CN113314230A - Intelligent epidemic prevention method, device, equipment and storage medium based on big data - Google Patents
Intelligent epidemic prevention method, device, equipment and storage medium based on big data Download PDFInfo
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Abstract
The application provides an intelligent epidemic prevention method, an intelligent epidemic prevention device, intelligent epidemic prevention equipment and a storage medium based on big data, wherein the intelligent epidemic prevention method based on the big data comprises the following steps: detecting the number of persons passing through the exit of the target area and the number of persons passing through the entrance of the target area; calculating the number of accommodated persons in the target area according to the number of persons passing through the exit of the target area and the number of persons passing through the entrance of the target area; when the number of people accommodated in the target area meets a preset first condition, allowing the human body to pass through the entrance of the target area; detecting the body temperature of the human body passing through the target area and obtaining a body temperature value; when the body temperature value meets a preset second condition, acquiring heat detection data of the human body and camera imaging of the human body; and judging whether the human body wears the mask or not according to the thermal detection data of the human body and the imaging of the camera of the human body and outputting a wearing detection result. The method and the device can reduce the labor cost of epidemic prevention, reduce epidemic situation risks faced by epidemic prevention workers, reduce the difficulty of epidemic prevention work and improve the accuracy of epidemic prevention work.
Description
Technical Field
The application relates to the field of computers, in particular to an intelligent epidemic prevention method, device, equipment and storage medium based on big data.
Background
New crown epidemic situation is abused globally, health epidemic prevention guarantee becomes mainstream demand, public places such as squares and campuses are taken as crowd intensive places, public health incidents are easily caused suddenly, and health data acquisition, tracking, supervision and diagnosis and treatment are normalized demands of public health. However, in these public places, a lot of human resources are needed to perform epidemic prevention and control work, which has the disadvantages of high human cost and epidemic risk faced by workers.
Disclosure of Invention
The embodiment of the application aims to provide an intelligent epidemic prevention method, device, equipment and storage medium based on big data. The method is used for reducing the labor cost of epidemic prevention, reducing epidemic situation risks faced by epidemic prevention workers, reducing the difficulty of epidemic prevention work and improving the accuracy of epidemic prevention work.
To this end, the first aspect of the present application discloses a big data-based intelligent epidemic prevention method, which comprises:
detecting the number of persons passing through the exit of the target area and the number of persons passing through the entrance of the target area;
calculating the number of accommodated people in the target area according to the number of people passing through the outlet of the target area and the number of people passing through the inlet of the target area;
when the number of persons accommodated in the target area meets a preset first condition, allowing the human body to pass through an entrance of the target area;
detecting the body temperature of the human body passing through the target area to obtain a body temperature value;
when the body temperature value meets a preset second condition, acquiring heat detection data of the human body and camera imaging of the human body;
and judging whether the human body wears the mask or not according to the thermal detection data of the human body and the imaging of the camera of the human body and outputting a wearing detection result.
In a first aspect of the present application, the body of the person passing through the target area includes the body of the person passing through the entrance of the target area, and the body of the person being within the target area, therefore, the embodiment of the application can detect whether the human body wears the mask at the entrance of the target area, and can also detect the real-time mask wearing behavior of the human body in the target area, for example, the human body can take off the mask to have a meal after entering the target area, however, after eating, people often forget to put on the mask, and at the moment, images of the human body are acquired through one or more cameras installed in the target area, and then judge whether human body wears the gauze mask, just so can automatic real time monitoring human action of wearing the gauze mask, improve epidemic prevention efficiency and reduce the human cost, on the other hand because the mode does not need the epidemic prevention personnel directly to face the human body, and then can reduce epidemic situation risk of epidemic prevention personnel. However, in the prior art, human body masks need to be judged manually, and especially when a plurality of human bodies are monitored in a large-area target area, a plurality of epidemic prevention personnel need to be arranged to detect at a plurality of point positions, so that the number of required epidemic prevention personnel is large, and the epidemic prevention personnel cannot frequently detect every corner, thereby causing great epidemic prevention difficulty and high labor cost.
In the first aspect of the present application, as an optional implementation manner, after determining whether the human body wears a mask according to the thermal detection data of the human body and the camera imaging of the human body, and outputting a wearing detection result, the method further includes:
acquiring a real-time image of the target area, wherein the real-time image is generated by a camera disposed in the target area;
determining the position information of each human body in the target area according to the real-time image;
calculating the relative position between the human bodies according to the internal reference matrix of the camera and the position information of each human body;
and judging whether the relative positions of the human bodies meet a preset third condition or not, and if not, outputting prompt information.
