An artificial intelligence-based visitor real-time tracking method and control system

Abstract

The application discloses a kind of based on artificial intelligence's real-time tracking method and control system of visitor, it is related to personnel tracking management technical field, including: obtaining the visit reason and identity level of visitor, the area division of access place is carried out, including target area, key area and other area, and access route planning is carried out, and each feasible access route is obtained;Each feasible access route feature data is obtained and analyzed, and each feasible access route recommendation value is obtained, the current action route of visitor is judged, and current action route recommendation value is obtained by processing;The state data of label equipment of visitor is collected, and state abnormal evaluation value of label equipment is obtained by processing;The action state data of visitor is monitored, and action abnormal evaluation value of visitor is obtained by processing, abnormity index of visitor is obtained by comprehensive analysis, and early warning feedback is carried out according to abnormity index of visitor.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of personnel tracking management, in particular to a real-time tracking method and control system for visiting personnel based on artificial intelligence. BACKGROUND

[0002] With the continuous expansion of the scale of power grid enterprises and the increasing complexity of business, personnel flow is increasingly frequent, and the access demand of various external personnel has increased significantly, which not only brings more communication opportunities for enterprises, but also increases security risks. As a key department of energy supply, the security of the internal facilities and data of power grid enterprises is crucial. In order to protect the safety and order within the unit, strengthen the management of visiting personnel, and prevent unauthorized personnel from entering sensitive areas, it has become an important part of the security of power grid enterprises.

[0003] For example, the invention patent with publication number CN111340998B is an intelligent office management system based on face recognition function, which includes an office building central processor and a door access system and an internal employee office computer connected with the office building central processor through a network. The door access system includes an entrance door access and an exit door access installed at the entrance and exit of the office building, the entrance door access and the exit door access are arranged in a row, a visitor access management cabinet is arranged between the entrance door access and the exit door access, an entrance face recognition device is arranged at the entrance door access, an exit face recognition device is arranged at the exit door access, and a handheld terminal is arranged in the visitor access management cabinet.

[0004] For example, the invention patent with publication number CN111179484B is a visitor management method, device, system and storage medium. The method includes: a first terminal generates a visitor identity electronic certificate according to a first identity information and a first access request input by a visitor, a visitor management system obtains the visitor identity electronic certificate and visitor biological information, performs identity confirmation on the visitor according to the visitor identity electronic certificate and the visitor biological information, performs behavior verification on the visitor by using an authentication management system after the identity confirmation is passed, sends an access request to an authentication employee to be accessed by the visitor when the behavior verification result is that there is no preset behavior record, and performs a door access restriction operation on the visitor based on the approval result of the access request.

[0005] However, in the process of implementing the technical scheme in the embodiments of the present application, it is found that the above-mentioned technology at least has the following technical problems: the management of visiting personnel at present mainly stays in the review and registration of door access, but there is a risk of misentry into important areas in the free activities of visiting personnel, which may cause production safety hazards and adverse effects. SUMMARY

[0006] In view of the deficiencies of the prior art, the present application provides a real-time tracking method and control system for visiting personnel based on artificial intelligence, which can effectively solve the problems involved in the background art.

[0007] To achieve the above object, the present application is implemented by the following technical solutions: the first aspect of the present application provides a real-time tracking method for visiting personnel based on artificial intelligence, comprising: obtaining the visiting reason and identity level of the visiting personnel, dividing the visiting place into regions, including target region, key region and other region, and planning the visiting route to obtain each feasible visiting route.

[0008] Obtaining the characteristic data of each feasible visiting route for analysis to obtain the recommended value of each feasible visiting route, judging the current action route of the visiting personnel, and processing to obtain the recommended value of the current action route.

[0009] Collecting the tag equipment state data of the visiting personnel, and processing to obtain the tag equipment state abnormality evaluation value.

[0010] Monitoring the action state data of the visiting personnel, processing to obtain the action abnormality evaluation value of the visiting personnel, comprehensively analyzing to obtain the abnormality index of the visiting personnel according to the recommended value of the current action route, the tag equipment state abnormality evaluation value and the action abnormality evaluation value of the visiting personnel, and giving early warning feedback according to the abnormality index of the visiting personnel.

[0011] As a further method, the region division of the visiting place is specifically as follows: extracting the demand access region from the visiting reason of the visiting personnel, and marking it as the target region; obtaining the demand access level of each region in the visiting place, comparing the demand access level of each region with the identity level of the visiting personnel, and marking the region as the key region if the demand access level of the region is greater than the identity level of the visiting personnel; marking the region as the other region if the demand access level of the region is greater than the identity level of the visiting personnel.

[0012] As a further method, the characteristic data of each feasible visiting route is obtained for analysis to obtain the recommended value of each feasible visiting route, and the specific analysis process is as follows: the characteristic data of each feasible visiting route includes the route length, the monitoring equipment density and the number of passing key regions; obtaining the shortest route length of each feasible visiting route, extracting the critical monitoring equipment density and the critical number of key regions from the visiting management database, and comprehensively analyzing to obtain the recommended value of each feasible visiting route.

