CN117666449B - Based on computer data acquisition analysis monitored control system - Google Patents
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Abstract
The invention discloses a computer-based data acquisition, analysis and monitoring system, which belongs to the field of monitoring systems and comprises an infrared sensing acquisition module, a sleep state acquisition module, a power acquisition module, an air acquisition module, a data receiving module, a data importing module, a data processing module, an early warning grading module and a display module; the infrared induction acquisition module is used for acquiring thermal imaging information in a campus dormitory; the sleep state acquisition module is used for acquiring respiratory and heart rate physiological information of a sleeper through a sensor placed under the mattress; the power acquisition module is used for acquiring total power information used by electric appliances in the campus dormitory. According to the invention, the health and dormitory safety of students in the campus dormitory can be effectively monitored on the premise of not affecting the privacy of the students, and the students can be better taught according to the combination of the health and dormitory safety of the students and the daily classroom learning state.
Description
Technical Field
The invention relates to the field of monitoring systems, in particular to a computer-based data acquisition analysis monitoring system.
Background
Computer data refers to the collective term for all the media of symbols that can be entered into a computer and processed by a computer program in computer science. The data can be numbers, letters, symbols, analog quantities and the like, and has a certain meaning. Data is a representation of information and a carrier, which can be symbols, characters, numbers, voice, images, video, etc., and computer-based data acquisition analysis is a process of collecting data from various data sources (such as sensors, databases, networks, etc.) for analysis.
Along with the acceleration of the construction of schools in China, the schools are more and more huge and dispersed, and the system is taken as an extremely important infrastructure of the schools, and has quite important significance for enhancing the collection and analysis of school schools' dormitory monitoring data. At present, a monitoring system mainly transmits monitoring picture images, the acquired transmission images are displayed on a display, and personnel in a monitoring room perform comparison and analysis. However, the monitoring system cannot effectively protect the privacy of students, so how to develop a monitoring system capable of effectively monitoring the health and dormitory safety of students on the premise of not affecting the privacy of the students becomes a current urgent problem to be solved.
Accordingly, a computer-based data acquisition analysis and monitoring system is provided by those skilled in the art to solve the above-mentioned problems.
Disclosure of Invention
The invention aims to provide a computer-based data acquisition, analysis and monitoring system which can effectively monitor the health and dormitory safety of students in a campus dormitory on the premise of not influencing the privacy of the students so as to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the computer-based data acquisition, analysis and monitoring system comprises an infrared sensing acquisition module, a sleep state acquisition module, a power acquisition module, an air acquisition module, a data receiving module, a data importing module, a data processing module, an early warning grading module and a display module;
the infrared induction acquisition module is used for acquiring thermal imaging information in a campus dormitory; the sleep state acquisition module is used for acquiring respiratory and heart rate physiological information of a sleeper through a sensor placed under the mattress; the power acquisition module is used for acquiring total power information used by electric appliances in the campus dormitory; the air collection module is used for collecting concentration information of each component in the air in the campus dormitory; the data receiving module is used for receiving all acquired information and sending the acquired information to the data processing module; the data importing module is used for importing preset information to the data processing module; the data processing module is used for analyzing all received information and outputting analysis results; the early warning grading module is used for carrying out early warning grading according to the analysis result; the display module is used for displaying analysis results and early warning grading results.
As a further scheme of the invention: the specific process of analyzing the total power information used by the electrical appliance by the data processing module is as follows:
s101: the total power of the appliance usage collected in real time is marked as Preal time, and then the total power used by the electric appliance at the same time in three months is marked as Pi, i=1····n, n is a positive integer;
s102: p is matched with Pi in real time, if any Pi is matched with P in real time, the output analysis result is that the power is normal, and if any Pi is not matched with P in real time, the next step is entered;
s103: marking the total power of the appliance use acquired in the previous minute of P in real time as P;
s104: comparing the P real-time value with the P real-time value, outputting an analysis result to be power abnormality if the P real-time value is larger than the preset value, and entering the next step if the P real-time value is smaller than or equal to the preset value;
s105: detecting whether P is zero in real time, if so, entering the next step;
s106: marking the time point when P is zero in real time as T1, marking the time point when P changes from zero in real time as T2, and marking the total power used by the electric appliance when the P changes as P changes;
s107: calculating a time difference t= (T2-T1) between T1 and T2, outputting an analysis result to be normal in power if T is larger than a preset value, and entering the next step if T is smaller than or equal to the preset value;
s108: comparing the P change with a preset value, outputting an analysis result as abnormal power if the P change is larger than the preset value, and outputting the analysis result as normal power if the P change is smaller than or equal to the preset value.
As still further aspects of the invention: the specific process of analyzing the concentration information of each component by the data processing module is as follows:
s201: the collected components in the dormitory air are respectively marked as Ai, i=1····n, and then the concentration corresponding to Ai is marked as Ai, and then corresponding Ai the concentration is marked as Ai and is indicated;
s202: matching Ai with a preset pollutant type, and if any Ai is matched with the preset pollutant type, entering the next step;
s203: comparing Ai corresponding to Ai matched with the preset pollutant types with a preset value, outputting an analysis result as an air abnormality if the Ai is larger than the preset value, and entering the next step if the Ai is smaller than or equal to the preset value;
s204: counting the quantity of all Ais matched with the preset pollutant types, and marking the quantity as B;
s205: if B is larger than a preset value, outputting an analysis result as air abnormality, and if B is smaller than or equal to the preset value, outputting the analysis result as air abnormality.
