Early-warning method and apparatus, and network device, electric energy device and medium

Abstract

An early-warning method and apparatus, and a network device, an electric energy device and a medium, which relate to the technical field of batteries. The method comprises: performing correlation analysis on sample production data and sample operation fault data of preset sub-component samples, so as to obtain production fault correlation data; if the production fault correlation data is greater than a predetermined threshold, determining an early-warning indicator according to the sample production data corresponding to the sample operation fault data; and then, acquiring a historical production data feature of a sub-component during production, and if the historical production data feature of the sub-component matches the early-warning indicator, sending early-warning prompt information.

Description

Early warning method, device, network equipment, electric energy equipment and medium

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This disclosure claims priority to Chinese patent application number 202310905195.3, filed on July 21, 2023, entitled “Early Warning Method, Device, Network Equipment, Electric Energy Equipment and Medium,” the entire contents of which are incorporated by reference into this disclosure. Technical Field

[0003] The present disclosure relates to the field of battery technology, and in particular to an early warning method, device, network equipment, electric energy equipment, and medium. Background Art

[0004] In the manufacturing industry, when the production of equipment subassemblies is completed, various tests are often carried out to distinguish qualified subassemblies from unqualified subassemblies. For example, in the relevant technology of the power battery industry, in order to distinguish faulty batteries from qualified batteries, the relevant parameters of the batteries need to be tested when the batteries are produced to screen out faulty batteries with relevant parameters outside the standard range. Batteries with relevant parameters within the standard range are identified as usable qualified batteries. However, when qualified batteries in the relevant technology are used, some unexpected faults may still occur. When qualified batteries in the relevant technology fail during use, the current common treatment method is to recycle all batteries from the same batch or all electric equipment using batteries from the batch, and identify all batteries from the batch as faulty batteries; or, only deal with batteries that have already failed. It can be seen that in the relevant technology, for batteries or electric energy equipment that actually have potential failure risks but do not currently show relevant abnormalities, the existing technology cannot provide timely early warning.

[0005] Public content

[0006] The embodiments of this specification aim to solve at least one of the technical problems in the related art to a certain extent. To this end, the embodiments of this specification propose an early warning method, device, network equipment, electric energy equipment and medium.

[0007] This specification provides a device early warning method, wherein the device includes subcomponents, and the method includes:

[0008] Obtaining historical production data characteristics of the subassembly during the production process;

[0009] If the historical production data characteristics of the sub-component match the warning indicators, a warning prompt message is issued;

[0010] Among them, the early warning indicator is created by obtaining production failure correlation data based on sample production data and sample operation failure data of preset sub-component samples; if the production failure correlation data is greater than a predetermined threshold, the early warning indicator is determined according to the sample production data corresponding to the sample operation failure data; wherein, the number of the preset sub-component samples is multiple.

[0011] In one embodiment, if the historical production data characteristics of the sub-component match the early warning index, an early warning prompt message is issued to the sub-component, thereby effectively discovering potential problems of the sub-component and issuing an early warning therefor.

[0012] In one embodiment, the device is an electric energy device, the subassembly is a battery, and the preset subassembly sample is a preset sample battery; before performing the correlation analysis between the sample production data and the sample operation fault data based on the preset subassembly sample, the method further includes:

[0013] Acquire sample actual production data of the preset sample battery during the production process; wherein the sample actual production data corresponds to sample expected production data;

[0014] According to the comparison result of the sample actual production data and the sample expected production data, abnormal production data is determined in the sample actual production data as the sample production data.

[0015] In one embodiment, the method of determining the abnormal production data includes at least one of the following steps:

[0016] determining the sample actual production data that is different from the sample expected production data as the abnormal production data;

[0017] The sample actual production data whose difference from the sample expected production data exceeds a preset difference threshold range is determined as the abnormal production data.

[0018] In one embodiment, the correlation analysis of sample production data and sample operation failure data based on the preset subassembly samples includes:

[0019] Extracting features from the abnormal production data to obtain target production data features;

[0020] A correlation analysis is performed on the target production data features and the sample operation fault data to obtain the production fault correlation data.

