CN117074840B

CN117074840B - DC charging pile on-line testing system

Info

Publication number: CN117074840B
Application number: CN202311331701.9A
Authority: CN
Inventors: 卢顺祥; 潘雷; 顾君; 熊学兵; 欧国徽; 黄爱龙; 汤黎黎
Original assignee: Nanjing Siyu Electric Technology Co ltd
Current assignee: Nanjing Siyu Electric Technology Co ltd
Priority date: 2023-10-16
Filing date: 2023-10-16
Publication date: 2023-12-19
Anticipated expiration: 2043-10-16
Also published as: CN117074840A

Abstract

The invention relates to the technical field of charging pile testing, and discloses a direct current charging pile online testing system which comprises a time determining module, a time determining module and a test module, wherein the time determining module is used for analyzing external related information of a future day and determining a future optimal testing time period; the test processing module is used for acquiring a test time-frequency diagram, analyzing the test time-frequency diagram and determining the running state of the direct current charging pile; the relation determining module is used for acquiring the temperature change coefficient of the DC charging pile in the abnormal operation state and determining abnormal analysis data corresponding to the temperature change coefficient based on the corresponding interval relation between the preset temperature change coefficient and the abnormal analysis data; the abnormality analysis module is used for acquiring an actual measurement vibration change diagram of the DC charging pile in an abnormal operation state, comparing the actual measurement vibration change diagram with the standard vibration change diagram, and recording an abnormality reason and an abnormality position corresponding to the standard vibration change diagram if the actual measurement vibration change diagram is different from the standard vibration change diagram.

Description

DC charging pile on-line testing system

Technical Field

The invention relates to the technical field of charging pile testing, in particular to a direct current charging pile online testing system.

Background

With the rapid development of electric vehicles, a direct current charging pile has become one of important facilities for charging electric vehicles; the direct-current charging pile is equipment for providing direct-current electric energy for the electric automobile, and compared with the alternating-current charging pile, the direct-current charging pile has higher charging power and faster charging speed, and can rapidly meet the charging requirement of the electric automobile, so that the direct-current charging pile becomes the most main charging terminal at present and is arranged in various charging service public places; the high traffic flow of the charging service public place further causes high use frequency of the direct current charging pile; frequent use, invasion of external factors or other influencing factors easily cause the direct-current charging pile to be extremely easy to generate faults; if timely fault early warning and fault maintenance cannot be carried out on the direct-current charging pile, the travel efficiency or arrangement of an electric car owner is easily affected, and the problems are most remarkable particularly in holidays; however, most of the existing direct current charging pile online test systems only test the post accident identification of the direct current charging pile, and cannot realize fault self-detection early warning, abnormal investigation and the like of the direct current charging pile; therefore, how to realize the timely on-line test of the direct current charging pile becomes the focus of the research.

At present, a system for timely and online testing the direct-current charging pile is lacking, most of the existing direct-current charging pile testing systems are realized by manually carrying a special instrument for testing on-site, and the method is time-consuming and labor-consuming and has low efficiency; of course, there are also partially automated dc charging stake testing systems, for example, chinese patent with grant notice number CN111458652B discloses a method, apparatus and device for determining faults of dc charging stake; although the method can carry out fault determination on the direct current charging pile, the inventor researches and finds that the method and the prior art have at least the following partial defects:

(1) The test time is relatively fixed, and the self-checking test before the electric car is charged can not be determined for the DC charging pile at the most appropriate time most often in the test of the electric car in the charging process, so that the fault occurrence probability of the electric car in the charging process is difficult to be reduced as much as possible;

(2) The test data are single, and most of the test data are fault tests in the accident, and the abnormal situation and the abnormal position of the direct current charging pile can not be timely determined through the combination analysis of the data, so that the maintenance personnel are difficult to be assisted to maintain and manage in time.

Disclosure of Invention

In order to overcome the above-mentioned drawbacks of the prior art, embodiments of the present invention provide an online test system for a dc charging pile.

In order to achieve the above purpose, the present invention provides the following technical solutions:

the utility model provides a direct current fills electric pile on-line testing system, the system relies on cloud computing server, cloud computing server and Q monitoring processing equipment remote communication connection, every monitoring processing equipment all electrical connection is inside direct current fills electric pile, direct current fills electric pile inside still includes energy storage battery, and Q is the positive integer collection that is greater than zero, the system includes:

the time determining module is used for acquiring external related information of the future date of the direct current charging pile, and analyzing the external related information of the future date based on a preset self-checking prediction model so as to determine the optimal test time period in the future; the external related information of the future date comprises future weather data, future vehicle flow data and future charging electricity price data;

the test processing module is used for waking up the monitoring processing equipment based on the optimal test time period, acquiring a test time-frequency diagram of the direct current charging pile by using the monitoring processing equipment, and analyzing the test time-frequency diagram to determine the running state of the direct current charging pile; the operation state comprises an abnormal operation state and a normal operation state;