As still further aspects of the invention: the specific process of the data processing module for analyzing the thermal imaging information is as follows:
s301: the acquired thermal images are labeled Ci respectively, i=1· n;
s302: dividing Ci into three types according to outline and body type, and marking as F1, F2 and F3 respectively, wherein the type corresponding to F1 refers to human, the type corresponding to F2 refers to mouse and the type corresponding to F3 refers to insect;
s303: analyzing F1 type thermal imaging, namely marking the number of F1 type thermal imaging acquired in real time as X, and marking the number of dormitory members as Y; if X=1, comparing the similarity between the F1 type thermal imaging and the preset dormitory member thermal imaging, if the similarity between any one dormitory member thermal imaging and the F1 type thermal imaging reaches a preset value, outputting an analysis result as normal thermal imaging, and if the similarity between any one dormitory member thermal imaging and the F1 type thermal imaging does not reach the preset value, outputting an analysis result as abnormal thermal imaging; if X=Y+1, carrying out gesture analysis on F1 type thermal imaging acquired in real time, and if Y thermal imaging is in a lying state and is kept still for a preset time, outputting an analysis result as thermal imaging abnormality if the last thermal imaging is in a walking and turning state;
s304: analyzing the F2 type thermal imaging, namely marking the number of F2 type thermal imaging acquired in real time as Z, outputting an analysis result as thermal imaging abnormality if the number of Z is larger than a preset value, and outputting the analysis result as thermal imaging normality if the number of Z is smaller than or equal to the preset value;
s305: and (3) analyzing the F3 type thermal imaging, namely marking the quantity of the F3 type thermal imaging acquired in real time as Q, outputting an analysis result as thermal imaging abnormality if the quantity of the Q is larger than a preset value, and outputting the analysis result as thermal imaging normality if the quantity of the Q is smaller than or equal to the preset value.
As still further aspects of the invention: the specific process of the data processing module for analyzing the breathing and heart rate physiological information of the sleeper is as follows:
s401: the respiratory rate of each sleeper in the dormitory is labeled Di, i=1· n is a ratio of the total number of the components, the heart rate of each sleeper is then marked as Ei, i=1· n;
s402: detecting whether Di is in a preset range interval, if Di is in the preset range interval, outputting an analysis result to be normal in breathing, and if Di is not in the preset range interval, outputting the analysis result to be abnormal in breathing;
s403: detecting whether Ei is in a preset range interval, if so, outputting an analysis result that the heart rate is normal, and if not, outputting an analysis result that the heart rate is abnormal.
As still further aspects of the invention: the specific process of the early warning grading module for early warning grading according to the analysis result is as follows:
step one: each campus dormitory was labeled Wi, i=1····n, n is a positive integer, wi is divided into three types according to analysis results, marked as L1, L2 and L3 respectively, wherein the type corresponding to L1 refers to dormitory with abnormal breathing or abnormal heart rate in the analysis result, the type corresponding to L2 refers to a dormitory with thermal imaging abnormality in an analysis result, and the type corresponding to L3 refers to a dormitory with power abnormality or air abnormality in the analysis result;
step two: setting a campus dormitory in L1 as a first priority sequence, setting a campus dormitory in L2 as a second priority sequence, and setting a campus dormitory in L3 as a third priority sequence;
step three: the first priority sequence, the second priority sequence and the third priority sequence are respectively subjected to priority sequencing to generate a first priority list, a second priority list and a third priority list;
step four: and outputting the first priority list, the second priority list and the third priority list as early warning grading results.
As still further aspects of the invention: the specific generation process of the first priority list is as follows:
the weight value is given to the abnormal sleep state, namely, the weight value given to the sleeper with the abnormal breathing in the analysis result is 0.4, the weight value given to the sleeper with the abnormal heart rate in the analysis result is 0.4, and the weight value given to the sleeper with the abnormal breathing and the abnormal heart rate in the analysis result is 1.0;
calculating the sum of the weight values of all sleepers in each dormitory in the first priority list, respectively marked Vi, i=1····k, wherein, k is a positive integer and is a number, and k refers to the number of dormitories in the first priority list;
and arranging Vi in order from big to small, and generating a first priority list according to the arrangement order.
As still further aspects of the invention: the specific generation process of the second priority list is as follows:
giving a weight value to the abnormal thermal imaging, namely giving a weight value of 0.7 to a dormitory in which an F1 type thermal imaging abnormality exists in the analysis result, giving a weight value of 0.2 to a dormitory in which an F2 type thermal imaging abnormality exists in the analysis result, and giving a weight value of 0.1 to a dormitory in which an F3 type thermal imaging abnormality exists in the analysis result;
calculating the sum of the abnormal thermal imaging weight values in each dormitory in the second priority list, respectively marked as Ui, i=1····j, wherein, j is a positive integer and is used to determine the total number of the sample, and j refers to the number of dormitories in the second priority list;
the Ui is arranged in the order from big to small, and a second priority list is generated according to the arrangement order.
As still further aspects of the invention: the specific generation process of the third priority list is as follows:
a weight value is given to a dormitory with abnormal power or abnormal air, wherein the weight value given to the dormitory with abnormal power is 0.3, and the weight value given to the dormitory with abnormal air is 0.2;
calculating the sum of the weight values of the dormitories in the third priority list, denoted Ri, i=1·····m, wherein, m is a positive integer, and is a positive integer, and m refers to the number of dormitories in the third priority list;
ri is arranged in the order from big to small, and then a third priority list is generated according to the arrangement order.
