CN116304582B - Abnormal mark correction method for monotone data in power battery - Google Patents
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Abstract
The invention belongs to the technical field of power batteries, and particularly relates to an anomaly marking correction method for monotone data in a power battery, which comprises the steps of analyzing and obtaining original data reported by the power battery, matching target data meeting monotone data characteristics in a set period in the original data, sequentially carrying out vectorization and continuous de-duplication pretreatment on the target data, taking each data in the vectorization data as a scattered point and connecting straight lines, and calculating the slope of the straight lines to construct a slope number set; marking the abnormal data after slope judgment, judging whether the data quantity of the abnormal data is smaller than the jump amplitude of the normal data, and judging whether scattered points in the abnormal data meet a preset second preset condition one by one; according to the invention, the slope judgment of a straight line formed by introducing scattered points to an abnormal mark in monotonic data is carried out, the scattered points of straight line segments with the slope not being 1 are marked as abnormal data, the abnormal data are stretched and changed, and the abnormal value in an actual data curve is smoothed, so that the data can be used further.
Description
Technical Field
The invention belongs to the technical field of power batteries, and particularly relates to an abnormality mark correction method for monotone data in a power battery.
Background
The power battery is used as a core component of the new energy automobile and is directly related to safe and stable running of the automobile. According to the requirements, the new energy automobile needs to send data to the big data platform according to the protocol format of the standard GBT32960 technical Specification of remote service and management System of electric automobile.
However, due to the fact that the working condition of the new energy automobile is complex and the problems of data storage and transmission of the T-BOX and the BMS, abnormal data exist in a message reported by the T-BOX, and for fields with monotonic non-decreasing or monotonic non-increasing requirements like mileage, discharging SOC, charging SOC and the like in the message, an abnormal data marking and correcting method is provided.
Disclosure of Invention
The invention aims to provide an abnormality mark correction method for monotone data in a power battery, so as to solve the problems in the background technology.
The invention realizes the above purpose through the following technical scheme:
an abnormality mark correction method of monotone data in a power battery is applied to processing a real-time message of a power battery related index reported to a TSP platform by a vehicle T-BOX, and comprises the following steps:
s1: analyzing the real-time message based on a first preset condition to obtain original data reported by a power battery, matching target data of which the original data meet monotonic data characteristics in a preset period, backing up the target data, sequentially carrying out vectorization and continuous de-duplication pretreatment on the target data, and generating pretreated data to be analyzed;
s2: taking each piece of data in the data to be analyzed as scattered points, analyzing a slope number set generated by the slope of each section of straight line constructed by two points, if the slopes in the slope number set are all 1, executing step S4, otherwise, calculating the data quantity with the slopes not being 1 in the slope number set, marking the straight line section scattered points with the slopes not being 1 as abnormal data, marking the straight line section scattered points with the slopes being 1 as normal data, and executing step S3 on the abnormal data;
s3: judging whether the data quantity is smaller than the jump amplitude of the normal data at two adjacent ends of the abnormal data, if yes, executing the step S4, otherwise, judging whether the scattered points in the abnormal data meet the second preset condition one by one, if yes, filling the scattered points in the abnormal data by adopting stretching transformation to obtain a corrected value, and if not, taking the corrected scattered point value meeting the second preset condition as the corrected value;
s4: and ending the abnormal mark correction flow.
The further improvement is that in step S1, the first preset condition is specifically a data format and a requirement of national standard GB/T32960 for reporting real-time messages by the power battery, and the obtained raw data at least includes mileage data, discharge SOC data and charge SOC data of the power battery.
The further improvement is that step S1 further comprises, after obtaining the original data reported by the power battery, sequentially performing sorting and data cleaning on the original data according to the time sequence of the original data acquisition to remove the repeated data and the null data.
The further improvement is that the continuous de-duplication method in the step S1 is as follows:
(1) Vectorizing the target data to generate vectorized data, and generating an index number corresponding to the vectorized data;
(2) If the data value in the vectorized data is continuously repeated, performing the de-duplication operation, reserving the first piece of data and the corresponding index number, and if the data value in the vectorized data is discontinuously repeated, not performing the de-duplication operation.
The further improvement is that the marking of the abnormal data in step S2 is specifically: and taking the tail index number and the head index number between the two sections of normal data, and marking the original data corresponding to the vectorized data between the tail index number and the head index number.
A further improvement is that in step S3, the second preset condition at least includes:
the current is not 0 when the cell works;
the temperature range of the battery cell is-30-60 ℃ when the battery cell works;
the soc of the battery cell ranges from 0 to 100;
the voltage range of the iron lithium battery core is 2500 mv-3650 mv when in operation;
the voltage range of the ternary lithium battery core is 2750 mv-4200 mv when the ternary lithium battery core works.
