Real-time visitor tracking method and control system based on artificial intelligence

Abstract

The invention discloses a visitor real-time tracking method and control system based on artificial intelligence, and relates to the technical field of personnel tracking management, and the method comprises the steps: obtaining a visiting reason and an identity level of a visitor, carrying out the regional division of a visiting place, including a target region, a key region and other regions, and carrying out the planning of a visiting route, obtaining each feasible access route; obtaining feature data of each feasible access route, analyzing the feature data to obtain a recommended value of each feasible access route, judging a current action route of the visitor, and processing to obtain a recommended value of the current action route; collecting label equipment state data of a visitor, and processing to obtain a label equipment state abnormity evaluation value; and monitoring the action state data of the visitor, processing to obtain an abnormal action evaluation value of the visitor, comprehensively analyzing to obtain an abnormal index of the visitor, and performing early warning feedback according to the abnormal index of the visitor.

Description

Technical Field

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

[0002] As the scale of power grid enterprises continues to expand and their business becomes increasingly complex, the flow of personnel becomes more frequent, and the demand for visits from various outsiders increases significantly, which not only brings more communication opportunities to enterprises, but also increases security risks. As a key department of energy supply, the security of internal facilities and data of power grid enterprises is of vital importance. In order to ensure the safety and order within the unit, strengthening the management of visitors and preventing unauthorized personnel from entering sensitive areas has become an important part of the security of power grid enterprises.

[0003] For example, the invention patent with announcement number CN111340998B is an intelligent office management system based on face recognition function, including an office building central processor, an access control system and internal employee office computers connected to the office building central processor through a network. The access control system includes an entrance access control and an exit access control installed at the entrance and exit of the office building. The entrance access control and the exit access control are arranged in a row. A visitor entry and exit management cabinet is set between the entrance access control and the exit access control. An entrance face recognition device is set at the entrance access control, and an exit face recognition device is set at the exit access control. A handheld terminal is set inside the visitor entry and exit management cabinet.

[0004] For example, the invention patent with announcement number CN111179484B is a visitor management method, device, system and storage medium. The method includes: the first terminal generates a visitor identity electronic certificate according to the first identity information and the first access request input by the visitor, the visitor management system obtains the visitor identity electronic certificate and the visitor biometric information, confirms the visitor's identity according to the visitor identity electronic certificate and the visitor biometric information, uses the authentication management system to verify the visitor's behavior after the identity is confirmed, sends an access request to the authentication employee that the visitor needs to visit when the behavior verification result shows that there is no preset behavior record, and maintains or releases the access restriction operation on the visitor based on the approval result of the access request.

[0005] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, it was found that the above technology has at least the following technical problems: currently the management of visitors mainly stays in the review and registration of access control, but there is a risk of visitors accidentally entering important areas during their free activities, which may cause production safety hazards and adverse effects. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a real-time visitor tracking method and control system based on artificial intelligence, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: The first aspect of the present invention provides a real-time tracking method for visitors based on artificial intelligence, including: obtaining the visitor's reason for visit and identity level, dividing the visit location into regions, including target areas, key areas and other areas, and planning the visit route to obtain various feasible visit routes.

[0008] Obtain the characteristic data of each feasible access route for analysis, obtain the recommended value of each feasible access route, determine the current action route of the visitor, and process it to obtain the recommended value of the current action route.

[0009] The tag device status data of visitors is collected and processed to obtain the abnormal tag device status assessment value.

[0010] The action status data of visitors is monitored and processed to obtain the abnormal action assessment value of visitors. According to the current action route recommendation value, the abnormal status assessment value of the tag device and the abnormal action assessment value of the visitor, the abnormal index of the visitor is obtained through comprehensive analysis, and early warning feedback is given based on the abnormal index of the visitor.

[0011] As a further method, the visited places are divided into regions, and the specific process is: extract the required visited areas from the reasons for the visitors' visits and mark them as target areas; obtain the required access levels of each area in the visited places, and compare the required access levels of each area with the identity levels of the visitors. If the required access level of an area is greater than the identity level of the visitor, the area is marked as a key area; if the required access level of an area is greater than the identity level of the visitor, the area is marked as other areas.

[0012] As a further method, the characteristic data of each feasible access route is obtained and analyzed to obtain the recommended value of each feasible access route. The specific analysis process is: the characteristic data of each feasible access route includes route length, monitoring equipment density and the number of critical areas passed; the shortest route length of each feasible access route is obtained, and the critical monitoring equipment density and the critical number of key areas are extracted from the visitor management database, and the recommended value of each feasible access route is obtained through comprehensive analysis.

[0013] As a further method, the current route of the visitor is determined and processed to obtain a recommended value of the current route. The specific process is: the actual route of the visitor is compared with each feasible route for overlap, 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; the overlap length of each feasible route is divided by the length of the actual route of the visitor to obtain the overlap degree of each feasible route; the overlap degrees of each feasible route are sorted in order from large to small, and the feasible route corresponding to the maximum feasible route overlap degree is extracted and marked as the current route of the visitor; the overlap degree of the feasible route of the visitor's current route is multiplied by the recommended value of the feasible route of the visitor's current route to obtain the recommended value of the current route.