[0013] As a further method, the current action route of the visitor is determined, and a current action route recommendation value is processed. The specific process is as follows: the actual action route of the visitor is compared with each feasible access route, the length of the actual action route of the visitor is extracted, and the length of the overlap between the actual action route of the visitor and each feasible access route is marked as the overlap length of each feasible access route; the overlap length of each feasible access route is divided by the length of the actual action route of the visitor to obtain the overlap degree of each feasible access route; the overlap degrees of each feasible access route are sorted in descending order, and the feasible access route corresponding to the maximum feasible access route overlap degree is extracted and marked as the current action route of the visitor; the feasible access route overlap degree of the current action route of the visitor is multiplied by the feasible access route recommendation value of the current action route of the visitor to obtain the current action route recommendation value.

[0014] As a further method, a label device state abnormality evaluation value is processed. The specific process is as follows: the label device state data includes vibration intensity, signal intensity, device response time, and data packet loss rate; the critical vibration intensity, critical signal intensity, critical device response time, and critical data packet loss rate are obtained from the visitor management database, and a comprehensive analysis is performed to obtain the label device state abnormality evaluation value.

[0015] As a further method, a visitor action abnormality evaluation value is processed. The specific analysis process is as follows: the action state data of the visitor includes repeated action route length, abnormal stay number, and each abnormal stay time and each abnormal stay location point; the straight-line distance between each abnormal stay location point and the nearest critical area is extracted and marked as the abnormal stay distance; the critical repeated action route length, critical abnormal stay number, critical abnormal stay time, and critical abnormal stay distance are obtained from the visitor management database, and a comprehensive analysis is performed to obtain the visitor action abnormality evaluation value.

[0016] As a further method, a visitor abnormality index is obtained through comprehensive analysis. The specific analysis process is as follows: based on the current action route recommendation value, the label device state abnormality evaluation value, and the visitor action abnormality evaluation value, a comprehensive analysis is performed to obtain the visitor abnormality index, which is used to quantitatively evaluate the abnormal state of the visitor.

[0017] As a further method, a warning feedback is given based on the visitor abnormality index. The specific process is as follows: the visitor abnormality index is compared with the preset visitor abnormality index threshold in the visitor management database. If the visitor abnormality index is less than the visitor abnormality index threshold, the visitor is marked as a normal person. If the visitor abnormality index is greater than or equal to the visitor abnormality index threshold, the visitor is marked as an abnormal person, and a warning feedback is given to the abnormal person.

[0018] The second aspect of the present application provides a real-time tracking control system for visiting personnel based on artificial intelligence, comprising: a region division module for obtaining the visiting reason and identity level of the visiting personnel, dividing the access location into target regions, key regions and other regions, and planning the access route to obtain each feasible access route.

[0019] An action route tracking module is configured to obtain and analyze the characteristic data of each feasible access route to obtain a recommended value of each feasible access route, determine the current action route of the visiting personnel, and obtain a recommended value of the current action route.

[0020] A device state monitoring module is configured to collect label device state data of the visiting personnel and obtain an abnormal evaluation value of the label device state after processing.

[0021] An early warning feedback module is configured to monitor the action state data of the visiting personnel, obtain an abnormal evaluation value of the action of the visiting personnel after processing, comprehensively analyze an abnormal index of the visiting personnel according to the recommended value of the current action route, the abnormal evaluation value of the label device state and the abnormal evaluation value of the action of the visiting personnel, and perform early warning feedback according to the abnormal index of the visiting personnel.

[0022] Compared with the prior art, the embodiments of the present application have at least the following beneficial effects:

[0023] (1) The present application provides a real-time tracking method and control system for visiting personnel based on artificial intelligence, which realizes fine management and efficient monitoring of visiting personnel. Real-time tracking of visiting personnel by label devices not only improves the intelligent level of visit management, but also enhances the security and protection capabilities of enterprises or units, providing a more convenient and safe access experience for visiting personnel. At the same time, it also provides strong support for management decisions of enterprises or units, ensuring the normal operation of the visit management system and the safety of the access area.

[0024] (2) The present application can improve the accuracy of judging the action route of the visiting personnel by comparing the actual action route of the visiting personnel with each feasible access route. Accurate determination of the current action route of the visiting personnel helps better access management, and timely adjustment of guidance measures can be made according to the actual route of the visiting personnel to ensure that the visiting personnel can reach the destination smoothly, while abnormal situations such as deviation from the route can be timely warned and handled to ensure the safety and order of the access area.

[0025] (3) The application evaluates the abnormal state of the tag device by comprehensively analyzing the vibration intensity, signal intensity, device response time and data packet loss rate, which helps to accurately reflect the actual operation of the tag device and can timely screen out abnormal tag devices. Measures can be taken in the early stage of device problems to prevent inaccurate visitor information acquisition caused by device failure and avoid the adverse effects caused by the misentry of visitors into important areas.

[0026] Of course, implementing any product of the application does not necessarily require all the advantages described above. BRIEF DESCRIPTION OF DRAWINGS

[0027] Figure 1 The figure is a schematic diagram of the method flow of the application.

[0028] Figure 2 The figure is a schematic diagram of the system module connection of the application.

[0029] Figure 3 The figure is a schematic diagram of the function relationship between the visitor action abnormality evaluation value and the visitor abnormality index in the embodiment of the application. DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the application.

[0031] It should be noted that, in this document, relationship terms such as first and second are used only to distinguish one entity or operation from another, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device.