As still further aspects of the invention: the display module comprises a display screen, and the first priority list, the second priority list and the third priority list are simultaneously displayed on the display screen, wherein the border of the first priority list on the display screen is red, the border of the second priority list is yellow and the border of the third priority list is black.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the dormitory monitoring system, various computer data in the dormitory are collected, whether a sleeper in each dormitory has breathing abnormality or heart rate abnormality is analyzed from multiple angles, whether thermal imaging in each dormitory has abnormality, whether power has abnormality or not, whether air has abnormality or not, priority grading is conducted on the dormitory with abnormality, three priority lists are generated, a manager can quickly know which dormitory has abnormality and severity of abnormality through checking the priority lists, and accordingly the sleeper can go to check in time, and health and dormitory safety of students are effectively monitored on the premise that privacy of the students is not affected.
Drawings
Fig. 1 is a block diagram of a computer-based data acquisition analysis monitoring system.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As mentioned in the background art of the application, the inventor finds that the existing monitoring system mainly transmits the monitoring picture image, the acquired transmission image is displayed on a display, and the monitoring room personnel perform comparison and analysis, but the monitoring system cannot effectively protect the privacy of students and has certain defects.
In order to solve the defects, the application discloses a computer data acquisition analysis monitoring system, which can effectively monitor the health of students and dormitory safety on the premise of not influencing the privacy of the students.
How the above technical problems are solved by the solutions of the present application will be described in detail below with reference to the accompanying drawings.
Referring to fig. 1, in an embodiment of the present invention, a computer-based data acquisition analysis and monitoring system includes an infrared sensing acquisition module, a sleep state acquisition module, a power acquisition module, an air acquisition module, a data receiving module, a data importing module, a data processing module, an early warning classification module and a display module; the infrared induction acquisition module is used for acquiring thermal imaging information in a campus dormitory; the sleep state acquisition module is used for acquiring respiratory and heart rate physiological information of a sleeper through a sensor placed under the mattress; the power acquisition module is used for acquiring total power information used by electric appliances in the campus dormitory; the air collection module is used for collecting concentration information of each component in the air in the campus dormitory; the data receiving module is used for receiving all the acquired information and sending the acquired information to the data processing module; the data importing module is used for importing preset information to the data processing module; the data processing module is used for analyzing all received information and outputting analysis results; the early warning grading module is used for carrying out early warning grading according to the analysis result; the display module is used for displaying analysis results and early warning grading results. According to the dormitory monitoring system, various computer data in the dormitory are collected, whether a sleeper in each dormitory has breathing abnormality or heart rate abnormality is analyzed from multiple angles, whether thermal imaging in each dormitory has abnormality, whether power has abnormality or not, whether air has abnormality or not, priority grading is conducted on the dormitory with abnormality, three priority lists are generated, a manager can quickly know which dormitory has abnormality and severity of abnormality through checking the priority lists, and accordingly the sleeper can go to check in time, and health and dormitory safety of students are effectively monitored on the premise that privacy of the students is not affected.
In this embodiment: the specific process of analyzing the total power information used by the electrical appliance by the data processing module is as follows: s101: the total power of the appliance usage collected in real time is marked as Preal time, and then the total power used by the electric appliance at the same time in three months is marked as Pi, i=1····n, n is a positive integer; s102: p is matched with Pi in real time, if any Pi is matched with P in real time, the output analysis result is that the power is normal, and if any Pi is not matched with P in real time, the next step is entered; s103: marking the total power of the appliance use acquired in the previous minute of P in real time as P; s104: comparing the P real-time value with the P real-time value, outputting an analysis result to be power abnormality if the P real-time value is larger than the preset value, and entering the next step if the P real-time value is smaller than or equal to the preset value; s105: detecting whether P is zero in real time, if so, entering the next step; s106: marking the time point when P is zero in real time as T1, marking the time point when P changes from zero in real time as T2, and marking the total power used by the electric appliance when the P changes as P changes; s107: calculating a time difference t= (T2-T1) between T1 and T2, outputting an analysis result to be normal in power if T is larger than a preset value, and entering the next step if T is smaller than or equal to the preset value; s108: comparing the P change with a preset value, outputting an analysis result as abnormal power if the P change is larger than the preset value, and outputting the analysis result as normal power if the P change is smaller than or equal to the preset value. The setting not only can find the abnormality when the power consumption suddenly rises, but also can detect whether the electric appliance still in the on state in the power failure stage exists when the power failure is restarted, if so, the electric appliance can be electrified at the first time of the incoming call, the corresponding power consumption can also change at the moment, but the electric appliance still in the on state in the power failure stage is easily damaged when the incoming call, and the electric appliance is planned to the abnormal state so as to lead dormitory members to close the electric appliance in the power failure stage after finding by dormitory administrators.
In this embodiment: the specific process of analyzing the concentration information of each component by the data processing module is as follows: s201: the collected components in the dormitory air are respectively marked as Ai, i=1····n, and then the concentration corresponding to Ai is marked as Ai, and then corresponding Ai the concentration is marked as Ai and is indicated; s202: matching Ai with a preset pollutant type, and if any Ai is matched with the preset pollutant type, entering the next step; s203: comparing Ai corresponding to Ai matched with the preset pollutant types with a preset value, outputting an analysis result as an air abnormality if the Ai is larger than the preset value, and entering the next step if the Ai is smaller than or equal to the preset value; s204: counting the quantity of all Ais matched with the preset pollutant types, and marking the quantity as B; s205: if B is larger than a preset value, outputting an analysis result as air abnormality, and if B is smaller than or equal to the preset value, outputting the analysis result as air abnormality. This setting can detect the dormitory air fast and whether have the abnormality, needs to say, smoking in the dormitory is a kind of action that seriously influences other people's health, and likewise, if dormitory member does not pay attention to health, also can send some healthy smell of influence, the air acquisition module of this application adopts air quality sensor, can detect the pollutant concentration in the air, like formaldehyde, benzene, ammonia etc. and the oxygen in the air, gas such as carbon dioxide. Common air quality sensors are electrochemical, optical, catalytic combustion, and the like.
