Method for determining the number of charging piles of an electric bus station

Abstract

The application discloses a charging pile number determination method for an electric bus station, and relates to the technical field of charging piles.The technical scheme mainly comprises the following steps: constructing a corresponding demand analysis data table according to vehicle operation data of a target bus station; identifying the demand analysis data table according to a preset demand analysis rule to obtain a target demand field; determining a module associated field associated with the target demand field based on the target demand field and a corresponding source data table structure; verifying the number management code and the module associated field according to a preset demand verification rule to obtain a verified target verification field and a corresponding source data table; and outputting an optimal charging pile configuration number of the electric bus station based on the target verification field, the source data table and the number management code, so that the operation efficiency and the return on investment of the charging facilities of the bus station are improved.

Description

Technical Field

[0001] This invention relates to the field of charging pile technology, and more specifically, to a method for determining the number of charging piles at electric bus stops. Background Technology

[0002] With the rapid development of the new energy vehicle industry and the continuous advancement of the electrification transformation of urban public transportation, electric buses have become the mainstream configuration of urban public transportation systems. As the core node for bus operation and refueling, the number of charging piles at electric bus stations directly determines the efficiency and cost of bus operation. If the number of charging piles is insufficient, it will lead to queuing for vehicle charging and delays in service, affecting the reliability of public transportation services and the passenger travel experience. If the number of charging piles is excessive, it will result in a waste of site resources, redundant power grid load, and a significant increase in construction and maintenance costs, seriously affecting the return on investment.

[0003] Current methods for determining the number of charging stations at electric bus stops do not fully incorporate actual vehicle operation data from the target bus stops, failing to capture the differences in charging demand across different operating hours and scenarios. For example, during morning rush hour, vehicle turnover is rapid and stop duration is short, leading to a primary demand for short-term fast charging, while at night, vehicle stop duration is longer, resulting in a primary demand for slow charging. Traditional experience-based configuration is ill-suited to match these time-varying needs. Furthermore, existing methods lack a correlation between vehicle operation data and charging demand, and lack systematic collection and analysis of vehicle stop duration, departure frequency, and power consumption status. This leads to significant biases in the quantitative analysis of charging demand, failing to provide reliable data support for charging station configuration and thus hindering precise optimization of charging station allocation. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a method for determining the number of charging piles at electric bus stations.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A method for determining the number of charging stations at electric bus stops, characterized by the following steps: Construct a corresponding demand analysis data table based on the vehicle operation data of the target bus station; The target requirement fields are obtained by identifying the requirement analysis data table according to the preset requirement analysis rules; Based on the target requirement fields and their corresponding source data table structure, determine the module association fields associated with the target requirement fields; The target requirement fields and their associated module fields are encoded according to the preset encoding rules to generate quantity management codes for the target requirement fields; The quantity management code and module association fields are verified according to the preset requirement verification rules to obtain the verified target verification fields and the corresponding source data table. The optimal number of charging piles for electric bus stations is output based on the target validation field, source data table, and quantity management code.

[0006] Preferably, a corresponding demand analysis data table is constructed based on the vehicle operation data of the target bus station, specifically including the following steps: By segmenting the vehicle operation data of the target bus station into different scenarios, target information related to vehicle stopping time, departure frequency, and power consumption status can be obtained. The target information is collected according to the station's operating time period to form the information summary content for the corresponding time period; Based on the information summary, a data table body is constructed. The data table body is then organized and logically validated to obtain the requirements analysis data table.

[0007] Preferably, the target requirement fields are identified in the requirement analysis data table according to preset requirement analysis rules, specifically including the following steps: The data in the demand analysis data table is divided into different categories according to the electric bus operation scenarios to obtain a classified data set; Based on the classified dataset, target data items related to charging demand for each category of data are extracted, and the inherent relationships between the target data items are sorted out to obtain the target data relationship; Based on the correlation of target data, key data items reflecting the intensity and frequency of charging demand are selected, and the validity of key data items is verified to obtain valid key data items; By combining preset demand analysis rules, effective key data items are filtered and integrated to obtain target demand fields that meet the requirements for calculating the number of charging piles.

[0008] Preferably, determining the module association fields associated with the target requirement fields based on the target requirement fields and their corresponding source data table structures specifically includes the following steps: The basic fields are obtained by processing the target requirement fields and their corresponding source data table structures; Based on the actual operational scenarios of electric bus station charging pile configuration, the number of charging piles and related target functional modules are divided and configured accordingly. Use the basic fields that match the functional requirements of the target functional module as candidate association fields; Perform correlation validation on candidate related fields to obtain the module related fields associated with the target requirement field.

[0009] Preferably, the target requirement fields and their corresponding source data table structures are processed to obtain the basic fields, specifically including the following steps: Clearly define the data ownership and relationships of the target requirement fields within the source data table structure; Based on data ownership and relationships, determine the basic fields in the source data table where data interaction occurs between the target requirement fields and the source data table.

[0010] Preferably, the target requirement field and its associated module fields are encoded according to a preset encoding rule to generate a quantity management code for the target requirement field. This specifically includes the following steps: Extract the core features corresponding to the target requirement fields and the associated attributes of the module-related fields according to the preset coding rules, and clarify the corresponding relationship between the core features and the associated attributes; The core features are hierarchically divided according to their corresponding relationships to obtain the core hierarchy; The core-level coding identifiers are combined with the corresponding auxiliary coding identifiers in an orderly manner to form quantity management codes.

[0011] Preferably, the quantity management code and module association fields are verified according to preset requirement verification rules to obtain the verified target verification fields and the corresponding source data table, specifically including the following steps: Combine the module-related fields that are associated with the quantity management code into a preliminary set of related fields; The validity of each field in the initial set of associated fields is verified according to the preset requirement verification rules to obtain the valid associated fields; Verify that the storage location of the valid related fields in the source data table meets the requirements to obtain the related fields that meet the requirements; Identify the relevant fields that meet the requirements as target validation fields, and confirm the source data table corresponding to each target validation field.

[0012] Preferably, verifying whether the storage location of the valid related fields in the source data table meets the requirements to obtain the related fields that meet the requirements specifically includes the following steps: Based on the corresponding quantity management code, confirm whether the storage location of the valid associated fields meets the requirements; Valid associated fields that meet the storage location requirements are identified as eligible associated fields.