[0032] Referring to Figure 1 The first aspect of the application provides a real-time tracking method for visitors based on artificial intelligence, which includes obtaining the visiting reason and identity level of the visitor, dividing the access location into regions including target regions, key regions and other regions, and planning the access route to obtain each feasible access route.

[0033] It should be understood that the visiting reasons in the embodiment include, but are not limited to, business cooperation, technical exchange and administrative affairs, different visiting reasons have fixed required access areas, for example, business cooperation needs to access production facility area and research and development center, technical exchange needs to access specific meeting room, therefore, obtaining the visiting reason of the visitor can directly obtain the required access area of the visitor.

[0034] It should be understood that the access site in the embodiment refers to the physical space range of the access activity of the visitor, which can be an independent building, a park, or a specific part in a larger building, for example, a working area of an enterprise or unit. According to specific working content, different areas in the access site can be preset with required access levels, and the identity level of the visitor can be determined according to the visiting reason of the visitor. It can be understood that the working content and the required access level and the visiting reason and the identity level are a one-to-one corresponding relationship preset in the access management database.

[0035] The characteristic data of each feasible access route is obtained for analysis to obtain a recommended value of each feasible access route, the current action route of the visitor is judged, and a recommended value of the current action route is obtained by processing; the state data of the tag equipment of the visitor is collected, and an abnormality evaluation value of the state of the tag equipment is obtained by processing.

[0036] It should be understood that the tag equipment in the embodiment refers to the visitor identity card issued to the visitor, which has unique identification and positioning functions, and usually exists in the form of active tag.

[0037] The action state data of the visitor is monitored, an abnormality evaluation value of the action of the visitor is obtained by processing, the visitor abnormality index is comprehensively analyzed according to the recommended value of the current action route, the abnormality evaluation value of the state of the tag equipment and the abnormality evaluation value of the action of the visitor, and the pre-warning feedback is performed according to the visitor abnormality index.

[0038] Specifically, the access site is divided into areas, and the specific process is as follows: the required access area of the visitor is extracted from the visiting reason of the visitor, and is marked as a target area; the required access levels of each area in the access site are obtained, the required access levels of each area are compared with the identity level of the visitor, if the required access level of a certain area is greater than the identity level of the visitor, the area is marked as a key area; if the required access level of a certain area is greater than the identity level of the visitor, the area is marked as other area.

[0039] In a specific embodiment, the required access levels of the areas include high, medium and public, and it is understood that the confidentiality requirement of the high level is greater than that of the medium level, and the confidentiality requirement of the medium level is greater than that of the public. The identity levels of the visitors include A, B and C, wherein A refers to allowing access to areas of high and lower levels, B refers to allowing access to areas of medium and lower levels, and C refers to allowing access to areas of public level. It is known that the required access level of the R&D center is high, the required access level of the conference room is medium, and the required access level of the leisure area is public.

[0040] The visiting reason of a certain visitor is business cooperation negotiation, and the required access area corresponding to the business cooperation negotiation in the visiting management database is the conference room. The conference room is marked as the target area, and the visitor is given the identity level of B. At the same time, the leisure area is marked as the other area, and the R&D center is marked as the key area. The visitor is allowed to access the conference room and the leisure area, and is not allowed to access the R&D center.

[0041] Specifically, the characteristic data of each feasible access route is obtained for analysis to obtain the recommendation value of each feasible access route. The specific analysis process is as follows: the characteristic data of each feasible access route includes route length, monitoring device density and number of passing key areas; the shortest route length of each feasible access route is obtained, and the critical monitoring device density and the critical number of key areas are extracted from the visiting management database, and the recommendation value of each feasible access route is obtained by comprehensive analysis.

[0042] It should be understood that the monitoring device density in the embodiment refers to the number of monitoring devices per unit area (such as 10 m²). The critical monitoring device density refers to the minimum monitoring device density that can cover the access route. The critical number of key areas refers to the maximum number of key areas allowed to pass through the access route.

[0043] In a specific embodiment, the recommendation value of each feasible access route is obtained in the following manner:

[0044] ;

[0045] In the formula, represents the recommendation value of the i th feasible access route, e represents a natural constant, represents the monitoring device density of the i th feasible access route, represents the critical monitoring device density, represents the route length of the i th feasible access route, represents the shortest route length, represents the number of passing key areas of the i th feasible access route, represents the critical number of key areas, represents the access route recommendation influence factor corresponding to the preset monitoring device density, represents the preset route length corresponding access route recommendation influence factor, represents the preset number of key area passing corresponding access route recommendation influence factor, i represents the number of each feasible access route, i=1, 2, 3,..., n, n represents the total number of feasible access routes.

[0046] In this embodiment , and are respectively the preset monitoring device density, route length and number of key area passing corresponding access route recommendation influence factor in the visit management database, and respectively represent the numerical value of the influence degree of the monitoring device density, route length and number of key area passing on the feasible access route recommendation value. When used, it can be directly obtained from the visit management database. For example, the access site area and the preset monitoring device density, route length and number of key area passing corresponding access route recommendation influence factor in the visit management database form a mapping set, and the monitoring device density, route length and number of key area passing corresponding access route recommendation influence factor is obtained by inputting the mapping set according to the access site area. The mapping relationship is one-to-one relationship. In this embodiment, the value range of the influence factor is between 0 and 1.

