Smart city security management and control method and system based on AI

Abstract

The invention is applicable to the technical field of security, and particularly relates to an AI-based smart city security management and control method and system, and the method comprises the steps: S100, obtaining the identity data of an operator of a production enterprise, writing the identity data into a dual-mode tag which is composed of an active RFID and a passive RFID, determining the integrated equipment of the dual-mode tag, and storing the integrated equipment of the dual-mode tag in a database; wherein the integrated equipment is a safety helmet or a work card; and S200: in the production enterprise, traversing key equipment, inputting the key equipment into a pre-constructed AI risk assessment model, outputting to obtain a security control level of each key equipment, determining an installation position of an RFID reader based on the security control level, and sending the installation position to the RFID reader. According to the invention, by constructing the balance chain table, whether the on-duty personnel accord with the production task of the day can be judged, and the risk probability of the safety accident can be visually displayed, so that decision support can be better provided for security management and control.

Description

Technical Field

[0001] The present invention relates to the field of security technology, and in particular to an AI-based smart city security management and control method and system. Background Art

[0002] Smart city security control refers to comprehensive measures to protect and manage production sites, personnel, equipment or information in the city in order to maintain safety and order. Security control plays a vital role. By using AI to divide the equipment in production sites in smart cities into security control levels, the efficiency of security control can be greatly improved.

[0003] Smart city security control requires many safety measures. For example, the tag-out and lock-out procedure during production and maintenance is centered on physically locking the start panel and hanging warning signs to ensure that the equipment cannot be started before it is unlocked, effectively protecting the safety of operators entering the equipment. However, it is precisely because of lax security control and the occurrence of special circumstances, and the failure to strictly implement the tag-out and lock-out procedure, that many accidents have occurred.

[0004] RFID is a technology that automatically identifies and tracks objects through radio waves. It can communicate with tags through radio frequency signals to collect and transmit data; therefore, "how to use RFID for security control" is the technical problem to be solved by the present invention. Summary of the invention

[0005] The purpose of the present invention is to provide an AI-based smart city security management method and system to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A smart city security control method and system based on AI, the method comprising:

[0008] S100: Acquire identity data of an operator of a manufacturing enterprise, and write the identity data into a dual-mode tag, wherein the dual-mode tag is composed of an active RFID and a passive RFID, and determine an integrated device of the dual-mode tag, wherein the integrated device is a safety helmet or a work badge;

[0009] S200: In the manufacturing enterprise, traverse key equipment, input the key equipment into a pre-built AI risk assessment model, output the security control level of each key equipment, determine the installation location of the RFID reader based on the security control level, and embed two monitoring modes, active and contact, into the RFID reader;

[0010] The active method is to use an RFID reader to perform real-time monitoring within a preset range to determine whether there is an active RFID within the preset range, and if so, read it;

[0011] The contact type is to activate the RFID reader by contacting the integrated device with the RFID reader to read the passive RFID;

[0012] S300: constructing a balance chain list corresponding to the key devices one by one, inserting a root node into the middle of the balance chain list, and embedding a balance mechanism, dividing the balance chain list into several levels, and mounting a left node and a right node on both sides of each level respectively;

[0013] S400: Obtain the production task of the manufacturing enterprise on the same day, determine the identity data of the on-duty personnel corresponding to the production task on the same day, transfer the production task of the same day and the identity data of the on-duty personnel to the left node, activate the active monitoring mode, transfer the identity data of the operator collected by the RFID reader to the right node, determine the inclination angle of the balance chain list through the balancing mechanism, and if the inclination angle is greater than a preset threshold, shut down the key equipment corresponding to the balance chain list;

[0014] S500: start the passive monitoring mode, and when the RFID reader reads the passive RFID in the integrated devices of all the on-duty personnel, the control authority of the key equipment is sent to the integrated devices of the on-duty personnel.

[0015] Furthermore, the S100 includes:

[0016] Draw a plan distribution map of the production enterprise, divide it into several blocks, and configure the corresponding relationship between each block and the operator;

[0017] In the block, the key equipment is marked, and the adjustment authority of the key equipment is opened to the on-duty personnel.

[0018] Furthermore, the S100 further includes:

[0019] Based on the integrated device, the factory personnel entering the production enterprise are classified, wherein the factory personnel at least include: operators, quality inspection personnel and visitors;

[0020] In the planar distribution map, a multi-level electronic fence is constructed, wherein the multi-level electronic fence is composed of several levels, and a mapping between each level and personnel in different factory areas is configured;

[0021] Integrate an alarm strategy into the multi-level electronic fence.

