Distributed on-road parking charging system and method based on charging piles

Abstract

The invention discloses a distributed curb parking charging system and method based on a charging pile, and relates to the technical field of parking charging, and the method specifically comprises the following steps: obtaining the resource use information of each parking space in real time, carrying out the analysis after the obtaining, and generating a resource occupation coefficient and a demand response index of each parking space; constructing a charging evaluation model for the generated resource occupation coefficient and demand response index of each parking space, generating a dynamic charging evaluation coefficient of each parking space, performing analysis after generation, and evaluating the resource tension degree grade of each parking space. The parking spaces are divided into high-tension parking spaces, normal-tension parking spaces and low-tension parking spaces according to evaluation results; and based on the division result of each parking space, corresponding charging strategies are adopted for the parking spaces with different resource tension levels. According to the invention, the problem of shortage of parking resources in peak periods is solved, the charging standard is dynamically adjusted, and the resource utilization efficiency and the operation income are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of parking fee collection, and in particular to a distributed on-street parking fee collection system based on charging piles and a method thereof. Background Art

[0002] Distributed on-street parking refers to setting up multiple scattered parking spaces on both sides of urban roads or in specific areas for temporary parking of vehicles, and providing parking and charging services in combination with infrastructure such as charging piles. Due to the limited parking resources in cities, the large demand for parking and the high cost of parking management and maintenance, charging for these parking spaces helps to reasonably allocate parking resources, improve parking turnover, and support the operation and maintenance of parking facilities. Distributed on-street parking charges based on charging piles refer to combining charging piles with these distributed parking spaces to provide comprehensive services for parking and charging vehicles, and dynamically generate parking and charging fees based on factors such as parking time and charging demand through an intelligent system, thereby achieving efficient use of parking resources and energy, while providing managers and users with convenient charging and payment methods.

[0003] The existing distributed on-street parking charging technology based on charging piles collects the usage status of the charging piles in each parking space, the parking time of the vehicle, the charging demand and other data in real time, and dynamically generates charging rules based on these data. After the vehicle enters the parking space, the system first records the vehicle's entry time and whether the charging service is turned on; if the vehicle starts charging, the system will simultaneously calculate the parking fee based on the charging power, duration and power consumption. When the vehicle leaves the parking space or ends charging, the system automatically calculates the parking fee, including the fees generated by pure parking time and charging time. These fees are dynamically adjusted according to different time periods, charging pile usage and parking demand fluctuations to ensure reasonable charges. Users complete payment through mobile phone APP, scan code payment or automatic deduction of bound accounts, and the whole process realizes non-sensory payment. At the same time, the management system optimizes the charging strategy by analyzing historical parking and charging data to improve the efficiency of parking resources and ensure the sustainable operation of facilities.

[0004] The prior art has the following deficiencies:

[0005] During peak hours of parking and charging demand, the frequency of use of parking spaces and charging piles increases dramatically, and the user demand for limited resources leads to resource shortages. The existing distributed on-street parking charging technology based on charging piles lacks real-time analysis of the resource usage of each parking space, and cannot accurately evaluate and predict the resource shortage of each parking space. Because the existing technology fails to classify the supply and demand of parking space resources, and the charging standards cannot be dynamically adjusted according to the actual shortage situation, some users occupy resources for a long time and fail to pay fees that match the shortage level, resulting in inefficient resource utilization and inequitable resource allocation. At the same time, the operator cannot flexibly adjust the charging standards to optimize resource utilization, affecting the overall operating income and user experience.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0007] The purpose of the present invention is to provide a distributed on-street parking fee collection system and method based on charging piles to solve the problems in the above-mentioned background technology.

[0008] In order to achieve the above-mentioned purpose, the present invention provides the following technical solution: a distributed on-street parking charging method based on charging piles, which specifically includes the following steps:

[0009] Determine the peak time periods for parking and charging demand, and establish a monitoring and charging management framework for all parking spaces and charging pile resources during these time periods based on the management objectives and resource allocation requirements of the parking area;

[0010] Obtain resource usage information of each parking space in real time, analyze it after acquisition, and generate resource occupancy coefficient and demand response index of each parking space;

[0011] A charging assessment model is constructed based on the resource occupancy coefficient and demand response index of each parking space generated, and a dynamic charging assessment coefficient of each parking space is generated. After the generation, the dynamic charging assessment coefficient is analyzed to assess the resource tension level of each parking space, and each parking space is divided into high tension parking space, normal tension parking space and low tension parking space according to the assessment results.

[0012] Based on the division results of each parking space, corresponding charging strategies are adopted for parking spaces with different resource tension levels;

[0013] Continuously monitor the resource usage of each parking space and the implementation effect of the charging strategy, dynamically adjust the charging standards of each parking space, regularly update the charging evaluation model, and optimize the parking resource allocation strategy.

[0014] Preferably, the resource usage information of each parking space is obtained in real time, and analyzed after being obtained to generate the resource occupancy coefficient and demand response index of each parking space respectively, which specifically includes the following steps:

[0015] Obtain resource usage information of each parking space in real time and perform preprocessing after acquisition;

[0016] Extracting resource occupancy status information and demand response dynamic information from the pre-processed resource usage information of each parking space;

[0017] The resource occupancy status information and demand response dynamic information in the extracted resource usage information of each parking space are analyzed to generate a resource occupancy coefficient and a demand response index of each parking space respectively.

[0018] Preferably, the logic for obtaining the resource occupancy coefficient of each parking space is as follows:

[0019] The resource occupancy status information of each parking space in the preprocessed resource usage information is extracted, including the cumulative occupancy time and usage frequency of each parking space at different times within a period of time, the average utilization rate of other parking spaces in the area where each parking space is located within the period of time, and the parking demand density in the area. The cumulative occupancy time and usage frequency of each parking space at different times within a period of time are respectively calculated according to the time series using the function TOC i (t) and FUS i (t) represents the time point, TOC i (t) represents the cumulative occupancy time of the i-th parking space at time t within a period of time, FUS i (t) represents the usage frequency of the i-th parking space at time t within a period of time. The time period is defined as [t1, t2], and the average usage rate of other parking spaces in the area where each parking space is located and the parking demand density in the area are marked as UAR respectively. i and DDC i , UAR i represents the average utilization rate of other parking spaces in the area where the i-th parking space is located during this time period, DDC i represents the parking demand density in the area where the i-th parking space is located during the time period, i=1, 2, 3, ..., k, where k is a positive integer;

[0020] Calculate the resource occupancy coefficient of each parking space. The specific calculation formula is as follows:

[0021]

[0022] In the formula, ROC i is the resource occupancy coefficient of the i-th parking space.

