Parking lot management method, system, device and medium based on dual-core recognition system

Abstract

The application relates to the technical field of parking lot management, in particular to a parking lot management method, system, equipment and medium based on a dual-core identification system. The application combines UWB positioning and RFID identification technology to realize dual confirmation of vehicle position and identity information. The method first acquires environmental parameters and operation modes, dynamically adjusts system working parameters, acquires position coordinates through a UWB module and corrects errors, reads vehicle information through an RFID module and verifies the identity, combines real-time parking space information to calculate recommended parking spaces, controls an intelligent lock to determine an occupancy state, and finally performs timing and charging. The complementary verification of the dual-core identification improves the reliability of the system, realizes intelligent management of the whole process from vehicle identification to fee settlement, and effectively improves the operation efficiency of the parking lot and the user experience.

Description

Technical Field

[0001] This application relates to the technical field of parking management, and in particular to parking management methods, systems, equipment and media based on dual-core identification systems. Background Technology

[0002] With the acceleration of urbanization and the continuous growth of motor vehicle ownership, the parking problem has become increasingly prominent. Intelligent parking systems, as an important means of solving parking difficulties, play a crucial role in improving parking management efficiency and enhancing the user parking experience. How to achieve intelligent management and precise services for parking lots has become a significant issue in urban transportation development.

[0003] Existing technologies employ video recognition and geomagnetic induction to manage parking lots. Video recognition systems capture vehicle images using cameras and combine them with image processing algorithms to identify vehicle information; geomagnetic induction systems detect changes in the Earth's magnetic field to determine the occupancy status of parking spaces, thus achieving automated parking lot management.

[0004] However, video recognition is susceptible to environmental factors such as lighting and weather, which reduces recognition accuracy; geomagnetic induction can only detect the occupancy status of parking spaces and cannot obtain vehicle identity information, making it difficult to achieve accurate vehicle positioning and reliable identity recognition at the same time, which affects the efficiency of parking lot management and service quality. This situation needs to be further improved. Summary of the Invention

[0005] To address the problem that existing parking management methods struggle to simultaneously achieve accurate vehicle location and reliable identification, thus impacting parking management efficiency and service quality, this application provides a parking management method, system, equipment, and medium based on a dual-core identification system, employing the following technical solution:

[0006] In a first aspect, this application provides a parking lot management method based on a dual-core recognition system, comprising the following steps:

[0007] Obtain parking lot environmental parameters and system operating mode, and determine the operating parameters of the dual-core recognition system based on the environmental parameters and system operating mode;

[0008] The vehicle is located using a UWB positioning module to obtain its real-time position coordinates; and the position coordinates are corrected for errors based on the operating parameters to obtain corrected position data.

[0009] Based on the corrected location data, vehicle information is read using an RFID identification module to obtain vehicle identification data; feature extraction and verification are performed on the vehicle identification data to obtain the vehicle identity credibility.

[0010] Based on the vehicle identification data, identity credibility, and corrected location data, combined with real-time parking space distribution information, parking space recommendation information is calculated and obtained through a cloud server.

[0011] Based on the parking space recommendation information, control the smart lock of the corresponding parking space to obtain the parking space occupancy status;

[0012] Based on the parking space occupancy status, the time and billing unit is activated to obtain parking fee data.

[0013] By adopting the above technical solution, this application proposes a parking lot management method based on a dual-core identification system. Through the coordinated use of UWB positioning and RFID identification, dual verification of vehicle location and identity information is achieved. First, the environmental parameters and system operating mode of the parking lot are acquired, and the operating parameters of the dual-core identification system are dynamically adjusted. Then, the real-time location coordinates of the vehicle are obtained using the UWB positioning module, and error correction is performed. Based on the corrected location data, vehicle information is read and identity credibility is verified using the RFID identification module. Next, combined with real-time parking space distribution information, a cloud server calculates recommended parking spaces. Then, the parking space occupancy status is determined by controlling the smart lock. Finally, the timekeeping and billing unit is activated to generate fee data. The complementary verification through dual-core identification improves system reliability and realizes intelligent management of the entire process from vehicle identification to parking space allocation to fee settlement, significantly improving the operational efficiency and user experience of the parking lot.

[0014] Optionally, the parking lot environmental parameters and system operating mode are obtained, and the operating parameters of the dual-core identification system are determined based on the environmental parameters and system operating mode, specifically including the following steps:

[0015] The signal interference intensity and multipath effect coefficient are extracted from the environmental parameters, and the signal interference intensity and multipath effect coefficient are used as the main correction targets.

[0016] Based on the signal interference intensity, multipath effect coefficient, and system operation mode, obtain UWB signal processing parameters, RFID reading parameters, and system coordination parameters;

[0017] The operating parameters of the dual-core identification system are obtained by calculating the correlation between the signal interference intensity, UWB signal processing parameters, RFID reading parameters, and system coordination parameters.

[0018] By adopting the above technical solution, the presence of numerous metal structures and moving vehicles in parking lots leads to complex electromagnetic signal propagation paths and diverse interference sources, resulting in unstable system recognition performance. This application first extracts signal interference intensity and multipath effect coefficients from environmental parameters as the main correction targets to ensure that the system can accurately grasp the characteristics of environmental changes. Then, based on the extracted interference intensity, multipath effect coefficients, and the current system operating mode, UWB signal processing parameters, RFID reading parameters, and system coordination parameters are comprehensively calculated to achieve multi-dimensional parameter optimization. Finally, through the correlation calculation of signal interference intensity and various parameters, the optimal operating parameter configuration of the dual-core recognition system is obtained. Through environmental perception and adaptive parameter adjustment, the system can dynamically optimize its performance according to the actual situation, effectively improving the reliability and recognition accuracy of the dual-core recognition system in complex environments.

[0019] Optionally, the UWB signal processing parameters include signal filtering coefficients and gain control coefficients, and the RFID reading parameters include reading power parameters and reading timing parameters.

[0020] Based on the signal interference intensity, multipath effect coefficient, and system operating mode, UWB signal processing parameters, RFID reading parameters, and system coordination parameters are obtained, specifically including the following steps:

[0021] Based on the signal interference intensity and multipath effect coefficient, obtain the signal filtering coefficient, the gain control coefficient, the readout power parameter, the readout timing parameter, and the system coordination parameter;

[0022] The RFID reading parameters are obtained based on the reading power parameters and the reading timing parameters;

[0023] Based on the system operation mode, various parameters are dynamically optimized and adjusted. The system operation mode includes peak mode, normal mode and energy-saving mode.

