Remote online monitoring method and system for charging pile cluster data

Abstract

The invention relates to the technical field of charging pile monitoring, and discloses a remote online monitoring method and system for charging pile cluster data, and the method comprises the steps: collecting the specification and position information of charging pile units, building a digital model for each charging pile unit based on the specification information, and forming a cluster digital model according to the position information; carrying out multi-dimensional data acquisition on the charging pile units, obtaining index parameters such as working states, working parameters and environment characteristics, substituting the index parameters into the cluster digital model, analyzing power utilization conditions, extracting power utilization distribution characteristics and trends, carrying out performance evaluation on each charging pile unit according to the index parameters, obtaining performance characteristics, and carrying out performance evaluation on each charging pile unit according to the performance characteristics. And the future resource scheduling trend of the charging pile cluster is predicted by using the power consumption trend and the performance evaluation characteristics, intelligent management of the charging pile cluster, optimization of resource use and improvement of the charging efficiency can be realized through the steps, and the problem of low efficiency of manual detection of the charging pile cluster in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of charging pile monitoring, and in particular to a remote online monitoring method and system for charging pile cluster data. Background Art

[0002] With the rapid popularization of electric vehicles (EVs), charging piles, as their key infrastructure, are receiving more and more attention. The increase in electric vehicles has directly led to an increase in the demand for charging piles. The management of charging pile clusters faces the problem of lack of real-time monitoring. Charging piles distributed in different locations face different workloads. The equipment usage experience of each charging pile leads to different equipment performance conditions of each charging pile. At present, the monitoring of charging piles relies on manual work, which is inefficient and cannot take into account the charging efficiency of the area where the charging pile cluster is located. Summary of the invention

[0003] The purpose of the present invention is to provide a remote online monitoring method and system for charging pile cluster data, aiming to solve the problem of low efficiency of manual detection of charging pile clusters in the prior art.

[0004] The present invention is implemented in this way. In a first aspect, the present invention provides a remote online monitoring method for charging pile cluster data, comprising: Acquire specification information and location information of each charging pile unit in the charging pile cluster, construct a unit digital model for digital feedback of each charging pile unit according to the specification information of each charging pile unit, and combine and process each unit digital model according to the location information of each charging pile unit to obtain a cluster digital model for digital feedback of the charging pile cluster; Continuously collect multi-dimensional data of each charging pile unit in the charging pile cluster to obtain index parameters of each charging pile unit, and substitute each index parameter into each unit digital model in the cluster digital model; wherein the index parameters include working state characteristics, working parameter characteristics and working environment characteristics; The cluster digital model is used to analyze the power consumption status of the charging pile cluster according to each of the indicator parameters to obtain the power consumption distribution characteristics of each time period, and the power consumption distribution characteristics of each time period are comprehensively analyzed to obtain the power consumption trend characteristics of the charging pile cluster; The cluster digital model is instructed to perform performance evaluation on each of the charging pile units according to each of the index parameters to obtain performance evaluation characteristics of each of the charging pile units; The resource scheduling trend of the charging pile cluster is predicted and processed according to the power consumption trend characteristics of the charging pile cluster and the performance evaluation characteristics of each of the charging pile units to obtain the resource scheduling trend of the charging pile cluster; wherein the resource scheduling trend is used to describe the charging and discharging demand and equipment performance prediction status of the charging pile cluster in a future time period.

[0005] In a second aspect, the present invention provides a remote online monitoring system for charging pile cluster data, which is used to implement a remote online monitoring method for charging pile cluster data as described in any one of the first aspects, including: A digital simulation module, used to obtain specification information and location information of each charging pile unit in the charging pile cluster, construct a unit digital model for digital feedback of each charging pile unit according to the specification information of each charging pile unit, and combine and process each unit digital model according to the location information of each charging pile unit to obtain a cluster digital model for digital feedback of the charging pile cluster; A data acquisition module, used for continuously performing multi-dimensional data acquisition on each of the charging pile units in the charging pile cluster to obtain index parameters of each of the charging pile units, and respectively substitute each of the index parameters into the digital model of each of the units in the cluster digital model; wherein the index parameters include working state characteristics, working parameter characteristics and working environment characteristics; A trend analysis module, used to enable the cluster digital model to analyze the power consumption status of the charging pile cluster according to each of the indicator parameters, obtain the power consumption distribution characteristics of each time period, and perform a comprehensive analysis on the power consumption distribution characteristics of each time period to obtain the power consumption trend characteristics of the charging pile cluster; A performance evaluation module, used to enable the cluster digital model to perform performance evaluation on each of the charging pile units according to each of the index parameters, so as to obtain performance evaluation characteristics of each of the charging pile units; A comprehensive analysis module is used to predict the resource scheduling trend of the charging pile cluster according to the power consumption trend characteristics of the charging pile cluster and the performance evaluation characteristics of each of the charging pile units, so as to obtain the resource scheduling trend of the charging pile cluster; wherein the resource scheduling trend is used to describe the charging and discharging demand and equipment performance prediction status of the charging pile cluster in a future time period.

[0006] The present invention provides a remote online monitoring method for charging pile cluster data, which has the following beneficial effects: The present invention collects specification and location information of charging pile units, establishes a digital model for each charging pile unit based on the specification information, and combines them into a cluster digital model according to the location information, collects multi-dimensional data of the charging pile units, obtains index parameters such as working status, working parameters and environmental characteristics, substitutes the index parameters into the cluster digital model, analyzes the power consumption status, extracts power consumption distribution characteristics and trends, performs performance evaluation on each charging pile unit based on the index parameters, obtains performance characteristics, and predicts future resource scheduling trends of the charging pile cluster by using the power consumption trend and performance evaluation characteristics. Through the above steps, intelligent management of the charging pile cluster can be achieved, resource utilization can be optimized, and charging efficiency can be improved, thereby solving the problem of low efficiency of manual detection of charging pile clusters in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 It is a schematic diagram of the steps of a remote online monitoring method of charging pile cluster data provided by an embodiment of the present invention; Figure 2 It is a structural diagram of a remote online monitoring system for charging pile cluster data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0008] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0009] The implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0010] Reference Figure 1 , Figure 2 As shown, a preferred embodiment of the present invention is provided.

