Security data processing method and system based on image recognition

Abstract

The present application relates to the field of image processing technology, and provides a security data processing method and system based on image recognition, wherein the method includes obtaining a first target image frame of a monitoring area in real time through a preset camera device, and obtaining a second target image frame of the monitoring area in a preset image database; wherein the second target image frame is the previous frame image of the first target image frame; determining the risk factor of each target object in the first target image frame based on the first target image frame and the second target image frame; judging whether there is a safety hazard in the monitoring area based on each risk factor; and if there is a safety hazard in the monitoring area, sending a danger warning message to the construction personnel in the monitoring area. The method realizes the timely prediction of the safety hazards that may exist in the monitoring area, and then provides timely and effective danger warning to the construction personnel in the monitoring area, so as to ensure the life safety of the construction personnel in the monitoring area.

Description

Technical Field

[0001] The present application relates to the field of image processing technology, and in particular to a security data processing method and system based on image recognition. Background Art

[0002] As a special working environment, construction scenes have many potential safety hazards. For example, high-altitude operations, the operation of on-site transportation facilities, and the operation of large-scale mechanical equipment are all likely to lead to dangerous incidents at the construction site, thus posing a great threat to the life safety of construction workers at the construction site. However, existing monitoring methods can usually only provide monitoring and recording functions, and cannot predict potential dangers at the construction site, and thus cannot provide timely and effective danger warnings to construction workers at the construction site. Summary of the invention

[0003] The present application provides a security data processing method and system based on image recognition to solve the problems raised by the above-mentioned background technology.

[0004] In a first aspect, the present application provides a security data processing method based on image recognition, comprising:

[0005] Acquire a first target image frame of a monitoring area in real time through a preset camera device, and acquire a second target image frame of the monitoring area in a preset image database; wherein the second target image frame is an image frame previous to the first target image frame;

[0006] Determining a risk factor of each target object in the first target image frame based on the first target image frame and the second target image frame;

[0007] Determine whether there is a potential safety hazard in the monitoring area based on each of the risk factors;

[0008] If there are potential safety hazards in the monitoring area, a danger warning message is sent to construction personnel in the monitoring area.

[0009] In a second aspect, the present application provides a security data processing system based on image recognition, comprising:

[0010] An acquisition module, used to acquire a first target image frame of a monitoring area in real time through a preset camera device, and acquire a second target image frame of the monitoring area in a preset image database; wherein the second target image frame is an image frame previous to the first target image frame;

[0011] A determination module, configured to determine a risk factor of each target object in the first target image frame based on the first target image frame and the second target image frame;

[0012] A judgment module, used for judging whether there is a potential safety hazard in the monitoring area based on each of the risk factors;

[0013] The sending module is used to send danger warning information to the construction personnel in the monitoring area if there are safety hazards in the monitoring area.

[0014] The present application provides a security data processing method and system based on image recognition, wherein the method includes: obtaining a first target image frame of a monitoring area in real time through a preset camera device, and obtaining a second target image frame of the monitoring area in a preset image database; wherein the second target image frame is the previous frame of the first target image frame; determining the risk factor of each target object in the first target image frame based on the first target image frame and the second target image frame; judging whether there is a safety hazard in the monitoring area based on each of the risk factors; if there is a safety hazard in the monitoring area, issuing a danger warning message to the construction personnel in the monitoring area. The method realizes the timely prediction of the possible safety hazards in the monitoring area, and then provides timely and effective danger warnings to the construction personnel in the monitoring area, thereby ensuring the life safety of the construction personnel in the monitoring area. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0016] Figure 1 A schematic diagram of a process flow of a security data processing method based on image recognition provided in an embodiment of the present application;

[0017] Figure 2 A schematic block diagram of the structure of a security data processing system based on image recognition provided in an embodiment of the present application;

[0018] Figure 3 A block diagram of the structure of a terminal device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0021] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0022] It should be further understood that the term “and / or” used in the specification and appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0023] As a special working environment, the construction scene has many potential safety hazards. For example, high-altitude operations, the operation of on-site transportation facilities, the operation of large-scale mechanical equipment, etc. are likely to lead to dangerous incidents at the construction site, thus posing a great threat to the life safety of construction workers at the construction site. However, existing monitoring methods can usually only provide monitoring and recording functions, and are unable to predict potential dangers at the construction site, and further, are unable to provide timely and effective danger warnings to construction workers at the construction site. To this end, the present application provides a security data processing method and system based on image recognition to solve the above problems.

