Crowd abnormal behavior detection method and device, electronic equipment and storage medium

Abstract

The invention discloses a crowd abnormal behavior detection method and device, electronic equipment and a storage medium, and relates to the technical field of safety detection. The crowd abnormal behavior detection method comprises the steps that S10, crowd data information of a current environment is monitored in real time through a sensor array, and a sensor combination is preferably selected according to the magnitude of noise during operation of all sensors of the sensor array; s20, whether the current crowd behavior is abnormal or not is judged according to the crowd data information monitored by the sensor combination, and if yes, an alarm is given out; s30, analyzing whether the abnormal behavior of the crowd corresponding to the alarm belongs to false alarms or not through the multi-mode large model, and if yes, filtering the false alarms and counting false alarm data; if not, data statistics is carried out, and a final alarm is output; and S40, according to the counted false alarm data, upgrading the parameters of the system to reduce false alarms. The method has the advantages that the crowd detection efficiency and accuracy are improved, and public safety can be effectively guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of security detection technology, and more specifically, to a method, device, electronic equipment and storage medium for detecting abnormal behavior of a crowd. Background Art

[0002] In public places, especially in security-sensitive areas such as airports, stations, shopping malls, etc., real-time monitoring and evaluation of crowd behavior is crucial to preventing accidents and crimes. Traditional surveillance systems often rely on a single type of sensor, such as closed-circuit television (CCTV) cameras, which may not provide enough information to identify potential risks in specific environments.

[0003] To this end, this solution provides a method, device, electronic device and storage medium for detecting abnormal crowd behavior, which can monitor various crowd data information in a specific environment, automatically select the best sensor combination, and dynamically adjust the perception strategy to optimize the detection of abnormal behavior, thereby improving the efficiency and accuracy of crowd detection and effectively ensuring public safety. Summary of the invention

[0004] In order to overcome the shortcomings of the prior art, the present invention provides a method, device, electronic device and storage medium for detecting abnormal crowd behavior, which solves the problem that the current crowd detection method cannot provide sufficient information to identify potential risks in a specific environment, thereby improving the efficiency and accuracy of crowd detection and effectively ensuring public safety.

[0005] The technical solution adopted by the present invention to solve the technical problem is: a method for detecting abnormal behavior of a crowd, which is applied to a computer system, wherein the computer system at least includes a sensor array and a multimodal large model; the improvement thereof is that the method for detecting abnormal behavior of a crowd includes the following steps:

[0006] S10, the computer system monitors the crowd data information of the current environment in real time through the sensor array, and selects the sensor combination based on the noise level of each sensor in the sensor array when it is running;

[0007] S20, the computer system determines whether there is any abnormality in the current crowd behavior through the crowd data information monitored by the sensor combination, and if so, issues an alarm;

[0008] S30, the computer system uses a multimodal large model to analyze whether the abnormal behavior of the crowd corresponding to the alarm is a false alarm. If so, the false alarm is filtered and the false alarm data is counted; if not, the data is counted and the final alarm is output;

[0009] S40, the computer system upgrades its own parameters according to the statistical false alarm data to reduce false alarms.

[0010] Furthermore, in step S10, the sensor array includes:

[0011] Temperature measurement cameras, used to monitor the body temperature data of individuals in the crowd;

[0012] Visible light cameras are used to monitor crowd density, flow status and behavior patterns;

[0013] Millimeter-wave radar, used to monitor hidden objects and crowd activities;

[0014] Microphone array, used to monitor sound events in the current environment.

[0015] Furthermore, in step S10, before selecting the sensor combination, the crowd data information monitored by the sensor array needs to be fused through a computer system, wherein the computer system also includes a centralized processing unit; specifically, the specific method of data fusion is:

[0016] Data aggregation: Transmitting crowd data information monitored by each sensor of the sensor array to a centralized processing unit through a network or serial port;

[0017] Time calibration: Based on the time of the centralized processing unit, the crowd data information monitored by each sensor is time-calibrated and synchronized;

[0018] Unified spatial coordinates: Using the common installation position of each sensor as the reference zero point, the crowd data information monitored by each sensor is converted into a unified spatial coordinate.

[0019] Furthermore, in step S20, the computer system determines whether the current crowd behavior is abnormal by using the crowd data information monitored by the preferred sensor combination in the following specific manner:

[0020] S201, presetting a normal crowd data information threshold in a computer system;

[0021] S202, determining whether the crowd data information detected by the sensor combination exceeds a normal crowd data information threshold, and if so, the current crowd behavior is abnormal.