[0023] Preferably, the logic for obtaining the demand response index of each parking space is as follows:

[0024] Extract the dynamic information of demand response from the preprocessed resource usage information of each parking space, including the traffic flow, parking demand pressure density, the average change rate of historical parking demand of each parking space in the time period, and the parking demand of each parking space at different times in a period of time in the area where each parking space is located. The traffic flow, parking demand pressure density and parking demand of each parking space at different times in a period of time in the area where each parking space is located are respectively calculated by the function TRF according to the time series. i (t), DDP i (t) and CPD i (t), t is the time point, TRF i (t) represents the traffic flow in the area of ​​the i-th parking space at time t within a period of time, DDP i (t) represents the parking demand pressure density of the i-th parking space at time t within a period of time, CPD i (t) represents the parking demand of the i-th parking space at time t within a period of time. The time period is defined as [t1, t2], and the average change rate of the historical parking demand of each parking space within this period is calibrated as HCD i , HCD i represents the average change rate of the historical parking demand of the i-th parking space in the time period, i = 1, 2, 3, ..., k, k is a positive integer;

[0025] Calculate the demand response index of each parking space. The specific calculation formula is as follows:

[0026]

[0027] Where, DRI i is the demand response index of the i-th parking space.

[0028] Preferably, a charging assessment model is constructed based on the resource occupancy coefficient and demand response index of each parking space generated to generate a dynamic charging assessment coefficient for each parking space, specifically including the following steps:

[0029] The resource occupancy coefficient, demand response index and dynamic charging evaluation coefficient of several parking spaces generated in the past period of time are collected and calibrated as and x represents the number of resource occupancy coefficients, demand response indexes, and dynamic charging assessment coefficients of several parking spaces generated in the past period of time, x=3, 4, 5, ..., d, d is a positive integer, and the data collected in the past period of time is formed into a historical data set;

[0030] Select the multivariate regression model as the fee assessment model, and train it through the historical data set to determine the value of the regression coefficient according to the formula:

[0031]

[0032] Where β0, β1 and β2 are regression coefficients;

[0033] By minimizing the error between the predicted value and the actual value, the regression coefficient is optimized, and finally the values ​​of the regression coefficients β0, β1 and β2 are determined;

[0034] Using the finalized regression coefficient, the constructed charging assessment model is input with the resource occupancy coefficient of each parking space generated in real time. and Demand Response Index Generate dynamic charging evaluation coefficient DPEC for each parking space in real time i .

[0035] Preferably, the dynamic charging evaluation coefficient DPEC of each parking space generated i The dynamic fee assessment coefficient threshold interval [DPEC min ,DPEC max ] to compare, evaluate the resource tension level of each parking space according to the comparison results, and divide each parking space into high tension parking space, normal tension parking space and low tension parking space according to the evaluation results. The specific comparison analysis and division are as follows:

[0036] If DPEC i <DPEC min , the resource tension level of the parking space is low, then the parking space is classified as a low tension parking space;

[0037] If DPEC min ≤DPEC i ≤DPEC max , the resource tension level of the parking space is medium, then the parking space is classified as a normal tension parking space;

[0038] If DPEC i >DPEC max , if the resource tension level of the parking space is high, the parking space is classified as a high tension parking space.

[0039] Preferably, based on the division results of each parking space, corresponding charging strategies are adopted for parking spaces with different resource tension levels, specifically:

[0040] For low-intensity parking spaces with low resource intensity levels, the charging strategy adopted is to reduce parking fee standards and provide parking discounts;

[0041] For normal parking spaces with medium resource shortage, the charging strategy adopted is to maintain the existing charging standards without making additional adjustments to maintain the current supply and demand balance;

[0042] For high-intensity parking spaces with a high level of resource intensity, the charging strategy adopted is to increase parking fee standards and adjust parking demand through price levers.

[0043] Preferably, the distributed on-street parking charging system based on charging piles includes a peak demand identification and management framework establishment module, a resource usage information collection and analysis module, a dynamic charging assessment construction and resource shortage analysis module, a charging strategy intelligent adjustment module, and a resource usage monitoring and assessment optimization module;

[0044] Peak demand identification and management framework establishment module, which determines the peak time period for parking and charging demand, and establishes a monitoring and charging management framework for all parking spaces and charging pile resources according to the management objectives and resource allocation requirements of the parking area during this period;

[0045] The resource usage information collection and analysis module obtains the resource usage information of each parking space in real time, analyzes it after acquisition, and generates the resource occupancy coefficient and demand response index of each parking space;

[0046] Dynamic charging assessment construction and resource tension analysis module, which constructs a charging assessment model based on the resource occupancy coefficient and demand response index of each parking space, generates a dynamic charging assessment coefficient for each parking space, and analyzes it after generation to assess the resource tension level of each parking space, and divides each parking space into high tension parking space, normal tension parking space and low tension parking space according to the assessment results;

[0047] The charging strategy intelligent adjustment module adopts corresponding charging strategies for parking spaces with different resource tension levels based on the division results of each parking space;

[0048] The resource usage monitoring and evaluation optimization module continuously monitors the resource usage of each parking space and the implementation effect of the charging strategy, dynamically adjusts the charging standards of each parking space, regularly updates the charging evaluation model, and optimizes the parking resource allocation strategy.

[0049] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0050] 1. The present invention solves the technical problem that the existing technology cannot accurately evaluate the degree of parking resource tension by real-time monitoring and analyzing the resource usage information of parking spaces. By combining the resource occupancy coefficient and the demand response index, the system can dynamically predict the resource tension of parking spaces. Its technical effect is that it can adjust the parking fee standard in real time to ensure the reasonable allocation of different parking spaces during peak hours and the optimization of charging strategies. This not only improves the intelligence level of the system, but also greatly improves the utilization rate of parking resources.

[0051] 2. The multivariate regression model in the present invention optimizes the resource tension assessment process by analyzing and learning the historical data of parking spaces. This model can generate a dynamic charging assessment coefficient for each parking space and adopt a corresponding charging strategy based on the assessment results. Its advantage is that the charging strategy can be flexibly adjusted according to the usage of the parking space, achieving accurate pricing for different parking needs and avoiding the situation where parking spaces are occupied for a long time without paying reasonable fees. This dynamic control strategy ensures the balance between the supply and demand of parking spaces and increases the revenue of the operator.

[0052] 3. The present invention also continuously monitors the execution effect of the charging strategy and regularly updates the evaluation model to ensure that the entire system can continuously optimize the configuration of parking resources over time. This feature makes the solution highly flexible and scalable, and can adapt to the dynamic changes in parking demand and market environment. It not only optimizes operational efficiency, but also improves user experience through a fair charging mechanism, providing a comprehensive and efficient solution for urban parking management. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0054] Figure 1 The figure is a flow chart of a distributed on-street parking fee collection system and method based on charging piles of the present invention.

