Environment abnormity early warning method, device, equipment, medium and program product

Abstract

The invention discloses an environment abnormity early warning method, device and equipment, a medium and a program product, real-time environment data and real-time people flow data of a target monitoring area are obtained, timely sensing of environment state abnormity of the target monitoring area can be ensured, environment abnormity identification is carried out by combining the real-time environment data and the real-time people flow data, and the early warning efficiency of the environment abnormity is improved. According to the invention, it can be ensured that environmental abnormality early warning of the target monitoring area is more accurate and timely, so that accurate early warning and quick response to environmental abnormality events can be realized.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to an environmental anomaly early warning method, device, electronic equipment, computer-readable storage medium, and computer program product. Background Technology

[0002] Currently, monitoring and alarm systems are commonly used to detect environmental anomalies. However, existing monitoring and alarm systems typically rely on manual monitoring and post-event anomaly analysis, which results in problems such as long alarm response times and difficulties in data analysis, making it impossible to achieve accurate early warning and rapid response to environmental anomalies. Summary of the Invention

[0003] This invention provides an environmental anomaly early warning method, device, equipment, medium, and program product to solve the technical problem that existing technologies cannot achieve accurate early warning and rapid response to environmental anomalies.

[0004] To address the aforementioned technical problems, a first aspect of this invention provides an environmental anomaly early warning method, comprising: Acquire real-time environmental data and real-time pedestrian flow data for the target monitoring area; Based on the real-time environmental data and the real-time pedestrian flow data, the environmental status detection data of the target monitoring area at each time moment is determined; Based on the environmental state detection data at each time point and the preset environmental state detection data threshold, environmental anomaly identification is performed on the target monitoring area to obtain environmental anomaly identification results. Based on the environmental anomaly identification results, an environmental anomaly warning is issued for the target monitoring area.

[0005] As a preferred embodiment, the method specifically obtains the real-time environmental data of the target monitoring area through the following steps: Based on at least one environmental monitoring unit configured at one or more preset monitoring locations in the target monitoring area, environmental monitoring data collected by each environmental monitoring unit is acquired, and the environmental monitoring data is used as the real-time environmental data.

[0006] As a preferred embodiment, the method specifically obtains the real-time pedestrian flow data of the target monitoring area through the following steps: Based on at least one camera configured at one or more preset monitoring locations in the target monitoring area, acquire the monitoring video stream output by each of the cameras; Extract image frames containing pedestrian areas at various times from the monitoring video stream; Identify the real-time number of people in the image frames at each time point, and use the real-time number of people as the real-time people flow data.

[0007] As a preferred embodiment, determining the environmental status detection data of the target monitoring area at various times based on the real-time environmental data and the real-time pedestrian flow data specifically includes: Based on the first preset weight coefficient corresponding to the real-time pedestrian flow data, the second preset weight coefficient corresponding to the real-time environmental data, the real-time environmental data, and the real-time pedestrian flow data, the environmental state detection parameters of the target monitoring area at each time are determined; The environmental state detection parameters at each time point are differentiated to obtain the changes in the environmental state detection parameters at each time point. Based on the environmental state detection parameters and the changes in the environmental state detection parameters, the environmental state detection data of the target monitoring area at each time point are determined.

[0008] As a preferred embodiment, the environmental state detection data threshold includes a preset range of environmental state detection parameters and a threshold for the change in environmental state detection parameters; The process of identifying environmental anomalies in the target monitoring area based on environmental state detection data at various times and preset environmental state detection data thresholds, and obtaining environmental anomaly identification results, specifically includes: When the environmental state detection parameter is detected to be outside the range of the environmental state detection parameter at any time, or when the change of the environmental state detection parameter is greater than or equal to the threshold of the change of the environmental state detection parameter, the environmental anomaly identification result of the target monitoring area at any time is determined to be an environmental state anomaly.

[0009] As a preferred embodiment, the step of providing an environmental anomaly warning for the target monitoring area based on the environmental anomaly identification results specifically includes: When the environmental anomaly identification result is that the environmental state is abnormal, the abnormal time and abnormal monitoring location corresponding to the environmental anomaly identification result are determined; wherein, the abnormal monitoring location is one of one or more monitoring locations preset in the target monitoring area; Acquire the anomaly monitoring video stream output by at least one camera pre-configured at the anomaly monitoring location; Based on the anomaly monitoring video stream, the area of ​​the target pedestrian flow region at the time of the anomaly is obtained; Based on the preset abnormal risk levels corresponding to different times and different pedestrian flow areas, the target abnormal risk level corresponding to the abnormal time and the target pedestrian flow area is determined; Based on the target's abnormal risk level, an early warning signal is generated to provide early warning of environmental anomalies in the target monitoring area.