[0021] In one embodiment, after extracting features from the abnormal production data, the method further includes:

[0022] Extracting features from the abnormal production data to obtain features of the abnormal production data;

[0023] The abnormal production data features that meet a first preset screening condition are screened out as the target production data features.

[0024] In one embodiment, before performing the correlation analysis between the sample production data based on the preset subassembly sample and the sample operation failure data, the method further includes:

[0025] The sample operation failure data features are obtained from the sample failure tracing database as the sample operation failure data.

[0026] In one embodiment, the method for constructing the sample fault tracing database includes:

[0027] Acquiring operational data characteristics of a faulty battery during operation; the faulty battery being the preset sample battery that has a fault during operation;

[0028] Filtering out the operation data features that meet a second preset screening condition as the sample operation fault data features;

[0029] The sample operation fault data features are stored in the sample fault tracing database.

[0030] In one embodiment, obtaining the operating data characteristics of the faulty battery during operation includes:

[0031] Acquiring initial operating data of the faulty battery during operation;

[0032] performing data cleaning on the initial operation data to obtain target operation data;

[0033] Feature extraction is performed on the target operation data to obtain the operation data features.

[0034] In one embodiment, after obtaining the target production data characteristics, the method includes:

[0035] Classified abnormal data features obtained by classifying the target production data features;

[0036] After obtaining the sample operation fault data characteristics, the method includes:

[0037] Classified fault data features obtained by classifying the sample operation fault data features;

[0038] Correlation analysis is performed on the classified abnormality data features and the classified fault data features to obtain the production fault correlation data.

[0039] In one embodiment, the analysis method for performing correlation analysis to obtain the production failure correlation data includes at least one of clustering, variance analysis, and proportion statistics.

[0040] The embodiments of this specification provide an electric energy device early warning method applied to a network device, wherein the electric energy device includes a battery, and the method includes:

[0041] Obtaining historical production data characteristics of the battery during the production process;

[0042] Matching the historical production data features with the early warning indicators, and if the match is successful, sending a corresponding early warning signal to the electric energy equipment, so that the electric energy equipment sends early warning prompt information according to the early warning signal; the method for creating the early warning indicator includes:

[0043] Performing a correlation analysis on sample production data and sample operation failure data of a preset sample battery to obtain production failure correlation data; the number of the preset sample batteries is multiple;

[0044] If the production fault correlation data is greater than a predetermined threshold, an early warning indicator is determined based on the sample production data corresponding to the sample operation fault data.

[0045] The embodiments of this specification provide an early warning method applied to an electric energy device, wherein the electric energy device includes a battery, and the method includes:

[0046] Receive a warning signal from a network device and issue corresponding warning prompt information according to the warning signal; the warning signal is determined by the network device based on the matching of the historical production data characteristics of the battery and the warning indicator, and the method for creating the warning indicator includes:

[0047] Performing a correlation analysis on sample production data and sample operation failure data of a preset sample battery to obtain production failure correlation data; the number of the preset sample batteries is multiple;

[0048] If the production fault correlation data is greater than a predetermined threshold, an early warning indicator is determined based on the sample production data corresponding to the sample operation fault data.

[0049] The embodiments of this specification provide an early warning method applied to an electric device, wherein the electric device includes a battery, and the electric device stores historical production data characteristics of the battery. The method includes:

[0050] Receive early warning indicators from network devices;

[0051] Matching the historical production data features with the early warning indicators, and if the match is successful, issuing corresponding early warning prompt information;

[0052] The method for creating the early warning indicator includes:

[0053] Performing a correlation analysis on sample production data and sample operation failure data of a preset sample battery to obtain production failure correlation data; the number of the preset sample batteries is multiple;

[0054] If the production fault correlation data is greater than a predetermined threshold, an early warning indicator is determined based on the sample production data corresponding to the sample operation fault data.

[0055] In an embodiment of the present specification, a network device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the method described in any of the above embodiments when executing the computer program.