[0047] It should be understood that the feasible access route recommendation value in this embodiment is used to quantitatively evaluate the recommendation degree of the feasible access route. The greater the monitoring device density, or the smaller the route length and the number of key area passing, the greater the corresponding feasible access route recommendation value, indicating that the access route is more recommended as the action route.

[0048] The algorithm of this embodiment combines monitoring device density, route length and number of key area passing, and comprehensively analyzes to obtain the feasible access route recommendation value. There is a correlation between the monitoring device density, route length and number of key area passing in the formula. The greater the route length, the more monitoring devices are needed to cover, and the monitoring device density and the number of key area passing have a positive correlation. The greater the monitoring device density, the stronger the safety and monitorability of the route, and the route with greater monitoring device density can better record the action trajectory of the visitor and timely discover abnormal situations. The smaller the route length, the faster the visitor can reach the destination, saving time and improving access efficiency. The smaller the number of key area passing, the less the opportunity for the visitor to contact sensitive areas, reducing the security risk.

[0049] Specifically, the current action route of the visitor is determined, and a current action route recommendation value is processed. The specific process is as follows: the actual action route of the visitor is compared with each feasible access route, the length of the actual action route of the visitor is extracted, and the length of the overlap between the actual action route of the visitor and each feasible access route is marked as the overlap length of each feasible access route; the overlap length of each feasible access route is divided by the length of the actual action route of the visitor to obtain the overlap degree of each feasible access route; the overlap degrees of each feasible access route are sorted in descending order, and the feasible access route corresponding to the maximum feasible access route overlap degree is extracted and marked as the current action route of the visitor.

[0050] In a specific embodiment, the actual action route of the visitor is tracked through a tag signal, an entrance is taken as the origin, an east-west direction is taken as the horizontal coordinate, and a south-north direction is taken as the vertical coordinate to establish a plane coordinate system of the access site, and the data of the actual action route and the feasible access route are mapped into the same coordinate system for comparison.

[0051] It should be understood that there are two cases for determining the current action route of the visitor in the embodiment. If the maximum feasible access route overlap degree is unique, the feasible access route corresponding to the maximum feasible access route overlap degree is marked as the current action route of the visitor. If the maximum feasible access route overlap degree is not unique, each feasible access route corresponding to the maximum feasible access route overlap degree is marked as a high overlap degree route, and the high overlap degree routes are further sorted according to the feasible access route recommendation values, and the high overlap degree route corresponding to the maximum feasible access route recommendation value is marked as the current action route of the visitor. Since the feasible access route recommendation value in the embodiment is obtained through comprehensive analysis of multiple factors, the probability of the same is low, and therefore the case that the maximum feasible access route recommendation value corresponds to multiple high overlap degree routes is not considered.

[0052] The feasible access route overlap degree of the current action route of the visitor is multiplied by the feasible access route recommendation value of the current action route of the visitor to obtain the current action route recommendation value.

[0053] It should be understood that in the embodiment, the actual action route of the visitor is compared with each feasible access route to determine the current action route of the visitor more accurately, avoiding misjudgment caused by relying on only one factor, thereby improving the accuracy of determining the action route of the visitor. Accurate determination of the current action route of the visitor helps better access management, timely adjustment of guidance measures according to the actual route of the visitor, ensures that the visitor can smoothly reach the destination, and also can timely warn and handle abnormal situations such as deviation from the route, and protect the safety and order of the access area.

[0054] The current action route recommendation value in the embodiment is used to quantify the normality of the current action route of the visitor. The smaller the feasible access route coincidence degree and the feasible access route recommendation value of the current action route of the visitor, the smaller the corresponding current action route recommendation value, indicating that the visitor deviates from the allowed access route more.

[0055] Specifically, a label device state abnormality evaluation value is obtained by processing, and the specific process is as follows: the label device state data includes vibration intensity, signal intensity, device response time and data packet loss rate.

[0056] It should be understood that the vibration intensity in the embodiment can be collected by an acceleration sensor. The device response time refers to the time required for the label device to respond to the base station instruction, and the data packet loss rate refers to the proportion of the number of data packets lost in the data transmission process of the label device to the total number of transmitted data packets. The signal intensity, device response time and data packet loss rate can be directly obtained from the software system of the base station, or can be extracted from the relevant information recorded in the system log of the base station and the label device.

[0057] The critical vibration intensity, the critical signal intensity, the critical device response time and the critical data packet loss rate are obtained from the visitor management database, and a label device state abnormality evaluation value is obtained by comprehensive analysis.

[0058] It should be understood that the critical vibration intensity in the embodiment refers to the maximum vibration intensity that may cause damage to the label device. The critical signal intensity refers to the minimum signal intensity that may cause the base station to fail to accurately receive the broadcast information of the label device. The critical device response time refers to the maximum allowed response time of the device. When the device response time exceeds the maximum allowed response time, it can be judged as response delay. The critical data packet loss rate refers to the maximum allowed data packet loss rate. A higher data packet loss rate (such as more than 5%) will seriously affect the acquisition and processing of visitor information by the system, and may cause problems such as inaccurate positioning and incomplete trajectory.

