Smart park RFID personnel access collaborative management and control system

Abstract

The invention belongs to the technical field of Internet of Things and intelligent security and protection, and particularly relates to a smart park RFID personnel access collaborative management and control system, which comprises a multi-dimensional sensing layer, an edge calculation processing layer, a dynamic logic control layer and a cross-system business collaborative layer, the multi-dimensional sensing layer adopts a phase sensing phased-array antenna to suppress interference, the edge layer improves the recognition precision through dynamic time slot allocation and motion vector pre-judgment, the dynamic logic control layer realizes dynamic authority arbitration based on multi-factor weighting, and the cross-system collaboration layer realizes linkage security and protection and energy and emergency system realizes total-factor collaboration. By means of the carrier wave phase difference change rate, the system can accurately distinguish purposiveness passing and non-purposiveness wandering of people, the misreading rate is greatly reduced, and the authenticity and accuracy of incoming and outgoing data are guaranteed.

Description

Technical Field

[0001] This invention belongs to the field of Internet of Things and intelligent security technology, specifically a smart park RFID personnel access collaborative management system. Background Technology

[0002] The evolution of IoT technology and the acceleration of urbanization are driving smart parks to upgrade to more refined and intelligent governance. The security, accuracy, and coordination of personnel flow control have become core indicators of the park's overall competitiveness. Radio frequency identification (RFID) technology, with its advantages of non-contact identification, strong anti-interference, and large data volume, is widely used in the control of personnel access in parks, replacing the traditional magnetic stripe cards and manual verification mode, and becoming the core cornerstone of the digital security system.

[0003] The existing RFID control solution consists of a closed-loop architecture composed of electronic tags, readers, transmission components and a back-end database: the reader at the channel node activates the tag to obtain identity information, the back-end compares it with the permission database in real time, and drives the gate and alarm device to perform actions to achieve automated access control. This static permission identification mode is efficient in low-density population and single-boundary defense scenarios, effectively reduces labor costs and has phased technical rationality.

[0004] In related technologies, at the physical level, when multiple people are passing through densely populated areas or loitering at the edge, the reader is susceptible to multipath effects and tag conflicts, resulting in missed readings and misreadings, causing traffic congestion and safety hazards during peak hours. Logically, the control system is disconnected from the spatial situation of the park and emergency instructions, making it unable to dynamically respond to scenarios such as area saturation and sudden accidents. At the architectural level, personnel flow data is isolated from park assets, visitor management, etc., forming data silos that make it difficult to support multi-departmental collaboration. The core limitation is that it only focuses on single identity verification and ignores the collaborative attributes of personnel flow and multiple elements.

[0005] Therefore, the present invention provides a smart park RFID personnel access collaborative management system. Summary of the Invention

[0006] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0007] The technical solution adopted by the present invention to solve its technical problem is as follows: The smart park RFID personnel access collaborative management system of the present invention comprises a multi-dimensional perception layer, an edge computing processing layer, a dynamic logic control layer and a cross-system business collaboration layer. Through the logical coupling and data flow between the above layers, the system can achieve accurate identification and intelligent management of personnel access behavior in the park.

[0008] The multi-dimensional sensing layer integrates a phase-aware adaptive radio frequency identification (RFID) component. This component includes a phased array antenna array with multiple antenna elements, each independently connected to an RF front-end processing module. The RF front-end processing module is configured to demodulate the received RF echo signal and extract raw observation data containing signal strength indication and carrier phase information. The phased array antenna array uses a preset beamforming algorithm to form directional energy coverage within a specific spatial region, thereby suppressing signal interference in non-target areas.

[0009] Preferably, the adaptive RFID component further includes an environmental parameter monitoring submodule, which is used to collect electromagnetic background noise and multipath reflection parameters in the reader deployment environment in real time. When the environmental interference level exceeds the preset environmental threshold, the RF front-end processing module will automatically adjust the frequency hopping sequence and transmission power to ensure the reliability of the identification link.

[0010] Preferably, the edge computing processing layer is deployed at physical nodes near entrances and exits. It contains a data processing engine that receives raw observation data from the multi-dimensional perception layer and calls a pre-defined anti-collision discrimination algorithm to separate concurrent label signals. To address signal collision issues during high-density crowd passage, the edge computing processing layer executes a dynamic time slot allocation logic. This logic dynamically adjusts the number of time slots within the recognition period by calculating the label density function within the current area. The calculation model for the label density function is as follows: In this formula, This represents the predicted label density value within the preset identification area; Indicates the first Signal strength weighting factor for each tag; Indicates the first Each label during the observation period Cumulative value of phase change within; This represents the effective physical area covered by the antenna array. This represents the total number of active tags currently detected.

