CN111402476A - Community epidemic prevention safety method based on machine learning - Google Patents
Community epidemic prevention safety method based on machine learning Download PDFInfo
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- CN111402476A CN111402476A CN202010503122.8A CN202010503122A CN111402476A CN 111402476 A CN111402476 A CN 111402476A CN 202010503122 A CN202010503122 A CN 202010503122A CN 111402476 A CN111402476 A CN 111402476A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
- G01K13/00—Thermometers specially adapted for specific purposes
- G01K13/20—Clinical contact thermometers for use with humans or animals
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C9/00—Individual registration on entry or exit
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/52—Network services specially adapted for the location of the user terminal
Abstract
The application discloses a community epidemic prevention safety method based on machine learning, which comprises the following steps: sending a message to a server through a communication module; receiving the epidemic prevention strategy returned by the server from the server through the communication module; after the residents are allowed to enter the entrance guard according to the epidemic prevention strategy, the first entrance guard is opened, the number of people entering between the first entrance guard and the second entrance guard is sensed through machine learning, and the second entrance guard is opened under the condition that the number of people is 1. According to the method and the system, the entrance guard system obtains the epidemic prevention strategy, and the residents entering the residential area are automatically judged and controlled based on machine learning according to the epidemic prevention strategy, so that the problem that human resources are consumed greatly due to the fact that the residents need to be controlled by people during the epidemic situation in the related art is solved, and the automatic control of the entrance guard along with the epidemic prevention grade in the epidemic situation prevention and control is realized.
Description
Technical Field
The application relates to the field of Internet of things, in particular to a community epidemic prevention safety method based on machine learning.
Background
Under the condition that epidemic situation exists in a certain area, expected epidemic prevention effect can be achieved by adopting an isolation policy. The most easily implemented isolation policy is to isolate the cell and control the persons entering the cell to achieve epidemic prevention.
Along with the change of the regional epidemic situation, the control strategy of the personnel entering the cell can also change, for example, when the epidemic situation is serious, the personnel entering the cell need to be strictly controlled; for another example, when the epidemic situation is light, people entering the cell can be relaxed appropriately.
At present, residents in a community are basically controlled by people, a large amount of human resources are needed to execute prevention and control of epidemic situations, people do not control the change of the epidemic situations timely, and if an automatic release strategy is used, the problem that people enter the system at the tail cannot be solved, so that the residents still need to manually check and release the system at present, and a large amount of human resources are wasted.
Disclosure of Invention
The application provides a community epidemic prevention safety method based on machine learning, and aims to solve the problem that human resources are consumed greatly due to the fact that people need to control the community epidemic situation in the related art.
According to an aspect of the present application, a machine learning-based community epidemic prevention safety method is provided, which is applied to a community access control system, and the community access control system includes: district entrance guard part and server, wherein, district entrance guard part includes: the system comprises a door, a communication module, a camera, a body temperature measuring module, a communication module and an access control card reading module; the door is used for the entrance and exit of residents; the door comprises a first entrance guard and a second entrance guard; the camera is used for acquiring images; the communication module is used for carrying out communication; the body temperature measuring module is used for measuring the body temperature of the residents; the access card reading module is used for reading the resident access cards; the method comprises the following steps: step S102, sending a message to the server through the communication module, wherein the message carries a geographical position of the community access control system, the geographical position is pre-configured and stored in the community access control system, and the message is used for requesting an epidemic prevention strategy; the message is sent regularly every day, and the sending time is preset; step S104, receiving the epidemic prevention strategy returned by the server from the server through the communication module, wherein the epidemic prevention strategy is a risk level corresponding to the geographic position, different risk levels correspond to different epidemic prevention strategies, and the epidemic prevention strategy is a combination of at least one of the following strategies: measuring whether the body temperature of the residents is in a normal range, judging whether the residents are residents in the local community, and judging whether the health certification of the residents meets the requirements; the body temperature is measured using the body temperature measurement module; judging whether the residents are residents of the local community or not according to whether the access control card reading module reads the access control card of the local community or not; shooting the health certification of the residents by using the camera module, and judging whether the health certification of the residents meets the requirements or not according to the shot pictures; step S106, opening a door after the resident is judged to be allowed to enter according to the epidemic prevention strategy; wherein, first way entrance guard opens after step S106, and the resident gets into this moment the second way entrance guard with be provided with personnel quantity induction system between the first way entrance guard for the response enters into first way entrance guard with personnel quantity between the second way entrance guard is opened under the condition that personnel quantity is 1 the second way entrance guard. If the number of the personnel is not unique, alarming is carried out to prompt all the personnel entering the first access control to exit and enter again in sequence; the community access control part sends the photos to the server for identifying the number of people, the server identifies the photos, and the identified photos are sent to the community access control part; the server recognizes the image by using a model obtained by machine learning training, wherein the model obtained by machine learning training is obtained by using a plurality of groups of training data, and each group of training data comprises a photo and a label for identifying the number of people in the photo. The input of the trained model is the picture, and the output is the number of people in the picture.
