CN116433410A - Method, system, electronic equipment and storage medium for identifying electric quantity metering abnormality - Google Patents
Method, system, electronic equipment and storage medium for identifying electric quantity metering abnormality Download PDFInfo
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Abstract
The invention discloses a method, a system, electronic equipment and a storage medium for identifying electric quantity metering abnormality, wherein the identification method comprises the following steps: acquiring battery charging data of a battery; acquiring charging metering data corresponding to the battery charging data recorded by an ammeter of the charging equipment; determining metering deviation data according to the battery charging data and the charging metering data; acquiring an abnormal interval range of the measurement deviation data; and determining abnormal data in the metering deviation data based on the abnormal interval range. According to the identification method, the abnormal interval range of the metering deviation data is determined based on the data analysis of the metering deviation data, and the metering deviation data in the abnormal interval range is determined to be the abnormal data, so that the automatic identification of the abnormal data in the electric quantity metering is realized, the efficiency and the accuracy of finding and positioning the electric meter generating the abnormal data are further improved, the loss caused by abnormal work of the electric meter is reduced, and the use experience of a user is improved.
Description
Technical Field
The invention relates to the technical field of intelligent management of electric quantity metering, in particular to a method, a system, electronic equipment and a storage medium for identifying abnormal electric quantity metering.
Background
As battery technology matures, rechargeable batteries are widely used in various fields. For example, in the battery charging process of an electric automobile, the current common electric quantity metering modes are: and when the battery is replaced and is normally fully charged to a preset electric quantity threshold value, acquiring a meter measurement value. However, if some electric meters are out of order due to faults, the abnormal conditions such as overlarge readings may occur, and the charge amount of the electric quantity order is too high during charging, so that user complaints are caused, and the use experience of the user is poor.
Disclosure of Invention
The invention aims to overcome the defect that an electric meter metering value is abnormal in the charging process of a rechargeable battery in the prior art, and provides an identification method, an identification system, electronic equipment and a storage medium for the electric meter metering abnormality.
The invention solves the technical problems by the following technical scheme:
the invention provides a method for identifying electric quantity metering abnormality, which comprises the following steps:
acquiring battery charging data of a battery;
Acquiring charging metering data corresponding to the battery charging data recorded by an ammeter of the charging equipment;
determining metering deviation data according to the battery charging data and the charging metering data;
acquiring an abnormal interval range of the measurement deviation data;
and determining abnormal data in the metering deviation data based on the abnormal interval range.
According to the method, the abnormal interval range of the metering deviation data is determined based on the data analysis of the metering deviation data, and the metering deviation data in the abnormal interval range is determined to be the abnormal data, so that the automatic identification of the abnormal data in the electric quantity metering is realized, the loss caused by abnormal work of the electric meter is reduced, and the use experience of a user is improved.
Preferably, the battery charging data includes a battery single charge power, a battery single charge start power and a battery single charge end power; the charging metering data comprises electric meter single-charge recording electric quantity corresponding to the battery single-charge electric quantity;
the step of determining metering deviation data from the battery charge data and the charge metering data comprises:
determining the unit charging electric quantity of the battery according to the single charging electric quantity of the battery, the initial electric quantity of the single charging of the battery and the final electric quantity of the single charging of the battery;
Determining the unit charging record electric quantity of the electric meter according to the single charging record electric quantity of the electric meter, the single charging start electric quantity of the battery and the single charging end electric quantity of the battery;
and determining the deviation of the unit charging electric quantity of the battery and the unit charging recorded electric quantity of the ammeter as the metering deviation data.
According to the scheme, the metering deviation data are determined based on the deviation of the unit charging electric quantity of the battery and the unit charging recording electric quantity of the ammeter, the processing standard of the electric quantity data is unified, and the efficiency of data processing and the accuracy of processing results are improved.
Preferably, the battery charging data further comprises at least one of a battery replacement station identifier, a charging equipment identifier, a bin identifier, a battery identifier, a single battery charging start time and a single battery charging end time; and/or the number of the groups of groups,
the charge metering data further includes at least one of an ammeter identification, a bin identification, a battery identification, a single charge start time and a single charge end time.
The scheme identifies the corresponding relation between the battery charging data and the charging metering data based on the multidimensional data such as the power station identifier, the charging equipment identifier, the bin identifier, the battery identifier and the like, and improves the accuracy of data identification and matching.
Preferably, the step of determining metering deviation data from the battery charging data and the charging metering data comprises:
screening out the battery charging data and the charging metering data of which the initial electric quantity of the battery and the final electric quantity of the battery meet the preset electric quantity conditions;
and determining metering deviation data according to the screened battery charging data and the screened charging metering data.
According to the scheme, the metering deviation data is determined based on the screened battery charging data and the charging metering data which meet the preset electric quantity condition, namely, the battery charging record of the charging process under the same condition is used as effective data, the pretreatment standards of the battery charging data and the charging metering data are unified, and the accuracy of the subsequent metering deviation data processing is improved.
Preferably, the step of acquiring the abnormal section range of the measurement deviation data includes:
and determining an abnormal interval range of the metering deviation data according to the normal distribution parameters of the metering deviation data.
According to the scheme, based on the characteristic that the measurement deviation data accords with normal distribution, the abnormal interval range of the measurement deviation data is determined by utilizing the probability distribution principle of normal distribution, and the accuracy of determining the abnormal interval range is improved.
Preferably, before the step of determining the abnormal section range of the metering deviation data according to the normal distribution parameter of the metering deviation data, the identifying method further includes:
verifying whether the metering deviation data accords with normal distribution;
when the measurement deviation data does not conform to the normal distribution, the measurement deviation data is subjected to normalization transformation so that the measurement deviation data conforms to the normal distribution.
According to the scheme, based on the normal distribution verification result of the measurement deviation data, the measurement deviation data which does not accord with the normal distribution is subjected to normal transformation so that the measurement deviation data accord with the normal distribution, and the subsequent data analysis on the measurement deviation data based on the characteristics of the normal distribution is more accurate.
