CN113012818A - Epidemic prevention and control system based on IOT temperature measurement, location bracelet and AI - Google Patents
Epidemic prevention and control system based on IOT temperature measurement, location bracelet and AI Download PDFInfo
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Abstract
The invention provides an epidemic prevention and control system based on IOT temperature measurement, a positioning bracelet and AI. Epidemic prevention and control system based on IOT temperature measurement, location bracelet and AI includes: the system comprises a supervised end, a server end and a supervisor APP end; the output end of the supervised end is connected with the input end of the server end, and the output end of the server end is connected with the input end of the supervisor end. The invention provides an epidemic prevention and control system based on IOT temperature measurement, a positioning bracelet and AI, which mainly comprises a supervised person end, a server end and a supervisor APP end, wherein the temperature and the position of the supervised person can be measured in real time by setting the temperature measurement and the positioning IOT bracelet, the obtained temperature data information and position data information are more accurate, the temperature of all the supervised persons can be observed in real time by setting the supervisor App, abnormal body temperature persons can be found in time, and the directions of all the supervised persons can be observed in real time.
Description
Technical Field
The invention relates to the field of epidemic prevention and control systems, in particular to an epidemic prevention and control system based on IOT temperature measurement, a positioning bracelet and AI.
Background
The IOT technology is to collect any object or process to be monitored, connected and interacted in real time, collect various information required by sound, light, heat, electricity, mechanics, chemistry, biology, location and the like through various devices and technologies such as various information sensors, radio frequency identification technologies, global positioning systems, infrared sensors, laser scanners and the like, realize ubiquitous connection of objects and objects, and objects and people through various possible network accesses, and realize intelligent sensing, identification and management of the objects and the processes. The internet of things is an information carrier based on the internet, a traditional telecommunication network and the like, and all common physical objects which can be independently addressed form an interconnected network;
artificial intelligence is a branch of computer science, which attempts to understand the essence of intelligence and produces a new intelligent machine that can respond in a manner similar to human intelligence, and the research in this field includes robots, language recognition, image recognition, natural language processing, expert systems, etc., and since birth, artificial intelligence is becoming more and more mature in theory and technology and its application field is expanding.
At present, epidemic prevention and control products used in society can be divided into the following three types: the first method is that pure user information is actively registered, a background of an administrator manually checks and identifies whether the user information is abnormal, and then the user information is checked in the next step; the second is to monitor the network, the public number, the data related to the epidemic situation published by each official website, such as the confirmed number of people and suspected cases, the confirmed and suspected behavior tracks, time nodes and the like, and integrate into the product by means of crawler; the third product which is made by the core data type pertinence and can identify or screen whether the epidemic situation exists or not or confirm the contact probability of the personnel in the epidemic situation; however, the three schemes lack real-time performance, cannot accurately control the observed person in real time, require a long time for emergency to be discovered and processed, and cannot timely receive and process the request and the demand of the observed person.
Therefore, it is necessary to provide an epidemic prevention and control system based on IOT temperature measurement, location bracelet and AI to solve the above technical problems.
Disclosure of Invention
The invention provides an epidemic prevention and control system based on IOT temperature measurement, a positioning bracelet and AI, which solves the problems that the existing epidemic prevention and control measures lack real-time performance, can not accurately control an observed person in real time, can find and process an emergency situation in a long time, and can not timely receive and process the request and the demand of the observed person.
In order to solve the above technical problems, the epidemic prevention and control system based on the IOT temperature measurement, the positioning bracelet and the AI provided by the invention comprises:
the system comprises a supervised end, a server end and a supervisor APP end;
wherein, by the output of supervisor's end with the input of server end is connected, the output of server end with the input of supervisor's end is connected, by the supervisor's end including temperature measurement and location IOT bracelet and by supervisor APP end, inside temperature measurement module, GPS orientation module and the 4G module of being provided with of temperature measurement and location IOT bracelet, the server end includes database module, crowd gathering analysis module, crowd gathering prediction module.
Preferably, the temperature measurement module is used for collecting temperature data of a monitored person and uploading the collected data to the server side, the GPS positioning module is used for collecting real-time position information and uploading the real-time position information to the server side, and the 4G module is used for communicating with the server side.
Preferably, the APP end of the supervised person is used for acquiring the positioning data, the temperature data and the crowd gathering area data of the supervised person from the server end, presenting the positioning data, the temperature data and the crowd gathering area data in the form of a map and a list, and providing a window for editing and sending requirements of the supervised person, so as to send the requirements to the supervisor in time.
