CN117157545A - Battery detection method, apparatus, device, storage medium, and computer program product - Google Patents
Battery detection method, apparatus, device, storage medium, and computer program product Download PDFInfo
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- 238000004590 computer program Methods 0.000 title claims abstract description 24
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- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 2
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- JLVVSXFLKOJNIY-UHFFFAOYSA-N Magnesium ion Chemical compound [Mg+2] JLVVSXFLKOJNIY-UHFFFAOYSA-N 0.000 description 1
- FKNQFGJONOIPTF-UHFFFAOYSA-N Sodium cation Chemical compound [Na+] FKNQFGJONOIPTF-UHFFFAOYSA-N 0.000 description 1
- JDZCKJOXGCMJGS-UHFFFAOYSA-N [Li].[S] Chemical compound [Li].[S] JDZCKJOXGCMJGS-UHFFFAOYSA-N 0.000 description 1
- 238000007792 addition Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004146 energy storage Methods 0.000 description 1
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- RVRCFVVLDHTFFA-UHFFFAOYSA-N heptasodium;tungsten;nonatriacontahydrate Chemical compound O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.[Na+].[Na+].[Na+].[Na+].[Na+].[Na+].[Na+].[W].[W].[W].[W].[W].[W].[W].[W].[W].[W].[W] RVRCFVVLDHTFFA-UHFFFAOYSA-N 0.000 description 1
- VVNXEADCOVSAER-UHFFFAOYSA-N lithium sodium Chemical compound [Li].[Na] VVNXEADCOVSAER-UHFFFAOYSA-N 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
Abstract
The application provides a battery detection method, a battery detection device (200), a battery detection apparatus, a battery detection storage medium and a battery detection computer program product. The battery detection method comprises the following steps: acquiring first operation data (110) of a first battery; determining a first detection result (120) of the first battery according to the fault detection model and the first operation data corresponding to the first battery, wherein the first detection result comprises a first score of the occurrence of the target fault of the first battery; and determining a second detection result (130) of the first battery according to the first score and a time influence factor of the first battery on the occurrence of the target fault, wherein the time influence factor is determined according to time information of the first battery on the occurrence of the target fault in a first preset time period. According to the embodiment of the application, the problems of low detection precision and high false alarm rate of the battery fault detection result can be solved.
Description
The present application relates to the field of batteries, and in particular, to a battery detection method, apparatus, device, storage medium, and computer program product.
With the continuous development of science and technology, batteries are widely applied to daily production and life, and the safety performance of the batteries is also receiving more and more attention. In the application process of the battery, the running state of the battery is often detected by collecting the electricity consumption parameters generated by the battery so as to judge whether the battery has faults or not.
However, the current common detection method, for example, uses the same detection model to perform fault detection on batteries in different application environments, and only obtains a rough judgment result on whether the batteries have faults, which results in low detection accuracy of the detection result and high false alarm rate, and is not beneficial to improving the safety of battery application.
Disclosure of Invention
In view of the above, the present application provides a battery detection method, apparatus, device, storage medium, and computer program product, which solve the problems of low detection accuracy and high false alarm rate of battery fault detection results.
In a first aspect, the present application provides a battery detection method, including:
acquiring first operation data of a first battery;
determining a first detection result of the first battery according to a fault detection model corresponding to the first battery and first operation data, wherein the first detection result comprises a first score of occurrence of a target fault of the first battery;
And determining a second detection result of the first battery according to the first score and a time influence factor of the first battery on the occurrence of the target fault, wherein the time influence factor is determined according to time information of the first battery on the occurrence of the target fault in a first preset time period.
According to the technical scheme, for the first operation data generated by the first battery, a first detection result of the first battery is determined according to the fault detection model corresponding to the first battery and the first operation data, wherein the first detection result comprises a first score of the first battery with the target fault, so that the first battery is primarily detected, next, the first battery is combined with a target fault time influence factor, the primary detection result is adjusted, and a second detection result is obtained.
In some embodiments, determining the second detection result of the first battery based on the first score and a time-impact factor of the occurrence of the target fault for the first battery includes:
Determining a second score of the first battery with the target fault according to the first score and a time influence factor of the first battery with the target fault;
and when the second score is larger than a preset fault threshold value, the second detection result comprises early warning information of the target fault of the first battery.
According to the embodiment of the application, the size of the first score is adjusted through the time influence factor, after the second score is obtained, the detection result is determined based on the second score and the preset fault threshold value, so that the difference between the detection results of whether the target fault occurs is more obvious, and accurate early warning information can be conveniently sent out.
In some embodiments, prior to the time impact factor for the target fault occurring based on the first score and the first battery, the method further comprises:
acquiring first time information of a target fault of a first battery in a first preset time period and second time information of first operation data acquired;
and determining a time influence factor according to the difference value of the first time information and the second time information, wherein the magnitude of the time influence factor is inversely related to the magnitude of the difference value.
According to the embodiment of the application, the magnitude of the time influence factor is determined by combining the difference value of the first time information and the second time information, so that different time influence factors can be determined according to different time spans, the first score can be adjusted in a self-adaptive manner, the second score with higher reliability can be obtained, the reliability of the battery detection result can be improved, and the fault false alarm rate of the battery can be reduced.
In some embodiments, determining the time impact factor based on the difference between the first time information and the second time information comprises:
determining a time influence factor according to a preset time attenuation formula and a difference value between the first time information and the second time information, wherein the preset time attenuation formula is as follows:
T＝C×e -Δt
wherein T is a time influence factor, deltat is a difference between the first time information and the second time information, and C is a preset constant.
According to the embodiment of the application, by combining the difference value of the first time information and the second time information and the preset time attenuation formula, different time influence factors can be determined according to different time spans, so that the first score can be adjusted in a self-adaptive manner, and the reliability of the battery detection result can be improved.