[0010] A second aspect of the present invention provides an environmental anomaly early warning device, comprising: The data acquisition module is used to acquire real-time environmental data and real-time pedestrian flow data of the target monitoring area; An environmental status detection module is used to determine the environmental status detection data of the target monitoring area at various times based on the real-time environmental data and the real-time pedestrian flow data. An environmental anomaly identification module is used to identify environmental anomalies in the target monitoring area based on the environmental state detection data at various times and a preset environmental state detection data threshold, and to obtain environmental anomaly identification results. An environmental anomaly early warning module is used to provide early warning of environmental anomalies to the target monitoring area based on the environmental anomaly identification results.

[0011] A third aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the environmental anomaly early warning method described in any of the first aspects.

[0012] A fourth aspect of the present invention provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the environmental anomaly early warning method described in any of the first aspects.

[0013] A fifth aspect of the present invention provides a computer program product, including a computer program / instructions, wherein when the computer program / instructions are executed by a processor, the steps of the environmental anomaly early warning method described in any one of the first aspects are implemented.

[0014] Compared with the prior art, the beneficial effects of the embodiments of the present invention are that by acquiring real-time environmental data and real-time pedestrian flow data of the target monitoring area, it is possible to ensure timely perception of abnormal environmental conditions in the target monitoring area. By combining real-time environmental data and real-time pedestrian flow data for environmental anomaly identification, it is possible to ensure more accurate and timely early warning of environmental anomalies in the target monitoring area, thereby enabling accurate early warning and rapid response to abnormal environmental events. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating the environmental anomaly early warning method in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of the environmental anomaly early warning device in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of the electronic device in an embodiment of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Please see Figure 1 The first aspect of this invention provides an environmental anomaly early warning method, comprising the following steps S1 to S4: Step S1: Obtain real-time environmental data and real-time pedestrian flow data for the target monitoring area; Step S2: Based on the real-time environmental data and the real-time pedestrian flow data, determine the environmental status detection data of the target monitoring area at each time. Step S3: Based on the environmental state detection data at each time point and the preset environmental state detection data threshold, perform environmental anomaly identification on the target monitoring area to obtain environmental anomaly identification results. Step S4: Based on the environmental anomaly identification results, issue an environmental anomaly warning for the target monitoring area.

[0018] Specifically, in order to achieve accurate early warning of the environmental status of the target monitoring area, this embodiment first acquires real-time environmental data and real-time pedestrian flow data. It can be understood that real-time environmental data can directly reflect whether the environmental status of the target monitoring area is abnormal, while real-time pedestrian flow data affects the degree of danger when the environmental status is abnormal. For example, when the environmental status is abnormal, if the pedestrian flow is dense, the number of people affected will be greater, and therefore the degree of danger will be higher.

[0019] Furthermore, this embodiment comprehensively considers real-time environmental data and real-time pedestrian flow data to determine the environmental status detection data of the target monitoring area at various times. This data serves as the basis for environmental anomaly identification. By pre-setting environmental status detection data thresholds, the environmental anomaly identification results of the target monitoring area at various times can be directly obtained. Based on these environmental anomaly identification results, environmental anomaly warnings can be issued, which helps to take timely and targeted emergency measures in emergency situations, especially in densely populated areas.

[0020] The environmental anomaly early warning method provided in this invention can ensure timely perception of environmental anomalies in the target monitoring area by acquiring real-time environmental data and real-time pedestrian flow data of the target monitoring area. By combining real-time environmental data and real-time pedestrian flow data for environmental anomaly identification, it can ensure more accurate and timely early warning of environmental anomalies in the target monitoring area, thereby enabling accurate early warning and rapid response to environmental anomaly events.

[0021] As a preferred embodiment, the method specifically obtains the real-time environmental data of the target monitoring area through the following steps: Based on at least one environmental monitoring unit configured at one or more preset monitoring locations in the target monitoring area, environmental monitoring data collected by each environmental monitoring unit is acquired, and the environmental monitoring data is used as the real-time environmental data.

