Park remote security monitoring management system

Abstract

The invention relates to the technical field of remote security and protection monitoring management, is used for solving the limitation of a traditional security and protection monitoring system, especially the problems that abnormal personnel and behaviors cannot be accurately recognized in real time, and the early warning and coping capacity is insufficient, and particularly discloses a remote security and protection monitoring management system for a park. Comprising a monitoring end, a server, an entrance identity recognition module, a locked personnel monitoring analysis module, a regional personnel monitoring analysis module, an early warning distribution module, a remote management terminal and a database. According to the invention, identity verification is carried out on personnel entering the park, so that only authorized personnel can enter the park, further identity authentication is carried out on personnel in the park, locking monitoring is carried out on unauthenticated in-service personnel, whether to arrive at a designated position on time or not is judged by analyzing a passing route of the personnel, and meanwhile, the security of the personnel is ensured. And behavior states of personnel outside the area are monitored and analyzed, and abnormal behaviors are intelligently identified, so that efficient park safety management is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of remote security monitoring and management, in particular to a park remote security monitoring and management system. Background Art

[0002] As the scale of the park continues to expand and the flow of people becomes more frequent, how to effectively manage and monitor people inside and outside the park has become a key issue in ensuring the safety of the park;

[0003] Traditional security monitoring systems usually rely on manual patrols or fixed camera monitoring. This method has significant limitations and cannot accurately identify abnormal personnel or behaviors in real time, which can easily lead to safety hazards. In addition, it is usually impossible to achieve efficient early warning and response in dealing with illegal intrusions, abnormal stays, and tracking the movements of important personnel by outsiders. These limitations often put the park in a passive state when dealing with complex security challenges and are unable to ensure the safety of the park in a timely manner.

[0004] In order to solve the above defects, a technical solution is now provided. Summary of the invention

[0005] The purpose of the present invention is to solve the problem that the traditional security monitoring system is relatively limited and cannot identify abnormal personnel or behaviors in real time and accurately, and cannot achieve efficient early warning and response, and proposes a campus remote security monitoring management system.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] A park remote security monitoring and management system, comprising:

[0008] The park entry identity recognition module is used to identify and analyze the facial images of people entering the park to determine whether they are certified employees of the park;

[0009] The locked person monitoring and analysis module is used to determine and judge the target travel route of the locked person, and analyze the route status of the target travel route accordingly to obtain the route time impact index, match the route time impact index with the estimated time corresponding to the set route time impact index, and obtain the estimated time, thereby judging whether the locked person arrives at the destination within the estimated time, and obtaining abnormal arrival warning signals and abnormal stay warning signals;

[0010] The regional personnel monitoring and analysis module is used to judge the personnel in each area to determine the personnel outside the area, thereby monitoring and analyzing the behavior trajectory parameters of the personnel outside the area, obtaining the movement deviation evaluation index, the stay time evaluation index and the repeated behavior evaluation index, and conducting a comprehensive analysis to obtain the behavior state evaluation coefficient, and judging the behavior state of the personnel outside the area based on the behavior state evaluation coefficient, thereby obtaining an abnormal behavior warning signal;

[0011] The warning allocation module is used to receive abnormal arrival warning signals, abnormal stay warning signals and abnormal behavior warning signals, and perform warning allocation operation processing accordingly.

[0012] Furthermore, the monitoring end is used to manage and control various types of high-definition camera equipment deployed in the park, including high-definition cameras at the entrance and inside the park, and transmit the collected video data to the server for storage and backup;

[0013] The high-definition camera at the entrance authenticates the identity of the person entering the park. If an unauthorized person is identified, an intrusion warning signal is immediately generated. If an authorized person is identified, the collected facial image is transmitted to the park entry identity recognition module.

[0014] The activities of people in the park are monitored in real time through high-definition cameras in the park.

[0015] Furthermore, the analysis process to determine whether a person is a certified employee of the park is as follows:

[0016] By matching the facial image of the person entering the park with the facial image of the authorized person stored in the database, the specific identity of the person is confirmed and the identity authentication status is extracted;

[0017] If the identity authentication status of the person is "unauthenticated employee in the park", a lock signal is generated to lock and monitor the person;

[0018] If the person's identity authentication status is "authenticated employee in the park", no lock signal is generated and normal monitoring continues.