In some embodiments, before determining the first detection result of the first battery according to the fault detection model and the first operation data corresponding to the first battery, the method further includes:
acquiring a plurality of second operation data generated by the first battery in a second preset time period and a second detection result corresponding to each second operation data;
and updating and training the initial fault detection model according to the plurality of second operation data and the second detection result corresponding to each second operation data to obtain a fault detection model corresponding to the first battery.
According to the embodiment of the application, the initial fault detection model is updated and trained by combining the historical operation data generated by the first battery to obtain the fault detection model corresponding to the first battery, so that the training cost can be reduced; and based on the fault detection model corresponding to the first battery, one-to-one battery fault detection can be realized, and the accuracy of the detection result is improved.
In some embodiments, the second detection result includes early warning information that the first battery has a target failure or that the first battery has no target failure; according to the plurality of second operation data and the second detection result corresponding to each second operation data, updating and training the initial fault detection model to obtain a fault detection model corresponding to the first battery, wherein the method comprises the following steps:
updating the first training sample of the initial fault detection model according to the plurality of first operation data and the second detection result corresponding to each second operation data to obtain a second training sample;
and updating and training the initial fault detection model according to the second training sample to obtain a fault detection model corresponding to the first battery.
In some embodiments, the second training sample includes a plurality of second operational data of the first battery and a plurality of third operational data of the second battery, each second operational data corresponding to a second test result, each third operational data corresponding to a third test result;
Updating and training the initial fault detection model according to the second training sample to obtain a fault detection model corresponding to the first battery, wherein the updating and training comprises the following steps:
and determining a first score of the target fault of the first battery according to a plurality of second operation data of the first battery and a plurality of third operation data of the second battery, a second detection result corresponding to each second operation data and a third detection result corresponding to each third operation data, wherein the first score is used for indicating a first probability of the target fault of the first battery when the first operation data meet a preset fault judgment condition.
According to the embodiment of the application, the initial fault detection model is updated and trained based on the operation data generated by the first battery, so that the fault detection model corresponding to the first battery can be obtained, the training cost can be reduced, and the accuracy of fault detection can be improved.
In a second aspect, the present application provides a battery detection device comprising:
the acquisition module is used for acquiring first operation data of the first battery;
the processing module is used for determining a first detection result of the first battery according to the fault detection model corresponding to the first battery and the first operation data, wherein the first detection result comprises a first score of the occurrence of the target fault of the first battery;
The processing module is further configured to determine a second detection result of the first battery according to the first score and a time impact factor of the first battery on occurrence of the target fault, where the time impact factor is determined according to time information of the first battery on occurrence of the target fault in a first preset time period.
According to the embodiment of the application, for the first operation data generated by the first battery, a first detection result of the first battery is determined according to the fault detection model corresponding to the first battery and the first operation data, wherein the first detection result comprises a first score of the occurrence of the target fault of the first battery, so that the preliminary detection of the first battery is realized, and then, the preliminary detection result is adjusted by combining the time influence factor of the occurrence of the target fault of the first battery, so as to obtain a second detection result.
In a third aspect, the present application provides a battery detection apparatus comprising: a processor and a memory storing computer program instructions; the processor, when executing the computer program instructions, implements the battery detection method as described in the first aspect or any of the realizations of the first aspect.
In a fourth aspect, the present application provides a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the battery detection method of the first aspect or any of the realizations of the first aspect.
In a fifth aspect, embodiments of the present application provide a computer program product, which, when executed by a processor of an electronic device, causes the electronic device to perform a battery detection method as described in the first aspect or any of the realizations of the first aspect.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the application. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
Fig. 1 is a schematic flow chart of a battery detection method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a battery detection device according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a battery detection device according to an embodiment of the present application.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description of the application and the claims and the description of the drawings above are intended to cover a non-exclusive inclusion.
In the description of embodiments of the present application, the technical terms "first," "second," and the like are used merely to distinguish between different objects and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated, a particular order or a primary or secondary relationship. In the description of the embodiments of the present application, the meaning of "plurality" is two or more unless specifically defined otherwise.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
In the description of the embodiments of the present application, the term "and/or" is merely an association relationship describing an association object, and indicates that three relationships may exist, for example, a and/or B may indicate: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
In the description of the embodiments of the present application, the term "plurality" means two or more (including two), and similarly, "plural sets" means two or more (including two), and "plural sheets" means two or more (including two).
In the description of embodiments of the application, unless expressly specified and limited otherwise, a first feature "up" or "down" on a second feature may be that the first and second features are in direct contact, or that the first and second features are in indirect contact via an intervening medium. Moreover, a first feature being "above," "over" and "on" a second feature may be a first feature being directly above or obliquely above the second feature, or simply indicating that the first feature is level higher than the second feature. The first feature being "under", "below" and "beneath" the second feature may be the first feature being directly under or obliquely below the second feature, or simply indicating that the first feature is less level than the second feature.
Along with the continuous development of science and technology, the battery is widely applied to daily production and life, and the battery is not only applied to energy storage power supply systems such as hydraulic power, firepower, wind power and solar power stations, but also widely applied to electric vehicles such as electric bicycles, electric motorcycles, electric automobiles, and a plurality of fields such as military equipment and aerospace. With the continuous expansion of the battery application field, the market demand thereof is also continuously expanding. The safety performance of batteries is also receiving increasing attention. Reference to a battery in accordance with an embodiment of the present application refers to a single physical module that includes one or more battery cells to provide higher voltage and capacity. For example, the battery referred to in the present application may include a battery module or a battery pack, or the like.
In the application process of the battery, the running state of the battery is often detected by collecting the electricity consumption parameters generated by the battery so as to judge whether the battery has faults or not.