[0024] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0025] See also Figure 1 , Figure 1 A schematic diagram of a process flow of a security data processing method based on image recognition provided in an embodiment of the present application, such as Figure 1 As shown, the security data processing method based on image recognition provided in the embodiment of the present application includes steps S100 to S400.

[0026] Step S100, obtaining a first target image frame of a monitoring area in real time through a preset camera device, and obtaining a second target image frame of the monitoring area in a preset image database; wherein the second target image frame is a previous frame image of the first target image frame.

[0027] Step S200: determining a risk factor of each target object in the first target image frame based on the first target image frame and the second target image frame.

[0028] Step S300: judging whether there are any potential safety hazards in the monitored area based on the risk factors.

[0029] Step S400: If there is a potential safety hazard in the monitoring area, a danger warning message is issued to construction personnel in the monitoring area.

[0030] In this embodiment, as described in step S100, a first target image frame of the monitoring area is acquired in real time by a preset camera device, and a second target image frame of the monitoring area is acquired in a preset image database. The preset image database is an image database corresponding to the camera device, and each graphic frame in the image database is marked with a shooting time, and the shooting time corresponding to the second target image frame is the latest.

[0031] As described in step S200 above, after obtaining the first target image frame and the second target image frame, the risk factor of each target object in the first target image frame is determined based on the first target image frame and the second target image frame. Specifically, step S200 includes the following steps:

[0032] Inputting the first target image frame into a preset semantic segmentation model to obtain a semantic category corresponding to each pixel of the first target image frame; wherein the semantic category includes at least one, and the semantic segmentation model includes an input layer, a feature extraction layer, a semantic category generation layer and an output layer;

[0033] For each of the semantic categories, extract each pixel corresponding to the semantic category in the target image frame to obtain a target area corresponding to the semantic category;

[0034] For each of the semantic categories, a target image segmentation model corresponding to the semantic category is obtained from a preset image segmentation model database, and a target area corresponding to the semantic category is input into the target image segmentation model to segment the target area into a plurality of target sub-areas; wherein one of the target sub-areas represents an image of a target object, and the target object is an object corresponding to the semantic category, and the target image segmentation model includes an input layer, a feature extraction layer, an image segmentation layer, and an output layer; it can be understood that there may be multiple objects corresponding to the same semantic category in the monitoring area, so it is necessary to perform segmentation processing on the target area corresponding to the semantic category;

[0035] For each of the target objects, judging whether the target object is a static object based on the semantic category corresponding to the target object; for example, if the semantic category corresponding to the target object is an excavator, then the target object is a dynamic object; if the semantic category corresponding to the target object is a brick on a wall, then the target object is a static object;

[0036] If so, determining the risk factor of the target object based on a preset static object risk factor generation rule;

[0037] If not, the risk factor of the target object is determined based on a preset dynamic object risk factor generation rule.

[0038] Wherein, determining the risk factor of the target object based on a preset static object risk factor generation rule comprises the following steps:

[0039] Mapping the first target image frame to a first preset plane rectangular coordinate system, and mapping the second target image frame to a second preset plane rectangular coordinate system; wherein the center point of the first target image frame coincides with the coordinate origin of the first preset plane rectangular coordinate system, the center point of the second target image frame coincides with the coordinate origin of the second preset plane rectangular coordinate system, the center point of the first target image frame is the center point of the circumscribed circle of the first target image frame, and the center point of the second target image frame is the center point of the circumscribed circle of the second target image frame;

[0040] Determine a first coordinate point corresponding to the center point of the target object in the first preset plane rectangular coordinate system, determine a reference object corresponding to the target object in the second target image frame, and determine a second coordinate point corresponding to the center point of the reference object in the second preset plane rectangular coordinate system; wherein the midline point of the target object is the center point of a circumscribed circle of a target sub-region corresponding to the target object, and the center point of the reference object is the center point of a circumscribed circle of a region corresponding to the reference object in the second target image frame;

[0041] Determine a first distance value between a first coordinate point and a first mapping point corresponding to the target object as the risk coefficient; wherein the first mapping point is a mapping point of the second coordinate point in the first preset plane rectangular coordinate system.

