CN115497159A

CN115497159A - Human body abnormal state detection method and device, computer equipment and storage medium

Info

Publication number: CN115497159A
Application number: CN202211117589.4A
Authority: CN
Inventors: 吴海鹏; 万慧; 高洪喜
Original assignee: Ping An Bank Co Ltd
Current assignee: Ping An Bank Co Ltd
Priority date: 2022-09-14
Filing date: 2022-09-14
Publication date: 2022-12-20

Abstract

The embodiment of the application provides a method and a device for detecting abnormal human body states, computer equipment and a storage medium, and the method comprises the following steps: acquiring video data of a target area through preset monitoring equipment; analyzing the video data based on a preset action recognition model to obtain an action risk value corresponding to each object to be recognized; and if the action risk value of any object to be identified exceeds a preset risk threshold value, sending human body abnormity prompt information to a preset terminal. According to the invention, the preset action recognition model is constructed, so that the abnormal actions of the human body in the target area can be accurately recognized, and the error recognition of the abnormal state of the human body can be effectively avoided in a way of accumulating action risk values, so that monitoring personnel can timely and accurately find the abnormal conditions in the target area, and the life safety of the personnel is ensured.

Description

Human body abnormal state detection method and device, computer equipment and storage medium

Technical Field

The present application relates to the field of intelligent monitoring, and in particular, to a method and an apparatus for detecting abnormal human body status, a computer device, and a storage medium.

Background

Health is one of the basic goals sought by contemporary employees, who often relax the work pressure by exercising or other entertainment means while working. The existing video monitoring scheme lacks a monitoring scheme for the health state of staff in a workplace during overtime or body building, and cannot timely cope with abnormal conditions.

Therefore, a detection scheme capable of accurately identifying the abnormal state of the employee is needed.

Disclosure of Invention

In order to solve the above technical problem, embodiments of the present application provide a method and an apparatus for detecting an abnormal state of a human body, a computer device, and a storage medium.

In a first aspect, an embodiment of the present application provides a method for detecting an abnormal state of a human body, including:

acquiring video data of a target area through preset monitoring equipment;

analyzing the video data based on a preset action recognition model to obtain an action risk value corresponding to each object to be recognized;

and if the action risk value of any object to be identified exceeds a preset risk threshold value, sending human body abnormity prompt information to a preset terminal.

According to a specific implementation manner of the embodiment of the present application, before the step of analyzing the video data based on the preset motion recognition model, the method further includes:

identifying the video data based on an image feature recognition model;

if a target characteristic image in the video data is identified, determining a video moment corresponding to the target characteristic image, intercepting video stream data of a preset time period before the video moment, inputting the video stream data to a preset action identification model, and continuously executing the step of analyzing the video data based on the preset action identification model.

According to a specific implementation manner of the embodiment of the present application, the step of analyzing the video data based on the preset motion recognition model to obtain the motion risk value corresponding to each object to be recognized includes:

acquiring a limb image set of each object to be identified in the video data;

identifying limb action information in the limb image set of each object to be identified based on the preset action identification model;

and matching the limb action information with preset risk action information, and if the limb action information is matched with the preset risk action information, increasing a preset action risk value for the corresponding object to be identified.

According to a specific implementation manner of the embodiment of the present application, the step of identifying the limb motion information in the limb image set of each object to be identified based on the preset motion identification model includes:

extracting position coordinates of human body key points in the limb image set, wherein the human body key points comprise a head and an upper limb;

and calculating the motion track of each human body key point according to the position coordinates, and generating a limb joint transformation matrix according to the position coordinates to obtain the limb action information, wherein the limb action information comprises the motion track and the limb joint transformation matrix.

According to a specific implementation manner of the embodiment of the application, the step of matching the limb movement information with the preset risk movement information, and if the limb movement information matches the preset risk movement information, adding a preset movement risk value to a corresponding object to be identified includes:

matching the limb action information with first risk action information, and if the limb action information matches the first risk action information, adding a first action risk value to a corresponding object to be identified;

matching the limb action information with second risk action information, and if the limb action information matches the second risk action information, adding a second action risk value to the corresponding object to be identified;

and matching the limb action information with third risk action information, and if the limb action information matches the third risk action information, adding a third action risk value to the corresponding object to be identified.