According to the optional embodiment, the distance between the human bodies in the target area is detected, so that on one hand, the people in the target area can be prevented from gathering, on the other hand, the epidemic prevention automatic monitoring degree of the target area can be further improved, and the labor cost can be further reduced.
In the first aspect of the present application, as an optional implementation manner, the determining, according to the real-time image, position information of each human body in the target area includes:
determining a human body image area according to the real-time image;
calculating the maximum value of the ordinate of the pixel coordinate of the human body image area and the average value of the abscissa of the pixel coordinate of the human body image area;
calculating to obtain a pixel coordinate of the observation point according to the maximum value of the ordinate of the pixel coordinate and the average value of the abscissa of the pixel coordinate;
and calculating the coordinates of the observation point in a camera coordinate system according to the pixel coordinates of the observation point, and taking the coordinates of the observation point in the camera coordinate system as the position information of the human body.
In this optional embodiment, by calculating the maximum value of the ordinate of the pixel coordinate of the human body image area and the average value of the abscissa of the pixel coordinate of the human body image area, the pixel coordinate of the observation point can be calculated according to the maximum value of the ordinate of the pixel coordinate and the average value of the abscissa of the pixel coordinate, so that the position information of the human body can be calculated.
In the first aspect of the present application, as an optional implementation manner, after the calculating the number of persons accommodated in the target area according to the number of persons who exit from the target area and the number of persons who enter from the target area, the method further includes:
using time dimension information, weather dimension information, temperature dimension information and people flow dimension information as input of a first neural network, and enabling the first neural network to output a limit threshold value of the target area according to a preset machine learning algorithm;
and comparing the number of persons accommodated in the target area with the limit threshold of the target area, and determining that the number of persons accommodated in the target area meets the preset first condition when the number of persons accommodated in the target area is smaller than the limit threshold of the target area.
In the optional embodiment, the personnel capacity of the public places can be more accurately evaluated through the time dimension information, the weather dimension information, the temperature dimension information and the people flow dimension information, so that an epidemic situation prevention and control mechanism is more accurately, truly and effectively established, and the prevention and control are convenient.
In the first aspect of the present application, as an optional implementation manner, the preset machine learning algorithm is one of a k-means algorithm, an adboost algorithm, and an xgboost algorithm.
In the first aspect of the present application, as an optional implementation, the determining whether the human body wears a mask and outputting a wearing detection result according to the thermal detection data of the human body and the camera image of the human body includes:
calculating a human body region image in the camera imaging of the human body according to the thermal detection data of the human body and a first deep learning network;
cropping the human body region image from the camera imaging of the human body;
and judging whether the human body wears the mask or not according to the human body area image and a second deep learning network and outputting a wearing detection result.
In this alternative embodiment, since the human body has a temperature, simultaneously using the thermal detection data from the human body and the camera imaging of the human body as inputs to the first deep learning network may enable the first deep learning network to more accurately determine the human body region image in the camera imaging of the human body.
In the first aspect of the present application, as an optional implementation manner, after the determining whether the human body wears a mask according to the thermal detection data of the human body and the camera imaging of the human body and outputting a wearing detection result, the method further includes:
and when the human body is detected not to wear the mask, generating alarm information.
In the optional implementation mode, through the alarm information, epidemic prevention personnel can know the epidemic prevention situation in time, and further the working efficiency of the working personnel is improved.
This application second aspect discloses an intelligence epidemic prevention device based on big data, the device includes:
the first detection module is used for detecting the number of people passing through the outlet of the target area and the number of people passing through the inlet of the target area;
the calculating module is used for calculating the number of accommodated people in the target area according to the number of people passing through the outlet of the target area and the number of people passing through the inlet of the target area;
the admission control module is used for permitting a human body to pass through the inlet of the target area when the number of people accommodated in the target area meets a preset first condition;
the second detection module is used for detecting the body temperature of the human body passing through the target area and obtaining a body temperature value;
the acquisition module is used for acquiring the thermal detection data of the human body and the camera imaging of the human body when the body temperature value meets a preset second condition;
and the judging module is used for judging whether the human body wears the mask or not according to the thermal detection data of the human body and the imaging of the camera of the human body and outputting a wearing detection result.
The device of the second aspect of the application can reduce the human cost of epidemic prevention and reduce epidemic situation risks faced by epidemic prevention workers by executing the intelligent epidemic prevention method based on big data.
