Resource elastic allocation and barrier gate linkage control system and method for mixed traffic of parking lot, medium and equipment

Abstract

The invention discloses an elastic resource allocation and barrier gate linkage control system and method for mixed traffic of parking lots, a medium and equipment, and the system comprises a vehicle type recognition module, a dynamic resource allocation module, a cooperative control decision module, an emergency channel management module and a multi-mode communication module. According to the parking lot mixed traffic management system integrating vehicle-road cooperative communication, double-layer game optimization and elastic resource pool division, an independent resource pool and a dynamic quota adjustment mechanism of automatic driving vehicles and manual driving vehicles are constructed, and a charging pile-barrier gate cooperative optimization unit and a multi-stage emergency response protocol are combined; the global optimization of parking lot resource allocation and traffic control is realized, and the problems of prominent resource competition contradiction, separation of barrier gate control and resource allocation, insufficient intelligence of an emergency response mechanism and short delay of multi-mode communication and decision cooperation in a mixed traffic scene are solved.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent transportation technology, and specifically relates to a resource elastic allocation and gate linkage control system, method, medium and equipment for mixed traffic in parking lots. Background Art

[0002] With the rapid development of autonomous driving technology and the widespread adoption of new energy vehicles, urban parking lots are facing complex management challenges brought about by the mixed traffic of traditional human-driven vehicles and autonomous vehicles. Existing parking management systems are mostly based on static resource allocation models for a single vehicle type, such as pre-allocating fixed parking spaces through license plate recognition, or relying on routing rules for human-driven vehicles. However, these two solutions have significant drawbacks in mixed traffic scenarios: (1) The contradiction of competition for mixed traffic resources is prominent: autonomous vehicles and manually driven vehicles have essential differences in charging pile requirements, route selection logic and response speed. The existing system does not divide dynamic resource pools for the two types of vehicles, resulting in frequent conflicts over charging pile resources during peak hours; For example, autonomous vehicles typically require precise matching of charging station power and battery status, while manually driven vehicles rely on manual decision-making. Charging station resource allocation lacks a flexible priority adjustment mechanism. Furthermore, research on the modeling and dynamic scheduling of cruising parking behaviors in mixed traffic environments remains elusive, exacerbating congestion and resource waste within parking lots. (2) Separation of gate control and resource allocation: The traditional parking lot gate system only serves as a switch control node for vehicle entry and exit, and does not form a collaborative decision-making closed loop with parking space allocation and charging pile scheduling; For example, in Baidu ViCAD 2.0's parking mode, while parking lot users can identify parking spaces and plan routes, the gate release strategy is still based on a fixed time sequence and cannot dynamically adjust release intervals or lane function switching based on real-time traffic density. In mixed traffic scenarios, the unpredictability of manually driven vehicles (such as sudden braking and lane changes) further increases gate response delays and the risk of route conflicts. (3) Insufficient intelligence of emergency response mechanism: The existing parking lot management system relies on manual intervention to support the emergency passage of special vehicles (ambulances, fire trucks), and lacks the ability to automatically clear the lot and recycle resources; For example, while the intelligent road classification standard (Level 3) states that a cloud platform can take over and control special vehicles, it does not define the preset path clearing rules for emergency lanes in parking lots and the reverse release protocol for charging pile resources. As a result, when an emergency occurs, the resources occupied by charging piles cannot be quickly released, and the parking lot gate system is not able to switch to emergency mode in a timely manner. (4) Bottlenecks in multimodal communication and decision-making collaboration: Although vehicle-road-cloud integrated technology has achieved collaborative perception and decision-making in high-level autonomous driving (L4-L5), low-latency communication and lightweight edge computing capabilities in mixed traffic scenarios in parking lots still have shortcomings; For example, existing vehicle-road cloud systems mostly use centralized cloud-based decision-making, which makes it difficult to meet the millisecond-level synchronization requirements for gate control commands within parking lots. Furthermore, the lack of a visual feedback mechanism for resource allocation strategies prevents management from monitoring optimization target weights in real time (e.g., the dynamic balance between charging demand satisfaction and traffic fairness). To this end, we propose a resource elastic allocation and gate linkage control system, method, medium and equipment for mixed traffic in parking lots. Summary of the Invention

[0003] The purpose of the present invention is to provide a resource elastic allocation and gate linkage control system, method, medium and equipment for mixed traffic in parking lots to solve the problems raised in the above background technology.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a resource elastic allocation and gate linkage control system for mixed traffic in parking lots, comprising: Vehicle type identification module: used to obtain vehicle control mode information and driving behavior characteristic data, and generate a vehicle type label with autonomous driving or manual driving classification attributes; Dynamic resource allocation module: This module divides the charging pile resource pool according to vehicle type tags, establishes a dynamic quota adjustment mechanism for autonomous vehicles and manually driven vehicles, and adjusts the total reserved ratio of parking lot resources based on the dynamic prediction of real-time charging demand in the parking lot; Collaborative control decision module: This module establishes a two-layer decision model consisting of a charging pile resource allocation layer and a gate access control layer. The charging pile resource allocation layer schedules charging pile resources based on the remaining battery level of the vehicle and the charging pile power. The gate access control layer generates gate release instructions based on the vehicle type priority and path conflict degree. Emergency channel management module: By identifying the vehicle identity, it triggers the preset path clearing mechanism, executes the forced release operation of the charging pile occupied resources, and controls the parking lot gate system to enter the emergency passage mode; Multimodal communication module: By deploying edge computing nodes, real-time data interaction between vehicle terminals, parking lot charging piles, and parking lot gate systems is achieved, and the weight parameters and operating status of the parking lot charging pile allocation strategy are synchronously displayed.