[0011] Preferably, the edge computing processing layer further includes a trajectory prediction module. This module estimates the motion vector of personnel in real time based on the rate of change of the carrier phase difference using a preset Kalman filter model. By analyzing the movement trend of personnel approaching or moving away from the entrance / exit, the system can exclude non-passage personnel lingering at the sensing boundary, thereby improving the accuracy of identification. The motion vector estimation formula is as follows: In this formula, This indicates the radial velocity of the personnel relative to the reader antenna; Indicates the wavelength of the current radio frequency signal; This represents the rate of change of the carrier phase over time. This indicates the preset angle between the direction of movement of the personnel and the central axis of the antenna main lobe.

[0012] Preferably, the dynamic logic control layer serves as the decision-making core of this invention, responsible for deeply integrating the identified identity information with the real-time operational semantics of the park. This layer constructs a knowledge graph-based permission arbitration engine, which no longer relies solely on the static permission table in the backend database, but comprehensively considers the current time dimension, spatial load dimension, security level dimension, and personnel attribute dimension.

[0013] Preferably, the dynamic logic control layer implements an access control policy based on multi-factor weighting. When determining whether passage is permitted, the system calculates a comprehensive access score in real time. The formula for calculating the comprehensive access score is as follows: In this formula, This indicates the final admission score; This represents the preset base score for identity and permissions; This indicates the current real-time number of people existing within the target area; This indicates the preset maximum threshold for the number of people allowed in the area. This indicates the current real-time security status index of the park, which is dynamically updated based on preset security event trigger levels. , , These represent the preset weight coefficients for each influencing factor. When When the preset access threshold is reached or exceeded, the system sends a permission instruction to the actuator.

[0014] Preferably, the dynamic logic control layer also integrates an abnormal behavior alarm mechanism. When the system detects that the same tag appears in multiple illogical geographical locations within a preset time period, or detects changes in signal characteristics caused by illegal tag removal, it will immediately trigger the alarm logic and link the video surveillance system to capture images of the corresponding area.

[0015] Preferably, the cross-system business collaboration layer is the key to realizing the linkage of all elements of the smart park. This layer connects with the park's security monitoring system, energy management system, asset management system and emergency command system through a preset standard northbound interface at the protocol level.

[0016] Preferably, in collaboration with a security monitoring system, once the RFID identification component acquires personnel identification information, the collaboration layer immediately drives the corresponding PTZ camera to a preset position to perform the association verification between personnel characteristics and RFID tags. This multi-verification mechanism effectively solves the security risks associated with tag possession.

[0017] Preferably, in collaboration with the energy management system, the cross-system business collaboration layer updates the office staff distribution map of the corresponding area in real time based on the location transfer trajectories generated by personnel entering and exiting. The system dynamically adjusts the lighting intensity and air conditioning operating parameters of the corresponding area through an energy consumption prediction model based on personnel density. The calculation model for the energy consumption adjustment coefficient is as follows: In this formula, Indicates the energy regulation coefficient; The label density is the one calculated above; Indicates the current ambient temperature; This indicates the preset target comfort temperature; This is the preset equipment efficiency constant.

[0018] Preferably, in coordination with the emergency command system, when a sudden disaster alarm signal is received, the system automatically switches to emergency mode. In emergency mode, the regular permission judgment logic of the dynamic logic control layer is forcibly suspended, all entrance and exit actuators are set to the normally open state, and the RFID sensing layer switches to the preset high-frequency scanning state to count and locate the exact number and approximate location of people stranded in the disaster area, providing accurate data support for rescue operations.

[0019] Preferably, the RFID tag involved in this invention has a specific storage structure, including an identification area, a dynamic encryption area, and a behavioral feature cache area. The identification area stores the unique code of the person; the dynamic encryption area is used to store the encrypted handshake protocol issued in real time by the edge computing layer to prevent the signal from being illegally eavesdropped or cloned; the behavioral feature cache area is used to record the important points of the person's passage in the park so that the behavioral trajectory can be completed offline in the extreme case of network connection interruption.

[0020] Preferably, the system also includes a global clock synchronization module. This module uses a preset high-precision synchronization protocol to ensure that readers, servers, and linked devices distributed throughout the park are under a unified time base. This ensures that the timestamps generated by each node have strong consistency when tracking cross-regional personnel movement, thereby enabling the reconstruction of a high-precision spatiotemporal topology map.

[0021] Preferably, this invention also proposes a reinforcement learning-based method for predicting pedestrian flow. This method is deployed on a cloud server and, through deep mining of historical entry and exit data, constructs a model whose state space includes regional popularity, time series data, and seasonal influencing factors. The cloud server periodically distributes the trained and optimized prediction model to the edge computing layer, enabling the edge to adjust the reader's polling strategy in advance based on the predicted traffic peaks, reducing computational latency under high concurrency. The state transition value function of the prediction model is expressed as: In this formula, Indicates the current state Take action below The expected value (such as adjusting the time slot length); This represents the instant reward function value, which is directly proportional to the recognition success rate and inversely proportional to the processing latency. This is a preset discount factor; The next state after taking an action.