Preferably, the normal distribution parameters include a mean value and a standard deviation;
the step of determining the abnormal interval range of the metering deviation data according to the normal distribution parameters of the metering deviation data comprises the following steps:
determining an abnormal boundary value of the metering deviation data according to the mean value and the standard deviation of the metering deviation data;
and determining the abnormal interval range based on the abnormal boundary value.
According to the scheme, the characteristic that the metering deviation data accords with normal distribution and the probability distribution principle of the normal distribution are fully utilized, the abnormal boundary value of the metering deviation data is determined based on the mean value and the standard deviation of the metering deviation data, and the accuracy of determining the abnormal boundary value is improved.
Preferably, the step of determining the abnormal data in the metering deviation data based on the abnormal section range includes:
normalizing the measurement deviation data to obtain a unit measurement deviation value;
and determining the unit measurement deviation value within the abnormal interval range as abnormal data.
According to the scheme, based on the unit measurement deviation obtained by carrying out normalization processing on the measurement deviation data, whether the unit measurement deviation is located in an abnormal interval range or not is judged, whether the measurement deviation data is abnormal data or not is further determined, the processing efficiency of the measurement deviation data is improved, and further the abnormal data can be found in time to reduce loss.
Preferably, the identification method further comprises:
and determining an abnormal ammeter according to the abnormal data.
The method and the device are used for determining the ammeter related to the abnormal data based on the identified abnormal data, so that the abnormal ammeter is automatically and accurately identified, overhauling or replacing the abnormal ammeter is facilitated, and loss caused by the abnormal condition of the ammeter is reduced.
Preferably, the step of determining the abnormal electricity meter according to the abnormal data includes:
acquiring the total amount of charging metering data recorded by the ammeter;
Calculating the proportion of the quantity of the abnormal data to the total quantity of the charging metering data;
and when the proportion is larger than a preset abnormal threshold value, determining that the ammeter is an abnormal ammeter.
According to the scheme, the proportion of the quantity of the abnormal data to the total quantity of the charging metering data recorded by the ammeter is determined based on the identified abnormal data, and when the proportion is larger than the preset abnormal threshold value, the ammeter is determined to be the abnormal ammeter, so that the abnormal ammeter is automatically and accurately identified, and loss caused by the abnormality of the ammeter is reduced.
The invention also provides an identification system of the electric quantity metering abnormality, which comprises:
the battery charging data acquisition module is used for acquiring battery charging data of the battery;
the charging metering data acquisition module is used for acquiring charging metering data corresponding to the battery charging data recorded by an ammeter of the charging equipment;
the metering deviation data determining module is used for determining metering deviation data according to the battery charging data and the charging metering data;
the abnormal interval range acquisition module is used for acquiring the abnormal interval range of the measurement deviation data;
and the abnormal data determining module is used for determining abnormal data in the metering deviation data based on the abnormal interval range.
According to the method, the abnormal interval range of the metering deviation data is determined based on the data analysis of the metering deviation data, and the metering deviation data in the abnormal interval range is determined to be the abnormal data, so that the automatic identification of the abnormal data in the electric quantity metering is realized, the loss caused by abnormal work of the electric meter is reduced, and the use experience of a user is improved.
Preferably, the battery charging data includes a battery single charge power, a battery single charge start power and a battery single charge end power; the charging metering data comprises electric meter single-charge recording electric quantity corresponding to the battery single-charge electric quantity;
the metering deviation data determining module is further used for determining the unit charging electric quantity of the battery according to the single charging electric quantity of the battery, the initial electric quantity of the single charging of the battery and the ending electric quantity of the single charging of the battery;
the metering deviation data determining module is further used for determining the unit charging record electric quantity of the electric meter according to the single charging record electric quantity of the electric meter, the single charging start electric quantity of the battery and the single charging end electric quantity of the battery;
the metering deviation data determining module is further used for determining deviation between the unit charging electric quantity of the battery and the unit charging record electric quantity of the ammeter as the metering deviation data.
According to the scheme, the metering deviation data are determined based on the deviation of the unit charging electric quantity of the battery and the unit charging recording electric quantity of the ammeter, the processing standard of the electric quantity data is unified, and the efficiency of data processing and the accuracy of processing results are improved.
Preferably, the battery charging data further comprises at least one of a battery replacement station identifier, a charging equipment identifier, a bin identifier, a battery identifier, a single battery charging start time and a single battery charging end time; and/or the number of the groups of groups,
the charge metering data further includes at least one of an ammeter identification, a bin identification, a battery identification, a single charge start time and a single charge end time.
The scheme identifies the corresponding relation between the battery charging data and the charging metering data based on the multidimensional data such as the power station identifier, the charging equipment identifier, the bin identifier, the battery identifier and the like, and improves the accuracy of data identification and matching.
Preferably, the measurement deviation data determining module is specifically configured to screen the battery charging data and the charging measurement data, where the battery charging start power and the battery charging end power meet a preset power condition, and determine measurement deviation data according to the screened battery charging data and the charging measurement data.
According to the scheme, the metering deviation data is determined based on the screened battery charging data and the charging metering data which meet the preset electric quantity condition, namely, the battery charging record of the charging process under the same condition is used as effective data, the pretreatment standards of the battery charging data and the charging metering data are unified, and the accuracy of the subsequent metering deviation data processing is improved.
Preferably, the abnormal interval range obtaining module is specifically configured to determine an abnormal interval range of the measurement deviation data according to a normal distribution parameter of the measurement deviation data.
According to the scheme, based on the characteristic that the measurement deviation data accords with normal distribution, the abnormal interval range of the measurement deviation data is determined by utilizing the probability distribution principle of normal distribution, and the accuracy of determining the abnormal interval range is improved.
Preferably, the identification system further comprises:
the normal distribution verification module is used for verifying whether the metering deviation data accords with normal distribution;
and the normal distribution transformation module is used for carrying out normal transformation on the metering deviation data so as to enable the metering deviation data to accord with normal distribution when the metering deviation data does not accord with normal distribution.
According to the scheme, based on the normal distribution verification result of the measurement deviation data, the measurement deviation data which does not accord with the normal distribution is subjected to normal transformation so that the measurement deviation data accord with the normal distribution, and the subsequent data analysis on the measurement deviation data based on the characteristics of the normal distribution is more accurate.