[0055] Figure 2 The module diagram of the distributed on-street parking fee collection system and method based on charging piles of the present invention is shown. DETAILED DESCRIPTION

[0056] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0057] The present invention provides Figure 1 The distributed on-street parking charging method based on charging piles shown in the figure specifically includes the following steps:

[0058] Determine the peak time periods for parking and charging demand, and establish a monitoring and charging management framework for all parking spaces and charging pile resources during these time periods based on the management objectives and resource allocation requirements of the parking area;

[0059] Determining the peak time periods for parking and charging demand can be achieved in the following ways: First, the system can analyze historical parking and charging data to extract parking and charging behavior patterns within a daily, weekly or monthly period, and identify high-frequency use time periods through statistical analysis and time series analysis algorithms. Secondly, combined with real-time traffic flow data, the system can monitor the traffic conditions in the area and predict future parking demand peaks. It is also possible to use machine learning models to dynamically predict peak periods based on historical data and other relevant external factors (such as holidays, weather changes, special events, etc.). In addition, the system can continuously adjust the definition of peak periods based on actual usage and user feedback to make the prediction results more accurate. The software will automatically and continuously update this data to generate dynamic peak period forecasts.

[0060] Once the peak time period is determined, the system will establish a monitoring and charging management framework during that period based on the management objectives and resource allocation needs of the parking area. Specifically, the system can delineate the monitoring area of ​​each parking space and charging pile according to the real-time usage status of the parking space and charging pile, and collect the usage data of the parking space and charging pile in real time through the installed sensor network and IoT devices. Then, the system will automatically generate charging rules and priority control strategies based on management objectives (such as improving resource utilization and optimizing charging revenue) and resource allocation strategies (such as giving priority to tight resources during peak hours). The framework will monitor in combination with real-time data and dynamically adjust the resource scheduling and charging standards of parking spaces. All these processes can be automatically managed through software, and updated and optimized in real time by the background system.

[0061] The reason for this approach is that it directly solves the problem of resource shortage during peak periods of parking and charging demand. By accurately predicting and identifying peak hours, the system can reasonably schedule the use of parking spaces and charging piles during resource-constrained periods, and use dynamic charging strategies to guide users to reasonably allocate limited resources to avoid the situation where some users occupy resources for a long time but pay unreasonable fees. At the same time, combined with a real-time monitoring and management framework, the system can automatically optimize resource allocation and charging standards during peak hours to ensure that resources are maximized. This approach can not only improve the efficiency of parking spaces and charging piles, but also improve the operator's revenue and user experience, solving the problem of unfair resource allocation and dynamic adjustment of charging standards.

[0062] Obtain resource usage information of each parking space in real time, analyze it after acquisition, and generate resource occupancy coefficient and demand response index of each parking space;

[0063] In this embodiment, the resource usage information of each parking space is obtained in real time, and analyzed after being obtained to generate the resource occupancy coefficient and demand response index of each parking space respectively, which specifically includes the following steps:

[0064] Obtain resource usage information of each parking space in real time and perform preprocessing after acquisition;

[0065] In order to achieve "real-time acquisition of resource usage information of each parking space", data collection can be carried out in the following ways. First, Internet of Things (IoT) sensors are installed in each parking space. These sensors can monitor whether the vehicle occupies the parking space, the parking time, the use of the charging pile, etc., and upload data in real time through wireless transmission technology (such as Wi-Fi, 5G or LoRa). Secondly, it is also possible to combine cameras and image recognition technology to analyze the video stream of the parking area to determine the occupancy of the parking space in real time. After the data is initially processed by the edge computing device, it is uploaded to the cloud server in real time to form the resource occupancy status information and demand response dynamic information of each parking space. In order to ensure the continuity and accuracy of data collection, the system will regularly collect the status information of each parking space, and use redundant technology to ensure that the data will not be lost or there will be a significant time delay in the event of a network interruption or sensor failure.

[0066] The purpose of preprocessing is to ensure that the resource usage information of each parking space is accurate, consistent and complete, so as to provide a reliable data basis for subsequent data analysis and parameter generation. In data preprocessing, the collected data must first be cleaned to remove abnormal data (such as sensor errors, misread data, etc.), and the missing data must be filled (historical data, average values ​​or values ​​of adjacent parking spaces can be used to fill in). Secondly, the data must be normalized, because different sensors may generate data in different formats or magnitudes. Normalization can convert the data into the same dimension to facilitate subsequent analysis. In addition, noise filtering can be performed, such as using Kalman filtering or mean filtering algorithms to remove high-frequency noise and errors generated by the sensor during the acquisition process. The preprocessing step is performed through software automation, relying on cloud computing and data processing frameworks to ensure that large-scale parking space information can be efficiently and seamlessly preprocessed, and provide high-quality data to support subsequent analysis and evaluation.

[0067] Extracting resource occupancy status information and demand response dynamic information from the pre-processed resource usage information of each parking space;

[0068] In order to extract the resource occupancy status information and demand response dynamic information of each pre-processed parking space, automatic data parsing and classification can be achieved through software. First, the pre-processed data will be stored in the database, and the software system will classify the data through defined rules and tags. Specifically, different types of resource usage information are identified through data tags. For example, information such as parking duration and occupancy frequency will be marked as resource occupancy status information, while information such as real-time traffic flow and current parking demand will be identified as demand response dynamic information. The software will extract the relevant data fields of each parking space according to the preset logical filtering and classification. For example, the system can directly filter out the indicator fields related to resource occupancy and demand response from the database through data query and filtering functions (such as SQL query or NoSQL filter). Then, the software will mark and store the extracted data in a hierarchical manner to ensure that the subsequent analysis module can directly access these two types of information and perform modeling and calculation. The entire extraction process is implemented through an automated data processing framework to ensure the efficiency and accuracy of the extraction.

[0069] The resource occupancy status information and demand response dynamic information in the extracted resource usage information of each parking space are analyzed to generate a resource occupancy coefficient and a demand response index of each parking space respectively.

[0070] In this embodiment, the logic for obtaining the resource occupancy coefficient of each parking space is as follows:

[0071] The resource occupancy status information of each parking space in the preprocessed resource usage information is extracted, including the cumulative occupancy time and usage frequency of each parking space at different times within a period of time, the average utilization rate of other parking spaces in the area where each parking space is located within the period of time, and the parking demand density in the area. The cumulative occupancy time and usage frequency of each parking space at different times within a period of time are respectively calculated according to the time series using the function TOC i (t) and FUS i (t) represents the time point, TOC i (t) represents the cumulative occupancy time of the i-th parking space at time t within a period of time, FUS i (t) represents the usage frequency of the i-th parking space at time t within a period of time. The time period is defined as [t1, t2], and the average usage rate of other parking spaces in the area where each parking space is located and the parking demand density in the area are marked as UAR respectively. i and DDC i , UAR i represents the average utilization rate of other parking spaces in the area where the i-th parking space is located during this time period, DDC i represents the parking demand density in the area where the i-th parking space is located during the time period, i=1, 2, 3, ..., k, where k is a positive integer;

[0072] In order to extract the resource occupancy status information of each pre-processed parking space, including the cumulative occupancy time, frequency of use, average utilization rate of other parking spaces in the area, and parking demand density, it can be achieved in the following ways. First, the cumulative occupancy time and frequency of use of parking spaces can be collected in real time through Internet of Things (IoT) sensors installed on parking spaces. These sensors can detect the time points when vehicles enter and leave parking spaces. The sensors record the occupancy and idle status of parking spaces, accumulate the occupancy time per hour or minute, and record the frequency of use through software. These data are uploaded to the cloud database via wireless networks (such as Wi-Fi, LoRa or 5G). In the database, the system automatically calculates the cumulative occupancy time and frequency of use based on the status information of each parking space.