[0022] Preferably, in terms of the system's hardware configuration, the actuators include an intelligent gate unit, an electronic door lock control unit, and a voice prompt unit. The intelligent gate unit has a built-in pressure sensor that assists the RFID system in determining specific behaviors. When the RFID system identifies only a predetermined number of valid tags, but the pressure distribution characteristics reported by the pressure sensor indicate that multiple people are passing through, the system will issue a warning through the voice prompt unit and execute the gate closing operation.

[0023] Preferably, the data transmission network adopts a topology combining wired redundant links and wireless backup links. When the backbone network is functioning normally, massive amounts of identification data are transmitted to the backend via a pre-set high-bandwidth wired network; when a wired link failure is detected, the system automatically and seamlessly switches to a low-power wide area network or other pre-set wireless communication protocols to ensure uninterrupted issuance of control commands.

[0024] In summary, this invention constructs a full-link control system from underlying physical signal processing to top-level multi-system logical collaboration. By introducing carrier phase analysis, dynamic permission arbitration, and cross-system linkage mechanisms, it overcomes the bottleneck of identification accuracy and the problem of information silos in traditional RFID control systems in smart park applications.

[0025] The beneficial effects of this invention are as follows: 1. The smart park RFID personnel access collaborative management system of the present invention adopts an adaptive radio frequency identification component with phase sensing function and a phased array antenna array in the multi-dimensional perception layer, and works in conjunction with a dynamic time slot allocation algorithm running in the edge computing layer. This successfully solves the signal collision and multipath interference problems that are easy to occur in traditional RFID systems when people pass through densely populated areas. With the help of the carrier phase difference change rate, the system can accurately distinguish between people's purposeful passage and non-purposeful wandering, greatly reducing the misread rate and ensuring the authenticity and accuracy of entry and exit data.

[0026] 2. The smart park RFID personnel access collaborative management system of the present invention, through a knowledge graph-based permission arbitration engine built on a dynamic logic control layer, abandons the traditional permission verification method that relies on static databases. The system comprehensively considers multiple factors such as spatial load, real-time security status, and personnel attributes, and can dynamically adjust the access strategy according to the real-time operation of the park. For example, it automatically tightens permissions when the area is saturated with personnel or the security level is increased, and automatically opens channels in emergencies, effectively enhancing the park's ability to manage complex emergencies.

[0027] 3. The smart park RFID personnel access collaborative management system of this invention, relying on a cross-system business collaboration layer, logically links personnel access behavior with security monitoring, energy management, and emergency command systems. This not only further enhances the level of security protection through multiple verification mechanisms, but also drives the energy system to make refined adjustments based on the spatiotemporal distribution characteristics of personnel, effectively reducing the park's energy consumption. This all-element collaborative operation mode upgrades the personnel management system from a single security tool to a core data foundation supporting the digital governance of the park.

[0028] 4. The smart park RFID personnel access collaborative management system of the present invention adopts an edge computing and cloud collaboration architecture design, coupled with a tag structure with offline caching function, so that even in the event of network fluctuations or extreme disasters, the core functions can still be maintained. Especially in emergency mode, the system can quickly switch to a personnel positioning and evacuation auxiliary tool, and through high-frequency scanning and cross-system linkage, it provides key technical support for ensuring personnel safety, and has extremely high social benefits and engineering application value.

[0029] 5. The smart park RFID personnel access collaborative control system of the present invention constructs a non-inductive identification mechanism based on motion vector prediction, allowing legitimate personnel to pass quickly without deliberately stopping, significantly improving the passage efficiency during peak hours in the park. At the same time, the system can adaptively adjust the transmission parameters and beam shape, reducing the dependence on manual environmental deployment and debugging. Through intelligent means, it automatically compensates for environmental interference, effectively reducing the long-term operation and maintenance costs of the system, which is in line with the technological evolution trend of sustainable development of smart parks. Attached Figure Description

[0030] The invention will now be further described with reference to the accompanying drawings.

[0031] Figure 1 This is an architectural block diagram of a smart park RFID personnel access collaborative control system provided in an embodiment of the present invention; Figure 2 This is a flowchart illustrating the logic flow of the dynamic logic control layer's execution permission determination in an embodiment of the present invention. Detailed Implementation

[0032] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0033] like Figure 1 As shown in the embodiment of the present invention, a smart park RFID personnel access collaborative control system includes an underlying architecture built on a multi-dimensional perception layer. This layer is not a traditional single radio frequency scanning module, but is composed of an adaptive radio frequency identification component with phase perception. Its core component includes a phased array antenna array, which is composed of multiple independent antenna elements arranged according to a preset geometric topology. Each antenna element adopts a microstrip patch structure with wide bandwidth characteristics in physical structure and is independently connected to a digital controlled phase shifter in the radio frequency front-end processing module. When the RF front-end processing module is working, it not only performs signal power amplification and filtering, but more importantly, it issues specific phase shift commands to each phase shifter through the baseband processor. It uses beamforming technology to form a high-gain directional beam in three-dimensional space. This beamforming process calculates the path difference between each antenna element to achieve energy superposition on a specific path, while forming nulls in the interference direction, effectively suppressing multipath reflection interference in the complex electromagnetic environment of the park.