[0024] By adopting the above technical solution, this application first clarifies that the UWB signal processing parameters include signal filtering coefficients and gain control coefficients, and the RFID reading parameters include reading power parameters and reading timing parameters. Then, based on the measured signal interference intensity and multipath effect coefficient, the signal filtering coefficients, gain control coefficients, reading power parameters, reading timing parameters, and system coordination parameters are uniformly calculated and obtained. Next, based on the combined optimization of reading power parameters and reading timing parameters, the final RFID reading parameters are obtained. Finally, according to different system operation modes such as peak mode, normal mode, and energy-saving mode, all parameters are dynamically optimized and adjusted. Through the coordinated optimization between parameters and the dynamic adjustment of operation modes, the overall system performance is optimized, effectively improving the adaptability and working efficiency of the dual-core identification system in different working scenarios.

[0025] Optionally, the vehicle identification data includes vehicle feature parameters and identity verification parameters. Based on the corrected location data, vehicle information is read using an RFID identification module, specifically including the following steps:

[0026] Obtain information on the status of the RFID antenna array and its signal coverage.

[0027] Based on the UWB signal processing parameters, the RFID reading parameters, and the signal coverage information, calculate the optimal reading angle and power configuration;

[0028] Based on the optimal reading angle and power configuration, vehicle RFID tag information is obtained;

[0029] Based on the tag information and system collaboration parameters, the vehicle feature parameters and identity verification parameters are extracted to generate the vehicle identity recognition data. The system collaboration parameters include time synchronization parameters, spatial matching parameters, and data fusion parameters.

[0030] By adopting the above technical solution, this application first obtains the working status and signal coverage information of the RFID antenna array to understand the current identification capability of the system; then, by combining UWB signal processing parameters, RFID reading parameters, and signal coverage information, the optimal reading angle and power configuration scheme is determined through comprehensive calculation; next, based on the optimized angle and power configuration, the vehicle RFID tag information is accurately read; finally, using system coordination parameters including time synchronization parameters, spatial matching parameters, and data fusion parameters, vehicle feature parameters and authentication parameters are extracted from the tag information to generate complete vehicle identification data; through location perception and parameter adaptive adjustment, intelligent optimization of the RFID reading process is achieved, significantly improving the accuracy and reliability of vehicle identification.

[0031] Optionally, by combining real-time parking space distribution information, parking space recommendation information can be calculated and obtained through a cloud server, specifically including the following steps:

[0032] Acquire real-time parking space status data and historical parking data of the parking lot. The real-time parking space status data includes parking space occupancy status, parking space type, and parking space environmental parameters.

[0033] Extract user parking preference features based on the historical parking data;

[0034] Based on the parking space environment parameters and parking space type, calculate the parking space convenience index and parking space matching score;

[0035] Based on the corrected location data, the optimal navigation path from the vehicle to each parking space is calculated, and the path accessibility score is obtained.

[0036] Using the user parking preference characteristics, parking space convenience index, parking space matching score and route accessibility score, a comprehensive evaluation result of the parking space is obtained through multi-factor weighted calculation.

[0037] Based on the comprehensive evaluation results of the parking spaces, the top N parking spaces with the highest scores are selected as recommended parking spaces, and the parking space recommendation information is generated, where N is the preset number of recommendations;

[0038] The availability of the recommended parking spaces is verified to ensure that the recommended parking spaces meet the access requirements of the vehicle identification data and identity credibility.

[0039] By adopting the above technical solution, when multiple parking spaces are available simultaneously, considering only distance factors may lead to recommendations that do not match the user's actual preferences, reducing user satisfaction. This application first obtains real-time parking space status data, including parking space occupancy, parking space type, and parking space environmental parameters, as well as historical parking data for analysis. Then, through in-depth analysis of historical data, it extracts the user's parking preference characteristics. Next, it calculates the convenience index and matching score for each parking space by combining parking space environmental parameters and type information. Simultaneously, based on the vehicle's current location, it calculates the optimal navigation path to each parking space and obtains the path accessibility score. Then, it performs multi-factor weighted calculation of user preference characteristics, convenience index, matching score, and path accessibility score to obtain a comprehensive evaluation result of the parking spaces. Based on the evaluation results, it selects the N parking spaces with the highest scores as recommended options. Finally, it verifies the availability of the recommended parking spaces to ensure that they meet the vehicle identification requirements. This achieves personalized parking space recommendations, effectively improving parking efficiency and user satisfaction.

[0040] Optionally, after generating the parking space recommendation information, the method further includes the following steps:

[0041] The system checks whether there are any available parking spaces in the comprehensive parking space evaluation results.

[0042] If no parking spaces are available, obtain real-time parking information from nearby parking lots and calculate the distance and estimated arrival time of the vehicle to each nearby parking lot.

[0043] Using the distance, estimated arrival time, and parking availability of each parking lot, a recommended list of alternative parking lots is generated;

[0044] Real-time monitoring of parking space availability in the current parking lot, and prediction of parking space vacancy time based on historical data analysis;

[0045] Push a list of alternative parking lots to users, along with the estimated availability of parking spaces in the current parking lot and the location of the temporary waiting area;

[0046] Regularly update push notifications until the user completes their parking selection or an available parking space becomes available in the current parking lot.

[0047] By adopting the above technical solution, during peak hours in commercial areas, users may find themselves shuttling between multiple parking lots without finding a suitable parking space. This application, based on multi-parking lot collaboration, first checks whether there are available parking spaces in the current parking lot. When no available spaces are found, the system automatically obtains real-time parking space information from surrounding parking lots and calculates the distance and estimated arrival time to each parking lot based on the vehicle's current location. Then, based on factors such as distance, arrival time, and parking space availability, a reasonable list of recommended alternative parking lots is generated. Simultaneously, the system continuously monitors the parking space dynamics of the current parking lot and predicts the possible vacancy time of parking spaces through historical data analysis. Next, it pushes comprehensive information to the user, including the list of alternative parking lots, estimated parking space vacancy time, and the location of temporary waiting areas. Finally, the system continuously updates this information until the user completes their parking selection or an available parking space becomes available in the current parking lot. This effectively alleviates the parking space shortage and improves overall parking efficiency.

[0048] Optionally, based on the parking space occupancy status, the time and billing unit is activated to obtain parking fee data, specifically including the following steps:

[0049] Obtain parking fee standards and discount strategies, including basic rates and rates for various time periods;

[0050] Check whether the parking space occupancy status and lock reliability data are normal;

[0051] If everything is normal, start the timer and billing system and record the parking start time;

[0052] Real-time monitoring of vehicle availability status; calculation of parking fees based on parking space type and the aforementioned billing standard.