[0022] Specifically, in this embodiment, one or more monitoring locations are pre-set in the target monitoring area. It is understood that the number of monitoring locations can be set according to the area of ​​the target monitoring area and the needs for monitoring environmental anomalies; this embodiment does not impose a specific limitation here. Furthermore, this embodiment also configures at least one environmental monitoring unit at each pre-set monitoring location. It is understood that the environmental monitoring unit can be different types of sensors, such as temperature sensors, humidity sensors, oxygen concentration sensors, carbon dioxide concentration sensors, etc. This embodiment does not impose a specific limitation here. By acquiring the environmental monitoring data collected by each environmental monitoring unit, the real-time environmental data of the current target monitoring area can be clearly determined.

[0023] As a preferred embodiment, the method specifically obtains the real-time pedestrian flow data of the target monitoring area through the following steps: Based on at least one camera configured at one or more preset monitoring locations in the target monitoring area, acquire the monitoring video stream output by each of the cameras; Extract image frames containing pedestrian areas at various times from the monitoring video stream; Identify the real-time number of people in the image frames at each time point, and use the real-time number of people as the real-time people flow data.

[0024] Specifically, in this embodiment, at least one camera is configured at each preset monitoring location in the target monitoring area. The number of cameras can be set according to the required monitoring range of the monitoring location and the needs for monitoring environmental anomalies. This embodiment does not make specific limitations here.

[0025] Furthermore, in order to accurately acquire real-time pedestrian flow data while avoiding low data processing efficiency, this embodiment extracts image frames containing pedestrian flow areas at various times from the monitoring video stream, while image frames not containing pedestrian flow areas are not processed, thus ensuring data processing efficiency and timely environmental anomaly warnings.

[0026] Furthermore, since there may be more than one camera at the same monitoring location, the image frames at each time may contain multiple frames. This embodiment can determine the real-time pedestrian flow data at each monitoring location by identifying the real-time pedestrian flow count in all image frames containing pedestrian flow areas at each time.

[0027] As a preferred embodiment, determining the environmental status detection data of the target monitoring area at various times based on the real-time environmental data and the real-time pedestrian flow data specifically includes: Based on the first preset weight coefficient corresponding to the real-time pedestrian flow data, the second preset weight coefficient corresponding to the real-time environmental data, the real-time environmental data, and the real-time pedestrian flow data, the environmental state detection parameters of the target monitoring area at each time are determined; The environmental state detection parameters at each time point are differentiated to obtain the changes in the environmental state detection parameters at each time point. Based on the environmental state detection parameters and the changes in the environmental state detection parameters, the environmental state detection data of the target monitoring area at each time point are determined.

[0028] Specifically, this embodiment calculates the environmental state detection parameters at each time point using the following expression: ; in, Let j be the environmental state detection parameter at time i. This refers to the real-time pedestrian flow data at time i. This is the first preset weighting coefficient; This refers to the j-th real-time environmental data at time i. This is the second preset weighting coefficient. This is the transformation function. It is understandable that multiple environmental data points may be monitored at the same time and location, such as temperature, humidity, and carbon dioxide concentration. Therefore, this embodiment requires calculating the corresponding environmental state detection parameters for each real-time environmental data point.

[0029] After obtaining the environmental state detection parameters at each time point, it is possible to obtain the curve of the environmental state detection parameters changing over time, so as to reflect their change process over time.

[0030] Furthermore, the change in environmental state detection parameters can also serve as a signal before an anomaly occurs. Therefore, in this embodiment, the environmental state detection parameters at each time point are differentiated to obtain the change in environmental state detection parameters at each time point, and thus obtain the curve of the change in environmental state detection parameters over time.

[0031] As a preferred embodiment, the environmental state detection data threshold includes a preset range of environmental state detection parameters and a threshold for the change in environmental state detection parameters; The process of identifying environmental anomalies in the target monitoring area based on environmental state detection data at various times and preset environmental state detection data thresholds, and obtaining environmental anomaly identification results, specifically includes: When the environmental state detection parameter is detected to be outside the range of the environmental state detection parameter at any time, or when the change of the environmental state detection parameter is greater than or equal to the threshold of the change of the environmental state detection parameter, the environmental anomaly identification result of the target monitoring area at any time is determined to be an environmental state anomaly.