The inventor notes that the current common battery detection method usually trains the historical data of all batteries to obtain a unified model parameter. However, because of the difference of production, storage, transportation and use conditions among different batteries, there is inconsistency, so that the sensitivity of each battery characteristic is different, for example, in the historical use process of some batteries, the situation of overlarge temperature difference often occurs, but the batteries can still be used normally; some batteries have no characteristic of overlarge temperature difference in the history use process, but the batteries cannot be normally used when the characteristic of overlarge temperature difference suddenly occurs. For example, the same detection model is used for detecting faults of batteries in different application environments, and only a rough judgment result can be obtained for judging whether the faults exist in the batteries, so that the detection accuracy of the detection result is low, the false alarm rate is high, and the safety of battery application is not improved.
Based on the above consideration, in order to solve the problems of low detection accuracy and high false alarm rate of the battery fault detection result. The inventors have conducted intensive studies to provide a battery detection method, apparatus, device, storage medium, and computer program product. In the battery detection process, for first operation data generated by a first battery, a first detection result of the first battery is determined according to a fault detection model corresponding to the first battery and the first operation data, wherein the first detection result comprises a first score of a target fault of the first battery, so that preliminary detection of the first battery is realized, next, a target fault time influence factor of the first battery is combined, the preliminary detection result is adjusted, and a second detection result is obtained.
The technical scheme described by the embodiment of the application is suitable for the battery and the power utilization device using the battery. The electric device may be a vehicle, a mobile phone, a portable device, a notebook computer, a ship, a spacecraft, an electric toy, an electric tool, or the like. The embodiment of the application does not limit the electric device in particular.
The battery detection method provided by the embodiment of the application is described in detail below through specific embodiments and application scenes thereof with reference to the accompanying drawings.
Referring to fig. 1, fig. 1 is a flowchart of a battery detection method according to an embodiment of the application, and as shown in fig. 1, the method may include the following steps 110 to 130.
Step 110, first operation data of a first battery is obtained.
Step 120, determining a first detection result of the first battery according to the fault detection model and the first operation data corresponding to the first battery.
The first detection result comprises a first score of the occurrence of the target fault of the first battery;
and 130, determining a second detection result of the first battery according to the first score and the time influence factor of the target fault of the first battery.
The time influence factor is determined according to time information of the first battery in the first preset time period when the target fault occurs.
In step 110 described above, the first battery may refer to a single physical module that includes one or more battery cells to provide higher voltage and capacity. For example, the battery referred to in the present application may include a battery module or a battery pack, or the like. The battery generally includes a case for enclosing one or more battery cells. The battery cell may include a lithium ion secondary battery cell, a lithium ion primary battery cell, a lithium sulfur battery cell, a sodium lithium ion battery cell, a sodium ion battery cell, or a magnesium ion battery cell, which is not limited in the embodiment of the present application.
The first operation data may include, for example, a temperature, a voltage, etc. of the first battery during application, and may also include a temperature variation, a voltage variation, etc., which are not specifically mentioned herein. For example, battery operation data such as temperature, voltage, etc. of the first battery may be collected according to a preset collection frequency. Taking the temperature as an example, the temperature of the first battery may be collected according to a preset collection frequency by a temperature sensor, and optionally, the temperature sensor may be a contact temperature sensor or a non-contact temperature sensor, which is not particularly limited herein. For each collected temperature, the current collected temperature can be compared with the last collected temperature, so that the temperature variation of the first battery is obtained.
After the first operational data of the first battery is obtained, step 120 of an embodiment of the present application may be performed.
In step 120, the fault detection model corresponding to the first battery is a trained model for fault detection of the battery. Optionally, the training sample of the fault detection model may include operation data generated in an application process or a test process of the first battery, and may further include third operation data generated in an application process or a test process of the second battery, so as to increase a data amount of the training data in the training sample, thereby increasing accuracy of fault detection. Alternatively, the second battery may be the same type of battery as the first battery. The second battery may be the same as, or different from, the application or test environment of the first battery, for example, and is not particularly limited herein.
According to the first operation data and in combination with the fault detection model corresponding to the first battery, a preliminary detection result of whether the first battery has a fault, namely, a first detection result, can be obtained. The first detection result may include a first score of the occurrence of the target failure of the first battery.
For example, the target fault may include a battery over-temperature fault, and a first score output based on the fault detection model may be used to represent a risk of the first battery over-temperature fault. For example, the higher the first score, the greater the risk of an over-temperature fault of the first battery.
In order to improve the accuracy of the first battery fault detection, step 130 of an embodiment of the present application may next be performed.
In the above step 130, the first preset time period may be a time period between the first battery generating the first operation data. Alternatively, the length of the first preset time period may be from the time when the first battery is first applied to the time when the first battery is operated to generate the first operation data. And in the first preset time period, if the first battery is determined to have the target fault, recording the time of the first battery with the target fault, and generating the time information of the first battery with the target fault. Alternatively, the time information of the occurrence of the target failure of the first battery may be stored in a preset database, which is not particularly limited herein.
According to the time information of the first battery generating the target fault in the first preset time period, the time influence factor of the first battery generating the target fault can be determined. For example, the longer the time information of the first battery in which the target fault occurs within the first preset period of time spans from the time of generating the first operation data, the smaller the influence of the influence factor; the smaller the span between the time information of the target fault of the first battery and the time of generating the first operation data of the first battery operation in the first preset time period, the larger the influence of the influence factor.
Therefore, the first score can be adjusted through the time influence factor so as to improve the accuracy of fault detection of the first battery. When the second detection result of the first battery is generated, the second detection result of the first battery may be determined according to the adjusted first score.
According to the embodiment of the application, for the first operation data generated by the first battery, the first detection result of the first battery is determined according to the fault detection model corresponding to the first battery and the first operation data, so that the preliminary detection of the first battery is realized, then the preliminary detection result is adjusted by combining the target fault time influence factor of the first battery, and the second detection result is obtained.