[0059] In a specific embodiment, the label device state abnormality evaluation value is obtained in the following manner:

[0060] ;

[0061] In the formula, e represents a natural constant, represents the signal intensity of the label device, represents the critical signal intensity, represents the vibration intensity of the label device, represents the critical vibration intensity, represents the device response time of the label device, represents the critical device response time, a data packet loss rate of the tag device, a critical data packet loss rate, a preset signal strength corresponding device state abnormality influence factor, a preset vibration strength corresponding device state abnormality influence factor, a preset device response time corresponding device state abnormality influence factor, a preset data packet loss rate corresponding device state abnormality influence factor.

[0062] In the embodiment , , and respectively are preset signal strength, vibration strength, device response time and data packet loss rate corresponding device state abnormality influence factors in the visitor management database, respectively represent the numerical values of the influence degrees of the signal strength, vibration strength, device response time and data packet loss rate on the tag device state abnormality evaluation value, which can be directly obtained from the visitor management database when used. For example, the access site area is mapped to the preset signal strength, vibration strength, device response time and data packet loss rate corresponding device state abnormality influence factors in the visitor management database to form a mapping set, and the signal strength, vibration strength, device response time and data packet loss rate corresponding device state abnormality influence factors are obtained by inputting the access site area into the mapping set, wherein the mapping relationship is a one-to-one relationship. In the embodiment, the value range of the influence factor is between 0 and 1.

[0063] It should be understood that the tag device state abnormality evaluation value in the embodiment is used to quantitatively evaluate the abnormal state degree of the tag device, the smaller the signal strength, or the greater the vibration strength, device response time and data packet loss rate, the greater the corresponding tag device state abnormality evaluation value, indicating that the state of the tag device is more abnormal.

[0064] The algorithm in the embodiment combines signal strength, vibration strength, device response time and data packet loss rate to comprehensively analyze the tag device state abnormality evaluation value, and there is a correlation between the signal strength, vibration strength, device response time and data packet loss rate in the formula. The smaller the signal strength, the more unstable the signal transmission, the longer the time for the device to receive and process instructions, and the greater the device response time and data packet loss rate. By comprehensively considering the vibration strength, signal strength, device response time and data packet loss rate, the abnormal state degree of the tag device can be quantitatively evaluated, so that the abnormal condition of the device can be found in time, and appropriate measures such as replacing the device, repairing the device, etc. can be taken to ensure the normal operation of the visitor behavior management system.

[0065] In a specific embodiment, the abnormal tag device screening is performed according to the tag device state abnormality evaluation value, the tag device state abnormality evaluation value is compared with a preset tag device state abnormality evaluation threshold in the visitor management database, if the tag device state abnormality evaluation value is greater than or equal to the tag device state abnormality evaluation threshold, the tag device is marked as an abnormal tag device, and the management personnel is notified to contact the visitor corresponding to the abnormal tag device in time, and the single tag device is replaced and repaired. If the tag device state abnormality evaluation value is less than the tag device state abnormality evaluation threshold, the tag device is marked as a normal tag device, and no special treatment is required. The normal state of the tag device is recorded in the visitor management database, and the state information of the tag device is updated. The number of abnormal tag devices is counted, and when the number of abnormal tag devices in a preset monitoring period is greater than or equal to two, the entire tag device needs to be repaired.

[0066] In the embodiment, the abnormal state of the tag device is evaluated by comprehensively analyzing the vibration intensity, the signal intensity, the device response time and the data packet loss rate, which helps to accurately reflect the actual operation of the tag device and can timely screen out abnormal tag devices. Measures can be taken in the early stage of device problems to prevent inaccurate visitor information acquisition, visitor misentry into important areas and other adverse consequences caused by device failure, avoid the misentry of visitors into important areas, and ensure the normal operation of the visitor management system and the safety of the access area.

[0067] Specifically, a visitor action abnormality evaluation value is obtained by processing, and the specific analysis process is as follows: the action state data of the visitor includes the length of the repeated action route, the number of abnormal stays, and the time and position point of each abnormal stay.

[0068] It should be understood that the length of the repeated action route in the embodiment refers to the length of the repeated action route of the visitor, i.e., the length of the repeated walking of the visitor on the route that has been walked. The number of abnormal stays refers to the number of long-time (e.g., greater than 3 minutes) stays of the visitor on the access route except for the target area. The base station receives the transmission signal of the tag device in real time, when the signal transmission positions of the tag devices coincide, the timer of the base station starts timing, when the signal transmission positions stop coinciding, the timer of the base station stops timing, and the signal transmission position and the stay time of the signal transmission position are recorded.

[0069] The straight-line distance between each abnormal stay position point and the nearest critical area is extracted and marked as the distance of each abnormal stay. The critical repeated action route length, the critical abnormal stay number, the critical abnormal stay time and the critical abnormal stay distance are obtained from the visitor management database, and the visitor action abnormality evaluation value is obtained by comprehensive analysis.

[0070] It should be understood that the critical repeated action route length in the embodiment refers to the maximum allowed repeated action route length, the critical abnormal stay number refers to the maximum allowed abnormal stay number, the critical abnormal stay time refers to the maximum allowed stay time for a single stay, and the critical abnormal stay distance refers to the minimum allowed distance between the abnormal stay position point and the nearest key area during abnormal stay.