[0011] In a first aspect, the present invention provides a remote online monitoring method for charging pile cluster data, comprising: S1: Obtain specification information and location information of each charging pile unit in a charging pile cluster, construct a unit digital model for digital feedback of each charging pile unit according to the specification information of each charging pile unit, and combine and process each unit digital model according to the location information of each charging pile unit to obtain a cluster digital model for digital feedback of the charging pile cluster; S2: Continuously collect multi-dimensional data of each charging pile unit in the charging pile cluster to obtain index parameters of each charging pile unit, and substitute each index parameter into each unit digital model in the cluster digital model; wherein the index parameters include working state characteristics, working parameter characteristics and working environment characteristics; S3: the cluster digital model is used to analyze the power consumption status of the charging pile cluster according to each of the indicator parameters to obtain the power consumption distribution characteristics of each time period, and the power consumption distribution characteristics of each time period are comprehensively analyzed to obtain the power consumption trend characteristics of the charging pile cluster; S4: enabling the cluster digital model to perform performance evaluation on each of the charging pile units according to each of the index parameters, so as to obtain performance evaluation characteristics of each of the charging pile units; S5: Predicting the resource scheduling trend of the charging pile cluster according to the power consumption trend characteristics of the charging pile cluster and the performance evaluation characteristics of each of the charging pile units to obtain the resource scheduling trend of the charging pile cluster; wherein the resource scheduling trend is used to describe the charging and discharging demand and equipment performance prediction status of the charging pile cluster in a future time period.

[0012] Specifically, in step S1 of the embodiment provided by the present invention, the charging pile cluster is a cluster composed of multiple charging pile units, each charging pile unit provides charging services respectively. In order to monitor each charging pile unit, it is necessary to pre-build a unit digital model corresponding to each charging pile unit to digitally simulate the charging pile unit, so as to substitute the collected data into the unit digital model for subsequent analysis.

[0013] More specifically, in order to construct a unit digital model corresponding to each charging pile unit, it is necessary to first obtain specification information of each charging pile unit, perform digital simulation on the charging pile unit based on the specification information, and obtain the corresponding unit digital model.

[0014] More specifically, the specification information includes charging power (such as 3kW, 7kW, 22kW, 50kW, etc.), charging interface type (such as Type 1, Type 2, CCS, etc.), supported charging standards (such as GB / T, CHAdeMO, etc.), maximum current and voltage, communication protocol (such as OCPP) and other elements.

[0015] More specifically, each charging pile unit has a corresponding setting position in the charging pile cluster. The setting position of the charging pile unit is also an element used for analysis. Therefore, it is necessary to collect the location information of each charging pile unit and combine the digital models of each unit according to the location information to obtain a cluster digital model that provides digital feedback to the charging pile cluster. This model can reflect the overall performance of the charging pile cluster, analyze the matching of charging demand and supply, and identify the mutual influence between charging piles.

[0016] It can be understood that the cluster digital model can monitor the status of charging piles in real time, provide instant feedback, and support rapid response to multiple subsequent processing requirements, including equipment failure prediction, resource scheduling, etc.

[0017] Specifically, in step S2 of the embodiment provided by the present invention, necessary sensors and equipment are installed on each charging pile unit to collect multi-dimensional data in real time, such as: current sensor, power meter, temperature sensor, humidity sensor, etc., to ensure that each sensor can transmit data to the central server via a wireless network or a wired network.

[0018] More specifically, the indicator parameters are defined, which include working status characteristics, working parameter characteristics and working environment characteristics. The working status characteristics include: online status (online, offline, fault, etc.), charging status (charging, standby, fault), user interaction status (whether the user is using it, whether the charging pile is occupied); working parameter characteristics include charging power (current charging power, maximum charging power), charging time (start time, end time, total charging time), voltage, current (real-time voltage value, real-time current value); working environment characteristics include ambient temperature and humidity (real-time monitoring of environmental conditions).

[0019] More specifically, various indicators and parameters are collected regularly through sensors and monitoring equipment. An appropriate collection frequency is set (such as every minute, every five minutes, etc.), and the collected data is transmitted to the centralized management platform in real time using the Internet of Things technology to ensure the real-time and integrity of the data.

[0020] More specifically, the index parameters of each charging pile unit are substituted into the digital models of each unit in the cluster digital model, the real-time status of the model is updated, the cluster digital model is dynamically operated using real-time data, the performance and status of each charging pile unit are evaluated, and based on the data analysis results, optimization suggestions for charging pile operations are put forward, such as adjusting the charging strategy, increasing the frequency of equipment maintenance, etc.

[0021] It is understandable that the real-time monitoring of the charging pile unit can quickly respond to faults and abnormal situations, reduce equipment failure rate, optimize the working parameters of the charging pile through multi-dimensional data analysis, improve charging efficiency, reduce energy consumption, adjust the working state of the charging pile according to the characteristics of the working environment, and improve its stability and reliability under different environmental conditions.

[0022] Specifically, in step S3 of the embodiment provided by the present invention, electricity consumption-related data is collected in the charging pile unit, including the current, voltage, charging power, etc. of each charging pile, and the electricity consumption data is recorded at predetermined time intervals (such as every minute, every hour) to ensure the timeliness and accuracy of the data. The collected data is stored in a database to ensure the integrity and queryability of the data.