Preferably, the database module comprises a supervised person position library, a supervised person identity information library, a supervised person body temperature library and a mobile phone user real-time position library, wherein the supervised person position library is used for storing and managing the collected position data of the supervised person, the supervised person identity information library is used for storing and managing the identity information of the supervised person, the supervised person body temperature library is used for storing and managing the body temperature data of the supervised person, and the mobile phone user real-time position library is used for storing and managing the positioning data of the user which is obtained by analyzing a specific interface provided by an operator.
Preferably, the crowd gathering analyzing module analyzes to obtain real-time location data of the user by using a specific interface provided by an operator, analyzes a current place where crowd gathering occurs at the place through a clustering algorithm and outputs longitude and latitude of the place, and develops a back-end data interface of the longitude and latitude providing JSON-format data externally, the crowd gathering predicting module analyzes to obtain the real-time location data of the user by using the specific interface provided by the operator, predicts a possibly occurring crowd gathering place at the next moment through an AI program and outputs the longitude and latitude, and develops the back-end data interface of the longitude and latitude providing the JSON-format data externally.
Preferably, the APP of the supervisor side is a software module, and is displayed in the form of executable software, the APP accesses the server to obtain data with a certain authority, the data is displayed in the form of a map and a list on an APP interface, and the APP accesses the server side to respectively obtain the body temperature data, the historical positioning data, the position data of the crowd gathering area and the predicted position data of the crowd gathering area of the supervised person.
Preferably, the APP end of the supervised person is compiled by Java, and is an executable file in an Android system, and the method is used for acquiring the positioning and temperature data and the crowd gathering area data of the observed person from the server end and presenting the data in the form of a map and a list, and providing a window for editing and sending requirements of the observed person, and meanwhile, making a page alarm for emergency situations such as abnormal body temperature of the observed person, approaching crowd gathering area, and the like, and the APP end of the supervised person is compiled by Java.
Preferably, the database module uses Mysql and has the functions of establishing a temperature library of a supervised person, an identity information library of the supervised person, a real-time position library of a mobile phone user and a position library of the supervised person, the crowd gathering analysis module and the crowd gathering prediction module are all realized by python, the analysis of emergency such as abnormal body temperature is realized by a logic judgment program, the crowd gathering analysis is realized by a clustering algorithm, the recognition of the crowd gathering condition is realized by deep learning with a Pytorch as a frame, and the model is obtained after machine training to predict the crowd gathering point at a later moment.
Compared with the related art, the epidemic prevention and control system based on the IOT temperature measurement, the positioning bracelet and the AI provided by the invention has the following beneficial effects:
the invention provides an epidemic prevention and control system based on IOT temperature measurement, positioning bracelet and AI, (1) the epidemic prevention and control system is mainly composed of a supervised person end, a server end and a supervisor APP end, wherein the temperature and the position of the supervised person can be measured in real time by setting the temperature measurement and positioning IOT bracelet, the obtained temperature data information and position data information are more accurate, the body temperature of all the supervised persons can be observed in real time by setting the supervisor App, abnormal body temperature persons can be found in time, the directions of all the supervised persons can be observed in real time, the supervised persons can be observed and tracked uniformly by the supervisor, the supervisor can receive and process the request and response of the supervised person in real time, and further the system can intelligently judge the crowd gathering area and predict the crowd gathering area by AI according to the crowd flowing condition, the situation can be found out in time and emergency, and people can be evacuated in time, the crowd gathering area is predicted and displayed in a list, so that a supervisor can take care of precaution conveniently and cross infection is effectively prevented;
(2) the system is based on the crowd gathering area prediction and the crowd gathering area analysis of artificial intelligence, so that the prevention and control system is more intelligent, the technologies of an embedded system, a sensor, a database, the Internet of things, artificial intelligence and the like are comprehensively applied, the whole epidemic prevention and control process is electronized, the intelligent degree of the epidemic prevention and control is increased, and the use requirement of the epidemic prevention and control is well met;
(3) can reach the cooperation with each region community communication operator simultaneously, write in back-end artificial intelligence training program with each community crowd's positioning data interface, make each community can directly use this system, increase the convenience that intelligent epidemic situation prevention and control system used.