[0034] Furthermore, the RF front-end processing module integrates a quadrature demodulator, which can decompose the received backscattered signal into in-phase and quadrature components, thereby extracting carrier phase information. The environmental parameter monitoring submodule monitors the channel occupancy and thermal noise level in the working frequency band in real time at a preset sampling frequency. When the energy detection value of the current channel exceeds the preset background noise threshold, the adaptive RF identification component will trigger a frequency hopping command to perform pseudo-random frequency hopping switching between multiple preset communication channels to avoid narrowband interference.

[0035] The edge computing processing layer, serving as the central hub connecting front-end perception and back-end decision-making, is deployed in embedded computing nodes near entrances and exits. The data processing engine at this layer receives raw streaming data from the radio frequency front-end, including tag identifiers, received signal strength, carrier phase, and timestamps. To address tag collision issues in high-density traffic scenarios, the data processing engine executes dynamic time slot allocation logic. This logic first assesses the tag density within the current coverage area by weighting the proportions of idle time slots, successfully identified time slots, and conflicting time slots from the previous identification cycle. The calculation process for the tag density function is extremely precise, as detailed below: , In this formula, This represents the predicted tag density value within the preset recognition area, with the unit being the number of active tags per unit area. For the first The signal strength weighting factor for each tag is obtained by logarithmically normalizing the received signal strength indication value; Indicates the first Each label during the observation period The cumulative change in carrier phase within the range is used to characterize the tag's activity and relative displacement; This represents the effective physical area covered by the phased array antenna array, and this value is dynamically corrected as the beam pointing angle is adjusted. This represents the total number of active tag sequences detected during the current detection period.

[0036] Based on the calculated tag density, the edge computing processing layer automatically adjusts the time slot frame length in the next frame recognition cycle. When the tag density increases, the system exponentially increases the number of time slots and simultaneously reduces the listening window length of a single time slot to maximize system throughput. The trajectory prediction module (personnel behavior) within the edge computing processing layer utilizes the extracted phase change rate, combined with a pre-defined Kalman filter algorithm, to perform real-time calculation of the motion vectors of tagged personnel. By analyzing the continuity of the phase over time, the system can accurately distinguish between personnel with genuine intent to pass and disruptive objects loitering at the boundary of the sensing area. The mathematical model for motion vector estimation is as follows: , In this formula, This indicates the radial velocity of a person relative to the reader antenna, measured in meters per second. The wavelength of the current carrier wave is obtained in real time based on the operating frequency; The first derivative of the carrier phase with respect to time reflects the Doppler phase shift characteristics of the tag echo; This represents the preset horizontal angle between the personnel movement trajectory vector and the central axis of the antenna array's main lobe; If the calculated motion vector direction deviates from the entrance / exit and the duration exceeds a preset time threshold, the edge computing processing layer will automatically block the label to prevent accidental triggering of subsequent permission determination logic.

[0037] like Figure 2 As shown, the dynamic logic control layer receives the filtered and behavior-determined identity data output by the edge computing layer. Its core permission arbitration engine introduces a semantic analysis model based on knowledge graphs. This engine takes the spatial attributes, time windows, personnel ranks, historical access characteristics, and current security status level within the park as input parameters. When determining whether passage is permitted, the system implements a multi-factor weighted access control policy. This policy not only verifies the legitimacy of the identity, but also focuses on the rationality of passage at this time, in this place, and for this person. The calculation logic for the comprehensive admission score is defined by the following formula: , In this formula, This is the final admission score result output; The basic weight score for personnel identity is determined by the security classification level of the department to which the personnel belong; This indicates the number of people currently stranded within the target area, as calculated in real-time by the system. The maximum capacity threshold set for the area by administrative planning or fire safety; This is the park's real-time safety index. When a fire alarm, intrusion alarm, or special control period occurs in the park, this index will be adjusted accordingly. , , These are the preset weight coefficients for each factor, and their sum is always equal to a constant value; Only when The dynamic logic control layer will send an enable signal to the actuator only when the threshold value is greater than or equal to the preset threshold value.

[0038] The cross-system business collaboration layer realizes deep decoupling and protocol-level linkage between the management and control system and other subsystems in the park. This layer pushes personnel entry and exit events to the security monitoring system in real time through the northbound interface based on RESTful architecture. When specific personnel trigger entry / exit behavior, the security monitoring system automatically activates nearby PTZ cameras, driving them to rotate to a preset position for close-up capture based on pre-defined coordinate mapping relationships. The cross-system business collaboration layer also interfaces with the energy management system, transmitting real-time personnel distribution density data to provide refined adjustment references for the building's HVAC and lighting systems. The calculation model for the energy consumption regulation coefficient is as follows: , In this formula, This is the energy regulation coefficient, used to linearly map to air conditioning fan speed or light brightness commands; This refers to the real-time calculation of the regional population density in the aforementioned steps; The real-time ambient temperature is fed back from the environmental sensor; The system's preset target temperature for human comfort; Energy efficiency constants are preset for different building areas (such as office areas, computer rooms, and corridors).

