A big data image processing method and system based on a security area

Abstract

The application discloses a kind of big data image processing method and system based on security area, it is related to intelligent door lock technical field, including data acquisition module, security area patrol work evaluation module, comparison analysis module, security area on duty work evaluation module, processing module, comprehensive feedback module;Data acquisition module, security area information of security personnel patrol is collected, including patrol personnel work information and patrol demand information, and the security area patrol work evaluation module is passed to patrol personnel work information and patrol demand information.The application can prevent the possibility of user information leakage in the security area by intelligently identifying the identity of the security personnel patrolling in the security area.The system can merge and sort the behavior and posture of the security personnel during the work process according to the length of the maintenance time in order to determine the working status of the security personnel, further protect the life and property safety of the residents in the security area, prevent the omissions caused by manual management, and reduce the loss of invalid labor.

Description

Technical Field

[0001] This invention relates to the field of big data image processing technology, and specifically to a big data image processing method and system based on a secure region. Background Technology

[0002] A big data image processing method and system based on secure areas can monitor the various activities of security personnel through image recognition and video surveillance technologies. Through a series of processes including image acquisition and storage, data labeling and classification, distributed processing, and image analysis and application, the system can review the behavior of security personnel and thus assess the security status of secure areas such as residential communities and factories.

[0003] Currently, the specific work management of security personnel is generally confirmed through methods such as taking photos and clocking in during patrols and random checks of on-duty surveillance videos. These methods play a role in promoting the work enthusiasm of security personnel and strengthening their motivation, while also greatly promoting the protection of the life and property safety of users in the area under their responsibility.

[0004] The existing technology has the following shortcomings:

[0005] Existing methods and systems for processing big data images of secure areas still have some loopholes in terms of privacy protection and the management of security personnel. Because security personnel take and upload photos during patrols, there is a possibility of information leakage regarding faces, license plates, or industrial production areas within their assigned areas, which is detrimental to privacy protection. At the same time, during daily shifts, the work status of security personnel is usually assessed through manual spot checks, which is not easy to obtain accurate and effective results and also leads to unnecessary waste of human resources. Therefore, an intelligent system is needed to manage the processing of big data images of secure areas.

[0006] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0007] The purpose of this invention is to provide a big data image processing method and system based on secure areas. This invention intelligently identifies the identities of security personnel patrolling within secure areas, avoiding the conventional method of manual review after taking photos, thus preventing the possibility of leakage of user information within secure areas. Then, the system merges and sorts the behavioral postures of security personnel during their work process according to the length of time they are maintained. By judging the working status of security personnel based on the duration of different postures, the system achieves the goal of fair management of security personnel within secure areas, further protecting the life and property safety of residents within secure areas, preventing oversights caused by manual management, and reducing the waste of ineffective manpower, thereby solving the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a big data image processing method and system based on a secure area, comprising a data acquisition module, a secure area patrol work evaluation module, a comparison and analysis module, a secure area duty work evaluation module, a processing module, and a comprehensive feedback module;

[0009] The data acquisition module collects information on security personnel patrolling the security area, including the personnel's work information and patrol needs information, and transmits the personnel's work information and patrol needs information to the security area patrol work evaluation module;

[0010] The security area patrol work evaluation module establishes a data analysis model based on the collected patrol personnel work information and patrol needs information, generates a security area patrol work evaluation index, and transmits the security area patrol work evaluation index to the comparison and analysis module.

[0011] The comparison and analysis module compares the security area patrol work evaluation index with the patrol work image recognition threshold, generates a security area patrol work qualification signal, and provides patrol personnel with review, summary and optimization suggestions based on the security area patrol work qualification signal and security area patrol work evaluation index. The system also transmits the data information to the security area duty work evaluation module.

[0012] The security area duty work assessment module will generate security personnel duty work information from the information transmitted from the comparison and analysis module, generate a security personnel duty work assessment index based on the security personnel duty work information and the accuracy information of the duty personnel images, and transmit the generated security personnel duty work assessment index to the processing module.