[0022] Furthermore, in step S40, before the computer system upgrades its own parameters, the computer system will also count whether there is any underreporting in the crowd data information.

[0023] Furthermore, the specific method in which the computer system upgrades its own parameters according to the filtered false alarms is:

[0024] When a false alarm is found in step S30, the sensitivity of the source sensor is lowered;

[0025] When the computer system finds that there are too few missed alarms or too few alarms, it increases the sensitivity of the source sensor;

[0026] The source sensor is a sensor that collects false positive or false negative data.

[0027] A device for detecting abnormal crowd behavior is applied to the above-mentioned method for detecting abnormal crowd behavior, and the improvement thereof is that the device for detecting abnormal crowd behavior comprises:

[0028] Sensor module, used to monitor crowd data information in the current environment in real time;

[0029] A data processing module is used to analyze crowd data information and obtain an optimal sensor combination;

[0030] The anomaly detection module analyzes crowd data information to determine whether there is an anomaly and issues an alarm signal if so;

[0031] Multimodal analysis module, used to analyze alarm data, determine whether there are false alarms, and if so, filter out false alarms and count false alarm data;

[0032] The false alarm processing module is used to upgrade the computer system's own parameters according to the statistical false alarm data to reduce false alarms.

[0033] An electronic device, the improvement of which is that it comprises at least one processor and at least one memory, wherein:

[0034] The memory has computer-readable instructions stored thereon;

[0035] The computer-readable instructions are executed by one or more processors, so that the electronic device implements the method for detecting abnormal crowd behavior as described above.

[0036] A storage medium having computer-readable instructions stored thereon, wherein the improvement lies in that the computer-readable instructions are executed by one or more processors to implement the above-mentioned method for detecting abnormal crowd behavior.

[0037] The beneficial effects of the present invention are as follows: the computer system of the present scheme first monitors the crowd data information of the current environment in real time through the sensor array, and selects the sensor combination by the noise level of each sensor of the sensor array during operation; then, the crowd data information monitored by the sensor combination is used to determine whether the current crowd behavior is abnormal, and if so, an alarm is issued; then, a multimodal large model is used to analyze whether the abnormal crowd behavior corresponding to the alarm is a false alarm, and if so, the false alarm is filtered and the false alarm data is counted; if not, data statistics are performed and a final alarm is output; finally, the own parameters are upgraded according to the statistical false alarm data to reduce false alarms; this process can monitor a variety of crowd data information in a specific environment, automatically select the best sensor combination, and dynamically adjust the perception strategy to optimize the detection of abnormal behavior, thereby improving the efficiency and accuracy of crowd detection and effectively ensuring public safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is an overall flow chart of a method for detecting abnormal behavior of a crowd of people according to the present invention;

[0039] Figure 2 A block diagram of a detection system for abnormal crowd behavior of the present invention;

[0040] Figure 3 A hardware structure diagram of an electronic device is shown in an exemplary embodiment;

[0041] Figure 4 The present invention is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0042] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0043] The following will clearly and completely describe the concept, specific structure and technical effects of the present invention in combination with the embodiments and drawings, so as to fully understand the purpose, characteristics and effects of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, other embodiments obtained by technicians in this field without creative work are all within the scope of protection of the present invention. In addition, all the connection / connection relationships involved in the patent do not refer to the direct connection of components, but refer to the formation of a better connection structure by adding or reducing connection accessories according to the specific implementation situation. The various technical features in the invention can be combined interchangeably without conflicting with each other.

[0044] Reference Figure 1As shown, the present invention discloses a method for detecting abnormal behavior of a crowd, which is applied to a computer system, wherein the computer system at least includes a sensor array and a multimodal large model; the method for detecting abnormal behavior of a crowd includes the following steps:

[0045] S10, the computer system monitors the crowd data information of the current environment in real time through the sensor array, and selects the sensor combination according to the noise level of each sensor in the sensor array when it is running; wherein the sensor array includes:

[0046] Temperature measurement cameras, used to monitor the body temperature data of individuals in the crowd;

[0047] Visible light cameras are used to monitor crowd density, flow status and behavior patterns;

[0048] Millimeter-wave radar, used to monitor hidden objects and crowd activities;

[0049] Array microphone, used to monitor sound events in the current environment;