[0073] The average utilization rate of other parking spaces in the area can be calculated using the data from all parking space sensors in the same system. The system first collects real-time usage data for all parking spaces in the same area, then aggregates and averages the data to generate the average utilization rate for the area where each parking space is located. The specific implementation method is to use the database query and data aggregation function, for example, using SQL or other database query languages ​​to aggregate the status information of parking spaces in the same area by time dimension and calculate the average utilization rate per hour or day. The result is then associated with a specific parking space to calculate the relative occupancy of the parking space in the area.

[0074] As for parking demand density, it can be calculated by combining traffic flow data and historical parking demand data. Traffic flow data can be obtained in real time through traffic monitoring equipment in the area (such as cameras or road traffic monitoring sensors). The software system combines these traffic data with the actual usage data of parking spaces to evaluate the changing trend of parking demand in the area. Historical data can be used to predict the current demand density by comparing the occupancy of parking spaces over a period of time, and use time series analysis algorithms to predict future parking demand. These analysis results are processed automatically by software, combining traffic flow and parking space usage information to generate real-time parking demand density.

[0075] Calculate the resource occupancy coefficient of each parking space. The specific calculation formula is as follows:

[0076]

[0077] In the formula, ROC i is the resource occupancy coefficient of the i-th parking space.

[0078] This formula is used to calculate the resource occupancy coefficient of each parking space. Its purpose is to evaluate the resource occupancy of parking spaces through multi-dimensional comprehensive factors, so as to provide a basis for subsequent dynamic charging strategies. First, the integral part of the formula Used to calculate the occupancy of parking spaces over a period of time, where TOC i (t) represents the cumulative occupancy time, FUS i (t) represents the frequency of use. After the two quantities are multiplied, the integration can dynamically reflect the resource usage intensity of parking spaces at different times. By capturing the changes within a time period through integration, we can avoid the transient data at a single moment misleading the analysis results and ensure that the overall trend is evaluated. Next, the denominator UAR irepresents the average utilization rate of other parking spaces in the area. By introducing this parameter, the occupancy of a parking space can be compared with the average utilization rate of the entire area to determine the relative tightness of the parking space. This comparison method can prevent the occupancy of a single parking space from being overestimated when the parking space utilization rate is generally low. Finally, [1+ln(1+DDC i )] is the logarithmic correction of parking demand density, DDC i Represents the parking demand density of the area. Logarithmic processing makes the impact of parking demand density not grow linearly, avoiding excessive influence of this indicator on the overall evaluation during peak demand periods. This not only takes into account the impact of actual demand on resource tension, but also avoids misjudgment caused by excessive weighting. Overall, the entire formula ensures that the calculation result of the resource occupancy coefficient can fully and accurately reflect the actual resource tension of each parking space through multiple calculation steps such as time integration, regional average utilization rate comparison, and demand density correction.

[0079] The resource occupation coefficient ROC of the i-th parking space i The size is directly related to the resource tension level of the parking space. The larger the resource occupation coefficient, the longer the parking space is occupied in a certain period of time and the higher the frequency of use. At the same time, the average utilization rate of other parking spaces in the area is low, and the parking demand density in the area is high, which means that the resource tension of the parking space is greater. Therefore, by comparing the resource occupation coefficients of various parking spaces, their resource tension levels can be classified: when ROC i When the value is high, the resource tension of the parking space is assessed as a high tension level, indicating that the demand for the parking space is high and the supply is insufficient; when ROC i When the value is in the middle range, it is assessed as a normal tension level, indicating that the supply and demand of parking spaces are relatively balanced; when ROC i When the value is low, it is evaluated as a low tension level, indicating that parking space resources are relatively loose and the use pressure is relatively small. Through this evaluation, it can provide a basis for subsequent dynamic charging adjustments to ensure the rational allocation of resources.

[0080] In this embodiment, the logic for obtaining the demand response index of each parking space is as follows:

[0081] Extract the dynamic information of demand response from the preprocessed resource usage information of each parking space, including the traffic flow, parking demand pressure density, the average change rate of historical parking demand of each parking space in the time period, and the parking demand of each parking space at different times in a period of time in the area where each parking space is located. The traffic flow, parking demand pressure density and parking demand of each parking space at different times in a period of time in the area where each parking space is located are respectively calculated by the function TRF according to the time series. i (t), DDP i (t) and CPD i (t), t is the time point, TRF i (t) represents the traffic flow in the area of ​​the i-th parking space at time t within a period of time, DDP i (t) represents the parking demand pressure density of the i-th parking space at time t within a period of time, CPD i (t) represents the parking demand of the i-th parking space at time t within a period of time. The time period is defined as [t1, t2], and the average change rate of the historical parking demand of each parking space within this period is calibrated as HCD i , HCD i represents the average change rate of the historical parking demand of the i-th parking space in the time period, i = 1, 2, 3, ..., k, k is a positive integer;

[0082] In order to extract the dynamic information of demand response of each pre-processed parking space, it can be achieved in the following ways. First, traffic flow data can be collected in real time through traffic flow sensors, cameras and intelligent transportation systems installed on the road. These devices use vehicle detection technology to capture the traffic flow on the road, generate traffic flow information at different times and upload it to the system database in real time through wireless networks. The parking demand pressure density is estimated by analyzing the occupancy of all parking spaces in the current area. The system will monitor the use of parking spaces in real time (such as vacancy and occupancy status) and conduct density analysis based on the supply and demand of parking spaces in the area. The density is calculated by the ratio of the number of available parking spaces in the area to the actual number of vehicles in demand. The number of vehicles in demand can be determined by the reservation system, historical data predictions and actual occupancy data.

[0083] The average change rate of historical parking demand is based on the analysis of historical parking data. By comparing the parking demand in the current time period with the same time period in the past, the fluctuation trend of parking demand is evaluated. The system will extract historical parking usage data (including parking space occupancy, vacancy, etc.) from the database and calculate the change rate of historical parking demand through time series analysis algorithms (such as ARIMA model). The parking demand can be obtained through the real-time parking space occupancy. The system will perform statistics at different time points based on the status of each parking space (such as vacant or occupied), and determine the actual demand for each parking space based on the total parking demand in the area.

[0084] The extraction of these quantitative data is achieved through software automation processes. First, devices such as sensors and cameras monitor traffic and parking space status in real time through edge computing, and transmit the collected data to the cloud. Then, the system uses database query languages ​​(such as SQL) to extract historical parking data and current parking demand data from the storage system, and pre-processes, cleans and analyzes the data through the data analysis module, and finally generates quantitative information on traffic flow, parking demand pressure density, historical parking demand change rate and parking demand. These data will be used as input for subsequent demand response index calculations and dynamic resource allocation.