[0023] More specifically, according to the analysis requirements, the data is divided into different time periods, for example, every half-hour interval is divided into a time period, and the power consumption data of each time period is statistically analyzed to extract the distribution characteristics of the power consumption of each charging pile unit in the charging pile cluster. The power consumption distribution characteristics of each time period are visualized through charts (such as bar charts, line charts, etc.) to facilitate analysis and understanding.

[0024] More specifically, according to the electricity consumption distribution characteristics in each time period, a power consumption trend analysis model can be constructed. Time series analysis, regression analysis and other methods can be used to extract the overall power consumption trend characteristics of the charging pile cluster through model analysis to describe the changing trend of the power consumption distribution characteristics of the charging pile cluster.

[0025] It is understandable that through systematic data analysis, we can accurately understand the electricity consumption of the charging pile cluster, help operators to grasp the electricity consumption dynamics in a timely manner, and visual analysis makes the electricity distribution characteristics more intuitive, which is convenient for various decision makers to understand and judge. By establishing a data model, we can predict future electricity demand and provide a scientific basis for equipment expansion and investment decisions.

[0026] Specifically, in step S4 of the embodiment provided by the present invention, the performance of the charging pile unit is evaluated according to the collected working state characteristics, working parameter characteristics and working environment characteristics to obtain performance evaluation characteristics for providing performance feedback to the charging pile unit.

[0027] It is understandable that by comprehensively evaluating the various indicators of the charging pile unit, we can fully understand the operating status and performance level of the charging pile, quickly identify the charging pile units with poor performance, and take timely measures to reduce the failure rate and improve service reliability.

[0028] Specifically, in step S5 of the embodiment provided by the present invention, the charging and discharging demand of the charging pile cluster in the future time period and the equipment performance are estimated according to the power consumption trend characteristics of the charging pile cluster and the performance evaluation characteristics of each charging pile unit, so as to obtain the resource scheduling trend of the charging pile cluster. The resource scheduling trend is a judgment on the future time period based on the data collected in the past and at present, that is, a monitoring conclusion of the charging pile cluster based on the existing data.

[0029] More specifically, resource scheduling trends include analyzing predicted charging and discharging demands and equipment performance, identifying peak and off-peak electricity demand, evaluating the load capacity of equipment, and formulating corresponding resource scheduling strategies based on the predicted results to ensure that there are enough charging piles available during peak periods while performing maintenance and overhaul during off-peak periods. A real-time monitoring mechanism is established to compare actual electricity usage with predicted results and adjust resource scheduling strategies in a timely manner.

[0030] It is understandable that by predicting the future charging and discharging needs of the charging pile cluster, efficient resource scheduling can be achieved to avoid resource waste and equipment idleness. The use of charging piles can be reasonably arranged according to the predicted power consumption trend, the overall utilization rate of the equipment can be improved, and the operating efficiency of the charging piles can be optimized.

[0031] The present invention provides a remote online monitoring method for charging pile cluster data, which has the following beneficial effects: The present invention collects specification and location information of charging pile units, establishes a digital model for each charging pile unit based on the specification information, and combines them into a cluster digital model according to the location information, collects multi-dimensional data of the charging pile units, obtains index parameters such as working status, working parameters and environmental characteristics, substitutes the index parameters into the cluster digital model, analyzes the power consumption status, extracts power consumption distribution characteristics and trends, performs performance evaluation on each charging pile unit based on the index parameters, obtains performance characteristics, and predicts future resource scheduling trends of the charging pile cluster by using the power consumption trend and performance evaluation characteristics. Through the above steps, intelligent management of the charging pile cluster can be achieved, resource utilization can be optimized, and charging efficiency can be improved, thereby solving the problem of low efficiency of manual detection of charging pile clusters in the prior art.

[0032] Preferably, the steps of obtaining specification information and location information of each charging pile unit in the charging pile cluster, constructing a unit digital model for digital feedback of each charging pile unit according to the specification information of each charging pile unit, and combining and processing each unit digital model according to the location information of each charging pile unit to obtain a cluster digital model for digital feedback of the charging pile cluster include: S11: Obtain specification information of each charging pile unit in the charging pile cluster; wherein the specification information includes the charging power, voltage range, charging protocol, interface model and number of interfaces of the charging pile unit; S12: constructing a corresponding basic unit model based on the charging pile unit, and performing deployment processing of model parameters on the basic unit model according to specification information of the charging pile unit, so as to obtain a unit digital model for digital feedback of the charging pile unit; S13: Obtaining location information of each of the charging pile units in the charging pile cluster; S14: Constructing a basic regional model for digital feedback of the area where the charging pile cluster is located, and performing model setting processing on each of the unit digital models according to the location information of each of the charging pile units to obtain each of the unit digital models set at a corresponding position on the basic regional model. The each of the unit digital models set at a corresponding position on the basic regional model together constitutes a cluster digital model for digital feedback of the charging pile cluster.

[0033] Specifically, the specification information of each charging pile unit in the charging pile cluster is collected, including: charging power (such as kW), voltage range (such as DC or AC voltage range), charging protocol (such as CCS, CHAdeMO, etc.), interface model and number of interfaces (such as Type 2, Type 1, etc.), and the collected specification information is organized into a structured data format (such as JSON, CSV, etc.) for subsequent processing.

[0034] More specifically, according to the specification information of the charging pile unit, a basic unit model is designed to reflect the basic functions and performance of the charging pile, and the parameters of the basic unit model (such as maximum power, charging time, efficiency, etc.) are set according to the specification information to generate a corresponding unit digital model.

[0035] More specifically, the location information of each charging pile unit is collected, including geographic coordinates (latitude and longitude), address, region, etc., and the location information is organized into a standard format for integration with the unit model.

