Parking space occupation detection and billing linkage control method for new energy automobile charging pile

Abstract

The invention discloses a new energy automobile charging pile parking space occupation detection and billing linkage control method, and relates to the technical field of urban public resource optimization distribution and new energy automobile charging pile parking space management. Comprising the steps of multi-source sensing and state fusion: sensing real-time occupation states, vehicle types and charging behavior states of all parking spaces in real time by using a parking space detector, an ALPR visual sensor, a Hall sensor and a charging pile state communication protocol, collecting original data, deploying edge nodes at the parking spaces, and fusing the original data in real time, outputting a unified parking space state object after fusion; original data are collected in real time through the parking space detector, the ALPR visual sensor, the Hall sensor and the charging pile state communication protocol, and through multi-dimensional behavior analysis of the parking space state, the abnormal behaviors that a fuel truck occupies a space for a long time, an electric vehicle occupies the space and is not charged, and the electric vehicle is detained after being charged can be accurately recognized; and an accurate basis is provided for subsequent charging processing and precise management.

Description

Technical Field

[0001] This invention relates to the field of urban public resource optimization and allocation and new energy vehicle charging station parking space management technology, specifically to a method for new energy vehicle charging station parking space occupancy detection and billing linkage control. Background Technology

[0002] With the increasing popularity of new energy vehicles, the demand for charging stations and parking spaces is growing rapidly. To promote urban economic development, efficiently allocate public resources, and respond to low-carbon and environmental protection policies, charging stations and parking spaces for new energy vehicles are being constructed and designated on urban roads. For example, charging stations are installed on the roadside railings on one side of the parking space, while cameras that recognize license plates are installed on the railings at both ends. However, in actual use, various unreasonable parking space occupancy issues have emerged, such as gasoline vehicles occupying charging station parking spaces for extended periods, large vehicles improperly parking in two spaces, affecting the utilization rate of charging station parking spaces, electric vehicles occupying spaces without charging, and electric vehicles lingering after charging. These problems seriously affect the normal use of charging station parking spaces and the effective utilization of resources.

[0003] For example, application number 202411074385.6 discloses a comprehensive management backend operation and maintenance system. By predicting peak hours, it adopts an entrance recommendation mechanism to effectively solve the entrance congestion problem. Combined with real-time monitoring and recommendation of the entrance with the shortest queuing time, it effectively disperses traffic flow and avoids traffic bottlenecks at the entrance, thereby improving parking lot operation efficiency, reducing vehicle waiting time, accelerating vehicle turnover, and improving the overall capacity utilization of the parking lot. However, it does not mention the deficiencies in the identification and occupancy of new energy vehicle charging spaces and the effective utilization of resources through differentiated billing. Application number 202411848284.X discloses a new energy vehicle charging management system, including temperature monitoring, overvoltage protection, leakage detection, etc. It can promptly alarm and take protective measures in abnormal situations to ensure the safety of the charging process. At the same time, through remote monitoring and control functions, users can view the charging status in real time, select the charging mode, and obtain cost estimates, which greatly improves the user experience. However, it still has the defects of not being able to distinguish and identify vehicle types and having difficulty in implementing differentiated billing strategies.

[0004] Existing technologies have the following shortcomings: The parking space detection technology in existing public charging station parking space management systems uses a single detection method, relying solely on cameras or charging station signals to simply detect whether a parking space is occupied. This fails to accurately distinguish between different vehicle types and charging behavior scenarios, making it difficult to precisely identify and effectively handle different abnormal parking behaviors, resulting in inefficient management and a high misjudgment rate. Furthermore, the management strategy is static and rigid, employing fixed time thresholds and charging standards. This fails to provide differentiated and refined penalties and guidance for violations of varying degrees, and the regulatory role of economic levers is not fully utilized, failing to adequately reflect the rational use of public resources and the punitive nature of violations. Finally, management stops at post-event charging, failing to fundamentally optimize parking space turnover efficiency, further contributing to the waste of charging station parking space resources.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a method for detecting and controlling the occupancy of parking spaces for new energy vehicle charging piles. This invention accurately identifies abnormal behaviors through multi-source data fusion and multi-dimensional behavior analysis, formulates differentiated billing strategies based on different abnormal behaviors, improves the turnover rate of parking spaces and charging pile resources through multi-level linkage and combined handling methods, continuously optimizes dynamic adjustment rules and strategies, and generates transparent bills, thereby solving the problems mentioned in the background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for linkage control of parking space occupancy detection and billing for new energy vehicle charging piles, comprising the following steps: S1. Multi-source perception and state fusion: Utilize parking space detectors, ALPR visual sensors, Hall sensors and charging pile state communication protocols to perceive the real-time occupancy status, vehicle type and charging behavior status of all parking spaces in real time and collect raw data. Deploy edge nodes in parking spaces to fuse the raw data in real time and output a unified fused parking space state object. S2. Abnormal Behavior Layered Judgment: Preset abnormal judgment rules and thresholds, perform comparative analysis based on the fused unified parking space status object, judge and generate structured abnormal event objects; S3. Dynamic Billing Strategy Generation and Linkage Instruction Arrangement: Configure a dynamic billing rule engine and a strategy arrangement engine to dynamically generate differentiated billing strategies based on the cumulative time of abnormal events, issue linkage handling instructions, and set up a cyclic optimization billing strategy mechanism. S4. System-linked real-time billing control: Dynamically loads and applies differentiated billing strategies to generate the final bill, supports multiple payment methods to complete settlement through the charging system linkage actuator, and monitors the status changes of the target parking space after the linkage disposal command is executed and updates the displayed status.