According to a specific implementation manner of the embodiment of the present application, the risk levels of the first risk action, the second risk action, and the third risk action are sequentially increased.

According to a specific implementation of the embodiments of the present application, the target area is a fitness area, an office area, or an entertainment area.

In a second aspect, an embodiment of the present application provides a human body abnormal state detection apparatus, including:

the video acquisition module is used for acquiring video data of a target area through preset monitoring equipment;

the video analysis module is used for analyzing the video data based on a preset action recognition model so as to obtain an action risk value corresponding to each object to be recognized;

and the abnormity alarm module is used for sending human abnormity prompt information to the preset terminal if the action risk value of any object to be identified exceeds a preset risk threshold value.

In a third aspect, an embodiment of the present application provides a computer device, where the computer device includes a memory and a processor, where the memory is used to store a computer program, and the computer program executes the human abnormal state detection method provided in any one of the first aspect and the first aspect when the processor runs.

In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program, and when the computer program runs on a processor, the method for detecting an abnormal state of a human body provided in any one of the first aspect and the first aspect is executed.

The application provides a human body abnormal state detection method, a human body abnormal state detection device, computer equipment and a storage medium, wherein the method comprises the following steps: acquiring video data of a target area through preset monitoring equipment; analyzing the video data based on a preset action recognition model to obtain an action risk value corresponding to each object to be recognized; and if the action risk value of any object to be identified exceeds a preset risk threshold value, sending human body abnormity prompt information to a preset terminal. According to the invention, the human body abnormal state occurring in the target area can be accurately identified by constructing the preset action identification model, and the error identification of the abnormal state can be effectively avoided by means of the accumulation of the action risk value, so that monitoring personnel can timely and accurately find the abnormal phenomenon in the target area, and the life safety of personnel is guaranteed.

Drawings

In order to more clearly explain the technical solutions of the present application, the drawings needed to be used in the embodiments are briefly introduced below, and 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 of protection of the present application. Like components are numbered similarly in the various figures.

Fig. 1 is a schematic method flow diagram illustrating a method for detecting an abnormal state of a human body according to an embodiment of the present application;

fig. 2 is a schematic diagram illustrating device modules of a human body abnormal state detection device according to an embodiment of the present application;

fig. 3 shows a device module schematic diagram of a computer device according to an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments.

The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.

Hereinafter, the terms "including", "having", and their derivatives, which may be used in various embodiments of the present application, are intended to indicate only specific features, numbers, steps, operations, elements, components, or combinations of the foregoing, and should not be construed as first excluding the existence of, or adding to, one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing.

Furthermore, the terms "first," "second," "third," and the like are used solely to distinguish one from another and are not to be construed as indicating or implying relative importance.

Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of the present application belong. The terms (such as terms defined in a commonly used dictionary) will be construed to have the same meaning as the contextual meaning in the related art and will not be construed to have an idealized or overly formal meaning unless expressly so defined in various embodiments of the present application.

Referring to fig. 1, which is one of a schematic flow chart of a method for detecting an abnormal state of a human body provided in an embodiment of the present disclosure, as shown in fig. 1, the method for detecting an abnormal state of a human body provided in the embodiment includes:

step S101, video data of a target area is obtained through preset monitoring equipment;

in a specific embodiment, the preset monitoring device is any camera device capable of acquiring image and video data, and the type of the preset monitoring device can be adaptively replaced according to an actual application scene.

The preset monitoring equipment is arranged at a position capable of shooting the video data of the target area.

In particular embodiments, the exercise area may be any area of an exercise room, such as a treadmill, yoga room, or an equipment area.

The office area may be any area of an office, such as an office location, a conference room, or a tea room.