[0071] In a specific embodiment, the method for obtaining the abnormal action evaluation value of the visitor is as follows:

[0072] ;

[0073] In the formula, e represents a natural constant, represents the abnormal action evaluation value of the visitor, represents the distance of the tth abnormal stay, represents the critical abnormal stay distance, represents the repeated action route length of the visitor, represents the critical repeated action route length, represents the abnormal stay number of the visitor, represents the critical abnormal stay number, represents the tth abnormal stay time, represents the critical abnormal stay time, represents the action abnormality evaluation influence factor corresponding to the preset abnormal stay distance, represents the action abnormality evaluation influence factor corresponding to the preset repeated action route length, represents the action abnormality evaluation influence factor corresponding to the preset abnormal stay number, represents the action abnormality evaluation influence factor corresponding to the preset abnormal stay time, and t represents the number of each abnormal stay, t = 1, 2, 3,..., s, and s represents the total number of abnormal stays.

[0074] In the embodiment, the critical repeated action route length refers to the maximum allowed repeated action route length, , , and respectively, are action abnormality evaluation influence factors corresponding to the preset abnormal stay distance, repeated action route length, abnormal stay times and abnormal stay time in the visit management database, and respectively represent the numerical values of the influence degree of the abnormal stay distance, repeated action route length, abnormal stay times and abnormal stay time on the visit personnel action abnormality evaluation value, which can be directly obtained from the visit management database. For example, the access site area is mapped with the action abnormality evaluation influence factors corresponding to the preset abnormal stay distance, repeated action route length, abnormal stay times and abnormal stay time in the visit management database to form a mapping set, and the action abnormality evaluation influence factors corresponding to the abnormal stay distance, repeated action route length, abnormal stay times and abnormal stay time are obtained by inputting the access site area into the mapping set, wherein the mapping relationship is a one-to-one relationship. In this embodiment, the value range of the influence factor is between 0 and 1.

[0075] It should be understood that the visit personnel action abnormality evaluation value in this embodiment is used to quantitatively evaluate the abnormality degree of the visit personnel action, and the smaller the abnormal stay distance, or the greater the repeated action route length, abnormal stay times and abnormal stay time, the greater the corresponding visit personnel action abnormality evaluation value, indicating that the visit personnel action is more abnormal.

[0076] The algorithm of this embodiment combines the abnormal stay distance, repeated action route length, abnormal stay times and abnormal stay time, and comprehensively analyzes to obtain the visit personnel action abnormality evaluation value, and there is a correlation between the abnormal stay distance, repeated action route length, abnormal stay times and abnormal stay time in the formula. The more the abnormal stay times, the more frequent the visit personnel stays in some areas, which may result in a longer abnormal stay time, and may also result in a smaller abnormal stay distance, and frequent stays on the repeated route may increase the repeated action route length. Comprehensive consideration of the abnormal stay distance, repeated action route length, abnormal stay times and abnormal stay time can quantitatively evaluate the abnormality degree of the visit personnel action, facilitate timely discovery of the abnormal behavior of the visit personnel, and take appropriate measures, such as strengthening monitoring and inquiring the visit personnel, to ensure the safety and normal order of the access area.

[0077] Specifically, the visit personnel abnormality index is obtained by comprehensive analysis, and the specific analysis process is: according to the current action route recommendation value, the tag device state abnormality evaluation value and the visit personnel action abnormality evaluation value, the visit personnel abnormality index is obtained by comprehensive analysis, and the visit personnel abnormality index is used to quantitatively evaluate the abnormal state of the visit personnel.

[0078] In a specific embodiment, the visit personnel abnormality index is obtained in the following manner:

[0079] ;

[0080] In the formula, This indicates abnormal indicators for visitors. This indicates the recommended value for the current action route. This indicates the abnormal status assessment value of the tag device. This indicates the assessment value for abnormal behavior of visitors. This represents the personnel anomaly assessment impact factor corresponding to the preset recommended value of the current action route. This represents the personnel anomaly assessment impact factor corresponding to the preset abnormal label device status assessment value. This represents the personnel abnormality assessment influence factor corresponding to the preset abnormal visitor behavior assessment value.

[0081] In this embodiment , and These are the personnel anomaly assessment influence factors corresponding to the current route recommendation value, tag device status anomaly assessment value, and visitor behavior anomaly assessment value preset in the visitor management database. These factors represent the numerical values ​​indicating the degree of influence of the current route recommendation value, tag device status anomaly assessment value, and visitor behavior anomaly assessment value on the visitor anomaly index. They can be directly obtained from the visitor management database. For example, a mapping set is formed between the area of ​​the visited location and the personnel anomaly assessment influence factors corresponding to the current route recommendation value, tag device status anomaly assessment value, and visitor behavior anomaly assessment value preset in the visitor management database. Inputting the area of ​​the visited location into the mapping set yields the personnel anomaly assessment influence factors corresponding to the current route recommendation value, tag device status anomaly assessment value, and visitor behavior anomaly assessment value. The mapping relationship is one-to-one. In this embodiment, the influence factor values ​​range from 0 to 1.