[0019] Furthermore, the analysis process for determining and judging the target route of the locked person is as follows:

[0020] By obtaining the current location and destination location of the locked person, marking them on the overall map of the park, calculating each accessible route between the two marked points, recording them as the pass route, and generating each pass route from the current location to the destination location of the locked person;

[0021] By extracting image information of each route and screening the extracted images, images without detected persons are eliminated and images containing persons are retained. Then, the retained person images are identified. If the locked person is identified in multiple consecutive images of a route, the route is marked as the target route.

[0022] Furthermore, the process of solving and analyzing the route traffic impact index is as follows:

[0023] By obtaining the slope of each slope in the target route, k and length Pcnad k , according to the formula: Calculate the time-dependent influence index θ of each slope k , where k represents the number of each slope in the target route, and its value range is [1, k]. a1 and a2 represent the set weight factors, respectively. * 、Pcnad * They represent the reference slope and reference length of the set slope respectively, and ΔPioad represents the allowable slope difference corresponding to the set slope;

[0024] The time-dependent influence index of each slope is summed to obtain the slope time-dependent influence index Rθ;

[0025] By obtaining the route length Ltaog of the target route, according to the formula: Calculate the route traffic impact index δ, Ltaog * Indicates the set reference route length.

[0026] Furthermore, the analysis process for determining whether the locked person arrives at the destination within the estimated time is as follows:

[0027] The high-definition camera video covering the destination area is retrieved and the video data is analyzed. If the locked person is not identified, an abnormal arrival warning signal is generated. If the locked person is identified, the length of stay of the locked person is obtained, and the actual length of stay of the locked person is compared and analyzed with the preset length of stay threshold. When the actual length of stay of the locked person exceeds the preset length of stay threshold, an abnormal stay warning signal is generated.

[0028] Furthermore, the behavior trajectory parameters of people outside the area are monitored and analyzed. The specific analysis process is as follows:

[0029] By obtaining the actual walking path data of people outside the area, the movement trajectory map of people outside the area is generated, and the movement trajectory map of people outside the area is overlapped and matched with the preset normal movement trajectory map, and the deflection angle value, distance value and plane value are extracted from them, marked as Lpz, Lpj and Lpm respectively, according to the formula: Calculate the moving deviation evaluation index Gjing, where Lpz * , Lpj * and Lpm * They represent the set reference deflection angle value, reference offset distance value and reference deflection plane value respectively, u represents a natural constant, b1, b2 and b3 represent the set weight factors respectively;

[0030] By obtaining the actual length of stay of people outside the area at a specific location, and calculating the difference between the actual length of stay and the preset standard length of stay, the length of stay evaluation index Tlscd is obtained;

[0031] The repetitive behavior assessment index Fcxin was obtained by analyzing the frequency and time interval of repeated appearance of people outside the area at the same location.

[0032] Furthermore, the analysis process for solving the behavior state evaluation coefficient is as follows:

[0033] The movement deviation evaluation index Gjing, the stay time evaluation index Tlscd and the repeated behavior evaluation index Fcxin of people outside the area are extracted and normalized according to the formula: The behavior state evaluation coefficient XWP is calculated, where η1, η2 and η3 represent the weight coefficients of the movement deviation evaluation index, the stay time evaluation index and the repeated behavior evaluation index, respectively.

[0034] Furthermore, the analysis process of the early warning allocation operation is as follows:

[0035] If an abnormal arrival warning signal is captured, the basic information of the locked person will be sent to the remote management terminal of the park security department for early warning notification within the set W time period;

[0036] If an abnormal stay warning signal is captured, the actual stay time of the locked person is retrieved, and the difference between the actual stay time and the preset stay time threshold is calculated to obtain the stay time difference value. The warning signal light is determined based on the stay time difference value and sent to the remote management terminal of the park security department for warning notification;

[0037] If an abnormal behavior warning signal is captured, the behavior status evaluation coefficient is retrieved, and the warning signal light is determined based on the behavior status evaluation coefficient, and sent to the remote management terminal of the park security department for warning notification.

[0038] Compared with the known prior art, the technical solution provided by the present invention has the following beneficial effects:

[0039] 1. The present invention obtains the current position and destination of the locked person in real time, calculates all accessible routes, and retrieves high-definition camera videos of the accessible routes for monitoring. Through video analysis and identity recognition, it can accurately track the movement trajectory of the locked person, obtain the slope gradient and length data in the route, calculate the time impact index using a set formula, evaluate the difficulty and time consumption of the route, and obtain the estimated travel time. If the locked person fails to arrive at the expected time, an abnormal arrival warning signal is generated. If the stay time exceeds the threshold, an abnormal stay warning signal is issued. Through dual monitoring, it is possible to timely identify and warn of abnormal behavior to ensure safety management.