[0013] Preferably, the optimal number of charging piles for electric bus stations is output based on the target verification field, the source data table, and the quantity management code, specifically including the following steps: Extract vehicle charging demand information represented by the target validation field from the source data table corresponding to the target validation field; By analyzing the correspondence between the target validation fields and information related to vehicle charging needs, a charging demand association set is obtained; Valid charging demand data is obtained by classifying the target verification fields based on the charging demand association set and quantity management code; Extract the corresponding relationship between the quantity management code and the target verification field, and determine the optimal number of charging piles for electric bus stations based on the corresponding relationship and the number of valid charging demands.

[0014] Preferably, the optimal number of charging piles for electric bus stations is determined based on the corresponding relationship and the effective charging demand, specifically including the following steps: The effective charging demand data is obtained by filtering the effective charging demand data according to the corresponding relationship; The effective screening of charging demand data is adapted according to the control standards of quantity management codes; The optimal number of charging piles for electric bus stations is determined based on the adjusted and effectively screened charging demand data.

[0015] Compared with the prior art, the present invention has the following beneficial effects: This invention quantifies charging demand by constructing a demand analysis data table. Based on the vehicle operation data of the target bus station, it distinguishes and aggregates vehicle stopping time, departure frequency, and power consumption status in a scenario-based and time-based manner to form a demand analysis data table. This table can capture the differences in charging demand in different time periods and scenarios, effectively avoiding unreasonable configuration problems caused by deviations in demand judgment. By extracting target demand fields and module-related fields, key data items reflecting the intensity and frequency of charging demand are filtered from massive operational data based on preset demand analysis rules. Simultaneously, related module-related fields are extracted from the source data table structure. Hierarchical coding of target demand fields and module-related fields is performed according to preset coding rules, transforming scattered business data into unified quantity management codes. This clarifies the correspondence between core features and related attributes, standardizes data identification, and facilitates data storage, retrieval, and verification management. Based on preset demand verification rules, the quantity management codes and module-related fields are verified layer by layer, eliminating invalid, abnormal, and non-compliant data. The source data table corresponding to each field is clearly identified, ensuring the reliability of the data source and fundamentally improving the credibility of the final configuration results. This avoids configuration errors caused by data mistakes and ultimately outputs the optimal number of charging piles. This not only fully meets the vehicle charging needs at different times, avoiding operational delays caused by insufficient charging piles, but also avoids the waste of site resources and increased investment costs caused by over-configuration. It achieves matching between charging facility configuration and public transport operation needs, significantly improving the operational efficiency and return on investment of public transport station charging facilities. Attached Figure Description

[0016] Figure 1 A schematic diagram illustrating the steps of a method for determining the number of charging piles at electric bus stations, as provided in an embodiment of the present invention; Figure 2This is a schematic diagram illustrating the steps of obtaining the demand analysis data table in the method for determining the number of charging piles at electric bus stations according to an embodiment of the present invention. Detailed Implementation

[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0018] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0019] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0020] Reference Figures 1-2 As shown.

[0021] The embodiments further illustrate the method for determining the number of charging piles at electric bus stations proposed in this invention.

[0022] A method for determining the number of charging stations at electric bus stops, characterized by the following steps: Construct a corresponding demand analysis data table based on the vehicle operation data of the target bus station; The target requirement fields are obtained by identifying the requirement analysis data table according to the preset requirement analysis rules; Based on the target requirement fields and their corresponding source data table structure, determine the module association fields associated with the target requirement fields; The target requirement fields and their associated module fields are encoded according to the preset encoding rules to generate quantity management codes for the target requirement fields; The quantity management code and module association fields are verified according to the preset requirement verification rules to obtain the verified target verification fields and the corresponding source data table. The optimal number of charging piles for electric bus stations is output based on the target validation field, source data table, and quantity management code.

[0023] Based on the vehicle operation data of the target bus station, a corresponding demand analysis data table is constructed, which includes the following steps: By segmenting the vehicle operation data of the target bus station into different scenarios, target information related to vehicle stopping time, departure frequency, and power consumption status can be obtained. The target information is collected according to the station's operating time period to form the information summary content for the corresponding time period; Based on the information summary, a data table body is constructed. The data table body is then organized and logically validated to obtain the requirements analysis data table.

[0024] Vehicle operation data is categorized and broken down according to actual bus operation scenarios to obtain target information related to vehicle dwell time, departure frequency, and power status. Dwell time refers to the actual time electric buses spend at target stations, including dwell time at the terminal stations and passenger pick-up / drop-off times at intermediate stations. Dwell time is a basis for determining whether a vehicle has charging capabilities. For example, if a vehicle's single dwell time at a certain route's terminal station is 30 minutes, it is used to determine whether the time requirement for fast charging is met. Departure frequency refers to the number of departures per unit time at the target station. Departure frequency = total departures within the statistical period ÷ statistical period duration. Departure frequency determines the station's vehicle turnover efficiency and charging demand. Peak demand, for example, during the morning rush hour when a station has 12 departures per hour, is much higher than during off-peak hours. Power status refers to the remaining battery power, driving energy consumption, and power load parameters of auxiliary equipment such as air conditioning when a vehicle arrives at a station. Remaining battery power = rated battery capacity - total driving energy consumption. Total driving energy consumption = energy consumption per unit mile × mileage + total energy consumption of auxiliary equipment. Power status directly reflects the intensity of the vehicle's immediate charging demand. For example, if a vehicle arrives at a station with 20% of its rated battery power remaining, there is an emergency charging demand.

[0025] The target information is collected according to the station's operating hours, forming a summary of information for each time period. The target information is categorized and integrated according to the time dimension, matching the time-specific characteristics of public transport operations to capture the differences in charging demand at different times. Based on the operating hours of the target stations, the entire day is divided into multiple consecutive operating periods, such as morning peak 6:00-9:00, off-peak 9:00-17:00, evening peak 17:00-20:00, and nighttime 20:00-6:00 the next day. Based on actual operational needs, this is further divided into hourly statistical periods, totaling 24 periods per day. Target information such as vehicle parking time, departure frequency, and power status are assigned to the corresponding operating periods according to vehicle arrival time, thus completing the time-dimensional data collection. For example, the parking time, remaining battery power, and departure frequency data of all arriving vehicles during the morning peak period are uniformly collected into the morning peak period information summary, forming a set of vehicle operation characteristics for the morning peak period. By aggregating data by time period, the distribution patterns of charging demand at different times are clearly presented. For example, during the morning peak hours, vehicles arrive at stations in a concentrated manner, depart frequently, and stop for a short period of time, with short-term fast charging demand being the main focus; during the night hours, vehicles stop for a long time and depart frequently, with slow charging demand being the main focus, providing data support for the time-sharing configuration of charging piles in the future.