[0008] Optionally, the steps for collecting the raw data are as follows: A1. Preset sensitivity threshold and use a geomagnetic parking space detector to detect changes in magnetic field strength in real time to determine the parking space occupancy status, so as to sense vehicle entry / exit events and record parking space occupancy status information and corresponding timestamps. A2. Use the ALPR vision sensor to capture license plate color and numerical information, and use a convolutional neural network (CNN) model to identify the license plate number and a real-time vehicle model recognition algorithm to make a preliminary judgment on the captured license plate color and numerical information, and identify the vehicle type and license plate number. A3. Obtain the charging pile status in real time and associate it with the parking space number through the charging pile status communication protocol; A4. Install Hall sensors on the charging gun of the charging station to directly detect whether the charging gun is physically connected to the vehicle charging port, and record the status information and timestamp of the physical connection. A5. Real-time sensing data of all parking space statuses from parking space detectors, ALPR vision sensors, Hall sensors, and charging pile status communication protocols are statistically integrated to form raw data.

[0009] Optionally, the process of the CNN model recognizing license plate numbers is as follows: B1. Use an object detection CNN to locate the license plate region in a vehicle image and output the bounding box coordinates of the license plate region; B2. Perform an affine transformation on the tilted license plate, and use Hough transform or corner detection algorithm to correct the rotation angle to correct the license plate. B3. Use binarization to convert the corrected license plate into a grayscale image, perform adaptive threshold segmentation, and vertically project to count pixel values ​​to segment individual characters, outputting an image of 7 independent characters consisting of Chinese province abbreviations and alphanumeric combinations; B4. Use a CNN model to identify 7 independent character images to obtain the license plate number, and assist in judging whether the second letter or number of the license plate number is D / F when identifying the license plate number as a new energy vehicle; B5. The ALPR vision sensor captures license plate colors to preliminarily classify vehicle types, including green license plates for pure electric / hybrid new energy vehicles, blue license plates for ordinary fuel vehicles, yellow license plates for large vehicles / motorcycles, white license plates for vehicles of the political and legal system, and black license plates with white characters for foreign vehicles. Among them, white license plates trigger special parking space free occupancy privileges. B6. Multimodal fusion of license plate number / vehicle appearance based on license plate color and CNN model recognition. It uses a dual-branch output layer of the improved ResNet-18 vehicle classification model. Branch 1 outputs the vehicle type probability and branch 2 outputs the vehicle attributes.

[0010] Optionally, the step of real-time fusion of the original data at the edge nodes is as follows: Edge nodes receive raw data, define time windows, and align the timestamps of the parking space detector, ALPR vision sensor, Hall sensor, and charging pile status communication protocol. The parking space occupancy event, vehicle type, and license plate recognition result are associated. When the parking space is occupied and the license plate is successfully recognized, the vehicle type information corresponding to the license plate is bound to the parking space number information. By comprehensively analyzing parking space occupancy events, charging gun connection status, and charging pile status, the precise charging behavior status of not charging, charging in progress, and charging completed can be determined. The comprehensive analysis includes the charging behavior status of not charging when the parking space is occupied, the charging gun is not connected, and the charging pile is idle. When the parking space is occupied, the charging gun is connected, and the charging pile is idle, the charging behavior status is "connected but not charging" in a fault state. When the parking space is occupied, the charging gun is connected, and the charging pile is in a charging state, the charging behavior status is "charging". When the parking space is occupied, the charging gun is connected, and the charging pile is in the charging completed state, the charging behavior status is charging completed. Edge nodes integrate the fused information into a unified parking space status object, which includes parking space ID, license plate number, vehicle type, occupancy status, charging gun connection status, charging pile status, and a set of timestamps corresponding to each status of the vehicle in the parking space.

[0011] Optionally, the preset content of the threshold is as follows: Collect historical parking and charging data, including information on vehicle parking duration, charging duration, and charging amount in different time periods and scenarios; Based on multiple information definitions, the following basic thresholds are established: the maximum free parking time for gasoline vehicles in charging station parking spaces is 30 minutes; the free parking time for new energy vehicles in charging station parking spaces without charging is 15 minutes; a preset buffer time threshold of 20 minutes after charging is completed for new energy vehicles; a preset full charge determination threshold of more than 95% charge; and a preset charging time threshold that must satisfy the formula. In the formula, This represents the preset charging time threshold. This refers to the vehicle's battery capacity. This represents the rated power of the charging station. This is expressed as the charging efficiency factor; The anomaly detection rules are as follows: Anomaly detection rules are formulated based on defined basic thresholds. For example, when a vehicle is identified as a fuel vehicle, the state machine time point tracking method is used to calculate in real time if the occupancy time of the fuel vehicle is greater than the maximum free parking time of 30 minutes, and then it is determined to be a fuel-consuming vehicle occupying the space for a long time. When the vehicle type is a new energy vehicle, if the charging gun is disconnected and the charging pile is idle / faulty and not charging, or if the charging gun is connected and the charging pile is idle / faulty and not charging, then if the new energy vehicle occupies the space without charging for a period of time longer than the free parking time without charging is 15 minutes, it is determined that the new energy vehicle is occupying the space without charging. When the vehicle type is a new energy vehicle, if the charging pile status is "charging complete", or the charging amount is not less than the preset full charge judgment threshold, or the actual charging time is not less than the preset charging time threshold, and the dwell time after charging complete / full charge is greater than the buffer time threshold of 20 minutes, it is determined that the new energy vehicle is staying after charging.