[0014] As a further method, the processing obtains the abnormal status evaluation value of the tag device, and the specific process is: the tag device status data includes vibration intensity, signal strength, device response time and data packet loss rate; the critical vibration intensity, critical signal strength, critical device response time and critical data packet loss rate are obtained from the visitor management database, and the abnormal status evaluation value of the tag device is obtained through comprehensive analysis.

[0015] As a further method, the processing obtains the abnormal behavior evaluation value of the visitor, and the specific analysis process is: the behavior status data of the visitor, including the length of the repeated action route, the number of abnormal stops, the time of each abnormal stop, and the location of each abnormal stop; the straight-line distance between each abnormal stop location and the nearest key area is extracted, and marked as the distance of each abnormal stop; the critical repeated action route length, critical abnormal stop number, critical abnormal stop time and critical abnormal stop distance are obtained from the visitor management database, and the abnormal behavior evaluation value of the visitor is obtained through comprehensive analysis.

[0016] As a further method, the comprehensive analysis obtains the visitor abnormality index. The specific analysis process is: based on the current action route recommendation value, the tag device status abnormality assessment value and the visitor action abnormality assessment value, the visitor abnormality index is obtained through comprehensive analysis. The visitor abnormality index is used to quantitatively evaluate the abnormal state of the visitor.

[0017] As a further method, the described early warning feedback is performed based on the abnormal indicators of the visitor. The specific process is: comparing the abnormal indicators of the future visitor with the abnormal indicator threshold of the future visitor preset in the visitor management database. If the abnormal indicator of the future visitor is less than the abnormal indicator threshold of the future visitor, the future visitor is marked as a normal person. If the abnormal indicator of the future visitor is greater than or equal to the abnormal indicator threshold of the future visitor, the future visitor is marked as an abnormal person, and early warning feedback is performed on the abnormal person.

[0018] The second aspect of the present invention provides a real-time tracking and control system for visitors based on artificial intelligence, including: an area division module for obtaining the visitor's reason for visit and identity level, dividing the visit location into areas, including target areas, key areas and other areas, and planning the visit route to obtain various feasible visit routes.

[0019] The action route tracking module is used to obtain the characteristic data of each feasible access route for analysis, obtain the recommended value of each feasible access route, determine the current action route of the visitor, and process to obtain the recommended value of the current action route.

[0020] The device status monitoring module is used to collect the tag device status data of visitors and obtain the tag device status abnormality assessment value after processing.

[0021] The early warning feedback module is used to monitor the action status data of visitors, obtain the visitor's action abnormality assessment value after processing, and obtain the visitor's abnormal index through comprehensive analysis based on the current action route recommendation value, tag device status abnormal assessment value and visitor action abnormal assessment value, and provide early warning feedback based on the visitor's abnormal index.

[0022] Compared with the prior art, the embodiments of the present invention have at least the following beneficial effects: (1) The present invention provides a method and control system for real-time tracking of visitors based on artificial intelligence, thereby achieving refined management and efficient monitoring of visitors. Real-time tracking of visitors based on tag devices not only improves the intelligence level of visitor management, but also enhances the security prevention capabilities of enterprises or units, providing visitors with a more convenient and safe access experience. At the same time, it also provides strong support for the management decisions of enterprises or units, and ensures the normal operation of the visitor management system and the safety of the access area.

[0023] (2) The present invention can improve the accuracy of the visitor's route by comparing the actual route of the visitor with each feasible access route. Accurately judging the visitor's current route helps to better manage the visit. Guidance measures can be adjusted in time according to the visitor's actual route to ensure that the visitor can reach the destination smoothly. At the same time, it can also timely warn and handle abnormal situations such as deviation from the route, thereby ensuring the safety and order of the visit area.

[0024] (3) The present invention evaluates the abnormal state of the tag device by comprehensively analyzing the vibration intensity, signal strength, 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. Timely measures can be taken at the early stage of equipment problems to prevent inaccurate visitor information acquisition caused by equipment failure and avoid adverse effects caused by visitors mistakenly entering important areas.

[0025] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a schematic diagram of the method flow of the present invention.

[0027] Figure 2 It is a schematic diagram of system module connection of the present invention.

[0028] Figure 3 Schematic diagram of the functional relationship between the visitor behavior abnormality assessment value and the visitor abnormality index involved in an embodiment of the present invention. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0030] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0031] Reference Figure 1 As shown, the first aspect of the present invention provides a real-time tracking method for visitors based on artificial intelligence, including: obtaining the visitor's reason for visit and identity level, dividing the visit location into areas, including target areas, key areas and other areas, and planning the visit route to obtain various feasible visit routes.

[0032] It should be understood that the reasons for the visit in this embodiment include but are not limited to business cooperation, technical exchanges and administrative affairs. Different reasons for the visit will have fixed required visit areas. For example, business cooperation requires visits to production facilities and R&D centers, and technical exchanges require visits to specific meeting rooms. Therefore, obtaining the reason for the visit of the visitor can directly obtain the visitor's required visit area.