[0026] Based on the aggregated information, a main data table is constructed. This table is then organized and logically validated to obtain a requirements analysis data table. The collected information is transformed into a standardized data table structure suitable for calculation and analysis, and data quality control is implemented. The main data table is constructed according to the dimensions of the target information. Rows correspond to different operating periods, and columns correspond to core target information such as vehicle parking duration, departure frequency, and power consumption status. Auxiliary fields such as vehicle number, arrival time, and route number are also included to form a complete data table framework. This framework includes fields such as period number, period start and end time, total number of vehicles arriving during that period, average parking duration, maximum parking duration, departure frequency, average remaining power, and minimum remaining power. The unit for vehicle parking duration is standardized to minutes, the unit for remaining power is standardized to kilowatt-hours, and the unit for departure frequency is standardized to trips per hour. Missing data is filled in, and abnormal data is corrected. For example, abnormal data with negative parking durations are removed, and missing remaining power data is estimated and filled in using vehicle mileage and energy consumption.

[0027] Logical validation is performed on the data tables. For example, it is verified whether the departure frequency of a certain period matches the total number of vehicles arriving at the station during that period, whether the remaining battery power of the vehicles is within a reasonable range of 0 to rated capacity, and whether the stop duration conforms to the actual operation pattern of public transportation. After content standardization and logical validation, a standardized, accurate, and complete requirements analysis data table is formed.

[0028] The target requirement fields are identified in the requirement analysis data table according to the preset requirement analysis rules. The specific steps include: The data in the demand analysis data table is divided into different categories according to the electric bus operation scenarios to obtain a classified data set; The structured data in the demand analysis data table is broken down into scenarios based on the actual operation scenarios of electric buses, achieving accurate data classification and focus, and avoiding cross-interference between data from different scenarios. The operation scenarios of electric buses are divided into several categories: terminal station, stationing scenario, intermediate stop, peak period scenario, turnover scenario, and nighttime shutdown scenario. Among them, the terminal stationing scenario corresponds to the long-term stay and departure preparation of vehicles at the terminal station; the intermediate stop scenario corresponds to the short-term passenger pick-up and drop-off stop of vehicles during the operation; the peak period turnover scenario corresponds to the high-frequency departure and rapid turnover during the morning and evening peak periods; and the nighttime shutdown scenario corresponds to the long-term stationary period of vehicles after the last shift. During the classification process, fields such as station type, stop duration, and time period attributes in the demand analysis data table are used as classification criteria to categorize the corresponding data into different scenario categories. For example, data with the first and last stations and a stop duration of more than 30 minutes are classified into the first and last station station scenario, data with intermediate stations and a stop duration of less than 5 minutes are classified into the intermediate station stop scenario, data from 6:00-9:00 and 17:00-20:00 are classified into the peak period turnover scenario, and data from 22:00 to 6:00 the next day are classified into the nighttime shutdown scenario. Finally, multiple classified data sets corresponding to different operating scenarios are formed.

[0029] Based on the classified dataset, target data items related to charging demand for each category of data are extracted, and the inherent relationships between the target data items are sorted out to obtain the target data relationship; From each categorized dataset, core data items directly related to vehicle charging demand are selected, and the logical relationships between these items are clarified to construct a complete charging demand data chain. The target data items extracted from the datasets differ depending on the scenario. For example, in the terminal station scenario, target data items include vehicle arrival time, remaining battery power, vehicle dwell time, departure time, vehicle battery rated capacity, and charging pile rated power; in the peak-hour turnover scenario, target data items include departure frequency per unit time, average remaining battery power, average dwell time, and route operating mileage; and in the nighttime shutdown scenario, target data items include remaining battery power at the end of the shift, vehicle departure time the following day, and vehicle nighttime dwell time. After extraction, the inherent relationships between each target data item are analyzed to clarify the causal and constraint relationships between the data. For example, the remaining battery power upon arrival is related to the vehicle's mileage: Remaining battery power upon arrival = Vehicle battery rated capacity - Vehicle energy consumption per unit mile × Mileage - Total energy consumption of auxiliary equipment; the available charging time is related to the vehicle's parking time and departure time: Available charging time = Vehicle departure time - Vehicle arrival time; the required charging power is related to the remaining battery power: Required charging power = Vehicle battery rated capacity - Remaining battery power upon arrival. By analyzing these relationships, a complete network of target data relationships is formed, clearly showing the impact path of each data item on charging demand.

[0030] Based on the correlation of target data, key data items reflecting the intensity and frequency of charging demand are selected, and the validity of key data items is verified to obtain valid key data items; Based on the correlation relationships of the target data, key data items reflecting the intensity and frequency of charging demand are selected, and the validity of these key data items is verified to obtain valid key data items. Core indicators that directly determine the intensity and frequency of charging demand are selected from the target data items based on the correlation relationships, and data validity is verified to remove invalid data, ensuring data quality. Charging demand intensity refers to the amount of electricity a vehicle needs to charge, directly reflected by the vehicle's remaining battery power and the amount of electricity needed for charging; charging demand frequency refers to the number of vehicles needing charging per unit of time, directly reflected by the departure frequency and the number of vehicles arriving at the station. Based on the correlation of target data, core data items with the greatest impact on the intensity and frequency of charging demand are prioritized. Validation of key data items is then performed, including verification of data completeness, rationality, and consistency. Completeness verification checks for missing key data items; for example, data items showing zero remaining battery power upon vehicle arrival are deemed invalid. Rationality verification checks whether the data is within a reasonable range; for example, data items showing remaining battery power exceeding the battery's rated capacity or below 0 are deemed invalid, as are data items showing negative charging time. Consistency verification checks the logical consistency between data items; for example, whether the sum of the vehicle's charging demand and remaining battery power equals the battery's rated capacity, and whether the vehicle's available charging time matches the parking duration. Validation eliminates invalid data items, retaining complete, reasonable, and logically consistent valid key data items, providing a high-quality data foundation for subsequent field integration.

[0031] By combining preset demand analysis rules, effective key data items are filtered and integrated to obtain target demand fields that meet the requirements for calculating the number of charging piles.