[0036] More specifically, according to the geographical area where the charging pile cluster is located, a basic regional model is created to reflect the characteristics of the area (such as traffic flow, surrounding facilities, etc.), and relevant parameters (such as regional capacity, user demand, etc.) are set in the basic regional model to provide background information for subsequent combination processing.

[0037] More specifically, according to the location information of the charging pile unit, each unit digital model is set at a corresponding position in the basic area model. For example, the charging pile unit digital model is placed at a corresponding position in the basic area model according to the latitude and longitude information.

[0038] More specifically, multiple unit digital models are combined into an overall cluster digital model to form a digital feedback system that includes all charging pile units. The constructed cluster digital model is verified and tested to ensure that it can accurately reflect the performance and status of the charging pile cluster. According to the test results, the model parameters and structure are adjusted to optimize the performance of the cluster digital model.

[0039] It can be understood that by building a cluster digital model, real-time monitoring and digital feedback of charging pile units can be achieved, management efficiency can be improved, specification information and location information can be integrated, and a comprehensive performance view of the charging pile cluster can be provided to support better decision-making.

[0040] Preferably, the steps of continuously collecting multi-dimensional data of each charging pile unit in the charging pile cluster to obtain index parameters of each charging pile unit, and substituting each index parameter into the digital model of each unit in the cluster digital model include: S21: Continuously collect data on the working state, working parameters and working environment of each charging pile unit in the charging pile cluster to obtain working state characteristics, working parameter characteristics and working environment characteristics of each charging pile unit in each time period; wherein the working state characteristics are used to describe the working state of the charging pile unit, the working parameter characteristics are used to describe various equipment parameters of the charging pile unit when charging and power supplying, and the working environment characteristics are used to describe the environmental parameters of the charging pile unit when working; S22: Combining and processing the working state characteristics, working parameter characteristics and working environment characteristics of the charging pile unit in the same time period to obtain an index parameter of the charging pile unit in the time period; S23: generating a first identifier of the indicator parameter according to the charging pile unit corresponding to the indicator parameter, and generating a second identifier of the indicator parameter according to the time period corresponding to the indicator parameter; S24: Substituting the indicator parameter into the unit digital model in the cluster digital model according to the first identifier of the indicator parameter, and sorting the positions of the indicator parameters substituted into the same unit digital model according to the second identifier of the indicator parameter.

[0041] Specifically, through sensors and monitoring systems, the working status characteristics of the charging pile unit are collected in real time, including but not limited to: whether the charging pile is in a working state (such as charging, standby, faulty, etc.), the start and end time of the charging session.

[0042] More specifically, various equipment parameters of the charging pile during charging and power supply are recorded to obtain working parameter characteristics, including input voltage and current, output power and charging rate, efficiency and energy consumption.

[0043] More specifically, the parameters of the environment in which the charging pile is located are monitored to obtain working environment characteristics, including the temperature and humidity of the charging pile unit.

[0044] More specifically, the working status, working parameters and working environment characteristics in each time period are combined to generate comprehensive index parameters of the charging pile unit, which will reflect the overall performance of the charging pile in a specific time period.

[0045] More specifically, a unique first identifier is generated for each indicator parameter to identify its corresponding charging pile unit, and a second identifier of a time period is generated for each indicator parameter to facilitate subsequent data sorting and analysis.

[0046] More specifically, according to the first identifier, the indicator parameter is substituted into the corresponding unit digital model in the cluster digital model to update its status and performance. According to the second identifier, the various indicator parameters substituted into the same unit digital model are sorted by position to ensure the accuracy and consistency of the data in the time dimension. Through continuous data collection and updating of indicator parameters, real-time performance feedback is provided for the charging pile cluster.

[0047] It is understandable that through continuous data collection, the status of each charging pile unit can be monitored in real time, problems can be discovered and solved in a timely manner, and real-time working status and parameter data can help identify potential failure risks, perform preventive maintenance, and reduce the occurrence of failures.

[0048] Preferably, the step of allowing the cluster digital model to analyze the power consumption status of the charging pile cluster according to each of the indicator parameters to obtain the power consumption distribution characteristics of each time period, and comprehensively analyzing the power consumption distribution characteristics of each time period to obtain the power consumption trend characteristics of the charging pile cluster includes: S31: Retrieving the working state characteristics and working parameter characteristics of each of the unit digital models in the same time period, integrating and analyzing the working state characteristics and working parameter characteristics of each of the unit digital models in the same time period, and obtaining the power consumption characteristics of each of the unit digital models in the same time period; S32: performing distribution characteristic analysis on the power consumption characteristics of each unit digital model in the cluster digital model in the same time period to obtain the power consumption distribution characteristics of the cluster digital model in the time period; S33: sorting the power consumption distribution features according to the time periods corresponding to the power consumption distribution features to obtain a power consumption distribution sequence of the charging pile cluster; S34: Analyzing the power consumption trend characteristics of the charging pile cluster based on the power consumption distribution sequence to obtain the power consumption trend characteristics of the charging pile cluster.

[0049] Specifically, the working status characteristics and working parameter characteristic data of all unit digital models in the same time period are retrieved from the database of the charging pile cluster, and the retrieved data are sorted to ensure the integrity and consistency of the data for subsequent analysis.

[0050] More specifically, the working status characteristics of the digital models of each unit are integrated, and their operating conditions within the time period (such as charging, standby, fault, etc.) are analyzed. The working parameter characteristics of the digital models of each unit are integrated, and key indicators such as the power consumption and charging power of each unit are calculated. Based on the integrated working status and parameter characteristics, the power consumption characteristics of the digital models of each unit within the time period are generated, including total power consumption, average power, etc.