In this embodiment: the specific process of the data processing module for analyzing the thermal imaging information is as follows: s301: the acquired thermal images are labeled Ci respectively, i=1· n; s302: dividing Ci into three types according to outline and body type, and marking as F1, F2 and F3 respectively, wherein the type corresponding to F1 refers to human, the type corresponding to F2 refers to mouse and the type corresponding to F3 refers to insect; s303: analyzing F1 type thermal imaging, namely marking the number of F1 type thermal imaging acquired in real time as X, and marking the number of dormitory members as Y; if X=1, comparing the similarity between the F1 type thermal imaging and the preset dormitory member thermal imaging, if the similarity between any one dormitory member thermal imaging and the F1 type thermal imaging reaches a preset value, outputting an analysis result as normal thermal imaging, and if the similarity between any one dormitory member thermal imaging and the F1 type thermal imaging does not reach the preset value, outputting an analysis result as abnormal thermal imaging; if X=Y+1, carrying out gesture analysis on F1 type thermal imaging acquired in real time, and if Y thermal imaging is in a lying state and is kept still for a preset time, outputting an analysis result as thermal imaging abnormality if the last thermal imaging is in a walking and turning state; s304: analyzing the F2 type thermal imaging, namely marking the number of F2 type thermal imaging acquired in real time as Z, outputting an analysis result as thermal imaging abnormality if the number of Z is larger than a preset value, and outputting the analysis result as thermal imaging normality if the number of Z is smaller than or equal to the preset value; s305: and (3) analyzing the F3 type thermal imaging, namely marking the quantity of the F3 type thermal imaging acquired in real time as Q, outputting an analysis result as thermal imaging abnormality if the quantity of the Q is larger than a preset value, and outputting the analysis result as thermal imaging normality if the quantity of the Q is smaller than or equal to the preset value. The setting not only can discern the quantity of mouse and insect in the dormitory, can also discern whether other people take the dormitory member and not be in or sleep and get into the dormitory to in time discover unusual notification managers, it is to be noted that infrared sensing collection module adopts infrared inductor to gather data, and infrared inductor can perceive the activity of mice equidimension animal, through the thermal radiation that senses its production, can judge whether there is mouse or insect, and this kind of inductor can not infringe user's privacy.
In this embodiment: the specific process of the data processing module for analyzing the breathing and heart rate physiological information of the sleeper is as follows: s401: the respiratory rate of each sleeper in the dormitory is labeled Di, i=1· n is a ratio of the total number of the components, the heart rate of each sleeper is then marked as Ei, i=1· n; s402: detecting whether Di is in a preset range interval, if Di is in the preset range interval, outputting an analysis result to be normal in breathing, and if Di is not in the preset range interval, outputting the analysis result to be abnormal in breathing; s403: detecting whether Ei is in a preset range interval, if so, outputting an analysis result that the heart rate is normal, and if not, outputting an analysis result that the heart rate is abnormal. This setting can discern whether the sleeper has breathing abnormality or rhythm of heart abnormality fast, and wherein, sleep state collection module adopts intelligent sleep monitor to gather data, and intelligent sleep monitor can monitor physiological parameters such as sleeper's breathing, rhythm of heart through the sensor of placing under the mattress, in case discovery abnormal conditions, can in time discover, and this kind of monitor also can not infringe user's privacy.
In this embodiment: the specific process of the early warning grading module for early warning grading according to the analysis result is as follows: step one: each campus dormitory was labeled Wi, i=1····n, n is a positive integer, wi is divided into three types according to analysis results, marked as L1, L2 and L3 respectively, wherein the type corresponding to L1 refers to dormitory with abnormal breathing or abnormal heart rate in the analysis result, the type corresponding to L2 refers to a dormitory with thermal imaging abnormality in an analysis result, and the type corresponding to L3 refers to a dormitory with power abnormality or air abnormality in the analysis result; step two: setting a campus dormitory in L1 as a first priority sequence, setting a campus dormitory in L2 as a second priority sequence, and setting a campus dormitory in L3 as a third priority sequence; step three: the first priority sequence, the second priority sequence and the third priority sequence are respectively subjected to priority sequencing to generate a first priority list, a second priority list and a third priority list; step four: and outputting the first priority list, the second priority list and the third priority list as early warning grading results. This setting is convenient when the dormitory appears unusual, divides the priority of each dormitory according to the light and heavy urgency of thing to make things convenient for dormitory manager's first time to look over to the worst dormitory.
In this embodiment: the specific generation process of the first priority list is as follows: the weight value is given to the abnormal sleep state, namely, the weight value given to the sleeper with the abnormal breathing in the analysis result is 0.4, the weight value given to the sleeper with the abnormal heart rate in the analysis result is 0.4, and the weight value given to the sleeper with the abnormal breathing and the abnormal heart rate in the analysis result is 1.0; calculating the sum of the weight values of all sleepers in each dormitory in the first priority list, respectively marked Vi, i=1····k, wherein, k is a positive integer and is a number, and k refers to the number of dormitories in the first priority list; and arranging Vi in order from big to small, and generating a first priority list according to the arrangement order. The dormitory with serious breathing abnormality and heart rate abnormality can be effectively ensured to be in the front of the list by the aid of the device.