[0032] Based on preset demand analysis rules, effective key data items are further filtered and integrated to form standardized target demand fields, which are directly used for subsequent calculation of the number of charging piles. The preset demand analysis rules are screening criteria formulated according to the core logic of charging pile number calculation. The core rules include charging demand intensity threshold rules, charging demand frequency statistics rules, and charging duration matching rules. For example, the charging demand intensity threshold rule sets that a vehicle is considered to have charging demand when its remaining battery level is below 30%. The charging demand frequency statistics rule sets the number of vehicles with charging demand in a 1-hour period as the statistical unit. The charging duration matching rule sets that a vehicle is considered to have valid charging conditions when its charging duration exceeds 15 minutes. Based on preset rules, valid key data items undergo a secondary screening, eliminating data items that do not meet the rules. For example, data items where the vehicle's remaining battery level is higher than 30% but the available charging time is less than 15 minutes are considered to have no valid charging demand and are removed. Valid key data items that meet the rules are then integrated, merging highly relevant data items into standardized target demand fields. For example, the vehicle's remaining battery level upon arrival and the vehicle's charging demand are combined into a vehicle charging demand intensity field; the frequency of departures per unit time and the number of vehicles requiring charging per unit time are combined into a vehicle charging demand frequency field; and the vehicle's available charging time and the charging pile's rated power are combined into a charging pile charging efficiency field. The final target demand fields perfectly match the charging pile quantity calculation logic.

[0033] Based on the target requirement fields and their corresponding source data table structure, the module association fields associated with the target requirement fields are determined, specifically including the following steps: The basic fields are obtained by processing the target requirement fields and their corresponding source data table structures, specifically including the following steps: Clearly define the data ownership and relationships of the target requirement fields within the source data table structure; Based on data ownership and relationships, determine the basic fields in the source data table where data interaction occurs between the target requirement fields and the source data table; The basic fields that directly interact with the target requirement fields are extracted from the source data table. These target requirement fields are extracted from the requirement analysis data table and used for calculating the number of charging piles. Examples include the vehicle charging demand intensity field, the vehicle charging demand frequency field, and the charging pile charging efficiency field. The source data table structure is the underlying data architecture storing the original vehicle operation data, including basic vehicle information, a vehicle operation timetable table, a battery status data table, and a charging pile equipment information table. First, the data source for each target requirement field is traced to clarify its storage location and data type in the source data table. For example, the data source for the vehicle charging demand intensity field is the vehicle remaining battery power field and the vehicle battery rated capacity field in the battery status data table; the data source for the vehicle charging demand frequency field is the departure time field and the arrival time field in the vehicle operation timetable table; and the data source for the charging pile charging efficiency field is the rated power field and the vehicle charging duration field in the charging pile equipment information table. Based on data attribution and relationships, extract the basic fields from the source data tables that interact with the target requirement fields. For example, for the vehicle charging demand intensity field, extract the vehicle remaining battery power, vehicle battery rated capacity, and vehicle energy consumption per unit mileage fields from the battery status data table as basic fields; for the vehicle charging demand frequency field, extract the departure time, arrival time, and route operating mileage fields from the vehicle schedule table as basic fields. Then, associate the target requirement fields with the underlying source data to obtain a complete set of basic fields.

[0034] Based on the actual operational scenarios of electric bus station charging pile configuration, the number of charging piles and related target functional modules are divided and configured accordingly. The actual operation scenario of electric bus station charging pile configuration involves multiple core business processes, corresponding to the following target functional modules: vehicle charging demand analysis module, charging pile load calculation module, site resource adaptation module, and operation scheduling coordination module. Specifically, the vehicle charging demand analysis module involves the statistics and analysis of vehicle charging demand, calculating the intensity and frequency of vehicle charging demand at different times; the charging pile load calculation module matches the charging capacity and load of charging piles, calculating the available charging power and the number of vehicles that can be served by each charging pile; the site resource adaptation module matches the available site area with the power grid capacity; and the operation scheduling coordination module coordinates bus operation scheduling and charging plans, optimizing charging timing based on vehicle departure schedules. Based on the business process of calculating the number of charging piles, the business boundaries and core functions of each target functional module are clearly defined. For example, the vehicle charging demand analysis module is to count the number of vehicles that need to be charged and the total charging demand within a unit time period; the charging pile load calculation module is to calculate the charging power completed by a single charging pile within a unit time period and the number of vehicles that can be served; the site resource adaptation module is to verify the maximum number of charging piles that can be installed at a site; and the operation scheduling and coordination module is to adjust the time distribution of charging demand in combination with the vehicle departure plan.

[0035] Use the basic fields that match the functional requirements of the target functional module as candidate association fields; A candidate set of related fields is formed by filtering the basic fields to match the functional requirements of each module, thus achieving the correspondence between basic fields and business modules. Each target functional module has clear functional requirements. For example, the functional requirement of the vehicle charging demand analysis module is to statistically analyze the intensity and frequency of charging demand, and the corresponding basic fields include the vehicle remaining battery power field, vehicle arrival time field, departure time field, and departure frequency per unit time period field; the charging pile load calculation module is to calculate the charging capacity of charging piles, and the corresponding basic fields include the rated power of charging piles, the vehicle charging demand power field, and the vehicle charging duration field; the functional requirement of the site resource adaptation module is to verify site resource conditions, and the corresponding basic fields include the available site area field, the power grid supply capacity field, and the area occupied by a single charging pile field; the functional requirement of the operation scheduling and coordination module is to coordinate operation plans, and the corresponding basic fields include the vehicle departure plan field, the line operation duration field, and the vehicle turnaround time field. Based on the functional requirements of the target functional modules, basic fields are screened one by one, and basic fields that match the functional requirements are included in the candidate related field set of the corresponding module. For example, the vehicle remaining battery power field is included in the candidate related field of the vehicle charging demand analysis module, and the charging pile rated power field is included in the candidate related field of the charging pile load calculation module. The scattered basic fields are classified according to business modules to form multiple candidate related field sets corresponding to different functional modules.

[0036] Perform correlation validation on candidate related fields to obtain the module related fields associated with the target requirement field.