Preferably, the normal distribution parameters include a mean value and a standard deviation;
the abnormal interval range acquisition module is specifically configured to determine an abnormal boundary value of the measurement deviation data according to a mean value and a standard deviation of the measurement deviation data, and determine the abnormal interval range based on the abnormal boundary value.
According to the scheme, the characteristic that the metering deviation data accords with normal distribution and the probability distribution principle of the normal distribution are fully utilized, the abnormal boundary value of the metering deviation data is determined based on the mean value and the standard deviation of the metering deviation data, and the accuracy of determining the abnormal boundary value is improved.
Preferably, the abnormal data determining module is specifically configured to normalize the measurement deviation data to obtain a unit measurement deviation value, and determine the unit measurement deviation value located in the abnormal interval range as abnormal data.
According to the scheme, based on the unit measurement deviation obtained by carrying out normalization processing on the measurement deviation data, whether the unit measurement deviation is located in an abnormal interval range or not is judged, whether the measurement deviation data is abnormal data or not is further determined, the processing efficiency of the measurement deviation data is improved, and further the abnormal data can be found in time to reduce loss.
Preferably, the identification system further comprises an abnormal ammeter determining module, which is used for determining an abnormal ammeter according to the abnormal data.
Preferably, the abnormal electricity meter determining module is specifically configured to obtain the total amount of charging metering data recorded by the electricity meter, calculate a proportion of the number of abnormal data to the total amount of charging metering data, and determine that the electricity meter is an abnormal electricity meter when the proportion is greater than a preset abnormal threshold.
The method and the device are used for determining the ammeter related to the abnormal data based on the identified abnormal data, so that the abnormal ammeter is automatically and accurately identified, overhauling or replacing the abnormal ammeter is facilitated, and loss caused by the abnormal condition of the ammeter is reduced.
The invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the identification method of the electric quantity metering abnormality when executing the computer program.
According to the method for identifying the electric quantity metering abnormality by the electronic equipment, the data analysis of the metering deviation data is used for determining the abnormal interval range of the metering deviation data, and the metering deviation data in the abnormal interval range is used as the abnormal data, so that the automatic identification of the abnormal data in the electric quantity metering is realized, and the loss caused by the abnormal work of the electric meter is reduced.
The present invention also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method of identifying a metering abnormality as described above.
According to the scheme, the computer program stored in the computer readable storage medium is called and executed when needed, the abnormal interval range of the metering deviation data is determined through data analysis of the metering deviation data, the metering deviation data in the abnormal interval range is determined to be the abnormal data, automatic identification of the abnormal data in electric quantity metering is achieved, and loss caused by abnormal work of the electric meter is reduced.
The invention has the positive progress effects that:
according to the identification method for the abnormal electricity metering, provided by the invention, the abnormal interval range of the metering deviation data is determined based on the data analysis of the metering deviation data by acquiring the battery charging data and the corresponding charging metering data in the battery charging process and determining the metering deviation data according to the battery charging data and the corresponding charging metering data, and the metering deviation data in the abnormal interval range is determined as the abnormal data, so that the automatic identification of the abnormal data in the electricity metering is realized, and the electric meter is associated with the abnormal data, so that the efficiency and the accuracy of finding and positioning the electric meter generating the abnormal data are further improved, the loss caused by the abnormal work of the electric meter is reduced, and the use experience of a user is improved.
Drawings
Fig. 1 is a flowchart of a method for identifying an abnormal electricity metering in embodiment 1 of the present invention.
Fig. 2 is a flowchart of a method for identifying an abnormal electricity metering in embodiment 2 of the present invention.
Fig. 3 is a schematic diagram of normal distribution of embodiment 2 of the present invention.
FIG. 4 is a graph showing the results of Q-Q plot analysis of the metrology bias data of example 2 of the present invention.
FIG. 5 is a graph showing the results of the Q-Q diagram analysis of the normalized conversion of the measurement deviation data in example 2 of the present invention.
Fig. 6 is a line graph of the metering deviation data of example 2 of the present invention under the 3σ principle.
Fig. 7 is a schematic block diagram of a system for identifying abnormal electricity metering in embodiment 3 of the present invention.
Fig. 8 is a schematic block diagram of a system for identifying abnormal electricity metering in embodiment 4 of the present invention.
Fig. 9 is a schematic hardware structure of an electronic device according to embodiment 5 of the present invention.
Detailed Description
The invention is further illustrated by means of the following examples, which are not intended to limit the scope of the invention.
Example 1
Please refer to fig. 1, which is a flowchart illustrating a method for identifying an abnormal electricity meter in the present embodiment. Specifically, as shown in fig. 1, the identification method includes:
S101, battery charging data of a battery are obtained. Specifically, the battery charging data can be obtained through a BMS (Battery Management System ) system, the BMS system collects battery data such as electric quantity, voltage, current, temperature and time of a battery in real time in the battery charging and discharging process, and part or all of the data recorded by the BMS system are used as battery charging data of each battery charging.
S102, acquiring charging metering data corresponding to the battery charging data, which are recorded by an ammeter of the charging equipment. The charging device may include a charging peg in a charging station, a charger in a battery-exchange station, etc. for charging the battery of the electric vehicle. The ammeter is used for metering the electric quantity of the charging equipment charged each time. In each process of charging the battery, the charging device has a one-to-one correspondence with the battery, so that the charging metering data of the ammeter and the battery charging data recorded by the battery BMS have a correspondence.
And S103, determining metering deviation data according to the battery charging data and the charging metering data. Specifically, in general, battery charging data recorded by the BMS and charging metering data recorded by the ammeter are not identical due to different recording modes and sensitivities of the devices, and metering deviation data can be determined according to the battery charging data and the charging metering data; under the normal working state of the ammeter, the metering deviation data are in a reasonable interval, and under the abnormal state of the ammeter, the metering deviation data can obviously exceed the reasonable interval.
S104, acquiring an abnormal interval range of the measurement deviation data. In an alternative embodiment, the normal interval range or the abnormal interval range of the metering deviation data may be determined based on a data analysis of the metering deviation data.