[0040] 2. The present invention retrieves high-definition camera videos covering various areas within a period of time, analyzes video data, screens out images containing people, distinguishes people inside and outside the area through face recognition, and analyzes the behavior trajectory parameters of people outside the area to obtain a behavior state evaluation coefficient, and compares it with a preset behavior state evaluation threshold. When the evaluation coefficient exceeds or equals the threshold, an abnormal behavior warning signal is generated, thereby achieving accurate monitoring and evaluation of the behavior state of people outside the area, and timely generating abnormal warnings to ensure safe management of the park. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0042] Figure 1 It is the overall module block diagram of the present invention. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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.

[0044] like Figure 1 As shown, a remote security monitoring and management system for a park includes: a monitoring terminal, a server, an entry identity recognition module, a locked personnel monitoring and analysis module, a regional personnel monitoring and analysis module, an early warning distribution module, a remote management terminal and a database;

[0045] The monitoring terminal is used to manage and control various types of high-definition camera equipment deployed in the park, ensure the real-time acquisition and transmission of video data, and transmit the collected video data to the server through a wireless network or wired network for storage and backup;

[0046] Among them, the monitoring end controls and manages the following types of high-definition cameras:

[0047] High-definition cameras at the entrance to the park: These cameras are deployed at the entrance of the park. The monitoring end verifies the identity of people entering the park by controlling these cameras. The cameras collect facial images of people entering the park and automatically determine whether they are authorized personnel by combining facial recognition technology.

[0048] If an unauthorized person is identified, an intrusion warning signal is immediately generated and transmitted to the remote management terminal;

[0049] If the person is identified as an authorized person, the collected facial image will be transmitted to the entry identification module for further analysis and processing;

[0050] HD cameras in the park: These HD cameras are deployed in different areas of the park, including but not limited to parking lots, work areas, office buildings, warehouses, etc. The monitoring end monitors the activities of people in the park in real time by controlling these HD cameras.

[0051] The park entry identification module is used to identify and analyze the facial images of people entering the park. The specific analysis is as follows:

[0052] Through face recognition technology, the facial images of people entering the park are compared and matched with the facial images of authorized personnel stored in the database to confirm the specific identity of the person and extract their identity authentication status;

[0053] If the identity authentication status of the person is "unauthenticated employee in the park", a lock signal is generated and transmitted to the monitoring end, and the monitoring end locks and monitors the person based on the lock signal. (That is, the person is considered locked personnel) and transmit its real-time video data in the park to the server;

[0054] If the person's identity authentication status is "authenticated employee in the park", no lock signal is generated and normal monitoring continues;

[0055] In a specific embodiment, the present invention automatically identifies the identity of people entering the park through a high-definition camera at the entrance combined with face recognition technology, can quickly distinguish authorized personnel from unauthorized personnel, ensure that only authorized personnel can enter the park freely, and effectively prevent illegal intrusion by outsiders. At the same time, it also performs identity recognition and authentication on authorized personnel, and implements locked monitoring of unauthenticated employees based on the comparison results, thereby effectively strengthening the precise management of people inside the park.

[0056] The locked person monitoring and analysis module is used to obtain the current location of the locked person and the destination location that the locked person needs to go to;

[0057] By extracting the overall map of the park from the database, marking the current position and destination position of the locked person on the overall map, and calculating the reachable routes between the two marked points according to the relative position relationship between the current position and the destination position, recording them as pass routes, and generating the pass routes of the locked person from the current position to the destination position;

[0058] Retrieve high-definition camera videos covering all routes of the targeted person from the server and parse the video data. Specifically, the parsing includes: extracting image information of each route, filtering the extracted images, removing images without detected persons, retaining images containing persons, and then performing identity recognition on the retained images of persons. If the targeted person is recognized in multiple consecutive images of a route, the route is marked as a target route.