[0053] When a vehicle is detected leaving, the timer stops and parking fee data is generated;

[0054] If the lock reliability data is detected to be abnormal, billing will be suspended and the abnormal time period will be recorded.

[0055] By adopting the above technical solution, this application first obtains parking fee standards including basic rates and various time-based rates, as well as related preferential strategies; then, it comprehensively detects parking space occupancy status and lock reliability data; when the system is in normal condition, it starts the time-based billing function and records the parking start time; the system continuously monitors the vehicle's presence status and calculates parking fees in real time according to the specific parking space type and applicable billing standards; when a vehicle is detected leaving, the system automatically stops timing and generates the final parking fee data; if an anomaly is detected in the lock reliability data during the billing process, the system automatically pauses billing and records the time period of the anomaly in detail; by introducing an anomaly detection and processing mechanism, precise management of parking fees is achieved, effectively ensuring the fairness of the fees and the reliability of the system.

[0056] Secondly, this application provides a parking management system based on a dual-core identification system, comprising:

[0057] The working parameter determination module is used to acquire parking lot environmental parameters and system operating mode, and determine the working parameters of the dual-core recognition system based on the environmental parameters and system operating mode.

[0058] The position data correction module is used to locate the vehicle using a UWB positioning module to obtain the vehicle's real-time position coordinates; and to perform error correction on the position coordinates based on the operating parameters to obtain corrected position data.

[0059] The vehicle identity credibility acquisition module is used to read vehicle information using an RFID identification module based on the corrected location data to obtain vehicle identity recognition data; and to perform feature extraction and verification on the vehicle identity recognition data to obtain vehicle identity credibility.

[0060] The parking space recommendation information acquisition module is used to calculate and obtain parking space recommendation information through a cloud server based on the vehicle identification data, identity credibility and corrected location data, combined with real-time parking space distribution information.

[0061] The parking space status acquisition module is used to control the smart lock of the corresponding parking space based on the parking space recommendation information to obtain the parking space occupancy status.

[0062] The parking fee calculation module is used to activate the timing and billing unit based on the parking space occupancy status to obtain parking fee data.

[0063] Thirdly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the parking management method based on the dual-core identification system described above.

[0064] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the parking management method based on the dual-core identification system described above.

[0065] In summary, this application includes at least one of the following beneficial technical effects:

[0066] 1. This application proposes a parking lot management method based on a dual-core identification system, combining UWB positioning and RFID identification technologies to achieve dual verification of vehicle location and identity information. The method first acquires environmental parameters and operating modes, and dynamically adjusts system operating parameters; it then acquires location coordinates and corrects errors through a UWB module; it uses an RFID module to read vehicle information and verify identity; it calculates recommended parking spaces based on real-time parking space information; it controls smart locks to determine occupancy status; and finally, it performs timekeeping and billing. The complementary verification through dual-core identification improves system reliability, achieving intelligent management of the entire process from vehicle identification to payment settlement, effectively improving parking lot operation efficiency and user experience.

[0067] 2. Due to the large number of metal structures and moving vehicles in parking lots, electromagnetic signal propagation is complex and interference sources are diverse, affecting the system's identification performance. This application first extracts the signal interference intensity and multipath effect coefficient as correction targets to accurately grasp the characteristics of environmental changes. Then, based on these parameters and the system operating mode, UWB signal processing parameters, RFID reading parameters, and system coordination parameters are calculated. Finally, the optimal operating parameter configuration is obtained through the correlation calculation of signal interference intensity and various parameters. Through environmental perception and adaptive parameter adjustment, the dynamic optimization of system performance is achieved, improving the reliability and identification accuracy in complex environments.

[0068] 3. When multiple parking spaces are available, recommendations based solely on distance may not align with user preferences. This application first acquires real-time parking space status data and historical parking data; extracts user parking preferences by analyzing historical data; calculates a convenience index and a matching score by combining parking space environment parameters and type information; calculates the optimal navigation route based on vehicle location to obtain an accessibility score; performs multi-factor weighted calculations on user preferences, convenience, matching score, and accessibility to obtain a comprehensive evaluation result; selects the N parking spaces with the highest scores as recommendations and verifies their availability; thus achieving personalized recommendations and improving parking efficiency and user satisfaction. Attached Figure Description

[0069] Figure 1 This is a flowchart illustrating a parking management method based on a dual-core identification system according to an embodiment of this application;

[0070] Figure 2 This is a flowchart illustrating step S100 in a parking lot management method based on a dual-core identification system according to an embodiment of this application.

[0071] Figure 3 This is a flowchart illustrating step S120 in a parking management method based on a dual-core identification system according to an embodiment of this application.

[0072] Figure 4This is a flowchart illustrating step S300 in a parking management method based on a dual-core identification system according to an embodiment of this application.

[0073] Figure 5 This is a flowchart illustrating step S400 in a parking lot management method based on a dual-core identification system according to an embodiment of this application.

[0074] Figure 6 This is a flowchart illustrating step S460 in a parking lot management method based on a dual-core identification system according to an embodiment of this application.

[0075] Figure 7 This is a flowchart illustrating step S600 in a parking lot management method based on a dual-core identification system according to an embodiment of this application.

[0076] Figure 8 This is a schematic diagram of a parking management system based on a dual-core identification system according to an embodiment of this application;

[0077] Figure 9 This is an internal structural diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0078] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0079] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0080] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0081] Firstly, this application provides a parking management method based on a dual-core recognition system, referring to... Figure 1 It includes the following steps:

[0082] S100: Obtain parking lot environmental parameters and system operating mode, and determine the working parameters of the dual-core identification system based on the environmental parameters and system operating mode.

[0083] In this embodiment, environmental parameters refer to physical quantities that affect the propagation of electromagnetic signals in the parking lot, mainly including signal interference intensity, multipath effect coefficient, temperature, humidity, and traffic density. Signal interference intensity characterizes the degree of influence of external electromagnetic waves on the system's operating signal, measured in decibels. The multipath effect coefficient reflects the combined intensity of reflection, scattering, and diffraction phenomena during signal propagation, with a value ranging from 0 to 1. The system operating modes include peak mode, normal mode, and energy-saving mode, automatically switching according to traffic flow. Operating parameters include configuration values ​​for three dimensions: signal processing, data acquisition, and system coordination.