[0056] An embodiment of the present specification provides an electric energy device including a memory and a processor, wherein the memory stores a computer program, and the processor implements the early warning method described in any of the above embodiments when executing the computer program.

[0057] In one embodiment, the electric energy device is a vehicle, a ship or an aircraft.

[0058] In an embodiment of the present specification, a computer-readable storage medium is provided. When instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the method described in any of the above embodiments.

[0059] In an embodiment of the present specification, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the method described in any of the above embodiments is implemented.

[0060] In an embodiment of the present specification, a battery warning device is provided, the device comprising:

[0061] A production feature acquisition module is used to obtain the actual production data features of the battery to be evaluated during the production process;

[0062] The early warning prompt issuing module is used to issue an early warning prompt message when the actual production data characteristics of the battery to be evaluated match the early warning indicator; wherein the method for creating the early warning indicator includes:

[0063] Performing a correlation analysis on sample production data and sample operation failure data of a preset sample battery to obtain production failure correlation data; the number of the preset sample batteries is multiple;

[0064] If the production fault correlation data is greater than a predetermined threshold, an early warning indicator is determined based on the sample production data corresponding to the sample operation fault data.

[0065] In an embodiment of the present specification, a vehicle warning device is provided, wherein the vehicle includes a power battery, and the device includes:

[0066] A production feature acquisition module, configured to acquire historical production data features during the production process of the power battery;

[0067] The early warning prompt issuing module is used to match the historical production data characteristics with the production early warning indicators. If the match is successful, a corresponding early warning signal is issued to the vehicle, so that the vehicle issues an early warning prompt information according to the early warning signal. The method for creating the early warning indicator includes:

[0068] Performing a correlation analysis on sample production data and sample operation failure data of a preset sample battery to obtain production failure correlation data; the number of the preset sample batteries is multiple;

[0069] If the production fault correlation data is greater than a predetermined threshold, an early warning indicator is determined based on the sample production data corresponding to the sample operation fault data.

[0070] In the above-mentioned implementation manner of the specification, production failure correlation data is obtained by performing a correlation analysis based on sample production data and sample operation failure data of preset sub-component samples; if the production failure correlation data is greater than a predetermined threshold, a warning indicator is created according to the sample production data corresponding to the sample operation failure data. Then, the historical production data characteristics of the sub-component in the production process are obtained, and if the historical production data characteristics of the sub-component match the warning indicators, a warning prompt message is issued. In the case that some equipment sub-components fail, the equipment sub-components that do not currently show relevant abnormalities but have potential failure risks can be determined by matching the warning indicators with the historical production data characteristics of the equipment sub-components, and a warning prompt message can be issued. Furthermore, the created warning indicators can also be used to guide the production process of the equipment sub-components to improve the quality of the equipment sub-components. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] The above and / or additional aspects and advantages of the present disclosure will become apparent and readily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0072] FIG1a is a schematic diagram of an application scenario of a method for creating a battery production early warning indicator according to an embodiment of this specification;

[0073] FIG1b is a schematic diagram of a flow chart of a device early warning method provided in an embodiment of this specification;

[0074] FIG2 is a schematic diagram of a process for determining sample production data according to an embodiment of this specification;

[0075] FIG3 is a schematic diagram of a process for determining production fault correlation data according to an embodiment of this specification;

[0076] FIG4a is a schematic diagram of a process for determining target production data characteristics according to an embodiment of this specification;

[0077] FIG4 b is a schematic diagram of battery voltage and current provided in an embodiment of this specification;

[0078] FIG4c is a schematic diagram of battery temperature and current provided in an embodiment of this specification;

[0079] FIG4 d is a schematic diagram of battery capacity and current according to an embodiment of this specification;

[0080] FIG5a is a schematic diagram of a process for determining operating data characteristics according to an embodiment of this specification;

[0081] FIG5 b is a schematic diagram of battery cell voltages provided in an embodiment of this specification;

[0082] FIG5 c is a schematic diagram of battery cell voltages provided in an embodiment of this specification;