[0013] The processing module compares the security personnel's duty performance evaluation index transmitted from the security area duty performance evaluation module with the duty performance image recognition threshold, and then provides performance processing for the security personnel during their duty work based on the comparison results.

[0014] Preferably, the patrol personnel's work information includes the patrol identification coefficient and the effective patrol coefficient. After collection, the data acquisition module will label the patrol identification coefficient and the effective patrol coefficient as F respectively. xc and Y xC The inspection demand information includes an inspection demand coefficient. After collection, the data acquisition module calibrates the inspection demand coefficient as X. xc .

[0015] Preferably, the logic for obtaining the patrol identity recognition coefficient is as follows:

[0016] The system collects facial recognition and personnel attire information from all cameras within an image acquisition device, and marks different patrol personnel to obtain the number of times each patrol personnel appears within a time period T. To ensure accuracy, for the same patrol personnel appearing within a time period K, only the time and location of their first appearance in the image recognition device are counted. The system analyzes the number of times the same patrol personnel appears within time period T, denoted as C. xc The marked patrol personnel are set to H. p p represents the inappropriate posture type number, p = 1, 2, 3, 4, ..., q, where q is a positive integer, and the patrol identity recognition coefficient.

[0017] The logic for obtaining the effective inspection coefficient is as follows:

[0018] Get the number of security issues reported by patrol personnel within a time period T in the safe area, and set it as J. aq Get the number of security issues that occurred in the safe zone within time T, and set it as P. aq Then the effective patrol coefficient Y xc =J aq / P aq ;

[0019] The logic for obtaining the inspection demand coefficient is as follows:

[0020] Obtain the security area patrol work evaluation index obtained from the security area patrol work evaluation module within the previous time period T. Set the minimum number of patrols required within the safe zone to X. zx The evaluation index for the largest security area patrol recorded in historical data is set at PG. max Then the inspection demand coefficient

[0021] Preferably, the security area patrol work evaluation module will obtain the patrol identity recognition coefficient F. xc Effective patrol coefficient Y xc And the inspection demand coefficient X xc Establish a data analysis model to generate a PG (Pressure Area Patrol) index for security area patrol work. xcThe formula used is: PG xc =(e1*F xc +e2*Y xc ) / (e3*X xc In the formula, e1, e2, and e3 are the patrol identification coefficients F. xc Effective patrol coefficient Y xc And the inspection demand coefficient X xc The preset proportional coefficients, and e1, e2, and e3 are all greater than 0;

[0022] The comparison and analysis module compares the security area patrol work evaluation index with the patrol work image recognition threshold, and classifies them into the following categories:

[0023] If the security area patrol work evaluation index is greater than or equal to the patrol work image recognition threshold, the security area patrol work qualification signal is generated through the comparison and analysis module, indicating that the security personnel in the security area can complete the required patrol work. The security area patrol work qualification signal is sent to the subsequent module and the security area duty work evaluation is carried out.

[0024] If the evaluation index of the security area patrol work is less than the image recognition threshold of the patrol work, the security area patrol work qualification signal will not be generated through the comparison and analysis module. This indicates that the security personnel in the security area have not been able to complete the required patrol work. It is necessary to review, summarize and optimize the work of the patrol personnel. After completion, the evaluation of subsequent modules will continue.

[0025] Preferably, after receiving the qualified signal and the summary of the security area patrol work evaluation index from the comparison and analysis module, the security area duty work evaluation module, combined with the on-duty personnel's work image information, generates security personnel duty work information and on-duty personnel image accuracy information within the system. The security personnel duty work information includes the improper posture maintenance time coefficient and the screening coefficient for passing personnel and vehicles, respectively denoted as T. wc and W pc The image accuracy information for on-duty personnel includes the failure coefficient of the image acquisition device, calibrated as G. tx .