[0012] Optionally, the steps for generating the exception event object are as follows: By setting the rule engine to continuously monitor the unified parking space status object, we can obtain the latest real-time status information of each parking space number; The state machine management mechanism is used to update the status of each parking space based on the latest real-time status information of each parking space, and to record the time points of vehicle entry time, charging start time, charging completion time, charging gun connection time, and charging gun disconnection time. The time points recorded by the state machine are input into the duration calculation model, and the vehicle occupancy time, the time of new energy vehicles occupying the space without charging, and the dwell time after charging is completed / fully charged are calculated based on the current time. The market calculated by the duration calculation model is compared and analyzed with the preset anomaly judgment rules and thresholds to determine whether the anomaly conditions are met. The rule engine is used for matching. When the abnormal judgment conditions are met, a structured abnormal event object is generated. The abnormal event object includes the abnormal type, parking space ID, license plate number, trigger time and trigger conditions.

[0013] Optionally, the steps for generating the differentiated billing strategy are as follows: Receive abnormal event objects, including abnormal type, parking space ID, license plate number, trigger time and trigger conditions, and extract evidence data of the trigger conditions as feature data; Based on the feature data extracted from the abnormal event object, the timeout duration is calculated according to the start time and current duration, combined with the abnormal type. The basic rate is preset based on the parking space occupancy status. The severity level of the abnormality is divided according to the timeout duration. Different penalty coefficients can be configured for different abnormality types according to the time interval corresponding to different levels, thus generating a differentiated billing strategy. Calculate the dynamic penalty multiplier based on the penalty coefficient, and calculate the vehicle occupancy fee; Set the billing rules so that when the timeout duration corresponding to the abnormal type is greater than zero, only the timeout portion will be subject to a penalty occupancy fee, while the portion that has not exceeded the timeout will be calculated independently according to the preset base rate rules for the basic parking fee.

[0014] Optionally, the linkage response command includes a multi-level response linkage command, which sends a warning message to the car owner via the mobile device APP associated with the parking space number; The parking space guidance linkage command marks the parking spaces with abnormal occupancy as "occupancy warning" or "high-cost parking space" on the indicator screen, guiding newly entering vehicles to prioritize the selection of compliant and vacant parking spaces; The barrier gate interception linkage command is used to intercept vehicles that violate regulations and exceed the time limit when they leave the gate. The command includes recording vehicle information and settlement fee processing requirements.

[0015] Optionally, the steps for setting up the cyclical optimized billing strategy mechanism are as follows: Statistics show the weekly decrease rate of parking space occupancy events and the cumulative charging volume of charging piles across all parking space IDs. Set a target threshold for parking space occupancy events, and set the target threshold to 30% of the total number of parking space IDs; The average charging pile capacity and the charging pile turnover rate threshold are preset, and the total charging amount is calculated based on the product of the average charging pile capacity and the charging pile turnover rate threshold. When the rate of decline is less than the target threshold, the duration increment coefficient is adaptively increased. When the accumulated charging amount is less than the total charging amount, the step value is increased by 2 times on the base multiple.

[0016] Optionally, the steps for generating the final bill are as follows: When the local magnetic parking space detector detects a vehicle leaving, the billing process is triggered; The billing engine loads the dynamic billing rules for the parking space from memory based on the parking space ID and adopts a differentiated billing strategy. When the billing engine fails to load the dynamic billing rules for a parking space from memory based on the parking space ID, it uses the preset base rate. The basic parking fee is calculated based on the total parking time from the time the vehicle enters to the time it leaves, according to the normal basic parking rate. The penalty fee for occupying a parking space is calculated based on the penalty fee for exceeding the free time in the dynamically loaded and differentiated billing strategy. It is necessary to determine the abnormal event of the vehicle occupying the parking space ID and accumulate the penalty fee in time segments. The basic parking fee and the penalty parking fee are combined to generate the final parking fee, and the amount of the basic parking fee and the penalty parking fee, the type of abnormal event of parking space occupation, and the duration information are recorded to generate the final bill.

[0017] The technical effects and advantages provided by the present invention in the above technical solution are as follows: This invention utilizes a parking space detector, an ALPR visual sensor, a Hall sensor, and a charging pile status communication protocol to collect raw data in real time. Through multi-dimensional behavioral analysis of parking space status, it can accurately identify abnormal behaviors such as gasoline vehicles occupying parking spaces for extended periods, electric vehicles occupying parking spaces without charging, and electric vehicles lingering after charging. Through multi-source perception fusion technology, it improves the judgment results of vehicle type and status, providing an accurate basis for subsequent billing and precise management. Furthermore, differentiated billing strategies are developed for different abnormal behaviors, further realizing reasonable costs for resource occupation and punitive measures for violations, guiding users to use parking spaces and charging piles resources rationally; through a multi-level linkage combination of APP-push alarm information, parking space guidance and gate linkage, the system effectively prompts illegal vehicles to leave in a timely manner, improving the turnover rate and resource utilization rate of parking spaces and charging piles, so that limited public resources can serve more new energy vehicle owners; Differentiated punitive charges and real-time vehicle relocation prompts can significantly reduce the incidence of illegal occupation, creating a positive incentive cycle. Combined with the generation of transparent and structured bills that clearly list all fees, it enhances persuasiveness, reduces disputes between car owners and management, and the entire process requires no manual intervention, greatly reducing management costs and improving response speed and processing efficiency. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0019] Figure 1 This is a flowchart of the new energy vehicle charging pile parking space occupancy detection and billing linkage control method of the present invention.

[0020] Figure 2 This is a flowchart illustrating the vehicle type identification process of the present invention.