The invention has the beneficial effects that:
the method for marking the abnormal mark in the monotonic data is more accurate and rapid in marking the position, and in the process of the mileage data analyzed from the GBT32960 message, the conditions of mileage jump, multi-frame mileage abnormal value, mileage abnormal jump low, lasting for a period of time and the like possibly exist, the problems of low efficiency and easy marking dislocation exist by adopting a first-order difference or window judgment method in the prior art, and the method can be avoided;
the correction strategy for the abnormal data is simple and effective, is in accordance with the actual situation, trowells the abnormal value in the actual data curve through stretching change, increases the effectiveness of the data, and is convenient for further use of the data.
Drawings
FIG. 1 is a schematic diagram of the overall execution flow of an abnormality flag correction method for monotonic data in a power cell according to the present invention;
FIG. 2 is a schematic diagram of a continuous deduplication operation of target data in the present invention;
fig. 3 is a schematic diagram of the correction of the abnormal mark of the monotone data in the present invention.
Detailed Description
The invention will now be described in further detail with reference to the accompanying drawings, wherein it is to be understood that the following detailed description is for the purpose of illustration only and is not to be construed as limiting the scope of the invention, as various insubstantial modifications and adaptations of the invention to those skilled in the art may be made in light of the foregoing disclosure.
As shown in fig. 1, the invention provides an anomaly flag correction method for monotonic data in a power battery, which is applied to processing real-time messages of power battery related indexes reported to a TSP platform by a vehicle T-BOX, and comprises the following steps:
s1: analyzing the real-time message based on a first preset condition to obtain original data reported by the power battery, matching the original data with target data meeting monotonic data characteristics in a preset period, backing up the target data, sequentially carrying out vectorization and continuous de-duplication pretreatment on the target data, and generating pretreated data to be analyzed.
The setting period can be set as one day, namely, the original data of the related indexes of the power battery in one day; the monotonic data characteristic refers to data meeting the requirements of monotonic non-increasing or monotonic non-decreasing conditions in the power battery index data; the related data such as the voltage array and the temperature array of the power battery in the prior art do not meet the characteristics of monotone data defined in the invention due to the uncertainty of instantaneous change.
In the invention, the first preset condition in step S1 is specifically the data format and requirement of national standard GB/T32960 for reporting real-time messages by the power battery, and the obtained raw data at least includes mileage data, discharge SOC data and charge SOC data of the power battery.
In the invention, step S1 further includes, after obtaining the original data reported by the power battery, sequentially performing sorting and data cleaning on the original data according to the time sequence of the original data acquisition to remove the repeated data and the null data.
In addition, the continuous de-duplication method in step S1 is as follows:
(1) Vectorizing the target data to generate vectorized data, and generating an index number corresponding to the vectorized data;
(2) If the data value in the vectorized data is continuously repeated, performing a de-duplication operation, reserving the first piece of data and the corresponding index number, and if the data value in the vectorized data is discontinuously repeated, not performing the de-duplication operation; the deduplication operation is specifically to remove duplicate data.
Referring specifically to fig. 2, a schematic diagram of a continuous deduplication operation of target data is shown.
S2: and (3) taking each piece of data in the data to be analyzed as scattered points, analyzing a slope number set generated by the slope of each section of straight line constructed by two points, if the slopes in the slope number set are all 1, executing step S4, otherwise, calculating the data quantity of which the slopes in the slope number set are not 1, marking the straight line section scattered points of which the slopes are not 1 as abnormal data, marking the straight line section scattered points of which the slopes are 1 as normal data, and executing step S3 on the abnormal data.
In the invention, the marking of the abnormal data in the step S2 is specifically as follows: if the slope is all "1" in the period, normal data is determined. If the slope in the period is not all '1', the abnormal data are proved to exist, the tail index number and the head index number between two sections of normal data are taken, and the original data corresponding to the vectorized data between the tail index number and the head index number are marked.
S3: and (3) judging whether the data quantity is smaller than the jump amplitude of the normal data at two adjacent ends of the abnormal data, if yes, executing the step S4, otherwise, judging whether the scattered points in the abnormal data meet the second preset condition one by one, if yes, filling the scattered points in the abnormal data by adopting stretching transformation to obtain a corrected value, and if not, taking the corrected scattered point value which meets the second preset condition as the corrected value.
In the present invention, the second preset condition at least includes:
the current is not 0 when the cell works;
the temperature range of the battery cell is-30-60 ℃ when the battery cell works;
the soc of the battery cell ranges from 0 to 100;
the voltage range of the iron lithium battery core is 2500 mv-3650 mv when in operation;
the voltage range of the ternary lithium battery core is 2750 mv-4200 mv when the ternary lithium battery core works.
In the invention, if aiming at the mileage data in the original data, the jump amplitude in the step S3 refers to the difference value of the normal mileage data at two adjacent ends of the abnormal mileage data; if the mileage data is abnormal data from 13000km to 13200km, the jump amplitude of the normal data at two adjacent ends of the abnormal data is 200km.