The relation determining module is used for acquiring the temperature change coefficient of the DC charging pile in the abnormal operation state and determining abnormal analysis data corresponding to the temperature change coefficient based on the corresponding interval relation between the preset temperature change coefficient and the abnormal analysis data; the abnormality analysis data comprises a plurality of abnormality reasons, abnormality positions corresponding to each abnormality reason and standard vibration change diagrams corresponding to each abnormality reason;

the abnormality analysis module is used for acquiring an actual measurement vibration change diagram of the DC charging pile in an abnormal operation state, comparing the actual measurement vibration change diagram with the standard vibration change diagram, and recording an abnormality reason and an abnormality position corresponding to the standard vibration change diagram if the actual measurement vibration change diagram is different from the standard vibration change diagram.

Further, the generation process of the preset self-checking prediction model specifically includes the following steps:

acquiring historical external related information of the direct current charging pile; the historical external related information comprises historical weather data, historical vehicle flow data, historical charging electricity price data and the number of the historical charging vehicles of the direct-current charging pile on a historical day;

processing historical external related information of the direct current charging pile to obtain a first characteristic data set, a second characteristic data set and a third characteristic data set;

Respectively extracting a first training feature set, a second training feature set and a third training feature set based on the first feature data set, the second feature data set and the third feature data set, and constructing a base learner, wherein the base learner comprises a first base learner, a second base learner and a third base learner, and the first training feature set, the second training feature set and the third training feature set are respectively trained by the first base learner, the second base learner and the third base learner to obtain a first prediction model, a second prediction model and a third prediction model;

respectively inputting historical weather data, historical vehicle flow data and historical charging electricity price data into a first prediction model, a second prediction model and a third prediction model for prediction to obtain first prediction data, second prediction data and third prediction data;

constructing an integrated learner, taking the first prediction data, the second prediction data and the third prediction data as prediction sample sets, dividing the prediction sample sets into prediction training sets and prediction test sets, inputting the prediction training sets into the integrated learner, training according to an integrated learning strategy to obtain an integrated learning model, testing the integrated learning model by using the prediction test sets, and outputting the integrated learning model meeting preset prediction accuracy as a preset self-checking prediction model.

Further, the processing of the historical external related information of the direct current charging pile comprises the following steps:

extracting the number of historical charging vehicles of the direct-current charging pile in the historical external related information of the direct-current charging pile;

randomly dividing each history day in the history external related information of the direct current charging pile to obtain a plurality of time periods, extracting the history weather data in each time period, and labeling the history weather data of each time period based on the number of the history charging vehicles of the direct current charging pile to obtain a first characteristic data set;

extracting historical vehicle flow data in each time period, and labeling the historical vehicle flow data in each time period based on the number of the historical charging vehicles of the direct current charging pile to obtain a second characteristic data set;

and extracting historical charging electricity price data in each time period, and labeling the historical charging electricity price data in each time period based on the number of the historical charging vehicles of the direct current charging pile to obtain a third characteristic data set.

Further, analyzing the external related information of the future date based on a preset self-checking prediction model, including:

inputting external related information of the future date of the direct current charging pile into a preset self-checking prediction model for prediction to obtain an idle state of the direct current charging pile in T time intervals, wherein T is a positive integer greater than zero;

Extracting the corresponding time interval of each idle state, and sequencing the corresponding time intervals of each idle state according to the interval size to obtain the corresponding time interval of the idle state of the first position, the corresponding time interval of the idle state of the second position, … … and the corresponding time interval of the idle state of the Nth position; the N is a positive integer greater than zero;

extracting the corresponding time interval of the idle state of the first position after sequencing, and marking the corresponding time interval of the idle state of the first position as a target time interval; acquiring the capacity of an energy storage battery in a target time interval based on the target time interval and a preset energy storage state prediction model;

comparing the capacity of the energy storage battery in the target time interval with the preset energy storage capacity, and taking the corresponding target time interval as the future optimal test time period if the capacity of the energy storage battery in the target time interval is larger than or equal to the preset energy storage capacity; otherwise, if the capacity of the energy storage battery in the target time interval is smaller than the preset energy storage capacity, marking the corresponding time interval of the idle state of the second position as the target time interval, and the like, stopping until the capacity of the energy storage battery in the target time interval is larger than or equal to the preset energy storage capacity, and obtaining the future optimal test time period.