[0042] It should be noted that each object in the first plane rectangular coordinate system is marked with a first identifier, and each of the first identifiers is different from each other, each object in the second plane rectangular coordinate system is marked with a second identifier, and each of the second identifiers is different from each other, and the method for determining the reference object corresponding to the target object in the second target image frame includes the following steps:

[0043] Determine a first identifier corresponding to the target object as a target first identifier;

[0044] A target second identifier is determined in the second plane rectangular coordinate system, and an object corresponding to the target second identifier is determined as a reference object corresponding to the target object; wherein the target second identifier is consistent with the target first identifier.

[0045] Wherein, determining the danger factor of the target object based on a preset dynamic object danger factor generation rule comprises the following steps:

[0046] Traversing a preset semantic category-acceleration mapping relationship table, and determining the acceleration corresponding to the target object based on the semantic category corresponding to the target object;

[0047] Determining a second distance value based on the acceleration and the target time difference; wherein the target time difference is a time difference between the first target image frame and the second target image frame;

[0048] Mapping the first target image frame to a first preset plane rectangular coordinate system, and mapping the second target image frame to a second preset plane rectangular coordinate system; wherein the center point of the first target image frame coincides with the origin of the coordinates of the first preset plane rectangular coordinate system, and the center point of the second target image frame coincides with the origin of the coordinates of the second preset plane rectangular coordinate system;

[0049] Determine a third coordinate point corresponding to the center point of the target object in the first preset plane rectangular coordinate system, determine a reference object corresponding to the target object in the second target image frame, and determine a fourth coordinate point corresponding to the center point of the reference object in the second preset plane rectangular coordinate system;

[0050] Determine a third distance value between a third coordinate point and a second mapping point corresponding to the target object; wherein the second mapping point is a mapping point of the fourth coordinate point in the first preset plane rectangular coordinate system;

[0051] A difference obtained by subtracting the third distance value from the second distance value is determined as the risk coefficient.

[0052] It should be noted that the solution of determining the second distance value based on the acceleration and the target time difference is implemented by formula (1), wherein S is the second distance value, a is the acceleration, and t is the target time difference.

[0053] (1)

[0055] It should be noted that the center point of the target object and the center point of the reference object have been introduced in detail above and will not be repeated here.

[0056] It should be noted that the method for determining the reference object corresponding to the target object in the second target image frame has been introduced in detail above and will not be repeated here.

[0057] It can be understood that the above method determines the danger factor of the target object based on a preset static object danger factor generation rule when the target object is a static object, and determines the danger factor of the target object based on a preset dynamic object danger factor generation rule when the target object is a dynamic object, thereby improving the reliability of the danger factor and helping to improve the accuracy of the security data processing method based on image recognition.

[0058] As described in the above step S300, after obtaining each of the risk factors, it is determined whether there is a safety hazard in the monitoring area based on each of the risk factors. Specifically, step S300 includes the following steps:

[0059] Determining whether the target object corresponding to the risk factor is a static object;

[0060] If so, comparing the risk factor with a first preset risk factor, and determining that there is a safety hazard in the monitored area when the risk factor is not less than the first preset risk factor;

[0061] If not, the risk factor is compared with a second preset risk factor, and when the risk factor is not less than the second preset risk factor, it is determined that there is a safety hazard in the monitored area.

[0062] It can be understood that when the target object is a static object, the method compares the risk factor with a first preset risk factor, and when the risk factor is not less than the first preset risk factor, it is determined that there is a safety hazard in the monitoring area; when the target object is a dynamic object, the method compares the risk factor with a second preset risk factor, and when the risk factor is not less than the second preset risk factor, it is determined that there is a safety hazard in the monitoring area. This improves the accuracy of the judgment result of whether there is a safety hazard in the monitoring area.