[0037] The candidate related fields are verified for relevance. Fields that are not substantially related to the target requirement field are eliminated. Finally, the module related fields related to the target requirement field are determined. It is verified whether the candidate related fields have direct data interaction or business relationship with the target requirement field and whether they have a substantial impact on the calculation results of the target requirement field. Data correlation verification verifies whether candidate correlation fields participate in the calculation process of the target requirement field. For example, for the vehicle charging demand intensity field, it verifies whether the vehicle remaining battery capacity field in the candidate correlation fields directly participates in the calculation of charging demand intensity. Vehicle charging demand intensity = vehicle battery rated capacity - vehicle remaining battery capacity. Fields participating in the calculation are considered valid data correlations, while fields not participating in the calculation are considered invalid and removed. Business correlation verification verifies whether candidate correlation fields have a substantial impact on the business logic of the target requirement field. For example, for the vehicle charging demand frequency field, it verifies whether the vehicle departure plan field in the candidate correlation fields affects the statistics of charging demand frequency. Adjustments to the departure plan will directly change the number of vehicles with charging demand per unit time period, thus the business correlation is considered valid. Fields without substantial impact are considered invalid and removed. Redundancy verification verifies whether there is data redundancy in the candidate correlation fields. For example, for the same target requirement field, if multiple candidate correlation fields express the same data meaning, only the core field is retained, and redundant fields are removed. Through multi-dimensional correlation verification, invalid and redundant fields are removed, ultimately yielding the module correlation fields that are strongly related to the target requirement field.

[0038] The target requirement fields and their associated module fields are encoded according to preset encoding rules to generate quantity management codes for the target requirement fields. The specific steps include: Extract the core features corresponding to the target requirement fields and the associated attributes of the module-related fields according to the preset coding rules, and clarify the corresponding relationship between the core features and the associated attributes; Based on preset coding rules, core features that determine the essence of charging demand are extracted from the target demand fields. Supporting attributes are extracted from module association fields, and a correspondence is established between the two. The preset coding rules are standardized coding specifications formulated based on the business logic of charging pile quantity calculation, clearly defining the extraction standards and corresponding logic of core features and supporting attributes. The core features of the target demand fields are key attributes that directly reflect the essence of charging demand. For example, the core feature of the vehicle charging demand intensity field is the total charging demand per unit time period; the core feature of the vehicle charging demand frequency field is the number of vehicles requiring charging per unit time period; and the core feature of the charging pile charging efficiency field is the number of vehicles that can be served by the charging pile per unit time period. The supporting attributes of the module association fields are the underlying data attributes that support the calculation of core features. For example, supporting attributes for the total charging demand per unit time period include the vehicle's remaining battery power upon arrival, the vehicle's rated battery capacity, and the number of vehicles arriving per unit time period; supporting attributes for the number of vehicles requiring charging per unit time period includes the departure frequency per unit time period and the vehicle's remaining battery power threshold; and supporting attributes for the number of vehicles that can be served by the charging pile per unit time period includes the charging pile's rated power and the vehicle's available charging time. Based on preset coding rules, the core features of each target requirement field are extracted one by one. At the same time, the associated attributes of the corresponding module-related fields are extracted to clarify the corresponding relationship between the core features and the associated attributes. For example, the core feature of the total charging demand per unit time period corresponds to the associated attributes of the vehicle's remaining battery power upon arrival, the vehicle's rated battery capacity, and the number of vehicles arriving per unit time period. The total charging demand per unit time period = the sum of the rated battery capacity of all vehicles within the unit time period - the sum of the remaining battery power of all vehicles arriving within the unit time period. The core feature of the number of vehicles required for charging per unit time period corresponds to the associated attributes of the frequency of departures within the unit time period and the vehicle's remaining battery power threshold. The number of vehicles required for charging per unit time period = the number of vehicles arriving within the unit time period with remaining battery power below the threshold.

[0039] The core features are hierarchically divided according to their corresponding relationships to obtain the core hierarchy; The hierarchical division follows the principle of prioritizing business functions from high to low, dividing core features into three levels: Level 1, Level 2, and Level 3. Level 1 has the highest priority and directly determines the core logic for calculating the number of charging piles. Level 2 supports Level 1 and is supplementary and detailed. For example, in the scenario of configuring charging piles at electric bus stations, Level 1 is divided into charging demand intensity, charging demand frequency, and charging pile service capacity levels. The charging demand intensity level corresponds to the total charging demand per unit time period, directly determining the total charging load; the charging demand frequency level corresponds to the number of vehicles charging per unit time period, directly determining the number of vehicles charging simultaneously; and the charging pile service capacity level corresponds to the number of vehicles a charging pile can serve per unit time period, directly determining the service efficiency of a single charging pile.

[0040] The second-level core hierarchy is a further subdivision of the first-level hierarchy. For example, the charging demand intensity hierarchy is divided into two levels: morning peak charging demand intensity, off-peak charging demand intensity, evening peak charging demand intensity, and nighttime charging demand intensity, corresponding to the differences in charging demand during different operating periods. The charging demand frequency hierarchy is divided into two levels: charging demand frequency at the first and last stops and charging demand frequency at intermediate stops, corresponding to the differences in charging demand for different station types. The charging pile service capability hierarchy is divided into two levels: fast charging pile service capability and slow charging pile service capability, corresponding to the differences in service capability for different charging pile types. The third-level core hierarchy is a supplementary refinement of the second-level hierarchy. For example, the morning peak charging demand intensity is divided into three levels: charging demand intensity for Line 1 and charging demand intensity for Line 2, corresponding to the differences in charging demand for different lines.

[0041] The core-level coding identifiers are combined with the corresponding auxiliary coding identifiers in an orderly manner to form quantity management codes.