In some embodiments of the present application, the step 130 may specifically include: determining a second score of the first battery with the target fault according to the first score and a time influence factor of the first battery with the target fault; and when the second score is larger than a preset fault threshold value, the second detection result comprises early warning information of the target fault of the first battery.
Illustratively, when the influence of the time influence factor is larger, the reliability of the first detection result of the first battery is higher, so that the first score can be increased by the time influence factor to obtain the second score; the reliability of the first detection result of the first battery is smaller as the influence of the time influence factor is smaller, and thus the first score can be reduced by the time influence factor to obtain the second score.
The preset fault threshold value can be set according to actual needs. Optionally, when the second score is greater than the preset fault threshold, the risk of the first battery generating the target fault may be considered to be large, and the early warning information is sent out, so that the second detection result may include the early warning information of the first battery generating the target fault. When the second score is smaller than or equal to the preset fault threshold, the risk of the target fault of the first battery is considered to be smaller or no fault risk exists, and therefore the second detection result does not include early warning information.
According to the embodiment of the application, the size of the first score is adjusted through the time influence factor, after the second score is obtained, the detection result is determined based on the second score and the preset fault threshold value, so that the difference between the detection results of whether the target fault occurs is more obvious, and accurate early warning information can be conveniently sent out.
In some embodiments of the application, the time-influencing factor is determined, in particular in accordance with the following steps: firstly, acquiring first time information of a target fault of a first battery in a first preset time period and second time information of first operation data acquired; next, a time influencing factor is determined according to the difference value between the first time information and the second time information, wherein the magnitude of the time influencing factor is inversely related to the magnitude of the difference value.
In some embodiments, the first time information may be a time when the target fault occurred last before the first operation data is generated for the first battery, and the second time information may be time information corresponding to the first operation data is generated for the first battery.
In this way, the magnitude of the time influence factor can be determined according to the difference between the first time information and the second time information. Wherein the magnitude of the time-affecting factor is inversely related to the magnitude of the difference. That is, if the time of the last occurrence of the target fault before the first battery generates the first operation data is longer from the second time information, the time influence factor is smaller; the time influence factor is greater if the time of the last occurrence of the target fault before the first battery generates the first operation data is shorter than the second time information.
Alternatively, when determining the time-influencing factor according to the difference between the first time information and the second time information, the determination may be performed according to a preset mapping relationship. For example, a table look-up mode may be adopted, a plurality of difference ranges are preset, and different difference ranges correspond to different time influence factors; for another example, the calculation may be performed according to a preset function, and the time influence factor may be obtained by calculating after the difference is input to the preset function. The above examples are merely illustrative and are not particularly limited herein.
According to the embodiment of the application, the magnitude of the time influence factor is determined by combining the difference value of the first time information and the second time information, so that different time influence factors can be determined according to different time spans, the first score can be adjusted in a self-adaptive manner, the second score with higher reliability can be obtained, the reliability of the battery detection result can be improved, and the fault false alarm rate of the battery can be reduced.
In some embodiments of the present application, the time impact factor is determined, and specifically, the time impact factor may be determined according to a preset time attenuation formula and a difference between the first time information and the second time information. The preset time attenuation formula may be shown in formula (1).
T＝C×e -Δt (1)
Wherein T is a time influence factor, deltat is a difference between the first time information and the second time information, and C is a preset constant.
As a specific example, the first operation data is obtained at the time t1, where the first operation data includes a temperature difference corresponding to t1, and a score is made according to the temperature difference and a fault detection model corresponding to the first battery, so as to obtain a first score S1 of the occurrence of the target fault of the first battery. Next, combining first time information of the last occurrence of the target fault of the first battery, wherein the first time information comprises time t2 of the last occurrence of the target fault of the first battery, so that a difference delta t between t1 and t2 can be obtained; and then combining a preset time attenuation formula, and calculating to obtain a time influence factor T. Alternatively, the product of the time impact factor and the first score may be used as the second score.
According to the embodiment of the application, by combining the difference value of the first time information and the second time information and the preset time attenuation formula, different time influence factors can be determined according to different time spans, so that the first score can be adjusted in a self-adaptive manner, and the reliability of the battery detection result can be improved.
In some embodiments of the present application, in order to improve accuracy of the first detection result, before determining the first detection result of the first battery, the embodiment of the present application may further include the steps of:
step 201, obtaining a plurality of second operation data generated by the first battery in a second preset time period and a second detection result corresponding to each second operation data;
step 202, updating and training the initial fault detection model according to the plurality of second operation data and the second detection result corresponding to each second operation data to obtain a fault detection model corresponding to the first battery.
Specifically, the second preset time period may be a time period between the first battery generating the first operation data. The first preset time periods may be the same or different, and are not particularly limited herein.
The second operation data may include, for example, a temperature, a voltage, etc. of the first battery during the application, and may include a temperature variation, a voltage variation, etc., which are not listed herein. For example, battery operation data such as temperature, voltage, etc. of the first battery may be collected according to a preset collection frequency. Taking the temperature as an example, the temperature of the first battery can be collected according to the preset collection frequency by the temperature sensor, and for each collected temperature, the current collected temperature can be compared with the last collected temperature, so that the temperature variation of the first battery is obtained.
The second detection result is a detection result obtained after the first battery is subjected to fault detection, and for example, the second detection result may include whether the first battery has a target fault.
The initial fault detection model may be a trained detection model, and in order to improve accuracy of a fault detection result, the initial fault detection model may be updated and trained by a plurality of second operation data and a second detection result corresponding to each second operation data, and then the fault detection model corresponding to the first battery may be obtained.
According to the embodiment of the application, the initial fault detection model is updated and trained by combining the historical operation data generated by the first battery to obtain the fault detection model corresponding to the first battery, so that the training cost can be reduced; and based on the fault detection model corresponding to the first battery, one-to-one battery fault detection can be realized, and the accuracy of the detection result is improved.