[0050] It should be noted that, in this embodiment, the sensor array can monitor a variety of crowd data information in the current environment. Specifically, the temperature measurement camera can monitor the abnormal body temperature of individuals in the crowd, which can help identify fever symptoms and thus conduct a preliminary screening of health conditions; the visible light camera can capture the density, flow state and behavior pattern of the crowd, and it can provide visual information of the crowd to help analyze the movement and behavior of the crowd; the millimeter wave radar can penetrate smoke and bad weather to detect hidden objects and human activities, and the millimeter wave radar can provide accurate information about the location and movement of objects, especially in the case of limited vision; the array microphone can capture sound events, such as screams, explosions or glass breaking, and the sound data is helpful for detecting emergencies and abnormal situations; in summary, the computer system can collect the crowd data information of the current environment more comprehensively through the various sensors of the sensor array, and can provide sufficient crowd data information for identifying potential risks in the current environment;

[0051] Before selecting the sensor combination, the crowd data information monitored by the sensor array needs to be fused through a computer system, wherein the computer system also includes a centralized processing unit; specifically, the specific method of data fusion is:

[0052] Data aggregation: Transmitting crowd data information monitored by each sensor of the sensor array to a centralized processing unit through a network or serial port;

[0053] Time calibration: Based on the time of the centralized processing unit, the crowd data information monitored by each sensor is time-calibrated and synchronized;

[0054] Unified spatial coordinates: Taking the common installation position of each sensor as the reference zero point, the crowd data information monitored by each sensor is converted into a unified spatial coordinate;

[0055] It should be noted that, in this embodiment, the data fusion integrates the crowd data information monitored by different sensors into a unified environmental description, including multiple types of information such as images, sounds, and distances; and by integrating the data of different sensors, a higher-dimensional unified description of the internal and external environment and targets is formed. This description set provides a comprehensive understanding of the environment and supports more accurate analysis and decision-making; this data fusion can improve the overall perception ability and accuracy of the system, making the final analysis and decision more reliable; for example, combined with abnormal body temperature detection, density monitoring, hidden object detection, and sound event capture, abnormal crowd behavior can be more comprehensively identified and responded to;

[0056] It should be noted that in this embodiment, the computer system also needs to analyze the fused data and evaluate the current environment and crowd status, that is, determine the current environmental challenges based on preset standards and learned behavior patterns; specifically, extract human-related feature information through deep learning, background models, threshold judgment and other methods to obtain crowd status feature information. For example, use YOLO and other deep learning methods to detect and extract people or cars in visible light images to obtain information such as the location, number, and accompanying objects of people. Use thresholds to segment human body temperature areas in heat maps to obtain information such as the location and number of people. Use Gaussian background models to model and threshold segment sounds and radar distances to obtain feature information such as the sound, location, and speed of suspected people. The parameters or data that characterize whether the working conditions of each sensor are ideal are analyzed through Gaussian background models, threshold judgment and other methods to obtain various challenge statistics of the environment. For example, the visible light sensor can determine whether the illumination is sufficient through the size C of the white balance gain compensation. If C>C 0 , 0<=C 0 <=100, the current lighting is poor, and the temperature camera can detect the temperature T in the high temperature area. 1 and low temperature zone temperature T 2 Deviation from human body temperature T 3 The degree of sensitivity of the current environment to human body temperature detection is used to determine whether it is sensitive and accurate. If (T 1 -T 3 )>M, 0<=M<=1000, or (T 3 -T 2 )>N, 0<=N<=200, the ambient temperature is too high or too low. The sound sensor can count the background sound intensity S to determine whether the environment is too noisy. If S>S 0 , 40 dB <= S 0, the environment is noisy, and the radar sensor can judge whether it is working under interference based on the signal-to-noise ratio; wherein, the signal-to-noise ratio refers to the ratio of signal power to noise power, which is directly related to whether the radar sensor can effectively detect the target. When there is interference, the interference signal may be mixed with the target signal, resulting in a change in the signal-to-noise ratio. Therefore, by monitoring the size of the signal-to-noise ratio, the radar sensor can determine whether it is affected by interference;

[0057] It should also be noted that, in this embodiment, the specific method of selecting the sensor combination is to dynamically determine the most effective sensor at present according to the current environmental conditions and the type of target to be monitored, and switch the mainly relied sensor when necessary, for example, infrared or millimeter wave radar is used preferentially under low light conditions. Specifically, after obtaining the crowd data information, the interference degree of each sensor is divided into three levels, among which the visible light camera is divided into: slightly poor light, poor light, and very poor light; the temperature measurement camera is divided into: slight high temperature / low temperature interference, high temperature / low temperature interference, and severe high temperature / low temperature interference; the array microphone is divided into: slight noise, noise interference, and severe noise interference; the millimeter wave radar is divided into slight radar interference, radar interference, and severe radar interference; according to the interference degree from slight to severe, each sensor is dynamically grouped by time slice, and the time slice size can be set between 0.001 seconds and 1 day. Sensors with slight interference are divided into the ideal group, those with medium interference are divided into the pending group, and those with severe interference are divided into the untrustworthy group. The ideal group forms the most effective sensor combination at present, and the acquisition of crowd information is preferentially obtained from the ideal group sensors;