Further, the judging whether the proof of health of the resident meets the requirement based on the photographed picture includes: acquiring characters in the photo, wherein the characters are used for indicating whether the residents are healthy or not; and determining whether the health certificate of the resident meets the requirement according to whether the characters in the photo are consistent with the characters configured in advance.
Further, acquiring the text in the photo comprises: sending the photo to the server, and extracting characters from the photo by the server; and receiving the characters from the server.
Further, acquiring the text in the photo comprises: sending the photo to a computer electrically connected with the community access control system, wherein software is installed in the computer and used for extracting characters from the photo; and receiving the characters extracted by the software in the computer.
According to another aspect of the present application, there is also provided a memory for storing software for performing the above method.
According to another aspect of the present application, there is also provided a processor for executing software, wherein the software is configured to perform the above method.
According to the method and the system, the entrance guard system obtains the epidemic prevention strategy, and the residents entering the residential area are automatically judged and controlled based on machine learning according to the epidemic prevention strategy, so that the problem that human resources are consumed greatly due to the fact that the residents need to be controlled by people during the epidemic situation in the related art is solved, and the automatic control of the entrance guard along with the epidemic prevention grade in the epidemic situation prevention and control is realized.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application. In the drawings:
fig. 1 is a flowchart of a cell epidemic prevention safety method based on machine learning according to an embodiment of the present application.
Detailed Description
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
In this embodiment, a machine learning-based community epidemic prevention safety method is provided, and the method is applied to a community access control system, and is applied to a community access control system, where the community access control system includes: district entrance guard part and server, wherein, district entrance guard part includes: the system comprises a door, a communication module, a camera, a body temperature measuring module, a communication module and an access control card reading module; the door (comprising a first access control and a second access control) is used for the entrance and exit of residents; the camera is used for acquiring images; the communication module is used for carrying out communication; the body temperature measuring module is used for measuring the body temperature of the residents; and the access control card reading module is used for reading the resident access control card. Fig. 1 is a flowchart of a cell epidemic prevention safety method based on machine learning according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
step S102, sending a message to a server through the communication module, wherein the message carries a geographical position of the community access control system, the geographical position is pre-configured and stored in the community access control system, and the message is used for requesting an epidemic prevention strategy; the message is sent regularly every day, and the sending time is preset; the server stores corresponding risk levels of different regions;
because every access control system all fixes at certain specific geographical position, for the consideration of saving cost this moment, generally need not increase orientation module in access control system, when installing access control system, with the geographical position configuration at this access control system place in access control system can.
For some old districts, if increase intelligent access control system temporarily, can make the access control system that can remove, only set up at the district temporarily during the epidemic situation to this kind of access control system, take into account this access control system's mobility this moment, can increase orientation module in this access control system, obtain the geographical position that this access control system belongs to by orientation module is automatic, this orientation module can be GPS orientation module, also can be big dipper orientation module.
When the access control system is configured with the positioning module, the geographic position is obtained through the positioning module, and if the geographic position obtained by the positioning module is not accordant with the pre-stored geographic position, the geographic position obtained by the positioning module is used. The geographic location is sent to a server to request an epidemic prevention strategy.
Step S104, receiving the epidemic prevention strategy returned by the server from the server through the communication module, wherein the epidemic prevention strategy is a risk level corresponding to the geographic position, different risk levels correspond to different epidemic prevention strategies, and the epidemic prevention strategy is a combination of at least one of the following strategies: measuring whether the body temperature of the residents is in a normal range, judging whether the residents are residents in the local community, and judging whether the health certification of the residents meets the requirements; the body temperature is measured using the body temperature measurement module; judging whether the residents are residents of the local community or not according to whether the access control card reading module reads the access control card of the local community or not; shooting the health certification of the residents by using the camera module, and judging whether the health certification of the residents meets the requirements or not according to the shot pictures;
for example, epidemic prevention grades may be divided into three grades: high, medium and low. Different levels correspond to different policies: the epidemic prevention grade is the highest, so that the temperature is normal, the health is proved to be normal, and residents in the community can enter the community. If the epidemic prevention level is medium, the temperature is normal and the residents in the community can enter the system with health certification. If the epidemic prevention level is low, the body temperature can be normal.
The access control system can also be configured with a default access control door opening mode, and the mode is used after the epidemic situation disappears. When the acquired risk level is zero, the default door opening mode of the entrance guard can be recovered.