[0022] Furthermore, the S100 further includes:

[0023] Based on the integrated device, the factory personnel entering the production enterprise are classified, wherein the factory personnel at least include: operators, quality inspection personnel and visitors;

[0024] In the planar distribution map, a multi-level electronic fence is constructed, wherein the multi-level electronic fence is composed of several levels, and a mapping between each level and personnel in different factory areas is configured;

[0025] Integrate an alarm strategy into the multi-level electronic fence.

[0026] Further, the S200 includes:

[0027] Constructing an inspection schedule, and judging whether the on-duty personnel conduct inspections according to the inspection schedule based on the reading result of the RFID reader;

[0028] If yes, use active RFID and RFID reader to record the inspection information of the on-duty personnel, wherein the inspection information at least includes: route, duration and location, compare the inspection information with the pre-established inspection strategy, obtain the deviation value, and if the deviation value is greater than the preset threshold, push the alarm information to the preset DCS central control system;

[0029] If not, the alarm information is directly pushed to the DCS central control system.

[0030] Further, the S200 includes:

[0031] Establishing a communication link between the RFID reader and a monitoring system within the manufacturing enterprise, and adjusting the monitoring system based on the location in the inspection information;

[0032] Obtaining video surveillance data of the on-duty personnel through the monitoring system, and performing feature extraction on the video surveillance data to determine whether there are dangerous features;

[0033] If so, the tilt angle of the balanced chain is corrected.

[0034] Further, the S300 includes:

[0035] Determine the priority of each of the key devices, and sort all the levels according to the order of the priority;

[0036] A coordinated relationship between the root node and all levels is established, with the root node as the center, and a left node and a right node are respectively hung on the left and right ends of each level.

[0037] Furthermore, the S400 includes:

[0038] Inserting a balance factor into the balance chain table, and determining influencing factors of the balance factor, wherein the influencing factors at least include: normal replacement shifts and temporary changes;

[0039] Based on the balancing factor, an adaptive adjustment strategy is constructed.

[0040] Furthermore, the system comprises:

[0041] A writing module is used to obtain the identity data of the operator of the production enterprise and write the identity data into the dual-mode tag, wherein the dual-mode tag is composed of an active RFID and a passive RFID, and determine an integrated device of the dual-mode tag, wherein the integrated device is a safety helmet or a work badge;

[0042] An embedding module is used to traverse key equipment in the production enterprise, input the key equipment into a pre-built AI risk assessment model, output the security control level of each key equipment, determine the installation location of the RFID reader based on the security control level, and embed two monitoring modes, active and contact, into the RFID reader;

[0043] The active method is to use an RFID reader to perform real-time monitoring within a preset range to determine whether there is an active RFID within the preset range, and if so, read it;

[0044] The contact type is to activate the RFID reader by contacting the integrated device with the RFID reader to read the passive RFID;

[0045] A mounting module is used to construct a balanced chain list corresponding to the key devices one by one, insert a root node into the middle of the balanced chain list, and embed a balancing mechanism to divide the balanced chain list into several levels, and mount a left node and a right node on both sides of each level respectively;

[0046] A shutdown module is used to obtain the production task of the production enterprise on the same day, determine the identity data of the on-duty personnel corresponding to the production task of the same day, transfer the production task of the same day and the identity data of the on-duty personnel to the left node, activate the active monitoring mode, transfer the identity data of the operator collected by the RFID reader to the right node, determine the inclination angle of the balance chain list through the balance mechanism, and if the inclination angle is greater than a preset threshold, shut down the key equipment corresponding to the balance chain list;

[0047] The sending module is used to start the passive monitoring mode. When the RFID reader reads the passive RFID in the integrated devices of all the on-duty personnel, the control authority of the key equipment is sent to the integrated devices of the on-duty personnel.

[0048] Furthermore, the writing module includes:

[0049] A configuration unit, used to draw a plan distribution map of the production enterprise, divide it into a plurality of blocks, and configure a corresponding relationship between each block and the operator;

[0050] An opening unit, used for marking the key equipment in the block and opening the adjustment authority of the key equipment to the on-duty personnel;

[0051] A classification unit, used for classifying the factory personnel entering the production enterprise according to the integrated device, wherein the factory personnel at least include: operators, quality inspection personnel and visitors;

[0052] A mapping unit, used to construct a multi-level electronic fence in the planar distribution map, wherein the multi-level electronic fence is composed of a plurality of levels, and configure a mapping between each level and personnel in different factory areas;

[0053] An integration unit is used to integrate an alarm strategy into the multi-level electronic fence.