In some embodiments of the present application, the second detection result includes early warning information that the first battery has a target failure or that the first battery has no target failure; step 202 may specifically be based on the following steps:
updating the first training sample of the initial fault detection model according to the plurality of first operation data and the second detection result corresponding to each second operation data to obtain a second training sample;
And updating and training the initial fault detection model according to the second training sample to obtain a fault detection model corresponding to the first battery.
Specifically, the first training sample of the initial failure may include third operation data generated during the application process or test of the second battery, which may be the same as, or different from, the application or test environment of the first battery, for example, and is not particularly limited herein. The first training sample further comprises detection information of battery faults corresponding to each third operation data.
According to the plurality of first operation data and the second detection result corresponding to each second operation data, the first training sample is updated, and the sample size of the first training sample can be effectively expanded.
For example, the second training sample may include a plurality of second operation data corresponding to the first battery and a second detection result corresponding to each second operation data, and a plurality of third operation data generated by the second battery and detection information of a battery fault corresponding to each third operation data.
According to the embodiment of the application, the initial fault detection model is updated and trained based on the operation data generated by the first battery, so that the fault detection model corresponding to the first battery can be obtained, the training cost can be reduced, and the accuracy of fault detection can be improved.
In some embodiments of the present application, the second training sample includes a plurality of second operation data of the first battery and a plurality of third operation data of the second battery, each of the second operation data corresponds to a second detection result, and each of the third operation data corresponds to a third detection result; for example, the initial fault detection model is updated and trained, which may specifically be: and determining a first score of the target fault of the first battery according to a plurality of second operation data of the first battery and a plurality of third operation data of the second battery, a second detection result corresponding to each second operation data and a third detection result corresponding to each third operation data, wherein the first score is used for indicating a first probability of the target fault of the first battery when the first operation data meet a preset fault judgment condition.
Specifically, the second battery may generate a plurality of third operation data during operation or test, and second battery operation information is corresponding to each third operation data, for example, whether the second battery has a fault or not when the second battery generates the third operation data, and the like.
The preset fault determination condition may be set according to a feature variable included in the first operation data, for example: the temperature, voltage, temperature variation, voltage variation, frequency at which the temperature exceeds a preset temperature, and the like are not particularly limited herein.
For example, taking the example that the second operation data and the third operation data both include the temperature variation, the preset fault judgment condition is that whether the temperature variation exceeds a preset temperature threshold value, and when the temperature variation exceeds the preset temperature threshold value, the occurrence of the event of overlarge temperature difference of the battery is indicated. Based on all the operation data of the second training sample, statistical analysis is performed, so that the data volume of the operation data when the temperature variation in the whole training sample exceeds the preset temperature threshold value can be obtained, and the data volume of the operation data corresponding to the target fault of the battery when the temperature variation exceeds the preset temperature threshold value can be obtained. In this way, the probability P of occurrence of the excessive temperature difference (excessive temperature difference), the probability P of occurrence of the target failure (target failure), and the probability P of occurrence of the target failure and the excessive temperature difference (excessive temperature difference |target failure) can be obtained.
Next, a first probability P that the first battery has a target fault when the temperature difference is too large (target fault|temperature difference is too large) may be calculated according to a preset bayesian probability model. Illustratively, the first probability may be derived according to the following calculation method: p (target fault |excessive temperature difference) = [ P (excessive temperature difference|target fault) ×p (target fault) ]/P (excessive temperature difference).
After the first probability is obtained, a first score may be determined based on the first probability. To simplify the calculation process, the first probability may be directly assigned to the first score.
According to the embodiment of the application, the initial fault detection model is updated by combining the plurality of second operation data of the first battery, so that the reliability of the first score output by the fault detection model can be improved, and the fault false alarm rate can be reduced.
In order to better understand the battery detection method provided by the embodiment of the present application, an embodiment of the battery detection method in practical application is provided herein for explanation.
Step 301, an initial fault detection model is obtained.
Specifically, a first training sample is obtained, and the constructed battery fault detection model is trained to obtain a trained initial fault detection model, wherein third operation data of the second battery and second battery operation information corresponding to each third operation data are obtained. Specifically, the second battery operates information. For example, whether the second battery has a target failure. The target failure, for example, causes the second battery to suffer from an over-temperature failure due to an excessive temperature difference of the second battery. The constructed battery fault detection model may be a bayesian probability model.
Step 302, acquiring a plurality of second operation data of the first battery and operation information of the first battery corresponding to each second operation data.
Specifically, the second operation data is battery operation data generated in the application or test process of the first battery, and after the second operation data is obtained, operation information of the first battery, specifically, operation information of the first battery, needs to be obtained when the second operation data is generated by the first battery. For example, whether the first battery has an over-temperature failure due to an excessive temperature difference of the first battery.
Step 303, updating the first training sample according to the plurality of second operation data of the first battery and the operation information of the first battery corresponding to each second operation data, so as to obtain a second training sample.
And step 304, updating and training the initial fault detection model according to the second training sample to obtain a fault detection model corresponding to the first battery.
After the fault detection model corresponding to the first battery is obtained, fault detection can be performed on the operation data generated by the first battery based on the fault detection model corresponding to the first battery.
When the initial fault detection model is updated and trained, a first probability P of the target fault of the first battery when the temperature difference is overlarge (the target fault|temperature difference is overlarge) can be calculated, so that a fault detection model corresponding to the first battery, namely a Bayesian probability model, is obtained. Illustratively, the first probability may be derived according to the following calculation method: p (target fault |excessive temperature difference) = [ P (excessive temperature difference|target fault) ×p (target fault) ]/P (excessive temperature difference).
Step 305, obtaining first operation data of a first battery.
Step 306, determining a first detection result of the first battery according to the fault detection model and the first operation data corresponding to the first battery.