S105, determining abnormal data in the metering deviation data based on the abnormal interval range. In an alternative embodiment, after the abnormal data in the metering deviation data is determined, the abnormal charging metering data can be further determined based on the corresponding relation, and the automatic discovery and positioning of the electric meter with abnormal working state can be realized by carrying out data analysis on the abnormal charging metering data.
According to the method for identifying the abnormal electricity metering, provided by the embodiment, the battery charging data and the corresponding charging metering data in the battery charging process are obtained, the metering deviation data are determined according to the battery charging data and the corresponding charging metering data, the abnormal interval range of the metering deviation data is determined based on the data analysis of the metering deviation data, the metering deviation data in the abnormal interval range are determined to be the abnormal data, automatic identification of the abnormal data in the electricity metering is realized, the electric meter is associated with the abnormal data, the efficiency and the accuracy of finding and positioning the electric meter generating the abnormal data are further improved, the loss caused by abnormal work of the electric meter is reduced, and the use experience of a user is improved.
Example 2
As shown in fig. 2, the method for identifying an abnormal electric quantity measurement of the present embodiment is a further improvement of embodiment 1, specifically:
in an alternative embodiment, the battery charge data may include a battery single charge level, a battery single charge start level, and a battery single charge end level; the charge metering data includes an ammeter single charge record power corresponding to the battery single charge power.
Specifically, the BMS system monitors the remaining battery power by estimating an SOC (State of Charge), and the battery single Charge start SOC and the battery single Charge end SOC and the battery single Charge power can be obtained by the BMS system.
In an alternative embodiment, step S103 further includes:
s1031, screening out battery charging data and charging metering data of which the initial electric quantity of the battery in single charging and the final electric quantity of the battery in single charging meet the preset electric quantity conditions, namely taking a battery charging record of the charging process under the same conditions as effective data to improve the accuracy of subsequent calculation. The preset power condition may be determined according to an actual situation, for example, battery charging data with a single-charge start SOC of 35 or less and a single-charge end SOC of 95 or more may be selected, and the data may be defined as data with a complete charging cycle. And then the metering deviation data can be determined according to the battery charging data and the charging metering data which are screened out to have complete charging periods, so that the accuracy of the metering deviation data is ensured.
In another alternative embodiment, step S103 further includes:
s1032, determining the unit charging electric quantity of the battery according to the single charging electric quantity of the battery, the initial electric quantity of the single charging of the battery and the ending electric quantity of the single charging of the battery. Specifically, the battery unit SOC charge amount=battery single charge amount/(battery single charge end soc—battery single charge start SOC).
S1033, determining the unit charge recording electric quantity of the electric meter according to the single charge recording electric quantity of the electric meter, the single charge starting electric quantity of the battery and the single charge ending electric quantity of the battery; specifically, the electric meter unit SOC charge recording electric quantity=electric meter single charge recording electric quantity/(battery single charge end soc—battery single charge start SOC) corresponds to the battery charge data.
And S1034, determining deviation of the unit charging electric quantity of the battery and the unit charging recorded electric quantity of the ammeter as metering deviation data. Specifically, the metering deviation data=the battery unit SOC charge amount-the electricity meter unit SOC charge record amount.
And the metering deviation data is determined based on the deviation of the unit charging electric quantity of the battery and the unit charging recording electric quantity of the ammeter, so that the processing standard of the electric quantity data is unified, and the efficiency of data processing and the accuracy of a processing result are improved.
In an alternative embodiment, the battery charging data further comprises at least one of a battery station identifier, a charging device identifier, a bin identifier, a battery identifier, a single battery charging start time, and a single battery charging end time; the charge metering data further includes at least one of an electricity meter identification, a bin identification, a battery identification, a single charge start time, and a single charge end time. By corresponding the data such as the charging equipment identifier, the bin identifier and the like in the battery charging data to the data such as the bin identifier, the battery identifier and the like in the charging metering data, the battery charging data and the charging metering data in one charging process can be associated.
In another alternative embodiment, the power conversion system may also generate a power conversion order for each charging of the battery, where the power conversion order may include historical data such as order identification, battery identification, charge level, battery station identification, battery down time, and the like.
In an alternative embodiment, step S104 may include: and determining the abnormal interval range of the metering deviation data according to the normal distribution parameters of the metering deviation data. As shown in FIG. 3, the normal distribution is a distribution with two parameters μ and σ 2 Probability distribution of continuous random variable (denoted as N (μ, σ) 2 ) Where μ is the mean value, σ, of a random variable that follows a normal distribution 2 Is the variance of the random variable. Probability law of random variables following normal distribution: the probability of taking a value adjacent to μ is large, while the probability of taking a value further away from μ is smaller; the smaller the σ, the more concentrated the distribution around μ, and the larger the σ, the more dispersed the distribution. Based on the data analysis of the metering deviation data, the metering deviation data of the complete charging cycle should conform to normal distribution under the condition that abnormal conditions such as charging interruption and the like do not occur.
In an alternative embodiment, before step S104, the identifying method further includes:
s201, verifying whether the metering deviation data accords with normal distribution. Specifically, as shown in fig. 4, it is possible to verify whether the measurement deviation data conforms to the normal distribution analysis using a Q-Q diagram (a probability distribution comparison diagram in statistics). Whether the data conforms to the normal distribution is judged by comparing whether the quantiles of the metering deviation data and the normal distribution are equal, and fig. 4 intuitively shows the difference between the metering deviation data and the normal distribution. Wherein the straight line represents normal distribution, the curved line represents measurement deviation data, and the closer the curved line is to the straight line, the more consistent the expected distribution is.
S202, when the metering deviation data does not accord with the normal distribution, carrying out normalization transformation on the metering deviation data so as to make the metering deviation data accord with the normal distribution. Specifically, because the charging equipment can have an abnormal interruption condition in the charging process, secondary charging is required to be started again, so that a plurality of charging records can be generated during primary charging of the battery, and the record deletion of the complete charging period is completed, so that the metering deviation data obtained through analysis is not full-quantity data, and the metering deviation data can be slightly different from the complete normal distribution, the metering deviation data needs to be converted to be in line with the normal distribution, and the influence of error data of the abnormal condition on a judgment result is reduced.