[0085] Calculate the demand response index of each parking space. The specific calculation formula is as follows:

[0086]

[0087] Where, DRI i is the demand response index of the i-th parking space.

[0088] The formula calculates the demand response index (DRI) of each parking space in detail by integrating traffic flow, parking demand, historical demand change rate and parking demand pressure density. i First, the numerator of the integral Used to dynamically evaluate traffic flow TRF over a period of time i (t) and parking demand CPD i (t) Changes in traffic flow TRF i (t) represents the vehicle flow at a certain point in time in the area, and the parking demand CPD i The square root of (t) reflects the actual demand intensity during the time period. The square root processing method does not overly amplify the impact on demand response when the demand is high, while still smoothly reflecting the impact of demand when the demand is weak. Through integration, the formula can capture the demand fluctuations at different time points and generate a comprehensive demand response evaluation.

[0089] In the denominator, the historical parking demand change rate HCD iIt is used to represent the average change trend of demand over a period of time in the past, and serves as a reference point to help evaluate whether the current demand is consistent with historical demand fluctuations. According to the parking demand pressure density DDP i (t) Modification of demand response, DDP i (t) reflects the current parking situation in the area. Through exponential decay, the formula can smoothly handle excessive or low demand pressure, avoid the impact of excessive demand pressure on the overall evaluation, and ensure that the response under low demand pressure is not underestimated. This calculation not only takes into account the immediate demand and historical data, but also combines the regional supply and demand pressure for correction, ensuring that the index can truly reflect the current parking demand response level and provide support for subsequent dynamic resource management.

[0090] Demand response index DRI of the i-th parking space i The size of is closely related to the resource tension level of the parking space. The larger the demand response index, the higher the traffic flow of the parking space in a period of time, the larger the parking demand, and the historical demand change rate shows that the current demand is significantly higher than the past, indicating that the parking space faces greater pressure in meeting the demand. At the same time, the parking demand pressure density in the area is high, reflecting that the demand for the use of the parking space far exceeds the available resources. Therefore, parking spaces with a higher demand response index are generally assessed as high tension levels. When the demand response index is small, it means that the traffic flow of the parking space is low, the parking demand is small, and the historical demand change rate is relatively stable, and the parking demand pressure density is low, indicating that the parking space is in a relatively loose resource environment, with low demand pressure and correspondingly low tension. Therefore, the size of the demand response index can accurately assess the resource tension of the parking space, and provide a basis for allocating different management and charging strategies to different parking spaces.

[0091] A charging assessment model is constructed based on the resource occupancy coefficient and demand response index of each parking space generated, and a dynamic charging assessment coefficient of each parking space is generated. After the generation, the dynamic charging assessment coefficient is analyzed to assess the resource tension level of each parking space, and each parking space is divided into high tension parking space, normal tension parking space and low tension parking space according to the assessment results.

[0092] In this embodiment, a charging assessment model is constructed based on the resource occupancy coefficient and demand response index of each parking space, and a dynamic charging assessment coefficient of each parking space is generated, which specifically includes the following steps:

[0093] The resource occupancy coefficient, demand response index and dynamic charging evaluation coefficient of several parking spaces generated in the past period of time are collected and calibrated as and x represents the number of resource occupancy coefficients, demand response indexes, and dynamic charging assessment coefficients of several parking spaces generated in the past period of time, x=3, 4, 5, ..., d, d is a positive integer, and the data collected in the past period of time is formed into a historical data set;

[0094] In order to collect the resource occupancy coefficient, demand response index and dynamic charging assessment coefficient of each parking space generated in the past period of time, it can be achieved in the following way. First, the system will store these data in the background database when calculating the resource occupancy coefficient, demand response index and dynamic charging assessment coefficient in real time. The software system regularly saves the relevant coefficients of each parking space according to the timestamp through an automated database management system (such as SQL or NoSQL database). Whenever the system updates the resource occupancy coefficient, demand response index and dynamic charging assessment coefficient of a parking space, these values ​​will be automatically timestamped and stored in a historical record table. The system can also collect and update these historical data in batches according to a preset time period (such as hourly, daily or weekly). In order to ensure data integrity and accuracy, the system can prevent data loss or storage errors through data checking and redundancy mechanisms. In addition, the software can also mark and classify historical data, so that the historical data of each parking space can be easily queried and analyzed according to time periods, supporting subsequent model optimization and regression analysis.

[0095] The value of x is a positive integer greater than or equal to 3. This is because the objective equation involves three variables: resource occupancy coefficient, demand response index, and dynamic charging assessment coefficient. In order to calculate the regression coefficients β0, β1, and β2 of these three variables, at least three independent equations are required to uniquely determine the values ​​of these three regression coefficients. Therefore, x must be greater than or equal to 3 to ensure that there are enough sets of equations for regression analysis. If there is insufficient data (that is, x is less than 3), all regression coefficients cannot be found and the solution of the model will become uncertain. Therefore, in order to ensure the stability and accuracy of the regression model, the minimum value of x is 3, which means that at least three sets of data are required to generate a valid multivariate regression model.

[0096] Select the multivariate regression model as the fee assessment model, and train it through the historical data set to determine the value of the regression coefficient according to the formula:

[0097]

[0098] Where β0, β1 and β2 are regression coefficients;

[0099] The reason for choosing the multivariate regression model as the fee assessment model is that the dynamic fee assessment coefficient Affected by multiple variables, including resource utilization factor and Demand Response Index The multiple regression model can simultaneously analyze the effects of these two independent variables on The impact of these two variables and the target variable are found by training the model with historical data. The best linear relationship between them. The regression coefficients β0, β1, and β2 represent the intercept term, the weight of the resource occupancy coefficient, and the weight of the demand response index, respectively. Specifically, β1 represents the intensity of the impact of resource occupancy on dynamic charging, β2 represents the degree of impact of the demand response index on dynamic charging, and β0, as an offset, represents the basic charging assessment coefficient when the impact of both is zero. By minimizing the error between the predicted value and the actual value and optimizing the three regression coefficients, the dynamic charging assessment coefficient of each parking space can be accurately estimated, so that the model can dynamically adjust the parking fee in different situations, so as to achieve the purpose of reasonable allocation of resources and optimal charging strategy.

[0100] By minimizing the error between the predicted value and the actual value, the regression coefficient is optimized, and finally the values ​​of the regression coefficients β0, β1 and β2 are determined;

[0101] The purpose of optimizing the regression coefficients by minimizing the error between the predicted and actual values ​​is to ensure that the model can predict future toll assessment coefficients as accurately as possible based on historical data. Minimizing the error can improve the model's fit, making the resource occupancy coefficient and Demand Response Index Predicted dynamic charging assessment coefficient The specific implementation method is through the least squares method (OLS), that is, selecting a set of regression coefficients β0, β1 and β2 so that the model predicts the Value and historical reality The sum of squared errors between the values ​​is minimized. The model continuously adjusts the regression coefficients to find a set of optimal coefficients that can minimize the overall error. The final regression coefficients can not only explain and right The contribution size can also be used to generate accurate charging assessments in a real-time environment, thereby achieving reasonable allocation of resources and optimization of dynamic charging.