Drawings
Fig. 1 is a system block diagram of an epidemic prevention and control system based on IOT temperature measurement, a positioning bracelet and an AI according to the present invention;
FIG. 2 is a schematic diagram of an internal architecture of an epidemic prevention and control system based on IOT temperature measurement, a positioning bracelet and AI according to the present invention;
FIG. 3 is a diagram of the software system architecture in the epidemic prevention and control system based on IOT temperature measurement, positioning bracelet and AI according to the present invention;
FIG. 4 is a block diagram of the supervisor side App software in the epidemic prevention and control system based on IOT temperature measurement, positioning bracelet and AI provided by the invention;
FIG. 5 is a block diagram of App software of a monitored end in the epidemic prevention and control system based on IOT temperature measurement, positioning bracelet and AI provided by the invention;
FIG. 6 is a schematic diagram of the K-MEANS algorithm of the epidemic prevention and control system based on IOT temperature measurement, positioning bracelet and AI provided by the invention;
FIG. 7 is a process diagram of a crowd analysis module algorithm in the epidemic prevention and control system based on IOT temperature measurement, positioning bracelet and AI according to the present invention;
FIG. 8 is a schematic diagram of a DBN deep belief network in the epidemic prevention and control system based on IOT temperature measurement, location bracelet and AI provided by the invention;
fig. 9 is a schematic diagram of a CNN convolutional neural network in an epidemic prevention and control system based on IOT temperature measurement, a positioning bracelet and AI according to the present invention;
FIG. 10 is a schematic diagram of an RNN recurrent neural network in the epidemic prevention and control system based on IOT temperature measurement, location bracelet and AI according to the present invention;
FIG. 11 is a schematic diagram of an LSTM neural network in the epidemic prevention and control system based on IOT temperature measurement, positioning bracelet and AI according to the present invention;
FIG. 12 is a schematic diagram of an LSTM neural network forgetting to remember a gate in the epidemic prevention and control system based on IOT temperature measurement, positioning bracelet and AI provided by the present invention;
FIG. 13 is a schematic diagram of an LSTM neural network input gate in the epidemic prevention and control system based on IOT temperature measurement, positioning bracelet and AI according to the present invention;
FIG. 14 is a schematic diagram of an LSTM neural network output gate in the epidemic prevention and control system based on IOT temperature measurement, positioning bracelet and AI according to the present invention;
fig. 15 is a model configuration diagram of a crowd prediction module in the epidemic prevention and control system based on IOT temperature measurement, a positioning bracelet and AI according to the present invention.
Detailed Description
The invention is further described with reference to the following figures and embodiments.
Please refer to fig. 1, fig. 2, fig. 3, fig. 4, fig. 5, fig. 6, fig. 7, fig. 8, fig. 9, fig. 10, fig. 11, fig. 12, fig. 13, fig. 14, and fig. 15 in combination, wherein fig. 1 is a system block diagram of an epidemic prevention and control system based on IOT temperature measurement, a positioning bracelet, and AI according to the present invention; FIG. 2 is a schematic diagram of an internal architecture of an epidemic prevention and control system based on IOT temperature measurement, a positioning bracelet and AI according to the present invention; FIG. 3 is a diagram of the software system architecture in the epidemic prevention and control system based on IOT temperature measurement, positioning bracelet and AI according to the present invention; FIG. 4 is a block diagram of the supervisor side App software in the epidemic prevention and control system based on IOT temperature measurement, positioning bracelet and AI provided by the invention; FIG. 5 is a block diagram of App software of a monitored end in the epidemic prevention and control system based on IOT temperature measurement, positioning bracelet and AI provided by the invention; FIG. 6 is a schematic diagram of the K-MEANS algorithm of the epidemic prevention and control system based on IOT temperature measurement, positioning bracelet and AI provided by the invention; FIG. 7 is a process diagram of a crowd analysis module algorithm in the epidemic prevention and control system based on IOT temperature measurement, positioning bracelet and AI according to the present invention; FIG. 8 is a schematic diagram of a DBN deep belief network in the epidemic prevention and control system based on IOT temperature measurement, location bracelet and AI provided by the invention; fig. 9 is a schematic diagram of a CNN convolutional neural network in an epidemic prevention and control system based on IOT temperature measurement, a positioning bracelet and AI according to the present invention; FIG. 10 is a schematic diagram of an RNN recurrent neural network in the epidemic prevention and control system based on IOT temperature measurement, location bracelet and AI according to the present invention; FIG. 11 is a schematic diagram of an LSTM neural network in the epidemic prevention and control system based on IOT temperature measurement, positioning bracelet and AI according to the present invention; FIG. 12 is a schematic diagram of an LSTM neural network forgetting to remember a gate in the epidemic prevention and control system based on IOT temperature measurement, positioning bracelet and AI provided by the present invention; FIG. 13 is a schematic diagram of an LSTM neural network input gate in the epidemic prevention and control system based on IOT temperature measurement, positioning bracelet and AI according to the present invention; FIG. 14 is a schematic diagram of an LSTM neural network output gate in the epidemic prevention and control system based on IOT temperature measurement, positioning bracelet and AI according to the present invention; fig. 15 is a model configuration diagram of a crowd prediction module in the epidemic prevention and control system based on IOT temperature measurement, a positioning bracelet and AI according to the present invention. Epidemic prevention and control system based on IOT temperature measurement, location bracelet and AI includes:
the system comprises a supervised end, a server end and a supervisor APP end;
wherein, by the output of supervisor's end with the input of server end is connected, the output of server end with the input of supervisor's end is connected, by the supervisor's end including temperature measurement and location IOT bracelet and by supervisor APP end, inside temperature measurement module, GPS orientation module and the 4G module of being provided with of temperature measurement and location IOT bracelet, the server end includes database module, crowd gathering analysis module, crowd gathering prediction module.