[0021] Figure 3This is a flowchart illustrating how the rule engine of this invention matches abnormal event types. Detailed Implementation

[0022] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0023] Example This invention provides, for example Figure 1-3 The method for detecting and controlling the occupancy of parking spaces for new energy vehicle charging piles, as shown, includes the following steps: S1. Multi-source perception and state fusion: Utilize parking space detectors, ALPR visual sensors, Hall sensors and charging pile state communication protocols to perceive the real-time occupancy status, vehicle type and charging behavior status of all parking spaces in real time and collect raw data. Deploy edge nodes in parking spaces to fuse the raw data in real time and output a unified fused parking space state object. Specifically, the steps for collecting the raw data are as follows: A1. A preset sensitivity threshold is set, and the change in magnetic field strength is detected in real time by a geomagnetic parking space detector to determine the parking space occupancy status, so as to detect vehicle entry / exit events and record the parking space occupancy status information and corresponding timestamp. Specifically, the preset sensitivity threshold is compared with the change in magnetic field strength. When the change in magnetic field strength is less than the preset sensitivity threshold, it is determined that a vehicle has left the parking space, and the parking space vacancy status information and corresponding timestamp are recorded. When the change in magnetic field strength is not less than the preset sensitivity threshold, it is determined that a vehicle has entered the parking space. A2. The ALPR vision sensor captures license plate color and numerical information. A convolutional neural network (CNN) model is used to identify the license plate number, and a real-time vehicle model recognition algorithm is employed to initially determine the captured license plate color and numerical information, identifying the vehicle type and license plate number. The CNN model's license plate number recognition process is as follows: B1. Use an object detection CNN to locate the license plate region in a vehicle image and output the bounding box coordinates of the license plate region; B2. Perform an affine transformation on the tilted license plate, and use Hough transform or corner detection algorithm to correct the rotation angle to correct the license plate. B3. Use binarization to convert the corrected license plate into a grayscale image, perform adaptive threshold segmentation, and vertically project to count pixel values ​​to segment individual characters, outputting an image of 7 independent characters consisting of Chinese province abbreviations and alphanumeric combinations; B4. Use a CNN model to identify 7 independent character images to obtain the license plate number, and assist in judging whether the second letter or number of the license plate number is D / F when identifying the license plate number as a new energy vehicle; B5. The ALPR vision sensor captures license plate colors to preliminarily classify vehicle types, including green license plates for pure electric / hybrid new energy vehicles, blue license plates for ordinary fuel vehicles, yellow license plates for large vehicles / motorcycles, white license plates for vehicles of the political and legal system, and black license plates with white characters for foreign vehicles. Among them, white license plates trigger special parking space free occupancy privileges. B6. Multimodal fusion of license plate number / vehicle appearance based on license plate color and CNN model recognition. It uses a dual-branch output layer of the improved ResNet-18 vehicle classification model. Branch 1 outputs the vehicle type probability and branch 2 outputs the vehicle attributes, which further improves the accuracy of vehicle type recognition. A3. The charging pile status is obtained in real time and associated with the parking space number through the charging pile status communication protocol. The charging pile status includes charging, charging completed, idle and ready for use and fault status. When charging new energy vehicles, the charging start time, end time and charging amount are recorded. The parking space number is the number of the planned parking space for waiting to park. A4. Install Hall sensors on the charging guns of the charging piles. Preset a magnetic flux threshold based on the critical value of the physical connection of the charging guns through experimental calibration. Compare and analyze the magnetic flux measured by the Hall sensors with the preset magnetic flux threshold to directly detect whether the charging gun is physically connected to the vehicle's charging port. Record the physical connection status information and timestamp to identify the states of "occupied but not charging" and "charging complete but not removed." When the charging gun head is inserted into the vehicle's charging port, the magnetic circuit closes, the measured magnetic flux increases and exceeds the preset magnetic flux threshold, indicating that the charging gun head has been inserted. Conversely, when the charging gun head is removed from the vehicle's charging port, the measured magnetic flux decreases and falls below the preset magnetic flux threshold, indicating that the charging gun head has been removed. A5. Real-time sensing data of all parking space statuses from parking space detectors, ALPR vision sensors, Hall sensors, and charging pile status communication protocols are statistically integrated to form raw data.

[0024] Specifically, the steps for real-time fusion of raw data at edge nodes are as follows: Edge nodes receive raw data, define time windows, and align the timestamps of the parking space detector, ALPR vision sensor, Hall sensor, and charging pile status communication protocol. The parking space occupancy event, vehicle type, and license plate recognition result are associated. When the parking space is occupied and the license plate is successfully recognized, the vehicle type information corresponding to the license plate is bound to the parking space number information. By comprehensively analyzing parking space occupancy events, charging gun connection status, and charging pile status, the precise charging behavior status of not charging, charging in progress, and charging completed can be determined. The comprehensive analysis includes the charging behavior status of not charging when the parking space is occupied, the charging gun is not connected, and the charging pile is idle. When the parking space is occupied, the charging gun is connected, and the charging pile is idle, the charging behavior status is "connected but not charging" in a fault state. When the parking space is occupied, the charging gun is connected, and the charging pile is in a charging state, the charging behavior status is "charging". When the parking space is occupied, the charging gun is connected, and the charging pile is in the charging completed state, the charging behavior status is charging completed. Edge nodes integrate the fused information into a unified parking space status object. This unified parking space status object includes the parking space ID, license plate number, vehicle type, occupancy status, charging gun connection status, charging pile status, and a set of timestamps corresponding to each status of the vehicle in the parking space. The expression for the unified parking space status object is as follows: In the formula, Represented as the first A unified parking space status object. Represented as the first Parking space ID This is represented by the license plate number. This indicates the vehicle type. This indicates that the device is in use. This indicates the charging gun is connected. This indicates the charging station status. It represents the set of timestamps corresponding to the various states of a vehicle in a parking space.