[0102] Using the finalized regression coefficient, the constructed charging assessment model is input with the resource occupancy coefficient of each parking space generated in real time. and Demand Response Index Generate dynamic charging evaluation coefficient DPEC for each parking space in real time i .

[0103] In this embodiment, the dynamic charging evaluation coefficient DPEC of each parking space is generated. iThe dynamic fee assessment coefficient threshold interval [DPEC min ,DPEC max ] to compare, evaluate the resource tension level of each parking space according to the comparison results, and divide each parking space into high tension parking space, normal tension parking space and low tension parking space according to the evaluation results. The specific comparison analysis and division are as follows:

[0104] If DPEC i <DPEC min , the resource tension level of the parking space is low, then the parking space is classified as a low tension parking space;

[0105] This situation means that the resource tension of the parking space is low. In this case, the occupancy of the parking space and the demand response index show that the parking space is not frequently used in the area, the vehicle demand is small, and the supply is relatively sufficient. Therefore, this type of parking space is usually idle or has not been used frequently for a long time. For this type of low-tension parking space, you can consider adopting a price reduction or preferential strategy to attract more vehicles to park and increase the utilization rate of the parking space. At the same time, this adjustment will also help reduce the pressure on other high-tension areas and optimize the overall resource allocation.

[0106] If DPEC min ≤DPEC i ≤DPEC max , the resource tension level of the parking space is medium, then the parking space is classified as a normal tension parking space;

[0107] This situation indicates that the resource tension of the parking space is in a normal or medium tension state. This means that the supply and demand of the parking space is relatively balanced, the parking demand is relatively stable, and there is neither obvious excessive tension nor long periods of idleness. Such parking spaces usually maintain the existing charging standards and do not require special adjustments. Through this division of medium tension, the system can ensure reasonable resource allocation and avoid uneven use of parking spaces due to excessive price increases or excessive discounts.

[0108] If DPEC i >DPEC max , if the resource tension level of the parking space is high, the parking space is classified as a high tension parking space.

[0109] This situation indicates that the resource tension of the parking space is high, indicating that the occupancy frequency and demand response index of the parking space show that the demand for parking spaces in the area far exceeds the supply, and the parking space supply is in short supply. In this case, the parking space may be in a state of full occupancy for a long time, and the demand for parking is strong. In order to cope with this high tension situation, the system should adopt a dynamic price increase strategy, adjust the parking demand through the price lever, and guide some vehicles to choose other idle or low-tension parking spaces, so as to effectively alleviate the resource tension in the area and optimize the overall utilization of parking resources.

[0110] In order to determine the pre-set dynamic charge assessment coefficient threshold range (DPEC min and DPEC max ), can be automatically calculated by combining a software system with a variety of data analysis methods. First, the system can perform statistical analysis based on historical parking data, extract the resource occupancy coefficient and demand response index of parking spaces in different time periods and under different usage conditions, and generate a large number of historical dynamic charging assessment coefficient data sets. Then, the system can use these historical DPEC data sets to determine the threshold using the percentile or standard deviation method in statistics. For example, you can choose to use the lower 10% of the historical DPEC data as the DPEC min , the upper 10% is used as DPEC max , or set different quantile standards according to actual needs. In addition, clustering algorithms (such as K-means clustering) can be used to perform cluster analysis on DPEC data to naturally divide the data into multiple tension level intervals. Based on these analysis results, the system will automatically generate threshold intervals for dynamic charging assessment coefficients and regularly update these thresholds to ensure that the model can reflect the current supply and demand of parking resources at any time and achieve the purpose of dynamically adjusting charging strategies.

[0111] Based on the division results of each parking space, corresponding charging strategies are adopted for parking spaces with different resource tension levels;

[0112] In this embodiment, based on the division results of each parking space, corresponding charging strategies are adopted for parking spaces with different resource tension levels, specifically:

[0113] For low-intensity parking spaces with a low resource intensity level, the charging strategy adopted is to reduce the parking fee standard and provide parking discounts to attract more vehicles to use the parking space;

[0114] In order to implement the charging strategy for low-tension parking spaces, the system can automatically identify parking spaces with low resource tension by monitoring the resource occupancy coefficient and demand response index of parking spaces in real time. Based on the real-time evaluation results, the system software determines whether the parking space belongs to the low tension level and triggers the corresponding strategy. The specific implementation method is to dynamically adjust the basic charging standard of the parking space through the parking space status information in the database. For example, the software will reduce the current rate of the parking space by a certain percentage, and update the display synchronously in the user's parking application. At the same time, it can generate coupons or discount codes to attract more users to choose this parking space. The purpose of this strategy is to increase the utilization rate of low-tension parking spaces by lowering prices, avoid idle waste of parking resources, and balance the allocation of parking resources in the region.

[0115] For normal parking spaces with medium resource shortage, the charging strategy adopted is to maintain the existing charging standards without making additional adjustments to maintain the current supply and demand balance;

[0116] For parking spaces with a normal level of resource tension, the system software needs to continuously monitor the resource occupancy and demand response status of the parking space. If the system assesses that the parking space is at a normal tension level, that is, the supply and demand are basically balanced, there is no need to adjust the existing charging standards. The software will keep the charging parameters of the parking space unchanged to ensure that the charging standards match the current supply and demand. The specific implementation method is that the system marks the normal tight parking spaces as not requiring adjustment through the automated charging management module, and avoids dynamic price increases or price reductions for such parking spaces through logical judgment. The rationality of this strategy lies in that it ensures the stable allocation of parking resources and does not cause an imbalance in supply and demand due to excessive adjustments, thereby maintaining the normal operation of parking spaces in the area.

[0117] For high-intensity parking spaces with a high level of resource intensity, the charging strategy adopted is to increase parking fee standards, adjust parking demand through price levers, and give priority to ensuring parking space turnover during high-demand periods.

[0118] For the charging strategy of high-tension parking spaces, the system will identify that the resource tension of the parking space exceeds the set threshold based on real-time monitoring data, and thus determine that the parking space belongs to the high tension level. In order to alleviate resource tension, the system software will automatically increase the charging standard of the parking space. The specific implementation method is that the software automatically increases the rate according to the dynamic charging evaluation coefficient (DPEC) of the parking space according to the preset rules, and the system will notify the user of the updated rate through the parking application or billing system. At the same time, the system can adjust the price to the optimal point of the supply and demand curve through a dynamic pricing algorithm to maximize the turnover rate and revenue of the parking space. This strategy guides users to choose parking spaces in other areas through price levers, thereby alleviating resource pressure in high-tension areas and optimizing the utilization of overall parking resources.

[0119] Continuously monitor the resource usage of each parking space and the implementation effect of the charging strategy, dynamically adjust the charging standards of each parking space, regularly update the charging evaluation model, and optimize the parking resource allocation strategy.