[0054] Furthermore, the embedded module includes:

[0055] A reading unit, used to construct an inspection schedule based on the reading results of the RFID reader;

[0056] A judgment unit, used to judge whether the on-duty personnel conduct inspections according to the inspection schedule, and if so, use active RFID and RFID readers to record the inspection information of the on-duty personnel, wherein the inspection information at least includes: route, duration and location, compare the inspection information with a pre-established inspection strategy, obtain a deviation value, and if the deviation value is greater than a preset threshold, push an alarm message to a preset DCS central control system, and if not, directly push the alarm message to the DCS central control system;

[0057] An adjustment unit, used to establish a communication link between the RFID reader and a monitoring system within the production enterprise, and adjust the monitoring system based on the position in the inspection information;

[0058] The correction unit is used to obtain the video monitoring data of the on-duty personnel through the monitoring system, and perform feature extraction on the video monitoring data to determine whether there are dangerous features. If so, correct the inclination angle of the balance chain table.

[0059] Compared with the prior art, the beneficial effects of the present invention are as follows: by constructing a dual-mode tag, the present invention greatly expands the scope of use of RFID in security control; by determining the integrated equipment, security control can be performed without affecting the normal work of the operator; by utilizing the AI ​​risk assessment model, the equipment can be quickly classified into security control levels; by constructing an active monitoring mode, the location and path of the operator can be monitored, thereby providing data support for the inspection of the production enterprise; by constructing a contact monitoring mode, the key equipment can be shut down after the on-duty personnel have overhauled the key equipment, and the integrated equipment of all on-duty personnel is required to restart the shut-down equipment, which greatly improves the safety of the on-duty personnel during the maintenance process and avoids safety accidents caused by mistaken startup; by constructing a balanced linked list, it can be judged whether the on-duty personnel meet the production tasks of the day, and the risk probability of safety accidents can be intuitively displayed, thereby better providing decision support for security control. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0061] Figure 1 A flowchart of the AI-based smart city security management and control method provided in an embodiment of the present invention.

[0062] Figure 2 A block diagram of the first sub-process of the AI-based smart city security management and control method provided in an embodiment of the present invention.

[0063] Figure 3 A second sub-process flowchart of the AI-based smart city security management and control method provided in an embodiment of the present invention.

[0064] Figure 4 A block diagram of the third sub-process of the AI-based smart city security management and control method provided in an embodiment of the present invention.

[0065] Figure 5 This is a block diagram of the fourth sub-process of the AI-based smart city security management and control method provided in an embodiment of the present invention.

[0066] Figure 6 A block diagram of the composition of the AI-based smart city security management and control system provided in an embodiment of the present invention.

[0067] Figure 7 A block diagram of the composition of the write module in the AI-based smart city security management and control system provided in an embodiment of the present invention.

[0068] Figure 8A block diagram of the composition of the embedded modules in the AI-based smart city security management and control system provided in an embodiment of the present invention.

[0069] Fig. 9 A block diagram of the composition of the mounting module in the AI-based smart city security management and control system provided in an embodiment of the present invention.

[0070] Fig.10 A block diagram of the composition of the shutdown module in the AI-based smart city security management and control system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0071] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0072] S100: Acquire identity data of an operator of a manufacturing enterprise, and write the identity data into a dual-mode tag, wherein the dual-mode tag is composed of an active RFID and a passive RFID, and determine an integrated device of the dual-mode tag, wherein the integrated device is a safety helmet or a work badge.

[0073] Obtain the identity data of the operating personnel of the production enterprise, where the identity data can be operators, technicians, quality inspectors, etc., write the identity data into the dual-mode tag; and install the dual-mode tag into the integrated device, where the integrated device can be a safety helmet or a work badge, etc.

[0074] S200: In the manufacturing enterprise, traverse key equipment, input the key equipment into a pre-built AI risk assessment model, output the security control level of each key equipment, determine the installation location of the RFID reader based on the security control level, and embed two monitoring modes, active and contact, into the RFID reader;

[0075] The active method is to use an RFID reader to perform real-time monitoring within a preset range to determine whether there is an active RFID within the preset range, and if so, read it;

[0076] The contact type is to activate the RFID reader by bringing the integrated device into contact with the RFID reader to read the passive RFID.

[0077] Use the AI ​​risk assessment model to determine the security control level of all equipment on the production site. The higher the probability of an accident, the higher the corresponding safety control level. Equipment with a security control level higher than the threshold is identified as key equipment, and the key equipment is used as the installation location of the RFID reader. In addition, the installation location of the RFID reader should also include inspection sections and important production nodes.

[0078] In actual use, RFID readers can be used in conjunction with dual-mode tags; one usage is active, when an operator wearing a safety helmet (here the integrated device is a safety helmet as an example) enters the preset range of the RFID reader, the RFID reader monitors the tag information of the active RFID and reads the active RFID to determine the identity data of the operator entering the preset range, where the preset range is determined by the manager or the hardware performance of the RFID reader; the other usage is contact, when an operator wearing a safety helmet brings the passive RFID part of the safety helmet into contact with the RFID reader, the RFID reader reads the passive RFID.