[0032] Specifically, the environmental state detection data threshold in this embodiment further includes a preset environmental state detection parameter range and an environmental state detection parameter change threshold. Therefore, during environmental anomaly identification, this embodiment first compares each environmental state detection parameter with its corresponding environmental state detection parameter range. If the environmental state detection parameter is within its corresponding range, it is determined to be normal; otherwise, it is determined to be abnormal. Furthermore, this embodiment also compares the change in each environmental state detection parameter with its corresponding environmental state detection parameter change threshold. If the change in the environmental state detection parameter is less than its corresponding threshold, it indicates that the change in the environmental state detection parameter is within the normal range, and the change in the environmental state detection parameter is determined to be normal. If the change in the environmental state detection parameter is greater than or equal to its corresponding threshold, it indicates that the change in the environmental state detection parameter is abnormal, and the change in the environmental state detection parameter is determined to be abnormal. If either the environmental state detection parameter or the change in the environmental state detection parameter is abnormal, the environmental anomaly identification result for the target monitoring area at the corresponding time is determined to be an environmental state anomaly.

[0033] As a preferred embodiment, the step of providing an environmental anomaly warning for the target monitoring area based on the environmental anomaly identification results specifically includes: When the environmental anomaly identification result is that the environmental state is abnormal, the abnormal time and abnormal monitoring location corresponding to the environmental anomaly identification result are determined; wherein, the abnormal monitoring location is one of one or more monitoring locations preset in the target monitoring area; Acquire the anomaly monitoring video stream output by at least one camera pre-configured at the anomaly monitoring location; Based on the anomaly monitoring video stream, the area of ​​the target pedestrian flow region at the time of the anomaly is obtained; Based on the preset abnormal risk levels corresponding to different times and different pedestrian flow areas, the target abnormal risk level corresponding to the abnormal time and the target pedestrian flow area is determined; Based on the target's abnormal risk level, an early warning signal is generated to provide early warning of environmental anomalies in the target monitoring area.

[0034] Specifically, when the environmental anomaly identification result indicates an abnormal environmental state, the abnormal moment when the environmental anomaly identification result indicates an abnormal environmental state is recorded at each monitoring location, and the monitoring location at that moment is regarded as an abnormal monitoring location. Further, the abnormal monitoring video stream output by at least one camera pre-configured at the abnormal monitoring location is acquired to determine the area of ​​the target pedestrian flow area at the abnormal moment. It is understood that the larger the pedestrian flow area, the higher the danger level when the anomaly occurs. This embodiment pre-sets corresponding abnormal danger levels for different moments and different pedestrian flow area areas, thereby directly determining the target abnormal danger level corresponding to the current abnormal moment and the target pedestrian flow area based on these pre-set multiple abnormal danger levels.

[0035] Based on the determined level of abnormal danger of the target, an early warning signal is generated. By issuing the early warning signal, the environmental anomalies in the target monitoring area can be warned in a more accurate manner, thus reducing unnecessary confusion and losses.

[0036] Please see Figure 2 A second aspect of the present invention provides an environmental anomaly early warning device 100, comprising: Data acquisition module 11 is used to acquire real-time environmental data and real-time pedestrian flow data of the target monitoring area; The environmental status detection module 12 is used to determine the environmental status detection data of the target monitoring area at various times based on the real-time environmental data and the real-time pedestrian flow data. The environmental anomaly identification module 13 is used to identify environmental anomalies in the target monitoring area based on the environmental state detection data at each time and the preset environmental state detection data threshold, and to obtain environmental anomaly identification results. The environmental anomaly early warning module 14 is used to provide an environmental anomaly early warning for the target monitoring area based on the environmental anomaly identification results.

[0037] As a preferred embodiment, the data acquisition module 11 acquires the real-time environmental data of the target monitoring area through the following steps: Based on at least one environmental monitoring unit configured at one or more preset monitoring locations in the target monitoring area, environmental monitoring data collected by each environmental monitoring unit is acquired, and the environmental monitoring data is used as the real-time environmental data.

[0038] As a preferred embodiment, the data acquisition module 11 acquires the real-time pedestrian flow data of the target monitoring area through the following steps: Based on at least one camera configured at one or more preset monitoring locations in the target monitoring area, acquire the monitoring video stream output by each of the cameras; Extract image frames containing pedestrian areas at various times from the monitoring video stream; Identify the real-time number of people in the image frames at each time point, and use the real-time number of people as the real-time people flow data.