[0026] The preferred logic for obtaining the improper posture maintenance time coefficient is as follows:

[0027] Image data is acquired from on-duty personnel during their working hours. This data is then merged, categorized, and timed based on behavior. For example, actions such as using mobile phones, sleeping, and behaviors outside the image acquisition range are identified as inappropriate postures based on motion capture. The duration of these inappropriate postures is set to T. cw Different types of improper postures have different effects; the degree of influence of improper postures is set as Z. nLet n represent the type number of the improper posture, n = 1, 2, 3, 4, ..., m, where m is a positive integer. Then, the improper posture duration coefficient...

[0028] The logic for obtaining the screening coefficients for incoming and outgoing personnel and vehicles is as follows:

[0029] Retrieve the number of vehicles and personnel recorded by the on-duty personnel during their working hours, as well as the actual number of vehicles passing by and the number of personnel requiring recording, and set them as X respectively. wl and S sj The number of people who need to be recorded refers to the number of people passing through during special periods such as nighttime or disease control periods. Therefore, the screening coefficient W for incoming and outgoing personnel and vehicles is calculated as follows. pc =X wl / S sj ;

[0030] The logic for obtaining the fault coefficient of the image acquisition device is as follows:

[0031] A self-diagnostic device is built for the image acquisition devices included in the system. When an image acquisition device experiences a prolonged monitoring malfunction or a black screen, the failure coefficient G of the image acquisition device is determined. tx =0; Self-test device, when the image acquisition device is operating normally, the failure coefficient G of the image acquisition device is 0. tx =1.

[0032] Preferably, the safety zone duty assessment module will obtain the improper posture maintenance time coefficient T. wc Personnel and vehicle screening coefficient W pc and the failure coefficient G of the image acquisition device tx Establish a data analysis model to generate a PG (Post-on-Duty Performance Index) for security personnel. ab The formula used is: PG ab =(d2*W pc / d1*T wc )*d3*G tx In the formula, d1, d2, and d3 are the improper posture maintenance time coefficients T. wc Personnel and vehicle screening coefficient W pc and the failure coefficient G of the image acquisition device tx The preset proportional coefficients, and d1, d2, and d3 are all greater than 0;

[0033] The processing module will receive the security personnel duty performance evaluation index (PG) from the security area duty performance evaluation module. ab Compared with the image recognition threshold for on-duty work, the security personnel's on-duty work evaluation index PG ab A higher PG value indicates better performance of security personnel during their shifts. abWhen the image recognition threshold for on-duty work is greater than or equal to the threshold, it indicates that there are no major problems in the on-duty work of security personnel and the security area is well protected.

[0034] If the security personnel's on-duty performance evaluation index is PG ab When the image recognition threshold for on-duty work is lower than the threshold, it indicates that the on-duty work of security personnel is not adequately ensuring the safety of the security area. In this case, the on-duty personnel should be punished and educated.

[0035] If the security personnel's on-duty performance evaluation index is PG ab When the value is 0, it indicates that the system's image acquisition device has malfunctioned. The security personnel's on-duty work assessment module deletes the existing records, restarts, and notifies maintenance personnel to repair it.

[0036] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0037] This invention intelligently identifies security personnel patrolling a secure area, avoiding the conventional method of manual review after taking photos. This prevents the potential leakage of user information within the secure area. The system then merges and sorts the security personnel's actions and postures according to the duration of each posture, determining their working status based on the length of time they are maintained. This achieves fair management of security personnel within the secure area, further protecting the lives and property of residents, preventing oversights caused by manual management, and reducing the waste of manpower.

[0038] This invention comprehensively analyzes the security area patrol work evaluation index generated by evaluating the behavior of security personnel in a secure area through big data image processing. By re-evaluating unsatisfactory patrol work and setting stability thresholds, it eliminates accidental anomalies in the behavior of security personnel during big data image processing, improves the accuracy of big data image processing in secure areas, thereby increasing user trust in the system and ensuring the efficient operation of big data image processing. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0040] Figure 1 This is a schematic diagram of a big data image processing method and system based on a secure area according to the present invention. Detailed Implementation

[0041] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0042] This invention provides, for example Figure 1 The above describes a big data image processing method and system based on a secure area, which includes a data acquisition module, a secure area patrol work evaluation module, a comparison and analysis module, a secure area duty work evaluation module, a processing module, and a comprehensive feedback module.