[0039] In an emergency, the cross-system business collaboration layer receives fire or earthquake alarm signals from the emergency command system. The system immediately entered emergency evacuation mode, and the dynamic logic control layer forcibly switched all turnstiles and electromagnetic locks to the power-off normally open state; The phased array antenna array switches to omnidirectional high-frequency scanning mode, and generates a heat map of the distribution of stranded personnel in real time by quickly polling the locations of all RFID tags in the park, and pushes the specific location coordinates of the stranded personnel to the mobile terminals of the rescuers.

[0040] To ensure the system's reliability in extreme environments, the RFID tag has a multi-zone storage structure. The identification zone uses a physically unforgeable function for hardware-level encryption, the dynamic encryption zone stores a random handshake key that is updated by the edge computing layer at preset time intervals, and the behavior feature cache zone records the tag's entry and exit point indexes within a preset number of past entries and exits. If the system's main backbone network is interrupted, the edge computing node can use the historical data in the tag's cache zone to perform a downgrade determination using the local offline permission table, and automatically synchronize the passage flow during the offline period after the network is restored.

[0041] The global clock synchronization module uses a precision time protocol to provide nanosecond-level synchronization accuracy for readers and writers deployed in a distributed manner throughout the park. This ensures that the timestamps generated by different nodes have absolute reference significance when calculating personnel motion vectors and tracking cross-regional trajectories. The cloud server utilizes massive amounts of historical traffic data to execute a reinforcement learning-based pedestrian flow prediction model. By training a state transition value function, it can predict future traffic peaks. , In this formula, Represents the current traffic load status Under these circumstances, a certain recognition strategy is adopted to adjust the action. The long-term value of (e.g., increasing recognition bandwidth or shortening sleep intervals); The reward value is an instantaneous value, determined by the ratio of the recognition success rate to the passage time. The preset discount factor determines the model's sensitivity to predicting future traffic fluctuations.

[0042] The physical execution part of the system includes an intelligent gate unit, an electronic door lock control unit, a voice prompt unit, and pressure sensors. The pressure sensor array is embedded in the bottom pedal of the gate channel, and its output weight distribution matrix is ​​transmitted to the edge computing processing layer in real time. If the RFID identifies a valid tag, but the pressure sensor shows that there are multiple pressure points and the total weight exceeds the preset range for a single person, the system will determine it as tailgating or proxy carrying behavior, immediately drive the voice prompt unit to play a warning voice, and link the gate to execute a forced locking action.

[0043] The technical solution of the present invention will be quantitatively described below through several specific embodiments.

[0044] Example 1: The system described in this invention was deployed at the main entrance of the headquarters office building of a large smart park. The phased array antenna array is installed on the inside of the glass curtain wall. It is a linear array composed of four antenna elements and the operating frequency is set to 920MHz to 925MHz. During the morning rush hour, the flow of people reaches several hundred per minute; Tag density calculated in real time by the system's edge computing layer The system reached the preset high value range. By dynamically adjusting the time slot frame length, the system completed the identification of fifty people wearing RFID name tags passing by in a dense crowd within three seconds, with a success rate of 99.9%. The system-linked air conditioning system increased the air volume in the lobby by 30%, achieving an advanced response in environmental regulation.

[0045] Example 2: At the entrance to a Class A classified laboratory within the park, the system has activated a multi-factor weighted access control strategy. Set as a high-weight score. Due to important experiments being conducted inside the laboratory, the real-time safety status index... Lowered; A person with basic access attempted to enter, but was denied entry as the laboratory had already reached its maximum capacity. The calculated admission score Below the preset threshold; The system informed personnel via voice prompts that the area was full and asked them to wait, while the electronic door locks remained locked, successfully preventing fluctuations in the experimental environment that could have been caused by overcrowding.

[0046] Example 3: During a simulated fire emergency drill, the system received a trigger signal from the emergency command system. The phased array antenna immediately switched from directional beam mode to wide beam coverage mode, and the scanning frequency increased from the usual ten times per second to one hundred times per second. Within ten seconds, the system identified and located the security guards on different floors and the visitors who could not be evacuated in time. By linking with the security monitoring system, the location of these personnel is displayed in real time on the large screen in the command center, and the optimal escape route is automatically planned. The voice prompts in each passage guide the evacuation of personnel.

[0047] Comparative Example 1: Traditional omnidirectional fixed-beam RFID reading and writing systems lack phase sensing and beamforming capabilities. In high-density traffic scenarios, this system suffers from severe tag signal interference due to its inability to suppress multipath reflections from walls and metal obstacles. Experimental data shows that when the number of people passing through per minute exceeds a certain threshold, the system's miss rate increases significantly, and it cannot distinguish between non-entry personnel passing through the gate, resulting in a large number of false gate openings.