[0039] As a preferred embodiment, the environmental status detection module 12 is used to determine the environmental status detection data of the target monitoring area at various times based on the real-time environmental data and the real-time pedestrian flow data, specifically including: Based on the first preset weight coefficient corresponding to the real-time pedestrian flow data, the second preset weight coefficient corresponding to the real-time environmental data, the real-time environmental data, and the real-time pedestrian flow data, the environmental state detection parameters of the target monitoring area at each time are determined; The environmental state detection parameters at each time point are differentiated to obtain the changes in the environmental state detection parameters at each time point. Based on the environmental state detection parameters and the changes in the environmental state detection parameters, the environmental state detection data of the target monitoring area at each time point are determined.

[0040] As a preferred embodiment, the environmental state detection data threshold includes a preset range of environmental state detection parameters and a threshold for the change in environmental state detection parameters; The environmental anomaly identification module 13 is used to identify environmental anomalies in the target monitoring area based on the environmental state detection data at various times and a preset environmental state detection data threshold, and to obtain environmental anomaly identification results, specifically including: When the environmental state detection parameter is detected to be outside the range of the environmental state detection parameter at any time, or when the change of the environmental state detection parameter is greater than or equal to the threshold of the change of the environmental state detection parameter, the environmental anomaly identification result of the target monitoring area at any time is determined to be an environmental state anomaly.

[0041] As a preferred embodiment, the environmental anomaly early warning module 14 is used to provide environmental anomaly early warning for the target monitoring area based on the environmental anomaly identification result, specifically including: When the environmental anomaly identification result is that the environmental state is abnormal, the abnormal time and abnormal monitoring location corresponding to the environmental anomaly identification result are determined; wherein, the abnormal monitoring location is one of one or more monitoring locations preset in the target monitoring area; Acquire the anomaly monitoring video stream output by at least one camera pre-configured at the anomaly monitoring location; Based on the anomaly monitoring video stream, the area of ​​the target pedestrian flow region at the time of the anomaly is obtained; Based on the preset abnormal risk levels corresponding to different times and different pedestrian flow areas, the target abnormal risk level corresponding to the abnormal time and the target pedestrian flow area is determined; Based on the target's abnormal risk level, an early warning signal is generated to provide early warning of environmental anomalies in the target monitoring area.

[0042] The environmental anomaly early warning device 100 provided in this embodiment of the invention can ensure timely perception of environmental anomalies in the target monitoring area by acquiring real-time environmental data and real-time pedestrian flow data of the target monitoring area. By combining real-time environmental data and real-time pedestrian flow data for environmental anomaly identification, it can ensure more accurate and timely environmental anomaly early warning for the target monitoring area, thereby enabling accurate early warning and rapid response to environmental anomaly events.

[0043] Please see Figure 3 The third aspect of the present invention provides an electronic device 200, including a memory 22, a processor 21, and a computer program stored in the memory 22 and executable on the processor 21. When the processor 21 executes the computer program, it implements the environmental anomaly early warning method described in any embodiment of the first aspect.

[0044] For example, the computer program may be divided into one or more modules / units, which are stored in the memory 22 and executed by the processor 21 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the electronic device 200.

[0045] The electronic device 200 may include, but is not limited to, a processor 21 and a memory 22. Those skilled in the art will understand that the schematic diagram is merely an example of the electronic device 200 and does not constitute a limitation on the electronic device 200. It may include more or fewer components than illustrated, or combine certain components, or different components. For example, the electronic device 200 may also include input / output devices, network access devices, buses, etc.

[0046] The processor 21 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, or the processor 21 can be any conventional processor 21. The processor 21 is the control center of the electronic device 200, connecting various parts of the electronic device 200 via various interfaces and lines.

[0047] The memory 22 can be used to store the computer programs and / or modules. The processor 21 implements various functions of the electronic device 200 by running or executing the computer programs and / or modules stored in the memory 22 and calling the data stored in the memory 22. The memory 22 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory 22 may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0048] A fourth aspect of the present invention provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the environmental anomaly early warning method described in any embodiment of the first aspect.

[0049] A fifth aspect of the present invention provides a computer program product, including a computer program / instructions, wherein when the computer program / instructions are executed by a processor, the steps of the environmental anomaly early warning method described in any embodiment of the first aspect are implemented.