[0005] Preferably, the vehicle type identification module includes: OBU interface parsing unit: receives OBU diagnostic interface information from the vehicle terminal through the RSU device deployed in the parking lot gate system, obtains the vehicle control mode identification code in real time, and parses and generates the initial classification identification of automatic driving or manual driving; Visual feature analysis unit: This unit collects vehicle trajectory video stream data through an array camera deployed at the parking lot entrance, extracts steering angle fluctuation rate and acceleration variance data, and constructs a driving behavior feature vector; Double verification decision unit: performs time series matching and verification on the initial classification identification results analyzed by the OBU interface analysis unit and the driving behavior feature vector results analyzed by the visual feature analysis unit, and generates a vehicle type label with a timestamp.

[0006] Preferably, the dynamic resource allocation module includes: A resource pool division unit divides charging pile resources into a dedicated pool for autonomous driving vehicle charging piles and a dedicated pool for manually driven vehicle charging piles, wherein the capacity ratio of the dedicated pool for autonomous driving vehicle charging piles is dynamically adjusted within a first preset ratio range, and the capacity ratio of the dedicated pool for manually driven vehicle charging piles is correspondingly adjusted within a second preset ratio range; Charging demand forecasting unit: uses a time series forecasting model to process historical charging data, peak parking flow data, and environmental data to generate periodic charging demand forecasts; Elastic adjustment unit: dynamically adjusts the total reserved ratio based on the periodic charging demand forecast value; Cross-pool borrowing unit: When the dedicated pool of charging piles for autonomous driving vehicles or the dedicated pool of charging piles for manually driven vehicles falls below the third preset threshold, the cross-pool resource allocation mechanism is activated. When the cross-pool resources are full, resources can be allocated, and the borrowing and returning operations are recorded in the distributed evidence storage node.

[0007] Preferably, the collaborative control decision module includes: Two-level game modeling unit: Establish a two-level decision-making model based on the leader-follower game framework: Among them, the charging pile resource allocation layer acts as a leader, with the joint optimization objectives of minimizing the weighted sum of the inverse of the remaining power of the vehicles and maximizing the power utilization of the charging piles, to construct the charging pile scheduling function. The calculation formula of the charging pile scheduling function is shown in formula (1): (1) Among them, SOC i Indicates the remaining battery percentage of vehicle i; P(e) represents the output power of the charging pile e in the parking lot; Indicates the power balance coefficient of the dynamic adjustment of the charging pile; That is, for each vehicle, the charging pile with the smallest F(i,e) is assigned. That is, e*(i) represents the charging pile assigned to the i-th vehicle. The calculation formula of e*(i) is shown in formula (2): (2) Among them, S represents the collection of all charging piles; argmin represents a universal numerical function, that is, the value of e when F(i,e) takes its minimum value; The gate access control layer acts as a follower and generates a vehicle access priority score according to the calculation formula (3): Score i =α·Type i +β(1 / Conflict i ) (3) Among them, Type i represents the weight coefficient of vehicle type i; Conflict i represents the path conflict index of vehicle i; α and β represent the preset weight factors for judging vehicle type and path conflict index, and α+β=1; Emergency response execution unit: When the SOC of vehicle i is detected i When the value is lower than the first preset threshold, a barrier lift command is generated with a response delay less than the second preset threshold; Conflict resolution decision unit: calculates the path conflict index between vehicles according to formula (4); Conflict i =Σ(Overlap i1i2 / Δt i1i2 ) (4) Among them, Overlap i1i2 Indicates the number of grid cells where the paths of vehicles i1 and i2 overlap; Δt i1i2 represents the estimated time difference between vehicle i1 and vehicle i2 arriving at the conflict point; When Conflict i When the third preset threshold is exceeded, the spatiotemporal window reallocation algorithm of the passing sequence is activated.

[0008] Preferably, the emergency channel management module includes: Identity authentication trigger unit: matches the vehicle identity code to the preset high-priority vehicle database, and generates a preset path clearing instruction set when the verification is passed; Resource preemption control unit: Sends a status reset command to the target charging pile, interrupts the current charging process and unlocks the charging gun. It also broadcasts a route replanning request to vehicles in the conflicting area through dynamic path topology analysis. Emergency Pass Execution Unit: After receiving the identity authentication trigger signal, it sets the associated parking lot gate system to the normally open state until one of the following conditions is met: The emergency vehicle completely passes through the preset monitoring area; The parking lot barrier system receives a manual release command; The duration of the emergency state exceeds the first response time threshold.

[0009] Preferably, the multimodal communication module includes: Edge communication relay unit: Deployed in the edge computing node in the parking lot area, it builds a heterogeneous communication channel cluster that includes V2X short-range communication links, charging pile control buses, and parking lot gate system RS485 interfaces; Policy data synchronization unit: performs the following data interaction operations in real time: Receive a data set of vehicle remaining power and estimated stay duration from the vehicle terminal; Send the power regulation coefficient matrix [λ1,λ2,...,λn] to the charging pile; Extracting the spatiotemporal encoding of the passage sequence from the parking lot gate system controller; Dynamic Visualization Unit: Generates an interactive interface consisting of the following layers: Strategy parameter map: maps the real-time weight ratio of the vehicle remaining power reciprocal term and the power balance term in the charging pile allocation strategy; Operation status topology diagram: displays the color temperature coding of the load intensity of each charging pile and the pulse signal of the opening and closing status of the parking lot gate system.