[0043] The data acquisition module collects information on security personnel patrolling the security area, including the personnel's work information and patrol needs information, and transmits the personnel's work information and patrol needs information to the security area patrol work evaluation module;

[0044] Patrol personnel work information includes patrol identification coefficient and effective patrol coefficient. After collection, the data acquisition module labels the patrol identification coefficient and effective patrol coefficient as F. xc and Y xc The inspection demand information includes an inspection demand coefficient. After collection, the data acquisition module calibrates the inspection demand coefficient as X. xc .

[0045] Example 1: A big data image processing method and image acquisition device based on a secure area collects data on the behavior of security personnel during their work in order to avoid infringing on user privacy and to ensure the work enthusiasm of security personnel in the secure area.

[0046] When evaluating big data images of secure areas, we prioritize using easily calculable and collectable information to reduce system computation and increase the feasibility of the control system. Since the system is primarily designed for identifying security personnel in secure areas such as residential communities and factories, and assesses the security level of the area based on their behavior, the external factors considered are largely relevant to the actual situation. This also improves the accuracy of the data to a certain extent, thus enabling the control system to be applied in practice.

[0047] Therefore, the logic for obtaining the patrol identity recognition coefficient is as follows:

[0048] The system collects facial recognition and personnel attire information from all cameras within an image acquisition device, and marks different patrol personnel to obtain the number of times each patrol personnel appears within a time period T. To ensure accuracy, for the same patrol personnel appearing within a time period K, only the time and location of their first appearance in the image recognition device are counted. The system analyzes the number of times the same patrol personnel appears within time period T, denoted as C. xcThe marked patrol personnel are set to H. p p represents the inappropriate posture type number, p = 1, 2, 3, 4, ..., q, where q is a positive integer, and the patrol identity recognition coefficient.

[0049] The logic for obtaining the effective inspection coefficient is as follows:

[0050] Get the number of security issues reported by patrol personnel within a time period T in the safe area, and set it as J. aq Get the number of security issues that occurred in the safe zone within time T, and set it as P. aq Then the effective patrol coefficient Y xc =J aq / P aq ;

[0051] The logic for obtaining the inspection demand coefficient is as follows:

[0052] Obtain the security area patrol work evaluation index obtained from the security area patrol work evaluation module within the previous time period T. Set the minimum number of patrols required within the safe zone to X. zx The evaluation index for the largest security area patrol recorded in historical data is set at PG. max Then the inspection demand coefficient

[0053] The security area patrol work evaluation module will obtain the patrol identity recognition coefficient F. xc Effective patrol coefficient Y xc And the inspection demand coefficient X xc Establish a data analysis model to generate a PG (Pressure Area Patrol) index for security area patrol work. xc The formula used is: PG xc =(e1*F xc +e2*Y xc ) / (e3*X xc In the formula, e1, e2, and e3 are the patrol identification coefficients F. xc Effective patrol coefficient Y xc And the inspection demand coefficient X xc The preset proportional coefficients, and e1, e2, and e3 are all greater than 0;

[0054] The comparison and analysis module compares the security area patrol work evaluation index with the patrol work image recognition threshold, and classifies them into the following categories:

[0055] If the security area patrol work evaluation index is greater than or equal to the patrol work image recognition threshold, the security area patrol work qualification signal is generated through the comparison and analysis module, indicating that the security personnel in the security area can complete the required patrol work. The security area patrol work qualification signal is sent to the subsequent module and the security area duty work evaluation is carried out.

[0056] If the evaluation index of the security area patrol work is less than the image recognition threshold of the patrol work, the security area patrol work qualification signal will not be generated through the comparison and analysis module. This indicates that the security personnel in the security area have not been able to complete the required patrol work. It is necessary to review, summarize and optimize the work of the patrol personnel. After completion, the evaluation of subsequent modules will continue.