The normalization transformation can be specifically a Box-Cox transformation, and the Box-Cox transformation is a data normalization transformation method commonly used in statistical modeling and is used for the condition that continuous response variables do not meet normal distribution. The correlation of the non-observable errors and the predicted variables can be reduced to some extent after transformation. The transformation is mainly characterized in that a parameter is introduced, the parameter is estimated through the data, the data transformation form to be adopted is determined, the transformation can obviously improve the normalization, symmetry and variance equality of the data, and the transformation is effective for a plurality of actual data. The Box-Cox conversion formula is as follows:
Wherein y is i As the measurement deviation data, λ is a parameter obtained based on analysis of the measurement deviation data.
As can be seen from a comparison of fig. 4 and 5, the data points of the curved line of fig. 5 are closer to a straight line than in fig. 4, and the measured deviation data are transformed to conform more to a normal distribution.
In an alternative embodiment, the normal distribution parameters include a mean and a standard deviation; at this time, step S104 may include:
s1041, determining an abnormal boundary value of the metering deviation data according to the mean value and the standard deviation of the metering deviation data; in particular, the abnormal boundary value of the metrology deviation data may be determined using the 3σ principle and based on the mean and standard deviation of the metrology deviation data. Outliers refer to individual values in a set of data that deviate significantly from the rest of the data. The 3σ principle is often used to identify outliers in data that conform to normal distributions. The 3 sigma principle is: calculating the mean value mu and standard deviation sigma of a group of data, assuming that the group of data only contains random errors, determining a section (mu-n sigma, mu+n sigma) according to a certain probability, and considering the errors which are not in the section, not the random errors but the significant errors. Based on the 3σ principle, the probability of the numerical distribution in (μ - σ, μ+σ) is 0.6827; the probability of the numerical distribution in (μ -2σ, μ+2σ) is 0.9545; the probability of the numerical distribution in (μ -3σ, μ+3σ) is 0.9973; it is considered that the data are almost entirely concentrated in the (μ -3σ, μ+3σ) interval, and the probability of exceeding the interval range is less than 0.3%. Therefore, the present embodiment can determine the measurement deviation data out of the interval range as an abnormal value, and based on the analysis of the history data, the present embodiment balances the measurement deviation data that are abnormal in the principle of (μ -2σ, μ+2σ), and the definition that the [ a, b ] range is exceeded as an abnormal value, that is, the abnormal boundary value is set to a=μ -2σ, b=μ+2σ. It will be appreciated that in other alternative embodiments, the anomaly boundary value may be set to, for example, μ - σ, μ+σ, or other values, depending on the actual requirements.
S1042, determining an abnormal interval range based on the abnormal boundary value.
In an alternative embodiment, step S105 includes:
s1051, carrying out normalization processing on measurement deviation data to obtain a unit measurement deviation value; specifically, the metering deviation data is normalized, the metering deviation data is changed into decimal between (0 and 1), and the metering deviation data is mapped to be processed within the range of 0 to 1, so that the data processing is more convenient and faster.
S1052, the unit measurement deviation value located within the abnormal section range is determined as the abnormal data. Specifically, as shown in fig. 6, the thick line is formed of abnormal data points, and data points outside the range of the (μ -2σ, μ+2σ) interval are identified as abnormal data points (data points have undergone Box-Cox inverse conversion).
In an alternative embodiment, the identification method further comprises: and determining the abnormal ammeter according to the abnormal data.
Specifically, the step of determining the abnormal electricity meter according to the abnormal data includes:
s203, acquiring the total amount of charging metering data recorded by the ammeter.
S204, calculating the proportion of the quantity of the abnormal data to the total quantity of the charging metering data.
S205, when the proportion is larger than a preset abnormal threshold value, determining that the ammeter is an abnormal ammeter.
According to the method for identifying the electric quantity metering abnormality, the deviation of the unit charging electric quantity of the battery and the recorded electric quantity of the electric meter unit charging is determined to be metering deviation data, whether the metering deviation data accords with normal distribution is verified, when the metering deviation data does not accord with the normal distribution, normal transformation is carried out on the metering deviation data, the abnormal boundary value of the metering deviation data is determined according to the mean value and the standard deviation of the metering deviation data, the metering deviation data in the abnormal interval range is determined to be abnormal data, the abnormal electric meter is determined according to the abnormal data, the automatic identification of the abnormal data in the electric quantity metering is realized through accurately and efficiently processing the battery data and the electric meter data, the electric meter is related through the abnormal data, the efficiency and the accuracy of finding and positioning the electric meter generating the abnormal data are further improved, the abnormal electric meter is convenient to overhaul or replace, the loss caused by abnormal work of the electric meter is reduced, and the use experience of a user is improved.
Example 3
Fig. 7 is a schematic block diagram of a system for identifying abnormal electricity metering in the present embodiment.
Specifically, as shown in fig. 7, the identification system includes:
And the battery charging data acquisition module 1 is used for acquiring battery charging data of the battery. Specifically, battery charging data can be obtained through the BMS system, and the BMS system collects battery data such as electric quantity, voltage, current, temperature and time of a battery in real time in the battery charging and discharging process, and takes part or all of the data recorded by the BMS system as battery charging data of each charging of the battery.
And the charging metering data acquisition module 2 is used for acquiring charging metering data corresponding to the battery charging data recorded by an ammeter of the charging equipment. The charging device may include a charging peg in various charging stations, a charger in a battery-exchange station, and the like for charging the battery of the electric vehicle. The ammeter is used for metering the electric quantity of the charging equipment charged each time. In each process of charging the battery, the charging device has a one-to-one correspondence with the battery, so that the charging metering data of the ammeter and the battery charging data recorded by the battery BMS have a correspondence.