[0033] It should be understood that the visit location in this embodiment refers to the physical space scope where the visitor conducts the visit activity, which can be an independent building, a park, or a specific part of a larger building, such as the work area of ​​a certain enterprise or unit. According to the specific work content, the required access level can be preset for different areas in the visit location, and the identity level of the visitor can be determined according to the reason for the visit of the visitor. It can be understood that the work content and the required access level, as well as the reason for the visit and the identity level are preset in a one-to-one correspondence in the visit management database.

[0034] Obtain the characteristic data of each feasible access route for analysis, obtain the recommended value of each feasible access route, determine the current route of the visitor, and process it to obtain the recommended value of the current route; collect the tag device status data of the visitor, and obtain the tag device status abnormality assessment value after processing.

[0035] It should be understood that the tag device in this embodiment refers to a visitor ID card issued to a visitor, which has a unique identification and positioning function and usually exists in the form of an active tag.

[0036] The action status data of visitors is monitored and processed to obtain the abnormal action assessment value of visitors. According to the current action route recommendation value, the abnormal status assessment value of the tag device and the abnormal action assessment value of the visitor, the abnormal index of the visitor is obtained through comprehensive analysis, and early warning feedback is given based on the abnormal index of the visitor.

[0037] Specifically, the visited places are divided into regions, and the specific process is: extract the required visit areas from the reasons for the visitors' visits and mark them as target areas; obtain the required access levels of each area in the visited places, and compare the required access levels of each area with the identity levels of the visitors. If the required access level of an area is greater than the identity level of the visitor, the area is marked as a key area; if the required access level of an area is greater than the identity level of the visitor, the area is marked as other areas.

[0038] In a specific embodiment, the required access levels of an area include advanced, intermediate and public. It is understandable that the confidentiality requirement of the advanced level is greater than that of the intermediate level, and the confidentiality requirement of the intermediate level is greater than that of the public. The identity levels of visitors include Class A, Class B and Class C, where Class A refers to areas that are allowed to access advanced and below, Class B refers to areas that are allowed to access intermediate and below, and Class C refers to areas that are allowed to access public levels. It is known that the required access level of the R&D center is advanced, the required access level of the conference room is intermediate, and the required access level of the leisure area is public.

[0039] The reason for a visitor's visit is business cooperation negotiation. In the visitor management database, the area that needs to be visited for business cooperation negotiation is the conference room. The conference room is marked as the target area, and the visitor is assigned a Class B identity level. At the same time, the leisure area is marked as other areas, and the R&D center is marked as a key area. The visitor is allowed to access the conference room and leisure area, but not the R&D center.

[0040] Specifically, characteristic data of each feasible access route is obtained for analysis to obtain recommended values ​​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 the number of critical areas passed through; 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 visitor management database, and the recommended values ​​of each feasible access route are obtained through comprehensive analysis.

[0041] It should be understood that in this embodiment, the monitoring device density refers to the number of monitoring devices per unit area (such as 10m²). The critical monitoring device density refers to the minimum monitoring device density that can cover the access route. The number of critical key areas refers to the maximum number of key areas that the access route is allowed to pass through.

[0042] In a specific embodiment, the method for obtaining the recommended value of each feasible access route is as follows: ; In the formula, represents the recommended value of the ith feasible access route, e represents a natural constant, represents the density of monitoring devices on the ith feasible access route, represents the density of critical monitoring equipment, represents the route length of the i-th feasible access route, represents the shortest route length, represents the number of key areas passed by the i-th feasible access route, represents the number of critical regions, Indicates the recommended impact factor of the access route corresponding to the preset monitoring device density. Indicates the recommended access route impact factor corresponding to the preset route length. It represents the recommended influencing factor of the access route corresponding to the preset number of key areas passed through, i represents the number of each feasible access route, i=1,2,3,...,n, and n represents the total number of feasible access routes.

[0043] In this embodiment , and The recommended impact factors of access routes corresponding to the monitoring device density, route length and number of key areas passed through preset in the visit management database respectively represent the numerical values ​​of the degree of influence of the monitoring device density, route length and number of key areas passed through on the recommended values ​​of feasible access routes, and can be directly obtained from the visit management database when used. For example, a mapping set is formed by combining the area occupied by the access location with the recommended impact factors of access routes corresponding to the monitoring device density, route length and number of key areas passed through preset in the visit management database, and the recommended impact factors of access routes corresponding to the monitoring device density, route length and number of key areas passed through are obtained according to the area occupied by the access location input mapping set, wherein the mapping relationship is a one-to-one relationship. In this embodiment, the value range of the impact factor is between 0 and 1.

[0044] It should be understood that the recommended value of the feasible access route in this embodiment is used to quantitatively evaluate the recommendation degree of the feasible access route. The greater the density of monitoring equipment, or the smaller the route length and the number of key areas passed through, the larger the corresponding recommended value of the feasible access route, indicating that the access route is more recommended as an action route.