In this embodiment: the specific generation process of the second priority list is as follows: giving a weight value to the abnormal thermal imaging, namely giving a weight value of 0.7 to a dormitory in which an F1 type thermal imaging abnormality exists in the analysis result, giving a weight value of 0.2 to a dormitory in which an F2 type thermal imaging abnormality exists in the analysis result, and giving a weight value of 0.1 to a dormitory in which an F3 type thermal imaging abnormality exists in the analysis result; calculating the sum of the abnormal thermal imaging weight values in each dormitory in the second priority list, respectively marked as Ui, i=1····j, wherein, j is a positive integer and is used to determine the total number of the sample, and j refers to the number of dormitories in the second priority list; the Ui is arranged in the order from big to small, and a second priority list is generated according to the arrangement order. This arrangement can effectively ensure that the dormitory in which the thermal imaging abnormality is serious is in the front of the present list.
In this embodiment: the specific generation process of the third priority list is as follows: a weight value is given to a dormitory with abnormal power or abnormal air, wherein the weight value given to the dormitory with abnormal power is 0.3, and the weight value given to the dormitory with abnormal air is 0.2; calculating the sum of the weight values of the dormitories in the third priority list, denoted Ri, i=1·····m, wherein, m is a positive integer, and is a positive integer, and m refers to the number of dormitories in the third priority list; ri is arranged in the order from big to small, and then a third priority list is generated according to the arrangement order. The arrangement can effectively ensure that dormitories with serious power abnormality or air abnormality are in the front of the list.
In this embodiment: the display module comprises a display screen, and the first priority list, the second priority list and the third priority list are simultaneously displayed on the display screen, wherein the border of the first priority list on the display screen is red, the border of the second priority list is yellow and the border of the third priority list is black. The setting is convenient for distinguishing the first priority list, the second priority list and the third priority list for a dormitory manager to check.
In this embodiment: the dormitories in the first priority list are endowed with points H=3, the dormitories in the second priority list are endowed with points H=2, the dormitories in the third priority list are endowed with points H=1, the points H are refreshed once a day, the total of the daily points of each dormitory is calculated at the end of each month, the bigger the total H is the worse the safety and sanitation of the dormitory, the smaller the total H is the better the safety and sanitation of the dormitory, and dormitory managers can screen the dormitory with the largest and smallest H and respectively serve as a reverse teaching material and a list to educate students in other dormitories. In addition, the students' office or teacher can also check the analysis results and the integral, when the study state of some students is found to be declined in the classroom, the analysis results and the integral of the dormitory where the students are located can be adjusted to check and analyze, whether the study state of the students is declined or not is judged whether the dormitory environment causes or not, and if so, the improvement can be carried out from the corresponding deficiency points.
According to the invention, through collecting various computer data in the dormitories, whether a sleeper in each dormitory has breathing abnormality or heart rate abnormality, whether thermal imaging in each dormitory has abnormality, whether power is abnormal or not, whether air is abnormal or not are analyzed from multiple angles, then priority grading is carried out on the dormitories with abnormality, three priority lists are generated, a manager can quickly know which dormitory has abnormality and severity of abnormality by checking the priority lists, so that the sleeper can go to check in time, and the health and the dormitory safety of students are effectively monitored on the premise of not affecting the privacy of the students.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
The foregoing description is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should make equivalent substitutions or modifications according to the technical solution of the present invention and the inventive concept thereof, and should be covered by the scope of the present invention.
Claims (9)
1. The computer-based data acquisition, analysis and monitoring system is characterized by comprising an infrared sensing acquisition module, a sleep state acquisition module, a power acquisition module, an air acquisition module, a data receiving module, a data importing module, a data processing module, an early warning grading module and a display module;
the infrared induction acquisition module is used for acquiring thermal imaging information in a campus dormitory; the sleep state acquisition module is used for acquiring respiratory and heart rate physiological information of a sleeper through a sensor placed under the mattress; the power acquisition module is used for acquiring total power information used by electric appliances in the campus dormitory; the air collection module is used for collecting concentration information of each component in the air in the campus dormitory; the data receiving module is used for receiving all acquired information and sending the acquired information to the data processing module; the data importing module is used for importing preset information to the data processing module; the data processing module is used for analyzing all received information and outputting analysis results; the early warning grading module is used for carrying out early warning grading according to the analysis result; the display module is used for displaying analysis results and early warning grading results;
the specific process of analyzing the total power information used by the electrical appliance by the data processing module is as follows:
s101: the total power of the appliance usage collected in real time is marked as Preal time, and then the total power used by the electric appliance at the same time in three months is marked as Pi, i=1····n, n is a positive integer;
s102: p is matched with Pi in real time, if any Pi is matched with P in real time, the output analysis result is that the power is normal, and if any Pi is not matched with P in real time, the next step is entered;
s103: marking the total power of the appliance use acquired in the previous minute of P in real time as P;
s104: comparing the P real-time value with the P real-time value, outputting an analysis result to be power abnormality if the P real-time value is larger than the preset value, and entering the next step if the P real-time value is smaller than or equal to the preset value;
s105: detecting whether P is zero in real time, if so, entering the next step;
s106: marking the time point when P is zero in real time as T1, marking the time point when P changes from zero in real time as T2, and marking the total power used by the electric appliance when the P changes as P changes;
s107: calculating a time difference t= (T2-T1) between T1 and T2, outputting an analysis result to be normal in power if T is larger than a preset value, and entering the next step if T is smaller than or equal to the preset value;
s108: comparing the P change with a preset value, outputting an analysis result as abnormal power if the P change is larger than the preset value, and outputting the analysis result as normal power if the P change is smaller than or equal to the preset value.