The first operation data may include, for example, a temperature change amount of the first battery during operation, and a first score of occurrence of the target failure of the first battery may be determined according to the temperature change amount and the failure detection model. May be used to indicate the risk of an overtemperature fault of the first battery. For example, the higher the first score, the greater the risk of an over-temperature fault of the first battery.
Step 307, obtaining a time impact factor of the first battery on the occurrence of the target fault.
Specifically, the first time information may be acquired first, including a time t2 when the target fault occurs last time in the first battery, and a time t1 when the first operation data is acquired, and then, the time influence factor may be determined according to a preset time attenuation formula and a difference value between the first time information and the second time information. The preset time attenuation formula may be shown in formula (1).
Step 308, determining a second detection result of the first battery according to the time influence factor.
Specifically, a second score of the first battery for the target fault may be determined according to the first score and a time impact factor of the first battery for the target fault; and when the second score is larger than a preset fault threshold value, the second detection result comprises early warning information of the target fault of the first battery.
According to the technical scheme, for first operation data generated by the first battery, a first detection result of the first battery is determined according to a fault detection model corresponding to the first battery and the first operation data, wherein the first detection result comprises a first score of a target fault of the first battery, so that preliminary detection of the first battery is realized, next, a target fault time influence factor of the first battery is combined, the preliminary detection result is adjusted, and a second detection result is obtained, and because the time influence factor is determined according to time information of the target fault of the first battery in a first preset time period, a second detection result can be generated by combining the condition of the target fault of the first battery in the first preset time period, so that reliability of the detection result is improved; in addition, the size of the first score is adjusted through the time influence factor, after the second score is obtained, the detection result is determined based on the second score and a preset fault threshold value, so that the difference between the detection results of whether the target fault occurs is more obvious, and accurate early warning information can be conveniently sent out.
Fig. 2 is a schematic structural diagram of a battery detection device according to an embodiment of the present application, and as shown in fig. 3, the battery detection device 200 may include: an acquisition module 210 and a processing module 220.
An acquiring module 210, configured to acquire first operation data of a first battery;
the processing module 220 is configured to determine a first detection result of the first battery according to the fault detection model and the first operation data corresponding to the first battery, where the first detection result includes a first score of the occurrence of the target fault of the first battery;
the processing module 220 is further configured to determine a second detection result of the first battery according to the first score and a time impact factor of the first battery on occurrence of the target fault, where the time impact factor is determined according to time information of the first battery on occurrence of the target fault in a first preset time period.
According to the embodiment of the application, for the first operation data generated by the first battery, a first detection result of the first battery is determined according to the fault detection model corresponding to the first battery and the first operation data, wherein the first detection result comprises a first score of the occurrence of the target fault of the first battery, so that the preliminary detection of the first battery is realized, and then, the preliminary detection result is adjusted by combining the time influence factor of the occurrence of the target fault of the first battery, so as to obtain a second detection result.
In some embodiments, the processing module 220 is further configured to determine a second score of the first battery experiencing the target fault according to the first score and a time-impact factor of the first battery experiencing the target fault; and when the second score is larger than a preset fault threshold value, the second detection result comprises early warning information of the target fault of the first battery.
According to the embodiment of the application, the size of the first score is adjusted through the time influence factor, after the second score is obtained, the detection result is determined based on the second score and the preset fault threshold value, so that the difference between the detection results of whether the target fault occurs is more obvious, and accurate early warning information can be conveniently sent out.
In some embodiments, the obtaining module 210 is further configured to obtain first time information of the first battery that the target fault occurs in the first preset time period and second time information of the first operation data;
the processing module 220 is further configured to determine a time-affecting factor according to the difference between the first time information and the second time information, where the magnitude of the time-affecting factor is inversely related to the magnitude of the difference.
According to the embodiment of the application, the magnitude of the time influence factor is determined by combining the difference value of the first time information and the second time information, so that different time influence factors can be determined according to different time spans, the first score can be adjusted in a self-adaptive manner, the second score with higher reliability can be obtained, the reliability of the battery detection result can be improved, and the fault false alarm rate of the battery can be reduced.
In some embodiments, the processing module 220 is further configured to determine the time impact factor according to a preset time attenuation formula and a difference between the first time information and the second time information, where the preset time attenuation formula is:
T＝C×e -Δt
wherein T is a time influence factor, deltat is a difference between the first time information and the second time information, and C is a preset constant.
According to the embodiment of the application, by combining the difference value of the first time information and the second time information and the preset time attenuation formula, different time influence factors can be determined according to different time spans, so that the first score can be adjusted in a self-adaptive manner, and the reliability of the battery detection result can be improved.
In some embodiments, the obtaining module 210 is further configured to obtain a plurality of second operation data generated by the first battery in a second preset period of time, and a second detection result corresponding to each second operation data;
the processing module 220 is further configured to update and train the initial fault detection model according to the plurality of second operation data and the second detection result corresponding to each second operation data, so as to obtain a fault detection model corresponding to the first battery.
According to the embodiment of the application, the initial fault detection model is updated and trained by combining the historical operation data generated by the first battery to obtain the fault detection model corresponding to the first battery, so that the training cost can be reduced; and based on the fault detection model corresponding to the first battery, one-to-one battery fault detection can be realized, and the accuracy of the detection result is improved.
In some embodiments, the second detection result includes early warning information that the first battery has a target failure or that the first battery has no target failure;
the processing module 220 is further configured to update the first training sample of the initial fault detection model according to the plurality of first operation data and the second detection result corresponding to each second operation data, so as to obtain a second training sample;
the processing module 220 is further configured to update and train the initial fault detection model according to the second training sample, so as to obtain a fault detection model corresponding to the first battery.
According to the embodiment of the application, the initial fault detection model is updated and trained based on the operation data generated by the first battery, so that the fault detection model corresponding to the first battery can be obtained, the training cost can be reduced, and the accuracy of fault detection can be improved.