Further, the generating process of the preset energy storage state prediction model is as follows: the method comprises the steps of obtaining historical data of an energy storage battery, pre-storing the historical data of the energy storage battery in a cloud computing server, wherein the historical data of the energy storage battery comprises, but is not limited to, historical charging time and historical energy storage battery capacity, taking the historical data of the energy storage battery as a battery data sample set, dividing the battery data sample set into a 80% battery data training set and a 20% battery data testing set, constructing a prediction model, inputting the battery data training set into the prediction model for training, obtaining an initial prediction model, verifying the initial prediction model by utilizing the battery data testing set, and outputting the initial prediction model with the prediction accuracy being larger than a preset prediction accuracy threshold as a preset energy storage state prediction model.

Further, analyzing the test time-frequency diagram includes:

extracting waveform amplitude values of each time point in the test time-frequency diagram;

comparing waveform amplitude values at each time point by using a preset abnormal rule, if anyJudging that no abnormal waveform amplitude value exists; if->＜/>Or there is->＜JudgingDetermining and recording abnormal waveform amplitude values; wherein: / >Is the minimum average waveform amplitude value,is the maximum average waveform amplitude value; />A waveform amplitude value representing an i-th point in time;

counting the total number of the abnormal waveform amplitude values, comparing the total number of the abnormal waveform amplitude values with a total amount threshold value of a preset abnormal waveform amplitude value, and judging that the running state of the direct current charging pile is a normal running state if the total number of the abnormal waveform amplitude values is smaller than the total amount threshold value of the preset abnormal waveform amplitude value; otherwise, if the total number of the abnormal waveform amplitude values is greater than or equal to the total number threshold value of the preset abnormal waveform amplitude values, judging that the running state of the direct current charging pile is the running abnormal state.

Further, obtaining a temperature change coefficient of the dc charging pile in an abnormal operation state includes:

collecting temperature data of each time point of the DC charging pile in an abnormal operation state in an optimal test time period;

carrying out formula calculation on the temperature data of each time point to obtain the temperature change coefficient of the DC charging pile in an abnormal operation state; the calculation formula is as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein: />Is the temperature change coefficient>For the temperature data at the j-th time point, +.>For the temperature data at the j-1 th time point, < >>Is the total duration of the optimal test period.

Further, obtaining an actually measured vibration change diagram of the DC charging pile in an abnormal operation state comprises the following steps:

collecting vibration data of the DC charging pile in an abnormal operation state at each time point in an optimal test time period;

carrying out formula calculation on the vibration data of each time point to obtain vibration change data of the DC charging pile in an abnormal operation state; the calculation formula is as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein: />Is->Vibration change data at time point, +.>For the vibration data at time g, +.>Vibration data for time point g-1;

and constructing a two-dimensional data graph by taking a time point as a horizontal axis and vibration change data as a vertical axis, and taking the two-dimensional data graph as an actually-measured vibration change graph.

Further, comparing the measured vibration variation map with the standard vibration variation map includes:

dividing the actually measured vibration change diagram and the standard vibration change diagram into R areas according to the same rule, wherein R is a positive integer set greater than zero;

comparing pixel points of the same position areas of the actually measured vibration change chart and the standard vibration change chart one by one, and recording a difference area where the actually measured vibration change chart and the standard vibration change chart are different;

if the number of the difference areas in the actually measured vibration change diagram is larger than the preset difference area number threshold, recording an abnormal reason and an abnormal position corresponding to the standard vibration change diagram.

Further, the direct current charging pile on-line testing system according to any one of the above is used for realizing a direct current charging pile on-line testing method.

An electronic device comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the direct current charging pile on-line testing method when executing the computer program.

A computer readable storage medium having a computer program stored thereon, which when executed by a processor, implements the dc charging stake on-line testing method described above.

Compared with the prior art, the invention has the beneficial effects that:

(1) The application discloses an online test system of a direct current charging pile, which is provided with a time determining module, wherein the time determining module is used for determining the self-checking test before the electric car is charged to the direct current charging pile at the most appropriate moment by acquiring the external related information of the direct current charging pile in the future and analyzing and processing the external related information through an integrated learning algorithm, so that the fault occurrence probability of the electric car during charging can be reduced as much as possible;

(2) The application discloses an online test system of a direct current charging pile, which comprises the steps of firstly, acquiring a test time-frequency diagram in the optimal test time period in the future, analyzing the test time-frequency diagram, and determining the running state of the direct current charging pile; then, acquiring a temperature change coefficient of the DC charging pile in an abnormal operation state, and determining abnormal analysis data corresponding to the temperature change coefficient based on a preset corresponding interval relation between the temperature change coefficient and the abnormal analysis data; finally, obtaining an actual measurement vibration change diagram of the DC charging pile in an abnormal operation state, comparing the actual measurement vibration change diagram with a standard vibration change diagram, and if the actual measurement vibration change diagram is different from the standard vibration change diagram, recording an abnormal reason and an abnormal position corresponding to the standard vibration change diagram.

