Water plant operation management method and system based on NFC label

Abstract

The invention belongs to the technical field of equipment room control, and discloses a water plant operation management method and system based on an NFC tag. The method comprises the following steps: collecting i equipment authority levels, personnel position data and personnel information data of i equipment in an equipment room; calculating to obtain an inter-device permission level based on the i device permission levels, taking the inter-device permission level as inter-device NFC identification end basic data, making an NFC label based on the personnel information data, matching the NFC label with the inter-device NFC identification end basic data, and determining whether the personnel can enter the device; by collecting the authority level of each device between the devices and judging the authority level between the devices based on the authority level of each device, dynamic adjustment of the authority levels between the devices is realized, so that personnel scheduling is freer, and the personnel utilization rate is improved.

Description

Technical Field

[0001] The present invention relates to the field of security management technology, and more specifically, to a water plant operation management method and system based on NFC tags. Background Art

[0002] NFC is a short-distance data exchange technology that is mostly used for information recognition, such as identity recognition or device information recognition. It is widely used in factory management systems, such as water plant management. The operation and maintenance of filtration equipment, oxidation equipment, disinfection equipment, pumping stations and other equipment in the water plant require clear equipment information. Using NFC to record equipment information allows staff to quickly obtain equipment information.

[0003] For example, a Chinese patent application with publication number CN118261581A discloses a water plant equipment management method, device, electronic device and storage medium, the method comprising: obtaining NFC tag inspection information of water plant equipment, the NFC tag inspection information comprising the equipment number and the NFC inspection video corresponding to the equipment number; performing fault diagnosis on the NFC inspection video according to a well-trained target fault diagnosis model to determine the equipment fault type of the target equipment.

[0004] This patent uses NFC tags to record inspection information and determine the type of equipment failure. However, the equipment failure type or inspection information can be directly transmitted to the control room remotely via wireless communication, and the characteristics of NFC tags cannot be brought into play. Currently, in the operation and management of water plants, NFC tags are used as identity recognition to manage personnel entering and exiting various equipment rooms, thereby achieving a security management effect. However, during personnel operations, it is impossible to limit personnel permissions based on NFC tags, which can easily lead to safety hazards caused by personnel with insufficient permissions operating equipment.

[0005] In view of this, the present invention proposes a water plant operation management method and system based on NFC tags to solve the above problems. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a water plant operation management method and system based on NFC tags.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A water plant operation management method based on NFC tags, comprising:

[0009] Collect i device authority levels, personnel location data and personnel information data of i devices in the equipment room, where i is an integer greater than or equal to 1;

[0010] The permission level between devices is calculated based on the i device permission levels. The permission level between devices is used as the basic data of the NFC identification terminal between devices. An NFC tag is made based on the personnel information data. The NFC tag is matched with the basic data of the NFC identification terminal between devices to determine whether the person can enter the device room.

[0011] A personnel identification model that is trained to identify personnel categories based on personnel information data and device permission levels;

[0012] Based on the personnel location data, the distance value between the personnel and the non-matching authority device is calculated;

[0013] A risk factor is generated based on personnel category and distance value, and the risk factor is compared and analyzed with the preset risk factor threshold to determine whether to generate an early warning instruction.

[0014] Preferably, the personnel information data includes personnel facial video data and personnel authority level.

[0015] Preferably, the personnel location data includes personnel location information and i device location information;

[0016] Personnel location information is the distance between the ground position of the personnel and the laser radar;

[0017] The location information of the i-th device is the distance between the i-th device and the laser radar.

[0018] Preferably, the training process of the personnel recognition model is as follows:

[0019] Match the personnel information data with the device permission level. When the personnel information data and the device permission level match, the corresponding actual label is 0, and when the personnel information data and the device permission level do not match, the corresponding actual label is 1; use the personnel information data and the device permission level as the input of the personnel identification model, the personnel identification model uses the predicted labeling of each group of personnel information data and device permission level as the output, the actual labeling as the prediction target, and minimizing the sum of the prediction accuracies of all personnel information data and device permission levels as the training target; train the personnel identification model until the sum of the prediction accuracies converges, stop the training, and use the trained personnel identification model as the personnel identification model.

[0020] Preferably, the personnel recognition model is any one of a two-stream convolutional neural network model or a 3D convolutional neural network model.

[0021] Preferably, the method for calculating the distance value between a person and a non-matching authority device includes:

[0022] The distance values ​​between the equipment and the personnel corresponding to the personnel category of non-matching are calculated in sequence, and g distance values ​​are obtained, where g is an integer greater than or equal to 0;

[0023] The minimum value among the g spacing values ​​is selected as the distance value.