The temperature measurement module is used for collecting temperature data of a monitored person and uploading the collected data to the server side, the GPS positioning module is used for collecting real-time position information and uploading the real-time position information to the server side, and the 4G module is used for communicating with the server side.
The APP terminal of the monitored person is respectively used for obtaining the positioning data, the temperature data and the crowd gathering area data of the monitored person from the server terminal, presenting the data in the form of a map and a list, and providing a window for editing and sending the requirements of the monitored person so as to send the requirements to the monitoring person in time.
The database module comprises a supervised person position library, a supervised person identity information library, a supervised person body temperature library and a mobile phone user real-time position library, wherein the supervised person position library is used for storing and managing the collected position data of the supervised person, the supervised person identity information library is used for storing and managing the identity information of the supervised person, the supervised person body temperature library is used for storing and managing the body temperature data of the supervised person, and the mobile phone user real-time position library is used for storing and managing the positioning data of the user obtained by analyzing a specific interface provided by an operator.
The crowd gathering and forecasting module analyzes and obtains the real-time positioning data of the user by utilizing a specific interface provided by an operator, analyzes and outputs the longitude and latitude of the current crowd gathering place at the position through a clustering algorithm, develops a back-end data interface of the longitude and latitude for providing JSON format data externally, predicts the crowd gathering place which is likely to occur at the next moment through an AI (artificial intelligence) program, outputs the longitude and latitude and develops the back-end data interface of the longitude and latitude for providing the JSON format data externally.
The APP of the supervisor side is a software module and is displayed in the form of executable software, the APP is accessed to the server to acquire data with a certain authority, the data are displayed in the form of a map and a list on an App interface, and the APP is accessed to the server to acquire the body temperature data, the historical positioning data, the position data of the crowd gathering area and the predicted position data of the crowd gathering area of the supervised person respectively.
The APP end of the observed person is compiled by Java and is an executable file under an Android system, the APP end of the observed person is used for obtaining the positioning data, the temperature data and the crowd gathering area data of the observed person from the server end and displaying the data in the form of a map and a list, and providing a window for editing and sending requirements of the observed person.
The database module uses Mysql and has the functions of establishing a supervised body temperature library, a supervised body identity information library, a mobile phone user real-time position library and a supervised body position library, the crowd gathering analysis module and the crowd gathering prediction module are all realized by python, the analysis of emergency such as abnormal body temperature is realized by using a logic judgment program, the crowd gathering analysis is realized by using a clustering algorithm, the recognition of the crowd gathering condition is realized by deep learning with Pythrch as a frame, and the model is obtained after machine training to predict the crowd gathering point at the later moment.