[0082] like Figure 3 As shown, in one specific embodiment, =1, =0.4, = =0.3, when When = 0.5, the functional relationship between the abnormal behavior assessment value and the abnormal visitor index is shown in Figure a; when When = 0.75, the functional relationship between the abnormal behavior assessment value and the abnormal visitor index is shown in b; when When the threshold is 1.2, the functional relationship between the visitor behavior anomaly assessment value and the visitor anomaly index is shown in Figure c. In this embodiment, the smaller the current route recommendation value, or the larger the tag device status anomaly assessment value and the visitor behavior anomaly assessment value, the larger the corresponding visitor anomaly index, indicating a higher degree of visitor anomaly.

[0083] The algorithm of the embodiment combines the current action route recommendation value, the label device state abnormality evaluation value and the visitor action abnormality evaluation value, and comprehensively analyzes to obtain the visitor action abnormality evaluation value. In the formula, there is a correlation between the current action route recommendation value, the label device state abnormality evaluation value and the visitor action abnormality evaluation value. The smaller the current action route recommendation value is, the greater the possibility that the action route selected by the visitor has factors that are not conducive to access management, resulting in an increase in the visitor action abnormality evaluation value. At the same time, the label device state abnormality may cause inaccurate monitoring and recording of the visitor's action. Comprehensive analysis of the current action route recommendation value, the label device state abnormality evaluation value and the visitor action abnormality evaluation value can accurately quantify the abnormality degree of the visitor.

[0084] Specifically, the visitor abnormality index is compared with the preset visitor abnormality index threshold in the visitor management database, if the visitor abnormality index is less than the visitor abnormality index threshold, the visitor is marked as a normal person, if the visitor abnormality index is greater than or equal to the visitor abnormality index threshold, the visitor is marked as an abnormal person, and the abnormal person is given a warning feedback.

[0085] In a specific embodiment, the abnormal person is given a warning feedback, when the visitor abnormality index is greater than or equal to the visitor abnormality index threshold, the identity information of the abnormal person is backed up in the visitor management database, and an alarm is sent to the abnormal person through the visitor identity card. When the visitor abnormality index is still greater than or equal to the visitor abnormality index threshold after three alarms, the management personnel are notified to supervise the abnormal person.

[0086] Referring to Figure 2 The second aspect of the present application provides a real-time tracking control system for visitors based on artificial intelligence, comprising: a region division module, an action route tracking module, a device state monitoring module, a warning feedback module and a visitor management database.

[0087] The region division module is used to obtain the visit reason and identity level of the visitor, divide the access location into target region, key region and other region, and plan the access route to obtain each feasible access route.

[0088] The action route tracking module is used to obtain the characteristic data of each feasible access route for analysis to obtain the recommendation value of each feasible access route, judge the current action route of the visitor and process to obtain the current action route recommendation value.

[0089] The device state monitoring module is used to collect the label device state data of the visitor, and process to obtain the label device state abnormality evaluation value.

[0090] The early warning feedback module is configured to monitor the action state data of the visitor, to obtain an action abnormality evaluation value of the visitor by processing, to obtain an abnormality index of the visitor by comprehensively analyzing the current action route recommendation value, the tag device state abnormality evaluation value and the action abnormality evaluation value of the visitor, and to perform early warning feedback according to the abnormality index of the visitor.

[0091] The visitor management database is configured to store visitor tracking management data, including critical monitoring device density, critical key area number, critical vibration intensity, critical signal intensity, critical device response time and critical data packet loss rate and the like. The data in the visitor management database can be integrated with other related systems (such as access control systems, monitoring systems and the like), and relevant data can be obtained from other systems in real time or periodically through API interfaces or data synchronization and the like.

[0092] In a specific embodiment, the present application provides a visitor real-time tracking method and control system based on artificial intelligence, which realizes fine management and efficient monitoring of visitors. The tag device is used to track the visitor in real time, which not only improves the intelligent level of visitor management, but also enhances the security protection ability of enterprises or units, and provides a more convenient and safe access experience for visitors. At the same time, it also provides strong support for the management decision of enterprises or units, and guarantees the normal operation of the visitor management system and the safety of the access area.

[0093] The preferred embodiments of the present application disclosed above are only used to help explain the present application. The preferred embodiments do not describe all the details, nor limit the application to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of the present application. The present application selects and describes these embodiments in order to better explain the principles and practical applications of the present application, so that those skilled in the art can well understand and utilize the present application. Any modification or variation that does not deviate from the structure of the present application or exceed the scope defined by the present application shall belong to the protection scope of the present application.