[0048] Comparative Example 2: A permission control scheme based on static database matching is adopted. The permission logic of this scheme depends only on whether the tag ID exists in the database; In simulated tests where the number of people in a region exceeded the limit or the security situation was upgraded, the system failed to automatically tighten permissions based on real-time environmental parameters, resulting in overloaded personnel density in sensitive areas such as laboratories during specific periods, indicating a lag in security management.

[0049] Comparative Example 3: The personnel management system operates in isolation and is not logically integrated with the monitoring, energy, and emergency response subsystems. In emergency drills, the system cannot provide real-time feedback on the physical location of personnel, nor can it dynamically adjust energy consumption based on personnel distribution. During normal operation, due to the lack of multiple layers of protection through video verification, the system is unable to effectively identify cases of unauthorized tag-holding or tailgating, resulting in a low level of security.

[0050] The table below provides a detailed comparison of the test data for key performance indicators between the three embodiments and the three comparative examples described above: Table 1: Comparison of System Performance Indicators Test metrics Example 1 Example 2 Example 3 Comparative Example 1 Comparative Example 2 Comparative Example 3 High-density recognition accuracy (%) 99.92 99.95 99.88 85.40 88.20 87.50 Average latency for access identification (ms) 120 145 110 450 380 410 False trigger rate (times / 10,000 passes) 0.8 0.5 1.2 45.0 32.0 38.0 Emergency response time (s) 0.5 0.4 0.6 5.5 4.8 6.2 Area population control error (people) 0 0 1 8 5 7 Combined energy-saving efficiency (%) 22.5 18.2 15.8 0.0 0.0 0.0 Abnormal behavior detection rate (%) 98.5 99.2 97.8 12.5 15.0 10.5 The data analysis in the table above shows that the present invention demonstrates significant technical advantages in terms of recognition accuracy, system response speed, anomaly detection capability, and multi-system linkage benefits. In particular, in the dimensions of high-density concurrent recognition and dynamic permission management, the present invention solves the technical pain point of traditional technologies that are difficult to balance accuracy and flexibility.

[0051] Further delving into the specific engineering implementation details of this invention, the adaptive radio frequency identification component of the multi-dimensional sensing layer, in terms of hardware implementation, its radio frequency front-end processing module adopts a baseband processing unit based on a high-performance FPGA. The internal logic of this unit is divided into a high-speed signal acquisition domain, a signal preprocessing domain, and a communication control domain. In the high-speed signal acquisition domain, the analog signal is converted into a digital sequence by a high-speed analog-to-digital converter, and the sampling rate meets the requirements for capturing small changes in the carrier phase. The signal preprocessing domain extracts the baseband IQ component containing tag reflection information through digital down-conversion and low-pass matched filtering. The communication control domain executes a preset anti-collision algorithm logic, which performs cluster analysis on the time and phase characteristics of the signals returned by different tags to deconstruct multiple concurrent signals in space.

[0052] In the edge computing processing layer, in order to achieve extremely low latency prediction of human behavior, the system adopts a processor cluster based on a reduced instruction set architecture. The data processing engine stores the received raw tag data stream in a circular buffer and uses a multi-threaded parallel processing mechanism to run tag recognition and trajectory prediction tasks simultaneously. When performing Kalman filtering, the personnel behavior trajectory prediction module's state vector contains the tag's position coordinates, velocity, and acceleration information. The observation vector consists of the carrier phase difference and the rate of change of signal strength. By correcting the prediction and observation matrices in real time, this module can output the personnel's movement tendency at millisecond-level frequencies.

[0053] The permission arbitration engine of the dynamic logic control layer includes a dynamic knowledge base in its logical architecture, which stores and updates various resource entities in the park and their interrelationships in real time. When a passage request occurs, the engine uses graph query languages ​​such as SPARQL to quickly retrieve all logical chains associated with the person and the entrance / exit. For example, if the query results show that the office area of ​​the person's department is under power outage maintenance at the current time, even if the person has basic access permissions, the access arbitration engine will lower their access score based on resource matching rules. This decision-making method based on semantic logic greatly improves the intelligence level of the management and control system.

[0054] The cross-system business collaboration layer employs a highly reliable message queue mechanism during data interaction. All incoming and outgoing events are first written to the local message cache and then distributed according to a preset priority. The linkage commands of the security monitoring system have the highest priority, ensuring that video capture and physical movement are synchronized. The distribution of energy management data is smooth, avoiding frequent switching of air conditioning or lighting systems due to rapid personnel movement in short periods, thus protecting the hardware lifespan of the end devices.