The entertainment area may be a KTV room, a script killing room, or other board game rooms.

Specifically, the target area may also be an area range designated by a user, and is not limited herein.

The preset monitoring device is arranged at any position capable of shooting human body characteristics in the target area, such as a corner of a gymnasium or a treadmill.

After the user starts the human body abnormal state detection device, the preset monitoring equipment continuously acquires video data of a target area and sends the video data to the human body abnormal state detection device so as to identify the human body abnormal state.

identifying the video data based on an image feature recognition model;

In a specific embodiment, before performing the abnormal recognition on the human body action in the video data, the human body abnormal state detection apparatus may determine whether the video data includes the target feature image through an image recognition model.

Specifically, the target feature image may be image data of a human head landing.

After the preset monitoring equipment is started, when an image frame that the head of a human body lands appears in video data acquired by the preset monitoring equipment, video stream data in a preset time period is immediately intercepted from a video moment corresponding to the image frame.

The preset time period may be adaptively set according to an actual application scenario, and is not specifically limited herein.

The target feature image may also be other image data that is custom set by the user, for example, the face presents a distorted expression.

After video stream data in a preset time period is intercepted, analyzing the video stream data through a preset action recognition model.

Step S102, analyzing the video data based on a preset action recognition model to obtain an action risk value corresponding to each object to be recognized;

in a specific embodiment, the human body abnormal state detection device identifies human body actions in the video data based on an action identification model constructed in advance, and associates a corresponding action risk value for each object to be identified when the human body actions are abnormal.

Specifically, the preset action recognition model can be obtained by performing simulation training through an image training set by a user.

The object to be identified is a human body.

The preset action recognition model is used for recognizing the self-set abnormal actions of the human body, and adding corresponding action risk values to the object to be recognized based on the action risk values associated with the abnormal actions of the human body.

It should be noted that the preset motion recognition model can effectively recognize abnormal motions of the human body such as falling and twitching.

And if the human body in the target area presents abnormal expression or abnormal limb movement, automatically adding a corresponding movement risk value to the human body.

The initial action risk value of each object to be recognized is 0, and if the preset action recognition model analyzes that the object to be recognized never has abnormal behaviors, the action risk value of the object to be recognized is always 0.

acquiring a limb image set of each object to be identified in the video data;

In a specific embodiment, all objects to be recognized in the video data are recognized based on a face recognition technology, and a limb image set corresponding to each object to be recognized in the video data is extracted.

And comparing and analyzing the limb action information in the limb image set based on the preset action model, and adding a corresponding action risk value for the object to be identified corresponding to the limb action information when the limb action information is judged to be matched with the preset risk action information.

For example, if the person a cannot fall over while moving in the gymnasium, the human body abnormal state detection device immediately intercepts the video data of the preset time period before the falling time of the person a for motion recognition, and the motion of the falling time of the person a is matched with the preset risk motion information, and then a 10-point motion risk value is added to the person a.

According to a specific implementation manner of the embodiment of the application, the step of identifying the limb motion information in the limb image set of each object to be identified based on the preset motion identification model includes:

and calculating the motion trail of each human body key point according to the position coordinates, and generating a limb joint transformation matrix according to the position coordinates to obtain the limb action information, wherein the limb action information comprises the motion trail and the limb joint transformation matrix.

In a specific embodiment, the specific process of recognizing the limb action information by the preset action recognition model includes acquiring position coordinates of the upper limbs and the head of the human body in the limb image set, calculating the movement tracks of the upper limbs and the head of the human body according to the change condition of the position coordinates, and outputting a human body key change matrix by using a preset connection layer to obtain corresponding limb action information.

The preset connection layer is a result output layer in a preset action recognition model obtained based on neural network model training.

In a specific embodiment, the first risk motion information is hand motion information, such as a palm pressing a chest, a palm pressing a head, and the like.

The second risk action information is facial expression information, for example, abnormal facial expressions such as frowning and closing eyes are presented.

The third risk motion information is body motion information, such as abnormal body motion like standing upside down, body twisting into a bow, etc.