[0051] More specifically, the distribution characteristics of the electricity consumption of each unit digital model in the cluster digital model in the same time period are analyzed, and statistical methods (such as mean, variance, quantile, etc.) are used to describe the distribution of electricity consumption, identify outliers and trends in the electricity consumption distribution characteristics, and find out the peak and trough periods.

[0052] More specifically, according to the time period corresponding to the power consumption distribution characteristics, these characteristics are sorted to form an ordered power consumption distribution sequence, and the power consumption distribution sequence of the charging pile cluster in the time period is generated to facilitate subsequent analysis.

[0053] More specifically, based on the electricity consumption distribution sequence, time series analysis techniques (such as moving average, exponential smoothing, etc.) are used to analyze the electricity consumption trend characteristics of the charging pile cluster, and finally the electricity consumption trend characteristics of the charging pile cluster are obtained, such as electricity consumption growth rate, volatility, etc.

[0054] It can be understood that by integrating and analyzing the working status and parameter characteristics of the charging pile units, we can fully understand the power consumption of each unit in a specific time period. In-depth analysis of the power distribution characteristics can help identify power consumption patterns and potential anomalies, support the optimization of charging strategies, and the sorted power distribution sequence provides a reliable data basis for further trend analysis, ensuring the accuracy of the analysis results.

[0055] Preferably, the step of analyzing the power consumption trend characteristics of the charging pile cluster based on the power consumption distribution sequence to obtain the power consumption trend characteristics of the charging pile cluster includes: S341: performing a characteristic analysis of the usage frequency of the charging piles on the charging pile cluster based on the power consumption distribution sequence to obtain the usage frequency characteristics of each of the charging pile units, and evaluating the usage frequency characteristics according to a preset standard to obtain the usage priority weight of each of the charging pile units; S342: Based on the power consumption distribution sequence, a characteristic analysis of the time period usage demand of the charging pile cluster is performed to obtain the overall demand characteristics of the charging pile cluster in each time period, and the overall demand characteristics of the charging pile cluster in each time period are arranged to obtain the overall demand sequence of the charging pile cluster; S343: obtaining current time period information, and performing estimation processing on the overall demand sequence for future time periods according to the current time period information, so as to obtain the estimated overall demand for the future time period; S344: Combining the estimated overall demand for the future time period with the usage priority weight of each of the charging pile units to obtain the power consumption trend characteristics of the charging pile cluster.

[0056] Specifically, the power consumption data of each charging pile unit in each time period is extracted from the power consumption distribution sequence, the number of times each charging pile unit is used in each time period is counted, and the usage frequency characteristics of each charging pile unit (such as the number of times used per day, the number of times used per week, etc.) are calculated.

[0057] More specifically, the usage frequency characteristics of each charging pile unit are evaluated according to preset standards (such as usage frequency threshold, usage time distribution, etc.) to obtain the usage priority weight of each charging pile unit.

[0058] More specifically, based on the electricity consumption distribution sequence, we analyze the overall electricity demand characteristics of the charging pile cluster in each time period, including peak periods, trough periods, and their corresponding electricity demand. We arrange the overall demand characteristics of each time period to form an orderly overall demand sequence, which is convenient for observing electricity consumption trends.

[0059] More specifically, relevant information of the current time period is obtained, including the current power consumption status and time period characteristics (such as weekdays, weekends, etc.). Based on the information of the current time period and the overall demand sequence, a prediction model (such as time series model, regression analysis, etc.) is applied to estimate the overall demand of the future time period to obtain the estimated overall demand.

[0060] More specifically, the estimated overall demand in the future time period is combined with the usage priority weight of the charging pile unit, and the potential load of each charging pile unit in the future time period is analyzed. By summarizing and analyzing the combined results, the power consumption trend characteristics of the charging pile cluster are obtained, including high-demand charging piles, low-demand charging piles, and their future power consumption change trends.

[0061] It is understandable that by analyzing the usage frequency characteristics of charging piles, the operation and maintenance of charging piles can be better optimized, thereby improving the efficiency of resource utilization. The analysis and estimation of usage demand characteristics based on time periods can provide a scientific basis for the layout and power supply of charging piles, ensuring sufficient power supply capacity during high-demand periods. The combination of priority weight calculation and demand estimation can help managers formulate more reasonable scheduling strategies and optimize the allocation and use of charging piles.

[0062] Preferably, the step of allowing the cluster digital model to perform performance evaluation on each of the charging pile units according to each of the index parameters to obtain the performance evaluation characteristics of each of the charging pile units includes: S41: performing working abnormality analysis on each of the charging pile units according to the working state characteristics and the working parameter characteristics of each of the charging pile units to obtain abnormal key characteristics of each of the charging pile units; S42: performing a comparison process based on the working environment characteristics of each of the charging pile units to obtain the abnormal environment characteristics of each of the charging pile units; S43: performing graded processing on the abnormal key features and the environmental abnormal features of the charging pile unit to obtain abnormal key features and environmental abnormal features of each grade; S44: performing frequency analysis on the abnormal key features and environmental abnormal features of each level of the charging pile unit, respectively, to obtain abnormal frequency features corresponding to multiple levels of the charging pile unit; S45: Performing an overall abnormal analysis on the charging pile unit according to the abnormal frequency characteristics corresponding to multiple levels to obtain abnormal characteristics of the charging pile unit, and evaluating the abnormal characteristics according to preset standards to obtain performance evaluation characteristics of the charging pile unit.

[0063] Specifically, the working status characteristics and working parameter characteristics are collected from the real-time monitoring system of the charging pile unit, and the working status and working parameters of each charging pile unit are analyzed using anomaly detection algorithms (such as Z-score, outlier detection model, etc.), potential working anomalies are identified, and key abnormal features (such as overload, short circuit, etc.) are extracted.