[0042] Each core level is assigned a unique code identifier, and corresponding auxiliary code identifiers are assigned to associated attributes. These are then combined in an orderly manner according to the priority order of the core levels to generate standardized quantity management codes. The code identifier allocation follows preset coding rules, with each core level corresponding to a unique code identifier. For example, in the first-level core level, the charging demand intensity level corresponds to code identifier 01, the charging demand frequency level corresponds to code identifier 02, and the charging pile service capacity level corresponds to code identifier 03; in the second-level core level, the morning peak charging demand intensity corresponds to code identifier 01, the off-peak charging demand intensity corresponds to code identifier 02, the evening peak charging demand intensity corresponds to code identifier 03, and the nighttime charging demand intensity corresponds to code identifier 04; in the third-level core level, line 1 corresponds to 01, line 2 corresponds to 02, and so on. Auxiliary code identifiers correspond to the associated attributes of the module's associated fields, used to supplement the specific parameters of the core level. For example, the vehicle's remaining battery power threshold corresponds to auxiliary code identifier 030, indicating a threshold of 30%, and the charging pile's rated power corresponds to auxiliary code identifier 120, indicating a rated power of 120 kilowatts. During the combination process, the codes are sequentially linked according to the priority order of the core hierarchy, from the first level to the second level and then to the third level. Auxiliary codes for related attributes are added after each corresponding level to form a complete quantity management code. For example, for the charging demand intensity at the first and last stops during the morning peak hours, the quantity management code combination is 010101+030+120, where 01 represents the first-level charging demand intensity, 01 represents the second-level morning peak, 01 represents the third-level first and last stops, 030 represents the vehicle's remaining battery threshold of 30%, and 120 represents the charging pile's rated power of 120 kilowatts. This orderly combination integrates scattered core features and related attributes into a unified quantity management code. The quantity management code uniquely identifies the business attributes and data parameters of the target demand field, achieving standardized control over charging demand data.

[0043] The quantity management code and module association fields are validated according to the preset requirement validation rules to obtain the validated target validation fields and the corresponding source data table. The specific steps include: Combine the module-related fields that are associated with the quantity management code into a preliminary set of related fields; Based on the quantity management code, all related module-related fields are traced back and collected to form a complete set of candidate fields. The quantity management code is a standardized identifier generated after encoding the target demand field and the module-related field. It is associated with core features and related attributes. Therefore, the module-related fields that are related to this code are all related fields that support the calculation of the target demand field. Using the quantity management code as an index, the module-related field library is traversed to extract the module-related fields that have established a corresponding relationship with this code. For example, if a quantity management code corresponds to the charging demand intensity field of the morning peak and the first and last stations, its associated module-related fields include the vehicle remaining power field, the vehicle battery rated capacity field, the number segment of vehicles arriving at the station during the morning peak, and the charging pile rated power field. All these fields are extracted and classified and integrated to form a preliminary set of related fields.

[0044] The validity of each field in the initial set of associated fields is verified according to the preset requirement verification rules to obtain the valid associated fields; Based on preset requirement verification rules, each field in the initial set of associated fields undergoes quality verification. Invalid, abnormal, or unqualified fields are removed. These preset requirement verification rules are validation standards based on the core logic of charging pile quantity calculation and include integrity rules, reasonableness rules, and consistency rules. Integrity rules check for missing data in a field; for example, if the vehicle's remaining battery power field contains null values, it is considered an invalid associated field. Reasonableness rules check if the field data is within a reasonable range; for example, the vehicle's remaining battery power must satisfy 0 ≤ vehicle remaining battery power ≤ vehicle battery rated capacity (unit: kilowatt-hours). If a vehicle's remaining battery power is 500 kilowatt-hours while its battery rated capacity is 300 kilowatt-hours, it is considered abnormal and removed. Consistency rules check the logical consistency between fields; for example, the vehicle's charging demand = vehicle battery rated capacity - vehicle remaining battery power (unit: kilowatt-hours). If the charging demand is negative, it indicates a logical contradiction in the field data and it is considered an invalid associated field.

[0045] Verify that the storage location of the valid related fields in the source data table meets the requirements to obtain the required related fields. This includes the following steps: Based on the corresponding quantity management code, confirm whether the storage location of the valid associated fields meets the requirements; Valid associated fields that meet the storage location requirements are identified as eligible associated fields; Identify the relevant fields that meet the requirements as target validation fields, and confirm the source data table corresponding to each target validation field.

[0046] Based on the corresponding quantity management code, confirm whether the storage location of the valid associated fields meets the requirements to obtain the compliant associated fields. Combining the business logic and data specifications corresponding to the quantity management code, verify whether the storage location of the valid associated fields in the source data table meets the requirements for data retrieval, access control, and data security, ensuring the compliance and callability of the fields. Storage location requirements include uniqueness requirements, data classification storage requirements, and access control requirements. Uniqueness requirements mean that each valid associated field must be stored in a unique source data table to avoid scattered storage leading to retrieval confusion. For example, the vehicle's remaining battery power field should be uniformly stored in the battery status data table, rather than scattered across the vehicle operation data table and the dispatch data table. Data classification storage requirements mean that associated fields are stored in the corresponding category of source data table according to their data attributes. For example, vehicle basic information fields are stored in the vehicle basic information table, and charging equipment fields are stored in the charging pile equipment information table. Access control requirements mean that the storage location must match data access permissions to ensure that the fields involved in the calculation can be normally accessed by authorized calculation modules. For example, core charging demand data must be stored in a data source with a higher access level to ensure data security. Based on the business scenario corresponding to the quantity management code, the storage location of each valid associated field is checked one by one. For example, for the valid associated fields corresponding to the charging demand intensity at the first and last stations during the morning peak, it is necessary to check whether the remaining battery power of the vehicle is stored in the battery status data table, whether the rated power of the charging pile is stored in the charging pile equipment information table, and whether the number of vehicles arriving at the station during the morning peak is stored in the vehicle operation timetable. If the storage location meets the above requirements, it is determined to be a qualified associated field. If there are problems such as incorrect or scattered storage location or insufficient permissions, it is removed.

[0047] Identify the relevant fields that meet the requirements as target verification fields and confirm the source data tables corresponding to each target verification field. Complete the final confirmation of the compliant relevant fields, clarifying their identity as target verification fields and locking their corresponding source data tables, providing a clear data direction for subsequent extraction of charging demand-related information. When identifying target verification fields, use the relevant fields as a basis for final identification, assigning them a target verification field identifier. Confirm the source data table corresponding to each target verification field, clarifying the specific source of the data. For example, identify the vehicle remaining battery power field as a target verification field and confirm its corresponding source data table as the battery status data table; identify the number segment of vehicles arriving during the morning peak as a target verification field and confirm its corresponding source data table as the vehicle operation timetable; identify the charging pile rated power field as a target verification field and confirm its corresponding source data table as the charging pile equipment information table. Complete the identification of target verification fields and source data tables, forming a complete list of target verification fields and their corresponding source data table mapping relationships.