[0010] A method for flexible resource allocation and barrier gate linkage control for mixed traffic in a parking lot is applied to the flexible resource allocation and barrier gate linkage control system for mixed traffic in a parking lot, comprising the following steps: A. Vehicle type identification: The vehicle control mode information is obtained through the vehicle terminal, and combined with the driving behavior characteristic data collected by the array camera, a vehicle type label with the classification identification of autonomous driving vehicle or manually driven vehicle is generated; B. Dynamic resource allocation: Based on vehicle type labels, dedicated charging pile pools for autonomous vehicles and manually driven vehicles are divided into dedicated charging pile pools. A quota adjustment mechanism is established for autonomous vehicle quotas and manually driven vehicle quotas. The total reserved proportion of charging piles is dynamically adjusted based on the real-time charging demand forecast values of each parking lot section. C. Collaborative control decision-making: Construct a two-tier decision-making model for charging pile resource allocation and parking lot gate system access control; The charging pile resource allocation layer generates charging pile scheduling instructions based on the vehicle's remaining power, estimated charging time, and charging pile output power; The gate access control layer calculates the parking lot gate system release sequence and time interval based on the vehicle type priority weight and path conflict detection results; D. Emergency channel management: When the identity characteristics of an emergency vehicle are recognized, the charging pile resource forced release program on the preset clearing path is activated, and an emergency passage mode activation instruction is sent to the gate system of the associated parking lot; E. Multimodal communication control: Real-time data interaction between vehicle terminals, parking lot charging piles and parking lot gate systems is achieved through edge computing nodes, the weight parameter matrix of the charging pile allocation strategy is dynamically updated, and the system operation status is visualized.

[0011] A computer-readable storage medium stores at least one computer program instruction, which is loaded and executed by a processor to implement the operations performed by the resource elastic allocation and gate linkage control method for mixed traffic in a parking lot.

[0012] An electronic device includes one or more processors and one or more memories, wherein the one or more memories store at least one program code, and the at least one program code is loaded and executed by the one or more processors to implement the operations performed by the resource elastic allocation and gate linkage control method for mixed traffic in a parking lot.

[0013] Compared with the prior art, the present invention has the following beneficial effects: 1. Resolve resource competition conflicts and improve charging pile utilization: By establishing independent resource pools for autonomous and manually driven vehicles and adopting a dynamic quota adjustment mechanism, optimal allocation of charging pile resources is achieved. This system intelligently matches charging piles based on vehicle SOC (remaining battery life), charging demand, and parking duration, effectively reducing resource preemption conflicts and improving charging pile power utilization. 2. Realize coordinated control of gates and resource allocation to optimize traffic efficiency: Traditional parking lot gate control is disconnected from resource allocation, resulting in low traffic efficiency. This invention incorporates vehicle access priority scores into gate release decisions through a charging pile-gate collaborative optimization unit. It dynamically adjusts release intervals and lane functions based on real-time traffic density, improving vehicle traffic efficiency and reducing path conflicts in mixed traffic environments. 3. Improve the level of intelligent emergency response and ensure the passage of special vehicles: The existing system's emergency response relies on manual intervention, which results in a delayed response. This invention introduces a multi-level emergency response protocol that automatically identifies high-priority vehicles (such as fire trucks and ambulances) through an identity authentication trigger unit and generates a path-clearing instruction. Simultaneously, it combines a resource preemption control unit to quickly release charging piles and optimize traffic paths, ensuring priority passage for special vehicles. 4. Enhance multimodal communication capabilities to achieve low-latency control and visual management: Edge computing + V2X (vehicle-to-infrastructure collaborative communication) technology is used to optimize low-latency communication capabilities within parking lots, ensuring millisecond-level synchronization of gate release control commands. At the same time, a visual interface for resource allocation is provided to monitor key optimization parameters such as charging demand satisfaction rate and traffic fairness in real time, making it easier for managers to adjust operating strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 Schematic diagram of the system structure of the present invention; Figure 2 Schematic diagram of the method of the present invention. DETAILED DESCRIPTION