[0058] S20, the computer system determines whether the current crowd behavior is abnormal through the crowd data information monitored by the sensor combination, and if so, issues an alarm; wherein the specific manner in which the computer system determines whether the current crowd behavior is abnormal through the crowd data information monitored by the preferred sensor combination is:

[0059] S201, presetting a normal crowd data information threshold in a computer system;

[0060] S202, determining whether the crowd data information detected by the sensor combination exceeds a normal crowd data information threshold, if so, the current crowd behavior is abnormal;

[0061] It should be noted that, in this embodiment, through real-time monitoring and threshold comparison, the computer system can quickly identify abnormal situations of crowd behavior and respond to potential security risks in a timely manner; and when an abnormality is detected, the system automatically issues an alarm to prompt security personnel to conduct further intervention and processing, thereby helping the security system to prevent and manage potential problems such as crowding and riots, and improve the efficiency and safety of crowd management;

[0062] S30, the computer system uses a multimodal large model to analyze whether the abnormal behavior of the crowd corresponding to the alarm is a false alarm. If so, the false alarm is filtered and the false alarm data is counted; if not, the data is counted and the final alarm is output;

[0063] It should be noted that, in this embodiment, the multimodal large model is a deep learning model trained by a large model data set, and the model can distinguish whether abnormal behavior of a crowd is a false alarm; when real-time data is input into the trained model, the model can evaluate whether the data is a false alarm; when there is a false alarm, the false alarm data is counted, and the statistical false alarm data serves as an important basis for subsequent adjustment of system parameters;

[0064] S40, the computer system upgrades its own parameters according to the statistical false alarm data to reduce false alarms; before upgrading its own parameters, the computer system also counts whether there are omissions in the crowd data information; the specific manner in which the computer system upgrades its own parameters according to the filtered false alarms is:

[0065] When a false alarm is found in step S30, the sensitivity of the source sensor is lowered;

[0066] When the computer system finds that there are too few missed alarms or too few alarms, it increases the sensitivity of the source sensor;

[0067] The source sensor is a sensor that collects false positive or false negative data.

[0068] It should be noted that, in this embodiment, by adjusting the sensitivity, the system can reduce unnecessary alarms and lower the false alarm rate, which helps users focus on real abnormal events rather than being disturbed by irrelevant alarms; by increasing the sensitivity, it can help the system capture more actual abnormal events, thereby reducing missed alarms and ensuring that all situations that require attention are handled in a timely manner; and the system can dynamically adjust parameters based on actual operating data so that it can maintain a high level of alarm accuracy in different environments and situations. This dynamic adjustment capability allows the system to maintain optimal performance at different times and under different conditions; through continuous analysis of false alarm and missed alarm data, the system continuously optimizes parameters and gradually improves the overall detection and alarm effects, which can not only reduce the interference of false alarm information, but also respond to all important events in a timely manner.

[0069] It should be noted that, in this embodiment, the computer system also includes a user interface and a control center to provide an intuitive user interface for security personnel to monitor and operate, realize remote control, parameter adjustment and real-time feedback, so as to ensure public safety.

[0070] The beneficial effects of the present invention are as follows: it is capable of monitoring data information of various groups of people in a specific environment, automatically selecting the best sensor combination, and dynamically adjusting the perception strategy to optimize the detection of abnormal behavior, thereby improving the efficiency and accuracy of crowd detection and effectively ensuring public safety.

[0071] Reference Figure 2 As shown, the present invention further discloses a detection device 600 for abnormal crowd behavior, which is applied to the detection method for abnormal crowd behavior as described in the above embodiment. The detection device 600 for abnormal crowd behavior includes:

[0072] The sensor module 601 is used to monitor the crowd data information of the current environment in real time;

[0073] The data processing module 602 is used to analyze the crowd data information and obtain the optimal sensor combination;

[0074] Anomaly detection module 603 analyzes crowd data information to determine whether there is anomaly, and if so, issues an alarm signal;

[0075] The multimodal analysis module 604 is used to analyze the alarm data to determine whether there are false alarms. If so, the false alarms are filtered out and the false alarm data is counted;

[0076] The false alarm processing module 605 is used to upgrade the parameters of the computer system itself according to the statistical false alarm data to reduce false alarms.