[0079] S300: construct a balanced linked list corresponding to the key devices one by one, insert a root node into the middle of the balanced linked list, embed a balancing mechanism, divide the balanced linked list into several levels, and mount a left node and a right node on both sides of each level respectively.

[0080] A balance list is created for each key device, a root node is inserted in the middle of the balance list, and a balance mechanism is embedded in the root node; at each level, a left node and a right node are mounted at both ends of the balance list.

[0081] S400: Obtain the production task of the manufacturing enterprise for the day, determine the identity data of the on-duty personnel corresponding to the production task of the day, transfer the production task of the day and the identity data of the on-duty personnel to the left node, activate the active monitoring mode, transfer the identity data of the operator collected by the RFID reader to the right node, and determine the inclination angle of the balance chain list through the balancing mechanism. If the inclination angle is greater than a preset threshold, shut down the key equipment corresponding to the balance chain list.

[0082] Determine the production task of the manufacturing enterprise for the day. It should be noted that the production task of the day here is not a specific production action, but an inspection route, maintenance task, etc.; determine the identity data of the on-duty personnel corresponding to each production task of the day, and also determine the key equipment corresponding to the production task of the day; transfer the identity data of the on-duty personnel to the left node, activate the active monitoring mode, where the active monitoring mode is the active mode of the two monitoring modes mentioned in S200, use the RFID reader to determine whether there are on-duty personnel within the preset range, and transfer the collected identity data to the right node, compare the identity data of the on-duty personnel in the production task in the left node with the identity data collected in the right node, and determine the difference between the two, where each difference corresponds to a tilt angle, and if the tilt angle is greater than the preset threshold, the corresponding key equipment is shut down.

[0083] For example, according to the production task, A, B and C will inspect and repair the key equipment A on that day, and the identity data of A, B and C will be transferred to the left node, where A and B are operators, C is an intern, and each person corresponds to a tilt angle, 10° for A and B, and 5° for C; using the RFID reader, it is collected that there are only A and C near the key equipment, and the identity data of A and C are transferred to the right node. At this time, the balance chain list is tilted by 10°; however, if it is collected that there is only C near the key equipment, the balance chain list is tilted by 20°. If the preset threshold is 15°, that is, the tilt angle of the balance chain list is greater than the preset threshold, the key equipment will be shut down; based on actual production experience, it can be known that there are safety hazards when C performs maintenance alone.

[0084] The balancing mechanism is the process of determining the tilt angle in the above example.

[0085] S500: start the passive monitoring mode, and when the RFID reader reads the passive RFID in the integrated devices of all the on-duty personnel, the control authority of the key equipment is sent to the integrated devices of the on-duty personnel.

[0086] When key equipment is shut down for maintenance, passive RFID is needed to regain control of the key equipment; for example, when a tank area equipment fails, the on-duty personnel need to enter the equipment for maintenance; during or after the maintenance, the passive RFID on the helmets of all on-duty personnel need to contact the RFID reader to regain control of the key equipment and restart the key equipment. The advantage of this is that it can prevent on-duty personnel outside the equipment from starting the equipment by mistake.

[0087] Actual production experience has proven that many safety accidents have occurred due to the mistaken start-up of equipment due to failure to strictly implement the tag-out and lock-out procedures. Therefore, key equipment can only be restarted after the passive RFID of all on-duty personnel has come into contact with the RFID reader. This can greatly reduce safety hazards.

[0088] In Example 2, Figure 2 The implementation process of the AI-based smart city security management and control method provided by an embodiment of the present invention is shown, and S100 is described in detail below, as follows:

[0089] S101: Draw a plan distribution map of the production enterprise, divide it into several blocks, and configure the corresponding relationship between each block and the operator.

[0090] Draw a floor plan of the production enterprise and mark key equipment and inspection routes in the floor plan. According to the floor plan, divide the production enterprise into several blocks and determine the operators responsible for each block.

[0091] S102: In the block, the key equipment is marked, and the adjustment authority of the key equipment is opened to the on-duty personnel.

[0092] The adjustment authority of key equipment in the block is opened to the corresponding on-duty personnel.

[0093] In Example 3, Figure 2 The implementation process of the AI-based smart city security control method provided by the embodiment of the present invention is shown, and S100 is further described in detail below, as follows:

[0094] S103: Based on the integrated device, the factory personnel entering the production enterprise are classified, wherein the factory personnel at least include: operators, quality inspection personnel and visitors.