Drawings

FIG. 1 is a schematic diagram of an on-line test system for DC charging piles provided by the invention;

FIG. 2 is a schematic diagram of an on-line test method for a DC charging pile according to the present invention;

fig. 3 is a schematic structural diagram of an electronic device according to the present invention;

fig. 4 is a schematic structural diagram of a computer readable storage medium according to the present invention.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

Example 1

Referring to fig. 2, the disclosure of the present embodiment provides an online test method for a dc charging pile, where the method relies on a cloud computing server, and the cloud computing server is in remote communication connection with Q monitoring processing devices, each of the monitoring processing devices is electrically connected to an inside of the dc charging pile, the inside of the dc charging pile further includes an energy storage battery, and Q is a positive integer set greater than zero, and the method includes:

Step 1: acquiring external related information of the future date of the direct current charging pile, and analyzing the external related information of the future date based on a preset self-checking prediction model to determine the optimal test time period in the future; the external related information of the future date comprises future weather data, future vehicle flow data and future charging electricity price data;

it should be appreciated that: the monitoring processing device comprises a direct current charging pile, a direct current vehicle interface circuit simulator (a device for simulating communication and electrical interfaces between an electric vehicle and the charging pile, a device for simulating signal interaction and communication protocols in the charging process of the electric vehicle so as to verify interoperability and standard consistency of the charging pile), a power analyzer (a device for measuring parameters such as current, voltage, power factor, frequency and the like and providing functions such as power waveform, harmonic analysis, energy consumption metering and the like), an oscilloscope (a device for observing and measuring electrical signal waveform), an insulation voltage-withstanding instrument (a device for testing insulation performance of electric equipment or cables), an impact voltage-withstanding instrument, an integrated control system and other communication auxiliary equipment and the like;

Specifically, the generation process of the preset self-checking prediction model specifically includes the following steps:

it should be appreciated that: future weather data including temperature, humidity and rainfall can be acquired by various sensors in a charging service public place where the direct current charging pile is located, and also can be acquired from weather forecast issued by a weather platform, and various sensors include but are not limited to temperature sensors, humidity sensors, rainfall sensors and the like; the future vehicle flow data is the future daily vehicle flow of the road near the charging service public place where the direct-current charging pile is located, and can be obtained through connecting a traffic management platform or can be obtained through monitoring by a sensor arranged on the road; future charging electricity price data can be obtained through connecting a power supply grid information system or a power transaction platform;

In an optional implementation step, the processing of the historical external related information of the direct current charging pile includes: preprocessing the historical external related information of the direct current charging pile, wherein the preprocessing comprises, but is not limited to, data cleaning, noise removal, missing value processing or error data restoration, and the following steps are needed: the pretreatment belongs to the prior art, and any pretreatment means can be used as an application object of the implementation step;

in another optional implementation step, the processing of the historical external related information of the direct current charging pile includes:

it should be appreciated that: the method comprises the steps that historical external related information of a direct-current charging pile is pre-stored in a cloud computing server, wherein the number of historical charging vehicles of each time period of the direct-current charging pile is specifically determined according to corresponding data pre-stored in the cloud computing server;

It should be noted that: the label is used for labeling the use state of the direct current charging pile corresponding to the historical weather data of each time period; the direct current charging pile use state comprises an idle state and an occupied state; further explaining, the specific process of labeling the historical weather data of each time period based on the number of the historical charging vehicles of the direct current charging pile is as follows: extracting the number of the historical charging vehicles of the direct current charging pile in each time period based on the historical weather data of each time period, and labeling the historical weather data of the corresponding time period with an idle state if the number of the historical charging vehicles of the direct current charging pile is zero; otherwise, if the number of the historical charging vehicles of the direct current charging pile is not zero, marking the historical weather data of the corresponding time period with a label of an occupied state;

it should be understood that: with the principle of labeling the historical weather data of each time period, the specific process of labeling the historical vehicle flow data of each time period based on the number of the historical charging vehicles of the direct current charging pile is as follows: extracting the number of the historical charging vehicles of the direct current charging pile in each time period based on the historical vehicle flow data in each time period, and marking the historical vehicle flow data in the corresponding time period with an idle state if the number of the historical charging vehicles of the direct current charging pile is zero; otherwise, if the number of the historical charging vehicles of the direct current charging pile is not zero, marking the historical vehicle flow data of the corresponding time period with a label of an occupied state;