[0057] In Example 2, after receiving the qualified signal and the summary of the security area patrol work evaluation index from the comparison and analysis module, the security area duty work evaluation module, combined with the work image information of the duty personnel in the security area, generates security personnel duty work information and duty personnel image accuracy information within the system. The security personnel duty work information includes the improper posture maintenance time coefficient and the screening coefficient of passing personnel and vehicles, respectively denoted as T. wc and W pc The image accuracy information for on-duty personnel includes the failure coefficient of the image acquisition device, calibrated as G. tx .

[0058] The logic for obtaining the improper posture maintenance time coefficient is as follows:

[0059] Image data is acquired from on-duty personnel during their working hours. This data is then merged, categorized, and timed based on behavior. For example, actions such as using mobile phones, sleeping, and behaviors outside the image acquisition range are identified as inappropriate postures based on motion capture. The duration of these inappropriate postures is set to T. cw Different types of improper postures have different effects; the degree of influence of improper postures is set as Z. n Let n represent the type number of the improper posture, n = 1, 2, 3, 4, ..., m, where m is a positive integer. Then, the improper posture duration coefficient...

[0060] The logic for obtaining the screening coefficients for incoming and outgoing personnel and vehicles is as follows:

[0061] Retrieve the number of vehicles and personnel recorded by the on-duty personnel during their working hours, as well as the actual number of vehicles passing by and the number of personnel requiring recording, and set them as X respectively. wl and S si The number of people who need to be recorded refers to the number of people passing through during special periods such as nighttime or disease control periods. Therefore, the screening coefficient W for incoming and outgoing personnel and vehicles is calculated as follows. pc =X wl / S si;

[0062] The logic for obtaining the fault coefficient of the image acquisition device is as follows:

[0063] A self-diagnostic device is built for the image acquisition devices included in the system. When an image acquisition device experiences a prolonged monitoring malfunction or a black screen, the failure coefficient G of the image acquisition device is determined. tx =0; Self-test device, when the image acquisition device is operating normally, the failure coefficient G of the image acquisition device is 0. tx =1.

[0064] The safe zone duty assessment module will obtain the inappropriate posture maintenance time coefficient T. wc Personnel and vehicle screening coefficient W pc and the failure coefficient G of the image acquisition device tx Establish a data analysis model to generate a PG (Post-on-Duty Performance Index) for security personnel. ab The formula used is: PG ab =(d2*W pc / d1*T wc )*d3*G tx In the formula, d1, d2, and d3 are the improper posture maintenance time coefficients T. wc Personnel and vehicle screening coefficient W pc and the failure coefficient G of the image acquisition device tx The preset proportional coefficients, and d1, d2, and d3 are all greater than 0;

[0065] The processing module will receive the security personnel duty performance evaluation index (PG) from the security area duty performance evaluation module. ab Compared with the image recognition threshold for on-duty work, the security personnel's on-duty work evaluation index PG ab A higher PG value indicates better performance of security personnel during their shifts. ab When the image recognition threshold for on-duty work is greater than or equal to the threshold, it indicates that there are no major problems in the on-duty work of security personnel and the security area is well protected.

[0066] If the security personnel's on-duty performance evaluation index is PG ab When the image recognition threshold for on-duty work is lower than the threshold, it indicates that the on-duty work of security personnel is not adequately ensuring the safety of the security area. In this case, the on-duty personnel should be punished and educated.

[0067] If the security personnel's on-duty performance evaluation index is PG ab When the value is 0, it indicates that the system's image acquisition device has malfunctioned. The security personnel's on-duty work assessment module deletes the existing records, restarts, and notifies maintenance personnel to repair it.

[0068] This invention intelligently identifies security personnel patrolling a secure area, avoiding the conventional method of taking photos and then manually reviewing them. This can prevent the possibility of user information leakage within the secure area. The system then merges and sorts the behavioral postures of security personnel during their work process according to the length of time they are maintained. By judging the working status of security personnel based on the duration of different postures, the system achieves the goal of fair management of security personnel within the secure area, further protecting the life and property safety of residents within the secure area, preventing oversights caused by manual management, and reducing the waste of ineffective manpower.