[0095] Use integrated equipment to classify factory personnel; for example, different factory personnel wear safety helmets of different colors, operators wear red safety helmets, and quality inspectors wear white safety helmets. Through this classification method, the identity of factory personnel can be quickly identified.

[0096] S104: constructing a multi-level electronic fence in the planar distribution map, wherein the multi-level electronic fence is composed of several levels, and mapping each level with different factory personnel is configured.

[0097] Construct multi-level electronic fences to determine the mapping between factory personnel of different identities and electronic fences; for example, when an operator of section A enters an electronic fence in section B, an alarm can be sent to the central control personnel through the preset DCS central control system. For another example, electronic fences can be set up in production areas with higher risk factors to prevent visitors from entering.

[0098] S105: Integrate an alarm strategy into the multi-level electronic fence.

[0099] Different alarm strategies are configured for different electronic fences, where the alarm strategy can be to send an alarm to the central control personnel through the preset DCS central control system, or to send an alarm message to the management personnel terminal.

[0100] In Example 4, Figure 3 The implementation process of the AI-based smart city security management and control method provided by an embodiment of the present invention is shown, and S200 is described in detail below, as follows:

[0101] S201: constructing an inspection schedule based on the reading result of the RFID reader;

[0102] Construct an inspection schedule and determine whether the on-duty personnel conduct inspections in accordance with regulations based on the reading results of the RFID reader.

[0103] S202: Determine whether the on-duty personnel conduct inspections according to the inspection schedule. If yes, use active RFID and RFID readers to record the inspection information of the on-duty personnel, wherein the inspection information at least includes: route, duration and location. Compare the inspection information with a pre-established inspection strategy to obtain a deviation value. If the deviation value is greater than a preset threshold, push an alarm message to a preset DCS central control system.

[0104] If not, the alarm information is directly pushed to the DCS central control system.

[0105] If the on-duty personnel conduct inspections according to the inspection schedule, the inspection time of the on-duty personnel, the inspection duration at key equipment and other data are recorded, and the recorded data is compared with the pre-established inspection strategy to determine the offset value to determine whether the on-duty personnel conduct inspections according to the inspection schedule. If the offset value is greater than the preset threshold, an alarm message is pushed to the preset DCS central control system, which means that the on-duty personnel only conducted inspections but did not conduct inspections according to the regulations in the inspection strategy, where the inspection strategy is the specific operation of the inspection.

[0106] If the on-duty personnel do not conduct inspections according to the inspection schedule, an alarm message is directly pushed to the DCS central control system.

[0107] In Example 5, Figure 3 The implementation process of the AI-based smart city security control method provided by the embodiment of the present invention is shown, and S200 is further described in detail below, as follows:

[0108] S203: Establishing a communication link between the RFID reader and a monitoring system within the production enterprise, and adjusting the monitoring system based on the position in the inspection information.

[0109] A communication link is established between the RFID reader and the monitoring system. When the position of the on-duty personnel is detected, the position of the monitoring system is adjusted. The monitoring system is composed of a camera and a monitor, etc. The position of the camera is adjusted to aim the camera at the on-duty personnel.

[0110] S204: Obtain the video surveillance data of the on-duty personnel through the monitoring system, and perform feature extraction on the video surveillance data to determine whether there are dangerous features. If so, correct the tilt angle of the balance chain table.

[0111] Use cameras to collect video surveillance data of on-duty personnel, extract features from the video surveillance data, and compare the extracted features with the preset dangerous feature library to determine whether there are dangerous features. If so, adjust the tilt angle of the balance chain table;

[0112] For example, after obtaining the video surveillance data of the on-duty personnel, it was found that the on-duty personnel did not wear a nitrogen oxide gas detector and entered the underground tank area; at this time, the inclination angle of the balance chain list was dynamically adjusted, and the closer the on-duty personnel were to the underground tank area, the greater the inclination angle of the balance chain list.

[0113] In Example 6, Figure 4 The implementation process of the AI-based smart city security control method provided by the embodiment of the present invention is shown, and S300 is further described in detail below, as follows:

[0114] S301: Determine the priority of each of the key devices, and sort all the levels according to the order of the priority.

[0115] Determine the priority of each key device, and establish the connection between priority, level and key devices. The higher the priority, the higher the level ranking.

[0116] S302: Establishing a coordinated relationship between the root node and all levels, with the root node as the center, and hanging a left node and a right node on the left and right ends of each level respectively.

[0117] The root node is used to coordinate the management of all levels, and the left node and the right node are respectively hung under each level.

[0118] In Example 7, Figure 5 The implementation process of the AI-based smart city security control method provided by an embodiment of the present invention is shown, and S400 is described in detail below, as follows:

[0119] S401: inserting a balance factor into the balance linked list, and determining influencing factors of the balance factor, wherein the influencing factors at least include: normal replacement shifts and temporary changes.

