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CN-121787861-B - Charging pile number configuration method based on big data analysis

CN121787861BCN 121787861 BCN121787861 BCN 121787861BCN-121787861-B

Abstract

The invention discloses a charging pile number configuration method based on big data analysis, which relates to the technical field of intelligent energy management and comprises the following steps of collecting traffic flow data, vehicle charging record data, power grid load curve data and air image time data, uniformly numbering time stamps of all the data, and generating a time dislocation sensitive list; based on the time dislocation sensitive list, data from different sources are compared item by item according to minute time intervals, offset positions of adjacent data in a time sequence are calculated, and a rhythm superposition contact list is formed. The invention realizes the time unification and false peak elimination of multi-source data by establishing the time dislocation sensitive list, the rhythm superposition contact list and the false peak identification index, improves the accuracy and the space matching degree of the configuration of the charging pile, combines a high mismatching area directory and a dynamic sampling control mechanism, carries out quota rollback and optimization adjustment according to real-time traffic and charging requirements, and realizes the intellectualization and dynamic balance of the configuration of the charging pile.

Inventors

  • Liao Yicai

Assignees

  • 福州能汇电力设计有限公司

Dates

Publication Date
20260505
Application Date
20260225

Claims (8)

  1. 1. The charging pile number configuration method based on big data analysis is characterized by comprising the following steps of: Collecting traffic flow data, vehicle charging record data, power grid load curve data and meteorological time data, uniformly numbering time stamps of all the data, and generating a time dislocation sensitive list; Based on the time dislocation sensitive list, carrying out item-by-item comparison on data from different sources according to minute-level time intervals, and calculating offset positions of adjacent data in a time sequence to form a rhythm superposition contact list; Carrying out statistical analysis on the repeated occurrence rule of each contact by utilizing a rhythm superposition contact list, identifying periodic reverberation points, positioning the peak position of the false charging demand, and generating a false peak identification index; Re-analyzing the regional charging pile configuration diagram according to the pseudo-peak identification index, calculating configuration quantity deviation of different regions, and summarizing to form a high-mismatching regional directory; Based on the high-mismatching area directory, by combining real-time traffic data and charging demand change information, performing dynamic adjustment operation, correcting the data sampling rhythm in a forward and reverse rhythm alternating mode and a short-time stoping window mode, implementing quota rollback according to demand fluctuation, and adjusting the configuration quantity of the charging piles in real time.
  2. 2. The method for configuring the number of charging piles based on big data analysis according to claim 1, wherein the time-offset sensitive list generation step is as follows: continuously acquiring traffic flow data, vehicle charging record data, power grid load curve data and meteorological time data at fixed time sampling intervals to form a multi-source original time sequence data set containing time stamps; taking the unified time base point as a reference, numbering all time stamps, mapping various data to the same time axis system, and supplementing the missing points through time interpolation to form a unified time sequence data set; Performing time offset correlation analysis on the data with uniform numbers, identifying time drift intervals of the data with different sources, and recording offset start and stop numbers and offset directions; and summarizing the offset information, generating a time dislocation sensitive list according to the time number sequence, and recording offset sources, offset amplitudes and frequencies.
  3. 3. The method for configuring the number of charging piles based on big data analysis according to claim 2, wherein the step of forming the rhythm superimposing contact table is as follows: According to the unified time number of the time dislocation sensitive list, carrying out minute-by-minute time alignment on traffic flow data, vehicle charging record data, power grid load curve data and meteorological time data, and combining all source data under each time number into the same time unit; Performing difference calculation on the data sets between adjacent time numbers according to the time number sequence, identifying offset positions of different source data in the time sequence, and recording offset start numbers, offset end numbers and offset duration time; Performing rule analysis on the identified time offset interval, and counting the offset direction, duration and occurrence frequency of each source data to form a time offset rule table; And integrating time number intervals with synchronous or periodic characteristics according to a time offset rule table, recording the offset direction, offset amplitude and offset occurrence frequency of each source data, and generating a rhythm superposition contact table.
  4. 4. The method for configuring the number of charging piles based on big data analysis according to claim 3, wherein the pseudo-peak identification index generation step is as follows: According to the time numbers, the offset directions, the offset duration, the offset amplitude and the offset occurrence frequency recorded in the rhythm superposition contact list, carrying out statistical analysis on the repeated distribution of various contacts to form an offset data sequence arranged according to the time numbers; Performing time interval analysis on the offset data sequence, extracting periodic reverberation characteristics, and recording offset source combinations, repeated occurrence intervals, duration and occurrence frequency to form a periodic reverberation analysis list; Identifying a false charging demand peak formed by overlapping time drift according to the periodic reverberation analysis list and the time dislocation sensitive list, establishing a false peak positioning list and recording a time numbering range, offset direction consistency and reverberation period length; and integrating all false peak information according to the false peak positioning list, and generating a false peak identification index according to the time numbering sequence.
  5. 5. The method for configuring the number of charging piles based on big data analysis according to claim 4, wherein in the generation process of the pseudo peak identification index, the types of offset sources recorded in the pseudo peak positioning list include traffic flow data, vehicle charging record data, power grid load curve data and meteorological time data, and the consistency of the offset direction and the length of the reverberation period are marked by a time number correspondence mode.
  6. 6. The method for configuring the number of charging piles based on big data analysis according to claim 4, wherein the step of generating the directory of the high-mismatch area is as follows: mapping with space nodes of the regional charging pile configuration map according to time numbers, offset source types, offset directions, reverberation periods, offset duration and geographic position identifiers in the pseudo-peak identification indexes to form a pseudo-peak influence region list, and recording region numbers, pseudo-peak times and configuration change trends corresponding to each pseudo-peak; according to the pseudo-peak influence area list, comparing and calculating the configuration quantity of each area in the pseudo-peak time period and the reference time period, and recording configuration quantity deviation, load change amplitude and utilization rate change value to form an area configuration quantity deviation table; Carrying out space aggregation analysis on deviation results by taking geographic proximity and pseudo-peak period consistency as principles to generate a regional deviation aggregation table, and recording deviation types, duration and pseudo-peak influence periods; And according to the regional deviation aggregation table, summarizing and arranging each aggregation unit, recording configuration error types, error amplitudes, error duration periods and pseudo-peak interference sources, and generating a high-error configuration regional directory.
  7. 7. The method for configuring the number of charging piles based on big data analysis according to claim 6, wherein in the regional deviation aggregation analysis, the geographical adjacent regions are judged in a correlated manner according to the power grid load transfer relationship, and when the number of charging piles in one region increases in a pseudo-peak period and the load of the adjacent region decreases, the phenomenon of space resource migration is recorded, and the phenomenon of resource allocation non-uniformity is marked in a high-mismatching region directory.
  8. 8. The method for configuring the number of charging piles based on big data analysis according to claim 6, wherein the dynamic adjustment operation is performed as follows: according to the high-mismatching area directory, carrying out real-time state identification on the area which is identified as over-configured or under-configured, and corresponding real-time traffic data and real-time charging demand data with the area number to form a real-time state matching matrix for recording traffic running conditions and energy demand changes; According to the real-time state matching matrix, carrying out forward and reverse rhythm alternating sampling correction on the data sampling process of the high mismatching area, keeping data distribution balance in a sampling mode of controlling the increasing and decreasing directions of time numbers, and recording the rising and falling rules of the change of the demand; setting a short-time stop sampling window in the process of sampling alternately in the forward and reverse directions, and correcting the sampling rhythm in a pause mode, so that time offset caused by high-frequency sampling is prevented, and the continuity and stability of a data time axis are maintained; And executing quota rollback according to the mismatching direction and deviation amplitude in the high mismatching area directory and combining the real-time traffic and charging demand change information, adjusting the configuration quantity of the charging piles in each area and recording the adjustment result.