[0025] S2. Abnormal Behavior Layered Judgment: Preset abnormal judgment rules and thresholds, perform comparative analysis based on the fused unified parking space status object, judge and generate structured abnormal event objects, where abnormal events include abnormal type, parking space ID, license plate number, trigger time and trigger conditions; abnormal types include fuel-consuming vehicles occupying spaces for a long time, new energy vehicles occupying spaces without charging, and new energy vehicles staying after charging. Specifically, the preset threshold values ​​are as follows: Collect historical parking and charging data, including multiple information such as vehicle parking time, charging time, and charging amount in different time periods and scenarios. By analyzing the distribution characteristics of these multiple information, we can statistically calculate the median of average parking time and charging time to understand users' parking and charging behavior habits. Based on multiple information definitions, the following basic thresholds are established: the maximum free parking time for gasoline vehicles in charging station parking spaces is 30 minutes; the free parking time for new energy vehicles in charging station parking spaces without charging is 15 minutes; a preset buffer time threshold of 20 minutes after charging is completed for new energy vehicles; a preset full charge determination threshold of more than 95% charge; and a preset charging time threshold that must satisfy the formula. In the formula, This represents the preset charging time threshold. This refers to the vehicle's battery capacity. This represents the rated power of the charging station. It is expressed as the charging efficiency factor.

[0026] Specifically, the rules for anomaly detection are as follows: Anomaly detection rules are formulated based on defined basic thresholds. For example, when a vehicle is identified as a fuel vehicle, the state machine time point tracking method is used to calculate in real time if the occupancy time of the fuel vehicle is greater than the maximum free parking time of 30 minutes, and then it is determined to be a fuel-consuming vehicle occupying the space for a long time. When the vehicle type is a new energy vehicle, if the charging gun is disconnected and the charging pile is idle / faulty and not charging, or if the charging gun is connected and the charging pile is idle / faulty and not charging, then if the new energy vehicle occupies the space without charging for a period of time longer than the free parking time without charging is 15 minutes, it is determined that the new energy vehicle is occupying the space without charging. When the vehicle type is a new energy vehicle, if the charging pile status is "charging complete", or the charging amount is not less than the preset full charge judgment threshold, or the actual charging time is not less than the preset charging time threshold, and the dwell time after charging complete / full charge is greater than the buffer time threshold of 20 minutes, it is determined that the new energy vehicle is staying after charging.

[0027] Specifically, the steps for generating an exception event object are as follows: By configuring the rule engine to continuously monitor the unified parking space status object, we can obtain the latest real-time status information for each parking space number. The set of timestamps corresponding to each status of the vehicle in the parking space, including parking space ID, license plate number, vehicle type, occupancy status, charging gun connection status, charging pile status, and the vehicle's status in the parking space. The state machine management mechanism is used to update the status of each parking space based on the latest real-time status information of each parking space, and to record the time points of vehicle entry time, charging start time, charging completion time, charging gun connection time, and charging gun disconnection time. The time points recorded by the state machine are input into the duration calculation model, and the vehicle occupancy time, the time of new energy vehicles occupying the space without charging, and the dwell time after charging is completed / fully charged are calculated based on the current time. The market calculated by the duration calculation model is compared and analyzed with the preset anomaly judgment rules and thresholds to determine whether the anomaly conditions are met. The rule engine is used for matching. When the anomaly judgment conditions are met, a structured anomaly event object is generated. The anomaly event object includes anomaly type, parking space ID, license plate number, trigger time and trigger conditions. The anomaly type includes long-term occupation of parking spaces by fuel-consuming vehicles, parking spaces occupied by new energy vehicles without charging, and parking spaces occupied by new energy vehicles after charging. The trigger conditions include start time, current duration or parking duration after charging is completed / fully charged, and evidence data of charging amount.

[0028] S3. Dynamic Billing Strategy Generation and Linkage Instruction Arrangement: Configure a dynamic billing rule engine and a strategy arrangement engine to dynamically generate differentiated billing strategies based on the cumulative time of abnormal events, issue linkage handling instructions, and set up a cyclic optimization billing strategy mechanism. Specifically, the steps for generating a differentiated billing strategy are as follows: Receive abnormal event objects, including abnormal type, parking space ID, license plate number, trigger time and trigger conditions, and extract evidence data of the trigger conditions as feature data. The feature data includes the start time, current duration or dwell time after charging is complete / fully charged in the trigger conditions. Based on the feature data extracted from the abnormal event object, the timeout duration is calculated according to the start time and current duration, combined with the abnormal type. The expression for the timeout duration, including for fuel-consuming vehicles occupying parking spaces for extended periods, is as follows: In the formula, This indicates the duration of time a fuel-consuming vehicle occupies a parking space for an extended period. This represents the current duration the fuel-consuming vehicle occupies the parking space. This indicates the start time of the fuel-consuming vehicle entering the parking space. This indicates the maximum free parking time that a gasoline-powered vehicle can spend in a charging station parking space; The expression for the timeout duration of a new energy vehicle occupying a charging spot is as follows: In the formula, This indicates the duration of timeout for new energy vehicles occupying charging spots. This represents the current duration for which a new energy vehicle occupies a parking space. This indicates the start time of the new energy vehicle entering the parking space. This refers to the free parking time for new energy vehicles that are not being charged in charging station parking spaces. The expression for the timeout duration of a new energy vehicle after charging is as follows: In the formula, This indicates the timeout duration for new energy vehicles after charging. This represents the current duration for which a new energy vehicle occupies a parking space. This represents the preset charging time threshold. This represents the buffer time threshold for new energy vehicles after charging is complete. A base rate is preset based on parking space occupancy status. The severity level of the anomaly is determined by the duration of the overdue period. Different anomaly types can be configured with different penalty coefficients according to different duration ranges, generating a differentiated billing strategy. The differentiated billing strategy table is as follows: ; The dynamic penalty multiplier is calculated based on the penalty coefficient, and the vehicle occupancy fee is also calculated. The formula for calculating the dynamic penalty multiplier is as follows: In the formula, This represents the dynamic penalty multiplier for fuel-consuming vehicles occupying parking spaces for extended periods. This represents the dynamic penalty multiplier for new energy vehicles occupying charging spots without charging. This represents the dynamic penalty multiplier for a new energy vehicle remaining stationary after charging. The formula for calculating the parking space fee is as follows: ,and In the formula, This refers to the fee for vehicles that occupy parking spaces for extended periods due to fuel consumption. This refers to a fee charged to new energy vehicles for exceeding the time limit for charging. This refers to the fee for occupying a parking space beyond the permitted time after a new energy vehicle has been charging. This is represented as a preset base rate; Set the billing rules so that when the timeout duration corresponding to the abnormal type is greater than zero, only the timeout portion will be subject to a penalty occupancy fee, while the portion that has not exceeded the timeout will be calculated independently according to the preset base rate rules for the basic parking fee.