2. The computer data acquisition, analysis and monitoring system according to claim 1, wherein the specific process of analyzing the concentration information of each component by the data processing module is as follows:
s201: the collected components in the dormitory air are respectively marked as Ai, i=1····n, and then the concentration corresponding to Ai is marked as Ai, and then corresponding Ai the concentration is marked as Ai and is indicated;
s202: matching Ai with a preset pollutant type, and if any Ai is matched with the preset pollutant type, entering the next step;
s203: comparing Ai corresponding to Ai matched with the preset pollutant types with a preset value, outputting an analysis result as an air abnormality if the Ai is larger than the preset value, and entering the next step if the Ai is smaller than or equal to the preset value;
s204: counting the quantity of all Ais matched with the preset pollutant types, and marking the quantity as B;
s205: if B is larger than a preset value, outputting an analysis result as air abnormality, and if B is smaller than or equal to the preset value, outputting the analysis result as air abnormality.
3. The computer data acquisition analysis and monitoring system according to claim 2, wherein the specific process of analyzing the thermal imaging information by the data processing module is as follows:
s301: the acquired thermal images are labeled Ci respectively, i=1· n;
s302: dividing Ci into three types according to outline and body type, and marking as F1, F2 and F3 respectively, wherein the type corresponding to F1 refers to human, the type corresponding to F2 refers to mouse and the type corresponding to F3 refers to insect;
s303: analyzing F1 type thermal imaging, namely marking the number of F1 type thermal imaging acquired in real time as X, and marking the number of dormitory members as Y; if X=1, comparing the similarity between the F1 type thermal imaging and the preset dormitory member thermal imaging, if the similarity between any one dormitory member thermal imaging and the F1 type thermal imaging reaches a preset value, outputting an analysis result as normal thermal imaging, and if the similarity between any one dormitory member thermal imaging and the F1 type thermal imaging does not reach the preset value, outputting an analysis result as abnormal thermal imaging; if X=Y+1, carrying out gesture analysis on F1 type thermal imaging acquired in real time, and if Y thermal imaging is in a lying state and is kept still for a preset time, outputting an analysis result as thermal imaging abnormality if the last thermal imaging is in a walking and turning state;
s304: analyzing the F2 type thermal imaging, namely marking the number of F2 type thermal imaging acquired in real time as Z, outputting an analysis result as thermal imaging abnormality if the number of Z is larger than a preset value, and outputting the analysis result as thermal imaging normality if the number of Z is smaller than or equal to the preset value;
s305: and (3) analyzing the F3 type thermal imaging, namely marking the quantity of the F3 type thermal imaging acquired in real time as Q, outputting an analysis result as thermal imaging abnormality if the quantity of the Q is larger than a preset value, and outputting the analysis result as thermal imaging normality if the quantity of the Q is smaller than or equal to the preset value.
4. A computer data acquisition analysis and monitoring system according to claim 3, wherein the specific process of analyzing the respiratory and heart rate physiological information of the sleeper by the data processing module is as follows:
s401: the respiratory rate of each sleeper in the dormitory is labeled Di, i=1· n is a ratio of the total number of the components, the heart rate of each sleeper is then marked as Ei, i=1· n;
s402: detecting whether Di is in a preset range interval, if Di is in the preset range interval, outputting an analysis result to be normal in breathing, and if Di is not in the preset range interval, outputting the analysis result to be abnormal in breathing;
s403: detecting whether Ei is in a preset range interval, if so, outputting an analysis result that the heart rate is normal, and if not, outputting an analysis result that the heart rate is abnormal.
5. The computer data acquisition, analysis and monitoring system according to claim 4, wherein the early warning classification module performs the specific process of early warning classification according to the analysis result:
step one: each campus dormitory was labeled Wi, i=1····n, n is a positive integer, wi is divided into three types according to analysis results, marked as L1, L2 and L3 respectively, wherein the type corresponding to L1 refers to dormitory with abnormal breathing or abnormal heart rate in the analysis result, the type corresponding to L2 refers to a dormitory with thermal imaging abnormality in an analysis result, and the type corresponding to L3 refers to a dormitory with power abnormality or air abnormality in the analysis result;
step two: setting a campus dormitory in L1 as a first priority sequence, setting a campus dormitory in L2 as a second priority sequence, and setting a campus dormitory in L3 as a third priority sequence;
step three: the first priority sequence, the second priority sequence and the third priority sequence are respectively subjected to priority sequencing to generate a first priority list, a second priority list and a third priority list;
step four: and outputting the first priority list, the second priority list and the third priority list as early warning grading results.
6. The computer-based data acquisition analysis and monitoring system according to claim 5, wherein the specific generation process of the first priority list is:
the weight value is given to the abnormal sleep state, namely, the weight value given to the sleeper with the abnormal breathing in the analysis result is 0.4, the weight value given to the sleeper with the abnormal heart rate in the analysis result is 0.4, and the weight value given to the sleeper with the abnormal breathing and the abnormal heart rate in the analysis result is 1.0;
calculating the sum of the weight values of all sleepers in each dormitory in the first priority list, respectively marked Vi, i=1····k, wherein, k is a positive integer and is a number, and k refers to the number of dormitories in the first priority list;
and arranging Vi in order from big to small, and generating a first priority list according to the arrangement order.
7. The computer-based data acquisition analysis and monitoring system according to claim 6, wherein the specific generation process of the second priority list is:
giving a weight value to the abnormal thermal imaging, namely giving a weight value of 0.7 to a dormitory in which an F1 type thermal imaging abnormality exists in the analysis result, giving a weight value of 0.2 to a dormitory in which an F2 type thermal imaging abnormality exists in the analysis result, and giving a weight value of 0.1 to a dormitory in which an F3 type thermal imaging abnormality exists in the analysis result;
calculating the sum of the abnormal thermal imaging weight values in each dormitory in the second priority list, respectively marked as Ui, i=1····j, wherein, j is a positive integer and is used to determine the total number of the sample, and j refers to the number of dormitories in the second priority list;
the Ui is arranged in the order from big to small, and a second priority list is generated according to the arrangement order.