[0045] The algorithm of this embodiment combines the density of monitoring equipment, the length of the route, and the number of key areas passed through, and comprehensively analyzes to obtain the recommended value of the feasible access route. In this formula, there is a correlation between the density of monitoring equipment, the length of the route, and the number of key areas passed through. The longer the route length, the more monitoring equipment is needed to cover it. At the same time, there is a positive correlation between the density of monitoring equipment and the number of key areas passed through. The greater the density of monitoring equipment, the stronger the security and monitorability of the route. Routes with a higher density of monitoring equipment can better record the movement trajectory of visitors and detect abnormal situations in a timely manner. The shorter the route length, the faster visitors can reach their destination, saving time and improving access efficiency. The fewer the number of key areas passed through, the less opportunities visitors have to contact sensitive areas, reducing security risks.

[0046] Specifically, the current action route of the visitor is determined, and the recommended value of the current action route is obtained through processing. The specific process is as follows: the actual action route of the visitor is compared with each feasible access route for overlap, the actual action route length of the visitor is extracted, and the overlap length 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 actual action route length of the visitor to obtain the overlap degree of each feasible access route; the overlap degrees of each feasible access route are sorted in order from large to small, 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.

[0047] In a specific embodiment, the actual movement route of the visitor is tracked through tag signals. With the entrance as the origin, the horizontal coordinate is established in the east-west direction, and the vertical coordinate is established in the north-south direction. A plane coordinate system of the visited location is constructed, and the data of the actual movement route and the feasible visit route are mapped to the same coordinate system for comparison.

[0048] It should be understood that there are two situations for judging the current route of the visitor in this embodiment. If the maximum feasible access route overlap is unique, the feasible access route corresponding to the maximum feasible access route overlap is marked as the current route of the visitor; if the maximum feasible access route overlap is not unique, each feasible access route corresponding to the maximum feasible access route overlap is marked as a high overlap route, and further sorted according to the recommended value of each feasible access route, and the high overlap route corresponding to the maximum feasible access route recommended value is marked as the current route of the visitor. Since the recommended value of the feasible access route in this embodiment is obtained through a comprehensive analysis of multiple factors, the probability of the same is low, so the situation where the maximum feasible access route recommended value corresponds to multiple high overlap routes is not considered.

[0049] The recommended value of the current action route is obtained by multiplying the overlap degree of the feasible access routes of the current action route of the visitor by the recommended value of the feasible access routes of the current action route of the visitor.

[0050] It should be understood that in this embodiment, by comparing the actual route of the visitor with each feasible access route, the current route of the visitor can be judged more accurately, avoiding misjudgment caused by relying solely on a certain factor, thereby improving the accuracy of the visitor's route judgment. Accurately judging the current route of the visitor helps to better manage the visit, and can timely adjust the guidance measures according to the actual route of the visitor to ensure that the visitor can reach the destination smoothly. At the same time, it can also timely warn and handle abnormal situations such as possible deviations from the route to ensure the safety and order of the visit area.

[0051] In this embodiment, the current action route recommendation value is used to quantify the normality of the visitor's current action route. The smaller the overlap of the feasible access route of the visitor's current action route and the feasible access route recommendation value, the smaller the corresponding current action route recommendation value, indicating that the visitor deviates more from the allowed access route.

[0052] Specifically, the tag device status abnormality evaluation value is obtained by processing, and the specific process is: the tag device status data includes vibration intensity, signal strength, device response time and data packet loss rate.

[0053] It should be understood that in this embodiment, the vibration intensity can be collected by an acceleration sensor. The device response time refers to the time required for the tag device to respond to the base station command, and the data packet loss rate refers to the ratio of the number of data packets lost by the tag device during data transmission to the total number of data packets sent. The signal strength, device response time and data packet loss rate can be directly obtained through the software system of the base station, or extracted from the relevant information of the command sending and response recorded in the system logs of the base station and the tag device.

[0054] 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 the abnormal status assessment value of the tag device is obtained through comprehensive analysis.

[0055] It should be understood that in this embodiment, the critical vibration intensity refers to the maximum vibration intensity that may cause damage to the tag device. The critical signal strength refers to the minimum signal strength that may cause the base station to be unable to accurately receive the broadcast information of the tag device. The critical device response time refers to the maximum response time allowed by the device. When the device response time exceeds the maximum allowed response time, it can be judged as a 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 system's acquisition and processing of visitor information, and may cause problems such as inaccurate positioning and incomplete trajectory.

[0056] In a specific embodiment, the tag device state abnormality evaluation value is obtained in the following manner: ; In the formula, represents the abnormal evaluation value of the tag device status, e represents a natural constant, Indicates the signal strength of the tag device. represents the critical signal strength, Indicates the vibration intensity of the tag device. Indicates the critical vibration intensity, Indicates the device response time of the tag device, Indicates the critical device response time, Indicates the data packet loss rate of the tag device. Indicates the critical data packet loss rate, Indicates the abnormal device status impact factor corresponding to the preset signal strength. Indicates the abnormal impact factor of the equipment status corresponding to the preset vibration intensity. Indicates the device status abnormality impact factor corresponding to the preset device response time. Indicates the device status abnormality impact factor corresponding to the preset data packet loss rate.