[0015] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0016] See also Figure 1 The present invention provides a resource elastic allocation and gate linkage control system for mixed traffic in parking lots, including: Vehicle type identification module: used to obtain vehicle control mode information and driving behavior characteristic data, and generate a vehicle type label with autonomous driving or manual driving classification attributes; The vehicle type identification module includes: OBU interface parsing unit: The OBU interface parsing unit is deployed in the RSU equipment at the entrance and exit of the parking lot. It receives the OBU diagnostic interface information of the vehicle terminal through the RSU equipment deployed in the parking lot gate system, obtains the vehicle control mode identification code in real time, and parses and generates the initial classification identification of automatic driving or manual driving; Visual Feature Analysis Unit: This unit is deployed in edge computing nodes near the parking lot entrances and exits. It uses an array camera deployed at the parking lot entrance to collect video stream data of vehicle driving trajectories, extract steering angle fluctuation rate and acceleration variance data, and construct a driving behavior feature vector. Double-verification decision unit: The double-verification decision unit is integrated into the edge computing node deployed near the entrance and exit of the parking lot. It performs time series matching and verification on the initial classification identification results analyzed by the OBU interface analysis unit and the driving behavior feature vector results analyzed by the visual feature analysis unit, and generates a vehicle type label with a timestamp. Dynamic resource allocation module: This module is deployed in the parking lot host server. It divides the charging pile resource pool according to vehicle type tags, establishes a dynamic quota adjustment mechanism for autonomous vehicles and manually driven vehicles, and adjusts the total reserved ratio of parking lot resources based on the dynamic prediction of real-time charging demand in the parking lot. The dynamic resource allocation module includes: A resource pool division unit divides charging pile resources into a dedicated pool for autonomous driving vehicle charging piles and a dedicated pool for manually driven vehicle charging piles, wherein the capacity ratio of the dedicated pool for autonomous driving vehicle charging piles is dynamically adjusted within a first preset ratio range, and the capacity ratio of the dedicated pool for manually driven vehicle charging piles is correspondingly adjusted within a second preset ratio range; Charging demand forecasting unit: uses a time series forecasting model to process historical charging data, peak parking flow data, and environmental data to generate periodic charging demand forecasts; Elastic adjustment unit: dynamically adjusts the total reserved ratio based on the periodic charging demand forecast value; Cross-pool borrowing unit: When the dedicated pool for autonomous driving vehicle charging piles or the dedicated pool for manual driving vehicle charging piles falls below a third preset threshold, the cross-pool resource allocation mechanism is activated. When the cross-pool resources are full, resources can be allocated and the borrowing and returning operations are recorded in the distributed evidence storage node; Collaborative control decision module: This module is deployed in edge computing nodes near the entrances and exits of parking lots. By establishing a two-layer decision model consisting of a charging pile resource allocation layer and a gate access control layer, the charging pile resource allocation layer schedules charging pile resources based on the remaining battery level of the vehicle and the charging pile power. The gate access control layer generates gate release instructions based on the vehicle type priority and path conflict degree. The collaborative control decision module includes: Two-level game modeling unit: Establish a two-level decision-making model based on the leader-follower game framework: Among them, the charging pile resource allocation layer acts as a leader, with the joint optimization objectives of minimizing the weighted sum of the inverse of the remaining power of the vehicles and maximizing the power utilization of the charging piles, to construct the charging pile scheduling function. The calculation formula of the charging pile scheduling function is shown in formula (1): (1) Among them, SOC i Indicates the remaining battery percentage of vehicle i; P(e) represents the output power of the charging pile e in the parking lot; Indicates the power balance coefficient of the dynamic adjustment of the charging pile; That is, for each vehicle, the charging pile with the smallest F(i,e) is assigned. That is, e*(i) represents the charging pile assigned to the i-th vehicle. The calculation formula of e*(i) is shown in formula (2): (2) Among them, S represents the collection of all charging piles; argmin represents a universal numerical function, that is, the value of e when F(i,e) takes its minimum value; The gate access control layer acts as a follower and generates a vehicle access priority score according to the calculation formula (3): Score i =α·Type i +β(1 / Conflict i ) (3) Among them, Type i represents the weight coefficient of vehicle type i; Conflict i represents the path conflict index of vehicle i; α and β represent the preset weight factors for judging vehicle type and path conflict index, and α+β=1; Emergency response execution unit: When the SOC of vehicle i is detected i When the value is lower than the first preset threshold, a barrier lift command is generated with a response delay less than the second preset threshold; Conflict resolution decision unit: calculates the path conflict index between vehicles according to formula (4); Conflict i =Σ(Overlap i1i2 / Δt i1i2 ) (4) Among them, Overlap i1i2 Indicates the number of grid cells where the paths of vehicles i1 and i2 overlap; Δt i1i2 represents the estimated time difference between vehicle i1 and vehicle i2 arriving at the conflict point; When Conflict i When the third preset threshold is exceeded, the spatiotemporal window reallocation algorithm of the pass sequence is activated; Emergency Channel Management Module: This module is deployed in edge computing nodes near the entrances and exits of parking lots. It identifies vehicles, triggers a preset path clearing mechanism, executes a forced release of charging pile resources, and controls the parking lot gate system to enter emergency access mode. The emergency channel management module includes: Identity authentication trigger unit: matches the vehicle identity code to the preset high-priority vehicle database, and generates a preset path clearing instruction set when the verification is passed; Resource preemption control unit: Sends a status reset command to the target charging pile, interrupts the current charging process and unlocks the charging gun. It also broadcasts a route replanning request to vehicles in the conflicting area through dynamic path topology analysis. Emergency Pass Execution Unit: After receiving the identity authentication trigger signal, it sets the associated parking lot gate system to the normally open state until one of the following conditions is met: The emergency vehicle completely passes through the preset monitoring area; The parking lot barrier system receives a manual release command; The duration of the emergency state exceeds the first response time threshold; Multimodal communication module: By deploying edge computing nodes, it enables real-time data interaction between vehicle terminals, parking lot charging piles, and parking lot gate systems, and simultaneously displays the weight parameters and operating status of the parking lot charging pile allocation strategy; The multimodal communication module includes: Edge communication relay unit: Deployed in the edge computing node in the parking lot area, it builds a heterogeneous communication channel cluster that includes V2X short-range communication links, charging pile control buses, and parking lot gate system RS485 interfaces; Policy data synchronization unit: performs the following data interaction operations in real time: Receive a data set of vehicle remaining power and estimated stay duration from the vehicle terminal; Send the power regulation coefficient matrix [λ1,λ2,...,λn] to the charging pile; Extracting the spatiotemporal encoding of the passage sequence from the parking lot gate system controller; Dynamic Visualization Unit: Generates an interactive interface consisting of the following layers: Strategy parameter map: maps the real-time weight ratio of the vehicle remaining power reciprocal term and the power balance term in the charging pile allocation strategy; Operation status topology diagram: displays the color temperature coding of the load intensity of each charging pile and the pulse signal of the opening and closing status of the parking lot gate system.