[0120] Insert a balance factor into the balance list, where the balance factor is determined by the influencing factor, which can be a normal replacement shift or a temporary change.

[0121] S402: Constructing an adaptive adjustment strategy based on the balance factor.

[0122] The balance factor is used to adaptively adjust the balance list. For example, the adaptive adjustment strategy can be that when active RFID monitoring detects that the person who inspects the key equipment is not the person on duty in the production task, the balance list is tilted. However, since the person on duty has changed shifts, the balance factor is inserted into the balance list, and the balance list will be rebalanced.

[0123] Figure 6 The following is a structural block diagram of the composition of the AI-based smart city security management and control system provided by an embodiment of the present invention. The AI-based smart city security management and control system 1 includes:

[0124] A writing module 11 is used to obtain the identity data of the operator of the production enterprise and write the identity data into a dual-mode tag, wherein the dual-mode tag is composed of an active RFID and a passive RFID, and determine an integrated device of the dual-mode tag, wherein the integrated device is a safety helmet or a work badge;

[0125] The embedding module 12 is used to traverse the key equipment in the production enterprise, input the key equipment into the pre-built AI risk assessment model, output the security control level of each key equipment, determine the installation location of the RFID reader based on the security control level, and embed two monitoring modes, active and contact, into the RFID reader;

[0126] The active method is to use an RFID reader to perform real-time monitoring within a preset range to determine whether there is an active RFID within the preset range, and if so, read it;

[0127] The contact type is to activate the RFID reader by contacting the integrated device with the RFID reader to read the passive RFID;

[0128] The mounting module 13 is used to construct a balanced chain list corresponding to the key devices one by one, insert a root node into the middle of the balanced chain list, and embed a balancing mechanism to divide the balanced chain list into several levels, and mount a left node and a right node on both sides of each level respectively;

[0129] The shutdown module 14 is used to obtain the production task of the production enterprise on the same day, determine the identity data of the on-duty personnel corresponding to the production task on the same day, transfer the production task on the same day and the identity data of the on-duty personnel to the left node, activate the active monitoring mode, transfer the identity data of the operator collected by the RFID reader to the right node, determine the inclination angle of the balance chain list through the balance mechanism, and if the inclination angle is greater than a preset threshold, shut down the key equipment corresponding to the balance chain list;

[0130] The sending module 15 is used to start the passive monitoring mode. When the RFID reader reads the passive RFID in the integrated devices of all the on-duty personnel, the control authority of the key equipment is sent to the integrated devices of the on-duty personnel.

[0131] Figure 7 The structure diagram of the composition of the AI-based smart city security management and control system provided by an embodiment of the present invention is shown, and the writing module 11 includes:

[0132] A configuration unit 111 is used to draw a plan distribution map of the production enterprise, divide it into a plurality of blocks, and configure a corresponding relationship between each block and the operator;

[0133] An opening unit 112 is used to mark the key equipment in the block and open the adjustment authority of the key equipment to the on-duty personnel;

[0134] A classification unit 113, configured to classify the factory personnel entering the production enterprise according to the integrated device, wherein the factory personnel at least include: operators, quality inspectors and visitors;

[0135] A mapping unit 114 is used to construct a multi-level electronic fence in the plane distribution map, wherein the multi-level electronic fence is composed of several levels, and a mapping between each level and different factory personnel is configured;

[0136] The integration unit 115 is used to integrate the alarm strategy into the multi-level electronic fence.

[0137] Figure 8 The structure diagram of the composition of the AI-based smart city security management and control system provided by an embodiment of the present invention is shown, and the embedded module 12 includes:

[0138] A reading unit 121, used to construct an inspection schedule based on the reading results of the RFID reader;

[0139] The judging unit 122 is used to judge whether the on-duty personnel conduct inspections according to the inspection schedule. If yes, the inspection information of the on-duty personnel is recorded by using active RFID and RFID readers, wherein the inspection information at least includes: route, duration and location, and the inspection information is compared with the pre-established inspection strategy to obtain a deviation value. If the deviation value is greater than a preset threshold, an alarm information is pushed to a preset DCS central control system. If not, an alarm information is directly pushed to the DCS central control system.

[0140] An adjustment unit 123, used to establish a communication link between the RFID reader and a monitoring system in the production enterprise, and adjust the monitoring system based on the position in the inspection information;

[0141] The correction unit 124 is used to obtain the video monitoring data of the on-duty personnel through the monitoring system, and perform feature extraction on the video monitoring data to determine whether there are dangerous features. If so, correct the tilt angle of the balance chain table.