And the metering deviation data determining module 3 is used for determining metering deviation data according to the battery charging data and the charging metering data. Specifically, in general, battery charging data recorded by the BMS and charging metering data recorded by the ammeter are not identical due to different recording modes and sensitivities of the devices, and metering deviation data can be determined according to the battery charging data and the charging metering data; under the normal working state of the ammeter, the metering deviation data are in a reasonable interval, and under the abnormal state of the ammeter, the metering deviation data can obviously exceed the reasonable interval.
And the abnormal interval range acquisition module 4 is used for acquiring the abnormal interval range of the measurement deviation data. In an alternative embodiment, the normal interval range or the abnormal interval range of the metering deviation data may be determined based on a data analysis of the metering deviation data.
An abnormal data determination module 5 for determining abnormal data in the measurement deviation data based on the abnormal section range. In an alternative embodiment, after the abnormal data in the metering deviation data is determined, the abnormal charging metering data can be further determined based on the corresponding relation, and the automatic discovery and positioning of the electric meter with abnormal working state can be realized by carrying out data analysis on the abnormal charging metering data.
According to the identification system for the abnormal electricity metering, provided by the embodiment, the battery charging data and the corresponding charging metering data in the battery charging process are obtained, metering deviation data are determined according to the battery charging data and the corresponding charging metering data, the abnormal interval range of the metering deviation data is determined based on the data analysis of the metering deviation data, the metering deviation data in the abnormal interval range are determined to be the abnormal data, automatic identification of the abnormal data in the electricity metering is realized, and the electric meter is associated with the abnormal data, so that the efficiency and the accuracy of finding and positioning the electric meter generating the abnormal data are further improved, the loss caused by abnormal work of the electric meter is reduced, and the use experience of a user is improved.
Example 4
As shown in fig. 8, the identification system of the electric quantity measurement abnormality of the present embodiment is a further improvement of embodiment 3, specifically:
in an alternative embodiment, the battery charge data may include a battery single charge level, a battery single charge start level, and a battery single charge end level; the charge metering data includes an ammeter single charge record power corresponding to the battery single charge power.
Specifically, the BMS system monitors the remaining battery power by estimating the SOC, and the battery single charge start SOC and the battery single charge end SOC and the battery single charge power can be obtained by the BMS system.
In an alternative embodiment, the measurement deviation data determining module 3 is further configured to screen out battery charging data and charging measurement data of which the initial charge capacity and the final charge capacity of the battery meet the preset charge capacity conditions, i.e. the battery charging record of the charging process under the same conditions is used as effective data, so as to improve the accuracy of subsequent calculation. The preset power condition may be determined according to an actual situation, for example, battery charging data with a single-charge start SOC of 35 or less and a single-charge end SOC of 95 or more may be selected, and the data may be defined as data with a complete charging cycle. And then the metering deviation data can be determined according to the battery charging data and the charging metering data which are screened out to have complete charging periods, so that the accuracy of the metering deviation data is ensured.
In another alternative embodiment, the metering deviation data determining module 3 is further configured to determine the battery unit charge level according to the battery single charge level, the battery single charge start level, and the battery single charge end level. Specifically, the battery unit SOC charge amount=battery single charge amount/(battery single charge end soc—battery single charge start SOC).
The metering deviation data determining module 3 is further configured to determine a unit charge recording electric quantity of the electric meter according to the electric quantity recorded by the electric meter for single charge, the initial electric quantity of the battery for single charge and the final electric quantity of the battery for single charge; specifically, the electric meter unit SOC charge recording electric quantity=electric meter single charge recording electric quantity/(battery single charge end soc—battery single charge start SOC) corresponds to the battery charge data.
The measurement deviation data determining module 3 is further configured to determine, as measurement deviation data, a deviation between the unit charge amount of the battery and the unit charge recorded amount of the electricity meter. Specifically, the metering deviation data=the battery unit SOC charge amount-the electricity meter unit SOC charge record amount.
And the metering deviation data is determined based on the deviation of the unit charging electric quantity of the battery and the unit charging recording electric quantity of the ammeter, so that the processing standard of the electric quantity data is unified, and the efficiency of data processing and the accuracy of a processing result are improved.
In an alternative embodiment, the battery charging data further comprises at least one of a battery station identifier, a charging device identifier, a bin identifier, a battery identifier, a single battery charging start time, and a single battery charging end time; the charge metering data further includes at least one of an electricity meter identification, a bin identification, a battery identification, a single charge start time, and a single charge end time. By corresponding the data such as the charging equipment identifier, the bin identifier and the like in the battery charging data to the data such as the bin identifier, the battery identifier and the like in the charging metering data, the battery charging data and the charging metering data in one charging process can be associated.
In another alternative embodiment, the power conversion system may also generate a power conversion order for each charging of the battery, where the power conversion order may include historical data such as order identification, battery identification, charge level, battery station identification, battery down time, and the like.
In an alternative embodiment, the abnormal interval range obtaining module 4 is specifically configured to determine an abnormal interval range of the measurement deviation data according to a normal distribution parameter of the measurement deviation data. As shown in FIG. 3, the normal distribution is a distribution with two parameters μ and σ 2 Probability distribution of continuous random variable (denoted as N (μ, σ) 2 ) Where μ is the mean value, σ, of a random variable that follows a normal distribution 2 Is the variance of the random variable. Probability law of random variables following normal distribution: the probability of taking a value adjacent to μ is large, while the probability of taking a value further away from μ is smaller; the smaller the σ, the more concentrated the distribution around μ, and the larger the σ, the more dispersed the distribution. Based on the data analysis of the metering deviation data, the metering deviation data of the complete charging cycle should conform to normal distribution under the condition that abnormal conditions such as charging interruption and the like do not occur.
In an alternative embodiment, the identification system further comprises:
the normal distribution verification module 6 is used for verifying whether the metering deviation data accords with normal distribution. Specifically, as shown in fig. 4, the Q-Q diagram can be used to verify whether the metering deviation data complies with the normal distribution analysis. Whether the data conforms to the normal distribution is judged by comparing whether the quantiles of the metering deviation data and the normal distribution are equal, and fig. 4 intuitively shows the difference between the metering deviation data and the normal distribution. Wherein the straight line represents normal distribution, the curved line represents measurement deviation data, and the closer the curved line is to the straight line, the more consistent the expected distribution is.