[0059] The route status of the target route is analyzed, and the specific analysis process is as follows:

[0060] The route length of the target route is obtained, and the route length of the target route is obtained, which is marked as Ltaog;

[0061] Obtain the slope of each slope in the target route, and obtain the slope of each slope in the target route, marked as Pioad k ;

[0062] Obtain the length of each slope in the target route, and obtain the length of each slope in the target route, marked as Pcnad k ;

[0063] According to the formula: Calculate the time influence index θ of each slope in the target route k , where k represents the number of each slope in the target route, and its value range is [1, k]. a1 and a2 represent the set weight factors, respectively. * 、Pcnad *They represent the reference slope and reference length of the set slope respectively, and ΔPioad represents the allowable slope difference corresponding to the set slope;

[0064] The time influence index of each slope in the target route is summed up and calculated to obtain the slope time influence index of the target route, which is marked as Rθ; the overall difficulty and time consumption of the target route are measured according to the slope time influence index;

[0065] According to the formula: Calculate the route time impact index δ of the target route, Ltaog * It is expressed as the set reference route length;

[0066] Matching the route time impact index of the target route with the estimated time corresponding to the set route time impact indexes to obtain the estimated time of the target route;

[0067] High-definition camera video covering the destination area is retrieved and the video data is analyzed to determine whether the locked person arrives at the destination as expected. If the locked person is not identified, an abnormal arrival warning signal is generated. If the locked person is identified, the length of stay of the locked person is acquired in real time to obtain the actual length of stay of the locked person. The actual length of stay of the locked person is compared and analyzed with the preset length of stay threshold. When the actual length of stay of the locked person exceeds the preset length of stay threshold, an abnormal stay warning signal is generated.

[0068] The generated abnormal arrival warning signal and abnormal stay warning signal are sent to the warning allocation module.

[0069] The regional personnel monitoring and analysis module is used to retrieve high-definition camera videos covering various areas within a period of time and analyze the video data. The specific analysis is as follows:

[0070] By extracting the image information of each area and screening the extracted images, images without detected persons are eliminated and images containing persons are retained, thereby obtaining a set of person images in each area, and extracting face images from the set of person images in each area, thereby obtaining face images of persons in each area, and matching the face images of persons in each area with the reference face images corresponding to each area stored in the database, if the match is successful, it is determined that the person belongs to the person in the area, otherwise, it is determined that the person belongs to the person outside the area, thereby monitoring and analyzing the behavior trajectory parameters of persons outside the area, and the specific analysis is as follows;

[0071] By obtaining the actual walking path data of people outside the area, the movement trajectory map of people outside the area is generated, and the movement trajectory map of people outside the area is overlapped and matched with the preset normal movement trajectory map, and the deflection angle value, distance value and plane value are extracted from them, marked as Lpz, Lpj and Lpm respectively, according to the formula: Calculate the moving deviation evaluation index Gjing, where Lpz * , Lpj * and Lpm * They represent the set reference deflection angle value, reference offset distance value and reference deflection plane value respectively, u represents a natural constant, b1, b2 and b3 represent the set weight factors respectively;

[0072] It should be noted that the deflection value represents the angle difference between the actual walking path and the preset path at each key point. The calculation steps are: obtain the angle difference between the actual walking path and the preset path at multiple key points (such as turning points and turns) according to the formula: Get the deflection value, where G actual (g) represents the actual walking path angle of the g-th key point, G expected (g) represents the angle of the preset path, g represents the number of the key point, and m represents the total number of key point numbers;

[0073] The offset value indicates the maximum horizontal distance deviation between the actual walking path and the preset path. The calculation steps are as follows: obtain each distance between the actual walking path and the preset path, record the horizontal distance deviation of each path, and take the maximum value of the horizontal distance deviation as the offset value;

[0074] The deviated value indicates the difference in area between the actual walking path and the preset path. The calculation steps are as follows: obtain the area formed between the actual walking path and the preset path, and sum up the areas of these areas according to the formula: Get the partial face value, where Area(P actual (x), P expected (x)) represents the area formed by the actual walking path and the preset path in the xth segment, and m1 represents the number of segments of the path;

[0075] By obtaining the actual length of stay of people outside the area at a specific location and calculating the difference between the actual length of stay and the preset standard length of stay, a length of stay evaluation index is obtained, which is marked as Tlscd;