The third aspect of the present application discloses an intelligent epidemic prevention equipment based on big data, the equipment includes:
a processor; and
a memory configured to store machine readable instructions that, when executed by the processor, cause the processor to perform the big-data based intelligent epidemic prevention method of the first aspect of the present application.
The device of the third aspect of the application reduces the labor cost of epidemic prevention, reduces epidemic situation risk faced by epidemic prevention workers, reduces the difficulty of epidemic prevention work and improves the accuracy of epidemic prevention work by executing the intelligent epidemic prevention method based on big data.
A fourth aspect of the present application discloses a storage medium, which stores a computer program, wherein the computer program is executed by a processor to perform the big data based intelligent epidemic prevention method of the first aspect of the present application.
The storage medium of the third aspect of the application reduces the labor cost of epidemic prevention, reduces epidemic situation risk faced by epidemic prevention workers, reduces the difficulty of epidemic prevention work and improves the accuracy of epidemic prevention work by executing an intelligent epidemic prevention method based on big data.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
FIG. 1 is a schematic flow chart of an intelligent epidemic prevention method based on big data, disclosed in the embodiments of the present application;
FIG. 2 is a schematic structural diagram of an intelligent epidemic prevention device based on big data, which is disclosed in the embodiment of the present application;
fig. 3 is a schematic structural diagram of an intelligent epidemic prevention equipment based on big data, which is disclosed in an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
Example one
Referring to fig. 1, fig. 1 is a schematic flow chart of an intelligent epidemic prevention method based on big data according to an embodiment of the present application. As shown in fig. 1, the method of the embodiment of the present application includes the steps of:
101. detecting the number of persons passing through the exit of the target area and the number of persons passing through the entrance of the target area;
102. calculating the number of accommodated persons in the target area according to the number of persons passing through the exit of the target area and the number of persons passing through the entrance of the target area;
103. when the number of people accommodated in the target area meets a preset first condition, allowing the human body to pass through the entrance of the target area;
104. detecting the body temperature of the human body passing through the target area and obtaining a body temperature value;
105. when the body temperature value meets a preset second condition, acquiring heat detection data of the human body and camera imaging of the human body;
106. and judging whether the human body wears the mask or not according to the thermal detection data of the human body and the imaging of the camera of the human body and outputting a wearing detection result.
In the embodiment of the application, the number of people passing through the outlet of the target area and the number of people passing through the inlet of the target area are detected, the current number of people in the target area can be determined, whether the target area can accommodate the human body again or not is judged according to the current number of people, if not, the new human body is limited to enter the target area, and therefore the situation that the target area is not crowded can be guaranteed, and the distance between the people in the target area is enabled to be a certain value preliminarily guaranteed.
In the embodiment of the present application, the human body passing through the target area includes the human body passing through the entrance of the target area, and the human body being in the target area, therefore, the embodiment of the application can detect whether the human body wears the mask at the entrance of the target area, and can also detect the real-time mask wearing behavior of the human body in the target area, for example, the human body can take off the mask to have a meal after entering the target area, however, after eating, people often forget to put on the mask, and at the moment, images of the human body are acquired through one or more cameras installed in the target area, and then judge whether human body wears the gauze mask, just so can automatic real time monitoring human action of wearing the gauze mask, improve epidemic prevention efficiency and reduce the human cost, on the other hand because the mode does not need the epidemic prevention personnel directly to face the human body, and then can reduce epidemic situation risk of epidemic prevention personnel. However, in the prior art, human body masks need to be judged manually, and especially when a plurality of human bodies are monitored in a large-area target area, a plurality of epidemic prevention personnel need to be arranged to detect at a plurality of point positions, so that the number of required epidemic prevention personnel is large, and the epidemic prevention personnel cannot frequently detect every corner, thereby causing great epidemic prevention difficulty and high labor cost.
In this embodiment of the present application, the target area may be an area such as a square, a supermarket, a campus, and the like, and this embodiment of the present application is not limited thereto.
In the embodiment of the present application, the preset first condition may mean that the number of persons who can enter the target area is 0.
In the embodiment of the application, the preset second condition is that the body temperature of the human body is less than or equal to 37 ℃ or other body temperature thresholds meeting epidemic prevention requirements.
In the embodiment of the present application, as an example of step 102, it is assumed that the number of people passing through the exit of the target area in a unit time is 10, and the number of people passing through the entrance of the target area in the same time is 15, and the number of people in the current target area is 20, where 15 is the number of people existing in the target area.