[0084] Specifically, this solution acquires environmental parameters by deploying environmental monitoring units at the parking lot entrance. These units include electromagnetic wave detectors, temperature and humidity sensors, and traffic flow detectors. First, a mapping table between environmental parameters and operating parameters is established, dividing signal interference intensity into three ranges: weak interference, moderate interference, and strong interference. Multipath effect coefficients are categorized into three levels: slight, moderate, and severe. When a vehicle is detected entering, the environmental monitoring unit collects current environmental data and queries the mapping table to obtain the corresponding basic operating parameters. Simultaneously, based on the current traffic flow threshold, the system operating mode is determined, enabling automatic switching between peak, normal, and energy-saving modes. Finally, the operating mode coefficient is multiplied by the basic operating parameters to obtain the actual operating parameter configuration.

[0085] S200 uses a UWB positioning module to locate the vehicle and obtain its real-time position coordinates; and performs error correction on the position coordinates based on the working parameters to obtain the corrected position data.

[0086] In this embodiment, the UWB positioning module refers to a device that uses nanosecond-level ultra-short pulse signals for wireless communication and positioning. Ultra-wideband (UWB) technology uses very narrow time-domain pulses to generate an extremely wide spectrum, characterized by strong penetration, high positioning accuracy, and low power consumption. This positioning module consists of a base station and an onboard tag. The base station is responsible for signal transmission and reception, while the onboard tag is used to respond to the signal. The vehicle's real-time position coordinates include x-axis coordinates, y-axis coordinates, and timestamp information, represented using a Cartesian coordinate system. Error correction mainly targets positioning deviations caused by multipath effects and signal attenuation; the corrected position data includes corrected coordinate values ​​and a reliability index.

[0087] Specifically, this solution deploys multiple UWB base stations in the parking lot and uses a two-way ranging principle to obtain the location information of the vehicle-mounted tags. First, a base station coordinate mapping table is established to record the fixed location information of each base station. Registered vehicles in the parking lot are issued a location tag. When a vehicle enters the parking lot, at least three base stations simultaneously receive the vehicle-mounted tag signal and record the round-trip time. The distance from the vehicle to each base station is calculated using the round-trip time. Combined with the base station coordinate mapping table, a trilateration algorithm is used to calculate the vehicle's real-time coordinates. Then, the original coordinates are corrected using the signal processing parameters in the operating parameters, including signal filtering and gain compensation, to obtain the corrected location data.

[0088] S300: Based on the corrected location data, the vehicle information is read using the RFID identification module to obtain vehicle identification data; the vehicle identification data is then subjected to feature extraction and verification to obtain the vehicle's identity credibility.

[0089] In this embodiment, the RFID identification module uses radio frequency identification technology to read vehicle electronic tag information. Vehicle identification data includes vehicle characteristic parameters and identity verification parameters, used to confirm the authenticity of the vehicle's identity. Vehicle identity credibility is a comprehensive evaluation index calculated based on multiple characteristic parameters, used to characterize the reliability of the identity recognition result.

[0090] Specifically, this solution involves setting up an RFID antenna array beside the lane to read vehicle tag information. First, a vehicle feature database is established to store the tag information and feature parameters of legitimate vehicles. When the system obtains corrected location data, the optimal reading angle is selected based on the signal coverage of the antenna array. Vehicle tag information is retrieved through multiple reading attempts, and the vehicle feature parameters are extracted from the tags and matched against records in the feature database for verification.

[0091] S400 calculates and obtains parking space recommendation information through a cloud server based on vehicle identification data, identity credibility, and corrected location data, combined with real-time parking space distribution information.

[0092] In this embodiment, real-time parking space distribution information includes the vacancy status, location information, and parking space type of each space. Parking space recommendation information includes the recommended parking space number, navigation route, and matching score. The cloud server comprehensively processes multiple parameters to select the most suitable parking location for the vehicle. The matching score reflects the suitability of the parking space for the current vehicle, considering three dimensions: parking space accessibility, convenience, and parking space type.

[0093] Specifically, this solution establishes a parking space feature database to store the basic attribute information of each parking space. Upon receiving vehicle identification data and location data, it first filters out vacant parking spaces that meet the identity credibility requirements; then it calculates the shortest path length from the vehicle to each candidate parking space and calculates a matching score based on the parking space type; finally, it selects the parking space with the highest score as the recommended parking space and generates a recommendation result containing navigation information.

[0094] The S500 controls the smart lock of the corresponding parking space based on parking space recommendation information to obtain the parking space occupancy status.

[0095] In this embodiment, the smart lock is an execution device used to control the occupancy status of a parking space, and includes a locking mechanism and a status detection unit. The parking space occupancy status includes three types: reserved, occupied, and vacant. The locking mechanism is used to physically restrict the use of the parking space, and the status detection unit is used to confirm whether the vehicle's parking position is accurate.

[0096] Specifically, this solution uses a parking space management controller to centrally manage the smart locks. Upon receiving parking space recommendation information, the controller sends a reservation command to the smart lock of the designated parking space; the system monitors the vehicle's location, and when the vehicle arrives at the target parking space according to the navigation route, the smart lock automatically lowers, and the status detection unit starts working; when the vehicle is detected to be parked correctly, the parking space status is updated to occupied.

[0097] S600: Based on the parking space occupancy status, activate the time and billing unit to obtain parking fee data.

[0098] In this embodiment, the time-based billing unit is used to manage the calculation and recording of parking fees. Parking fee data includes billing duration, fee rate information, and the amount due. The billing duration begins when the parking space status changes from "occupied" to "occupied," and the fee rate information is determined based on the parking space type and time period.

[0099] Specifically, this solution establishes a billing rule database to store the basic rates and time-based rates for different types of parking spaces. When a parking space becomes occupied, the time-based billing unit starts a timer and records the start time; the system periodically checks the parking space occupancy status and calculates the cumulative fee in real time according to the billing rules; when a vehicle leaves the parking space, the time-based billing unit stops timing and generates the final parking fee data.

[0100] In one embodiment, refer to Figure 2 In step S100, the parking lot environmental parameters and system operating mode are obtained. Based on the environmental parameters and system operating mode, the operating parameters of the dual-core recognition system are determined, specifically including the following steps:

[0101] S110. Extract the signal interference intensity and multipath effect coefficient from the environmental parameters, and use the signal interference intensity and multipath effect coefficient as the main correction targets.

[0102] In this embodiment, signal interference intensity refers to the degree of influence of external electromagnetic waves on the system's operating signal, obtained by measuring the power ratio of the received signal to the noise, expressed in decibels. The multipath effect coefficient characterizes the degree of signal distortion caused by reflection, scattering, and diffraction during propagation, with a value ranging from 0 to 1. The main correction target refers to the physical quantity that is the primary consideration during system parameter optimization.