Extracting historical charging electricity price data in each time period, and labeling the historical charging electricity price data in each time period based on the number of the historical charging vehicles of the direct current charging pile to obtain a third characteristic data set;

it should be understood that: the specific principle of labeling the historical charging electricity price data in each time period is the same as the specific process of labeling the historical weather data in each time period, and details can be referred to above without redundant description;

it should be noted that: the specific process for respectively extracting the first training feature set, the second training feature set and the third training feature set based on the first feature data set, the second feature data set and the third feature data set comprises the following steps: firstly, taking a first characteristic data set as a first sample set, dividing the first sample set into a first training characteristic set and a first test characteristic set, taking a second characteristic data set as a second sample set, dividing the second sample set into a second training characteristic set and a second test characteristic set, taking a third characteristic data set as a third sample set, dividing the third sample set into a third training characteristic set and a third test characteristic set, and finally extracting the first training characteristic set, the second training characteristic set and the third training characteristic set respectively;

The first base learner, the second base learner and the third base learner can be homogeneous base learners or heterogeneous base learners, and the base learners comprise one or more of prediction models such as linear regression, decision trees, random forests or support vector regression;

constructing an integrated learner, taking the first prediction data, the second prediction data and the third prediction data as prediction sample sets, dividing the prediction sample sets into a prediction training set and a prediction test set, inputting the prediction training set into the integrated learner, training according to an integrated learning strategy to obtain an integrated learning model, testing the integrated learning model by using the prediction test set, and outputting the integrated learning model meeting preset prediction accuracy as a preset self-checking prediction model;

it should be noted that: the integrated learner is specifically a neural network model, and the integrated learning strategy is specifically a weighted average strategy;

Specifically, the analysis of the external related information of the future date based on a preset self-checking prediction model comprises the following steps:

it should be noted that: the generation process of the preset energy storage state prediction model is as follows: acquiring historical data of an energy storage battery, wherein the historical data of the energy storage battery is pre-stored in a cloud computing server, the historical data of the energy storage battery comprises, but is not limited to, historical charging time, historical energy storage battery capacity and the like, the historical data of the energy storage battery is used as a battery data sample set, the battery data sample set is divided into a 80% battery data training set and a 20% battery data testing set, a prediction model is constructed, the battery data training set is input into the prediction model for training, an initial prediction model is obtained, the battery data testing set is utilized for verifying the initial prediction model, and an initial prediction model with prediction accuracy greater than a preset prediction accuracy threshold is output to serve as a preset energy storage state prediction model;

Comparing the capacity of the energy storage battery in the target time interval with the preset energy storage capacity, and taking the corresponding target time interval as the future optimal test time period if the capacity of the energy storage battery in the target time interval is larger than or equal to the preset energy storage capacity; otherwise, if the capacity of the energy storage battery in the target time interval is smaller than the preset energy storage capacity, marking the corresponding time interval of the idle state of the second position as the target time interval, and the like, stopping until the capacity of the energy storage battery in the target time interval is larger than or equal to the preset energy storage capacity, and obtaining the future optimal test time period;

it should be understood that: the corresponding time interval of a certain idle state is at a first position, which indicates that the time interval is the largest, that is, the corresponding time interval span is the largest, further explanation is that the time of the direct current charging pile in the corresponding time interval in the idle state is the longest, but consideration is needed to be given, if the capacity of the energy storage battery in the corresponding time interval does not meet the condition, the corresponding time interval in the idle state of the second position needs to be judged, and so on until the corresponding time interval span of the certain idle state is the largest, and the capacity of the energy storage battery in the time interval meets the condition, and the corresponding time interval is taken as the future optimal test time interval;

Step 2: waking up the monitoring processing equipment based on the optimal test time period, acquiring a test time-frequency diagram of the direct current charging pile by using the monitoring processing equipment, and analyzing the test time-frequency diagram to determine the running state of the direct current charging pile; the operation state comprises an abnormal operation state and a normal operation state;

it should be noted that: when the monitoring processing equipment is awakened based on the optimal test time period, the capacity of the energy storage battery does not reach 100% in the optimal test time period, and further, according to the above, the capacity of the energy storage battery is low at the moment, and the energy storage battery is in a charging state at the moment;

it should be noted that: the method for obtaining the test time-frequency diagram of the direct current charging pile by using the monitoring and processing equipment comprises the following steps: collecting current and voltage data of the energy storage battery in a charging state in an optimal test time period; generating a test time-frequency chart according to the current-voltage data;

specifically, analyzing the test time-frequency diagram includes:

comparing waveform amplitude values at each time point by using a preset abnormal rule, if anyJudging that no abnormal waveform amplitude value exists; if- >＜/>Or there is->＜Judging that abnormal waveform amplitude values exist, and recording; wherein: />Is the minimum average waveform amplitude value,is the maximum average waveform amplitude value; />A waveform amplitude value representing an i-th point in time;