[0064] More specifically, the working environment characteristics of the charging pile units are collected, the environmental characteristics of the various charging pile units are compared with each other, and it is determined which charging pile units have environmental conditions that deviate from the normal range, thereby extracting environmental abnormality characteristics (such as extreme temperature, abnormal humidity, etc.).

[0065] More specifically, based on the numerical range of abnormal key features and environmental abnormal features, a grading standard is formulated (such as classifying abnormal indicators into mild, moderate, and severe), the abnormal key features and environmental abnormal features of each charging pile unit are classified into grades, and the characteristic information of each grade is recorded.

[0066] More specifically, the frequency statistics of abnormal key features and environmental abnormal features of each charging pile unit at different levels are performed, the number of occurrences is recorded, and the abnormal frequency features of different levels are extracted to form an abnormal frequency feature data set for subsequent analysis.

[0067] More specifically, based on the abnormal frequency characteristics of each charging pile unit, an overall abnormal analysis model is established to identify abnormal patterns and their impact levels. Based on preset performance evaluation standards (such as reliability, safety, efficiency, etc.), the abnormal characteristics of the charging pile units are evaluated to obtain the performance evaluation characteristics of each charging pile unit.

[0068] It is understandable that through in-depth analysis of working status and environmental characteristics, potential working anomalies can be discovered and repaired in a timely manner, improving the reliability and safety of charging piles. The classification and analysis of abnormal key characteristics and environmental abnormal characteristics can provide maintenance personnel with clear maintenance strategies and reduce the occurrence of sudden failures. Through frequency analysis and overall abnormality analysis, charging pile units with low usage frequency and high failure rate can be identified, thereby optimizing the use and scheduling of charging piles and improving overall utilization efficiency.

[0069] Preferably, the step of predicting the resource scheduling trend of the charging pile cluster according to the power consumption trend characteristics of the charging pile cluster and the performance evaluation characteristics of each of the charging pile units to obtain the resource scheduling trend of the charging pile cluster includes: S51: performing characteristic analysis on the power usage probability of each charging pile unit in the future time period according to the estimated overall demand of the charging pile cluster in the future time period and the usage priority of each charging pile unit, so as to obtain the power usage probability characteristics of each charging pile unit in the future time period; S52: performing performance risk simulation processing on each charging pile unit in the future time period according to the performance evaluation characteristics of each charging pile unit and the power consumption probability characteristics in the future time period, so as to obtain the performance risk characteristics of each charging pile unit in the future time period; S53: Analyzing and processing the future power consumption quota of each charging pile unit according to the power consumption probability characteristics and performance risk characteristics of each charging pile unit in the future time period to obtain the power consumption limit quota of each charging pile unit in the future time period; S54: Taking the power consumption probability characteristics, performance risk characteristics and power consumption limit quota of the charging pile unit in the future time period as the resource scheduling characteristics of the charging pile unit; S55: Combining and processing the resource scheduling characteristics of each of the charging pile units to obtain a resource scheduling trend of the charging pile cluster.

[0070] Specifically, the historical electricity consumption data, user behavior patterns, charging demand fluctuations and other information of the charging pile cluster are collected, and time series analysis and machine learning models (such as ARIMA, LSTM, etc.) are used to predict the overall demand in the future time period to obtain the overall electricity demand of the charging pile cluster.

[0071] More specifically, the weight of each charging pile is adjusted according to the usage priority of the charging pile unit (for example, the charging pile location, charging speed, maintenance status, etc.), and the power consumption probability characteristics of each charging pile unit in the future time period are calculated by using the overall demand and priority to form a probability characteristic data set.

[0072] More specifically, a performance risk simulation is performed on the power consumption probability characteristics in the future time period according to the performance evaluation characteristics, and the performance risks that may occur under different power consumption conditions are simulated to form a performance risk characteristic data set.

[0073] More specifically, based on the combination of electricity consumption probability characteristics and performance risk characteristics, the electricity consumption limit quota of each charging pile unit in the future time period is calculated, considering the maximum carrying capacity and risk tolerance, and the electricity consumption quota is refined and adjusted through optimization algorithms (such as linear programming, genetic algorithms, etc.) to ensure rational allocation of resources.

[0074] More specifically, the power consumption probability characteristics, performance risk characteristics and power consumption limit quota of each charging pile unit are integrated to form a complete resource scheduling characteristic of the charging pile unit. The resource scheduling characteristics of all charging pile units are aggregated and analyzed to identify the scheduling trend of the entire charging pile cluster and generate a resource scheduling strategy to ensure that the charging pile cluster can efficiently and reasonably meet future power demand.

[0075] It can be understood that through comprehensive analysis of the power usage probability, performance risk and power usage limit of the charging pile unit, the resource allocation of the charging pile cluster can be effectively optimized to ensure that user needs can be efficiently met during high demand periods. Performance risk simulation processing can identify potential faults in advance, thereby reducing the system failure rate and improving the overall reliability of the charging pile.