The function of the supervisor side App mainly comprises real-time monitoring of the body temperature and the position of a supervised person, viewing of a journey track of the supervised person, presentation of crowd gathering area analysis and prediction conditions, and processing and response of the demands of the supervised person;
designing a server-side database, and storing a mobile phone user real-time position library: the database is used for storing real-time position data of all supervised persons, and the database of the body temperature database of the supervised persons comprises: the real-time body temperature storage system is used for storing the real-time body temperatures of all the supervised persons, and the identity information base of the supervised persons comprises: the identity information base of the monitored person is used for storing the identity information of the monitored person, such as account number, password name and the like;
the method comprises the steps that a module function is designed, a supervisor side App logs in an interface, the main function of a login module is to allow a user to input in a user name edit box and a password edit box, when the input user name and the password are matched with a certain item (the database name is Person-check) in a manager database at the same time, the user obtains authority to log in the system, if the user name or the password is not input by the user, the system prompts that the user name or the password cannot be empty and cannot log in the system, and if the input user name or the password is wrong, the system prompts that the user name or the password is input by mistake and cannot log in the system;
the supervisor side App user main interface provides entry points of four functions of the system, namely function selection, which are respectively the position of a supervised person on a map, receives and confirms the request of the supervised person, checks the personnel gathering area and the prediction condition, checks the body temperature of all the supervised persons, the positions of the supervised person on the map, calls the API of the Goods map after receiving the position data of the supervised person of a server, presents the positions of all the supervised persons in the form of the map, receives and confirms the request of the supervisor side App of the supervised person to request the demand message of the supervised person from the server, responds and edits the demand message of the supervised person by using an edit box, checks the body temperature of all the supervised persons, requests the body temperature data of the supervised person from the server by the supervisor side App, and presents the body temperature data of the supervisor side by using the App of the supervisor side in the form of a list, the method comprises the steps that a person gathering area and a prediction situation are checked, a supervisor side App requests a server for data of a person gathering area and the prediction situation and presents the data in a list + map form at the supervisor side App, the supervisor side App requests historical positioning data of a supervised person from the supervised person, the supervisor can check a travel track of the supervised person by calling an API of a hundred-degree map, and a forming track of the supervised person is presented to the supervisor at an App interface in a map + list form;
the functions of the monitored person end App mainly include real-time monitoring of the body temperature and the position of a monitored person, early warning of emergency situations such as the monitored person approaching a crowd gathering area and the monitored person having overhigh body temperature, analysis and prediction of the crowd gathering area, and processing and response of the requirements of the monitored person;
designing a server-side database, and storing a mobile phone user real-time position library: the database is used for storing real-time position data of all supervised persons, and the database of the body temperature database of the supervised persons comprises: the real-time body temperature storage system is used for storing the real-time body temperatures of all the supervised persons, and the identity information base of the supervised persons comprises: the identity information base of the monitored person is used for storing the identity information of the monitored person, such as account number, password name and the like;
the module function design, the supervisor side App software module is divided into three parts of a login interface, a registration interface and a user main interface, the supervisor side App login interface is provided, the login module mainly has the functions of allowing a user to input in a user name editing frame and a password editing frame, when the input user name and the password are matched with a certain item (the database name is Person-check) in a manager database, the user obtains authority and logs in the system, if the user name or the password is not input by the user, the system prompts that the user name or the password cannot be empty and cannot log in the system, if the input user name or the password is wrong, the system prompts that the user name or the password is input incorrectly and cannot log in the system, meanwhile, the login interface provides an interface for entering the supervisor login interface, the supervisor side App user main interface provides entry points of four functions of the system, namely function selection, namely the position of the supervised person on a map, editing and sending requirements, checking a person gathering area and a prediction condition, checking the body temperature of the supervised person, the position of the supervised person on the map, after receiving the position data of the supervised person of the server, calling an API of a Gade map by an App end of the supervised person, presenting the position of the supervised person in the map, editing and sending requirements of the supervised person, editing and sending requirements of the observed person to the supervisor, checking the body temperature of the supervised person, requesting the body temperature data of the supervised person from the server by an App end of the supervised person, presenting the App at the supervised person end, checking the person gathering area and the prediction condition, requesting the person gathering area data from the server by the App at the supervised person end, presenting the App at the supervised person end in a list + map form, registering the supervised person, after the supervisor fills the own identity information in the interface, the supervisor App sends the identity information to the server, and after the server database records the identity information, the supervisor completes registration, namely the supervisor can log in the App by using the registered user name and password;
the main requirement of the crowd gathering analysis module is to acquire crowd gathering conditions, abstract the crowd densities of different areas according to the concrete positions of users in the database so as to know the crowd gathering conditions, so as to warn the users to avoid crowd gathering areas and remind supervisors to manage and control, and also obtain data for artificial intelligence model training of the crowd gathering prediction module,
algorithm selection, a proper clustering algorithm is needed to be used for analyzing the distribution condition of the crowd, the crowd distribution condition of each region needs to be known for quantitative analysis and artificial intelligence model training, a clustering algorithm based on distance needs to be used, and the classification mode mainly adopts two choices: the partition method uses a clustering algorithm of the partition method, and automatically classifies according to the position information of the crowd, and the clustering algorithm which can be used in the occasion has the advantages of K-MEANS algorithm, K-MEDOIDS algorithm, CLARANS algorithm and the like: the main position of crowd gathering can be calculated only by crowd position data without giving any map information, and redundant position points are few.
The model algorithm, a clustering algorithm using the model algorithm, provides the system with the marked locations on the map, and takes the locations as the basis of the assumed model, namely classification, and has the advantages that: the divided regions are often meaningful, each of which can be easily named and easily provided as training data to the artificial intelligence model.