it should be noted that: the method comprises the steps of acquiring a historical standard test time-frequency diagram of a normal state direct current charging pile (namely, no abnormal direct current charging pile), extracting waveform amplitude values of each time point in the historical standard test time-frequency diagram, and dividing the waveform amplitude values of each time point in the historical standard test time-frequency diagram into a first waveform amplitude value set and a second waveform amplitude value set according to a preset amplitude interval, wherein each waveform amplitude value in the first waveform amplitude value set belongs to a high dimension, and each waveform amplitude value in the second waveform amplitude value set belongs to a low dimension; obtaining the minimum average waveform amplitude value according to the first waveform amplitude value set and the second waveform amplitude value set and through formula calculationAnd maximum average waveform amplitude value +.>The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1) >The formula of (2) is: />；/>The formula of (2) is:the method comprises the steps of carrying out a first treatment on the surface of the Wherein: />Representing the ith waveform amplitude value in the second set of waveform amplitude values,/th waveform amplitude value>Representing the ith waveform amplitude value in the first set of waveform amplitude values,/th waveform amplitude value>Is the size of the second waveform amplitude value set, i.e. the total number of elements of the second waveform amplitude value set, +.>The size of the first waveform amplitude value set is the total number of elements of the first waveform amplitude value set;

counting the total number of the abnormal waveform amplitude values, comparing the total number of the abnormal waveform amplitude values with a total amount threshold value of a preset abnormal waveform amplitude value, and judging that the running state of the direct current charging pile is a normal running state if the total number of the abnormal waveform amplitude values is smaller than the total amount threshold value of the preset abnormal waveform amplitude value; otherwise, if the total number of the abnormal waveform amplitude values is greater than or equal to the total number threshold value of the preset abnormal waveform amplitude values, judging that the running state of the direct current charging pile is an abnormal running state;

step 3: acquiring a temperature change coefficient of a DC charging pile in an abnormal operation state, and determining abnormal analysis data corresponding to the temperature change coefficient based on a preset corresponding interval relation between the temperature change coefficient and the abnormal analysis data; the abnormality analysis data comprises a plurality of abnormality reasons, abnormality positions corresponding to each abnormality reason and standard vibration change diagrams corresponding to each abnormality reason;

Specifically, obtaining a temperature change coefficient of the dc charging pile in an abnormal operation state includes:

carrying out formula calculation on the temperature data of each time point to obtain the temperature change coefficient of the DC charging pile in an abnormal operation state; the calculation formula is as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein: />Is the temperature change coefficient>For the temperature data at the j-th time point, +.>For the temperature data at the j-1 th time point, < >>The total duration of the optimal test period; it should be noted that: when j is 1, the temperature data of the j-1 time point is zero;

it should be noted that: the cloud computing server is pre-stored with a plurality of temperature change coefficient intervals, and each temperature change coefficient interval is associated with a plurality of abnormal reasons, an abnormal position corresponding to each abnormal reason and a standard vibration change chart corresponding to each abnormal reason; when the DC charging pile is in an abnormal state, the abnormal state is usually accompanied by the reflection characteristic of temperature change, so that a plurality of temperature change coefficient sections of the DC charging pile in the abnormal state are established through the prior recording or experimental analysis, then the acquired temperature change coefficient of the DC charging pile in the abnormal state is compared with each temperature change coefficient section, the attribution of the corresponding section of the temperature change coefficient is judged, namely, a plurality of corresponding abnormal reasons can be determined according to the corresponding section relation between the temperature change coefficient and abnormal analysis data, thereby being beneficial to shortening the investigation time of the DC charging pile in the abnormal state, and then the abnormal detection efficiency of the DC charging pile is beneficial to being improved through further analysis according to the standard vibration change diagram in the abnormal analysis data;

Step 4: obtaining an actual measurement vibration change diagram of the DC charging pile in an abnormal operation state, comparing the actual measurement vibration change diagram with a standard vibration change diagram, and if the actual measurement vibration change diagram is different from the standard vibration change diagram, recording an abnormal reason and an abnormal position corresponding to the standard vibration change diagram;

specifically, obtaining an actually measured vibration change diagram of the dc charging pile in an abnormal operation state includes:

carrying out formula calculation on the vibration data of each time point to obtain vibration change data of the DC charging pile in an abnormal operation state; the calculation formula is as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein: />Is->Vibration change data at time point, +.>For the vibration data at time g, +.>For vibration data at time g-1, it should be noted that: when g is 1, vibration data at the g-1 time point is zero;