[0120] In order to achieve continuous monitoring of the resource usage of each parking space, the system can collect the status data of the parking space in real time through Internet of Things (IoT) sensors and cameras. These sensors are installed on each parking space and can monitor the vehicle entry and exit, occupancy time, and usage of charging piles in real time. These data are transmitted to the background system through wireless networks (such as 5G, LoRa or Wi-Fi), and the system will store these data in the database and analyze them regularly. The specific implementation method is that the data processing module in the system combines these real-time data with the resource occupancy coefficient and demand response index of the parking space to form a real-time resource usage monitoring chart. The software continuously tracks these data through the automated monitoring system and sets alarm thresholds. When the usage of a parking space is abnormal (such as long-term idleness or excessive occupancy), the system will trigger an alarm or adjust the strategy. Continuous monitoring can ensure that the system can quickly respond to changes in the use of parking resources to avoid waste of resources or insufficient supply.

[0121] In order to monitor the effectiveness of the charging strategy, the system needs to evaluate whether the current charging strategy can reasonably reflect the supply and demand of parking spaces through real-time data collection and historical data analysis. The specific implementation method is that after the charging adjustment is implemented, the system software will judge the effectiveness of the charging strategy by comparing the parking space utilization rate, turnover rate and income changes before and after the adjustment. For example, the system will automatically track the changes in the frequency of use and demand response index of the parking space within a period of time after the parking rate is adjusted, and compare these changes with the target model. If it is found that the utilization rate of the parking space has not increased significantly after the charging adjustment or the supply and demand have not yet reached a balance, the system will further optimize the charging strategy. By dynamically monitoring the implementation effect of the charging strategy, the system can respond in a timely manner to ensure that the charging standards can accurately reflect the tension of parking resources and achieve efficient use of parking spaces.

[0122] The system can adjust the charging standards of each parking space through a dynamic pricing algorithm. The specific implementation method is that the system automatically calculates the dynamic charging evaluation coefficient (DPEC) of the parking space at regular intervals based on the resource occupancy coefficient (ROC) and demand response index (DRI) of each parking space. The software adjusts the price to an appropriate level through the set price adjustment rules. For example, when the parking space is in a high-tension state, the system will automatically increase the rate, and vice versa. The frequency of fee adjustment can be set to every hour, every day, or other appropriate time periods according to changes in real-time data. The purpose of dynamic adjustment is to adjust the supply and demand balance of parking spaces through price levers, avoid excessive congestion when resources are tight, and avoid waste when resources are idle, so as to ensure that the overall resource allocation of the system can adapt to fluctuations in parking demand at any time.

[0123] Regular updating of the charging assessment model can be achieved through regular review of historical data and model training. The system will regularly extract historical parking space usage data from the database, including resource occupancy, demand response, and changing trends of the dynamic charging assessment coefficient (DPEC). The software uses this data to retrain the charging assessment model through machine learning algorithms (such as multivariate regression analysis or cluster analysis) to ensure that the model can reflect the latest parking behavior and demand changes. The updated model will optimize the ROC and DRI calculation weights of each parking space according to the new data trends and adjust the pricing strategy of the charging standard. The significance of regularly updating the charging assessment model is to ensure that the system can continue to adapt to the changing parking needs, ensure the accuracy of price adjustments and the effectiveness of charging strategies.

[0124] The configuration strategy for optimizing parking resources can be achieved through the resource allocation and scheduling plan automatically generated by the system. The system monitors and analyzes the resource tension of each parking space over a long period of time, identifies areas with low resource utilization and high tension areas, and adjusts the configuration of parking resources based on historical data and prediction models. For example, in areas with low utilization, the number of parking spaces can be reduced or discounts can be provided to increase utilization; in areas with high tension, parking spaces can be increased or prices can be raised to regulate demand. Based on the prediction of demand changes, the system automatically adjusts the parking resource configuration plan for each area through an intelligent scheduling algorithm to ensure the reasonable allocation of parking resources in different time periods and areas, thereby maximizing the efficiency of parking space use and improving overall revenue.

[0125] like Figure 2 The distributed on-street parking charging system based on charging piles shown in the figure includes a peak demand identification and management framework establishment module, a resource usage information collection and analysis module, a dynamic charging evaluation construction and resource tension analysis module, a charging strategy intelligent adjustment module, and a resource usage monitoring and evaluation optimization module;

[0126] Peak demand identification and management framework establishment module, which determines the peak time period for parking and charging demand, and establishes a monitoring and charging management framework for all parking spaces and charging pile resources according to the management objectives and resource allocation requirements of the parking area during this period;

[0127] The resource usage information collection and analysis module obtains the resource usage information of each parking space in real time, analyzes it after acquisition, and generates the resource occupancy coefficient and demand response index of each parking space;

[0128] Dynamic charging assessment construction and resource tension analysis module, which constructs a charging assessment model based on the resource occupancy coefficient and demand response index of each parking space, generates a dynamic charging assessment coefficient for each parking space, and analyzes it after generation to assess the resource tension level of each parking space, and divides each parking space into high tension parking space, normal tension parking space and low tension parking space according to the assessment results;

[0129] The charging strategy intelligent adjustment module adopts corresponding charging strategies for parking spaces with different resource tension levels based on the division results of each parking space;

[0130] The resource usage monitoring and evaluation optimization module continuously monitors the resource usage of each parking space and the implementation effect of the charging strategy, dynamically adjusts the charging standards of each parking space, regularly updates the charging evaluation model, and optimizes the parking resource allocation strategy.

[0131] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0132] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of 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, the process or function described in the embodiment of the present application is generated in whole or in part. 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 computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). 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 containing one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0133] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean 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 the present application.

[0134] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0135] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0136] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0137] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0138] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A distributed on-street parking charging method based on charging piles, characterized in that: The specific steps include: Determine the peak time periods for parking and charging demand, and establish a monitoring and charging management framework for all parking spaces and charging pile resources during these time periods based on the management objectives and resource allocation requirements of the parking area; Obtain resource usage information of each parking space in real time, analyze it after acquisition, and generate resource occupancy coefficient and demand response index of each parking space; A charging assessment model is constructed based on the resource occupancy coefficient and demand response index of each parking space generated, and a dynamic charging assessment coefficient of each parking space is generated. After the generation, the dynamic charging assessment coefficient is analyzed to assess the resource tension level of each parking space, and each parking space is divided into high tension parking space, normal tension parking space and low tension parking space according to the assessment results. Based on the division results of each parking space, corresponding charging strategies are adopted for parking spaces with different resource tension levels; Continuously monitor the resource usage of each parking space and the implementation effect of the charging strategy, dynamically adjust the charging standards of each parking space, regularly update the charging evaluation model, and optimize the parking resource allocation strategy.