[0103] Specifically, this solution acquires raw data by deploying signal monitors at key locations in the parking lot. First, a signal characteristic data table is established to record baseline signal strength values ​​for different time periods. Then, by comparing the measured signal strength with the baseline values, the current signal interference intensity is calculated. Simultaneously, using the multiple signal waveforms received by the signal monitors, the degree of signal distortion is calculated to obtain the multipath effect coefficient.

[0104] S120. Based on the signal interference intensity, multipath effect coefficient, and system operating mode, obtain the UWB signal processing parameters, RFID reading parameters, and system coordination parameters.

[0105] In this embodiment, the UWB signal processing parameters include signal amplification factor, filtering threshold, and sampling frequency. RFID reading parameters include reading power, reading time interval, and antenna gain. System coordination parameters are used to adjust the operating status of the two subsystems of the dual-core identification system, including operating mode switching threshold, data fusion weight, and sampling synchronization delay.

[0106] Specifically, this solution establishes a parameter optimization mapping table, recording the optimal parameter combinations under different environmental conditions. Once the signal interference intensity and multipath effect coefficient are obtained, the corresponding parameter configuration is retrieved from the mapping table based on the current system operating mode. For operating conditions not included in the mapping table, interpolation algorithms are used to calculate parameter values. This table-based approach can quickly obtain optimal parameter configurations.

[0107] S130. Based on the correlation calculation of signal interference intensity, UWB signal processing parameters, RFID reading parameters and system coordination parameters, the working parameters of the dual-core identification system are obtained.

[0108] In this embodiment, correlation calculation refers to the calculation process that considers the mutual influence between various parameters. The operating parameters are the actual configuration values ​​executed by the dual-core recognition system, encompassing a set of parameters across three dimensions: signal processing, data acquisition, and system coordination. These parameters determine the system's operating state and performance.

[0109] Specifically, this solution establishes a parameter correlation matrix to perform correlation calculations between various parameters. First, the signal interference strength is used as a baseline input to establish a mapping relationship with other parameters. Then, the initially obtained UWB signal processing parameters, RFID reading parameters, and system coordination parameters are substituted into the correlation matrix. Matrix operations are used to obtain correction values ​​that consider the mutual influence between parameters. Finally, the correction values ​​are added to the initial parameters to obtain the actual operational parameter configuration.

[0110] In one embodiment, refer to Figure 3 The UWB signal processing parameters include signal filtering coefficients and gain control coefficients, and the RFID reading parameters include reading power parameters and reading timing parameters. In step S120, the UWB signal processing parameters, RFID reading parameters, and system coordination parameters are obtained based on the signal interference intensity, multipath effect coefficient, and system operating mode. Specifically, this includes the following steps:

[0111] S121. Based on the signal interference intensity and multipath effect coefficient, obtain the signal filtering coefficient, gain control coefficient, read the power parameters, read the timing parameters and system coordination parameters.

[0112] In this embodiment, the signal filtering coefficients are used to suppress interference components in the signal, and include cutoff frequency parameters for low-pass and band-pass filters. The gain control coefficients are used to adjust the signal amplification factor to ensure the signal amplitude is within a suitable range. The read power parameters determine the RFID antenna's transmit power, and the read timing parameters include the read interval time and duration. The system coordination parameters are used to coordinate the working rhythm of the UWB positioning and RFID identification subsystems.

[0113] Specifically, this scheme establishes a parameter lookup table, using signal interference intensity and multipath effect coefficients as dual indexes. First, based on the currently detected signal interference intensity and multipath effect coefficients, the corresponding parameter configuration range is located in the lookup table. For signal filtering coefficients, a narrower passband is used under strong interference conditions. For gain control coefficients, the gain is appropriately reduced when the multipath effect is significant. Power parameters and timing parameters are read and adjusted accordingly based on the interference level. The system coordination parameters change the sampling synchronization method as the interference intensity changes.

[0114] S122. Obtain RFID reading parameters based on reading power parameters and reading timing parameters.

[0115] In this embodiment, the RFID reading parameters are a set of configuration values ​​used to control the operating state of the RFID reader. These parameters are formed by combining reading power parameters and reading timing parameters, and must ensure reading reliability while avoiding mutual interference. There is a mutual constraint relationship between the reading power parameters and the reading timing parameters.

[0116] Specifically, this solution uses a power-timing mapping rule to calculate the final reading parameters. First, a power-timing association table is established to record the optimal timing configuration under different power levels. After obtaining the initial reading power parameters, the association table is queried to obtain the corresponding timing adjustment suggestions. Combined with the initial reading timing parameters, a weighted average is used to obtain the actual RFID reading parameter configuration.

[0117] S123. Based on the system operation mode, various parameters are dynamically optimized and adjusted. The system operation modes include peak mode, normal mode and energy-saving mode.

[0118] In this embodiment, dynamic optimization and adjustment refers to the process of adjusting various parameters in real time according to the system's operating mode. Peak mode prioritizes processing speed and concurrency capabilities, normal mode seeks a balance between performance and power consumption, and energy-saving mode focuses on reducing power consumption.

[0119] Specifically, this scheme establishes an operating mode parameter adjustment matrix. In peak mode, the read power and sampling frequency are increased, the read interval is shortened, and the signal processing bandwidth is increased; in normal mode, the default parameter configuration is used; in energy-saving mode, the read power is reduced, the sampling interval is extended, and the signal processing bandwidth is reduced. The correction coefficients for each mode are calculated through the parameter adjustment matrix, and the correction coefficients are multiplied by the original parameters to obtain the final operating parameters.

[0120] In one embodiment, refer to Figure 4 The vehicle identification data includes vehicle feature parameters and identity verification parameters. In step S300, based on the corrected location data, the vehicle information is read using the RFID identification module, specifically including the following steps:

[0121] S310: Obtain RFID antenna array status and signal coverage information.

[0122] In this embodiment, the RFID antenna array status includes three basic attributes: antenna operating status, gain parameters, and directional angle. Signal coverage information describes the effective identification area of ​​each antenna, including coverage radius and directional characteristics. This information is used to determine the optimal tag reading position and angle.

[0123] Specifically, this solution collects data in real time through an antenna status monitoring unit. First, an antenna status table is established to record the current operating status of each antenna; simultaneously, a signal coverage model is maintained, which divides the parking lot into multiple identification zones and records the coverage intensity distribution of available antennas in each zone; when vehicle information needs to be read, the system queries the antenna status table and coverage model to determine the available antenna combinations.