[0017] See also Figure 2 As shown, the resource elastic allocation and barrier linkage control method for mixed traffic in a parking lot provided in this embodiment is applied to the resource elastic allocation and barrier linkage control system for mixed traffic in a parking lot, and includes the following steps: A. Vehicle type identification: Vehicle control mode information is obtained through the vehicle terminal, and combined with the driving behavior characteristic data collected by the array camera, a vehicle type label is generated with the classification identification of autonomous driving vehicle or manual driving vehicle. The specific process is as follows: A1: When a vehicle drives to the parking lot gate system, it obtains the vehicle control mode identification code in real time through the OBU diagnostic interface information of the RSU device vehicle terminal placed at the parking lot gate system. It receives the control mode identification code sent by the on-board OBU. For example, the identification code 0x01 indicates an autonomous driving vehicle, and the identification code 0x02 indicates manual driving. A2: Use an array camera (e.g., 4K resolution, 30fps) deployed at the parking lot entrance to collect vehicle trajectory video stream data, extract the vehicle's steering angle fluctuation rate and acceleration variance, and construct a driving behavior feature vector. The steering angle fluctuation rate is used to calculate the standard deviation of the steering angle per second (the threshold is set to ≤1.5°), and the acceleration variance is used to calculate the discrete degree of acceleration change (the threshold is set to ≤0.5m / s³). A3: Enables dual verification decisions, which involves time series matching of the OBU interface parsing results and visual feature analysis results. When the OBU result indicates autonomous driving, but both the steering angle fluctuation rate and acceleration variance exceed thresholds, a manual review mechanism is initiated. (For example, if the OBU obtains a vehicle as an autonomous vehicle with identification code 0x01, but visually calculated steering angle fluctuation rate is 5° and acceleration variance is 1 m / s³, manual review is triggered.) Finally, a vehicle type label with a timestamp is generated. An example label is 0x01_202504051430. B. Dynamic Resource Allocation: Based on vehicle type labels, separate dedicated pools of charging piles for autonomous vehicles and for manually driven vehicles. A quota adjustment mechanism is established for autonomous and manually driven vehicles. The total reserved proportion of charging piles is dynamically adjusted based on the real-time charging demand forecast for each parking lot section. The specific process is as follows: B1: Preliminary division of the charging pile resource pool ratio, i.e., dividing all charging pile resources in the parking lot into a dedicated pool of charging piles for autonomous vehicles (the first resource pool) and a dedicated pool of charging piles for manually driven vehicles (the second resource pool). The capacity ratio of the first resource pool is dynamically adjusted within a first preset ratio range (set to 60%-80%), and the capacity ratio of the second resource pool is correspondingly adjusted within a second preset ratio range (set to 20%-40%). B2: Input historical parking lot charging data, historical peak parking flow data, weather temperature data, and holiday data into the time series prediction model (LSTM neural network) to generate a predicted periodic charging demand value and dynamically adjust the total reserved ratio based on the predicted value; For example, the time series model predicts that between 8 and 10 a.m. on Monday morning when the temperature is -1°C, autonomous vehicles will account for 60% of all charging piles. In this case, the first resource pool will be dynamically adjusted to account for 60% and the second resource pool will account for 40%.