[0050] Wherein, if the modules / units integrated in the electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0051] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for early warning of environmental anomalies, characterized in that, include: Acquire real-time environmental data and real-time pedestrian flow data for the target monitoring area; Based on the real-time environmental data and the real-time pedestrian flow data, the environmental status detection data of the target monitoring area at each time moment is determined; Based on the environmental state detection data at each time point and the preset environmental state detection data threshold, environmental anomaly identification is performed on the target monitoring area to obtain environmental anomaly identification results. Based on the environmental anomaly identification results, an environmental anomaly warning is issued for the target monitoring area.

2. The environmental anomaly early warning method as described in claim 1, characterized in that, The method specifically obtains the real-time environmental data of the target monitoring area through the following steps: Based on at least one environmental monitoring unit configured at one or more preset monitoring locations in the target monitoring area, environmental monitoring data collected by each environmental monitoring unit is acquired, and the environmental monitoring data is used as the real-time environmental data.

3. The environmental anomaly early warning method as described in claim 1, characterized in that, The method specifically obtains the real-time pedestrian flow data of the target monitoring area through the following steps: Based on at least one camera configured at one or more preset monitoring locations in the target monitoring area, acquire the monitoring video stream output by each of the cameras; Extract image frames containing pedestrian areas at various times from the monitoring video stream; Identify the real-time number of people in the image frames at each time point, and use the real-time number of people as the real-time people flow data.

4. The environmental anomaly early warning method as described in claim 1, characterized in that, The step of determining the environmental status detection data of the target monitoring area at various times based on the real-time environmental data and the real-time pedestrian flow data specifically includes: Based on the first preset weight coefficient corresponding to the real-time pedestrian flow data, the second preset weight coefficient corresponding to the real-time environmental data, the real-time environmental data, and the real-time pedestrian flow data, the environmental state detection parameters of the target monitoring area at each time are determined; The environmental state detection parameters at each time point are differentiated to obtain the changes in the environmental state detection parameters at each time point. Based on the environmental state detection parameters and the changes in the environmental state detection parameters, the environmental state detection data of the target monitoring area at each time point are determined.

5. The environmental anomaly early warning method as described in claim 4, characterized in that, The environmental state detection data threshold includes a preset range of environmental state detection parameters and a threshold for the change in environmental state detection parameters. The process of identifying environmental anomalies in the target monitoring area based on environmental state detection data at various times and a preset environmental state detection data threshold, and obtaining environmental anomaly identification results, specifically includes: When the environmental state detection parameter is detected to be outside the range of the environmental state detection parameter at any time, or the change in the environmental state detection parameter is greater than or equal to the threshold of the change in the environmental state detection parameter, the environmental anomaly identification result of the target monitoring area at any time is determined to be an environmental state anomaly.

6. The environmental anomaly early warning method as described in claim 1, characterized in that, The step of issuing an environmental anomaly warning for the target monitoring area based on the environmental anomaly identification results specifically includes: When the environmental anomaly identification result is that the environmental state is abnormal, the abnormal time and abnormal monitoring location corresponding to the environmental anomaly identification result are determined; wherein, the abnormal monitoring location is one of one or more monitoring locations preset in the target monitoring area; Acquire the anomaly monitoring video stream output by at least one camera pre-configured at the anomaly monitoring location; Based on the anomaly monitoring video stream, the area of ​​the target pedestrian flow region at the time of the anomaly is obtained; Based on the preset abnormal risk levels corresponding to different times and different pedestrian flow areas, the target abnormal risk level corresponding to the abnormal time and the target pedestrian flow area is determined; Based on the target's abnormal risk level, an early warning signal is generated to provide early warning of environmental anomalies in the target monitoring area.

7. An environmental anomaly early warning device, characterized in that, include: The data acquisition module is used to acquire real-time environmental data and real-time pedestrian flow data of the target monitoring area; An environmental status detection module is used to determine the environmental status detection data of the target monitoring area at various times based on the real-time environmental data and the real-time pedestrian flow data. An environmental anomaly identification module is used to identify environmental anomalies in the target monitoring area based on the environmental state detection data at various times and a preset environmental state detection data threshold, and to obtain environmental anomaly identification results. An environmental anomaly early warning module is used to provide early warning of environmental anomalies to the target monitoring area based on the environmental anomaly identification results.

8. An electronic device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the environmental anomaly early warning method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the environmental anomaly early warning method according to any one of claims 1 to 6.

10. A computer program product, characterized in that, It includes a computer program / instruction that, when executed by a processor, implements the steps of the environmental anomaly early warning method according to any one of claims 1 to 6.

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