[0063] As described in the above step S400, if there is a potential safety hazard in the monitoring area, a danger warning message is sent to the construction personnel in the monitoring area. Specifically, step S400 includes the following steps:

[0064] Obtaining the location information of each construction worker at the construction site; wherein each of the construction workers wears a positioning device, and the location information of each of the construction workers is obtained through the positioning device worn by each of the construction workers;

[0065] For each of the construction workers, it is determined whether the construction worker is located in the monitoring area based on the location information corresponding to the construction worker, and when the construction worker is located in the monitoring area, a danger warning message is issued to the construction worker.

[0066] It can be understood that the time interval between the first target image frame and the second target image frame is very short. This embodiment determines the danger factor of each target object in the first target image frame through the first target image frame and the second target image frame, and judges whether there are safety hazards in the monitoring area based on each of the danger factors, thereby realizing timely prediction of possible safety hazards in the monitoring area, and then providing timely and effective danger warnings to construction personnel in the monitoring area, thereby ensuring the life safety of construction personnel in the monitoring area.

[0067] In some embodiments, the step of obtaining the target image segmentation model corresponding to the semantic category in a preset image segmentation model database includes the following steps:

[0068] Obtaining a category code of the semantic category; wherein the category code includes a first field, a second field, and a third field;

[0069] Acquire a target video in a preset database based on the first field;

[0070] Obtaining a hash value corresponding to the second field based on a preset hash algorithm;

[0071] For each target number in the hash value, a decrypted image frame is determined in the target video based on the target number; wherein the number corresponding to the playback sequence corresponding to the decrypted image frame in the target video is consistent with the target number;

[0072] For each of the decrypted image frames, matrix encoding is performed on the decrypted image frame based on a preset matrix encoding rule to obtain an encoding matrix corresponding to the decrypted image frame;

[0073] For each target character of the third field, determine a target row vector in each encoding matrix; wherein the target row vector includes the target character;

[0074] Arrange the target row vectors in order from top to bottom based on a preset sorting rule to obtain a decryption matrix;

[0075] Decryption processing is performed on each image segmentation model in the image segmentation model database based on the decryption matrix, and the successfully decrypted image segmentation model is determined as the target image segmentation model.

[0076] The method provided in this embodiment, on the one hand, can prevent the target image segmentation model from being tampered with by setting an encryption password for the target image segmentation model, thereby improving the security of the target image segmentation model, and further improving the accuracy of the security data processing method based on image recognition; on the other hand, the decryption matrix is ​​generated by each of the decrypted image frames, which increases the difficulty of cracking the decryption matrix and further improves the security of the target image segmentation model.

[0077] See also Figure 2 , Figure 2 The schematic block diagram of the structure of the security data processing system 100 based on image recognition provided in the embodiment of the present application is as follows: Figure 2 As shown, the security data processing system 100 based on image recognition includes:

[0078] The acquisition module 110 is used to acquire a first target image frame of the monitoring area in real time through a preset camera device, and acquire a second target image frame of the monitoring area in a preset image database; wherein the second target image frame is a previous frame image of the first target image frame.

[0079] The determination module 120 is configured to determine a risk factor of each target object in the first target image frame based on the first target image frame and the second target image frame.

[0080] The judgment module 130 is used to judge whether there is a safety hazard in the monitoring area based on each of the risk factors.

[0081] The sending module 140 is used to send danger warning information to the construction personnel in the monitoring area if there is a safety hazard in the monitoring area.

[0082] In some embodiments, the determining module includes:

[0083] An input unit, used to input the first target image frame into a preset semantic segmentation model to obtain a semantic category corresponding to each pixel of the first target image frame; wherein the semantic category includes at least one;

[0084] An extraction unit, configured to extract, for each of the semantic categories, pixels corresponding to the semantic category in the target image frame to obtain a target area corresponding to the semantic category;

[0085] A first acquisition unit is used to acquire, for each of the semantic categories, a target image segmentation model corresponding to the semantic category in a preset image segmentation model database, and input a target area corresponding to the semantic category into the target image segmentation model to segment the target area into a plurality of target sub-areas; wherein one of the target sub-areas represents an image of a target object, and the target object is an object corresponding to the semantic category;

[0086] A judging unit, configured to judge, for each of the target objects, whether the target object is a static object based on a semantic category corresponding to the target object;

[0087] A first determining unit, configured to determine the risk factor of the target object based on a preset static object risk factor generation rule;

[0088] The second determining unit is used to determine the risk factor of the target object based on a preset dynamic object risk factor generation rule if no.