[0057] In this embodiment , , and The device status abnormality impact factors corresponding to the signal strength, vibration intensity, device response time and data packet loss rate preset in the visitor management database respectively represent the numerical values ​​of the degree of influence of the signal strength, vibration intensity, device response time and data packet loss rate on the tag device status abnormality evaluation value, and can be directly obtained from the visitor management database when used. For example, a mapping set is formed by combining the area of ​​the visit location with the device status abnormality impact factors corresponding to the signal strength, vibration intensity, device response time and data packet loss rate preset in the visitor management database, and the device status abnormality impact factors corresponding to the signal strength, vibration intensity, device response time and data packet loss rate are obtained according to the mapping set input based on the area of ​​the visit location, wherein the mapping relationship is a one-to-one relationship. In this embodiment, the value range of the impact factor is between 0 and 1.

[0058] It should be understood that in this embodiment, the tag device state abnormality evaluation value is used to quantitatively evaluate the degree of abnormal state of the tag device. The smaller the signal strength, or the greater the vibration intensity, device response time and data packet loss rate, the larger the corresponding tag device state abnormality evaluation value, indicating that the state of the tag device is more abnormal.

[0059] The algorithm of this embodiment combines signal strength, vibration intensity, device response time and data packet loss rate, and comprehensively analyzes to obtain the abnormal evaluation value of the tag device status. In this formula, there is a correlation between signal strength, vibration intensity, device response time and data packet loss rate. 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 intensity, signal strength, device response time and data packet loss rate, the degree of abnormal status of the tag device can be quantitatively evaluated, which is convenient for timely detection of abnormal conditions of the equipment and taking corresponding measures, such as replacing equipment, repairing equipment, etc., to ensure the normal operation of the visitor behavior management system.

[0060] In a specific embodiment, abnormal tag devices are screened according to the abnormal evaluation value of the tag device state, and the abnormal evaluation value of the tag device state is compared with the abnormal evaluation threshold of the tag device state preset in the visitor management database. If the abnormal evaluation value of the tag device state is greater than or equal to the abnormal evaluation threshold of the tag device state, the tag device is marked as an abnormal tag device, and the management personnel are notified to contact the visitor corresponding to the abnormal tag device in time, and the individual tag device is replaced and repaired. If the abnormal evaluation value of the tag device state is less than the abnormal evaluation threshold of the tag device state, the tag device is marked as a normal tag device without special processing. 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. When the number of abnormal tag devices within the preset monitoring period is greater than or equal to two, all tag devices need to be repaired.

[0061] In this embodiment, the abnormal state of the tag device is evaluated by comprehensively analyzing the vibration intensity, signal strength, 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. Timely measures can be taken in the early stage of equipment problems to prevent adverse consequences such as inaccurate visitor information acquisition and visitors mistakenly entering important areas due to equipment failures, avoid adverse effects caused by visitors mistakenly entering important areas, and ensure the normal operation of the visitor management system and the safety of the access area.

[0062] Specifically, the visitor's behavior abnormality assessment value is obtained by processing, and the specific analysis process is as follows: the visitor's behavior status data includes the length of repeated action routes, the number of abnormal stops, the time of each abnormal stop, and the location of each abnormal stop.

[0063] It should be understood that the length of the repeated action route in this embodiment refers to the length of the visitor's repeated action route, that is, the length of the visitor's repeated walking on the route he has walked. The number of abnormal stops refers to the number of times the visitor has stayed for a long time (such as more than 3 minutes) on the access route except the target area. The base station will receive the transmission signal of the tag device in real time. When the signal transmission position of the tag device overlaps, the timer of the base station starts counting. When the signal transmission position stops overlapping, the timer of the base station stops counting and records the signal transmission position and the stay time at the signal transmission position.

[0064] The straight-line distance between each abnormal stop location and the nearest key area is extracted and marked as the abnormal stop distance. The critical repeated action route length, critical abnormal stop number, critical abnormal stop time and critical abnormal stop distance are obtained from the visitor management database, and the visitor's action abnormality assessment value is obtained through comprehensive analysis.

[0065] It should be understood that, in this embodiment, the critical repeated action route length refers to the maximum allowed repeated action route length, the critical abnormal stop number refers to the maximum allowed abnormal stop number, the critical abnormal stop time refers to the maximum stop time allowed for a single stop, and the critical abnormal stop distance refers to the minimum allowable distance between the abnormal stop location point and the nearest critical area during the abnormal stop.

[0066] In a specific embodiment, the method for obtaining the visitor's behavior abnormality assessment value is as follows: ; In the formula, represents the visitor's abnormal behavior assessment value, e represents a natural constant, represents the tth abnormal stay distance, Indicates the critical abnormal stay distance, Indicates the length of the visitor's repeated action route, represents the critical repeated action route length, Indicates the number of abnormal stays of visitors. Indicates the number of critical abnormal stays, represents the tth abnormal residence time, represents the critical abnormal residence time, Indicates the action abnormality assessment impact factor corresponding to the preset abnormal stay distance, Indicates the action anomaly assessment impact factor corresponding to the preset repeated action route length, Indicates the action abnormality assessment impact factor corresponding to the preset abnormal stay times, It represents the action abnormality assessment impact factor corresponding to the preset abnormal stay time, t represents the number of each abnormal stay, t=1,2,3,...,s, s represents the total number of abnormal stays.