2. The distributed on-street parking charging method based on charging piles according to claim 1 is characterized in that: The resource usage information of each parking space is obtained in real time, and analyzed after acquisition to generate the resource occupancy coefficient and demand response index of each parking space, which specifically includes the following steps: Obtain resource usage information of each parking space in real time and perform preprocessing after acquisition; Extracting resource occupancy status information and demand response dynamic information from the pre-processed resource usage information of each parking space; The resource occupancy status information and demand response dynamic information in the extracted resource usage information of each parking space are analyzed to generate a resource occupancy coefficient and a demand response index of each parking space respectively.

3. The distributed on-street parking charging method based on charging piles according to claim 2 is characterized in that: The logic for obtaining the resource occupancy coefficient of each parking space is as follows: The resource occupancy status information of each parking space is extracted from the pre-processed resource usage information, including the cumulative occupancy time and usage frequency of each parking space at different times within a period of time, the average utilization rate of other parking spaces in the area where each parking space is located during the period of time, and the parking demand density in the area. The cumulative occupancy time and usage frequency of each parking space at different times within a period of time are respectively calculated according to the time series using the function TOC i (t) and FUS i (t) represents the time point, TOC i (t) represents the cumulative occupancy time of the i-th parking space at time t within a period of time, FUS i (t) represents the usage frequency of the i-th parking space at time t within a period of time. The time period is defined as [t1, t2], and the average usage rate of other parking spaces in the area where each parking space is located and the parking demand density in the area are marked as UAR respectively. i and DDC i , UAR i represents the average utilization rate of other parking spaces in the area where the i-th parking space is located during this time period, DDC i represents the parking demand density in the area where the i-th parking space is located during the time period, i=1, 2, 3, ..., k, where k is a positive integer; Calculate the resource occupancy coefficient of each parking space. The specific calculation formula is as follows: In the formula, ROC i is the resource occupancy coefficient of the i-th parking space.

4. The distributed on-street parking charging method based on charging piles according to claim 3 is characterized in that: The logic for obtaining the demand response index of each parking space is as follows: Extract the dynamic information of demand response from the preprocessed resource usage information of each parking space, including the traffic flow, parking demand pressure density, the average change rate of historical parking demand of each parking space in the time period, and the parking demand of each parking space at different times in a period of time in the area where each parking space is located. The traffic flow, parking demand pressure density and parking demand of each parking space at different times in a period of time in the area where each parking space is located are respectively calculated by the function TRF according to the time series. i (t), DDP i (t) and CPD i (t), t is the time point, TRF i (t) represents the traffic flow in the area of ​​the i-th parking space at time t within a period of time, DDP i (t) represents the parking demand pressure density of the i-th parking space at time t within a period of time, CPD i (t) represents the parking demand of the i-th parking space at time t within a period of time. The time period is defined as [t1, t2], and the average change rate of the historical parking demand of each parking space within this period is calibrated as HCD i , HCD i represents the average change rate of the historical parking demand of the i-th parking space in the time period, i = 1, 2, 3, ..., k, k is a positive integer; Calculate the demand response index of each parking space. The specific calculation formula is as follows: Where DRI i is the demand response index of the i-th parking space.

5. The distributed on-street parking charging method based on charging piles according to claim 4 is characterized in that: A charging assessment model is constructed based on the resource occupancy coefficient and demand response index of each parking space, and a dynamic charging assessment coefficient of each parking space is generated, which specifically includes the following steps: The resource occupancy coefficient, demand response index and dynamic charging evaluation coefficient of several parking spaces generated in the past period of time are collected and calibrated as and x represents the number of resource occupancy coefficients, demand response indexes, and dynamic charging assessment coefficients of several parking spaces generated in the past period of time, x=3, 4, 5, ..., d, d is a positive integer, and the data collected in the past period of time is formed into a historical data set; Select the multivariate regression model as the fee assessment model, and train it through the historical data set to determine the value of the regression coefficient according to the formula: Where β0, β1 and β2 are regression coefficients; By minimizing the error between the predicted value and the actual value, the regression coefficient is optimized, and finally the values ​​of the regression coefficients β0, β1 and β2 are determined; Using the finalized regression coefficient, the constructed charging assessment model is input with the resource occupancy coefficient of each parking space generated in real time. and Demand Response Index Generate dynamic charging evaluation coefficient DPEC for each parking space in real time i .

6. The distributed on-street parking charging method based on charging piles according to claim 5 is characterized in that: The dynamic charging evaluation coefficient DPEC of each parking space will be generated i The dynamic fee assessment coefficient threshold interval [DPEC min ,DPEC max ] to compare, evaluate the resource tension level of each parking space according to the comparison results, and divide each parking space into high tension parking space, normal tension parking space and low tension parking space according to the evaluation results. The specific comparison analysis and division are as follows: If DPEC i <DPEC min , the resource tension level of the parking space is low, then the parking space is classified as a low tension parking space; If DPEC min ≤DPEC i ≤DPEC max , the resource tension level of the parking space is medium, then the parking space is classified as a normal tension parking space; If DPEC i >DPEC max , if the resource tension level of the parking space is high, the parking space is classified as a high tension parking space.

7. The distributed on-street parking charging method based on charging piles according to claim 6 is characterized in that: Based on the division results of each parking space, corresponding charging strategies are adopted for parking spaces with different resource tension levels, specifically: For low-intensity parking spaces with low resource intensity levels, the charging strategy adopted is to reduce parking fee standards and provide parking discounts; For normal parking spaces with medium resource shortage, the charging strategy adopted is to maintain the existing charging standards without making additional adjustments to maintain the current supply and demand balance; For high-intensity parking spaces with a high level of resource intensity, the charging strategy adopted is to increase parking fee standards and adjust parking demand through price levers.

8. A distributed on-street parking fee collection system based on charging piles, used to implement the distributed on-street parking fee collection method based on charging piles as described in any one of claims 1 to 7, characterized in that: It includes modules for peak demand identification and management framework establishment, resource usage information collection and analysis, dynamic charging assessment construction and resource shortage analysis, charging strategy intelligent adjustment, and resource usage monitoring and assessment optimization. Peak demand identification and management framework establishment module, which determines the peak time period for parking and charging demand, and establishes a monitoring and charging management framework for all parking spaces and charging pile resources according to the management objectives and resource allocation requirements of the parking area during this period; The resource usage information collection and analysis module obtains the resource usage information of each parking space in real time, analyzes it after acquisition, and generates the resource occupancy coefficient and demand response index of each parking space; Dynamic charging assessment construction and resource tension analysis module, which constructs a charging assessment model based on the resource occupancy coefficient and demand response index of each parking space, generates a dynamic charging assessment coefficient for each parking space, and analyzes it after generation to assess the resource tension level of each parking space, and divides each parking space into high tension parking space, normal tension parking space and low tension parking space according to the assessment results; The charging strategy intelligent adjustment module adopts corresponding charging strategies for parking spaces with different resource tension levels based on the division results of each parking space; The resource usage monitoring and evaluation optimization module continuously monitors the resource usage of each parking space and the implementation effect of the charging strategy, dynamically adjusts the charging standards of each parking space, regularly updates the charging evaluation model, and optimizes the parking resource allocation strategy.

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