[0077] It should be noted that the detection strategy for abnormal crowd behavior provided in the above embodiment is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the detection system 600 for abnormal crowd behavior will be divided into different functional modules to complete all or part of the functions described above.

[0078] In addition, the abnormal crowd behavior detection system 600 provided in the above embodiment and the abnormal crowd behavior detection method embodiment belong to the same concept, wherein the specific manner in which each module performs the operation has been described in detail in the method embodiment and will not be repeated here.

[0079] Figure 3 The present invention schematically illustrates the structure of an electronic device according to an exemplary embodiment.

[0080] It should be noted that the electronic device is only an example adapted to the present invention and cannot be considered to provide any limitation on the scope of use of the present invention. The electronic device cannot be interpreted as requiring dependence on or having Figure 3 One or more components of exemplary electronic device 2000 are shown.

[0081] The hardware structure of the electronic device 2000 may vary greatly due to differences in configuration or performance, such as Figure 3 As shown, the electronic device 2000 includes: a power supply 210, an interface 230, at least one memory 250, and at least one central processing unit (CPU) 270.

[0082] Specifically, the power supply 210 is used to provide operating voltage for each hardware device on the electronic device 2000 .

[0083] The interface 230 includes at least one wired or wireless network interface 231 for interacting with external devices. Of course, in other examples adapted by the present invention, the interface 230 may further include at least one serial-to-parallel conversion interface 233, at least one input-output interface 235, and at least one USB interface 237, etc. Figure 3 As shown, this is not intended to be a specific limitation.

[0084] The memory 250 is a carrier for storing resources, which may be a read-only memory, a random access memory, a disk or an optical disk, etc. The resources stored thereon include an operating system 251, an application 253 and data 255, etc. The storage method may be temporary storage or permanent storage.

[0085] Among them, the operating system 251 is used to manage and control various hardware devices and application programs 253 on the electronic device 2000 to enable the central processing unit 270 to calculate and process the massive data 255 in the memory 250. It can be Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, etc.

[0086] The application program 253 is a computer-readable instruction that performs at least one specific task based on the operating system 251, and may include at least one module ( Figure 3 (not shown), each module may respectively include computer-readable instructions for the electronic device 2000. For example, the device for assessing task priority may be regarded as an application 253 deployed on the electronic device 2000.

[0087] The data 255 may be signal information, etc., stored in the memory 250 .

[0088] The central processor 270 may include one or more processors, and is configured to communicate with the memory 250 through at least one communication bus to read the computer-readable instructions stored in the memory 250, thereby realizing the operation and processing of the mass data 255 in the memory 250. For example, the method for detecting abnormal behavior of a crowd is completed by the central processor 270 reading a series of computer-readable instructions stored in the memory 250.

[0089] In addition, the present invention can also be implemented by hardware circuits or hardware circuits combined with software. Therefore, the implementation of the present invention is not limited to any specific hardware circuits, software, or a combination of the two.

[0090] See also Figure 4 In an embodiment of the present invention, an electronic device 4000 is provided. The electronic device 4000 may include: a desktop computer, a laptop computer, a server, etc. with sensor recognition capability.

[0091] exist Figure 4 In the embodiment, the electronic device 4000 includes at least one processor 4001 and at least one memory 4003.

[0092] The data interaction between the processor 4001 and the memory 4003 can be realized through at least one communication bus 4002. The communication bus 4002 may include a path for transmitting data between the processor 4001 and the memory 4003. The communication bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The communication bus 4002 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0093] Optionally, the electronic device 4000 may further include a transceiver 4004, which may be used for data interaction between the electronic device and other electronic devices, such as data transmission and / or data reception, etc. It should be noted that in actual applications, the transceiver 4004 is not limited to one, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present invention.

[0094] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of the present invention. Processor 4001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0095] The memory 4003 can be a ROM (Read On Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrical Erasable Programmable Read On Memory), a CD-ROM (Compact Disk Read On Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program instructions or codes in the form of instructions or data structures and can be accessed by the electronic device 400, but is not limited to this.

[0096] Computer-readable instructions are stored in the memory 4003 , and the processor 4001 can read the computer-readable instructions stored in the memory 4003 through the communication bus 4002 .