[0069] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0070] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0071] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0072] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0073] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0074] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0075] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0076] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0077] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A big data image processing system based on a secure area, characterized in that: It includes a data acquisition module, a security area patrol work evaluation module, and a comparison and analysis module; The data acquisition module collects information on security personnel patrolling the security area, including the personnel's work information and patrol needs information, and transmits the personnel's work information and patrol needs information to the security area patrol work evaluation module; The security area patrol work evaluation module establishes a data analysis model based on the collected patrol personnel work information and patrol needs information, generates a security area patrol work evaluation index, and transmits the security area patrol work evaluation index to the comparison and analysis module. The comparison and analysis module compares the security area patrol work evaluation index with the patrol work image recognition threshold, generates a security area patrol work qualification signal, and provides patrol personnel with review, summary and optimization suggestions based on the security area patrol work qualification signal and security area patrol work evaluation index. The system also transmits the data information to the security area duty work evaluation module. The logic for obtaining the patrol identity recognition coefficient is as follows: The system collects facial recognition and personnel attire information from all cameras within an image acquisition device, and marks different patrol personnel to obtain the number of times each patrol personnel appears within a time period T. To ensure accuracy, for the same patrol personnel appearing within a time period K, only the time and location of their first appearance in the image recognition device are counted. The system analyzes the number of times the same patrol personnel appears within a time period T, denoted as [missing information]. The marked patrol personnel are set to p represents the inappropriate posture type number, p = 1, 2, 3, 4, ..., q, where q is a positive integer, and the patrol identity recognition coefficient. ; The logic for obtaining the effective inspection coefficient is as follows: Get the number of security issues reported by patrol personnel within a time period T in the safe area, and set it as... Get the number of security issues that occurred in the safe zone within time T, and set it as... The effective patrol coefficient ; The logic for obtaining the inspection demand coefficient is as follows: Obtain the security area patrol work evaluation index obtained from the security area patrol work evaluation module within the previous time period T. Set the minimum number of patrols required within the safe zone to . The evaluation index for the largest security area patrol recorded in historical data was set as follows: Then the inspection demand coefficient .

2. The big data image processing system based on a secure area according to claim 1, characterized in that, Patrol personnel work information includes patrol identification coefficient and effective patrol coefficient. After collection, the data acquisition module will label the patrol identification coefficient and effective patrol coefficient as follows: and The inspection demand information includes an inspection demand coefficient. After collection, the data acquisition module calibrates the inspection demand coefficient as follows: .

3. The big data image processing system based on a secure area according to claim 1, characterized in that, The security area patrol work evaluation module will obtain the patrol identity recognition coefficient. Effective patrol coefficient and patrol demand coefficient Establish a data analysis model to generate an evaluation index for security area patrol work. The formula used is: In the formula, e1, e2, and e3 are the patrol identification coefficients, respectively. Effective patrol coefficient and patrol demand coefficient The preset proportional coefficients, and e1, e2, and e3 are all greater than 0; The comparison and analysis module compares the security area patrol work evaluation index with the patrol work image recognition threshold, and classifies them into the following categories: If the evaluation index of the security area patrol work is greater than or equal to the image recognition threshold of the patrol work, the security area patrol work qualification signal is generated through the comparison and analysis module, and the security area patrol work qualification signal is sent to the subsequent modules to conduct security area duty work evaluation. If the evaluation index of the security area patrol work is less than the image recognition threshold of the patrol work, the security area patrol work qualification signal will not be generated through the comparison and analysis module. Instead, the work content of the patrol personnel needs to be reviewed, summarized and optimized before the evaluation of subsequent modules can continue.