In the embodiment of the application, in order to detect the body temperature of the human body, temperature measuring instruments can be arranged at the inlet and the inner area of the current area, and then the body temperature of the human body can be detected through the temperature measuring instruments.
In the embodiment of the present application, as an optional implementation manner, in the step: after judging whether the human body wears the mask and outputting a wearing detection result according to the thermal detection data of the human body and the imaging of the camera of the human body, the method of the embodiment of the application further comprises the following steps:
acquiring a real-time image of a target area, wherein the real-time image is generated by a camera arranged in the target area;
determining the position information of each human body in the target area according to the real-time image;
calculating the relative position between the human bodies according to the internal reference matrix of the camera and the position information of each human body;
and judging whether the relative position between the human bodies meets a preset third condition or not, and if not, outputting prompt information.
In an embodiment of the present application, the real-time image of the target area may be a camera image of the human body, and on the other hand, if the previously acquired camera image of the body does not meet the condition, it may be acquired again by the camera.
In the embodiment of the application, the preset third condition may be greater than 0.5m, or greater than 0.6m, or other distance conditions meeting the epidemic prevention requirement.
In the embodiment of the application, the distance between human bodies in the target area is detected, so that on one hand, the gathering of people in the target area can be prevented, on the other hand, the epidemic prevention automatic monitoring degree of the target area can be further improved, and the labor cost is further reduced.
In the embodiment of the present application, as an optional implementation manner, the steps of: determining position information of each human body in the target area according to the real-time image, comprising:
determining a human body image area according to the real-time image;
calculating the maximum value of the ordinate of the pixel coordinate of the human body image area and the average value of the abscissa of the pixel coordinate of the human body image area;
calculating to obtain the pixel coordinate of the observation point according to the maximum value of the ordinate of the pixel coordinate and the average value of the abscissa of the pixel coordinate;
and calculating the coordinates of the observation point in the camera coordinate system according to the pixel coordinates of the observation point, and taking the coordinates of the observation point in the camera coordinate system as the position information of the human body.
In this alternative embodiment, the real-time image includes a human body and a beijing image, and therefore a human body image region needs to be partitioned from the real-time image, wherein the image features of the real-time image may be input into a pre-trained image recognition model, so that the image recognition model outputs the human body image region. It should be noted that, please refer to the prior art for how the image recognition model recognizes the human body image region in the real-time image, which is not described in detail in the embodiments of the present application.
In this optional embodiment, by calculating the maximum value of the ordinate of the pixel coordinate of the human body image area and the average value of the abscissa of the pixel coordinate of the human body image area, the pixel coordinate of the observation point can be calculated according to the maximum value of the ordinate of the pixel coordinate and the average value of the abscissa of the pixel coordinate, so that the position information of the human body can be calculated.
In the embodiment of the present application, as an optional implementation manner, in step 102: after calculating the number of persons accommodated in the target area according to the number of persons passing through the exit of the target area and the number of persons passing through the entrance of the target area, the method of the embodiment of the application further comprises the following steps:
the time dimension information, the weather dimension information, the temperature dimension information and the people flow dimension information are used as input of a first neural network, so that the first neural network outputs a suggested threshold value capable of containing people number and a limit threshold value of a target area according to a preset machine learning algorithm;
and comparing the number of persons accommodated in the target area with the limit threshold of the target area, and determining that the number of persons accommodated in the target area meets a preset first condition when the number of persons accommodated in the target area is smaller than the limit threshold of the target area.
In this optional embodiment, the first neural network can also output a suggested threshold value of the number of persons that can be accommodated in the target area according to a preset machine learning algorithm, thereby further facilitating control of the number of persons in the target area by epidemic prevention personnel.
In the optional embodiment, the personnel capacity of the public places can be more accurately evaluated through the time dimension information, the weather dimension information, the temperature dimension information and the people flow dimension information, so that an epidemic situation prevention and control mechanism is more accurately, truly and effectively established, and the prevention and control are facilitated.
In the embodiment of the application, as an optional implementation manner, the preset machine learning algorithm is one of a k-means algorithm, an adboost algorithm and an xgboost algorithm.
It should be noted that, for detailed descriptions of the k-means algorithm, the adboost algorithm, and the xgboost algorithm, reference is made to the prior art, and details thereof are not described in the embodiments of the present application.