[0024] Preferably, the method for calculating the inter-device permission level based on i device permission levels includes:

[0025] The i devices are numbered in sequence, and the device permission level of the i device is marked as L ij , where i is the device number and j is the permission level;

[0026] For each device, the permission level L ij Assign a weight factor w i ;

[0027] Calculate the sum of the products between the weight and the permission level of each device, and then divide it by the sum of the weights of all devices to get the device-to-device permission level between devices. The device-to-device permission level is marked as L. total , L total The calculation expression is: Where n is the total number of devices, m is the total number of permission levels, and L ij is the permission level of the ith device, w i is the weight factor of the ith device.

[0028] Preferably, the method for generating the risk factor includes:

[0029] The risk factor is marked as danger, the weight q is assigned to the personnel category, and the weight y is assigned to the distance value;

[0030] The formula for generating the risk factor is: Where B is the personnel category and H is the distance value.

[0031] Preferably, the method for determining whether to generate a warning instruction includes: comparing and analyzing the risk factor with a preset risk factor threshold; if the risk factor is greater than or equal to the preset risk factor threshold, generating a risk instruction, reducing the personnel authority based on the risk instruction and issuing a warning; if the risk factor is less than the preset risk factor threshold, not generating a risk instruction.

[0032] A water plant operation management system based on NFC tags, applied to the above-mentioned water plant operation management method based on NFC tags, comprising:

[0033] A data collection module is used to collect i device permission levels, personnel location data and personnel information data;

[0034] The data processing module calculates the permission level between devices based on the i device permission levels. The permission level between devices is used as the basic data of the NFC identification terminal between devices. An NFC tag is made based on the personnel information data, and the NFC tag is matched with the basic data of the NFC identification terminal between devices to determine whether the person can enter the device room.

[0035] An analysis module calculates the distance between a person and a non-matching authority device based on the person's location data;

[0036] Model training module, which trains a personnel recognition model to identify personnel categories based on personnel information data and device permission levels;

[0037] The dynamic permission module generates a risk factor based on personnel category and distance value, sets a risk factor threshold and compares and analyzes the risk factor to generate a risk instruction, and adjusts personnel permissions based on the risk instruction.

[0038] The technical effects and advantages of the water plant operation management method and system based on NFC tags of the present invention are as follows:

[0039] By collecting the authority level of each device in the equipment room and judging the authority level between devices based on the authority level of each device, dynamic adjustment of the authority level between devices can be achieved, thereby making personnel scheduling more flexible and improving personnel utilization. In addition, by judging the risks of personnel's working conditions in the equipment, dangerous behaviors of personnel will reduce their authority. Subsequently, safety awareness training can be strengthened for such personnel to improve the safety of water plant operation and management. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a schematic diagram of a water plant operation management system based on NFC tags of the present invention;

[0041] Figure 2 This is a schematic diagram of a water plant operation management method based on NFC tags of the present invention. DETAILED DESCRIPTION

[0042] 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.

[0043] Example 1

[0044] See also Figure 1As shown, the water plant operation and management system based on NFC tags described in this embodiment includes a data collection module, a data processing module, a model training module, an analysis module and a dynamic permission module, and each module is connected through a wired and / or wireless network.

[0045] The data collection module is used to collect i device permission levels, personnel location data and personnel information data.

[0046] The data processing module calculates the permission level between devices based on the i device permission levels. The permission level between devices is used as the basic data of the NFC identification terminal between devices. The NFC tag is made based on the personnel information data, that is, the personnel information data is written into the NFC tag, such as a card that supports identity identification. The NFC tag is matched with the basic data of the NFC identification terminal between devices to determine whether the person can enter the device room. If the personnel information data in the NFC tag matches the basic data of the NFC identification terminal, it is determined that the person can enter the device room. If the personnel information data in the NFC tag does not match the basic data of the NFC identification terminal, it is determined that the person can enter the device room.