[0089] In some embodiments, the sending module includes:

[0090] The second acquisition unit is used to acquire the location information of each construction worker at the construction site;

[0091] The sending unit is used to determine, for each of the construction workers, whether the construction worker is located in the monitoring area based on the location information corresponding to the construction worker, and send danger warning information to the construction worker when the construction worker is located in the monitoring area.

[0092] It should be noted that technicians in the relevant technical field can clearly understand that for the convenience and conciseness of description, the specific working processes of the system, modules and units described above can refer to the corresponding processes in the aforementioned security data processing method based on image recognition, and will not be repeated here.

[0093] The security data processing system 100 based on image recognition provided in the above embodiment can be implemented in the form of a computer program. The computer program can be used in Figure 3 The system is run on the terminal device 200 shown.

[0094] See also Figure 3 , Figure 3 The present invention provides a schematic block diagram of the structure of a terminal device 200 according to an embodiment of the present application. The terminal device 200 includes a processor 201 and a memory 202. The processor 201 and the memory 202 are connected via a device bus 203, wherein the memory 202 may include a non-volatile storage medium and an internal memory.

[0095] The non-volatile storage medium can store a computer program. The computer program includes program instructions, and when the program instructions are executed by the processor 201, the processor 201 can execute any of the above-mentioned security data processing methods based on image recognition.

[0096] The processor 201 is used to provide computing and control capabilities to support the operation of the entire terminal device 200.

[0097] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor 201, the processor 201 can execute any of the above-mentioned security data processing methods based on image recognition.

[0098] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the terminal device 200 involved in the scheme of the present application. The specific terminal device 200 may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0099] It should be understood that the processor 201 may be a central processing unit (CPU), and the processor 201 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0100] In some embodiments, the processor 201 is used to run a computer program stored in the memory to implement the following steps:

[0101] Acquire a first target image frame of a monitoring area in real time through a preset camera device, and acquire a second target image frame of the monitoring area in a preset image database; wherein the second target image frame is an image frame previous to the first target image frame;

[0102] Determining a risk factor of each target object in the first target image frame based on the first target image frame and the second target image frame;

[0103] Determine whether there is a potential safety hazard in the monitoring area based on each of the risk factors;

[0104] If there are potential safety hazards in the monitoring area, a danger warning message is sent to construction personnel in the monitoring area.

[0105] It should be noted that technicians in the relevant field can clearly understand that for the convenience and simplicity of description, the specific working process of the terminal device 200 described above can refer to the corresponding process of the aforementioned security data processing method based on image recognition, and will not be repeated here.

[0106] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by one or more processors, the one or more processors implement the security data processing method based on image recognition as provided in the embodiment of the present application.

[0107] The computer-readable storage medium may be an internal storage unit of the terminal device 200 in the aforementioned embodiment, such as a hard disk or memory of the terminal device 200. The computer-readable storage medium may also be an external storage device of the terminal device 200, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped with the terminal device 200.