[0083] FIG6 is a schematic diagram of a process for determining production fault correlation data according to an embodiment of this specification;

[0084] FIG7 is a pie chart showing the statistical proportions of fault type data characteristics and abnormal characteristics of battery production data provided in an embodiment of this specification;

[0085] FIG8 is a flow chart of a method for determining production early warning indicators according to an embodiment of this specification;

[0086] FIG9 is a flow chart of a vehicle warning method according to an embodiment of the present disclosure;

[0087] FIG10 is a flow chart of a vehicle warning method according to an embodiment of the present disclosure;

[0088] FIG11 is a schematic flow chart of a method for creating battery production early warning indicators according to an embodiment of this specification;

[0089] FIG12 is a schematic diagram of a device for creating a battery production early warning indicator according to an embodiment of this specification;

[0090] FIG13 is a schematic diagram of a battery warning device provided in an embodiment of this specification;

[0091] FIG14 is a schematic diagram of a vehicle warning device provided in an embodiment of this specification;

[0092] FIG15 is a schematic diagram of a vehicle warning device provided in an embodiment of this specification;

[0093] FIG16 is a schematic diagram of a battery warning device provided in an embodiment of this specification;

[0094] FIG17 is a schematic diagram of a vehicle warning device provided in an embodiment of this specification. DETAILED DESCRIPTION

[0095] The following describes in detail embodiments of the present disclosure, examples of which are illustrated in the accompanying drawings. The same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary, taking the manufacturing process of the power battery industry as an example for analysis and the production early warning indicator as an example to illustrate the indicator creation process. These embodiments are intended to explain the present disclosure and are not to be construed as limiting the present disclosure.

[0096] In the manufacturing industry, after a product is manufactured, various tests are often performed to distinguish qualified from unqualified products. However, even after the finished product is shipped as qualified, it may still contain potential failure risks. Existing technologies cannot provide early warnings for these potential risks. To facilitate understanding, the following text uses batteries as an example to illustrate the embodiments of the present disclosure.

[0097] With society urgently demanding energy conservation and emissions reduction, the development of environmentally friendly or energy-efficient new energy vehicles has become a consensus within the automotive industry. As the primary development direction for new energy vehicles, the safety and stability of electric vehicles have long been a focus of public attention. Frequent power battery safety accidents have resulted in significant losses of life and property. Therefore, comprehensive and rigorous process optimization methods and accurate and effective fault warning methods are needed to provide strong assurance for the safe operation and stability of power batteries.

[0098] Related technologies monitor the battery production process and issue early warnings for abnormal production conditions to prevent power battery failures. However, these monitoring methods for the battery production process do not specifically detect abnormal production conditions associated with battery operational failures.

[0099] The battery production process involves electrochemical processing, which operates under smooth, uniform operating conditions. The battery is in a quasi-steady state, and various battery parameters are determined during this stage. Therefore, the initial quality of the battery depends on the production process, and this quality is also correlated to operational failures that may occur during post-installation operation. Further analysis by the inventors has shown that, since the initial quality of the battery depends on the production process, and that the production process generates actual production data, mining this actual production data reveals that abnormal characteristics exhibited by this data are indeed the root cause of operational failures that may occur during post-installation operation.

[0100] Based on this, the embodiment of this specification provides a method for creating early warning indicators. Specifically, first, the sample production data and sample operation failure data of the sample battery are obtained, and a correlation analysis is performed based on the obtained sample production data and sample operation failure data to obtain production failure correlation data. It should be noted that the number of the sample batteries is multiple, and the sample operation failure data can be of multiple types. Secondly, the production failure correlation data is compared with a predetermined threshold. If the production failure correlation data is greater than the predetermined threshold, the sample operation failure data corresponding to the production failure correlation data can be determined, and then the early warning indicators are determined based on the sample production data corresponding to the sample operation failure data. It should be noted that the sample operation failure data in the embodiment of this application refers to the data obtained from the failures generated in the normal operation process of the product that is shipped as a qualified product in the later stage.