[0047] The purpose is that as the authority level of i equipment changes, the authority level between equipment is also adjusted accordingly, which facilitates the scheduling of personnel for water plant operation and management and makes better use of human resources;

[0048] For example, in the pump room of the equipment room of a water plant, the pump station equipment has dangers such as high pressure and high temperature, and careless operation may cause safety risks such as squeezing and scalding. Therefore, the pump station equipment in the pump room needs to be repaired by professionals, but the normal inspection does not necessarily require professionals who can repair the pump station equipment. If the authority between the equipment rooms is consistent, for safety reasons, the authority should be limited to professionals who can only repair the pump station equipment, and the inspection work can only be carried out by professionals who can repair the pump station equipment. If the professionals who can repair the pump station equipment work at other sites or take leave, the inspection work of the pump room will be insufficient. However, under normal circumstances, ordinary personnel can perform the inspection work of the pump room. If the dynamic authority of the present embodiment is used, the authority level of the equipment room can be adjusted according to the actual situation.

[0049] It should be noted that this embodiment only takes a single pump room as an example. If the equipment in the equipment room is of various types and employs a large number of people, the benefits of dynamic permissions on personnel scheduling can be more realized, and no further elaboration will be given here.

[0050] The analysis module calculates the distance value between the personnel and the non-matching authority device based on the personnel location data, and the personnel location data includes the personnel location information and i equipment location information; the personnel location information is the distance between the ground position of the personnel and the laser radar; the i equipment location information is the distance between the i equipment and the laser radar; the method for calculating the distance value between the personnel and the non-matching authority device includes: sequentially calculating the spacing values ​​between the equipment corresponding to the personnel category of non-matching and the personnel, and obtaining g spacing values, g is an integer greater than or equal to 0; selecting the minimum value of the g spacing values ​​as the distance value; for example, although ordinary personnel can enter the equipment room to perform inspection-type low-risk work, the authority level of the equipment room will be comprehensively judged based on the authority level of the internal equipment in the equipment room. Ordinary personnel match the authority level of the equipment room and enter the equipment room, which does not mean that the personnel can operate all equipment. Therefore, in order to avoid the personnel from mistakenly operating the equipment that does not match their equipment authority level, the distance value of the personnel from the non-matching authority device is calculated, and the distance value is used to judge the possible danger of the personnel;

[0051] It is worth mentioning that the distance between a person and i devices can be calculated by triangulation. Triangulation is a known and public calculation method and will not be elaborated on here.

[0052] Personnel information data includes personnel facial video data and personnel authority level; specifically, the facial video data of personnel in the equipment room is obtained by pre-shooting with the camera in the equipment room, and the personnel authority level represents the ability of the personnel. For example, each level corresponds to the type of equipment that can be operated and the equipment situation that can be dealt with. The facial video data of the personnel is used for facial recognition. The purpose is to bind the personnel skill level with the facial video data of the personnel. In this way, even if the NFC tag used by the non-person matches the basic data of the NFC recognition end between the devices and enters the equipment room, it will be judged as a dangerous behavior. Alternatively, the NFC recognition end is equipped with a face recognition device, which can cooperate with the NFC recognition end to authenticate the personnel entering the equipment room. The personnel recognition model training method is described below;

[0053] Match the personnel information data with the device permission level. When the personnel information data and the device permission level match, the corresponding actual label is 0, and when the personnel information data and the device permission level do not match, the corresponding actual label is 1; use the personnel information data and the device permission level as the input of the personnel identification model, the personnel identification model uses the predicted label of each group of personnel information data and device permission level as the output, the actual label as the prediction target, and minimizes the sum of the prediction accuracy of all personnel information data and device permission levels as the training target; wherein the calculation formula for prediction accuracy is: zk = (ak-wk) 2, wherein k is the number of the personnel information data and the device authority level, zk is the prediction accuracy, ak is the predicted labeling value corresponding to the k-th group of personnel information data and the device authority level, and wk is the actual labeling corresponding to the k-th group of personnel information data and the device authority level; the personnel identification model is trained until the sum of the prediction accuracies converges. It should be noted that the convergence standard is determined by technical personnel in this field according to actual conditions, and the training is stopped and the trained personnel identification model is used as the personnel identification model; the personnel identification model is any one of a two-stream convolutional neural network model or a 3D convolutional neural network model.

[0054] The dynamic permission module generates a danger factor based on personnel category and distance value, sets a danger factor threshold and generates a danger instruction by comparing and analyzing the danger factor, and adjusts personnel permissions based on the danger instruction; specifically: compare and analyze the danger factor with a preset danger factor threshold; if the danger factor is greater than or equal to the preset danger factor threshold, a danger instruction is generated, and based on the danger instruction, the personnel authority is reduced and a warning is issued, thereby improving the personnel's safety awareness; if the danger factor is less than the preset danger factor threshold, no danger instruction is generated; it is worth mentioning that the danger factor threshold is determined by technical personnel in this field according to actual conditions. The higher the danger factor threshold is set, the lower the sensitivity of judging dangerous instructions.