The normal distribution conversion module 7 is configured to perform a normal conversion on the measurement deviation data so that the measurement deviation data conforms to the normal distribution when the measurement deviation data does not conform to the normal distribution. Specifically, because the charging equipment can have an abnormal interruption condition in the charging process, secondary charging is required to be started again, so that a plurality of charging records can be generated during primary charging of the battery, and the record deletion of the complete charging period is completed, so that the metering deviation data obtained through analysis is not full-quantity data, and the metering deviation data can be slightly different from the complete normal distribution, the metering deviation data needs to be converted to be in line with the normal distribution, and the influence of error data of the abnormal condition on a judgment result is reduced.
The normalization transformation can be specifically a Box-Cox transformation, and the Box-Cox transformation is a data normalization transformation method commonly used in statistical modeling and is used for the condition that continuous response variables do not meet normal distribution. The correlation of the non-observable errors and the predicted variables can be reduced to some extent after transformation. The transformation is mainly characterized in that a parameter is introduced, the parameter is estimated through the data, the data transformation form to be adopted is determined, the transformation can obviously improve the normalization, symmetry and variance equality of the data, and the transformation is effective for a plurality of actual data. The Box-Cox conversion formula is as follows:
Wherein y is i As the measurement deviation data, λ is a parameter obtained based on analysis of the measurement deviation data.
As can be seen from a comparison of fig. 4 and 5, the data points of the curved line of fig. 5 are closer to a straight line than in fig. 4, and the measured deviation data are transformed to conform more to a normal distribution.
In an alternative embodiment, the normal distribution parameters include a mean and a standard deviation; at this time, the abnormal interval range obtaining module 4 is further configured to determine an abnormal boundary value of the measurement deviation data according to the mean value and the standard deviation of the measurement deviation data; in particular, the abnormal boundary value of the metrology deviation data may be determined using the 3σ principle and based on the mean and standard deviation of the metrology deviation data. Outliers refer to individual values in a set of data that deviate significantly from the rest of the data. The 3σ principle is often used to identify outliers in data that conform to normal distributions. The 3 sigma principle is: calculating the mean value mu and standard deviation sigma of a group of data, assuming that the group of data only contains random errors, determining a section (mu-n sigma, mu+n sigma) according to a certain probability, and considering the errors which are not in the section, not the random errors but the significant errors. Based on the 3σ principle, the probability of the numerical distribution in (μ - σ, μ+σ) is 0.6827; the probability of the numerical distribution in (μ -2σ, μ+2σ) is 0.9545; the probability of the numerical distribution in (μ -3σ, μ+3σ) is 0.9973; it is considered that the data are almost entirely concentrated in the (μ -3σ, μ+3σ) interval, and the probability of exceeding the interval range is less than 0.3%. Therefore, the present embodiment can determine the measurement deviation data out of the interval range as an abnormal value, and based on the analysis of the history data, the present embodiment balances the measurement deviation data that are abnormal in the principle of (μ -2σ, μ+2σ), and the definition that the [ a, b ] range is exceeded as an abnormal value, that is, the abnormal boundary value is set to a=μ -2σ, b=μ+2σ. It will be appreciated that in other alternative embodiments, the anomaly boundary value may be set to, for example, μ - σ, μ+σ, or other values, depending on the actual requirements.
The abnormal section range obtaining module 4 is specifically configured to determine an abnormal section range based on the abnormal boundary value.
In an optional embodiment, the abnormal data determining module 5 is specifically configured to normalize the measurement deviation data to obtain a unit measurement deviation value; specifically, the metering deviation data is normalized, the metering deviation data is changed into decimal between (0 and 1), and the metering deviation data is mapped to be processed within the range of 0 to 1, so that the data processing is more convenient and faster.
The abnormal data determining module 5 is specifically configured to determine, as abnormal data, a unit measurement deviation value that is within an abnormal section range. Specifically, as shown in fig. 6, the thick line is formed of abnormal data points, and data points outside the range of the (μ -2σ, μ+2σ) interval are identified as abnormal data points (data points have undergone Box-Cox inverse conversion).
In an alternative embodiment, the identification system further comprises an abnormal electricity meter determination module 8 for determining an abnormal electricity meter from the abnormal data.
Specifically, the abnormal electricity meter determining module 8 is further configured to obtain the total amount of the charge metering data recorded by the electricity meter, calculate a proportion of the number of abnormal data to the total amount of the charge metering data, and determine that the electricity meter is the abnormal electricity meter when the proportion is greater than a preset abnormal threshold.
According to the identification system for the abnormal electricity metering, the deviation of the unit charging electricity quantity of the battery and the unit charging recording electricity quantity of the electricity meter is determined to be metering deviation data, whether the metering deviation data accords with normal distribution is verified, when the metering deviation data does not accord with the normal distribution, normal transformation is carried out on the metering deviation data, the abnormal boundary value of the metering deviation data is determined according to the mean value and the standard deviation of the metering deviation data, the metering deviation data in the abnormal interval range is determined to be abnormal data, the abnormal electricity meter is determined according to the abnormal data, automatic identification of the abnormal data in the electricity metering is achieved through accurately and efficiently processing the battery data and the electricity meter data, and the electricity meter is related through the abnormal data, so that the efficiency and the accuracy of finding and locating the electricity meter generating the abnormal data are further improved, the abnormal electricity meter is convenient to overhaul or replace, the loss caused by abnormal work of the electricity meter is reduced, and the use experience of a user is improved.
Example 5
Fig. 9 is a schematic structural diagram of an electronic device according to embodiment 5 of the present invention. The electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor, which when executed implements the method of identifying a metering abnormality of embodiment 1 or embodiment 2. The electronic device 30 shown in fig. 9 is merely an example, and should not be construed as limiting the functionality and scope of use of embodiments of the present invention.
As shown in fig. 9, the electronic device 30 may be embodied in the form of a general purpose computing device, which may be a server device, for example. Components of electronic device 30 may include, but are not limited to: the at least one processor 31, the at least one memory 32, a bus 33 connecting the different system components, including the memory 32 and the processor 31.