In the embodiment of the present application, as an optional implementation manner, the steps of: according to human hot detected data and human camera formation of image judgement human whether wears the gauze mask and output and wear the testing result, include:
calculating a human body region image in the camera imaging of the human body according to the thermal detection data of the human body and the first deep learning network;
cutting out a human body area image from the camera imaging of the human body;
and judging whether the human body wears the mask or not according to the human body area image and the second deep learning network and outputting a wearing detection result.
In this alternative embodiment, since the human body has a temperature, simultaneously using the thermal detection data from the human body and the camera imaging of the human body as inputs to the first deep learning network may enable the first deep learning network to more accurately determine the human body region image in the camera imaging of the human body. For example, the specific structures of the first deep learning network and the second deep learning network can refer to deep learning networks such as YOLO, SSD, fast-RCNN, centrnet, etc. In the embodiment of the present application, as an optional implementation manner, after determining whether the human body wears the mask according to the thermal detection data of the human body and the camera imaging of the human body, and outputting a wearing detection result, the method further includes:
and when the human body is detected not to wear the mask, alarm information is generated.
In this optional implementation mode, when it is detected that the human body does not wear the mask, the alarm device can be triggered to produce alarm information.
In the optional implementation mode, through the alarm information, epidemic prevention personnel can know the epidemic prevention situation in time, and further the working efficiency of the working personnel is improved.
Example two
Please refer to fig. 2, fig. 2 is a schematic structural diagram of an intelligent epidemic prevention apparatus based on big data according to an embodiment of the present application. As shown in fig. 2, the apparatus of the embodiment of the present application includes:
a first detecting module 201 for detecting the number of persons passing through the exit of the target area and the number of persons passing through the entrance of the target area;
the calculating module 202 is used for calculating the number of accommodated people in the target area according to the number of people passing through the outlet of the target area and the number of people passing through the inlet of the target area;
the admission control module 203 is used for admitting a human body to pass through the inlet of the target area when the number of people accommodated in the target area meets a preset first condition;
the second detection module 204 is used for detecting the body temperature of the human body passing through the target area and obtaining a body temperature value;
the acquiring module 205 is configured to acquire thermal detection data of a human body and camera imaging of the human body when the body temperature value meets a preset second condition;
and the judging module 206 is configured to judge whether the human body wears the mask according to the human body thermal detection data and the human body camera imaging, and output a wearing detection result.
In the embodiment of the application, the number of people passing through the outlet of the target area and the number of people passing through the inlet of the target area are detected, the current number of people in the target area can be determined, whether the target area can accommodate the human body again or not is judged according to the current number of people, if not, the new human body is limited to enter the target area, and therefore the situation that the target area is not crowded can be guaranteed, and the distance between the people in the target area is enabled to be a certain value preliminarily guaranteed.
In the embodiment of the present application, the human body passing through the target area includes the human body passing through the entrance of the target area, and the human body being in the target area, therefore, the embodiment of the application can detect whether the human body wears the mask at the entrance of the target area, and can also detect the real-time mask wearing behavior of the human body in the target area, for example, the human body can take off the mask to have a meal after entering the target area, however, after eating, people often forget to put on the mask, and at the moment, images of the human body are acquired through one or more cameras installed in the target area, and then judge whether human body wears the gauze mask, just so can automatic real time monitoring human action of wearing the gauze mask, improve epidemic prevention efficiency and reduce the human cost, on the other hand because the mode does not need the epidemic prevention personnel directly to face the human body, and then can reduce epidemic situation risk of epidemic prevention personnel. However, in the prior art, human body masks need to be judged manually, and especially when a plurality of human bodies are monitored in a large-area target area, a plurality of epidemic prevention personnel need to be arranged to detect at a plurality of point positions, so that the number of required epidemic prevention personnel is large, and the epidemic prevention personnel cannot frequently detect every corner, thereby causing great epidemic prevention difficulty and high labor cost.
In this embodiment of the present application, the target area may be an area such as a square, a supermarket, a campus, and the like, and this embodiment of the present application is not limited thereto.
In the embodiment of the present application, the preset first condition may mean that the number of persons who can enter the target area is 0.
In the embodiment of the application, the preset second condition is that the body temperature of the human body is less than or equal to 37 ℃ or other body temperature thresholds meeting epidemic prevention requirements.
In the embodiment of the present application, as an example of step 102, it is assumed that the number of people passing through the exit of the target area in a unit time is 10, and the number of people passing through the entrance of the target area in the same time is 15, and the number of people in the current target area is 20, where 15 is the number of people existing in the target area.