[0076] Reference Figure 2 As shown, in a second aspect, the present invention provides a remote online monitoring system for charging pile cluster data, which is used to implement a remote online monitoring method for charging pile cluster data as described in any one of the first aspects, including: A digital simulation module, used to obtain specification information and location information of each charging pile unit in the charging pile cluster, construct a unit digital model for digital feedback of each charging pile unit according to the specification information of each charging pile unit, and combine and process each unit digital model according to the location information of each charging pile unit to obtain a cluster digital model for digital feedback of the charging pile cluster; A data acquisition module, used for continuously performing multi-dimensional data acquisition on each of the charging pile units in the charging pile cluster to obtain index parameters of each of the charging pile units, and respectively substitute each of the index parameters into the digital model of each of the units in the cluster digital model; wherein the index parameters include working state characteristics, working parameter characteristics and working environment characteristics; A trend analysis module, used to enable the cluster digital model to analyze the power consumption status of the charging pile cluster according to each of the indicator parameters, obtain the power consumption distribution characteristics of each time period, and perform a comprehensive analysis on the power consumption distribution characteristics of each time period to obtain the power consumption trend characteristics of the charging pile cluster; A performance evaluation module, used to enable the cluster digital model to perform performance evaluation on each of the charging pile units according to each of the index parameters, so as to obtain performance evaluation characteristics of each of the charging pile units; A comprehensive analysis module is used to predict the resource scheduling trend of the charging pile cluster according to the power consumption trend characteristics of the charging pile cluster and the performance evaluation characteristics of each of the charging pile units, so as to obtain the resource scheduling trend of the charging pile cluster; wherein the resource scheduling trend is used to describe the charging and discharging demand and equipment performance prediction status of the charging pile cluster in a future time period.

[0077] In this embodiment, for the specific implementation of each module in the above system embodiment, please refer to the above method embodiment, which will not be repeated here.

[0078] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A remote online monitoring method for charging pile cluster data, characterized in that: include: Acquire specification information and location information of each charging pile unit in the charging pile cluster, construct a unit digital model for digital feedback of each charging pile unit according to the specification information of each charging pile unit, and combine and process each unit digital model according to the location information of each charging pile unit to obtain a cluster digital model for digital feedback of the charging pile cluster; Continuously collect multi-dimensional data of each charging pile unit in the charging pile cluster to obtain index parameters of each charging pile unit, and substitute each index parameter into each unit digital model in the cluster digital model; wherein the index parameters include working state characteristics, working parameter characteristics and working environment characteristics; The cluster digital model is used to analyze the power consumption status of the charging pile cluster according to each of the indicator parameters to obtain the power consumption distribution characteristics of each time period, and the power consumption distribution characteristics of each time period are comprehensively analyzed to obtain the power consumption trend characteristics of the charging pile cluster; The cluster digital model is instructed to perform performance evaluation on each of the charging pile units according to each of the index parameters to obtain performance evaluation characteristics of each of the charging pile units; The resource scheduling trend of the charging pile cluster is predicted and processed according to the power consumption trend characteristics of the charging pile cluster and the performance evaluation characteristics of each of the charging pile units to obtain the resource scheduling trend of the charging pile cluster; wherein the resource scheduling trend is used to describe the charging and discharging demand and equipment performance prediction status of the charging pile cluster in a future time period.

2. The remote online monitoring method for charging pile cluster data according to claim 1, characterized in that: The steps of obtaining specification information and location information of each charging pile unit in the charging pile cluster, constructing a unit digital model for digital feedback of each charging pile unit according to the specification information of each charging pile unit, and combining and processing each unit digital model according to the location information of each charging pile unit to obtain a cluster digital model for digital feedback of the charging pile cluster include: Obtain specification information of each charging pile unit in the charging pile cluster; wherein the specification information includes the charging power, voltage range, charging protocol, interface model and number of interfaces of the charging pile unit; Constructing a corresponding basic unit model based on the charging pile unit, and performing deployment processing of model parameters on the basic unit model according to specification information of the charging pile unit, so as to obtain a unit digital model for digital feedback of the charging pile unit; Obtaining location information of each of the charging pile units in the charging pile cluster; A basic regional model for digital feedback of the area where the charging pile cluster is located is constructed, and model setting processing is performed on each of the unit digital models according to the position information of each of the charging pile units to obtain each of the unit digital models set at a corresponding position on the basic regional model. The each of the unit digital models set at a corresponding position on the basic regional model together constitutes a cluster digital model for digital feedback of the charging pile cluster.

3. The remote online monitoring method for charging pile cluster data according to claim 1, characterized in that: The steps of continuously collecting multi-dimensional data of each charging pile unit in the charging pile cluster to obtain index parameters of each charging pile unit, and substituting each index parameter into the digital model of each unit in the cluster digital model include: Continuously collect data on the working state, working parameters and working environment of each charging pile unit in the charging pile cluster to obtain the working state characteristics, working parameter characteristics and working environment characteristics of each charging pile unit in each time period; wherein the working state characteristics are used to describe the working state of the charging pile unit, the working parameter characteristics are used to describe the various equipment parameters of the charging pile unit when charging and power supplying, and the working environment characteristics are used to describe the environmental parameters of the charging pile unit when working; Combining and processing the working state characteristics, working parameter characteristics and working environment characteristics of the charging pile unit in the same time period to obtain the index parameter of the charging pile unit in the time period; Generate a first identifier of the indicator parameter according to the charging pile unit corresponding to the indicator parameter, and generate a second identifier of the indicator parameter according to the time period corresponding to the indicator parameter; The indicator parameter is substituted into the unit digital model in the cluster digital model according to the first identifier of the indicator parameter, and the positions of the indicator parameters substituted into the same unit digital model are sorted according to the second identifier of the indicator parameter.

4. The remote online monitoring method for charging pile cluster data according to claim 1, characterized in that: The steps of making the cluster digital model analyze the power consumption status of the charging pile cluster according to each of the index parameters to obtain the power consumption distribution characteristics of each time period, and comprehensively analyzing the power consumption distribution characteristics of each time period to obtain the power consumption trend characteristics of the charging pile cluster include: Retrieving the working state characteristics and working parameter characteristics of each of the unit digital models in the same time period, integrating and analyzing the working state characteristics and working parameter characteristics of each of the unit digital models in the same time period, and obtaining the power consumption characteristics of each of the unit digital models in the same time period; Performing distribution characteristic analysis on the power consumption characteristics of each unit digital model in the cluster digital model in the same time period to obtain the power consumption distribution characteristics of the cluster digital model in the time period; According to the time periods corresponding to the respective power consumption distribution characteristics, the power consumption distribution characteristics are sorted to obtain a power consumption distribution sequence of the charging pile cluster; The power consumption trend characteristics of the charging pile cluster are analyzed based on the power consumption distribution sequence to obtain the power consumption trend characteristics of the charging pile cluster.