8. The computer-based data acquisition analysis and monitoring system according to claim 7, wherein the specific generation process of the third priority list is:
a weight value is given to a dormitory with abnormal power or abnormal air, wherein the weight value given to the dormitory with abnormal power is 0.3, and the weight value given to the dormitory with abnormal air is 0.2;
calculating the sum of the weight values of the dormitories in the third priority list, denoted Ri, i=1·····m, wherein, m is a positive integer, and is a positive integer, and m refers to the number of dormitories in the third priority list;
ri is arranged in the order from big to small, and then a third priority list is generated according to the arrangement order.
9. The computer data acquisition, analysis and monitoring system according to claim 8, wherein the display module comprises a display screen, and the first priority list, the second priority list and the third priority list are simultaneously displayed on the display screen, wherein a border of the first priority list on the display screen is red, a border of the second priority list is yellow and a border of the third priority list is black.
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Citations (30)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5550476A (en) * | 1994-09-29 | 1996-08-27 | Pacific Gas And Electric Company | Fault sensor device with radio transceiver |
| JP2011176919A (en) * | 2010-02-23 | 2011-09-08 | Chugoku Electric Power Co Inc:The | Equipment monitoring device and equipment monitoring system |
| CN104122819A (en) * | 2014-07-18 | 2014-10-29 | 东北电力大学 | User habit based intelligent household electricity utilization method and system thereof |
| JP2015082154A (en) * | 2013-10-22 | 2015-04-27 | 三菱電機株式会社 | Home electric appliance and home power control system |
| CN104777800A (en) * | 2014-12-28 | 2015-07-15 | 上海电机学院 | Integrated dormitory management control system |
| CN105653294A (en) * | 2016-02-29 | 2016-06-08 | 苏州集泰信息科技有限公司 | Outage resetting method for hardware equipment |
| CN107526008A (en) * | 2017-09-12 | 2017-12-29 | 益和电气集团股份有限公司 | Business electrical monitoring device and failure analysis methods |
| CN108255079A (en) * | 2016-12-29 | 2018-07-06 | 广东美的生活电器制造有限公司 | Household electrical appliance, attaching plug electric discharge device and its control method |
| CN208190987U (en) * | 2018-05-29 | 2018-12-04 | 刘根文 | Power failure energy-saving protector |
| CN110051329A (en) * | 2019-04-26 | 2019-07-26 | 广东工业大学 | A kind of sleep monitor method, apparatus, system and readable storage medium storing program for executing |
| CN111323644A (en) * | 2020-03-17 | 2020-06-23 | 陶齐雄 | Intelligent monitoring method and system based on electric energy meter |
| CN111428984A (en) * | 2020-03-18 | 2020-07-17 | 浩云科技股份有限公司 | Risk early warning and analysis method of intelligent safety electricity utilization management system |
| CN111652325A (en) * | 2020-06-28 | 2020-09-11 | 广东诺信安科技有限公司 | Enterprise power consumption mode identification method and device based on clustering and storage medium |
| CN112068439A (en) * | 2020-09-18 | 2020-12-11 | 上海电力大学 | Family energy management optimization method based on renewable energy sharing model |
| CN112258356A (en) * | 2020-10-28 | 2021-01-22 | 湖南华恒智能有限公司 | Big data platform of wisdom campus |
| CN112631242A (en) * | 2020-12-07 | 2021-04-09 | 国网四川省电力公司电力科学研究院 | Power abuse analysis method and device for household electrical equipment |
| CN112905670A (en) * | 2020-12-11 | 2021-06-04 | 国网冀北电力有限公司计量中心 | Electric energy meter system for indoor power failure fault study and judgment and indoor power failure fault study and judgment method |
| CN113311270A (en) * | 2021-05-28 | 2021-08-27 | Tcl家用电器（合肥）有限公司 | Monitoring method and detection circuit of household appliance |
| CN113947886A (en) * | 2021-12-20 | 2022-01-18 | 北京易联通达科技有限公司 | Power consumption behavior monitoring device based on non-invasive ammeter |
| CN215576668U (en) * | 2021-09-06 | 2022-01-18 | 江阴市华姿中等专业学校(无锡农业技术学校) | Dormitory management system |
| CN114167781A (en) * | 2021-11-30 | 2022-03-11 | 杭州老板电器股份有限公司 | Early warning system and method for abnormal conditions of kitchen appliances |
| CN114236208A (en) * | 2021-11-17 | 2022-03-25 | 国网天津市电力公司 | Commercial default power consumption behavior detection device of civilian electricity |
| CN114509975A (en) * | 2022-02-16 | 2022-05-17 | 珠海格力电器股份有限公司 | Electric appliance control method, control device and nonvolatile storage medium |
| CN115116210A (en) * | 2022-06-28 | 2022-09-27 | 武汉翰林文融教育咨询有限公司 | Wisdom campus construction student electricity consumption safety monitoring early warning system of internet of things |
| CN115689821A (en) * | 2022-10-09 | 2023-02-03 | 淄博职业学院 | Students' dormitory information acquisition management system |
| CN115767066A (en) * | 2022-11-05 | 2023-03-07 | 四川水利职业技术学院 | Immersive AR system and propaganda method for water conservancy and higher vocational schools |
| CN116205600A (en) * | 2023-02-27 | 2023-06-02 | 南京莱博赛克智能科技有限公司 | Laboratory off-duty safety management system |