[0055] Embodiment 2

[0056] A water plant operation management method based on NFC tags, comprising:

[0057] Collect i device authority levels, personnel location data and personnel information data of i devices in the equipment room, where i is an integer greater than or equal to 1;

[0058] The permission level between devices is calculated based on the i device permission levels. The permission level between devices is used as the basic data of the NFC identification terminal between devices. An NFC tag is made based on the personnel information data. The NFC tag is matched with the basic data of the NFC identification terminal between devices to determine whether the person can enter the device room.

[0059] A personnel identification model for identifying personnel categories is trained based on personnel information data and device authority levels. The training process of the personnel identification model is as follows: matching personnel information data with device authority levels. When the personnel information data and device authority levels match, the corresponding actual label is 0, and when the personnel information data and device authority levels do not match, the corresponding actual label is 1; the personnel information data and device authority levels are used as inputs of the personnel identification model, and the personnel identification model uses the predicted labeling of each group of personnel information data and device authority levels as output, the actual labeling as the prediction target, and minimizing the sum of the prediction accuracy of all personnel information data and device authority levels as the training target; wherein the calculation formula for the prediction accuracy is: zk = (ak-wk) 2 , where k is the number of the personnel information data and the device authority level, zk is the prediction accuracy, ak is the predicted annotation value corresponding to the k-th group of personnel information data and the device authority level, and wk is the actual annotation corresponding to the k-th group of personnel information data and the device authority level; the personnel identification model is trained until the sum of the prediction accuracies reaches convergence. It should be noted that the convergence standard is determined by those skilled in the art according to the actual situation, and the training is stopped and the trained personnel identification model is used as the personnel identification model; the personnel identification model is any one of a two-stream convolutional neural network model or a 3D convolutional neural network model;

[0060] Based on the personnel location data, the distance value between the personnel and the non-matching authority device is calculated;

[0061] A risk factor is generated based on personnel category and distance value, and the risk factor is compared and analyzed with the preset risk factor threshold to determine whether to generate an early warning instruction.

[0062] Specifically, the personnel information data includes personnel facial video data and personnel authority level.

[0063] More specifically, the personnel location data includes personnel location information and i device location information;

[0064] Personnel location information is the distance between the ground position of the personnel and the laser radar;

[0065] The location information of the i-th device is the distance between the i-th device and the laser radar.

[0066] Furthermore, the method for calculating the distance value between a person and a non-matching authority device includes: calculating the distance values ​​between the device corresponding to the person whose person category is non-matching and the person in turn, obtaining g distance values, where g is an integer greater than or equal to 0; and selecting the minimum value among the g distance values ​​as the distance value.

[0067] Specifically, the method for calculating the inter-device permission level based on the i-device permission levels includes:

[0068] The i devices are numbered in sequence, and the device permission level of the i device is marked as L ij , where i is the device number and j is the permission level;

[0069] For each device, the permission level L ij Assign a weight factor w i ;

[0070] Calculate the sum of the products between the weight and the permission level of each device, and then divide it by the sum of the weights of all devices to get the device-to-device permission level between devices. The device-to-device permission level is marked as L. total , L total The calculation expression is: Where n is the total number of devices, m is the total number of permission levels, and L ij is the permission level of the ith device, w i is the weight factor of the ith device, w i It is obtained by technicians in this field based on the specific conditions of the equipment or a large number of simulation experiments.

[0071] Furthermore, the method for generating the risk factor includes: marking the risk factor as danger, assigning a weight q to the personnel category, and assigning a weight y to the distance value; the formula for generating the risk factor danger is: Where B is the personnel category, and H is the distance value; it is worth mentioning that q and y are obtained by technicians in this field based on the specific conditions between devices or on a large number of simulation experiments.

[0072] Furthermore, the method for determining whether to generate a warning instruction includes: comparing and analyzing the risk factor with a preset risk factor threshold; if the risk factor is greater than or equal to the preset risk factor threshold, generating a risk instruction, reducing the personnel authority based on the risk instruction and issuing a warning; if the risk factor is less than the preset risk factor threshold, not generating a risk instruction.