[0067] In this embodiment , , and The abnormal behavior assessment impact factors corresponding to the abnormal stay distance, repeated action route length, abnormal stay number and abnormal stay time preset in the visitor management database respectively represent the numerical values ​​of the degree of influence of the abnormal stay distance, repeated action route length, abnormal stay number and abnormal stay time on the abnormal behavior assessment value of the visitor, and can be directly obtained from the visitor management database when used. For example, a mapping set is formed by combining the area of ​​the visit location with the abnormal behavior assessment impact factors corresponding to the abnormal stay distance, repeated action route length, abnormal stay number and abnormal stay time preset in the visitor management database, and the mapping set is input according to the area of ​​the visit location to obtain the abnormal behavior assessment impact factors corresponding to the abnormal stay distance, repeated action route length, abnormal stay number and abnormal stay time, wherein the mapping relationship is a one-to-one relationship. In this embodiment, the value range of the impact factor is between 0 and 1.

[0068] It should be understood that in this embodiment, the visitor's behavior abnormality assessment value is used to quantitatively assess the degree of abnormality of the visitor's behavior. The smaller the abnormal stay distance, or the longer the repeated action route, the number of abnormal stops and the abnormal stay time, the larger the corresponding visitor's behavior abnormality assessment value, indicating that the visitor's behavior is more abnormal.

[0069] The algorithm of this embodiment combines the abnormal stay distance, the length of the repeated action route, the number of abnormal stops and the abnormal stay time, and comprehensively analyzes to obtain the visitor's action abnormality evaluation value. In this formula, there is a correlation between the abnormal stay distance, the length of the repeated action route, the number of abnormal stops and the abnormal stay time. The more abnormal stops, it usually means that the visitor frequently stops in some areas, which may lead to a longer abnormal stay time and may also make the abnormal stay distance smaller. At the same time, frequent stops on repeated routes may increase the length of the repeated action route. Comprehensively considering the abnormal stop distance, the length of the repeated action route, the number of abnormal stops and the abnormal stay time, it is possible to quantitatively evaluate the degree of abnormal behavior of the visitor, facilitate timely detection of the abnormal behavior of the visitor, and take corresponding measures, such as strengthening monitoring, questioning the visitor, etc., to ensure the safety and normal order of the visited area.

[0070] Specifically, a comprehensive analysis is performed to obtain an abnormal index of a visitor. The specific analysis process is as follows: based on the current action route recommendation value, the tag device status abnormal evaluation value and the visitor action abnormal evaluation value, a comprehensive analysis is performed to obtain an abnormal index of a visitor. The abnormal index of a visitor is used to quantitatively evaluate the abnormal status of a visitor.

[0071] In a specific embodiment, the method for obtaining the visitor abnormality indicator is: ; In the formula, Indicates abnormal indicators of visitors. Indicates the recommended value of the current action route. Indicates the abnormal evaluation value of the tag device status. Indicates the visitor's abnormal behavior assessment value. Indicates the personnel abnormality assessment impact factor corresponding to the preset current action route recommendation value, Indicates the personnel abnormality assessment impact factor corresponding to the preset tag device status abnormality assessment value, Indicates the personnel abnormality assessment impact factor corresponding to the preset visitor action abnormality assessment value.

[0072] In this embodiment , and The personnel anomaly assessment impact factors corresponding to the current action route recommendation value, tag device status abnormality assessment value and visitor action abnormality assessment value preset in the visitor management database respectively represent the numerical values ​​of the degree of influence of the current action route recommendation value, tag device status abnormality assessment value and visitor action abnormality assessment value on the visitor abnormality index respectively, and can be directly obtained from the visitor management database when used. For example, a mapping set is formed by combining the area occupied by the visit location with the personnel anomaly assessment impact factors corresponding to the current action route recommendation value, tag device status abnormality assessment value and visitor action abnormality assessment value preset in the visitor management database, and the mapping set is input according to the area occupied by the visit location to obtain the personnel anomaly assessment impact factors corresponding to the current action route recommendation value, tag device status abnormality assessment value and visitor action abnormality assessment value, wherein the mapping relationship is a one-to-one relationship. In this embodiment, the value range of the impact factor is between 0 and 1.

[0073] like Figure 3 As shown, in a specific embodiment, =1, =0.4, = = 0.3, when =0.5, the functional relationship between the visitor's action abnormality assessment value and the visitor's abnormality index is shown in a; when =0.75, the functional relationship between the visitor's action abnormality assessment value and the visitor's abnormality index is shown in b; when =1.2, the functional relationship between the visitor's action abnormality assessment value and the visitor's abnormality index is shown in c. In this embodiment, the smaller the current action route recommendation value, or the larger the tag device state abnormality assessment value and the visitor's action abnormality assessment value, the larger the corresponding visitor abnormality index, indicating that the visitor's abnormality is higher.