The method comprises the steps that a design is constructed, a crowd gathering analysis module uses an algorithm combining a model algorithm and a grid algorithm, mark places on a map are provided for a system to serve as 'gathering points', each person is classified into an area represented by one 'gathering point' nearest to the person, the number of people of each 'gathering point' can be obtained after all people are counted, the crowd density of each 'gathering point' can be calculated according to the area of the area represented by the 'gathering point', and the crowd gathering condition of the areas can be known by comparing the density with a crowd gathering threshold value;
on the basis, a grid algorithm is combined to improve the calculation speed, when the 'gathering points' are provided, a grid is generated, the 'gathering points' which can be classified by people in the grid are recorded in each grid, when people are classified, the grid where the people are located is found first, only the distance of a few 'gathering points' needs to be calculated, the classification can be finished, and the operation speed is greatly improved;
the crowd gathering prediction module is designed, the main requirement of the crowd gathering prediction module is to predict the crowd gathering condition in the future, and the crowd gathering condition in the future is predicted according to the crowd density distribution at present and in a past period of time, so that a user is reminded to avoid a future crowd gathering area in advance, and a supervisor is facilitated to prevent the crowd gathering;
the model algorithm is selected, the crowd aggregation is a very subjective behavior, the things that everyone can do and the places where the people go are quite random, and it is almost impossible to accurately predict the detail change of the crowd, however, the crowd distribution still follows a few statistical rules along with the time, the rules can be found to roughly predict the future change, and the deep learning method is most suitable for finding the crowd aggregation change rule by considering the development degree of the prior art;
the basic models for deep learning are: (1) and DBN (deep belief network), the advantages are as follows: the generation model learns the joint probability density distribution, so that the distribution condition of the data can be represented from the statistical angle, and the similarity of the same-class data can be reflected; the generated model can restore condition probability distribution, which is equivalent to a discriminant model, and the discriminant model cannot obtain joint distribution, so that the generated model cannot be used as the generated model; (2) CNN (constraint Neural networks) convolutional Neural networks, the advantages are as follows: the weight sharing strategy reduces parameters needing to be trained, and the same weight can enable the filter not to be influenced by the signal position to detect the characteristics of the signal, so that the generalization capability of the trained model is stronger; the spatial resolution of the network can be reduced through the pooling operation, so that the tiny offset and distortion of signals are eliminated, and the requirement on the translation invariance of input data is not high; (3) RNN (Current neural network) recurrent neural network, advantage: the model is a depth model in a time dimension, and can model the sequence content; (4) LSTM (Long-Short Term Memory) Long-Short Term Memory artificial neural network has the advantages that: compared with a general RNN recurrent neural network, the LSTM neural network can better process and predict important events with very long intervals and delays in a time sequence, and by comprehensive consideration, the LSTM neural network is most suitable for population aggregation prediction, information is continuously circulated during RNN training, the updating of weights of a neural network model is very large, because error gradients are accumulated in the updating process, the network is unstable, in an extreme case, the values of the weights can become large to overflow and result in NaN values, explosion is generated by the repeated accumulation of gradients by a network layer with the values larger than 1, the explosion disappears if the values are smaller than 1, the problem of gradient dissipation or gradient explosion can be well solved by a long-term memory unit of the LSTM, the LSTM is stronger than other models when the model is expected to learn from long-term dependence, the LSTM is forgotten, the ability to remember and update information makes it more advanced than classical RNNs;
model construction design, LSTM neural network: the data memory of the LSTM neural network consists of a hidden layer and a memory layer, wherein the processing consists of the following 3 gates: forgetting to remember the door: the key of long-term memory is to determine how much content in the memory layer needs to be forgotten, and data which is not easy to forget can be kept for a long time, and the input gate: the method determines how much information is added into the memory layer, and because the information is added through addition, original contents in memory are easy to retain, and the method is key to avoid gradient dissipation and gradient explosion, and the output gate is as follows: the hidden layer, the memory layer and the input layer are integrated, the output content of the hidden layer is calculated, and the content of the hidden layer provided for the next calculation is also the output content;
the crowd position condition is directly used as the input layer content, input into the LSTM neural network for calculation, and output the content of the hidden layer, but the content of the hidden layer is abstract and limited between [ -1,1], so that the content of the hidden layer is linearly transformed through a linear layer to obtain the predicted next crowd position condition, and then the predicted next crowd position condition is input into the LSTM neural network again to predict the next crowd position condition, and the process is circulated, so that the change of a long time in the future can be predicted;
difficulty and solution, when designing the model structure of the crowd gathering prediction module, originally thought that repeatedly taking the last prediction result as the next input may cause the error to be amplified continuously, which may cause the result to become unreliable when predicting the time far in the future, but in the process of testing, it is found that the prediction module can always predict the important future change, probably because of the memory capacity of the LSTM neural network, the real situation of far time can be remembered, and the error of the prediction situation at near time has little influence on the result;