| CN116386120A (en) * | 2023-05-24 | 2023-07-04 | 杭州企智互联科技有限公司 | Noninductive monitoring management system |
| CN116820872A (en) * | 2023-05-19 | 2023-09-29 | 四川水利职业技术学院 | Experimental data processing system of mobile intelligent platform |
| CN117239942A (en) * | 2023-11-16 | 2023-12-15 | 天津瑞芯源智能科技有限责任公司 | Ammeter with monitoring function |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6998962B2 (en) * | 2000-04-14 | 2006-02-14 | Current Technologies, Llc | Power line communication apparatus and method of using the same |
| FI118865B (en) * | 2006-05-19 | 2008-04-15 | Valtion Teknillinen | Procedure and apparatus for monitoring property |
| ITUB20159599A1 (en) * | 2015-12-28 | 2017-06-28 | Beghelli Spa | CONTROL DEVICE FOR ELECTRIC CONTROL UNITS |
-
2024
- 2024-01-31 CN CN202410135789.5A patent/CN117666449B/en active Active
Patent Citations (30)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5550476A (en) * | 1994-09-29 | 1996-08-27 | Pacific Gas And Electric Company | Fault sensor device with radio transceiver |
| JP2011176919A (en) * | 2010-02-23 | 2011-09-08 | Chugoku Electric Power Co Inc:The | Equipment monitoring device and equipment monitoring system |
| JP2015082154A (en) * | 2013-10-22 | 2015-04-27 | 三菱電機株式会社 | Home electric appliance and home power control system |
| CN104122819A (en) * | 2014-07-18 | 2014-10-29 | 东北电力大学 | User habit based intelligent household electricity utilization method and system thereof |
| CN104777800A (en) * | 2014-12-28 | 2015-07-15 | 上海电机学院 | Integrated dormitory management control system |
| CN105653294A (en) * | 2016-02-29 | 2016-06-08 | 苏州集泰信息科技有限公司 | Outage resetting method for hardware equipment |
| CN108255079A (en) * | 2016-12-29 | 2018-07-06 | 广东美的生活电器制造有限公司 | Household electrical appliance, attaching plug electric discharge device and its control method |
| CN107526008A (en) * | 2017-09-12 | 2017-12-29 | 益和电气集团股份有限公司 | Business electrical monitoring device and failure analysis methods |
| CN208190987U (en) * | 2018-05-29 | 2018-12-04 | 刘根文 | Power failure energy-saving protector |
| CN110051329A (en) * | 2019-04-26 | 2019-07-26 | 广东工业大学 | A kind of sleep monitor method, apparatus, system and readable storage medium storing program for executing |
| CN111323644A (en) * | 2020-03-17 | 2020-06-23 | 陶齐雄 | Intelligent monitoring method and system based on electric energy meter |
| CN111428984A (en) * | 2020-03-18 | 2020-07-17 | 浩云科技股份有限公司 | Risk early warning and analysis method of intelligent safety electricity utilization management system |
| CN111652325A (en) * | 2020-06-28 | 2020-09-11 | 广东诺信安科技有限公司 | Enterprise power consumption mode identification method and device based on clustering and storage medium |
| CN112068439A (en) * | 2020-09-18 | 2020-12-11 | 上海电力大学 | Family energy management optimization method based on renewable energy sharing model |
| CN112258356A (en) * | 2020-10-28 | 2021-01-22 | 湖南华恒智能有限公司 | Big data platform of wisdom campus |
| CN112631242A (en) * | 2020-12-07 | 2021-04-09 | 国网四川省电力公司电力科学研究院 | Power abuse analysis method and device for household electrical equipment |
| CN112905670A (en) * | 2020-12-11 | 2021-06-04 | 国网冀北电力有限公司计量中心 | Electric energy meter system for indoor power failure fault study and judgment and indoor power failure fault study and judgment method |
| CN113311270A (en) * | 2021-05-28 | 2021-08-27 | Tcl家用电器（合肥）有限公司 | Monitoring method and detection circuit of household appliance |
| CN215576668U (en) * | 2021-09-06 | 2022-01-18 | 江阴市华姿中等专业学校(无锡农业技术学校) | Dormitory management system |
| CN114236208A (en) * | 2021-11-17 | 2022-03-25 | 国网天津市电力公司 | Commercial default power consumption behavior detection device of civilian electricity |
| CN114167781A (en) * | 2021-11-30 | 2022-03-11 | 杭州老板电器股份有限公司 | Early warning system and method for abnormal conditions of kitchen appliances |
| CN113947886A (en) * | 2021-12-20 | 2022-01-18 | 北京易联通达科技有限公司 | Power consumption behavior monitoring device based on non-invasive ammeter |
| CN114509975A (en) * | 2022-02-16 | 2022-05-17 | 珠海格力电器股份有限公司 | Electric appliance control method, control device and nonvolatile storage medium |
| CN115116210A (en) * | 2022-06-28 | 2022-09-27 | 武汉翰林文融教育咨询有限公司 | Wisdom campus construction student electricity consumption safety monitoring early warning system of internet of things |
| CN115689821A (en) * | 2022-10-09 | 2023-02-03 | 淄博职业学院 | Students' dormitory information acquisition management system |
| CN115767066A (en) * | 2022-11-05 | 2023-03-07 | 四川水利职业技术学院 | Immersive AR system and propaganda method for water conservancy and higher vocational schools |
| CN116205600A (en) * | 2023-02-27 | 2023-06-02 | 南京莱博赛克智能科技有限公司 | Laboratory off-duty safety management system |
| CN116820872A (en) * | 2023-05-19 | 2023-09-29 | 四川水利职业技术学院 | Experimental data processing system of mobile intelligent platform |
| CN116386120A (en) * | 2023-05-24 | 2023-07-04 | 杭州企智互联科技有限公司 | Noninductive monitoring management system |
| CN117239942A (en) * | 2023-11-16 | 2023-12-15 | 天津瑞芯源智能科技有限责任公司 | Ammeter with monitoring function |
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