[0124] S320: Calculate the optimal reading angle and power configuration based on UWB signal processing parameters, RFID reading parameters, and signal coverage information.

[0125] In this embodiment, the optimal reading angle refers to the best operating angle of the RFID antenna array relative to the vehicle, at which the signal reception quality is the best. Power configuration includes two key parameters: transmit power and receive sensitivity.

[0126] Specifically, this solution employs an angle optimization algorithm to calculate the optimal configuration. First, based on the corrected vehicle position, an antenna combination within the signal coverage area is selected. Then, combining the signal quality indicators in the UWB signal processing parameters, a relationship model between antenna angle and read success rate is established. Simultaneously, RFID read parameters are used as constraints, and the optimal read angle is obtained through simple iterative calculation. Finally, the operating angle and power parameters of the antenna array are set based on the calculation results.

[0127] S330 acquires vehicle RFID tag information based on optimal reading angle and power configuration.

[0128] In this embodiment, the vehicle RFID tag information is the data content stored in the electronic tag, including basic vehicle information and an identification code. Reading the tag information needs to be performed under appropriate angle and power conditions to ensure data integrity and reliability.

[0129] Specifically, this solution attempts to obtain tag information through multiple reads. First, the antenna array direction is adjusted according to the calculated optimal angle; after setting the corresponding power configuration, the reading process is initiated; the system adopts a segmented reading strategy, dividing the tag information into basic information segments and authentication information segments for separate reading; data segments that fail to be read are retried until complete tag information is obtained or the maximum number of retries is reached.

[0130] S340. Based on the tag information and system collaboration parameters, extract vehicle feature parameters and identity verification parameters to generate vehicle identity recognition data.

[0131] The system coordination parameters include time synchronization parameters, spatial matching parameters, and data fusion parameters.

[0132] In this embodiment, vehicle characteristic parameters include three basic attributes: vehicle type, size, and weight. Authentication parameters include authentication code and timestamp. Time synchronization parameters are used to align the timing of UWB positioning and RFID identification, spatial matching parameters are used to verify the consistency between location data and tag information, and data fusion parameters determine the weight allocation of the two types of data.

[0133] Specifically, this solution establishes a feature extraction template and a verification rule base. First, the acquired tag information is parsed according to the preset template to extract vehicle feature parameters; then, the time synchronization parameter in the system collaboration parameters is used to ensure the time correspondence between feature extraction and location data; the spatial matching parameter is used to verify the matching degree between tag information and vehicle location; finally, the data fusion parameter is used to integrate location data and tag information to generate complete vehicle identification data.

[0134] In one embodiment, refer to Figure 5 In step S400, parking space recommendation information is calculated and obtained through a cloud server based on real-time parking space distribution information. This includes the following steps:

[0135] S410. Obtain real-time parking space status data and historical parking data. Real-time parking space status data includes parking space occupancy status, parking space type, and parking space environmental parameters.

[0136] In this embodiment, real-time parking space status data reflects the current operational status of the parking lot. Parking space occupancy includes three states: vacant, reserved, and occupied; parking space types include regular parking spaces, charging parking spaces, and accessible parking spaces; parking space environmental parameters include lighting conditions, degree of obstruction, and ventilation. Historical parking data records past parking behavior information.

[0137] Specifically, this solution collects real-time data through a parking space monitoring system. First, a parking space status database is established to update the occupancy status of each parking space in real time; simultaneously, the distribution locations of different types of parking spaces are recorded; environmental parameters of the parking spaces are collected through environmental sensors; and the system saves recent parking records, including vehicle type, parking duration, and parking space selection information.

[0138] S420: Extract user parking preference features based on historical parking data.

[0139] In this embodiment, user parking preference features describe the tendencies of car owners when choosing parking spaces, including location preference, parking space type preference, and environmental requirement preference.

[0140] Specifically, this solution establishes a user profile model to analyze parking preferences. First, it extracts users' parking selection patterns from historical parking data; through statistical analysis, it determines commonly used parking space areas and preferred parking space types; it establishes a simple rating matrix to record the weight coefficients of different features; and based on the historical behavior of the current vehicle, it matches corresponding preference features.

[0141] S430. Calculate the parking space convenience index and parking space matching score based on parking space environment parameters and parking space type.

[0142] In this embodiment, the parking space convenience index reflects the ease of using a parking space, taking into account factors such as entrance / exit distance, lighting conditions, and obstruction. The parking space matching score indicates the degree of matching between parking space characteristics and vehicle needs, including size matching and type matching.

[0143] Specifically, this solution calculates index values ​​by establishing scoring rules. First, a basic convenience score is calculated based on parking space environment parameters; then, location weights are set based on the distance to entrances and exits; the matching coefficient between parking space type and vehicle is obtained through a lookup table; finally, the scores of each item are weighted and combined to obtain the convenience index and matching score.

[0144] S440. Based on the corrected location data, calculate the optimal navigation path from the vehicle to each parking space and obtain the path accessibility score.

[0145] In this embodiment, the optimal navigation path refers to the best route for the vehicle from its current location to the target parking space. Path accessibility scoring considers three factors: path length, number of turns, and lane width.

[0146] S450: By utilizing user parking preference characteristics, parking space convenience index, parking space matching score, and route accessibility score, a comprehensive evaluation result of parking spaces is obtained through multi-factor weighted calculation.

[0147] S460. Based on the comprehensive evaluation results of parking spaces, select the top N parking spaces with the highest scores as recommended parking spaces and generate parking space recommendation information.

[0148] Where N is the preset number of recommendations.

[0149] S470. Verify the availability of recommended parking spaces to ensure that the recommended parking spaces meet the access requirements for vehicle identification data and identity credibility.

[0150] In this embodiment, a recommended parking space refers to the parking space most suitable for the current vehicle, selected by the system. Availability verification is a check process to ensure that the recommended parking space meets the vehicle access requirements. The parking space recommendation information includes the parking space number, navigation route, and estimated parking fee.

[0151] Specifically, this solution determines recommended parking spaces through sorting and filtering. First, the comprehensive evaluation results of the parking spaces are sorted in descending order; the N parking spaces with the highest scores are selected as candidate recommendations; it is verified whether these parking spaces meet the vehicle's identity requirements and credit rating; a recommendation result containing basic parking space information and navigation guidance is generated; if a candidate parking space fails the verification, the next highest-scoring parking space is selected sequentially until a sufficient number of valid recommendations are obtained.

[0152] In one embodiment, refer to Figure 6 In step S460, after generating parking space recommendation information, the method also includes the following steps:

[0153] S461. Check whether there are available parking spaces in the comprehensive evaluation results of the parking spaces.