[0018] B3: When the utilization rate of a single charging pile resource pool falls below the third preset threshold (set to 30%), the cross-pool resource allocation mechanism is activated. When the other resource pool is full, resources can be allocated, and the borrowing and returning operations are recorded and stored in the blockchain network node; For example, if the utilization rate of the second resource pool is only 20% at a certain moment, it is marked as borrowable. At this time, if the capacity of the first resource pool is full, the charging piles of the second resource pool can be allocated to the first resource pool, and the blockchain network nodes are used to store and record them; C. Collaborative control decision-making: A two-layer decision-making model is constructed for charging pile resource allocation and parking lot gate system access control. The charging pile resource allocation layer generates charging pile scheduling instructions based on the vehicle's remaining battery life, estimated charging time, and charging pile output power. The gate access control layer calculates the parking lot gate system release sequence and time interval based on the vehicle type priority weight and path conflict detection results. The specific process is as follows: C1: Establish a two-level decision-making model based on the leader-follower game framework: Among them, the charging pile resource allocation layer acts as a leader, with the joint optimization objectives of minimizing the weighted sum of the inverse of the remaining power of the vehicles and maximizing the power utilization of the charging piles, to construct the charging pile scheduling function. The calculation formula of the charging pile scheduling function is shown in formula (1): (1) Among them, SOC i Indicates the remaining battery percentage of vehicle i; P(e) represents the output power of the charging pile e in the parking lot; Indicates the power balance coefficient of the dynamic adjustment of the charging pile; That is, for each vehicle, the charging pile with the smallest F(i,e) is assigned. That is, e*(i) represents the charging pile assigned to the i-th vehicle. The calculation formula of e*(i) is shown in formula (2): (2) Among them, S represents the collection of all charging piles; argmin represents a universal numerical function, that is, the value of e when F(i,e) takes its minimum value; For example, the adjustment rule of λ can be set as: λ=0.5•(1-SOCavg), SOCavg represents the average remaining power of vehicles in the parking lot; When SOCavg=40%, vehicle A with 15% remaining power enters and is preferentially assigned to a 22KW charging station. Vehicle B with 50% remaining power enters and is preferentially assigned to a 7KW charging station. The gate access control layer acts as a follower and generates a vehicle access priority score according to the calculation formula (3): Score i =α·Type i +β(1 / Conflict i ) (3) Among them, Type i represents the weight coefficient of vehicle type i; Conflict i represents the path conflict index of vehicle i; α and β represent the preset weight factors for judging vehicle type and path conflict index, and α+β=1; For example, the Type i Using graded assignment, in parking lots in areas where autonomous driving is being vigorously promoted, the Type i Set to 1.0, manually driven vehicle Type i Set to 0.6 to ensure the priority of autonomous driving vehicles; C2: When the SOC of vehicle i is detected i When the value is lower than the first preset threshold, a barrier lift command is generated with a response delay less than the second preset threshold; For example, the first preset value can be set to 5% and the second preset value can be set to 500ms. That is, when the system detects that the vehicle's SOC is lower than 5%, it generates a barrier lift with a delay of less than 500ms to ensure that the vehicle's power can reach the parking space smoothly, reducing waiting time and power consumption in front of the barrier. C3: Calculate the path conflict index between vehicles according to formula (4); Conflict i =Σ(Overlap i1i2 / Δt i1i2 ) (4) Among them, Overlap i1i2 Indicates the number of grid cells where the paths of vehicles i1 and i2 overlap; Δt i1i2 represents the estimated time difference between vehicle i1 and vehicle i2 arriving at the conflict point; When Conflict i When the third preset threshold is exceeded, the time-space window reallocation algorithm of the passage sequence is activated; For example, if the third preset threshold is set to 2, then Conflict i <2.0 (low conflict, keep original sequence) 2.0≤Conflict i <4.0 (moderate conflict, local adjustments) Conflicti ≥4.0 (high conflict, global reorganization); Assume that the number of grids that overlap between the paths of vehicles C and D is 10, and the estimated time difference between the two arriving at the conflict location is 10 seconds, then Conflict i If it is 1, the original sequence is kept; D. Emergency channel management: When the identity of an emergency vehicle is recognized, the charging pile resource forced release program on the preset clearing path is activated, and an emergency passage mode activation instruction is sent to the gate system of the associated parking lot. The specific process is as follows: D1: Match the vehicle identification code to the preset high-priority vehicle database and generate a path clearing instruction set when the verification is passed; The high-priority vehicle database includes: encrypted digital certificates of emergency rescue vehicles and special operation vehicles; For example, when a vehicle is identified as about to enter a parking lot, the high-priority vehicle database matches the current vehicle as a fire truck (e.g., VIN code: LSKGJ4CLXHA******), and a path clearing instruction is generated; D2: Resource preemption control unit, configured to perform hierarchical response operations: First, a status reset command is sent to the target charging pile, interrupting the current charging process and unlocking the charging gun. Through dynamic path topology analysis, a path replanning request is broadcast to vehicles in the conflicting area. The state reset instruction includes the charging pile power-off sequence: ①Stop power output, ②Release the charging gun mechanical lock, ③Light up the emergency indicator light; D3: Emergency passage execution unit, configured as: After receiving the identity authentication trigger signal, the associated gate is set to the normally open state until one of the following conditions is met: (1) The emergency vehicle completely passes through the preset monitoring area; (2) The system receives a manual release command; (3) The duration of the emergency state exceeds the first response time threshold; The first response time threshold is set to 120 seconds by default and can be adjusted dynamically based on the size of the parking lot; E. Multimodal communication control: This system uses edge computing nodes to implement real-time data exchange between vehicle terminals, parking lot charging piles, and parking lot gate systems. It dynamically updates the weight parameter matrix of the charging pile allocation strategy and visualizes the system's operating status. The specific process is as follows: E1: Build an edge communication relay unit and deploy it at the edge computing node in the parking lot area. This will create a heterogeneous communication channel cluster that includes a V2X short-range communication link, a charging pile control bus, and a gate RS485 interface. The V2X communication link uses the IEEE802.11p protocol, with a data update period of ≤200ms. E2: Policy data synchronization unit, performs the following data interaction operations in real time: (1) Receive the vehicle remaining charge (SOC) and estimated stay time data set from the vehicle terminal; (2) Send the power regulation coefficient matrix [λ1,λ2,...,λn] to the charging pile; The power adjustment coefficient matrix is calculated by the following formula: λj=(Prated,j-Pcurrent,j) / Prated,j Where Prated,j is the rated power of charging pile j, and Pcurrent,j is the real-time output power of charging pile j.

[0019] (3) Extracting the spatiotemporal coding of the passage sequence from the gate controller; E3: Dynamic visualization unit, generating an interactive interface containing the following layers: (1) Strategy parameter map: maps the real-time weight ratio of the SOC inverse term and power balance term in the charging pile allocation strategy; (2) Operation status topology diagram: Displays the color temperature coding of the load intensity of each charging pile and the pulse signal of the gate opening and closing status; The load intensity color temperature coding rule is: Low load: blue series (λj>0.7); Medium load: green series (0.3≤λj≤0.7); High load: red series (λj<0.3); During actual operation, the edge communication relay unit simultaneously receives the SOC data and dwell time predictions of multiple vehicles and sends the latest strategy parameters (for example, [λ1=0.22,λ2=0.18,…]) to each charging pile via the charging pile control bus. At the same time, the gate controller feedbacks the current pass sequence code "TS-20250319-15:10". This data is displayed in real-time graphical form on the monitoring platform via the dynamic visualization unit, allowing managers to adjust strategies and monitor the operating status of the system in a timely manner.

[0020] This embodiment provides a computer-readable storage medium, in which at least one computer program instruction is stored. The at least one computer program instruction is loaded and executed by a processor to implement the operations performed by the resource elastic allocation and gate linkage control method for mixed traffic in a parking lot.

[0021] An electronic device provided in this embodiment includes one or more processors and one or more memories, wherein at least one program code is stored in the one or more memories, and the at least one program code is loaded and executed by the one or more processors to implement the operations performed by the resource elastic allocation and gate linkage control method for mixed traffic in parking lots.