[0074] The algorithm of this embodiment combines the current action route recommendation value, the tag device status abnormality assessment value and the visitor action abnormality assessment value, and comprehensively analyzes to obtain the visitor action abnormality assessment value. In this formula, there is a correlation between the current action route recommendation value, the tag device status abnormality assessment value and the visitor action abnormality assessment value. The smaller the current action route recommendation value, the greater the factors that may be unfavorable to access management in the action route selected by the visitor, resulting in an increase in the visitor action abnormality assessment value. At the same time, the abnormal tag device status may lead to inaccurate monitoring and recording of the visitor's actions. Comprehensive analysis of the current action route recommendation value, the tag device status abnormality assessment value and the visitor action abnormality assessment value can accurately quantify the degree of abnormality of the visitor.

[0075] Specifically, early warning feedback is given based on the abnormal indicators of the visitor. The specific process is: compare the abnormal indicators of the visitor with the abnormal indicator thresholds of the visitor preset in the visitor management database. If the abnormal indicators of the visitor are less than the abnormal indicator threshold of the visitor, the visitor is marked as a normal person. If the abnormal indicators of the visitor are greater than or equal to the abnormal indicator threshold of the visitor, the visitor is marked as an abnormal person, and early warning feedback is given to the abnormal person.

[0076] In a specific embodiment, early warning feedback is provided to abnormal personnel. When the abnormal index of a visitor is greater than or equal to the abnormal index threshold of the visitor, the identity information of the abnormal personnel is backed up in the visitor management database, and an alarm is issued to the abnormal personnel through the visitor ID card. When the abnormal index of the visitor is still greater than or equal to the abnormal index threshold of the visitor after three alarms are issued, the management personnel is notified to supervise the abnormal personnel.

[0077] Reference Figure 2 As shown, the second aspect of the present invention provides a real-time tracking and control system for visitors based on artificial intelligence, including: an area division module, a movement route tracking module, an equipment status monitoring module, an early warning feedback module and a visitor management database.

[0078] The area division module is used to obtain the visitor's reason for visit and identity level, divide the visit location into regions, including target areas, key areas and other areas, and plan the visit route to obtain various feasible visit routes.

[0079] The action route tracking module is used to obtain the characteristic data of each feasible access route for analysis, obtain the recommended value of each feasible access route, determine the current action route of the visitor, and process to obtain the recommended value of the current action route.

[0080] The device status monitoring module is used to collect the tag device status data of visitors and obtain the tag device status abnormality assessment value after processing.

[0081] The early warning feedback module is used to monitor the action status data of visitors, obtain the visitor's action abnormality assessment value after processing, and obtain the visitor's abnormal index through comprehensive analysis based on the current action route recommendation value, tag device status abnormal assessment value and visitor action abnormal assessment value, and provide early warning feedback based on the visitor's abnormal index.

[0082] The visitor management database is used to store visitor tracking management data, including critical monitoring equipment density, critical key area number, critical vibration intensity, critical signal intensity, critical equipment response time, critical data packet loss rate, etc. The data in the visitor management database can be integrated with other related systems (such as access control systems, monitoring systems, etc.), and relevant data can be obtained from other systems in real time or regularly through API interfaces or data synchronization.

[0083] In a specific embodiment, the present invention provides a method and control system for real-time tracking of visitors based on artificial intelligence, thereby achieving refined management and efficient monitoring of visitors. Real-time tracking of visitors based on tag devices not only improves the intelligence level of visitor management, but also enhances the security prevention capabilities of enterprises or units, providing visitors with a more convenient and safe access experience. At the same time, it also provides strong support for the management decisions of enterprises or units, and ensures the normal operation of the visitor management system and the safety of the access area.

[0084] 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 the specific implementation methods described. 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 technicians in the relevant technical field can understand and use the present invention well. As long as they do not deviate from the structure of the present invention or exceed the scope defined by the present invention, they should all belong to the protection scope of the present invention.

Claims

1. A method for real-time tracking of visitors based on artificial intelligence, characterized in that: include: Obtain the visitor's reason for visit and identity level, divide the visit location into regions, including target areas, key areas and other areas, and plan the visit route to obtain all feasible visit routes; Obtain the characteristic data of each feasible access route for analysis, obtain the recommended value of each feasible access route, determine the current action route of the visitor, and process to obtain the recommended value of the current action route; Collect the tag device status data of the visitor and obtain the abnormal evaluation value of the tag device status after processing; The action status data of visitors is monitored and processed to obtain the abnormal action assessment value of visitors. According to the current action route recommendation value, the abnormal status assessment value of the tag device and the abnormal action assessment value of the visitor, the abnormal index of the visitor is obtained through comprehensive analysis, and early warning feedback is given based on the abnormal index of the visitor.