[0101] The embodiment of this specification also provides a scenario example to exemplify the method for creating early warning indicators. First, obtain the target production data features used to characterize the deviation of the actual production data of the battery from the expected production data of the battery during the production process, as well as the operation fault data features related to the fault type during the operation of the faulty battery; then, use the target production data features and the operation fault data features to perform correlation analysis to obtain production fault correlation data that describes the degree of correlation between the target production data features and the fault type; finally, mine the early warning indicators related to the fault type in the target production data features based on the production fault correlation data. By determining the early warning indicators that are more relevant to the fault type, when the production data features corresponding to the early warning indicators appear in the battery production process, early warning prompts can be issued accurately and timely for abnormal production situations related to battery operation failures; or, when the battery has been tested and qualified but has not yet been loaded for use, and is still in the battery warehouse storage stage or transportation stage, as long as the production data features corresponding to the early warning indicators appear in a certain battery production process, an early warning prompt message can be issued to the battery. It should be understood that for batteries with an early warning function, the battery itself can issue a warning message; for batteries without an early warning function, the early warning message can be directly issued by a server or other computing terminal, and the early warning message contains identity information, location information or batch information that can point to the battery.

[0102] Furthermore, on the one hand, by providing reminders for production anomalies of batteries in production, the outflow of unqualified batteries can be reduced and battery safety can be improved; on the other hand, for batteries that have already left the factory and are in use, early warning indicators can be used to analyze the actual production data of the batteries in use, and early warning of battery failures can be issued as early as possible. It should be noted that the faulty battery in the embodiments of this application refers to a battery that has passed the quality inspection at the time of production completion and then fails during subsequent use, and does not refer to a unqualified battery that fails the quality inspection at the time of production completion.

[0103] This example scenario also provides a device early warning method that combines the aforementioned early warning indicators related to operational fault types. The device can be an electrical device. Electrical devices can include vehicles, ships, or aircraft. This example scenario uses a vehicle as an example.

[0104] In related technologies, it is possible to obtain historical operating data generated by the battery during vehicle-side operation, mine data features related to operational failures, and combine unsupervised learning and supervised learning methods to complete the training of the operational failure warning model, thereby completing the early warning of battery operational failures. However, because batteries are complex electrochemical systems with a high degree of nonlinearity and are affected by road conditions and the driving environment, the vehicle's operating conditions are complex and changeable. All of these factors lead to high noise in the historical operating data of the vehicle-side battery, which is not conducive to the mining and extraction of operational data features related to failures. Therefore, the method of using only the historical operating data of the battery for vehicle-side battery fault warning also needs to be improved.

[0105] For batteries that have already left the factory and are currently operating on the vehicle, if the battery on the vehicle exhibits operational fault data characteristics related to a certain operational fault type during operation, the actual production data of the power battery on the vehicle can be combined to further determine whether to issue an early warning for that particular operational fault type, thereby improving the accuracy of the early warning.

[0106] Based on this, as previously mentioned, warning indicators related to operational fault types have been established. A correspondence between operational fault types and warning indicators can be further generated. By extracting features from the operational data of the battery on the vehicle during operation, operational data characteristics of the battery on the vehicle can be obtained. If these operational data characteristics indicate that the battery has experienced operational fault characteristics related to the fault type during operation, actual production data of the battery during production is obtained, and feature extraction is performed on the actual production data to obtain historical production data characteristics. Based on the correspondence between the operational fault type and the warning indicator, a target warning indicator corresponding to the operational fault type is determined to further verify whether a warning should be issued for that operational fault type. The historical production data characteristics are compared with the target warning indicator. If the historical production data characteristics match the target warning indicator, it indicates that the battery on the vehicle is likely to have an operational fault, and a warning message is issued. If the historical production data characteristics do not match the target warning indicator, it indicates that the production process of the battery under evaluation is normal and no warning is currently required. Therefore, by combining the historical production data characteristics and battery operational data characteristics over the entire battery life cycle to perform vehicle warnings, the accuracy of vehicle fault warnings can be improved.