In the embodiment of the application, in order to detect the body temperature of the human body, temperature measuring instruments can be arranged at the inlet and the inner area of the current area, and then the body temperature of the human body can be detected through the temperature measuring instruments.
For another description of the embodiments of the present application, reference is made to another description of the first embodiment of the present application, and the embodiments of the present application are not limited thereto.
EXAMPLE III
Referring to fig. 3, fig. 3 is a schematic structural diagram of an intelligent epidemic prevention equipment based on big data according to an embodiment of the present application. As shown in fig. 3, the apparatus of the embodiment of the present application includes:
a processor 301; and
the memory 302 is configured to store machine-readable instructions, which when executed by the processor 301, cause the processor 301 to execute the big data based intelligent epidemic prevention method according to the first embodiment of the present application.
The device of the embodiment of the application can reduce the labor cost of epidemic prevention, reduce epidemic situation risks faced by epidemic prevention workers, reduce the difficulty of epidemic prevention work and improve the accuracy of epidemic prevention work by executing the intelligent epidemic prevention method based on big data.
Example four
The embodiment of the application discloses a storage medium, wherein a computer program is stored in the storage medium, and the computer program is executed by a processor to execute the big data-based intelligent epidemic prevention method in the embodiment of the application.
The storage medium of the embodiment of the application can reduce the labor cost of epidemic prevention, reduce epidemic situation risks faced by epidemic prevention workers, reduce the difficulty of epidemic prevention work and improve the accuracy of epidemic prevention work by executing the intelligent epidemic prevention method based on big data.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
In addition, units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
Furthermore, the functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
It should be noted that the functions, if implemented in the form of software functional modules and sold or used as independent products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. An intelligent epidemic prevention method based on big data, which is characterized by comprising the following steps:
detecting the number of persons passing through the exit of the target area and the number of persons passing through the entrance of the target area;
calculating the number of accommodated people in the target area according to the number of people passing through the outlet of the target area and the number of people passing through the inlet of the target area;
when the number of persons accommodated in the target area meets a preset first condition, allowing the human body to pass through an entrance of the target area;
detecting the body temperature of the human body passing through the target area to obtain a body temperature value;
when the body temperature value meets a preset second condition, acquiring heat detection data of the human body and camera imaging of the human body;
and judging whether the human body wears the mask or not according to the thermal detection data of the human body and the imaging of the camera of the human body and outputting a wearing detection result.
2. The method according to claim 1, wherein after the determining whether the human body wears a mask based on the thermal detection data of the human body and the camera imaging of the human body and outputting a wearing detection result, the method further comprises:
acquiring a real-time image of the target area, wherein the real-time image is generated by a camera disposed in the target area;
determining the position information of each human body in the target area according to the real-time image;
calculating the relative position between the human bodies according to the internal reference matrix of the camera and the position information of each human body;
and judging whether the relative positions of the human bodies meet a preset third condition or not, and if not, outputting prompt information.
3. The method of claim 2, wherein said determining position information for each of said human bodies of said target region from said real-time images comprises:
determining a human body image area according to the real-time image;
calculating the maximum value of the ordinate of the pixel coordinate of the human body image area and the average value of the abscissa of the pixel coordinate of the human body image area;
calculating to obtain a pixel coordinate of the observation point according to the maximum value of the ordinate of the pixel coordinate and the average value of the abscissa of the pixel coordinate;
and calculating the coordinates of the observation point in a camera coordinate system according to the pixel coordinates of the observation point, and taking the coordinates of the observation point in the camera coordinate system as the position information of the human body.
4. The method of claim 1, wherein after said calculating the number of people accommodated by the target area based on the number of people who exit the target area and the number of people who enter the target area, the method further comprises:
using time dimension information, weather dimension information, temperature dimension information and people flow dimension information as input of a first neural network, and enabling the first neural network to output a limit threshold value of the target area according to a preset machine learning algorithm;
and comparing the number of persons accommodated in the target area with the limit threshold of the target area, and determining that the number of persons accommodated in the target area meets the preset first condition when the number of persons accommodated in the target area is smaller than the limit threshold of the target area.
5. The method of claim 4, wherein the pre-set machine learning algorithm is one of a k-means algorithm, an adboost algorithm, and an xgboost algorithm.
6. The method according to claim 4, wherein the determining whether the human body wears a mask and outputting a wearing detection result based on the thermal detection data of the human body and the camera image of the human body comprises:
calculating a human body region image in the camera imaging of the human body according to the thermal detection data of the human body and a first deep learning network;
cropping the human body region image from the camera imaging of the human body;
and judging whether the human body wears the mask or not according to the human body area image and a second deep learning network and outputting a wearing detection result.