The bus 33 includes a data bus, an address bus, and a control bus.
The processor 31 executes various functional applications and data processing such as the method of identifying a metering abnormality of embodiment 1 or embodiment 2 of the present invention by running a computer program stored in the memory 32.
The electronic device 30 may also communicate with one or more external devices 34 (e.g., keyboard, pointing device, etc.). Such communication may be through an input/output (I/O) interface 35. Also, model-generating device 30 may also communicate with one or more networks, such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet, via network adapter 36. As shown, network adapter 36 communicates with the other modules of model-generating device 30 via bus 33. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in connection with the model-generating device 30, including, but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, data backup storage systems, and the like.
It should be noted that although several units/modules or sub-units/modules of an electronic device are mentioned in the above detailed description, such a division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more units/modules described above may be embodied in one unit/module in accordance with embodiments of the present invention. Conversely, the features and functions of one unit/module described above may be further divided into ones that are embodied by a plurality of units/modules.
Example 6
The present embodiment provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of identifying a metering abnormality of embodiment 1 or embodiment 2.
More specifically, among others, readable storage media may be employed including, but not limited to: portable disk, hard disk, random access memory, read only memory, erasable programmable read only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.
In a possible embodiment, the invention may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the identification method of the electric quantity metering anomaly implementing embodiment 1 or embodiment 2, when said program product is run on the terminal device.
Wherein the program code for carrying out the invention may be written in any combination of one or more programming languages, which program code may execute entirely on the user device, partly on the user device, as a stand-alone software package, partly on the user device and partly on the remote device or entirely on the remote device.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that this is by way of example only, and the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the principles and spirit of the invention, but such changes and modifications fall within the scope of the invention.
Claims (13)
1. A method for identifying an abnormality in electrical quantity measurement, the method comprising:
acquiring battery charging data of a battery;
acquiring charging metering data corresponding to the battery charging data recorded by an ammeter of the charging equipment;
determining metering deviation data according to the battery charging data and the charging metering data;
acquiring an abnormal interval range of the measurement deviation data;
and determining abnormal data in the metering deviation data based on the abnormal interval range.
2. The method of claim 1, wherein the battery charge data includes a battery single charge level, a battery single charge start level, and a battery single charge end level; the charging metering data comprises electric meter single-charge recording electric quantity corresponding to the battery single-charge electric quantity;
The step of determining metering deviation data from the battery charge data and the charge metering data comprises:
determining the unit charging electric quantity of the battery according to the single charging electric quantity of the battery, the initial electric quantity of the single charging of the battery and the final electric quantity of the single charging of the battery;
determining the unit charging record electric quantity of the electric meter according to the single charging record electric quantity of the electric meter, the single charging start electric quantity of the battery and the single charging end electric quantity of the battery;
and determining the deviation of the unit charging electric quantity of the battery and the unit charging recorded electric quantity of the ammeter as the metering deviation data.
3. The method of claim 2, wherein the battery charging data further comprises at least one of a battery station identifier, a charging device identifier, a bin identifier, a battery single charge start time, and a battery single charge end time; and/or the number of the groups of groups,
the charge metering data further includes at least one of an ammeter identification, a bin identification, a battery identification, a single charge start time and a single charge end time.
4. The method of identifying a metering anomaly of claim 2, the step of determining metering deviation data from the battery charge data and the charge metering data comprising:
Screening out the battery charging data and the charging metering data of which the initial electric quantity of the battery and the final electric quantity of the battery meet the preset electric quantity conditions;
and determining metering deviation data according to the screened battery charging data and the screened charging metering data.
5. The method of identifying an abnormality in electric quantity measurement according to claim 1, wherein the step of acquiring an abnormal section range of the measurement deviation data includes:
and determining an abnormal interval range of the metering deviation data according to the normal distribution parameters of the metering deviation data.
6. The method of identifying a metering abnormality according to claim 5, wherein before the step of determining an abnormal section range of the metering deviation data from a normal distribution parameter of the metering deviation data, the method further comprises:
verifying whether the metering deviation data accords with normal distribution;
when the measurement deviation data does not conform to the normal distribution, the measurement deviation data is subjected to normalization transformation so that the measurement deviation data conforms to the normal distribution.
7. The method of claim 5, wherein the normal distribution parameters include a mean and a standard deviation;
The step of determining the abnormal interval range of the metering deviation data according to the normal distribution parameters of the metering deviation data comprises the following steps:
determining an abnormal boundary value of the metering deviation data according to the mean value and the standard deviation of the metering deviation data;
and determining the abnormal interval range based on the abnormal boundary value.
8. The method of identifying a metering abnormality of claim 1, wherein the step of determining abnormal data in the metering deviation data based on the abnormal section range includes:
normalizing the measurement deviation data to obtain a unit measurement deviation value;
and determining the unit measurement deviation value within the abnormal interval range as abnormal data.
9. The method of identifying a metering anomaly in a power supply of claim 1, further comprising:
and determining an abnormal ammeter according to the abnormal data.
10. The method of claim 9, wherein the step of determining an abnormal meter from the abnormal data comprises:
acquiring the total amount of charging metering data recorded by the ammeter;
calculating the proportion of the quantity of the abnormal data to the total quantity of the charging metering data;
And when the proportion is larger than a preset abnormal threshold value, determining that the ammeter is an abnormal ammeter.
11. A system for identifying a metering anomaly, the system comprising:
the battery charging data acquisition module is used for acquiring battery charging data of the battery;
the charging metering data acquisition module is used for acquiring charging metering data corresponding to the battery charging data recorded by an ammeter of the charging equipment;
the metering deviation data determining module is used for determining metering deviation data according to the battery charging data and the charging metering data;
the abnormal interval range acquisition module is used for acquiring the abnormal interval range of the measurement deviation data;
and the abnormal data determining module is used for determining abnormal data in the metering deviation data based on the abnormal interval range.
12. 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 method of identifying a metering anomaly as claimed in any one of claims 1 to 10 when the computer program is executed.
13. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the method of identifying a metering abnormality according to any one of claims 1 to 10.
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