constructing a two-dimensional data graph by taking a time point as a horizontal axis and vibration change data as a vertical axis, and taking the two-dimensional data graph as an actually-measured vibration change graph;

specifically, comparing the measured vibration variation map with the standard vibration variation map includes:

if the number of the difference areas in the actually measured vibration change diagram is larger than a preset difference area number threshold value, recording an abnormal reason and an abnormal position corresponding to the standard vibration change diagram;

it should be noted that: the method comprises the steps that a plurality of areas obtained after an actual measurement vibration change chart and a standard vibration change chart are divided through the same rule, wherein the size and the dividing mode of the dividing areas are identical, namely the dividing areas in the actual measurement vibration change chart are not different from the dividing areas in the standard vibration change chart in terms of the size and the dividing mode of the dividing areas; when the divided areas of the areas at the same positions in the actually measured vibration change chart and the standard vibration change chart are subjected to pixel-by-pixel comparison, if the pixel points with differences between the areas at the same positions exceed a preset ratio, judging that the areas at the same positions are different, and when a plurality of different areas exist and are larger than a preset difference area number threshold value, determining that the abnormal reason corresponding to the standard vibration change chart is the reason for causing the DC charging pile to be in an abnormal operation state, and recording the corresponding abnormal position, thereby being beneficial to determining the abnormal reason and the abnormal position of the DC charging pile in the abnormal operation state and further being beneficial to assisting in timely abnormal feedback of the DC charging pile;

Also to be described is: the standard vibration change diagram is obtained according to the analysis and treatment of the normal state direct current charging pile (i.e. the direct current charging pile without any abnormality), the specific treatment process is the same as the principle of the actual measurement vibration change diagram, and the detailed description is not repeated.

Example 2

Referring to fig. 1, the disclosure of the present embodiment provides an online test system for a dc charging pile, the system relies on a cloud computing server, the cloud computing server is in remote communication connection with Q monitoring and processing devices, each monitoring and processing device is electrically connected to an inside of the dc charging pile, the inside of the dc charging pile further includes an energy storage battery, Q is a positive integer set greater than zero, and the system includes:

the time determining module 210 is configured to obtain external related information of a future date of the dc charging pile, analyze the external related information of the future date based on a preset self-checking prediction model, and determine a future optimal test time period; the external related information of the future date comprises future weather data, future vehicle flow data and future charging electricity price data;

The test processing module 220 is configured to wake up the monitoring processing device based on the optimal test time period, acquire a test time-frequency diagram of the dc charging pile by using the monitoring processing device, and analyze the test time-frequency diagram to determine an operation state of the dc charging pile; the operation state comprises an abnormal operation state and a normal operation state;

it should be noted that: the method for obtaining the test time-frequency diagram of the direct current charging pile by using the monitoring and processing equipment comprises the following steps: collecting current and voltage data of the energy storage battery in a charging state in an optimal test time period; generating a test time-frequency chart according to the current-voltage data; the horizontal axis of the test time-frequency diagram is time, and the vertical axis is current-voltage data;

specifically, analyzing the test time-frequency diagram includes:

comparing waveform amplitude values at each time point by using a preset abnormal rule, if any Judging that no abnormal waveform amplitude value exists; if->＜/>Or there is->＜Judging that abnormal waveform amplitude values exist, and recording; wherein: />Is the minimum average waveform amplitude value,is the maximum average waveform amplitude value; />A waveform amplitude value representing an i-th point in time;

it should be noted that: the method comprises the steps of acquiring a historical standard test time-frequency diagram of a normal state direct current charging pile (namely, no abnormal direct current charging pile), extracting waveform amplitude values of each time point in the historical standard test time-frequency diagram, and dividing the waveform amplitude values of each time point in the historical standard test time-frequency diagram into a first waveform amplitude value set and a second waveform amplitude value set according to a preset amplitude interval, wherein each waveform amplitude value in the first waveform amplitude value set belongs to a high dimension, and each waveform amplitude value in the second waveform amplitude value set belongs to a low dimension; obtaining the minimum average waveform amplitude value according to the first waveform amplitude value set and the second waveform amplitude value set and through formula calculation And maximum average waveform amplitude value +.>The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>The formula of (2) is: />；/>The formula of (2) is:the method comprises the steps of carrying out a first treatment on the surface of the Wherein: />Representing the ith waveform amplitude value in the second set of waveform amplitude values,/th waveform amplitude value>Representing the ith waveform amplitude value in the first set of waveform amplitude values,/th waveform amplitude value>The size of the two waveform amplitude value sets, i.e. the total number of elements of the second waveform amplitude value set, +.>The size of the first waveform amplitude value set is the total number of elements of the first waveform amplitude value set;

the relationship determining module 230 is configured to obtain a temperature change coefficient of the dc charging pile in an abnormal operation state, and determine abnormal analysis data corresponding to the temperature change coefficient based on a corresponding interval relationship between a preset temperature change coefficient and the abnormal analysis data; the abnormality analysis data comprises a plurality of abnormality reasons, abnormality positions corresponding to each abnormality reason and standard vibration change diagrams corresponding to each abnormality reason;