In some embodiments, the second training sample includes a plurality of second operational data of the first battery and a plurality of third operational data of the second battery, each second operational data corresponding to a second test result, each third operational data corresponding to a third test result;
the processing module 220 is further configured to determine a first score of the first battery for generating the target fault according to the plurality of second operation data of the first battery and the plurality of third operation data of the second battery, the second detection result corresponding to each second operation data, and the third detection result corresponding to each third operation data, where the first score is used to indicate a first probability of the first battery generating the target fault when the first operation data meets a preset fault judgment condition.
According to the embodiment of the application, the initial fault detection model is updated by combining the plurality of second operation data of the first battery, so that the reliability of the first score output by the fault detection model can be improved, and the fault false alarm rate can be reduced.
It can be appreciated that the battery detection device 200 of the embodiment of the present application may correspond to the execution body of the battery detection method provided by the embodiment of the present application, and specific details of the operations and/or functions of each module/unit of the battery detection device 200 may be referred to the description of the corresponding parts of the battery detection method provided by the embodiment of the present application, which is not repeated herein for brevity.
Fig. 3 is a schematic diagram showing the structure of a battery detection apparatus according to an embodiment of the present application. As shown in fig. 3, the device may include a processor 301 and a memory 302 storing computer program instructions.
In particular, the processor 301 may include a central processing unit (Central Processing Unit, CPU), or an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or may be configured as one or more integrated circuits implementing embodiments of the present application.
Memory 302 may include mass storage for information or instructions. By way of example, and not limitation, memory 302 may comprise a Hard Disk Drive (HDD), floppy Disk Drive, flash memory, optical Disk, magneto-optical Disk, magnetic tape, or universal serial bus (Universal Serial Bus, USB) Drive, or a combination of two or more of the foregoing. In one example, memory 302 may include removable or non-removable (or fixed) media, or memory 302 may be a non-volatile solid state memory. The memory 302 may be internal or external to the battery detection device.
The memory may include Read Only Memory (ROM), random Access Memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical/tangible memory storage devices. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software comprising computer-executable instructions and when the software is executed (e.g., by one or more processors) it is operable to perform the operations described with reference to methods in accordance with aspects of the present disclosure.
The processor 301 reads and executes the computer program instructions stored in the memory 302 to implement the method described in the embodiment of the present application, and achieves the corresponding technical effects achieved by executing the method in the embodiment of the present application, which is not described herein for brevity.
In one example, the battery detection device may also include a communication interface 303 and a bus 310. As shown in fig. 3, the processor 301, the memory 302, and the communication interface 303 are connected to each other by a bus 310 and perform communication with each other.
The communication interface 303 is mainly used to implement communication between each module, device, unit and/or apparatus in the embodiment of the present application.
Bus 310 includes hardware, software, or both that couple the components of the online information-flow billing device to each other. By way of example, and not limitation, the buses may include an accelerated graphics port (Accelerated Graphics Port, AGP) or other graphics Bus, an enhanced industry standard architecture (Extended Industry Standard Architecture, EISA) Bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an industry standard architecture (Industry Standard Architecture, ISA) Bus, an infiniband interconnect, a Low Pin Count (LPC) Bus, a memory Bus, a micro channel architecture (MCa) Bus, a Peripheral Component Interconnect (PCI) Bus, a PCI-Express (PCI-X) Bus, a Serial Advanced Technology Attachment (SATA) Bus, a video electronics standards association local (VLB) Bus, or other suitable Bus, or a combination of two or more of the above. Bus 310 may include one or more buses, where appropriate. Although embodiments of the application have been described and illustrated with respect to a particular bus, the application contemplates any suitable bus or interconnect.
The battery detection device can execute the battery detection method of the embodiment of the application, thereby realizing the corresponding technical effects of the battery detection method described by the embodiment of the application.
In addition, in combination with the battery detection method of the above embodiment, the embodiment of the present application may be implemented by providing a readable storage medium. The readable storage medium having stored thereon computer program instructions; the computer program instructions, when executed by a processor, implement any of the battery detection methods of the above embodiments. Examples of readable storage media may be non-transitory machine readable media such as electronic circuits, semiconductor Memory devices, read-Only Memory (ROM), floppy disks, compact discs (Compact Disc Read-Only Memory, CD-ROMs), optical discs, hard disks, and the like.
It should be understood that the application is not limited to the particular arrangements and instrumentality described above and shown in the drawings. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between steps, after appreciating the spirit of the present application.
The functional blocks shown in the above-described structural block diagrams may be implemented in hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), suitable firmware, a plug-in, a function card, or the like. When implemented in software, the elements of the application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine readable medium or transmitted over transmission media or communication links by a data signal carried in a carrier wave. A "machine-readable medium" may include any medium that can store or transfer information. Examples of machine-readable media include electronic circuitry, semiconductor Memory devices, read-Only Memory (ROM), flash Memory, erasable Read-Only Memory (Erasable Read Only Memory, EROM), floppy disks, compact discs (Compact Disc Read-Only Memory, CD-ROM), optical disks, hard disks, fiber optic media, radio Frequency (RF) links, and the like. The code segments may be downloaded via computer networks such as the internet, intranets, etc.
It should also be noted that the exemplary embodiments mentioned in this disclosure describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, or may be performed in a different order from the order in the embodiments, or several steps may be performed simultaneously.
In addition, in combination with the battery detection method, the apparatus, and the readable storage medium in the above embodiments, embodiments of the present application may be implemented by providing a computer program product. The instructions in the computer program product, when executed by a processor of an electronic device, cause the electronic device to perform any of the battery detection methods of the above embodiments.
Aspects of the present disclosure are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to being, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware which performs the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In the foregoing, only the specific embodiments of the present application are described, and it will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the systems, modules and units described above may refer to the corresponding processes in the foregoing method embodiments, which are not repeated herein. It should be understood that the scope of the present application is not limited thereto, and any equivalent modifications or substitutions can be easily made by those skilled in the art within the technical scope of the present application, and they should be included in the scope of the present application.