[0142] Fig. 9 The structure diagram of the composition of the AI-based smart city security management and control system provided by an embodiment of the present invention is shown, and the mounting module 13 includes:

[0143] A sorting unit 131 is used to determine the priority of each of the key devices and sort all the levels according to the order of the priority;

[0144] The hanging unit 132 is used to establish a coordinated relationship between the root node and all the levels, with the root node as the center, and hanging a left node and a right node on the left and right ends of each level respectively.

[0145] Fig.10 The structure diagram of the composition of the AI-based smart city security management and control system provided by an embodiment of the present invention is shown, and the shutdown module 14 includes:

[0146] An inserting unit 141 is used to insert a balance factor into the balance chain table and determine the influencing factors of the balance factor, wherein the influencing factors at least include: normal replacement shifts and temporary changes;

[0147] The construction unit 142 is used to construct an adaptive adjustment strategy based on the balance factor.

[0148] The writing module 11 is mainly used to complete step S100, the embedding module 12 is mainly used to complete step S200, the mounting module 13 is mainly used to complete step S300, the shutdown module 14 is mainly used to complete step S400, and the sending module 15 is mainly used to complete step S500;

[0149] The configuration unit 111 is mainly used to complete step S101, the opening unit 112 is mainly used to complete step S102, the classification unit 113 is mainly used to complete step S103, the mapping unit 114 is mainly used to complete step S104, and the integration unit 115 is mainly used to complete step S105;

[0150] The reading unit 121 is mainly used to complete step S201, the judging unit 122 is mainly used to complete step S202, the adjusting unit 123 is mainly used to complete step S203, and the correcting unit 124 is mainly used to complete step S204;

[0151] The sorting unit 131 is mainly used to complete step S301, and the hanging unit 132 is mainly used to complete step S302;

[0152] The inserting unit 141 is mainly used to complete step S401, and the constructing unit 142 is mainly used to complete step S402.

[0153] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A smart city security management and control method based on AI, characterized in that: The method comprises: S100: Acquire identity data of an operator of a manufacturing enterprise, and write the identity data into a dual-mode tag, wherein the dual-mode tag is composed of an active RFID and a passive RFID, and determine an integrated device of the dual-mode tag, wherein the integrated device is a safety helmet or a work badge; S200: In the manufacturing enterprise, traverse key equipment, input the key equipment into a pre-built AI risk assessment model, output the security control level of each key equipment, determine the installation location of the RFID reader based on the security control level, and embed two monitoring modes, active and contact, into the RFID reader; The active method is to use an RFID reader to perform real-time monitoring within a preset range to determine whether there is an active RFID within the preset range, and if so, read it; The contact type is to activate the RFID reader by contacting the integrated device with the RFID reader to read the passive RFID; S300: constructing a balance chain list corresponding to the key devices one by one, inserting a root node into the middle of the balance chain list, and embedding a balance mechanism, dividing the balance chain list into several levels, and mounting a left node and a right node on both sides of each level respectively; S400: Obtain the production task of the manufacturing enterprise on the same day, determine the identity data of the on-duty personnel corresponding to the production task on the same day, transfer the production task of the same day and the identity data of the on-duty personnel to the left node, activate the active monitoring mode, transfer the identity data of the operator collected by the RFID reader to the right node, determine the inclination angle of the balance chain list through the balancing mechanism, and if the inclination angle is greater than a preset threshold, shut down the key equipment corresponding to the balance chain list; S500: start the passive monitoring mode, and when the RFID reader reads the passive RFID in the integrated devices of all the on-duty personnel, the control authority of the key equipment is sent to the integrated devices of the on-duty personnel.

2. The AI-based smart city security management and control method according to claim 1 is characterized in that: The S100 includes: Draw a plan distribution map of the production enterprise, divide it into several blocks, and configure the corresponding relationship between each block and the operator; In the block, the key equipment is marked, and the adjustment authority of the key equipment is opened to the on-duty personnel.

3. The AI-based smart city security management and control method according to claim 2 is characterized in that: The S100 further includes: Based on the integrated device, the factory personnel entering the production enterprise are classified, wherein the factory personnel at least include: operators, quality inspection personnel and visitors; In the planar distribution map, a multi-level electronic fence is constructed, wherein the multi-level electronic fence is composed of several levels, and a mapping between each level and personnel in different factory areas is configured; Integrate an alarm strategy into the multi-level electronic fence.

4. The AI-based smart city security management and control method according to claim 1 is characterized in that: The S200 includes: Constructing an inspection schedule, and judging whether the on-duty personnel conduct inspections according to the inspection schedule based on the reading result of the RFID reader; If yes, use active RFID and RFID reader to record the inspection information of the on-duty personnel, wherein the inspection information at least includes: route, duration and location, compare the inspection information with the pre-established inspection strategy, obtain the deviation value, and if the deviation value is greater than the preset threshold, push the alarm information to the preset DCS central control system; If not, the alarm information is directly pushed to the DCS central control system.