[0108] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A security data processing method based on image recognition, characterized in that: include: Acquire a first target image frame of a monitoring area in real time through a preset camera device, and acquire a second target image frame of the monitoring area in a preset image database; wherein the second target image frame is an image frame previous to the first target image frame; Determining a risk factor of each target object in the first target image frame based on the first target image frame and the second target image frame; Determine whether there is a potential safety hazard in the monitoring area based on each of the risk factors; If there are potential safety hazards in the monitoring area, a danger warning message is sent to the construction personnel in the monitoring area; The step of determining the risk factor of each target object in the first target image frame based on the first target image frame and the second target image frame includes: Inputting the first target image frame into a preset semantic segmentation model to obtain a semantic category corresponding to each pixel of the first target image frame; wherein the semantic category includes at least one; For each of the semantic categories, extract each pixel corresponding to the semantic category in the target image frame to obtain a target area corresponding to the semantic category; For each of the semantic categories, a target image segmentation model corresponding to the semantic category is obtained from a preset image segmentation model database, and a target area corresponding to the semantic category is input into the target image segmentation model to segment the target area into a plurality of target sub-areas; wherein one of the target sub-areas represents an image of a target object, and the target object is an object corresponding to the semantic category; For each of the target objects, judging whether the target object is a static object based on the semantic category corresponding to the target object; If so, determining the risk factor of the target object based on a preset static object risk factor generation rule; If not, determining the danger factor of the target object based on a preset dynamic object danger factor generation rule; The determining the danger factor of the target object based on a preset dynamic object danger factor generation rule includes: Traversing a preset semantic category-acceleration mapping relationship table, and determining the acceleration corresponding to the target object based on the semantic category corresponding to the target object; Determining a second distance value based on the acceleration and the target time difference; wherein the target time difference is a time difference between the first target image frame and the second target image frame; Mapping the first target image frame to a first preset plane rectangular coordinate system, and mapping the second target image frame to a second preset plane rectangular coordinate system; wherein the center point of the first target image frame coincides with the origin of the coordinates of the first preset plane rectangular coordinate system, and the center point of the second target image frame coincides with the origin of the coordinates of the second preset plane rectangular coordinate system; Determine a third coordinate point corresponding to the center point of the target object in the first preset plane rectangular coordinate system, determine a reference object corresponding to the target object in the second target image frame, and determine a fourth coordinate point corresponding to the center point of the reference object in the second preset plane rectangular coordinate system; Determine a third distance value between a third coordinate point and a second mapping point corresponding to the target object; wherein the second mapping point is a mapping point of the fourth coordinate point in the first preset plane rectangular coordinate system; A difference obtained by subtracting the third distance value from the second distance value is determined as the risk coefficient.

2. The security data processing method based on image recognition according to claim 1 is characterized in that: The determining the risk factor of the target object based on a preset static object risk factor generation rule includes: Mapping the first target image frame to a first preset plane rectangular coordinate system, and mapping the second target image frame to a second preset plane rectangular coordinate system; wherein the center point of the first target image frame coincides with the origin of the coordinates of the first preset plane rectangular coordinate system, and the center point of the second target image frame coincides with the origin of the coordinates of the second preset plane rectangular coordinate system; Determine a first coordinate point corresponding to the center point of the target object in the first preset plane rectangular coordinate system, determine a reference object corresponding to the target object in the second target image frame, and determine a second coordinate point corresponding to the center point of the reference object in the second preset plane rectangular coordinate system; Determine a first distance value between a first coordinate point and a first mapping point corresponding to the target object as the risk coefficient; wherein the first mapping point is a mapping point of the second coordinate point in the first preset plane rectangular coordinate system.

3. The security data processing method based on image recognition according to claim 1 is characterized in that: The determining whether there is a potential safety hazard in the monitoring area based on each of the risk factors includes: Determining whether the target object corresponding to the risk factor is a static object; If so, comparing the risk factor with a first preset risk factor, and determining that there is a safety hazard in the monitored area when the risk factor is not less than the first preset risk factor; If not, the risk factor is compared with a second preset risk factor, and when the risk factor is not less than the second preset risk factor, it is determined that there is a safety hazard in the monitored area.

4. The security data processing method based on image recognition according to claim 1 is characterized in that: The sending of danger warning information to the construction personnel in the monitoring area includes: Obtain the location information of each construction worker at the construction site; For each of the construction workers, it is determined whether the construction worker is located in the monitoring area based on the location information corresponding to the construction worker, and when the construction worker is located in the monitoring area, a danger warning message is issued to the construction worker.