Claims

1. A method for real-time tracking of visitors based on artificial intelligence, characterized in that: include: Obtain the reasons for and status of visitors, divide the visiting locations into regions, including target areas, key areas and other areas, and plan the visiting routes to obtain each feasible visiting route. The feature data of each feasible access route is obtained and analyzed to obtain the recommended value of each feasible access route. The current action route of the visitor is determined and processed to obtain the recommended value of the current action route. Collect the status data of the tag devices of visitors, and process it to obtain the abnormal status assessment value of the tag devices; The system monitors the movement status data of visitors, processes it to obtain anomaly assessment values ​​for visitor movement, and comprehensively analyzes the current movement route recommendation value, tag device status anomaly assessment value, and visitor movement anomaly assessment value to obtain visitor anomaly indicators. Early warning feedback is then provided based on the visitor anomaly indicators. The process of acquiring and analyzing the feature data of each feasible access route to obtain a recommended value for each feasible access route is as follows: The characteristic data of each feasible access route includes route length, monitoring device density, and number of key areas passed through; Obtain the shortest route length for each feasible access route, and extract the density of critical monitoring devices and the number of critical key areas from the visitor management database. A comprehensive analysis is then conducted to obtain the recommended value for each feasible access route. The process of determining the visitor's current route and obtaining a recommended route value is as follows: The actual route of the visitor is compared with each feasible route, the length of the actual route of the visitor is extracted, and the overlap length between the actual route of the visitor and each feasible route is marked as the overlap length of each feasible route. Divide the overlap length of each feasible access route by the actual travel length of the visitor to obtain the overlap degree of each feasible access route. Sort the overlap of each feasible access route in descending order, and extract the feasible access route corresponding to the highest overlap as the visitor's current action route; The recommended value of the current route is obtained by multiplying the overlap of the visitor's current route with the recommended value of the current route. The processing yields an assessment value for abnormal visitor behavior, and the specific analysis process is as follows: The visitor's movement status data includes the length of repeated movement routes, the number of abnormal stops, the time of each abnormal stop, and the location of each abnormal stop. Extract the straight-line distance between each abnormal dwell location and the nearest critical area, and mark it as the abnormal dwell distance; The length of critically repeated action routes, the number of critically abnormal stops, the time of critically abnormal stops, and the distance of critically abnormal stops are obtained from the visitor management database, and a comprehensive analysis is performed to obtain the abnormal behavior assessment value of the visitors.

2. The method for real-time tracking of visitors based on artificial intelligence according to claim 1, characterized in that: The specific process of dividing the access locations into regions is as follows: Extract the areas that visitors need to access from the reasons for their visits, and mark them as target areas; Obtain the required access level for each area in the visited location, compare the required access level of each area with the identity level of the visitor, and mark the area as a critical area if the required access level of a certain area is greater than the identity level of the visitor. If the access requirement level of a certain area is higher than the identity level of the visitor, then the area will be marked as another area.

3. The method for real-time tracking of visitors based on artificial intelligence according to claim 1, characterized in that: The process yields an abnormal status assessment value for the tag device, specifically as follows: The tag device status data includes vibration intensity, signal strength, device response time, and data packet loss rate; The critical vibration intensity, critical signal intensity, critical equipment response time, and critical data packet loss rate are obtained from the visitor management database, and a comprehensive analysis is performed to obtain the abnormal status assessment value of the tag equipment.

4. The method for real-time tracking of visitors based on artificial intelligence according to claim 1, characterized in that: The comprehensive analysis yielded abnormal indicators for visitors, and the specific analysis process is as follows: Based on the current recommended route value, the abnormal status assessment value of the tag device, and the abnormal behavior assessment value of the visitor, a comprehensive analysis is conducted to obtain the abnormal visitor index, which is used to quantitatively assess the abnormal status of the visitor.

5. The method for real-time tracking of visitors based on artificial intelligence according to claim 1, characterized in that: The process of issuing early warnings based on abnormal indicators of visitors is as follows: The abnormal indicators of visitors are compared with the preset abnormal indicator thresholds of visitors in the visitor management database. If the abnormal indicator of visitors is less than the abnormal indicator threshold, the visitors are marked as normal visitors. If the abnormal indicator of visitors is greater than or equal to the abnormal indicator threshold, the visitors are marked as abnormal visitors, and an early warning is issued to the abnormal visitors.

6. The method for real-time tracking of visitors based on artificial intelligence according to claim 3, characterized in that: The method for obtaining the abnormal status assessment value of the tag device is as follows: ； In the formula, This represents the abnormal status assessment value of the tag device, where e represents the natural constant. Indicates the signal strength of the tag device. Indicates the critical signal strength. Indicates the vibration intensity of the tagging equipment. Indicates the critical vibration intensity. This indicates the device response time of the tag device. This indicates the response time of critical equipment. This indicates the data packet loss rate of the tag device. This represents the critical data packet loss rate. This indicates the device status anomaly impact factor corresponding to the preset signal strength. This represents the abnormal equipment condition impact factor corresponding to the preset vibration intensity. This represents the device status anomaly impact factor corresponding to the preset device response time. This represents the device status anomaly impact factor corresponding to the preset data packet loss rate.

7. A control system applying the real-time visitor tracking method based on artificial intelligence as described in any one of claims 1-6, characterized in that: include: The area division module is used to obtain the reason for the visit and the identity level of the visitor, divide the visited location into areas, including target area, key area and other areas, and plan the visit route to obtain each feasible visit route. The movement route tracking module is used to acquire and analyze the feature data of each feasible access route, obtain the recommended value of each feasible access route, determine the current movement route of the visitor, and process it to obtain the recommended value of the current movement route. The equipment status monitoring module is used to collect the status data of the tag equipment of visitors and process it to obtain the abnormal status assessment value of the tag equipment. The early warning feedback module is used to monitor the movement status data of visitors, process it to obtain the visitor movement abnormality assessment value, and comprehensively analyze the visitor abnormality index based on the current movement route recommendation value, the tag device status abnormality assessment value, and the visitor movement abnormality assessment value to obtain the visitor abnormality index, and issue early warning feedback based on the visitor abnormality index.

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