Claims (11)
- A battery detection method, characterized by comprising:acquiring first operation data of a first battery;determining a first detection result of the first battery according to a fault detection model corresponding to the first battery and the first operation data, wherein the first detection result comprises a first score of a target fault of the first battery;and determining a second detection result of the first battery according to the first score and a time influence factor of the first battery when the target fault occurs, wherein the time influence factor is determined according to time information of the first battery when the target fault occurs in a first preset time period.
- The method of claim 1, wherein the determining the second detection result of the first battery based on the first score and a time-impact factor of the first battery on the occurrence of the target fault comprises:determining a second score of the first battery with the target fault according to the first score and a time influence factor of the first battery with the target fault;and when the second score is larger than a preset fault threshold value, the second detection result comprises early warning information of the target fault of the first battery.
- The method of claim 1, wherein prior to the time-impact factor based on the first score and the occurrence of the target fault for the first battery, the method further comprises:acquiring first time information of the target fault of the first battery in a first preset time period and second time information of the first operation data acquired;and determining the time influence factor according to the difference value of the first time information and the second time information, wherein the size of the time influence factor is inversely related to the size of the difference value.
- A method according to claim 3, wherein said determining said time-affecting factor based on a difference between said first time information and said second time information comprises:determining the time influence factor according to a preset time attenuation formula and a difference value between the first time information and the second time information, wherein the preset time attenuation formula is as follows:T＝C×e -Δtwherein T is the time influence factor, Δt is the difference between the first time information and the second time information, and C is a preset constant.
- The method of claim 1, wherein prior to said determining a first detection result for said first battery based on said first operational data and a corresponding fault detection model for said first battery, said method further comprises:acquiring a plurality of second operation data generated by the first battery in a second preset time period and a second detection result corresponding to each second operation data;and updating and training the initial fault detection model according to the plurality of second operation data and the second detection result corresponding to each second operation data to obtain a fault detection model corresponding to the first battery.
- The method of claim 5, wherein the second detection result includes early warning information of the first battery experiencing the target fault or the first battery not experiencing the target fault; and updating and training the initial fault detection model according to the plurality of second operation data and the second detection result corresponding to each second operation data to obtain a fault detection model corresponding to the first battery, wherein the updating and training comprises the following steps:updating the first training sample of the initial fault detection model according to the plurality of first operation data and the second detection result corresponding to each second operation data to obtain a second training sample;and updating and training the initial fault detection model according to the second training sample to obtain a fault detection model corresponding to the first battery.
- The method of claim 6, wherein the second training sample comprises a plurality of second operational data of the first battery and a plurality of third operational data of a second battery, each of the second operational data corresponding to a second test result, each of the third operational data corresponding to a third test result;The updating training is carried out on the initial fault detection model according to the second training sample to obtain a fault detection model corresponding to the first battery, and the updating training comprises the following steps:and determining a first score of the target fault of the first battery according to a plurality of second operation data of the first battery and a plurality of third operation data of the second battery, wherein the second detection result corresponds to each second operation data, and the third detection result corresponds to each third operation data, and the first score is used for indicating a first probability of the target fault of the first battery when the first operation data meet a preset fault judgment condition.
- A battery detection device, characterized by comprising:the acquisition module is used for acquiring first operation data of the first battery;the processing module is used for determining a first detection result of the first battery according to the fault detection model corresponding to the first battery and the first operation data, wherein the first detection result comprises a first score of the target fault of the first battery;the processing module is further configured to determine a second detection result of the first battery according to the first score and a time impact factor of the first battery on occurrence of the target fault, where the time impact factor is determined according to time information of the first battery on occurrence of the target fault in a first preset time period.
- A battery detection apparatus, characterized in that the apparatus comprises: a processor and a memory storing computer program instructions;the processor, when executing the computer program instructions, implements the battery detection method of any one of claims 1-7.
- A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon computer program instructions, which when executed by a processor, implement the battery detection method according to any of claims 1-7.
- A computer program product, characterized in that instructions in the computer program product, when executed by a processor of an electronic device, cause the electronic device to perform the battery detection method according to any of claims 1-7.
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| CN103596208B (en) * | 2013-11-15 | 2017-02-15 | 大唐移动通信设备有限公司 | Method and system for judging fault of network element |
| CN107967520A (en) * | 2017-09-27 | 2018-04-27 | 国网安徽省电力有限公司 | A kind of synthetical condition assessment theoretical method towards novel intelligent station relay protection device |
| CN107769972B (en) * | 2017-10-25 | 2019-12-10 | 武汉大学 | Power communication network equipment fault prediction method based on improved LSTM |
| CN108445410B (en) * | 2018-04-02 | 2021-02-26 | 国家计算机网络与信息安全管理中心 | Method and device for monitoring running state of storage battery pack |
| CN108537386A (en) * | 2018-04-13 | 2018-09-14 | 上海财经大学 | Maintenance forecast device based on history maintenance record |
| CN108717577A (en) * | 2018-05-29 | 2018-10-30 | 景祝强 | Maintenance forecast device based on history maintenance record big data and method |
| FR3088440B1 (en) * | 2018-11-14 | 2020-10-30 | Commissariat Energie Atomique | PROCESS FOR DIAGNOSING A FUEL CELL |
| CN112083244B (en) * | 2020-08-30 | 2022-10-28 | 西南电子技术研究所（中国电子科技集团公司第十研究所） | Integrated intelligent diagnosis system for faults of avionic equipment |
| CN113408154A (en) * | 2021-08-02 | 2021-09-17 | 广东电网有限责任公司中山供电局 | Transformer substation relay protection equipment state monitoring method and system based on digital twinning |
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