When various types of risk action information are matched, corresponding action risk values can be added to corresponding objects to be identified after the limb action information is matched with the preset risk action information for a certain time, so that misjudgment is avoided.

In the actual application process, action risk values corresponding to each type of risk action are different.

In the detection process, the third action risk value is the highest, the second action risk value is the next lowest, and the first action risk value is the lowest.

And step S103, if the action risk value of any object to be identified exceeds a preset risk threshold value, sending human body abnormity prompt information to a preset terminal.

In a specific embodiment, after the video data is processed by the preset motion recognition model, a motion risk value corresponding to each object to be recognized in the target area can be obtained.

The preset risk threshold may be set to a sum of the first risk value, the second risk value and the third risk value.

In the practical application process, if the continuous matching time of the limb action information of the object to be identified and the first risk action information exceeds a certain time, the action risk value is continuously added to the object to be identified under the condition that the first risk value is added once until the action risk value of the object to be identified exceeds a preset threshold value.

For example, if character a keeps pressing the chest for more than 10 seconds with the palm, then a first risk value is added for a; if the palm of the hand A continuously presses the chest for more than 1 minute, continuously increasing a second risk value for the hand A on the basis of the first risk value; if the palm of hand A is continuously pressing on the chest for more than 5 minutes, the third risk value is continuously increased on the basis of the first risk value.

The action risk value in the above example is a stepwise increasing manner, and the action risk value may also continuously increase with time, which is not limited in this embodiment.

The preset risk threshold value can be set in a self-adaptive manner according to an actual application scene.

When the action risk value of the object to be identified exceeds a preset risk threshold value, the human body abnormal state detection device immediately sends human body abnormal prompt information to a monitoring terminal in a duty room, and performs voice alarm to inform related personnel that human body abnormal conditions occur in a target area and the personnel need to send medical treatment and treatment in time.

Specifically, preset terminal also can be the mobile terminal of the regional responsible person of target to the responsible person carries out remote rescue, prevents that only one person in gymnasium or office from, can't obtain the condition of timely rescue.

The embodiment of the application provides a method for detecting abnormal human body state, which can quickly and accurately judge whether human body twitches fall down in public areas such as gymnasiums or offices in an AI intelligent mode, and timely alarm prompt processing is carried out when human body twitch fall down actions are recognized in a target area, so that security or other related personnel can quickly react, the abnormal human body state can be quickly reacted, and medical treatment and treatment can be timely carried out.

Referring to fig. 2, a schematic diagram of device modules of a human body abnormal state detection device 200 is provided for an embodiment of the present disclosure, and as shown in fig. 2, the human body abnormal state detection device 200 provided in the embodiment of the present disclosure includes:

the video acquisition module 201 is configured to acquire video data of a target area through preset monitoring equipment;

the video analysis module 202 is configured to analyze the video data based on a preset action recognition model to obtain an action risk value corresponding to each object to be recognized;

and the abnormity warning module 203 is used for sending human abnormity prompt information to the preset terminal if the action risk value of any object to be identified exceeds the preset risk threshold value.

The specific implementation process of the human body abnormal state detection apparatus 200 provided in this embodiment may refer to the specific implementation process in the method embodiment 1, and is not described herein again for avoiding repetition.

Furthermore, an embodiment of the present disclosure provides a computer device, which includes a memory and a processor, where the memory stores a computer program, and the computer program executes, when running on the processor, the human abnormal state detection method provided in the above method embodiment 1.

Specifically, as shown in fig. 3, the computer device 300 provided in this embodiment includes:

radio frequency unit 301, network module 302, audio output unit 303, input unit 304, sensor 305, display unit 306, user input unit 307, interface unit 308, memory 309, processor 310, and power supply 311. Those skilled in the art will appreciate that the computer device architecture illustrated in FIG. 3 is not intended to be limiting of computer devices, which may include more or fewer components than those illustrated, or some of the components may be combined, or a different arrangement of components. In the embodiments of the present application, the computer device includes, but is not limited to, a mobile phone, a tablet computer, a notebook computer, a palm computer, a vehicle-mounted computer device, a wearable device, a pedometer, and the like.