5. The AI-based smart city security management and control method according to claim 4 is characterized in that: The S200 includes: Establishing a communication link between the RFID reader and a monitoring system within the manufacturing enterprise, and adjusting the monitoring system based on the location in the inspection information; Obtaining video surveillance data of the on-duty personnel through the monitoring system, and performing feature extraction on the video surveillance data to determine whether there are dangerous features; If so, the tilt angle of the balanced chain is corrected.

6. The AI-based smart city security management and control method according to claim 2 is characterized in that: The S300 includes: Determine the priority of each of the key devices, and sort all the levels according to the order of the priority; A coordinated relationship between the root node and all levels is established, with the root node as the center, and a left node and a right node are respectively hung on the left and right ends of each level.

7. The AI-based smart city security management and control method according to claim 1 is characterized in that: The S400 includes: Inserting a balance factor into the balance chain table, and determining influencing factors of the balance factor, wherein the influencing factors at least include: normal replacement shifts and temporary changes; Based on the balancing factor, an adaptive adjustment strategy is constructed.

8. An AI-based smart city security control system, characterized in that: The system comprises: A writing module is used to obtain the identity data of the operator of the production enterprise and write the identity data into the dual-mode tag, wherein the dual-mode tag is composed of an active RFID and a passive RFID, and determine an integrated device of the dual-mode tag, wherein the integrated device is a safety helmet or a work badge; An embedding module is used to traverse key equipment in the production enterprise, input the key equipment into a pre-built AI risk assessment model, output the security control level of each key equipment, determine the installation location of the RFID reader based on the security control level, and embed two monitoring modes, active and contact, into the RFID reader; The active method is to use an RFID reader to perform real-time monitoring within a preset range to determine whether there is an active RFID within the preset range, and if so, read it; The contact type is to activate the RFID reader by contacting the integrated device with the RFID reader to read the passive RFID; A mounting module is used to construct a balanced chain list corresponding to the key devices one by one, insert a root node into the middle of the balanced chain list, and embed a balancing mechanism to divide the balanced chain list into several levels, and mount a left node and a right node on both sides of each level respectively; A shutdown module is used to obtain the production task of the production enterprise on the same day, determine the identity data of the on-duty personnel corresponding to the production task of the same day, transfer the production task of the same day and the identity data of the on-duty personnel to the left node, activate the active monitoring mode, transfer the identity data of the operator collected by the RFID reader to the right node, determine the inclination angle of the balance chain list through the balance mechanism, and if the inclination angle is greater than a preset threshold, shut down the key equipment corresponding to the balance chain list; The sending module is used to start the passive monitoring mode. When the RFID reader reads the passive RFID in the integrated devices of all the on-duty personnel, the control authority of the key equipment is sent to the integrated devices of the on-duty personnel.

9. The AI-based smart city security management and control system according to claim 8 is characterized in that: The writing module comprises: A configuration unit, used to draw a plan distribution map of the production enterprise, divide it into a plurality of blocks, and configure a corresponding relationship between each block and the operator; An opening unit, used for marking the key equipment in the block and opening the adjustment authority of the key equipment to the on-duty personnel; A classification unit, used for classifying the factory personnel entering the production enterprise according to the integrated device, wherein the factory personnel at least include: operators, quality inspection personnel and visitors; A mapping unit, used to construct a multi-level electronic fence in the planar distribution map, wherein the multi-level electronic fence is composed of a plurality of levels, and configure a mapping between each level and personnel in different factory areas; An integration unit is used to integrate an alarm strategy into the multi-level electronic fence.

10. The AI-based smart city security management and control system according to claim 9 is characterized in that: The embedded module comprises: A reading unit, used to construct an inspection schedule based on the reading results of the RFID reader; A judgment unit, used to judge whether the on-duty personnel conduct inspections according to the inspection schedule, and if so, use active RFID and RFID readers to record the inspection information of the on-duty personnel, wherein the inspection information at least includes: route, duration and location, compare the inspection information with a pre-established inspection strategy, obtain a deviation value, and if the deviation value is greater than a preset threshold, push an alarm message to a preset DCS central control system, and if not, directly push the alarm message to the DCS central control system; An adjustment unit, used to establish a communication link between the RFID reader and a monitoring system within the production enterprise, and adjust the monitoring system based on the position in the inspection information; The correction unit is used to obtain the video monitoring data of the on-duty personnel through the monitoring system, and perform feature extraction on the video monitoring data to determine whether there are dangerous features. If so, correct the inclination angle of the balance chain table.

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