[0022] By introducing vehicle-road cooperative communication, two-layer game optimization, and flexible resource pool partitioning, this invention significantly improves the intelligent level of mixed traffic management in parking lots. Compared with existing technologies, it has the following beneficial effects: (1) Resolve resource competition conflicts and improve the utilization rate of charging piles: By establishing independent resource pools for autonomous and manually driven vehicles and adopting a dynamic quota adjustment mechanism, optimal allocation of charging pile resources is achieved. This system intelligently matches charging piles based on vehicle SOC (remaining battery life), charging demand, and parking duration, effectively reducing resource preemption conflicts and improving charging pile power utilization. (2) Realize coordinated control of gates and resource allocation to optimize traffic efficiency: Traditional parking lot gate control is disconnected from resource allocation, resulting in low traffic efficiency. This invention incorporates vehicle access priority scores into gate release decisions through a charging pile-gate collaborative optimization unit. It dynamically adjusts release intervals and lane functions based on real-time traffic density, improving vehicle traffic efficiency and reducing path conflicts in mixed traffic environments. (3) Improve the level of intelligent emergency response and ensure the passage of special vehicles: The existing system's emergency response relies on manual intervention, which results in a delayed response. This invention introduces a multi-level emergency response protocol that automatically identifies high-priority vehicles (such as fire trucks and ambulances) through an identity authentication trigger unit and generates a path-clearing instruction. Simultaneously, it combines a resource preemption control unit to quickly release charging piles and optimize traffic paths, ensuring priority passage for special vehicles. (4) Strengthen multimodal communication capabilities to achieve low-latency control and visual management: Edge computing + V2X (vehicle-to-infrastructure collaborative communication) technology is used to optimize low-latency communication capabilities within parking lots, ensuring millisecond-level synchronization of gate release control commands. At the same time, a visual interface for resource allocation is provided to monitor key optimization parameters such as charging demand satisfaction rate and traffic fairness in real time, making it easier for managers to adjust operating strategies.

[0023] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A resource elastic allocation and gate linkage control system for mixed traffic in parking lots, characterized by: include: Vehicle type identification module: used to obtain vehicle control mode information and driving behavior characteristic data, and generate a vehicle type label with autonomous driving or manual driving classification attributes; Dynamic resource allocation module: This module divides the charging pile resource pool according to vehicle type tags, establishes a dynamic quota adjustment mechanism for autonomous vehicles and manually driven vehicles, and adjusts the total reserved ratio of parking lot resources based on the dynamic prediction of real-time charging demand in the parking lot; Collaborative control decision module: This module establishes a two-layer decision model consisting of a charging pile resource allocation layer and a gate access control layer. The charging pile resource allocation layer schedules charging pile resources based on the remaining battery level of the vehicle and the charging pile power. The gate access control layer generates gate release instructions based on the vehicle type priority and path conflict degree. Emergency channel management module: By identifying the vehicle identity, it triggers the preset path clearing mechanism, executes the forced release operation of the charging pile occupied resources, and controls the parking lot gate system to enter the emergency passage mode; Multimodal communication module: By deploying edge computing nodes, real-time data interaction between vehicle terminals, parking lot charging piles, and parking lot gate systems is achieved, and the weight parameters and operating status of the parking lot charging pile allocation strategy are synchronously displayed.

2. The resource flexible allocation and gate linkage control system for mixed traffic in parking lots according to claim 1 is characterized by: The vehicle type identification module includes: OBU interface parsing unit: receives OBU diagnostic interface information from the vehicle terminal through the RSU device deployed in the parking lot gate system, obtains the vehicle control mode identification code in real time, and parses and generates the initial classification identification of automatic driving or manual driving; Visual feature analysis unit: This unit collects vehicle trajectory video stream data through an array camera deployed at the parking lot entrance, extracts steering angle fluctuation rate and acceleration variance data, and constructs a driving behavior feature vector; Double verification decision unit: performs time series matching and verification on the initial classification identification results analyzed by the OBU interface analysis unit and the driving behavior feature vector results analyzed by the visual feature analysis unit, and generates a vehicle type label with a timestamp.

3. The resource flexible allocation and gate linkage control system for mixed traffic in parking lots according to claim 2 is characterized by: The dynamic resource allocation module includes: A resource pool division unit divides charging pile resources into a dedicated pool for autonomous driving vehicle charging piles and a dedicated pool for manually driven vehicle charging piles, wherein the capacity ratio of the dedicated pool for autonomous driving vehicle charging piles is dynamically adjusted within a first preset ratio range, and the capacity ratio of the dedicated pool for manually driven vehicle charging piles is correspondingly adjusted within a second preset ratio range; Charging demand forecasting unit: uses a time series forecasting model to process historical charging data, peak parking flow data, and environmental data to generate periodic charging demand forecasts; Elastic adjustment unit: dynamically adjusts the total reserved ratio based on the periodic charging demand forecast value; Cross-pool borrowing unit: When the dedicated pool of charging piles for autonomous driving vehicles or the dedicated pool of charging piles for manually driven vehicles falls below the third preset threshold, the cross-pool resource allocation mechanism is activated. When the cross-pool resources are full, resources can be allocated, and the borrowing and returning operations are recorded in the distributed evidence storage node.