[0029] Specifically, the linkage handling instructions include multi-level response linkage instructions, which send warning information to the car owner via the mobile device APP associated with the parking space number. For example, the primary response is "Charging is complete, please leave in time", the intermediate response is "You have exceeded the time limit and will be charged a high space occupancy fee", or the advanced response is "Improper parking in the parking space, obscuring the license plate number is a serious violation". The parking space guidance linkage command marks the parking spaces that are abnormally occupied as "occupancy warning" or "high-cost parking space" on the indicator screen, guiding newly entering vehicles to prioritize the selection of compliant and vacant parking spaces. The command also sends a warning message to the car owner via the parking space number-linked mobile device APP for vehicles that are abnormally occupied. After the car owner drives the vehicle away from the parking space, the indicator screen updates the display of the parking space status. The barrier gate interception linkage command is used to send an interception command to vehicles that have exceeded the time limit and violated regulations when they leave the gate. The command also includes vehicle information and settlement fee processing requirements.

[0030] Specifically, the steps for setting up the cyclical optimization billing strategy mechanism are as follows: Statistics show the weekly decrease rate of parking space occupancy events and the cumulative charging volume of charging piles across all parking space IDs. Set a target threshold for parking space occupancy events, and the target threshold is 30% of the total number of parking space IDs. For example, if there are 100 parking spaces, the target threshold is 30. The average charging pile capacity and the charging pile turnover rate threshold are preset, and the total charging amount is calculated based on the product of the average charging pile capacity and the charging pile turnover rate threshold. When the rate of decline is less than the target threshold, the duration increment coefficient is adaptively increased to increase the penalty coefficient for the timeout effect. When the accumulated charging amount is less than the total charging amount, the step value is increased by 2 times on the base multiple.

[0031] S4. System-linked real-time billing control: Dynamically loads and applies differentiated billing strategies to generate the final bill, supports multiple payment methods to complete settlement through the charging system linkage actuator, and monitors the status changes of the target parking space after the linkage disposal command is executed and updates the displayed status.

[0032] Specifically, the steps for generating the final bill are as follows: When the local magnetic parking space detector detects a vehicle leaving, the billing process is triggered; The billing engine loads the dynamic billing rules for the parking space from memory based on the parking space ID and adopts a differentiated billing strategy. When the billing engine fails to load the dynamic billing rules for a parking space from memory based on the parking space ID, it uses the preset base rate. The basic parking fee is calculated based on the total parking time from the time the vehicle enters to the time it leaves, according to the normal basic parking rate. The formula for calculating the basic parking fee is as follows: In the formula, This represents the basic parking fee. This is represented as the preset base rate. This represents the total time a vehicle spends parking in a parking space. The penalty fee for occupying a parking space is calculated based on the penalty fee for exceeding the free time limit in a dynamically loaded and differentiated billing strategy. This requires determining the abnormal event for the vehicle occupying the space corresponding to the parking space ID and accumulating the penalty fee in time segments. For example, if a new energy vehicle enters a parking space and occupies it without charging for an extended period... Then it was charged again, and after being fully charged, it remained in the charging state for an extended period. If someone leaves the field, the formula for calculating the penalty for reserving a spot is as follows: In the formula, This is represented as a punitive parking space occupancy fee for new energy vehicles; for example, if a gasoline-powered vehicle enters a parking space and occupies it for an extended period of time, the fee is... The formula for calculating the punitive reservation fee is: In the formula, This refers to a punitive parking fee levied on gasoline-powered vehicles. The system combines the basic parking fee and the penalty parking fee to generate the final parking fee, and records the amount of the basic parking fee and the penalty parking fee, the type of abnormal event of parking space occupation, and the duration information. The final bill is then generated and displayed to the car owner through the charging system. When the vehicle is stopped at the exit gate, it supports multiple payment methods such as WeChat, Alipay, and ETC to complete the settlement before the vehicle leaves.

[0033] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0034] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0035] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes 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.