the crowd gathering prediction module is realized by using a python language, applying a PyTorch framework, using an LSTM neural network and Linear transformation algorithm, using crowd data as a training sample, and continuously training a model along with the time;
the data set is used for temporarily difficultly obtaining actual crowd data, a simulation scene is designed for testing, 10000 students in a simulated campus are simulated, the process of continuously acting among a teaching building, a dining room and a dormitory every day is realized, the PyTorch is realized, linux + Python3.7+ torch 1.2.0+ PyCharm is used for the PyTorch, a crowd gathering prediction module model structure and an LSTM neural network layer (an additional linear layer) are used for the PyTorch realization;
the test conclusion shows that under the simulated small-scale test data, the time required by the first training is about 10 seconds, then only about 3 seconds are required each time, after 2-3 times of training, the accuracy rate of prediction on the future crowd gathering condition basically reaches over 90 percent, the serious crowd gathering condition can be predicted correctly, and the prediction time is less than 0.1 second;
system configuration according to the system function and the requirements for designing embedded systems, developing this system requires the following main configurations:
a, developing environment: xshell + ARM LINUX, Windows system, Android system;
b, developing a language: C/C + + language, Qt language, Python language, Java language;
c, hardware: the Cortex-A series bottom plates (number: 1) are magic-A series bottom plates provided by Beijing Bo Chuang Intelligent alliance company, the IMX6 core controller is an IMX6 core controller provided by Beijing Bo Chuang Intelligent alliance company, the temperature measuring sensors (number: 1) are LS-TM02 non-contact infrared temperature measuring sensors, and the 4G modules (number: 1) are 4G modules provided by Beijing Bo Chuang Intelligent alliance company; GPS modules (number: 1), 280GPS modules.
The working principle of the epidemic prevention and control system based on the IOT temperature measurement, the positioning bracelet and the AI provided by the invention is as follows:
when the system is used, the temperature measuring and positioning IOT bracelet is worn by a monitored person, the body temperature and position data of the monitored person are collected from the wearing position through the temperature measuring and positioning IOT bracelet, the system starts to normally run by logging in an App end of the monitored person and inputting related information, the App end of the monitored person realizes early warning of the monitored person approaching a crowd gathering area, analysis of the crowd gathering area and presentation of prediction conditions and report of requirements of the monitored person, the data processing of the monitored person, prediction and analysis of the crowd gathering area and processing and storage of travel tracks of the monitored person are realized at a server end, and the App end of the monitor realizes real-time monitoring of the body temperature and the position of the monitored person, supervision of the travel tracks of the monitored person, analysis of the crowd gathering area and presentation of the prediction conditions and processing and response of the requirements of the monitored person.
Compared with the related art, the epidemic prevention and control system based on the IOT temperature measurement, the positioning bracelet and the AI provided by the invention has the following beneficial effects:
(1) the epidemic prevention and control system mainly comprises a supervised person end, a server end and a supervisor APP end, wherein the body temperature and the position of a supervised person can be measured in real time by setting a temperature measurement and positioning IOT bracelet, the obtained temperature data information and position data information are more accurate, the body temperatures of all the supervised persons can be observed in real time by setting the supervisor App, abnormal body temperature persons can be found in time, the directions of all the supervised persons can be observed in real time, the supervised persons can be conveniently and uniformly observed and tracked, the supervisor can receive and process the request of the supervised person in real time and respond, further, the system can intelligently judge a crowd gathering area and predict the crowd gathering area according to the crowd flowing condition, can timely find emergency, timely evacuate the persons, predict the crowd gathering area and display in a list, so that the supervisor can take care of caution, cross infection is effectively prevented;
(2) the system is based on the crowd gathering area prediction and the crowd gathering area analysis of artificial intelligence, so that the prevention and control system is more intelligent, the technologies of an embedded system, a sensor, a database, the Internet of things, artificial intelligence and the like are comprehensively applied, the whole epidemic prevention and control process is electronized, the intelligent degree of the epidemic prevention and control is increased, and the use requirement of the epidemic prevention and control is well met;
(3) can reach the cooperation with each region community communication operator simultaneously, write in back-end artificial intelligence training program with each community crowd's positioning data interface, make each community can directly use this system, increase the convenience that intelligent epidemic situation prevention and control system used.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. The utility model provides an epidemic prevention and control system based on IOT temperature measurement, location bracelet and AI, its characterized in that includes:
the system comprises a supervised end, a server end and a supervisor APP end;
wherein, by the output of supervisor's end with the input of server end is connected, the output of server end with the input of supervisor's end is connected, by the supervisor's end including temperature measurement and location IOT bracelet and by supervisor APP end, inside temperature measurement module, GPS orientation module and the 4G module of being provided with of temperature measurement and location IOT bracelet, the server end includes database module, crowd gathering analysis module, crowd gathering prediction module.