[0055] The identification area of ​​the RFID tag uses a random number generator based on transistor-level physical characteristics to generate a seed key. After each successful identification, the dynamic encryption area sends a new random offset via the downlink from the reader. The internal encryption circuit of the tag updates its internal state accordingly. This one-time key dynamic authentication mechanism completely eliminates the possibility of unauthorized personnel deceiving the system by recording and replaying radio frequency signals. The tag's behavioral feature cache uses a non-volatile storage chip, which ensures that the stored historical path information is not lost even when there is no power.

[0056] To achieve distributed sub-microsecond alignment, the global clock synchronization module employs Ethernet-based hardware timestamp technology. At the physical layer of each network interface controller, synchronization pulses are precisely injected. Through periodic master-slave synchronization message exchange, each edge computing node can automatically compensate for asymmetric delays on the network transmission path, thereby ensuring a high degree of uniformity in the time dimension of the entire park management and control system. This provides an indispensable spatiotemporal benchmark for tracking the rapid movement trajectory of personnel across buildings and for conducting full-scene behavior audits afterward.

[0057] The actuator of this invention is designed with user-friendliness and safety in mind in mind. The intelligent gate unit adopts a DC brushless motor drive system with anti-pinch function, which can adjust the closing speed and force of the gate wings in real time according to the human distance information fed back by the pressure sensor. The voice prompt unit is equipped with digital sound wave guiding technology, which can accurately and directionally deliver the prompt voice to the ear area of ​​the person passing through in noisy environments, reducing sound pollution and improving the privacy of the interaction.

[0058] In summary, this invention constructs a smart park personnel management ecosystem with extremely fine granularity, comprehensive data support, and adaptive capabilities by introducing phased array technology and phase calculation at the physical perception layer, edge intelligence and trajectory prediction at the computing layer, knowledge graph-driven dynamic arbitration at the decision-making layer, and full-element system linkage at the collaboration layer. This full-stack engineering optimization, from underlying signal characteristics to top-level management logic, not only improves the security level of the park but also provides a solid technical guarantee for the digital management and green operation of the smart park.

[0059] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A smart park RFID personnel access collaborative control system, characterized in that, include: The multidimensional sensing layer acquires raw signals from RFID tags through an adaptive RFID component. The multidimensional sensing layer includes a phased array antenna array, an RF front-end processing module, and an environmental parameter monitoring submodule. The phased array antenna array forms a detection beam under the beamforming control of the RF front-end processing module to obtain raw observation data containing received signal strength indication and carrier phase information. An edge computing processing layer is electrically connected to the multi-dimensional sensing layer. The edge computing processing layer includes a data processing engine and a trajectory prediction module, which are used to process concurrent tag signals through dynamic time slot allocation logic and use the rate of change of carrier phase difference to estimate the motion vector of personnel carrying radio frequency tags in order to eliminate interference tags that are not intended to pass through. The dynamic logic control layer interacts with the edge computing processing layer. The dynamic logic control layer has a knowledge graph-based permission arbitration engine. The permission arbitration engine is configured to take into account time, spatial load, security level and personnel attribute dimensions, execute access control policies based on multi-factor weighting, calculate the comprehensive access score in real time and output access control instructions. The cross-system business collaboration layer connects with the park's security monitoring system, energy management system, and emergency command system through a preset standard northbound interface, based on the identity information and location trajectory output by the dynamic logic control layer. The multi-dimensional perception layer, edge computing processing layer, dynamic logic control layer, and cross-system business collaboration layer achieve unified spatiotemporal data flow through a communication network.

2. The smart park RFID personnel access collaborative control system according to claim 1, characterized in that, In the adaptive radio frequency identification component of the multidimensional sensing layer, the phased array antenna array comprises multiple antenna elements, each of which is connected to a digitally controlled phase shifter. The radio frequency front-end processing module forms a directional beam by issuing phase shift commands to each of the digitally controlled phase shifters; The environmental parameter monitoring submodule is used to collect electromagnetic background noise and multipath reflection parameters in the environment in which the reader is deployed. When the environmental interference level exceeds the preset environmental threshold, the radio frequency front-end processing module automatically adjusts the frequency hopping sequence and transmission power.

3. The smart park RFID personnel access collaborative control system according to claim 1, characterized in that, When executing the dynamic time slot allocation logic, the data processing engine of the edge computing processing layer dynamically adjusts the number of time slots in the recognition cycle by calculating the tag density function in the current recognition area. The calculation model of the tag density function is as follows: , In this formula, This represents the predicted label density value within the preset identification area; Indicates the first The signal strength weighting factor for each tag is obtained by logarithmically normalizing the received signal strength indication value; Indicates the first Each label during the observation period The cumulative value of carrier phase change within the period; This represents the effective physical area covered by the phased array antenna array. This represents the total number of active tags currently detected. When the predicted tag density increases, the data processing engine proportionally increases the number of time slots in the next frame recognition cycle and simultaneously shortens the listening window duration of a single time slot.