And S106, opening a door after the resident is judged to be allowed to enter according to the epidemic prevention strategy.
And sending out alarm information after judging that the resident is not allowed to enter. The alarm information includes the contact information of the administrator and the reason why the resident is not allowed to enter. The administrator is used for opening the access control authority. If the administrator judges that the resident can enter, the access control system can take a picture when the administrator opens the access control system, and the picture of the resident allowed to enter by the administrator is recorded. This is taken as the basis for subsequent processing.
As an optional implementation manner, for greater security, two access controls may be used, the first access control opens the door after step S106, and the resident enters this moment, and a personnel number sensing device is provided between the second access control and the first access control to sense the quantity of personnel entering between the first access control and the second access control, and the second access control is opened under the condition that the quantity of personnel is 1. And if the number of the personnel is not unique, alarming is carried out, and all the personnel entering the first access control are prompted to exit and enter again in sequence.
Personnel quantity induction system can shoot through the camera and realize, sets up the second camera between first entrance guard and second way entrance guard, and this camera is used for shooing the personnel between first way entrance guard and the second way entrance guard, discerns the quantity of personnel in the photo. Of course, other types of sensing devices can be applied to the embodiments of the present application, and are not described herein.
The community access control part sends the photos to the server for identifying the number of people, the server identifies the photos, and the identified photos are sent to the community access control part; the server recognizes the image by using a model obtained by machine learning training, wherein the model obtained by machine learning training is obtained by using a plurality of groups of training data, and each group of training data comprises a photo and a label for identifying the number of people in the photo. The input of the trained model is the picture, and the output is the number of people in the picture.
By this alternative embodiment, it is possible to avoid people entering the cell following others.
Through the steps, the entrance guard system acquires the epidemic prevention strategy, and automatically judges and controls residents entering the residential area according to the epidemic prevention strategy, so that the problem that human resources are consumed greatly due to the fact that the residents need to be controlled by people during the epidemic situation in the related art is solved, and the automatic control of the epidemic situation by the entrance guard along with the epidemic prevention grade is achieved.
During an epidemic, a health certificate is typically used, which may be a page on APP, or a paper certificate. When judging the health certificate, it is first judged whether the health certificate is a legitimate health certificate. For example, a template of the required health certification is acquired in advance in a storage space in the access control system. The template is issued by a server, and the access control system stores the template after the issuing. After the health certificate held by the resident is photographed, whether the health certificate of the resident is a legal health certificate is determined according to the comparison result of the picture of the health certificate and a template saved in advance.
When the access control system compares the images, a comparison mode of various pictures can be adopted, and when the similarity degree of the pictures in the resident health certificate and the template exceeds a threshold value, the health certificate is determined to be a legal health certificate.
Or, when the comparison is performed, the comparison may be performed on a server, that is, the access control system sends the picture to the server, and the server performs the comparison. The server may perform machine translation, and train a machine learning model using training data, where the model uses multiple sets of training data, each set of training data includes a template picture and a health certificate picture, and further includes a label, where the label is used to indicate whether the health certificate picture in the training data matches the template picture. After training, the input of the model is the template picture and the photographed health certificate picture of the resident, and the output is whether the health certificate picture is legal or not.
And after judging whether the health certificate is a legal health certificate or not, the server sends a judgment result to the access control system.
After the health certification of the resident is judged to be a legal health certification, whether the content in the health certification indicates whether the health condition of the resident is normal or not is also identified.
For example, it is possible to judge whether the health condition of the resident is normal in the following manner.
Judging whether the health certification of the resident meets the requirements according to the shot picture comprises the following steps: acquiring characters in the photo, wherein the characters are used for indicating whether the residents are healthy or not; and determining whether the health certificate of the resident meets the requirement according to whether the characters in the photo are consistent with the characters configured in advance. Compliance indicates that the health condition indicated in the health certification is normal.
The obtaining mode can be judged by the server or the computer. For example: sending the photo to the server, and extracting characters from the photo by the server; and receiving the characters from the server. For another example, the photo is sent to a computer electrically connected with the community access control system, wherein software is installed in the computer and used for extracting characters from the photo; and receiving the characters extracted by the software in the computer.
As another alternative, the access control system may send the number of people of the access control system of the current day to the server at a fixed time of day, wherein the number of people is the number of residents entering through the access control system, and the number of people who are determined not to be allowed to enter the cell. Further, information of persons who do not enter the cell may be transmitted to the server.
Because the server is connected with a plurality of different access control systems, the server can obtain an epidemic situation prevention and control map after summarizing the data reported by each access control system, and contributes to the statistics of prevention and control data.