Description

Charging pile number configuration method based on big data analysis Technical Field The invention relates to the technical field of intelligent energy management, in particular to a charging pile number configuration method based on big data analysis. Background The configuration of the number of the charging piles based on big data analysis refers to a process of scientifically determining the number and types of the charging piles required by different areas through comprehensive analysis of vehicle charging demand distribution, time period characteristics, residence time, electricity price fluctuation and regional power supply capacity under the support of multidimensional data such as urban traffic, user travel, energy supply, power grid load and the like. The method utilizes big data technology to collect, clean, cluster and forecast and model historical and real-time data, so as to identify high-demand areas in space, describe the charging peak law in time, and dynamically generate an optimal configuration scheme by combining power supply capacity and land resource constraint. Through the data driving mode, the transformation of the construction of the charging infrastructure from empirical planning to intelligent and fine configuration can be realized, and the energy utilization efficiency and the charging convenience of users are improved. The prior art has the following defects: in the prior art, periodic dislocation of multi-source data is easy to occur in the time synchronization process, and especially in the multi-source information fusion stage of traffic flow, vehicle track, charging order, power grid load, meteorological parameters and the like, timestamp drift is caused by different sampling frequencies and uploading delays. The drift can generate false charging demand peak signals at the period superposition points, and the model can easily misjudge the peak as a real charging concentration period in the identification process, so that the configuration quantity of the charging piles in a certain area is increased wrongly. Such misjudgment can lead to long-term idle of the partial-area charging piles, and the pile position is insufficient in the real high-demand area, so that obvious space mismatching is formed. The above information disclosed in the background section is only for enhancement of understanding of the background of the disclosure and therefore it may include information that does not form the prior art that is already known to a person of ordinary skill in the art. Disclosure of Invention The invention aims to provide a charging pile number configuration method based on big data analysis, so as to solve the problems in the background technology. In order to achieve the above purpose, the invention provides a charging pile number configuration method based on big data analysis, which comprises the following steps: Collecting traffic flow data, vehicle charging record data, power grid load curve data and meteorological time data, uniformly numbering time stamps of all the data, and generating a time dislocation sensitive list for establishing a time reference to support subsequent comparison analysis; Based on the time dislocation sensitive list, carrying out item-by-item comparison on data from different sources according to minute-level time intervals, and calculating offset positions of adjacent data in a time sequence to form a rhythm superposition contact list; Carrying out statistical analysis on the repeated occurrence rule of each contact by utilizing a rhythm superposition contact table, identifying periodic reverberation points, positioning the peak position of the false charging demand, and generating a false peak identification index for correcting abnormal peaks in data; Re-analyzing the regional charging pile configuration diagram according to the pseudo-peak identification index, calculating configuration quantity deviation of different regions, and summarizing to form a high-mismatching regional directory serving as target input of a dynamic adjustment stage; Based on the high-mismatching area directory, by combining real-time traffic data and charging demand change information, performing dynamic adjustment operation, correcting the data sampling rhythm in a forward and reverse rhythm alternating mode and a short-time stoping window mode, and implementing quota rollback according to demand fluctuation to adjust the configuration quantity of the charging piles in real time, so that the configuration result of the charging piles is consistent with the actual demand. Preferably, the time-shift sensitive list generation steps are as follows: continuously acquiring traffic flow data, vehicle charging record data, power grid load curve data and meteorological time data at fixed time sampling intervals to form a multi-source original time sequence data set containing time stamps; taking the unified time base point as a reference, numbering all time stamps, mapping v