[0036] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0037] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for linkage control of parking space occupancy detection and billing for new energy vehicle charging piles, characterized in that, Includes the following steps: S1. Multi-source perception and state fusion: Utilize parking space detectors, ALPR visual sensors, Hall sensors and charging pile state communication protocols to perceive the real-time occupancy status, vehicle type and charging behavior status of all parking spaces in real time and collect raw data. Deploy edge nodes in parking spaces to fuse the raw data in real time and output a unified fused parking space state object. S2. Abnormal Behavior Layered Judgment: Preset abnormal judgment rules and thresholds, perform comparative analysis based on the fused unified parking space status object, judge and generate structured abnormal event objects; S3. Dynamic Billing Strategy Generation and Linkage Instruction Arrangement: Configure a dynamic billing rule engine and a strategy arrangement engine to dynamically generate differentiated billing strategies based on the cumulative time of abnormal events, issue linkage handling instructions, and set up a cyclic optimization billing strategy mechanism. S4. System-linked real-time billing control: Dynamically loads and applies differentiated billing strategies to generate the final bill, supports multiple payment methods to complete settlement through the charging system linkage actuator, and monitors the status changes of the target parking space after the linkage disposal command is executed and updates the displayed status.

2. The method for linkage control of parking space occupancy detection and billing for new energy vehicle charging piles according to claim 1, characterized in that, The steps for collecting the raw data are as follows: A1. Preset sensitivity threshold and use a geomagnetic parking space detector to detect changes in magnetic field strength in real time to determine the parking space occupancy status, so as to sense vehicle entry / exit events and record parking space occupancy status information and corresponding timestamps. A2. Use the ALPR vision sensor to capture license plate color and numerical information, and use a convolutional neural network (CNN) model to identify the license plate number and a real-time vehicle model recognition algorithm to make a preliminary judgment on the captured license plate color and numerical information, and identify the vehicle type and license plate number. A3. Obtain the charging pile status in real time and associate it with the parking space number through the charging pile status communication protocol; A4. Install Hall sensors on the charging gun of the charging station to directly detect whether the charging gun is physically connected to the vehicle charging port, and record the status information and timestamp of the physical connection. A5. Real-time sensing data of all parking space statuses from parking space detectors, ALPR vision sensors, Hall sensors, and charging pile status communication protocols are statistically integrated to form raw data.

3. The method for linkage control of parking space occupancy detection and billing for new energy vehicle charging piles according to claim 2, characterized in that, The process of the CNN model recognizing license plate numbers is as follows: B1. Use an object detection CNN to locate the license plate region in a vehicle image and output the bounding box coordinates of the license plate region; B2. Perform an affine transformation on the tilted license plate, and use Hough transform or corner detection algorithm to correct the rotation angle to correct the license plate. B3. Use binarization to convert the corrected license plate into a grayscale image, perform adaptive threshold segmentation, and vertically project to count pixel values ​​to segment individual characters, outputting an image of 7 independent characters consisting of Chinese province abbreviations and alphanumeric combinations; B4. Use a CNN model to identify 7 independent character images to obtain the license plate number, and assist in judging whether the second letter or number of the license plate number is D / F when identifying the license plate number as a new energy vehicle; B5. The ALPR vision sensor captures license plate colors to preliminarily classify vehicle types, including green license plates for pure electric / hybrid new energy vehicles, blue license plates for ordinary fuel vehicles, yellow license plates for large vehicles / motorcycles, white license plates for vehicles of the political and legal system, and black license plates with white characters for foreign vehicles. Among them, white license plates trigger special parking space free occupancy privileges. B6. Multimodal fusion of license plate number / vehicle appearance based on license plate color and CNN model recognition. It uses a dual-branch output layer of the improved ResNet-18 vehicle classification model. Branch 1 outputs the vehicle type probability and branch 2 outputs the vehicle attributes.

4. The method for linkage control of parking space occupancy detection and billing for new energy vehicle charging piles according to claim 1, characterized in that, The steps for real-time fusion of raw data at the edge nodes are as follows: Edge nodes receive raw data, define time windows, and align the timestamps of the parking space detector, ALPR vision sensor, Hall sensor, and charging pile status communication protocol. The parking space occupancy event, vehicle type, and license plate recognition result are associated. When the parking space is occupied and the license plate is successfully recognized, the vehicle type information corresponding to the license plate is bound to the parking space number information. By comprehensively analyzing parking space occupancy events, charging gun connection status, and charging pile status, the precise charging behavior status of not charging, charging in progress, and charging completed can be determined. The comprehensive analysis includes the charging behavior status of not charging when the parking space is occupied, the charging gun is not connected, and the charging pile is idle. When the parking space is occupied, the charging gun is connected, and the charging pile is idle, the charging behavior status is "connected but not charging" in a fault state. When the parking space is occupied, the charging gun is connected, and the charging pile is in a charging state, the charging behavior status is "charging". When the parking space is occupied, the charging gun is connected, and the charging pile is in the charging completed state, the charging behavior status is charging completed. Edge nodes integrate the fused information into a unified parking space status object, which includes parking space ID, license plate number, vehicle type, occupancy status, charging gun connection status, charging pile status, and a set of timestamps corresponding to each status of the vehicle in the parking space.