7. The method according to claim 1, wherein after the determining whether the human body wears a mask based on the thermal detection data of the human body and the camera imaging of the human body and outputting a wearing detection result, the method further comprises:
and when the human body is detected not to wear the mask, generating alarm information.
8. An intelligent epidemic prevention apparatus based on big data, the apparatus comprising:
the first detection module is used for detecting the number of people passing through the outlet of the target area and the number of people passing through the inlet of the target area;
the calculating module is used for calculating the number of accommodated people in the target area according to the number of people passing through the outlet of the target area and the number of people passing through the inlet of the target area;
the admission control module is used for permitting a human body to pass through the inlet of the target area when the number of people accommodated in the target area meets a preset first condition;
the second detection module is used for detecting the body temperature of the human body passing through the target area and obtaining a body temperature value;
the acquisition module is used for acquiring the thermal detection data of the human body and the camera imaging of the human body when the body temperature value meets a preset second condition;
and the judging module is used for judging whether the human body wears the mask or not according to the thermal detection data of the human body and the imaging of the camera of the human body and outputting a wearing detection result.
9. An intelligent epidemic prevention equipment based on big data, which is characterized in that the equipment comprises:
a processor; and
a memory configured to store machine readable instructions that, when executed by the processor, cause the processor to perform the big data based intelligent epidemic prevention method of any one of claims 1-7.
10. A storage medium storing a computer program for executing the big data based intelligent epidemic prevention method according to any one of claims 1-7 by a processor.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113837030A (en) * | 2021-09-06 | 2021-12-24 | 中国福利会国际和平妇幼保健院 | Intelligent personnel management and control method and system for epidemic situation prevention and control and computer equipment |
| CN115497639A (en) * | 2022-11-17 | 2022-12-20 | 上海维智卓新信息科技有限公司 | Epidemic prevention spatiotemporal region determination method and device |
Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102831683A (en) * | 2012-08-28 | 2012-12-19 | 华南理工大学 | Pedestrian flow counting-based intelligent detection method for indoor dynamic cold load |
| CN106096521A (en) * | 2016-06-02 | 2016-11-09 | 苏州大学 | A kind of swarm and jostlement method for early warning based on stress and strain model and device |
| CN205722300U (en) * | 2016-05-06 | 2016-11-23 | 牛凯 | Volume of the flow of passengers real-time monitoring system in scenic spot, city |
| CN110222004A (en) * | 2019-05-15 | 2019-09-10 | 江苏大学 | A kind of wisdom Scenery Management System |
| CN110363255A (en) * | 2019-08-19 | 2019-10-22 | 公安部交通管理科学研究所 | A kind of Speed Limitation on Freeway current-limiting method based on deep learning algorithm |
| CN111311799A (en) * | 2020-03-08 | 2020-06-19 | 吴立新 | Gate control system based on intelligent identification |
| CN111443674A (en) * | 2020-04-09 | 2020-07-24 | 中建科技有限公司 | Control system and control method thereof |
| CN111582052A (en) * | 2020-04-17 | 2020-08-25 | 深圳市优必选科技股份有限公司 | Crowd intensive early warning method and device and terminal equipment |
| CN111595453A (en) * | 2020-05-27 | 2020-08-28 | 成都电科崇实科技有限公司 | Infrared temperature measurement system and method based on face recognition |
| CN111681770A (en) * | 2020-04-15 | 2020-09-18 | 梅州市青塘实业有限公司 | Intelligent detection method and device for abnormal target, computer equipment and storage medium |
| CN111680830A (en) * | 2020-05-25 | 2020-09-18 | 广州衡昊数据科技有限公司 | Epidemic situation prevention method and device based on aggregation risk early warning |
| CN111709285A (en) * | 2020-05-09 | 2020-09-25 | 五邑大学 | Epidemic situation protection monitoring method and device based on unmanned aerial vehicle and storage medium |