4. The big data image processing system based on a secure area according to claim 3, characterized in that, It also includes a security zone duty work assessment module, a processing module, and a comprehensive feedback module; The security area duty work assessment module will generate security personnel duty work information from the information transmitted from the comparison and analysis module, generate a security personnel duty work assessment index based on the security personnel duty work information and the accuracy information of the duty personnel images, and transmit the generated security personnel duty work assessment index to the processing module. The processing module compares the security personnel's duty performance evaluation index transmitted from the security area duty performance evaluation module with the duty performance image recognition threshold, and then gives the performance processing of the security personnel during their duty work based on the comparison results. After receiving the security area patrol work qualification signal and the security area patrol work evaluation index summary from the comparison and analysis module, the security area duty work evaluation module, combined with the on-duty personnel's work image information, generates security personnel duty work information and on-duty personnel image accuracy information within the system. The security personnel duty work information includes the improper posture maintenance time coefficient and the screening coefficient for passing personnel and vehicles, which are respectively calibrated as follows: and The image accuracy information for on-duty personnel includes the failure coefficient of the image acquisition device, calibrated as follows: .

5. A big data image processing system based on a secure area according to claim 4, characterized in that, The logic for obtaining the improper posture maintenance time coefficient is as follows: The logic for obtaining the improper posture maintenance time coefficient is as follows: Image data is captured during the shift work hours of the staff, and the data is merged, summarized, and categorized according to behavior. The duration of improper posture is set as follows: Different types of improper postures have different effects; the degree of impact of improper postures is set as follows: Let n represent the type number of the improper posture, n = 1, 2, 3, 4, ..., m, where m is a positive integer. Then, the improper posture duration coefficient... ; The logic for obtaining the screening coefficients for incoming and outgoing personnel and vehicles is as follows: The system retrieves the number of vehicles and personnel recorded by on-duty personnel during their working hours, as well as the actual number of vehicles passing through and the number of personnel requiring recording, and sets them as follows: and The coefficient for screening incoming and outgoing personnel and vehicles ; The logic for obtaining the fault coefficient of the image acquisition device is as follows: A self-diagnostic device is built for the image acquisition devices included in the system. When an image acquisition device experiences prolonged monitoring abnormalities or a black screen, the failure coefficient of the image acquisition device is calculated. Self-test device; when the image acquisition device is operating normally, the failure rate of the image acquisition device is... .

6. A big data image processing system based on a secure area according to claim 5, characterized in that, The safe zone duty assessment module will obtain the coefficient of improper posture maintenance time. Personnel and vehicle screening coefficient and the failure coefficient of the image acquisition device Establish a data analysis model to generate an evaluation index for security personnel's on-duty work. The formula used is: In the formula, d1, d2, and d3 are the coefficients for maintaining the improper posture for a certain period of time. Personnel and vehicle screening coefficient and the failure coefficient of the image acquisition device The preset proportional coefficients, and d1, d2, and d3 are all greater than 0; The processing module will receive the security personnel's duty performance evaluation index from the security area duty performance evaluation module. Compared with the image recognition threshold for on-duty work, if the security personnel's on-duty work evaluation index... When the image recognition threshold for on-duty work is greater than or equal to the threshold, the safe zone is well protected; If the security personnel's on-duty work evaluation index When the image recognition threshold for on-duty work is lower than the threshold, the on-duty personnel should be punished and educated. If the security personnel's on-duty work evaluation index When the value is 0, the security personnel duty assessment module deletes existing records, restarts, and notifies maintenance personnel to repair them.

7. A method for processing large data images based on a secure region, implemented based on a large data image processing system based on a secure region as described in any one of claims 1-6, characterized in that: Includes the following steps: S1: Collect information on security personnel patrolling security areas, including personnel work information and patrol needs, and organize multi-source data; S2: Establish a data analysis model based on the work information and patrol needs of patrol personnel to generate a security area patrol work evaluation index; S3: Compare the security area patrol work evaluation index with the patrol work image recognition threshold, and generate a security area patrol work qualification signal at the same time. S4: Based on the qualified signal of the security area patrol work based on the processing results, a data model is established by combining the security personnel's on-duty work information and the accuracy information of the on-duty personnel's images, and an evaluation index of the security personnel's on-duty work is generated; S5: Compare the security personnel's on-duty work evaluation index with the on-duty work image recognition threshold, and then give the security personnel's performance processing when on duty based on the comparison results.

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