2. The epidemic prevention and control system based on the IOT temperature measurement, the positioning bracelet and the AI as claimed in claim 1, wherein the temperature measurement module is used for collecting temperature data of a supervised person and uploading the collected data to the server, the GPS positioning module is used for collecting real-time position information and uploading the real-time position information to the server, and the 4G module is used for communicating with the server.
3. The epidemic prevention and control system based on IOT temperature measurement, positioning bracelet and AI as claimed in claim 1 wherein, the supervised APP terminal is used to obtain the positioning, temperature data and crowd gathering area data of the supervised person from the server terminal, and present them in the form of map and list, and provide the window for editing and sending the supervised person's needs, so as to send the needs to the supervisor in time.
4. The epidemic prevention and control system based on IOT temperature measurement, location bracelet and AI of claim 1, wherein the database module comprises a supervised person location library, a supervised person identity information library, a supervised person temperature library and a mobile phone user real-time location library, the supervised person location library is used for storing and managing the collected supervised person location data.
5. The IOT temperature measurement, location bracelet and AI based epidemic prevention and control system of claim 1, wherein the supervised person identity information repository is used to store and manage the identity information of the supervised person, the supervised person body temperature repository is used to store and manage the body temperature data of the supervised person, and the mobile phone user real-time location repository is used to store and manage the location data of the user parsed from the operator provided specific interface.
6. The epidemic prevention and control system based on IOT temperature measurement, positioning bracelet and AI according to claim 1, wherein the crowd gathering analysis module analyzes to obtain real-time positioning data of a user by using a specific interface provided by an operator, analyzes a current crowd gathering place at the place through a clustering algorithm and outputs longitude and latitude thereof, and develops a back-end data interface that externally provides the longitude and latitude of JSON-format data, the crowd gathering prediction module analyzes to obtain the real-time positioning data of the user by using the specific interface provided by the operator, predicts a crowd gathering place that is likely to occur at a next moment through an AI program and outputs the longitude and latitude, and develops the back-end data interface that externally provides the longitude and latitude of the JSON-format data.
7. The epidemic prevention and control system based on the IOT temperature measurement, the positioning bracelet and the AI according to claim 1, wherein the supervisor side APP is a software module, and is presented in the form of executable software, accesses the server to obtain data with a certain authority, presents the data in the form of a map and a list on an APP interface, and respectively obtains the temperature data, the historical positioning data, the position data of the crowd gathering area and the position data of the forecasted crowd gathering area of the supervised person by accessing the server side APP.
8. The epidemic prevention and control system based on the IOT temperature measurement, the positioning bracelet and the AI according to claim 1, wherein the supervised person APP is written using Java, and is an executable file under an Android system, and functions to obtain positioning, temperature data and crowd gathering area data of the observed person from the server, present the positioning, temperature data and crowd gathering area data in a map and list form, provide a window for the observed person to edit and send, and make a page alarm for emergency situations such as abnormal body temperature of the observed person, approaching crowd gathering area, and the like.
9. The epidemic prevention and control system based on the IOT temperature measurement, the positioning bracelet and the AI according to claim 1, wherein the supervisor APP side is written using Java, is an executable file under an Android system, and functions to obtain the positioning data, the temperature data and the crowd gathering area data of the observed person from the server side, present them in the form of a map and a list, provide a window for the observed person to edit and send, and make a page alarm for emergency situations such as abnormal body temperature of the observed person, approaching the crowd gathering area, and the like.
10. The epidemic prevention and control system based on IOT temperature measurement, location bracelet and AI of claim 1, characterized in that the database module uses Mysql for establishing a supervised person body temperature library, a supervised person identity information library, a mobile phone user real-time location library and a supervised person location library, the crowd gathering analysis module and the crowd gathering prediction module are implemented by python, the analysis of emergency such as abnormal body temperature is implemented by using a logic judgment program, the crowd gathering analysis is implemented by using a clustering algorithm, the recognition of crowd gathering conditions is implemented by deep learning with Pythrch as a frame, and the model is obtained after machine training for predicting the crowd gathering points at the later moment.
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