[0048] The optimal number of charging piles for electric bus stations is output based on the target validation field, source data table, and quantity management code. This includes the following steps: Extract vehicle charging demand information represented by the target validation field from the source data table corresponding to the target validation field; Based on the mapping relationship between target verification fields and source data tables, core information directly related to vehicle charging needs is extracted from the source data tables. Target verification fields are compliant fields determined after multiple rounds of verification, and each field corresponds to a unique source data table. For example, the vehicle remaining battery power field corresponds to the battery status data table, the vehicle arrival time field corresponds to the vehicle operation schedule, and the charging pile rated power field corresponds to the charging pile equipment information table. Using the target verification fields as indexes, all valid data for each field is read from the corresponding source data tables and transformed into information related to vehicle charging needs. For example, vehicle remaining battery power data is extracted from the battery status data table and transformed into information on the vehicle's immediate charging demand intensity; vehicle arrival and departure time data are extracted from the vehicle operation schedule and transformed into information on the vehicle's available charging time; and rated power data is extracted from the charging pile equipment information table and transformed into information on the charging pile's service capacity. Data units must be standardized during extraction; for example, vehicle remaining battery power is standardized to kilowatt-hours, available charging time to hours, and charging pile power to kilowatts to ensure consistency in subsequent calculations.

[0049] By analyzing the correspondence between the target validation fields and information related to vehicle charging needs, a charging demand association set is obtained; Establish a one-to-one correspondence logic between target verification fields and charging demand information, integrate scattered field data into a structured set of associations, and clearly present the causal and supporting relationships between data. Using the core dimension of charging demand as a framework, bind target verification fields with corresponding information. For example, bind the vehicle's remaining battery power field with the vehicle's charging demand power information: vehicle charging demand power = vehicle battery rated capacity - vehicle remaining battery power; bind the vehicle's available charging time field with the vehicle's charging condition information: vehicle available charging time = vehicle departure time - vehicle arrival time, with the unit uniformly set in hours; bind the number of vehicles arriving per unit time period with the charging demand frequency information: number of vehicles requiring charging per unit time period = number of vehicles arriving per unit time period with remaining battery power below a threshold, thereby obtaining a set of charging demand associations.

[0050] Valid charging demand data is obtained by classifying the target verification fields based on the charging demand association set and quantity management code; By combining the structure of the charging demand association set with the hierarchical identifiers of the quantity management code, the target verification fields are classified in multiple dimensions to remove invalid data and extract valid charging demand data that meets the calculation requirements. The quantity management code includes a core level and auxiliary code identifiers to distinguish charging demand at different times and locations. For example, the first level distinguishes the intensity and frequency of charging demand with service capacity; the second level distinguishes the operating hours during morning peak, off-peak, evening peak, and nighttime; and the third level distinguishes the types of stations such as terminals, intermediate stations, etc. Using the hierarchy of the quantity management code as the classification dimension, and combining the correspondence of the charging demand association set, the target verification fields are assigned to the corresponding categories. For example, the field of remaining vehicle battery power at terminals during the morning peak is assigned to the category of charging demand intensity at terminals during the morning peak, and the field of departure frequency during the evening peak is assigned to the category of charging demand frequency during the evening peak. Data fields that do not conform to the corresponding coding scenario are removed. For example, vehicle data during nighttime hours are removed from the morning peak category, and short-duration stop data at intermediate stations are removed from the first and last station categories. Finally, effective charging demand data by scenario, time period, and station is obtained.

[0051] Extracting the relationship between quantity management codes and target verification fields includes the following steps: The effective charging demand data is obtained by filtering the effective charging demand data according to the corresponding relationship; The effective screening of charging demand data is adapted according to the control standards of quantity management codes; The optimal number of charging piles for electric bus stations is determined based on the adjusted and effectively screened charging demand data.

[0052] Effective charging demand data is filtered based on the corresponding relationships to obtain valid charging demand data. The inherent correlation between quantity management codes and target verification fields is explored, and effective charging demand data is filtered based on the correlation logic to improve the relevance and accuracy of the data. When extracting the corresponding relationships, the core features and correlation attribute correspondence logic of the coding generation stage are traced back to clarify the target verification fields corresponding to each coding level. For example, in the coding, the charging demand intensity level corresponds to the vehicle remaining battery power field, the charging demand frequency level corresponds to the departure frequency field, and the charging pile service capacity level corresponds to the rated power field. During the filtering process, based on the corresponding relationships, only effective charging demand data directly related to the coding level is retained. For example, for charging demand intensity calculation, only data related to vehicle remaining battery power and battery rated capacity are retained, while irrelevant data such as departure frequency are removed; for charging pile service capacity calculation, only data related to rated power and available charging time are retained, while irrelevant data such as vehicle remaining battery power is removed. At the same time, the control threshold in the coding is used for filtering. For example, the remaining battery level is set to be less than 30% in the coding as the charging demand threshold. Only the data of vehicles with remaining battery level less than 30% are retained, and the data of vehicles with no charging demand are removed, so as to obtain effective filtered charging demand data.

[0053] Based on the control standards of the quantity management code, the effectively screened charging demand data is adapted and adjusted. The data is then adjusted for business compatibility and parameter correction according to the built-in control standards of the quantity management code to ensure that the data conforms to actual operational scenarios and planning requirements. The control standards of the quantity management code include operational scenario standard resources, constraint standards, and service efficiency standards. For example, the operational scenario standard sets the maximum allowable queuing time during morning peak hours to 10 minutes; the resource constraint standard sets the maximum power supply capacity of the site's power grid to 1000 kilowatts; and the service efficiency standard sets the number of vehicles that a fast charging pile can serve per hour to 2. The data is then corrected and optimized based on these control standards. For example, for charging demand data during morning peak hours, the time distribution of charging demand is adjusted in conjunction with the maximum queuing time standard, diverting some non-emergency charging demand to off-peak hours; the number of charging piles operating simultaneously is adjusted to avoid exceeding the power supply limit; and the service capacity parameters of the charging piles are corrected according to the service efficiency standard to ensure that the calculations conform to actual charging efficiency. Finally, optimized charging demand data adapted to actual operational conditions is obtained.