The abnormality analysis module 240 is configured to obtain an actually measured vibration variation graph of the dc charging pile in an abnormal operation state, compare the actually measured vibration variation graph with a standard vibration variation graph, and record an abnormality reason and an abnormality position corresponding to the standard vibration variation graph if the actually measured vibration variation graph is different from the standard vibration variation graph;

Example 3

Referring to fig. 3, the disclosure provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor implements any one of the methods provided by the above methods when executing the computer program.

Example 4

Referring to fig. 4, the disclosure provides a computer readable storage medium, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements any one of the methods provided by the above methods when executing the computer program.

The above formulas are all formulas with dimensionality removed and numerical value calculated, the formulas are formulas with the latest real situation obtained by software simulation by collecting a large amount of data, and preset parameters, weights and threshold selection in the formulas are set by those skilled in the art according to the actual situation.

The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present invention are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center over a wired network or a wireless network. The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.

In the several embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely one, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other forms.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.

The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.

Claims

1. The utility model provides a direct current fills electric pile on-line testing system, its characterized in that, the system relies on cloud computing server, cloud computing server and Q monitoring processing equipment remote communication connection, every monitoring processing equipment all electric connection is inside direct current fills electric pile, direct current fills electric pile inside still includes energy storage battery, Q is the positive integer collection that is greater than zero, the system includes:

the analyzing the external related information of the future date based on the preset self-checking prediction model comprises the following steps:

2. The online test system of the direct current charging pile according to claim 1, wherein the generation process of the preset self-checking prediction model is specifically as follows:

3. The direct current charging pile on-line testing system according to claim 2, wherein the processing of the historical external related information of the direct current charging pile comprises:

4. The online test system of the direct current charging pile according to claim 3, wherein the generating process of the preset energy storage state prediction model is as follows: the method comprises the steps of obtaining historical data of an energy storage battery, pre-storing the historical data of the energy storage battery in a cloud computing server, wherein the historical data of the energy storage battery comprises, but is not limited to, historical charging time and historical energy storage battery capacity, taking the historical data of the energy storage battery as a battery data sample set, dividing the battery data sample set into a 80% battery data training set and a 20% battery data testing set, constructing a prediction model, inputting the battery data training set into the prediction model for training, obtaining an initial prediction model, verifying the initial prediction model by utilizing the battery data testing set, and outputting the initial prediction model with the prediction accuracy being larger than a preset prediction accuracy threshold as a preset energy storage state prediction model.

5. The direct current charging pile online test system according to claim 4, wherein analyzing the test time-frequency diagram comprises:

comparing waveform amplitude values at each time point by using a preset abnormal rule, if anyJudging that no abnormal waveform amplitude value exists; if->Or there is->Judging that abnormal waveform amplitude values exist, and recording; wherein: />Is the minimum average waveform amplitude value, +.>Is the maximum average waveform amplitude value; />A waveform amplitude value representing an i-th point in time;

6. The direct current charging pile on-line testing system according to claim 5, wherein obtaining the temperature change coefficient of the direct current charging pile in the abnormal operation state comprises:

7. The direct current charging pile on-line testing system according to claim 6, wherein obtaining the measured vibration variation graph of the direct current charging pile in the abnormal operation state comprises:

carrying out formula calculation on the vibration data of each time point to obtain vibration change data of the DC charging pile in an abnormal operation state; the calculation formula is as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein: />Is->Vibration change data at a point in time,for the vibration data at time g, +.>Vibration data for time point g-1;

8. The direct current charging pile on-line testing system according to claim 7, wherein comparing the measured vibration variation map with the standard vibration variation map comprises:

9. The direct current charging pile online test method based on the direct current charging pile online test system of any one of claims 1-8, comprising the following steps:

step 4: and acquiring an actual measurement vibration change diagram of the DC charging pile in an abnormal operation state, comparing the actual measurement vibration change diagram with a standard vibration change diagram, and recording an abnormal reason and an abnormal position corresponding to the standard vibration change diagram if the actual measurement vibration change diagram is different from the standard vibration change diagram.

10. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the dc charging stake on-line testing method of claim 9 when the computer program is executed by the processor.

11. A computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and when the computer program is executed by a processor, the direct current charging pile on-line testing method of claim 9 is realized.