[0076] By obtaining the frequency and time interval of repeated appearance of people outside the area at the same place, according to the formula: Calculate the repetitive behavior assessment index Fcxin, where N re Indicates the number of times people outside the area appear in the same place, T totIndicates the total monitoring time period, which refers to the monitoring time range, such as half an hour or one hour. avg It represents the average time interval between the appearance of people outside the area at the same location. The calculation formula is: Among them, t represents the time point of the i-th occurrence, T min represents the preset shortest reasonable time interval, b4 and b5 represent the preset weight factors respectively;

[0077] Based on this, the behavior status of people outside the area is analyzed. The specific analysis process is as follows:

[0078] The movement deviation evaluation index Gjing, the stay time evaluation index Tlscd and the repeated behavior evaluation index Fcxin of people outside the area are extracted and normalized according to the formula: The behavior state evaluation coefficient XWP is calculated, where η1, η2 and η3 represent the weight coefficients of the movement deviation evaluation index, the stay time evaluation index and the repeated behavior evaluation index, respectively, and η1>η2>η3;

[0079] Compare and analyze the behavior status evaluation coefficient of the person outside the area with the behavior status evaluation threshold. When the behavior status evaluation coefficient of the person outside the area is greater than or equal to the behavior status evaluation threshold, an abnormal behavior warning signal is generated, otherwise, no warning signal is generated;

[0080] The generated abnormal behavior warning signal is sent to the warning distribution module.

[0081] The warning allocation module is used to receive abnormal arrival warning signals, abnormal stay warning signals and abnormal behavior warning signals, and perform warning allocation analysis. The specific analysis process is as follows:

[0082] If an abnormal arrival warning signal is captured, the basic information of the locked person will be sent to the remote management terminal of the park security department for early warning notification within the set W time period, where the basic information includes the person's name, face image, contact information, etc.;

[0083] If an abnormal stay warning signal is captured, the actual stay time of the locked person is retrieved, and the difference between the actual stay time and the preset stay time threshold is calculated to obtain the stay time difference value, and the stay time difference value is matched and analyzed with the stay time difference state table stored in the database to obtain the stay time difference level, and each stay time difference value corresponds to a stay time difference level, and at the same time, it is matched with the warning signal light corresponding to the stay time difference level, thereby obtaining the warning signal light of the locked person, and sending it to the remote management terminal of the park security department for warning notification, wherein the warning signal light includes a red warning signal light, an orange warning signal light and a yellow warning signal light, and the severity level of the warning signal light is red warning signal light> orange warning signal light> yellow warning signal light;

[0084] If an abnormal behavior warning signal is captured, the behavior status evaluation coefficient is retrieved, and the behavior status evaluation coefficient is matched and analyzed with the behavior status table stored in the database, thereby obtaining the behavior status level, and each behavior status evaluation coefficient corresponds to a behavior status level, and it is matched with the warning signal light corresponding to the behavior status level, thereby obtaining the warning signal light of the locked person, and sending it to the remote management terminal of the park security department for warning notification;

[0085] Remote management terminals include mobile phones, computers and tablets;

[0086] The database is used to store the facial images of authorized personnel, the reference facial images corresponding to each area, the stay time difference state table and the behavior state table.

[0087] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A park remote security monitoring and management system, characterized in that: include: The park entry identity recognition module is used to identify and analyze the facial images of people entering the park to determine whether they are certified employees of the park. If they are uncertified employees of the park, a lock signal is generated to lock and monitor the person; The locked person monitoring and analysis module is used to determine and judge the target route of the locked person, and analyze the route status of the target route accordingly to obtain the route time impact index, match the route time impact index with the estimated time, and obtain the estimated time, thereby judging whether the locked person arrives at the destination within the estimated time, and obtaining abnormal arrival warning signals and abnormal stay warning signals; The regional personnel monitoring and analysis module is used to identify personnel outside the region, thereby monitoring and analyzing the behavior trajectory parameters of personnel outside the region, obtaining the movement deviation evaluation index, the stay time evaluation index and the repeated behavior evaluation index, and performing a comprehensive analysis to obtain the behavior state evaluation coefficient. Based on the behavior state evaluation coefficient, the behavior state of personnel outside the region is judged, thereby obtaining an abnormal behavior warning signal; The warning allocation module is used to receive abnormal arrival warning signals, abnormal stay warning signals and abnormal behavior warning signals, and perform warning allocation operation processing accordingly.