[0054] The optimal number of charging piles for electric bus stations is determined based on the adjusted and effectively screened charging demand data. The optimal number of charging piles that meets operational needs and is economically reasonable is obtained based on the optimized charging demand data. The optimal number of charging piles is calculated as follows: Maximum number of vehicles with the highest charging demand per unit time period ÷ Number of vehicles that a single charging pile can serve per unit time period. The maximum number of vehicles with the highest charging demand per unit time period is derived from the adjusted and effectively screened charging demand data. The number of vehicles that a single charging pile can serve per unit time period is calculated as: Charging pile rated power × Unit time period duration ÷ Average charging demand per vehicle. The calculation needs to be performed separately for different time periods and scenarios, such as calculating the number of charging piles during morning peak, off-peak, evening peak, and nighttime. The maximum value is taken as the final configuration number, and adjustments are made based on site resource constraints to ensure that the configuration number does not exceed the capacity limits of the site and the power grid. For example, the maximum number of vehicles requiring charging during the morning peak hours at a certain terminal station is 20. A single 120 kW fast charging pile can serve 2 vehicles per hour. After calculation, the number of charging piles required during the morning peak hours is 10. Considering the station's power grid capacity and site area constraints, the optimal configuration number is finally determined to be 10, which satisfies the charging demand during the morning peak hours while avoiding over-configuration and resource waste.

[0055] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0056] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0057] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for determining the number of charging piles at electric bus stops, characterized in that, Includes the following steps: Construct a corresponding demand analysis data table based on the vehicle operation data of the target bus station; The target requirement fields are obtained by identifying the requirement analysis data table according to the preset requirement analysis rules; Based on the target requirement fields and their corresponding source data table structure, determine the module association fields associated with the target requirement fields; The target requirement fields and their associated module fields are encoded according to the preset encoding rules to generate quantity management codes for the target requirement fields; The quantity management code and module association fields are verified according to the preset requirement verification rules to obtain the verified target verification fields and the corresponding source data table. The optimal number of charging piles for electric bus stations is output based on the target validation field, source data table, and quantity management code.

2. The method for determining the number of charging piles at electric bus stations according to claim 1, characterized in that, Based on the vehicle operation data of the target bus station, a corresponding demand analysis data table is constructed, which includes the following steps: By segmenting the vehicle operation data of the target bus station into different scenarios, target information related to vehicle stopping time, departure frequency, and power consumption status can be obtained. The target information is collected according to the station's operating time period to form the information summary content for the corresponding time period; Based on the information summary, a data table body is constructed. The data table body is then organized and logically validated to obtain the requirements analysis data table.

3. The method for determining the number of charging piles at electric bus stations according to claim 1, characterized in that, The target requirement fields are identified in the requirement analysis data table according to the preset requirement analysis rules. The specific steps include: The data in the demand analysis data table is divided into different categories according to the electric bus operation scenarios to obtain a classified data set; Based on the classified dataset, target data items related to charging demand for each category of data are extracted, and the inherent relationships between the target data items are sorted out to obtain the target data relationship; Based on the correlation of target data, key data items reflecting the intensity and frequency of charging demand are selected, and the validity of key data items is verified to obtain valid key data items; By combining preset demand analysis rules, effective key data items are filtered and integrated to obtain target demand fields that meet the requirements for calculating the number of charging piles.

4. The method for determining the number of charging piles at electric bus stations according to claim 1, characterized in that, Based on the target requirement fields and their corresponding source data table structure, the module association fields associated with the target requirement fields are determined, specifically including the following steps: The basic fields are obtained by processing the target requirement fields and their corresponding source data table structures; Based on the actual operational scenarios of electric bus station charging pile configuration, the target functional modules related to the number of charging piles and configuration are divided; Use the basic fields that match the functional requirements of the target functional module as candidate association fields; Perform correlation validation on candidate related fields to obtain the module related fields associated with the target requirement field.

5. The method for determining the number of charging piles at electric bus stations according to claim 4, characterized in that, The basic fields are obtained by processing the target requirement fields and their corresponding source data table structures, specifically including the following steps: Clearly define the data ownership and relationships of the target requirement fields within the source data table structure; Based on data ownership and relationships, determine the basic fields in the source data table where data interaction occurs between the target requirement fields and the source data table.

6. The method for determining the number of charging piles at electric bus stations according to claim 1, characterized in that, The target requirement fields and their associated module fields are encoded according to preset encoding rules to generate quantity management codes for the target requirement fields. The specific steps include: Extract the core features corresponding to the target requirement fields and the associated attributes of the module-related fields according to the preset coding rules, and clarify the corresponding relationship between the core features and the associated attributes; The core features are hierarchically divided according to their corresponding relationships to obtain the core hierarchy; The core-level coding identifiers are combined with the corresponding auxiliary coding identifiers in an orderly manner to form quantity management codes.

7. The method for determining the number of charging piles at electric bus stations according to claim 1, characterized in that, The quantity management code and module association fields are validated according to the preset requirement validation rules to obtain the validated target validation fields and the corresponding source data table. The specific steps include: Combine the module-related fields that are associated with the quantity management code into a preliminary set of related fields; The validity of each field in the initial set of associated fields is verified according to the preset requirement verification rules to obtain the valid associated fields; Verify that the storage location of the valid related fields in the source data table meets the requirements to obtain the related fields that meet the requirements; Identify the relevant fields that meet the requirements as target validation fields, and confirm the source data table corresponding to each target validation field.

8. The method for determining the number of charging piles at electric bus stations according to claim 7, characterized in that, Verify that the storage location of the valid related fields in the source data table meets the requirements to obtain the required related fields. This includes the following steps: Based on the corresponding quantity management code, confirm whether the storage location of the valid associated fields meets the requirements; Valid associated fields that meet the storage location requirements are identified as eligible associated fields.

9. The method for determining the number of charging piles at electric bus stations according to claim 1, characterized in that, The optimal number of charging piles for electric bus stations is output based on the target validation field, source data table, and quantity management code. This includes the following steps: Extract vehicle charging demand information represented by the target validation field from the source data table corresponding to the target validation field; By analyzing the correspondence between the target validation fields and information related to vehicle charging needs, a charging demand association set is obtained; Valid charging demand data is obtained by classifying the target verification fields based on the charging demand association set and quantity management code; Extract the corresponding relationship between the quantity management code and the target verification field, and determine the optimal number of charging piles for electric bus stations based on the corresponding relationship and the number of valid charging demands.

10. The method for determining the number of charging piles at electric bus stations according to claim 9, characterized in that, The optimal number of charging piles for electric bus stations is determined based on the corresponding relationships and the effective charging demand. This involves the following steps: The effective charging demand data is obtained by filtering the effective charging demand data according to the corresponding relationship; The effective screening of charging demand data is adapted according to the control standards of quantity management codes; The optimal number of charging piles for electric bus stations is determined based on the adjusted and effectively screened charging demand data.

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