4. The smart park RFID personnel access collaborative control system according to claim 1, characterized in that, The trajectory prediction module of the edge computing processing layer uses the extracted carrier phase difference change rate, combined with a preset Kalman filter model, to estimate the motion vector of the person in real time. The motion vector estimation formula is as follows: , In this formula, This indicates the radial velocity of the personnel relative to the reader antenna; Indicates the wavelength of the current radio frequency signal; This represents the rate of change of the carrier phase over time. This represents the preset horizontal angle between the personnel movement direction vector and the central axis of the antenna main lobe; When the motion vector calculated by the trajectory prediction module deviates from the entrance / exit direction and the duration exceeds a preset time threshold, the edge computing processing layer logically blocks the corresponding label.

5. The smart park RFID personnel access collaborative control system according to claim 1, characterized in that, The dynamic logic control layer's permission arbitration engine calculates the comprehensive access score in real time using the following formula when determining whether passage is permitted: , In this formula, This indicates the final admission score; The preset identity and access level score is determined by the security classification level of the department to which the person belongs. This indicates the number of people currently stranded within the target area in real time. This indicates the preset maximum threshold for the number of people allowed in the area. This indicates the current real-time security status index of the park; , , These represent the preset weight coefficients of each factor; When the comprehensive admission score reaches or exceeds the preset admission threshold, the dynamic logic control layer sends a permission instruction to the execution mechanism.

6. The smart park RFID personnel access collaborative control system according to claim 1, characterized in that, When the cross-system business collaboration layer collaborates with the energy management system, it updates the personnel distribution map of the corresponding area in real time based on the location transfer trajectories generated by personnel entering and exiting. It also dynamically adjusts the lighting intensity and air conditioning operating parameters of the corresponding area using an energy consumption prediction model based on personnel density. The calculation model for its energy consumption adjustment coefficient is as follows: , In this formula, Indicates the energy regulation coefficient; The tag density calculated for the edge computing processing layer; This indicates the real-time temperature value fed back by the ambient temperature sensor. This indicates the preset target comfort temperature; The preset equipment efficiency constants for different functional areas of a building.

7. The smart park RFID personnel access collaborative control system according to claim 1, characterized in that, The dynamic logic control layer also integrates an abnormal behavior alarm mechanism and emergency mode switching logic: The abnormal behavior alarm mechanism is configured to: when the system detects that the same RFID tag appears in multiple geographical locations that do not conform to the motion logic within a preset time, or detects changes in signal characteristics caused by illegal removal of the tag, the alarm logic is immediately triggered and the security monitoring system is linked to capture video of the corresponding area. The emergency mode switching logic is configured such that when a sudden disaster alarm signal is received, the system automatically switches to emergency mode. At this time, the normal permission judgment logic of the dynamic logic control layer is suspended, all entrance and exit actuators are set to the normally open state, and the multi-dimensional perception layer switches to a preset high-frequency scanning state to locate the location of stranded personnel in the park.

8. The smart park RFID personnel access collaborative control system according to claim 1, characterized in that, The system involves radio frequency tags with a specific multi-zone storage structure, including: The identity identification area is used to store a unique personnel code generated using hardware-level encryption with a physically unforgeable function; A dynamic encryption zone is used to store random handshake keys periodically issued by the edge computing processing layer. The encryption circuit inside the RFID tag updates its internal authentication status based on the random handshake keys. The behavior feature cache is used to record the indexes of important path points that people pass through in the park, so that behavior trajectory can be completed offline in the event of network connection interruption.

9. The smart park RFID personnel access collaborative control system according to claim 1, characterized in that, The system also includes a cloud server, which uses historical traffic data to execute a reinforcement learning-based method for predicting pedestrian flow. The state transition value function of the prediction model is expressed as: , In this formula, Indicates the current traffic load status The following identification strategy was adopted to adjust the action. The expected long-term value; This represents the instant reward function value, which is determined by the ratio of recognition success rate to processing latency; This is a preset discount factor; The next state after taking an action; The cloud server periodically sends the optimized prediction model to the edge computing processing layer to pre-adjust the polling strategy of the adaptive RFID component.

10. The smart park RFID personnel access collaborative control system according to claim 1, characterized in that, The system also includes: The actuator includes an intelligent gate unit, an electronic door lock control unit, and a voice prompt unit. The intelligent gate unit has a built-in pressure sensor array, which is used to collect the pressure distribution matrix at the bottom of the passage in real time. When the system identifies a predetermined number of valid RFID tags, but the characteristics of the pressure distribution matrix indicate that the number of people passing through exceeds the predetermined number, the system issues a warning through the voice prompt unit and drives the smart gate unit to perform a gate closing operation. The global clock synchronization module uses a preset high-precision synchronization protocol to ensure that the readers in the multi-dimensional perception layer, the servers in the edge computing processing layer, and the execution mechanisms are all under a unified time reference. The data transmission network adopts a topology that combines wired redundant links with wireless backup links. When a fault is detected in the backbone wired link, the system automatically switches to the wireless backup link to transmit control commands.

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