5. The security data processing method based on image recognition according to claim 1 is characterized in that: The step of obtaining the target image segmentation model corresponding to the semantic category in a preset image segmentation model database includes: Obtaining a category code of the semantic category; wherein the category code includes a first field, a second field, and a third field; Acquire a target video in a preset database based on the first field; Obtaining a hash value corresponding to the second field based on a preset hash algorithm; For each target number in the hash value, a decrypted image frame is determined in the target video based on the target number; wherein the number corresponding to the playback sequence corresponding to the decrypted image frame in the target video is consistent with the target number; For each of the decrypted image frames, matrix encoding is performed on the decrypted image frame based on a preset matrix encoding rule to obtain an encoding matrix corresponding to the decrypted image frame; For each target character of the third field, determine a target row vector in each encoding matrix; wherein the target row vector includes the target character; Arrange the target row vectors in order from top to bottom based on a preset sorting rule to obtain a decryption matrix; Decryption processing is performed on each image segmentation model in the image segmentation model database based on the decryption matrix, and the successfully decrypted image segmentation model is determined as the target image segmentation model.

6. A security data processing system based on image recognition, characterized in that: include: An acquisition module, used to acquire a first target image frame of a monitoring area in real time through a preset camera device, and acquire a second target image frame of the monitoring area in a preset image database; wherein the second target image frame is an image frame previous to the first target image frame; a determination module, configured to determine a risk factor of each target object in the first target image frame based on the first target image frame and the second target image frame; A judgment module, used for judging whether there is a potential safety hazard in the monitoring area based on each of the risk factors; A sending module, used to send danger warning information to construction personnel in the monitoring area if there are safety hazards in the monitoring area; Wherein, the determination module includes: An input unit, used to input the first target image frame into a preset semantic segmentation model to obtain a semantic category corresponding to each pixel of the first target image frame; wherein the semantic category includes at least one; An extraction unit, configured to extract, for each of the semantic categories, pixels corresponding to the semantic category in the target image frame to obtain a target area corresponding to the semantic category; A first acquisition unit is used to acquire, for each of the semantic categories, a target image segmentation model corresponding to the semantic category in a preset image segmentation model database, and input a target area corresponding to the semantic category into the target image segmentation model to segment the target area into a plurality of target sub-areas; wherein one of the target sub-areas represents an image of a target object, and the target object is an object corresponding to the semantic category; A judging unit, configured to judge, for each of the target objects, whether the target object is a static object based on a semantic category corresponding to the target object; A first determining unit, configured to determine the risk factor of the target object based on a preset static object risk factor generation rule; a second determining unit, for determining the risk factor of the target object based on a preset dynamic object risk factor generation rule if no; The determining the danger factor of the target object based on a preset dynamic object danger factor generation rule includes: Traversing a preset semantic category-acceleration mapping relationship table, and determining the acceleration corresponding to the target object based on the semantic category corresponding to the target object; Determining a second distance value based on the acceleration and the target time difference; wherein the target time difference is a time difference between the first target image frame and the second target image frame; Mapping the first target image frame to a first preset plane rectangular coordinate system, and mapping the second target image frame to a second preset plane rectangular coordinate system; wherein the center point of the first target image frame coincides with the origin of the coordinates of the first preset plane rectangular coordinate system, and the center point of the second target image frame coincides with the origin of the coordinates of the second preset plane rectangular coordinate system; Determine a third coordinate point corresponding to the center point of the target object in the first preset plane rectangular coordinate system, determine a reference object corresponding to the target object in the second target image frame, and determine a fourth coordinate point corresponding to the center point of the reference object in the second preset plane rectangular coordinate system; Determine a third distance value between a third coordinate point and a second mapping point corresponding to the target object; wherein the second mapping point is a mapping point of the fourth coordinate point in the first preset plane rectangular coordinate system; A difference obtained by subtracting the third distance value from the second distance value is determined as the risk coefficient.

7. The security data processing system based on image recognition according to claim 6, characterized in that: The sending module comprises: The second acquisition unit is used to acquire the location information of each construction worker at the construction site; The sending unit is used to determine, for each of the construction workers, whether the construction worker is located in the monitoring area based on the location information corresponding to the construction worker, and send danger warning information to the construction worker when the construction worker is located in the monitoring area.

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