5. The method for linkage control of parking space occupancy detection and billing for new energy vehicle charging piles according to claim 1, characterized in that, The preset content of the threshold is as follows: Collect historical parking and charging data, including information on vehicle parking duration, charging duration, and charging amount in different time periods and scenarios; Based on multiple information definitions, the following basic thresholds are established: the maximum free parking time for gasoline vehicles in charging station parking spaces is 30 minutes; the free parking time for new energy vehicles in charging station parking spaces without charging is 15 minutes; a preset buffer time threshold of 20 minutes after charging is completed for new energy vehicles; a preset full charge determination threshold of more than 95% charge; and a preset charging time threshold that must satisfy the formula. In the formula, This represents the preset charging time threshold. This refers to the vehicle's battery capacity. This represents the rated power of the charging station. Represented as the charging efficiency factor; The anomaly detection rules are as follows: Anomaly detection rules are formulated based on defined basic thresholds. For example, when a vehicle is identified as a fuel vehicle, the state machine time point tracking method is used to calculate in real time if the occupancy time of the fuel vehicle is greater than the maximum free parking time of 30 minutes, and then it is determined to be a fuel-consuming vehicle occupying the space for a long time. When the vehicle type is a new energy vehicle, if the charging gun is disconnected and the charging pile is idle / faulty and not charging, or if the charging gun is connected and the charging pile is idle / faulty and not charging, then if the new energy vehicle occupies the space without charging for a period of time longer than the free parking time without charging is 15 minutes, it is determined that the new energy vehicle is occupying the space without charging. When the vehicle type is a new energy vehicle, if the charging pile status is "charging complete", or the charging amount is not less than the preset full charge judgment threshold, or the actual charging time is not less than the preset charging time threshold, and the dwell time after charging complete / full charge is greater than the buffer time threshold of 20 minutes, it is determined that the new energy vehicle is staying after charging.

6. The method for linkage control of parking space occupancy detection and billing for new energy vehicle charging piles according to claim 1, characterized in that, The steps for generating the exception event object are as follows: By setting the rule engine to continuously monitor the unified parking space status object, we can obtain the latest real-time status information of each parking space number; The state machine management mechanism is used to update the status of each parking space based on the latest real-time status information of each parking space, and to record the time points of vehicle entry time, charging start time, charging completion time, charging gun connection time, and charging gun disconnection time. The time points recorded by the state machine are input into the duration calculation model, and the vehicle occupancy time, the time of new energy vehicles occupying the space without charging, and the dwell time after charging is completed / fully charged are calculated based on the current time. The market calculated by the duration calculation model is compared and analyzed with the preset anomaly judgment rules and thresholds to determine whether the anomaly conditions are met. The rule engine is used for matching. When the abnormal judgment conditions are met, a structured abnormal event object is generated. The abnormal event object includes the abnormal type, parking space ID, license plate number, trigger time and trigger conditions.

7. The method for linkage control of parking space occupancy detection and billing for new energy vehicle charging piles according to claim 1, characterized in that, The steps for generating the differentiated billing strategy are as follows: Receive abnormal event objects, including abnormal type, parking space ID, license plate number, trigger time and trigger conditions, and extract evidence data of the trigger conditions as feature data; Based on the feature data extracted from the abnormal event object, the timeout duration is calculated according to the start time and current duration, combined with the abnormal type. The basic rate is preset based on the parking space occupancy status. The severity level of the abnormality is divided according to the timeout duration. Different penalty coefficients can be configured for different abnormality types according to the time interval corresponding to different levels, thus generating a differentiated billing strategy. Calculate the dynamic penalty multiplier based on the penalty coefficient, and calculate the vehicle occupancy fee; Set the billing rules so that when the timeout duration corresponding to the abnormal type is greater than zero, only the timeout portion will be subject to a penalty occupancy fee, while the portion that has not exceeded the timeout will be calculated independently according to the preset base rate rules for the basic parking fee.

8. The method for linkage control of parking space occupancy detection and billing for new energy vehicle charging piles according to claim 1, characterized in that, The coordinated response instructions include multi-level response instructions, which send warning information to the car owner via the mobile device APP associated with the parking space number; The parking space guidance linkage command marks the parking spaces with abnormal occupancy as "occupancy warning" or "high-cost parking space" on the indicator screen, guiding newly entering vehicles to prioritize the selection of compliant and vacant parking spaces; The barrier gate interception linkage command is used to intercept vehicles that violate regulations and exceed the time limit when they leave the gate. The command includes recording vehicle information and settlement fee processing requirements.

9. The method for linkage control of parking space occupancy detection and billing for new energy vehicle charging piles according to claim 1, characterized in that, The steps for setting up the cyclical optimized billing strategy mechanism are as follows: Statistics show the weekly decrease rate of parking space occupancy events and the cumulative charging volume of charging piles across all parking space IDs. Set a target threshold for parking space occupancy events, and set the target threshold to 30% of the total number of parking space IDs; The average charging pile capacity and the charging pile turnover rate threshold are preset, and the total charging amount is calculated based on the product of the average charging pile capacity and the charging pile turnover rate threshold. When the rate of decline is less than the target threshold, the duration increment coefficient is adaptively increased. When the accumulated charging amount is less than the total charging amount, the step value is increased by 2 times on the base multiple.

10. The method for linkage control of parking space occupancy detection and billing for new energy vehicle charging piles according to claim 1, characterized in that, The steps for generating the final bill are as follows: When the local magnetic parking space detector detects a vehicle leaving, the billing process is triggered; The billing engine loads the dynamic billing rules for the parking space from memory based on the parking space ID and adopts a differentiated billing strategy. When the billing engine fails to load the dynamic billing rules for a parking space from memory based on the parking space ID, it uses the preset base rate. The basic parking fee is calculated based on the total parking time from the time the vehicle enters to the time it leaves, according to the normal basic parking rate. The penalty fee for occupying a parking space is calculated based on the penalty fee for exceeding the free time in the dynamically loaded and differentiated billing strategy. It is necessary to determine the abnormal event of the vehicle occupying the parking space ID and accumulate the penalty fee in time segments. The basic parking fee and the penalty parking fee are combined to generate the final parking fee, and the amount of the basic parking fee and the penalty parking fee, the type of abnormal event of parking space occupation, and the duration information are recorded to generate the final bill.

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