| CN111860230A (en) * | 2020-07-03 | 2020-10-30 | 北京航空航天大学 | Automatic detection system and method based on behavior of video monitoring personnel not wearing mask |
| CN111916219A (en) * | 2020-07-17 | 2020-11-10 | 深圳中集智能科技有限公司 | Intelligent safety early warning method, device and electronic system for inspection and quarantine |
| CN112183471A (en) * | 2020-10-28 | 2021-01-05 | 西安交通大学 | Automatic detection method and system for standard wearing of epidemic prevention mask of field personnel |
| CN112287869A (en) * | 2020-11-10 | 2021-01-29 | 上海依图网络科技有限公司 | Image data detection method and device |
| CN112381696A (en) * | 2020-11-13 | 2021-02-19 | 国网山东省电力公司泰安供电公司 | Intelligent park epidemic situation prevention and control system and user terminal |
| CN112434578A (en) * | 2020-11-13 | 2021-03-02 | 浙江大华技术股份有限公司 | Mask wearing normative detection method and device, computer equipment and storage medium |
-
2021
- 2021-05-27 CN CN202110587066.5A patent/CN113314230A/en active Pending
Patent Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102831683A (en) * | 2012-08-28 | 2012-12-19 | 华南理工大学 | Pedestrian flow counting-based intelligent detection method for indoor dynamic cold load |
| CN205722300U (en) * | 2016-05-06 | 2016-11-23 | 牛凯 | Volume of the flow of passengers real-time monitoring system in scenic spot, city |
| CN106096521A (en) * | 2016-06-02 | 2016-11-09 | 苏州大学 | A kind of swarm and jostlement method for early warning based on stress and strain model and device |
| CN110222004A (en) * | 2019-05-15 | 2019-09-10 | 江苏大学 | A kind of wisdom Scenery Management System |
| CN110363255A (en) * | 2019-08-19 | 2019-10-22 | 公安部交通管理科学研究所 | A kind of Speed Limitation on Freeway current-limiting method based on deep learning algorithm |
| CN111311799A (en) * | 2020-03-08 | 2020-06-19 | 吴立新 | Gate control system based on intelligent identification |
| CN111443674A (en) * | 2020-04-09 | 2020-07-24 | 中建科技有限公司 | Control system and control method thereof |
| CN111681770A (en) * | 2020-04-15 | 2020-09-18 | 梅州市青塘实业有限公司 | Intelligent detection method and device for abnormal target, computer equipment and storage medium |
| CN111582052A (en) * | 2020-04-17 | 2020-08-25 | 深圳市优必选科技股份有限公司 | Crowd intensive early warning method and device and terminal equipment |
| CN111709285A (en) * | 2020-05-09 | 2020-09-25 | 五邑大学 | Epidemic situation protection monitoring method and device based on unmanned aerial vehicle and storage medium |
| CN111680830A (en) * | 2020-05-25 | 2020-09-18 | 广州衡昊数据科技有限公司 | Epidemic situation prevention method and device based on aggregation risk early warning |
| CN111595453A (en) * | 2020-05-27 | 2020-08-28 | 成都电科崇实科技有限公司 | Infrared temperature measurement system and method based on face recognition |
| CN111860230A (en) * | 2020-07-03 | 2020-10-30 | 北京航空航天大学 | Automatic detection system and method based on behavior of video monitoring personnel not wearing mask |
| CN111916219A (en) * | 2020-07-17 | 2020-11-10 | 深圳中集智能科技有限公司 | Intelligent safety early warning method, device and electronic system for inspection and quarantine |
| CN112183471A (en) * | 2020-10-28 | 2021-01-05 | 西安交通大学 | Automatic detection method and system for standard wearing of epidemic prevention mask of field personnel |
| CN112287869A (en) * | 2020-11-10 | 2021-01-29 | 上海依图网络科技有限公司 | Image data detection method and device |
| CN112381696A (en) * | 2020-11-13 | 2021-02-19 | 国网山东省电力公司泰安供电公司 | Intelligent park epidemic situation prevention and control system and user terminal |
| CN112434578A (en) * | 2020-11-13 | 2021-03-02 | 浙江大华技术股份有限公司 | Mask wearing normative detection method and device, computer equipment and storage medium |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113837030A (en) * | 2021-09-06 | 2021-12-24 | 中国福利会国际和平妇幼保健院 | Intelligent personnel management and control method and system for epidemic situation prevention and control and computer equipment |
| CN113837030B (en) * | 2021-09-06 | 2024-03-22 | 中国福利会国际和平妇幼保健院 | Personnel intelligent management and control method and system for epidemic situation prevention and control and computer equipment |
| CN115497639A (en) * | 2022-11-17 | 2022-12-20 | 上海维智卓新信息科技有限公司 | Epidemic prevention spatiotemporal region determination method and device |
| CN115497639B (en) * | 2022-11-17 | 2023-05-05 | 上海维智卓新信息科技有限公司 | Epidemic prevention space-time region determining method and device |
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