[0097] The computer-readable instructions are executed by one or more processors 4001 to implement the method for detecting abnormal crowd behavior in the above-mentioned embodiments.

[0098] In addition, an embodiment of the present invention provides a storage medium on which computer-readable instructions are stored. The computer-readable instructions are executed by one or more processors to implement the above-mentioned method for detecting abnormal behavior of a crowd.

[0099] The above is a specific description of the preferred implementation of the present invention, but the invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A method for detecting abnormal behavior of a crowd, applied to a computer system, wherein the computer system comprises at least a sensor array and a multimodal large model; characterized in that: The method for detecting abnormal behavior of a crowd comprises the following steps: S10, the computer system monitors the crowd data information of the current environment in real time through the sensor array, and selects the sensor combination based on the noise level of each sensor in the sensor array when it is running; S20, the computer system determines whether there is any abnormality in the current crowd behavior through the crowd data information monitored by the sensor combination, and if so, issues an alarm; S30, the computer system uses a multimodal large model to analyze whether the abnormal behavior of the crowd corresponding to the alarm is a false alarm. If so, the false alarm is filtered and the false alarm data is counted; if not, the data is counted and the final alarm is output; S40, the computer system upgrades its own parameters according to the statistical false alarm data to reduce false alarms.

2. A method for detecting abnormal behavior of a crowd according to claim 1, characterized in that: In step S10, the sensor array includes: Temperature measurement cameras, used to monitor the body temperature data of individuals in the crowd; Visible light cameras are used to monitor crowd density, flow status and behavior patterns; Millimeter-wave radar, used to monitor hidden objects and crowd activities; Microphone array, used to monitor sound events in the current environment.

3. A method for detecting abnormal behavior of a crowd according to claim 2, characterized in that: In step S10, before selecting the sensor combination, the crowd data information monitored by the sensor array needs to be fused through a computer system, wherein the computer system also includes a centralized processing unit; specifically, the specific method of data fusion is: Data aggregation: Transmitting crowd data information monitored by each sensor of the sensor array to a centralized processing unit through a network or serial port; Time calibration: Based on the time of the centralized processing unit, the crowd data information monitored by each sensor is time-calibrated and synchronized; Unified spatial coordinates: Using the common installation position of each sensor as the reference zero point, the crowd data information monitored by each sensor is converted into a unified spatial coordinate.

4. A method for detecting abnormal behavior of a crowd according to claim 3, characterized in that: In step S20, the computer system determines whether the current crowd behavior is abnormal by using the crowd data information monitored by the preferred sensor combination in the following specific manner: S201, presetting a normal crowd data information threshold in a computer system; S202, determining whether the crowd data information detected by the sensor combination exceeds a normal crowd data information threshold, and if so, the current crowd behavior is abnormal.

5. A method for detecting abnormal behavior of a crowd according to claim 4, characterized in that: In step S40, before the computer system upgrades its own parameters, the computer system will also count whether there is any underreporting in the crowd data information.

6. A method for detecting abnormal behavior of a crowd according to claim 5, characterized in that: The specific method in which the computer system upgrades its own parameters according to the filtered false alarms is: When a false alarm is found in step S30, the sensitivity of the source sensor is lowered; When the computer system finds that there are too few missed alarms or too few alarms, it increases the sensitivity of the source sensor; The source sensor is a sensor that collects false positive or false negative data.

7. A device for detecting abnormal behavior of a crowd, applied to the method for detecting abnormal behavior of a crowd according to any one of claims 1 to 6, characterized in that: The device for detecting abnormal crowd behavior comprises: Sensor module, used to monitor crowd data information in the current environment in real time; A data processing module is used to analyze crowd data information and obtain an optimal sensor combination; The anomaly detection module analyzes crowd data information to determine whether there is an anomaly and issues an alarm signal if so; Multimodal analysis module, used to analyze alarm data, determine whether there are false alarms, and if so, filter out false alarms and count false alarm data; The false alarm processing module is used to upgrade the computer system's own parameters according to the statistical false alarm data to reduce false alarms.

8. An electronic device, characterized in that: comprises at least one processor and at least one memory, wherein: The memory has computer-readable instructions stored thereon; The computer-readable instructions are executed by one or more processors, so that the electronic device implements the method for detecting abnormal behavior of a crowd as described in any one of claims 1-6.

9. A storage medium having computer-readable instructions stored thereon, characterized in that: The computer-readable instructions are executed by one or more processors to implement the method for detecting abnormal crowd behavior as described in any one of claims 1-6.

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