In this embodiment, a memory is provided for storing software for performing the above-described method.
In this embodiment, a processor is provided for executing software for performing the above-described method.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than presented herein.
An embodiment of the present invention provides a storage medium on which a program or software is stored, the program implementing the above method when executed by a processor. The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (4)
1. A community epidemic prevention safety method based on machine learning is characterized in that the method is applied to a community access control system, and the community access control system comprises the following steps: district entrance guard part and server, wherein, district entrance guard part includes: the system comprises a door, a communication module, a camera, a body temperature measuring module, a communication module and an access control card reading module; the door is used for the entrance and exit of residents; the door comprises a first entrance guard and a second entrance guard; the camera is used for acquiring images; the communication module is used for carrying out communication; the body temperature measuring module is used for measuring the body temperature of the residents; the access card reading module is used for reading the resident access cards; the method comprises the following steps:
sending a message to the server through the communication module, wherein the message carries a geographical position of the community access control system, the geographical position is pre-configured and stored in the community access control system, and the message is used for requesting an epidemic prevention strategy; the message is sent regularly every day, and the sending time is preset;
receiving the epidemic prevention strategy returned by the server from the server through the communication module, wherein the epidemic prevention strategy is a risk level corresponding to the geographic position, different risk levels correspond to different epidemic prevention strategies, and the epidemic prevention strategy is a combination of at least one of the following strategies: measuring whether the body temperature of the residents is in a normal range, judging whether the residents are residents in the local community, and judging whether the health certification of the residents meets the requirements; the body temperature is measured using the body temperature measurement module; judging whether the residents are residents of the local community or not according to whether the access control card reading module reads the access control card of the local community or not; shooting the health certification of the residents by using the camera module, and judging whether the health certification of the residents meets the requirements or not according to the shot pictures;
opening a door after the resident is judged to be allowed to enter according to the epidemic prevention strategy; wherein, opening the door includes: opening the first entrance guard, wherein residents enter the first entrance guard, a personnel number sensing device is arranged between the second entrance guard and the first entrance guard and used for identifying the quantity of personnel entering the space between the first entrance guard and the second entrance guard, the second entrance guard is opened under the condition that the personnel number is 1, and if the personnel number is not unique, an alarm is given to prompt all personnel entering the first entrance guard to exit and enter the first entrance guard again in sequence; the community access control part sends the photos to the server for identifying the number of people, the server identifies the photos, and the identified photos are sent to the community access control part; the server identifies the model obtained by machine learning training when identifying, the model obtained by machine learning training is obtained by training a plurality of groups of training data, each group of training data comprises a photo and a label for identifying the number of people in the photo, the input of the model obtained by training is the photo, and the output is the number of people in the photo.
2. The method according to claim 1, wherein the judging whether the proof of health of the resident meets the requirement based on the taken picture comprises: acquiring characters in the photo, wherein the characters are used for indicating whether the residents are healthy or not;
and determining whether the health certificate of the resident meets the requirement according to whether the characters in the photo are consistent with the characters configured in advance.
3. The method of claim 2, wherein obtaining text in the photograph comprises: sending the photo to the server, and extracting characters from the photo by the server; and receiving the characters from the server.
4. The method of claim 2, wherein obtaining text in the photograph comprises:
sending the photo to a computer electrically connected with the community access control system, wherein software is installed in the computer and used for extracting characters from the photo; and receiving the characters extracted by the software in the computer.
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| CN112133008A (en) * | 2020-08-11 | 2020-12-25 | 浙江大华技术股份有限公司 | Park pedestrian identification method and system and computer equipment |
| CN112037395A (en) * | 2020-08-26 | 2020-12-04 | 武汉普利商用机器有限公司 | Access control method and device, electronic equipment and storage medium |
| CN112819326A (en) * | 2021-01-30 | 2021-05-18 | 成都航空职业技术学院 | Epidemic situation prevention and control management system suitable for residential community and construction method thereof |
| CN112819326B (en) * | 2021-01-30 | 2023-10-17 | 成都航空职业技术学院 | Epidemic situation prevention and control management system suitable for residential communities and construction method thereof |
| CN113066214A (en) * | 2021-03-26 | 2021-07-02 | 深圳市博盛科电子有限公司 | Access control system based on 5G network remote monitoring |
| CN113808313A (en) * | 2021-08-16 | 2021-12-17 | 茂名粤云信息技术有限公司 | Access control method of intelligent access control system with epidemic situation prevention and control function |
| CN115019428A (en) * | 2022-06-24 | 2022-09-06 | 杭州海康威视数字技术股份有限公司 | Passage management method and device based on place codes |
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|---|---|
| CN111402476B (en) | 2020-10-09 |
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