It should be understood that, in the embodiment of the present application, the radio frequency unit 301 may be used for receiving and sending signals during a message sending and receiving process or a call process, and specifically, receives downlink data from a base station and then processes the received downlink data to the processor 310; in addition, the uplink data is transmitted to the base station. In general, the radio frequency unit 301 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like. In addition, the radio frequency unit 301 can also communicate with a network and other devices through a wireless communication system.

The computer device provides wireless broadband internet access to the user via the network module 302, such as assisting the user in sending and receiving e-mails, browsing web pages, and accessing streaming media.

The audio output unit 303 may convert audio data received by the radio frequency unit 301 or the network module 302 or stored in the memory 309 into an audio signal and output as sound. Also, the audio output unit 303 may also provide audio output related to a specific function performed by the computer device 300 (e.g., a call signal reception sound, a message reception sound, etc.). The audio output unit 303 includes a speaker, a buzzer, a receiver, and the like.

The input unit 304 is used to receive audio or video signals. The input Unit 304 may include a Graphics Processing Unit (GPU) 3041 and a microphone 3042, the Graphics processor 3041 Processing image data of still pictures or video obtained by an image capturing computer device (such as a camera) in a video capturing mode or an image capturing mode. The processed image frames may be video played on the display unit 306. The image frames processed by the graphic processor 3041 may be stored in the memory 309 (or other storage medium) or transmitted via the radio frequency unit 301 or the network module 302. The microphone 3042 may receive sounds and can process such sounds into audio data. The processed audio data may be converted into a format output transmittable to a mobile communication base station via the radio frequency unit 301 in case of the phone call mode.

The computer device 300 also includes at least one sensor 305, such as light sensors, motion sensors, and other sensors. Specifically, the light sensor includes an ambient light sensor that adjusts the brightness of the display panel 3061 according to the brightness of the ambient light, and a proximity sensor that turns off the display panel 3061 and/or the backlight when the computer device 300 is moved to the ear. As one type of motion sensor, an accelerometer sensor can detect the magnitude of acceleration in various directions (generally three axes), detect the magnitude and direction of gravity when stationary, and can be used to identify the posture of a computer device (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), and vibration identification related functions (such as pedometer, tapping); the sensors 305 may also include fingerprint sensors, pressure sensors, iris sensors, molecular sensors, gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc., which are not described in detail herein.

The display unit 306 is used for video playing of information input by the user or information provided to the user. The Display unit 306 may include a Display panel 3061, and the Display panel 3061 may be configured in the form of a Liquid Crystal Display (LCD), an Organic Light-Emitting Diode (OLED), or the like.

The user input unit 307 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the computer apparatus. Specifically, the user input unit 307 includes a touch panel 3071 and other input devices 3072. The touch panel 3071, also referred to as a touch screen, may collect touch operations by a user on or near the touch panel 3071 (e.g., operations by a user on or near the touch panel 3071 using a finger, a stylus, or any suitable object or attachment). The touch panel 3071 may include two parts of a touch detection computer device and a touch controller. The touch detection computer equipment detects the touch direction of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch-sensing computer device, converts the touch information into touch point coordinates, sends the touch point coordinates to the processor 310, receives a command from the processor 310, and executes the command. In addition, the touch panel 3071 may be implemented using various types, such as resistive, capacitive, infrared, and surface acoustic wave. The user input unit 307 may include other input devices 3072 in addition to the touch panel 3071. Specifically, the other input devices 3072 may include, but are not limited to, a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, and a joystick, which are not described herein.