[0073] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center through a wired network or a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0074] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0075] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0076] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a water plant operation management method and system based on NFC tags. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0077] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0078] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0079] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0080] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A water plant operation management method based on NFC tags, characterized in that: include: Collect i device authority levels, personnel location data and personnel information data of i devices in the equipment room, where i is an integer greater than or equal to 1; The permission level between devices is calculated based on the i device permission levels. The permission level between devices is used as the basic data of the NFC identification terminal between devices. An NFC tag is made based on the personnel information data. The NFC tag is matched with the basic data of the NFC identification terminal between devices to determine whether the person can enter the device room. A personnel identification model that is trained to identify personnel categories based on personnel information data and device permission levels; Based on the personnel location data, the distance value between the personnel and the non-matching authority device is calculated; A risk factor is generated based on personnel category and distance value, and the risk factor is compared and analyzed with the preset risk factor threshold to determine whether to generate an early warning instruction.

2. A water plant operation management method based on NFC tags according to claim 1, characterized in that: Personnel information data includes personnel facial video data and personnel authority level.

3. A water plant operation management method based on NFC tags according to claim 2, characterized in that: Personnel location data includes personnel location information and i device location information; Personnel location information is the distance between the ground position of the personnel and the laser radar; The location information of the i-th device is the distance between the i-th device and the laser radar.

4. The water plant operation management method based on NFC tags according to claim 1 is characterized in that: The training process of the personnel recognition model is as follows: Matching personnel information data with device permission levels, when the personnel information data and device permission levels match, the corresponding actual label is 0, when the personnel information data and device permission levels do not match, the corresponding actual label is 1; using the personnel information data and device permission levels as inputs to a personnel identification model, the personnel identification model uses the predicted labeling of each set of personnel information data and device permission levels as output, the actual labeling as the prediction target, and minimizing the sum of the prediction accuracies of all personnel information data and device permission levels as a training target; The personnel recognition model is trained until the sum of the prediction accuracies reaches convergence, the training is stopped and the trained personnel recognition model is used as the personnel recognition model.

5. A water plant operation management method based on NFC tags according to claim 4, characterized in that: The personnel recognition model is any one of a two-stream convolutional neural network model or a 3D convolutional neural network model.

6. A water plant operation management method based on NFC tags according to claim 4, characterized in that: The calculation method of the distance value between a person and a non-matching authority device includes: The distance values ​​between the equipment and the personnel corresponding to the personnel category of non-matching are calculated in sequence, and g distance values ​​are obtained, where g is an integer greater than or equal to 0; The minimum value among the g spacing values ​​is selected as the distance value.

7. The water plant operation management method based on NFC tags according to claim 1 is characterized in that: The method for calculating the inter-device permission level based on the i-device permission levels includes: The i devices are numbered in sequence, and the device permission level of the i device is marked as L ij , where i is the device number and j is the permission level; For each device, the permission level L ij Assign a weight factor w i ; Calculate the sum of the products between the weight and the permission level of each device, and then divide it by the sum of the weights of all devices to get the device-to-device permission level between devices. The device-to-device permission level is marked as L. total , L total The calculation expression is: Where n is the total number of devices, m is the total number of permission levels, and L ij is the permission level of the ith device, w i is the weight factor of the ith device.

8. The water plant operation management method based on NFC tags according to claim 1 is characterized in that: The methods for generating risk factors include: The risk factor is marked as danger, the personnel category is assigned a weight q, and the distance value is assigned a weight y; The formula for generating the risk factor is: Where B is the personnel category and H is the distance value.

9. The water plant operation management method based on NFC tags according to claim 1 is characterized in that: The method for determining whether to generate a warning instruction includes: comparing and analyzing the risk factor with a preset risk factor threshold; if the risk factor is greater than or equal to the preset risk factor threshold, generating a risk instruction, reducing the personnel authority based on the risk instruction and issuing a warning; if the risk factor is less than the preset risk factor threshold, not generating a risk instruction.

10. A water plant operation management system based on NFC tags, applied to a water plant operation management method based on NFC tags according to any one of claims 1 to 9, characterized in that: include: A data collection module is used to collect i device permission levels, personnel location data and personnel information data; The data processing module calculates the permission level between devices based on the i device permission levels. The permission level between devices is used as the basic data of the NFC identification terminal between devices. An NFC tag is made based on the personnel information data, and the NFC tag is matched with the basic data of the NFC identification terminal between devices to determine whether the person can enter the device room. An analysis module calculates the distance between a person and a non-matching authority device based on the person's location data; Model training module, which trains a personnel recognition model to identify personnel categories based on personnel information data and device permission levels; The dynamic permission module generates a risk factor based on personnel category and distance value, sets a risk factor threshold and compares and analyzes the risk factor to generate a risk instruction, and adjusts personnel permissions based on the risk instruction.

Images