4. The resource flexible allocation and gate linkage control system for mixed traffic in parking lots according to claim 3 is characterized by: The collaborative control decision module includes: Two-level game modeling unit: Establish a two-level decision-making model based on the leader-follower game framework: Among them, the charging pile resource allocation layer acts as a leader, with the joint optimization objectives of minimizing the weighted sum of the inverse of the remaining power of the vehicles and maximizing the power utilization of the charging piles, to construct the charging pile scheduling function. The calculation formula of the charging pile scheduling function is shown in formula (1): （1） Among them, SOC i Indicates the remaining battery percentage of vehicle i; P(e) represents the output power of the charging pile e in the parking lot; Indicates the power balance coefficient of the dynamic adjustment of the charging pile; That is, for each vehicle, the charging pile with the smallest F(i,e) is assigned. That is, e*(i) represents the charging pile assigned to the i-th vehicle. The calculation formula of e*(i) is shown in formula (2): （2） Among them, S represents the collection of all charging piles; argmin represents a universal numerical function, that is, the value of e when F(i,e) takes its minimum value; The gate access control layer acts as a follower and generates a vehicle access priority score according to the calculation formula (3): Score i =α·Type i +β(1 / Conflict i ) (3) Among them, Type i represents the weight coefficient of vehicle type i; Conflict i represents the path conflict index of vehicle i; α and β represent the preset weight factors for judging vehicle type and path conflict index, and α+β=1; Emergency response execution unit: When the SOC of vehicle i is detected i When the value is lower than the first preset threshold, a barrier lift command is generated with a response delay less than the second preset threshold; Conflict resolution decision unit: calculates the path conflict index between vehicles according to formula (4); Conflict i =Σ(Overlap i1i2 / Δt i1i2 ) (4) Among them, Overlap i1i2 Indicates the number of grid cells where the paths of vehicles i1 and i2 overlap; Δt i1i2 represents the estimated time difference between vehicle i1 and vehicle i2 arriving at the conflict point; When Conflict i When the third preset threshold is exceeded, the spatiotemporal window reallocation algorithm of the passing sequence is activated.

5. The resource flexible allocation and gate linkage control system for mixed traffic in parking lots according to claim 4 is characterized by: The emergency channel management module includes: Identity authentication trigger unit: matches the vehicle identity code to the preset high-priority vehicle database, and generates a preset path clearing instruction set when the verification is passed; Resource preemption control unit: Sends a status reset command to the target charging pile, interrupts the current charging process and unlocks the charging gun. It also broadcasts a route replanning request to vehicles in the conflicting area through dynamic path topology analysis. Emergency Pass Execution Unit: After receiving the identity authentication trigger signal, it sets the associated parking lot gate system to the normally open state until one of the following conditions is met: The emergency vehicle completely passes through the preset monitoring area; The parking lot barrier system receives a manual release command; The duration of the emergency state exceeds the first response time threshold.

6. The resource flexible allocation and gate linkage control system for mixed traffic in parking lots according to claim 5 is characterized by: The multimodal communication module includes: Edge communication relay unit: Deployed in the edge computing node in the parking lot area, it builds a heterogeneous communication channel cluster that includes V2X short-range communication links, charging pile control buses, and parking lot gate system RS485 interfaces; Policy data synchronization unit: performs the following data interaction operations in real time: Receive a data set of vehicle remaining power and estimated stay duration from the vehicle terminal; Send the power regulation coefficient matrix [λ1,λ2,...,λn] to the charging pile; Extracting the spatiotemporal encoding of the passage sequence from the parking lot gate system controller; Dynamic Visualization Unit: Generates an interactive interface consisting of the following layers: Strategy parameter map: maps the real-time weight ratio of the vehicle remaining power reciprocal term and the power balance term in the charging pile allocation strategy; Operation status topology diagram: displays the color temperature coding of the load intensity of each charging pile and the pulse signal of the opening and closing status of the parking lot gate system.

7. A method for flexible resource allocation and barrier gate linkage control for mixed traffic in a parking lot, applied to a flexible resource allocation and barrier gate linkage control system for mixed traffic in a parking lot according to any one of claims 1 to 6, characterized in that: The steps include: A. Vehicle type identification: The vehicle control mode information is obtained through the vehicle terminal, and combined with the driving behavior characteristic data collected by the array camera, a vehicle type label with the classification identification of autonomous driving vehicle or manually driven vehicle is generated; B. Dynamic resource allocation: Based on vehicle type labels, dedicated charging pile pools for autonomous vehicles and manually driven vehicles are divided into dedicated charging pile pools. A quota adjustment mechanism is established for autonomous vehicle quotas and manually driven vehicle quotas. The total reserved proportion of charging piles is dynamically adjusted based on the real-time charging demand forecast values of each parking lot section. C. Collaborative control decision-making: Construct a two-tier decision-making model for charging pile resource allocation and parking lot gate system access control; The charging pile resource allocation layer generates charging pile scheduling instructions based on the vehicle's remaining power, estimated charging time, and charging pile output power; The gate access control layer calculates the parking lot gate system release sequence and time interval based on the vehicle type priority weight and path conflict detection results; D. Emergency channel management: When the identity characteristics of an emergency vehicle are recognized, the charging pile resource forced release program on the preset clearing path is activated, and an emergency passage mode activation instruction is sent to the gate system of the associated parking lot; E. Multimodal communication control: Real-time data interaction between vehicle terminals, parking lot charging piles and parking lot gate systems is achieved through edge computing nodes, the weight parameter matrix of the charging pile allocation strategy is dynamically updated, and the system operation status is visualized.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one computer program instruction, which is loaded and executed by the processor to implement the operations performed by the resource elastic allocation and gate linkage control method for mixed traffic in a parking lot as described in claim 7.

9. An electronic device, characterized in that: It includes one or more processors and one or more memories, wherein at least one program code is stored in the one or more memories, and the at least one program code is loaded and executed by the one or more processors to implement the operations performed by the resource elastic allocation and gate linkage control method for mixed traffic in a parking lot as described in claim 7.

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