Further, the touch panel 3071 may be overlaid on the display panel 3061, and when the touch panel 3071 detects a touch operation on or near the touch panel, the touch operation is transmitted to the processor 310 to determine the type of the touch event, and then the processor 310 provides a corresponding visual output on the display panel 3061 according to the type of the touch event. Although the touch panel 3071 and the display panel 3061 are shown in fig. 3 as two separate components to implement the input and output functions of the computer device, in some embodiments, the touch panel 3071 and the display panel 3061 may be integrated to implement the input and output functions of the computer device, which is not limited herein.

The interface unit 308 is an interface for connecting an external computer device to the computer device 300. For example, the external computer device may include a wired or wireless headset port, an external power supply (or battery charger) port, a wired or wireless data port, a memory card port, a port for connecting a computer device having an identification module, an audio input/output (I/O) port, a video I/O port, an earphone port, and the like. Interface unit 308 may be used to receive input (e.g., data information, power, etc.) from an external computer device and transmit the received input to one or more elements within computer device 300 or may be used to transmit data between computer device 300 and an external computer device.

The memory 309 may be used to store software programs as well as various data. The memory 309 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. Further, the memory 309 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.

The processor 310 is a control center of the computer device, connects various parts of the entire computer device using various interfaces and lines, performs various functions of the computer device and processes data by operating or executing software programs and/or modules stored in the memory 309 and calling data stored in the memory 309, thereby monitoring the computer device as a whole. Processor 310 may include one or more processing units; preferably, the processor 310 may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 310.

The computer device 300 may further include a power supply 311 (such as a battery) for supplying power to various components, and preferably, the power supply 311 may be logically connected to the processor 310 through a power management system, so as to implement functions of managing charging, discharging, and power consumption through the power management system.

In addition, the computer device 300 includes some functional modules that are not shown, and are not described in detail here.

The present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program runs on a processor, the method for detecting a human abnormal state described in embodiment 1 may be performed, and details are not repeated herein in order to avoid repetition.

In this embodiment, the computer-readable storage medium may be a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk.

It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or terminal that comprises the element.

Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.

While the present embodiments have been described with reference to the accompanying drawings, it is to be understood that the invention is not limited to the precise embodiments described above, which are meant to be illustrative and not restrictive, and that various changes may be made therein by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.

Claims

1. A method for detecting an abnormal state of a human body, comprising:

acquiring video data of a target area through preset monitoring equipment;

2. The method of claim 1, wherein the step of analyzing the video data based on the preset motion recognition model is preceded by the method further comprising:

identifying the video data based on an image feature recognition model;

if a target characteristic image in the video data is identified, determining a video moment corresponding to the target characteristic image, intercepting video stream data of a preset time period before the video moment, inputting the video stream data to the preset action identification model, and continuously executing the step of analyzing the video data based on the preset action identification model.

3. The method according to claim 1, wherein the step of analyzing the video data based on the preset motion recognition model to obtain the motion risk value corresponding to each object to be recognized comprises:

acquiring a limb image set of each object to be identified in the video data;

4. The method according to claim 3, wherein the step of identifying the limb motion information in the limb image set of each object to be identified based on the preset motion identification model comprises:

extracting position coordinates of human key points in the limb image set, wherein the human key points comprise a head and an upper limb;

5. The method according to claim 3, wherein the step of matching the limb movement information with preset risk movement information and adding a preset movement risk value to the corresponding object to be recognized if the limb movement information matches the preset risk movement information comprises:

matching the limb action information with first risk action information, and if the limb action information matches the first risk action information, adding a first action risk value for a corresponding object to be identified;

and matching the limb action information with third risk action information, and if the limb action information is matched with the third risk action information, adding a third action risk value to the corresponding object to be identified.

6. The method of claim 5, wherein the first risk action, the second risk action, and the third risk action are sequentially ranked higher.

7. The method of claim 1, wherein the target area is a fitness area, an office area, or an entertainment area.

8. A human body abnormal state detection device, comprising:

9. A computer device, characterized in that the computer device comprises a memory and a processor, the memory storing a computer program which, when the processor is run, performs the human abnormal state detection method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that it stores a computer program which, when run on a processor, performs the human abnormal state detection method of any one of claims 1 to 7.