2. A park remote security monitoring and management system according to claim 1, characterized in that: It also includes a monitoring terminal, which is used to manage and control various types of high-definition camera equipment deployed in the park, including high-definition cameras at the entrance and inside the park, and transmit the collected video data to the server for storage and backup; The high-definition camera at the entrance authenticates the identity of the person entering the park. If an unauthorized person is identified, an intrusion warning signal is immediately generated. If an authorized person is identified, the collected facial image is transmitted to the park entry identity recognition module. The activities of people in the park are monitored in real time through high-definition cameras in the park.

3. A park remote security monitoring and management system according to claim 1, characterized in that: The analysis process for determining the target route of the locked person is as follows: By obtaining the current location and destination location of the locked person, marking them on the overall map of the park, calculating each accessible route between the two marked points, recording them as the pass route, and generating each pass route from the current location to the destination location of the locked person; By extracting image information of each route and screening the extracted images, images without detected persons are eliminated, images containing persons are retained, and identity recognition is performed on the retained person images. If the locked person is identified in multiple consecutive images of a certain route, the route is marked as the target route.

4. A park remote security monitoring and management system according to claim 1, characterized in that: The analysis process of solving the route traffic time impact index is as follows: By obtaining the slope of each slope in the target route, k and length Pcnad k , according to the formula: Calculate the time-dependent influence index θ of each slope k , where k represents the number of each slope in the target route, and its value range is [1, k]. a1 and a2 represent the set weight factors, respectively. * 、Pcnad * They represent the reference slope and reference length of the set slope respectively, and ΔPioad represents the allowable slope difference corresponding to the set slope; The time-dependent influence index of each slope is summed to obtain the slope time-dependent influence index Rθ; By obtaining the route length Ltaog of the target route, according to the formula: Calculate the route traffic impact index δ, Ltaog * Indicates the set reference route length.

5. A park remote security monitoring and management system according to claim 1, characterized in that: The analysis process to determine whether the locked person arrives at the destination within the expected time is as follows: The high-definition camera video covering the destination area is retrieved and analyzed. If the locked person is not identified, an abnormal arrival warning signal is generated. If the locked person is identified, the length of stay of the locked person is obtained, and the actual length of stay is compared with the length of stay threshold to determine whether to generate an abnormal stay warning signal.

6. A park remote security monitoring and management system according to claim 1, characterized in that: Monitor and analyze the behavior trajectory parameters of people outside the area. The specific analysis process is as follows: By obtaining the actual walking path data of people outside the area, the movement trajectory map of people outside the area is generated, and the movement trajectory map of people outside the area is overlapped and matched with the preset normal movement trajectory map, and the deflection angle value, distance value and plane value are extracted from them, marked as Lpz, Lpj and Lpm respectively, according to the formula: Calculate the moving deviation evaluation index Gjing, where Lpz * , Lpj * and Lpm * They represent the set reference deflection angle value, reference offset distance value and reference deflection plane value respectively, u represents a natural constant, b1, b2 and b3 represent the set weight factors respectively; By obtaining the actual length of stay of people outside the area at a specific location, and calculating the difference between the actual length of stay and the preset standard length of stay, the length of stay evaluation index Tlscd is obtained; The repetitive behavior assessment index Fcxin was obtained by analyzing the frequency and time interval of repeated appearance of people outside the area at the same location.

7. A park remote security monitoring and management system according to claim 6, characterized in that: The analysis process for solving the behavior state evaluation coefficient is as follows: The movement deviation evaluation index Gjing, the stay time evaluation index Tlscd and the repeated behavior evaluation index Fcxin of people outside the area are extracted and normalized according to the formula: The behavior state evaluation coefficient XWP is calculated, where η1, η2 and η3 represent the weight coefficients of the movement deviation evaluation index, the stay time evaluation index and the repeated behavior evaluation index, respectively.

8. The remote security monitoring and management system for a park according to claim 1, characterized in that: The analysis process for early warning allocation operation processing is as follows: If an abnormal arrival warning signal is captured, the basic information of the locked person will be sent to the remote management terminal for warning notification within the set W time period; If an abnormal stay warning signal is captured, the actual stay time of the locked person is retrieved, and the difference between the actual stay time and the preset stay time threshold is calculated to obtain the stay time difference value. The warning signal light is determined based on the stay time difference value and sent to the remote management terminal for warning notification; If an abnormal behavior warning signal is captured, the behavior status evaluation coefficient is retrieved, a warning signal light is determined based on the behavior status evaluation coefficient, and the signal light is sent to the remote management terminal for warning notification.

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