5. The remote online monitoring method for charging pile cluster data according to claim 4, characterized in that: The step of analyzing the power consumption trend characteristics of the charging pile cluster based on the power consumption distribution sequence to obtain the power consumption trend characteristics of the charging pile cluster includes: Based on the power consumption distribution sequence, the charging pile cluster is subjected to a characteristic analysis of the frequency of use of the charging piles to obtain the frequency of use characteristics of each of the charging pile units, and the frequency of use characteristics are evaluated according to a preset standard to obtain the priority weight of use of each of the charging pile units; Based on the power consumption distribution sequence, a characteristic analysis of the time period usage demand of the charging pile cluster is performed to obtain the overall demand characteristics of the charging pile cluster in each time period, and the overall demand characteristics of the charging pile cluster in each time period are arranged to obtain the overall demand sequence of the charging pile cluster; Acquire current time period information, and perform estimation processing on the overall demand sequence for future time periods according to the current time period information to obtain the estimated overall demand for the future time period; The estimated overall demand in the future time period is combined with the usage priority weight of each of the charging pile units to obtain the power consumption trend characteristics of the charging pile cluster.

6. The remote online monitoring method for charging pile cluster data according to claim 1, characterized in that: The step of causing the cluster digital model to perform performance evaluation on each of the charging pile units according to each of the index parameters to obtain performance evaluation characteristics of each of the charging pile units comprises: Performing working abnormality analysis on each of the charging pile units according to the working state characteristics and the working parameter characteristics of each of the charging pile units to obtain abnormal key characteristics of each of the charging pile units; Comparing the working environment characteristics of each charging pile unit with each other to obtain the abnormal environment characteristics of each charging pile unit; The abnormal key features and the abnormal environment features of the charging pile unit are graded to obtain the abnormal key features and the abnormal environment features of each grade; Perform frequency analysis on the abnormal key features and environmental abnormal features of each level of the charging pile unit to obtain abnormal frequency features corresponding to multiple levels of the charging pile unit; An overall abnormal analysis is performed on the charging pile unit according to the abnormal frequency characteristics corresponding to multiple grades to obtain abnormal characteristics of the charging pile unit, and the abnormal characteristics are evaluated according to preset standards to obtain performance evaluation characteristics of the charging pile unit.

7. The remote online monitoring method for charging pile cluster data according to claim 5, characterized in that: The step of predicting the resource scheduling trend of the charging pile cluster according to the power consumption trend characteristics of the charging pile cluster and the performance evaluation characteristics of each of the charging pile units to obtain the resource scheduling trend of the charging pile cluster includes: According to the estimated overall demand of the charging pile cluster in the future time period and the usage priority of each of the charging pile units, a characteristic analysis is performed on the power usage probability of each of the charging pile units in the future time period to obtain the power usage probability characteristics of each of the charging pile units in the future time period; According to the performance evaluation characteristics of each charging pile unit and the probability characteristics of electricity consumption in the future time period, a performance risk simulation process is performed on each charging pile unit in the future time period to obtain the performance risk characteristics of each charging pile unit in the future time period; According to the power consumption probability characteristics and performance risk characteristics of each charging pile unit in the future time period, the future power consumption quota of each charging pile unit is analyzed and processed to obtain the power consumption limit quota of each charging pile unit in the future time period; The power consumption probability characteristics, performance risk characteristics and power consumption limit quota of the charging pile unit in the future time period are used as resource scheduling characteristics of the charging pile unit; The resource scheduling characteristics of each of the charging pile units are combined and processed to obtain a resource scheduling trend of the charging pile cluster.

8. A remote online monitoring system for charging pile cluster data, characterized in that: A remote online monitoring method for charging pile cluster data according to any one of claims 1 to 7, comprising: A digital simulation module, used to obtain specification information and location information of each charging pile unit in the charging pile cluster, construct a unit digital model for digital feedback of each charging pile unit according to the specification information of each charging pile unit, and combine and process each unit digital model according to the location information of each charging pile unit to obtain a cluster digital model for digital feedback of the charging pile cluster; A data acquisition module, used for continuously performing multi-dimensional data acquisition on each of the charging pile units in the charging pile cluster to obtain index parameters of each of the charging pile units, and respectively substitute each of the index parameters into the digital model of each of the units in the cluster digital model; wherein the index parameters include working state characteristics, working parameter characteristics and working environment characteristics; A trend analysis module, used to enable the cluster digital model to analyze the power consumption status of the charging pile cluster according to each of the indicator parameters, obtain the power consumption distribution characteristics of each time period, and perform a comprehensive analysis on the power consumption distribution characteristics of each time period to obtain the power consumption trend characteristics of the charging pile cluster; A performance evaluation module, used to enable the cluster digital model to perform performance evaluation on each of the charging pile units according to each of the index parameters, so as to obtain performance evaluation characteristics of each of the charging pile units; A comprehensive analysis module is used to predict the resource scheduling trend of the charging pile cluster according to the power consumption trend characteristics of the charging pile cluster and the performance evaluation characteristics of each of the charging pile units, so as to obtain the resource scheduling trend of the charging pile cluster; wherein the resource scheduling trend is used to describe the charging and discharging demand and equipment performance prediction status of the charging pile cluster in a future time period.

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