Referring specifically to fig. 3, which is an abnormal mark correction schematic diagram of monotonic data; in the process of abnormality correction, the last value and the first value of the normal data segment before and after the abnormal data segment are recorded as followsy 1 ,y 2 Andx1,x2the data amount of the abnormal data is recorded asnThe method comprises the steps of carrying out a first treatment on the surface of the When scattered points in the abnormal data meet the compliance condition, performing straight line fitting on the previous normal data segment of the abnormal data, and marking the fitted straight line as，/>As an inverse function thereof; when->When in use, correction is carried out; when->In this case, no correction (data deletion) is performed. Wherein->Correction value ∈1 satisfying the condition>The method comprises the following steps: />。
First, theCorrection value which does not satisfy the condition->The method comprises the following steps:
。
wherein, the liquid crystal display device comprises a liquid crystal display device,for rounding function, ++>The value of the most recent correction point satisfying the condition with a larger index number is taken.
In the invention, the data quantity and the jump amplitude of the abnormal data need to be judged preferentially in the correction process of the abnormal data, and if the data quantity is less than the jump amplitude, the abnormal data is not corrected; if the data quantity is more than or equal to the jump amplitude, judging whether the reference data meets the compliance condition or not during correction, if so, filling the scattered points meeting the condition in a stretching mode to obtain a correction value, and if not, taking the previous value meeting the compliance condition as the correction value; in the correction method, scattered points meeting the conditions are corrected in sequence according to the size of the index number.
It is emphasized that the monotone characteristic of the original data is ensured by the abnormal marking of the monotone data and the index number generation mode in the correction process, and scientificity and rationality of the corrected data are also ensured by adding preset compliance conditions.
The foregoing detailed description of the embodiments is specific and detailed, but is not therefore to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention.
Claims (6)
1. The abnormal mark correction method of monotonic data in the power battery is applied to processing real-time messages of power battery related indexes reported to a TSP platform by a vehicle T-BOX, and is characterized by comprising the following steps:
s1: analyzing the real-time message based on a first preset condition to obtain original data reported by a power battery, matching target data of which the original data meet monotonic data characteristics in a preset period, backing up the target data, sequentially carrying out vectorization and continuous de-duplication pretreatment on the target data, and generating pretreated data to be analyzed;
s2: taking each piece of data in the data to be analyzed as scattered points, analyzing a slope number set generated by the slope of each section of straight line constructed by two points, if the slopes in the slope number set are all 1, executing step S4, otherwise, calculating the data quantity with the slopes not being 1 in the slope number set, marking the straight line section scattered points with the slopes not being 1 as abnormal data, marking the straight line section scattered points with the slopes being 1 as normal data, and executing step S3 on the abnormal data;
s3: judging whether the data quantity is smaller than the jump amplitude of the normal data at two adjacent ends of the abnormal data, if yes, executing the step S4, otherwise, judging whether the scattered points in the abnormal data meet the second preset condition one by one, if yes, filling the scattered points in the abnormal data by adopting stretching transformation to obtain a corrected value, and if not, taking the corrected scattered point value meeting the second preset condition as the corrected value;
s4: and ending the abnormal mark correction flow.
2. The abnormality flag correction method for monotonic data in a power cell according to claim 1, wherein: in step S1, the first preset condition is specifically a data format and a requirement of national standard GB/T32960 of a real-time report of the power battery, and the obtained raw data at least includes mileage data, discharge SOC data, and charge SOC data of the power battery.
3. The abnormality flag correction method for monotonic data in a power cell according to claim 1, wherein: and step S1, after the original data reported by the power battery is obtained, sequencing and data cleaning are sequentially carried out on the original data according to the time sequence of the original data acquisition so as to remove the repeated data and the null data.
4. The abnormality flag correction method for monotonic data in a power cell according to claim 1, wherein: the pretreatment method in the step S1 is as follows:
(1) Vectorizing the target data to generate vectorized data, and generating an index number corresponding to the vectorized data;
(2) If the data value in the vectorized data is continuously repeated, performing the de-duplication operation, reserving the first piece of data and the corresponding index number, and if the data value in the vectorized data is discontinuously repeated, not performing the de-duplication operation.
5. The abnormality flag correction method for monotonic data in a power cell as defined in claim 4, wherein: in step S2, the marking of the abnormal data specifically includes: and taking the tail index number and the head index number between the two sections of normal data, and marking the original data corresponding to the vectorized data between the tail index number and the head index number.
6. The abnormality flag correction method for monotonic data in a power cell according to claim 1, wherein: in step S3, the second preset condition at least includes:
the current is not 0 when the cell works;
the temperature range of the battery cell is-30-60 ℃ when the battery cell works;
the soc of the battery cell ranges from 0 to 100;
the voltage range of the iron lithium battery core is 2500 mv-3650 mv when in operation;
the voltage range of the ternary lithium battery core is 2750 mv-4200 mv when the ternary lithium battery core works.
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