[0154] In this embodiment, the comprehensive evaluation results for parking spaces record the scores and availability status of all parking spaces. Available parking spaces refer to those that meet the vehicle access requirements and are currently vacant.

[0155] S462. If no parking spaces are available, obtain real-time parking information of surrounding parking lots and calculate the distance and estimated arrival time of the vehicle to each surrounding parking lot.

[0156] In this embodiment, surrounding parking lots refer to other parking lots within a preset distance of the current parking lot. Real-time parking information includes the number of remaining parking spaces, the distribution of parking space types, and the charging standard. The list of recommended alternative parking lots includes the parking lot name, location coordinates, route guidance, and estimated cost.

[0157] Specifically, this solution establishes a networked parking database. First, it acquires parking information within a preset range; then, it queries the parking space status of each parking lot in real time via a data interface; it uses navigation services to calculate the distance and estimated travel time from vehicles to each parking lot; it establishes a scoring model to convert distance, time, and parking space status into recommendation scores; and finally, it generates a list of candidate parking lots based on the recommendation scores.

[0158] S463. Using distance, estimated arrival time, and parking availability at each parking lot, generate a recommended list of alternative parking lots.

[0159] S464. Real-time monitoring of parking space availability in the current parking lot, and prediction of parking space vacancy time based on historical data analysis.

[0160] In this embodiment, the parking space release status reflects the dynamic process of parking spaces becoming vacant in the parking lot. Predicting the parking space vacancy time involves analyzing historical data to estimate the possible time points when parking spaces may become available.

[0161] Specifically, this solution analyzes parking space turnover using a predictive model. First, it collects historical data on parking space usage, including parking duration distribution and turnover patterns. Then, it establishes a time series model to analyze the probability of parking space availability during the current period. Finally, it combines real-time monitoring data to predict the number and timing of potentially vacant parking spaces in the near future.

[0162] S465: Push a list of alternative parking lot recommendations, the estimated availability of parking spaces in the current parking lot, and the location of the temporary waiting area to the user.

[0163] In this embodiment, the temporary waiting area refers to a designated area for vehicles to temporarily stop and wait. Push notifications are real-time suggestions sent by the system to the user, including multiple options.

[0164] S466. Regularly update push notifications until the user completes the parking selection or an available parking space becomes available in the current parking lot.

[0165] In one embodiment, refer to Figure 7 In step S600, based on the parking space occupancy status, the time and billing unit is activated to obtain parking fee data, specifically including the following steps:

[0166] S610: Obtain parking fee standards and preferential policies, including basic rates and rates for various time periods.

[0167] S620: Check if the parking space occupancy status and lock reliability data are normal.

[0168] In this embodiment, the lock reliability data reflects the working status of the parking space lock, including locking status, sensor data, and communication status. The initiation of time-based billing requires ensuring that the parking space is occupied normally and that the lock is working reliably to avoid billing disputes.

[0169] Specifically, this solution uses a status monitoring program for checks. First, it verifies whether the data from the parking space occupancy sensor is stable; it checks whether the mechanical condition and electrical parameters of the locks are within the normal range; it confirms that the communication link is working properly; once all checks are passed, the system records the current time as the billing start point and starts the timing unit.

[0170] S630 If normal, start the timer and billing system and record the parking start time.

[0171] S640: Real-time monitoring of vehicle availability and calculation of parking fees based on parking space type and billing standards.

[0172] S650: When a vehicle is detected leaving, stop timing and generate parking fee data.

[0173] S660. If abnormal lock reliability data is detected, billing is suspended and the abnormal time period is recorded.

[0174] In this embodiment, "vehicle leaving" refers to the vehicle completely leaving the parking space. Parking fee data includes parking duration, applicable rate, discount amount, and amount due. Abnormal lock reliability data indicates that the lock's operating condition does not meet normal standards and requires special handling.

[0175] Specifically, this solution ensures accurate billing through an anomaly handling mechanism. First, when a vehicle leaves the signal, the system stops timing; it generates a complete billing calculation detail, including charges and discounts for each time period; if an abnormality is detected in the lock, the system automatically marks the abnormal period; it suspends billing accumulation for that period; it records the start and end times of the abnormality as the basis for billing calculation; and it restarts billing once the lock returns to normal.

[0176] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0177] Secondly, this application provides a parking management system based on a dual-core recognition system. The parking management system based on a dual-core recognition system of this application will be described below in conjunction with the above-mentioned parking management method based on a dual-core recognition system.

[0178] Reference Figure 8 A parking management system based on a dual-core identification system includes:

[0179] The working parameter determination module is used to acquire parking lot environmental parameters and system operating mode, and determine the working parameters of the dual-core recognition system based on the environmental parameters and system operating mode.

[0180] The position data correction module is used to locate the vehicle using the UWB positioning module, obtain the vehicle's real-time position coordinates, and correct the position coordinates based on the working parameters to obtain the corrected position data.

[0181] The vehicle identity credibility acquisition module is used to read vehicle information using an RFID identification module based on corrected location data to obtain vehicle identity recognition data; and to extract and verify features from the vehicle identity recognition data to obtain vehicle identity credibility.

[0182] The parking space recommendation information acquisition module is used to calculate and obtain parking space recommendation information through a cloud server based on vehicle identification data, identity credibility and corrected location data, combined with real-time parking space distribution information.

[0183] The parking space status acquisition module is used to control the smart lock of the corresponding parking space based on the parking space recommendation information and obtain the parking space occupancy status.

[0184] The parking fee calculation module is used to activate the time and billing unit based on the parking space occupancy status to obtain parking fee data.

[0185] In one embodiment, this application provides an electronic device, which may be a server, and its internal structure diagram may be as follows: Figure 9As shown, the electronic device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The database stores data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements a parking management method based on a dual-core identification system.