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 visited places into regions is as follows: Extract the required visit area from the visitor's visit reason and mark it as the target area; Obtain the required access level of each area in the visited location, compare the required access level of each area with the identity level of the visitor, and if the required access level of an area is greater than the identity level of the visitor, mark the area as a key area; If the required access level of a certain area is greater than the identity level of the visitor, the area will be marked as other areas.

3. The method for real-time tracking of visitors based on artificial intelligence according to claim 1, characterized in that: The characteristic data of each feasible access route is obtained and analyzed to obtain the recommended value of each feasible access route. The specific analysis process is as follows: The characteristic data of each feasible access route, including route length, monitoring equipment density and number of key areas passed; The shortest route length of each feasible access route is obtained, and the density of critical monitoring equipment and the number of critical key areas are extracted from the visitor management database. The recommended value of each feasible access route is obtained through comprehensive analysis.

4. The method for real-time tracking of visitors based on artificial intelligence according to claim 3 is characterized in that: The specific process of determining the current action route of the visitor and obtaining the recommended value of the current action route 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 overlap length between the actual action route of the visitor and each feasible access route is marked as the overlap length of each feasible access route; Divide the overlap length of each feasible access route by the length of the actual route of the visitor to obtain the overlap degree of each feasible access route; Sort the overlap of each feasible access route in descending order, extract the feasible access route corresponding to the maximum feasible access route overlap and mark it as the current action route of the visitor; The recommended value of the current action route is obtained by multiplying the overlap degree of the feasible access routes of the current action route of the visitor by the recommended value of the feasible access routes of the current action route of the visitor.

5. The method for real-time tracking of visitors based on artificial intelligence according to claim 1, characterized in that: The processing obtains the abnormal evaluation value of the tag device state, and the specific process is: 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 device response time and critical data packet loss rate are obtained from the visitor management database, and the abnormal status assessment value of the tag device is obtained through comprehensive analysis.

6. The method for real-time tracking of visitors based on artificial intelligence according to claim 1, characterized in that: The processing obtains the visitor's behavior abnormality assessment value, and the specific analysis process is as follows: The movement status data of the visitor, including the length of the repeated movement route, 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 stop location and the nearest key area, and mark it as each abnormal stop distance; The critical repeated action route length, critical abnormal stop times, critical abnormal stop time and critical abnormal stop distance are obtained from the visitor management database, and the visitor action abnormality assessment value is obtained through comprehensive analysis.

7. The method for real-time tracking of visitors based on artificial intelligence according to claim 1, characterized in that: The comprehensive analysis obtains abnormal indicators of visitors, and the specific analysis process is as follows: According to the current action route recommendation value, the tag device state abnormality assessment value and the visitor action abnormality assessment value, a comprehensive analysis is performed to obtain a visitor abnormality index, which is used to quantitatively assess the visitor's abnormal state.

8. The method for real-time tracking of visitors based on artificial intelligence according to claim 1, characterized in that: The specific process of providing early warning feedback based on abnormal indicators of visitors is as follows: The abnormal index of the visitor will be compared with the abnormal index threshold of the visitor preset in the visitor management database. If the abnormal index of the visitor is less than the abnormal index threshold of the visitor, the visitor will be marked as a normal person. If the abnormal index of the visitor is greater than or equal to the abnormal index threshold of the visitor, the visitor will be marked as an abnormal person and early warning feedback will be given to the abnormal person.

9. The method for real-time tracking of visitors based on artificial intelligence according to claim 5, characterized in that: The method for obtaining the abnormal evaluation value of the tag device state is as follows: ； In the formula, represents the abnormal evaluation value of the tag device status, e represents a natural constant, Indicates the signal strength of the tag device. represents the critical signal strength, Indicates the vibration intensity of the tag device. Indicates the critical vibration intensity, Indicates the device response time of the tag device, Indicates the critical device response time, Indicates the data packet loss rate of the tag device. Indicates the critical data packet loss rate, Indicates the device status abnormality impact factor corresponding to the preset signal strength, Indicates the abnormal impact factor of the equipment status corresponding to the preset vibration intensity. Indicates the device status abnormality impact factor corresponding to the preset device response time. Indicates the device status abnormality impact factor corresponding to the preset data packet loss rate.

10. A control system using the method for real-time tracking of visitors based on artificial intelligence as claimed in any one of claims 1 to 9, characterized in that: include: The area division module is used to obtain the visitor's reason for visit and identity level, divide the visit location into regions, including target areas, key areas and other areas, and plan the visit route to obtain various feasible visit routes; The action route tracking module is used to obtain the characteristic data of each feasible access route for analysis, obtain the recommended value of each feasible access route, determine the current action route of the visitor, and process to obtain the recommended value of the current action route; The equipment status monitoring module is used to collect the tag equipment status data of visitors and obtain the abnormal evaluation value of the tag equipment status after processing; The early warning feedback module is used to monitor the action status data of visitors, obtain the visitor's action abnormality assessment value after processing, and obtain the visitor's abnormal index through comprehensive analysis based on the current action route recommendation value, tag device status abnormal assessment value and visitor action abnormal assessment value, and provide early warning feedback based on the visitor's abnormal index.

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