[0186] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the electronic device to which the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0187] In one embodiment, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0188] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0189] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A parking lot management method based on a dual-core recognition system, characterized by, Includes the following steps: The system acquires parking lot environmental parameters and system operating modes. Based on these parameters, it determines the operating parameters of the dual-core identification system. The environmental parameters include signal interference intensity and multipath effect coefficient. The system operating modes include peak mode, normal mode, and energy-saving mode. A mapping table between environmental parameters and operating parameters is established. Signal interference intensity is divided into three ranges: weak interference, moderate interference, and strong interference. The multipath effect coefficient is divided into three levels: slight, moderate, and severe. Based on the mapping table and the current system operating mode, the operating parameters of the dual-core identification system are calculated using a parameter correlation matrix. The operating mode coefficient corresponding to the system operating mode is multiplied by the operating parameters to obtain the actual operating parameter configuration. These operating parameters include UWB signal processing parameters, RFID reading parameters, and system coordination parameters. The vehicle is located using a UWB positioning module to obtain its real-time position coordinates; and the position coordinates are corrected for errors based on the operating parameters to obtain corrected position data. Based on the corrected location data, vehicle information is read using an RFID identification module to obtain vehicle identification data; feature extraction and verification are performed on the vehicle identification data to obtain the vehicle identity credibility, wherein the feature extraction and verification are performed through tag information and system coordination parameters, and the system coordination parameters include time synchronization verification, spatial matching verification and data fusion verification. Based on the vehicle identification data, identity credibility, and corrected location data, combined with real-time parking space distribution information, parking space recommendation information is calculated and obtained through a cloud server. The parking space recommendation information is obtained by multi-factor weighting based on user parking preference characteristics, parking space convenience index, parking space matching score, and path accessibility score. Based on the parking space recommendation information, control the smart lock of the corresponding parking space to obtain the parking space occupancy status; Based on the parking space occupancy status, the timing and billing unit is activated to obtain parking fee data. If the reliability data of the smart lock is detected to be abnormal, billing is suspended and the abnormal time period is recorded.

2. The parking lot management method based on a dual-core recognition system according to claim 1, characterized in that, The UWB signal processing parameters include signal filtering coefficients and gain control coefficients, and the RFID reading parameters include reading power parameters and reading timing parameters. Based on the signal interference intensity, multipath effect coefficient, and system operating mode, UWB signal processing parameters, RFID reading parameters, and system coordination parameters are obtained, specifically including the following steps: Based on the signal interference intensity and multipath effect coefficient, obtain the signal filtering coefficient, the gain control coefficient, the readout power parameter, the readout timing parameter, and the system coordination parameter; The RFID reading parameters are obtained based on the reading power parameters and the reading timing parameters; Based on the system's operating mode, various parameters are dynamically optimized and adjusted.

3. The parking lot management method based on a dual-core recognition system according to claim 1, characterized in that, The vehicle identification data includes vehicle feature parameters and identity verification parameters. Based on the corrected location data, vehicle information is read using an RFID identification module, specifically including the following steps: Obtain information on the status of the RFID antenna array and its signal coverage. Based on the UWB signal processing parameters, the RFID reading parameters, and the signal coverage information, calculate the optimal reading angle and power configuration; Based on the optimal reading angle and power configuration, vehicle RFID tag information is obtained; Based on the tag information and system collaboration parameters, the vehicle feature parameters and identity verification parameters are extracted to generate the vehicle identity recognition data.

4. The parking lot management method based on a dual-core recognition system according to claim 1, characterized in that, Based on real-time parking space distribution information, parking space recommendation information is calculated and obtained through a cloud server, specifically including the following steps: Acquire real-time parking space status data and historical parking data of the parking lot. The real-time parking space status data includes parking space occupancy status, parking space type, and parking space environmental parameters. Extract user parking preference features based on the historical parking data; Based on the parking space environment parameters and parking space type, calculate the parking space convenience index and parking space matching score; Based on the corrected location data, the optimal navigation path from the vehicle to each parking space is calculated, and the path accessibility score is obtained. Using the user parking preference characteristics, parking space convenience index, parking space matching score and route accessibility score, a comprehensive evaluation result of the parking space is obtained through multi-factor weighted calculation. Based on the comprehensive evaluation results of the parking spaces, the top N parking spaces with the highest scores are selected as recommended parking spaces, and the parking space recommendation information is generated, where N is the preset number of recommendations; The availability of the recommended parking spaces is verified to ensure that the recommended parking spaces meet the access requirements of the vehicle identification data and identity credibility.

5. The parking lot management method based on a dual-core recognition system according to claim 4, characterized in that, After generating the parking space recommendation information, the method further includes the following steps: The system checks whether there are any available parking spaces in the comprehensive parking space evaluation results. If no parking spaces are available, obtain real-time parking information from nearby parking lots and calculate the distance and estimated arrival time of the vehicle to each nearby parking lot. Using the distance, estimated arrival time, and parking availability of each parking lot, a recommended list of alternative parking lots is generated; Real-time monitoring of parking space availability in the current parking lot, and prediction of parking space vacancy time based on historical data analysis; Push a list of alternative parking lots to users, along with the estimated availability of parking spaces in the current parking lot and the location of the temporary waiting area; Regularly update push notifications until the user completes their parking selection or an available parking space becomes available in the current parking lot.

6. The parking lot management method based on a dual-core recognition system according to claim 1, characterized in that, Based on the parking space occupancy status, the time and billing unit is activated to obtain parking fee data, specifically including the following steps: Obtain parking fee standards and discount strategies, including basic rates and rates for various time periods; Check whether the parking space occupancy status and lock reliability data are normal; If everything is normal, start the timer and billing system and record the parking start time; Real-time monitoring of vehicle availability status; calculation of parking fees based on parking space type and the aforementioned billing standard. When a vehicle is detected leaving, the timer stops and parking fee data is generated.

7. A parking lot management system based on a dual core recognition system, characterized by, The parking management method based on a dual-core identification system according to any one of claims 1-6 includes: The working parameter determination module is used to acquire parking lot environmental parameters and system operating mode, and determine the working parameters of the dual-core recognition system based on the environmental parameters and system operating mode. The position data correction module is configured to locate the vehicle by using the UWB positioning module to obtain real-time position coordinates of the vehicle, and correct errors of the position coordinates based on the working parameters to obtain corrected position data. The vehicle identity credibility acquisition module is configured to read vehicle information by using the RFID identification module based on the corrected position data to obtain vehicle identity recognition data, and perform feature extraction and verification on the vehicle identity recognition data to acquire vehicle identity credibility. The parking space recommendation information acquisition module is configured to obtain parking space recommendation information by calculating the parking space recommendation information through a cloud server based on the vehicle identity recognition data, the identity credibility, and the corrected position data in combination with real-time parking space distribution information. The parking space state acquisition module is configured to control an intelligent lock of a corresponding parking space based on the parking space recommendation information to obtain a parking space occupancy state. The parking fee calculation module is configured to start a timing and billing unit based on the parking space occupancy state to obtain parking fee data.

8. An electronic device, comprising: The computer program is executed by the processor to implement the steps of the parking lot management method based on the dual-core identification system according to any one of claims 1-6.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the parking lot management method based on the dual-core identification system according to any one of claims 1-6.

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