[0107] Referring to FIG. 1a , the vehicle warning method provided in this scenario example can be applied to the vehicle 110 in FIG. 1a , or to other devices with vehicle control functions (such as the cloud server 120 and mobile terminal 130 in FIG. 1a ). The vehicle can be an autonomous vehicle, which can be a vehicle with partial autonomous driving functions or a vehicle with full autonomous driving functions. That is, the level of autonomous driving of the vehicle can be classified as no automation (L0), driving assistance (L1), partial automation (L2), conditional automation (L3), high automation (L4), or full automation (L5) according to the classification standards of the Society of Automotive Engineers (SAE). The vehicle or other device can implement the method for creating the warning indicator through the components it contains (including hardware and software). It is understood that the vehicle can be a car, truck, motorcycle, bus, recreational vehicle, amusement park vehicle, construction equipment (such as an engineering vehicle), tram, golf cart, train, cart, or other vehicle that uses a power battery. The embodiments of this specification do not specifically limit this.

[0108] For example, the vehicle-side Tbox (Telematics box, Internet of Vehicles system) sends the initial operating data of battery A during operation to cloud server 120. Cloud server 120 can analyze and extract features from the received initial operating data to obtain operational fault data features related to fault type X. Cloud server 120 stores historical production data features F that describe situations where the battery's actual production data deviates from the battery's expected production data. It can also store the correspondence between operational fault types and production warning indicators. Based on fault type X, cloud server 120 matches the correspondence between operational fault types and production warning indicators to obtain production warning indicator Q related to fault type X. If the historical production data features F match the production warning indicator Q, cloud server 120 sends a warning message to the vehicle-side.

[0109] The cloud server 120 can be an electronic device with certain computing and processing capabilities. It can include a network communication module, a processor, and memory. The server can be a distributed server, a system with multiple processors, memories, network communication modules, and the like operating in coordination. Alternatively, the cloud server can be a server cluster consisting of multiple servers. Alternatively, with the advancement of science and technology, the server can also be a new technological means capable of implementing the functions described in the embodiments of this specification. For example, it can be a new form of "server" based on quantum computing.

[0110] This specification provides a device early warning method. The device includes subcomponents. Please refer to Figure 1b. The device early warning method may include the following steps:

[0111] S110: Obtain historical production data characteristics of the sub-component during the production process.

[0112] S120: If the historical production data characteristics of the sub-component match the warning indicators, issue a warning prompt message.

[0113] Among them, the early warning indicator is created by performing a correlation analysis between the sample production data and the sample operation failure data based on the preset sub-component samples, thereby obtaining the production failure correlation data; the number of preset sub-component samples is multiple; if the production failure correlation data is greater than a predetermined threshold, the early warning indicator is determined based on the sample production data corresponding to the sample operation failure data. Subcomponents can be different modules, parts or functional modules in a device, which together constitute the function of the entire device or system. For example, the main chassis of a computer can be regarded as a device, and the main chassis includes multiple subcomponents, such as the motherboard, processor, memory, hard disk, graphics card, etc. These subcomponents are responsible for different functions and work together to ensure the normal operation of the entire computer system. The historical production data characteristics can characterize the situation where the historical production data of the subcomponent in the production process steps deviates from the expected production data.

[0114] Early warning indicators can be used to monitor the subassembly production process for abnormal production conditions related to subassembly operational failures. These indicators can directly utilize target production data that meets the indicator screening criteria, or they can utilize target production data features extracted from the target production data that meet the indicator screening criteria. Early warning indicators can serve as an important basis for process optimization, quality control, and early warning of existing equipment failures.

[0115] Warning information can be used to alert or notify the manufacturer of any potential production anomalies in the subassembly being evaluated. This information can include the subassembly's unique identification code, actual production data characteristics, and the type of subassembly failure. This information can be delivered via voice or to a display device, such as a mobile terminal, a vehicle-mounted display unit, or an in-vehicle tablet.

[0116] Specifically, during the production process of a subassembly, historical production data of the subassembly during the production process is obtained. By extracting features from the historical production data of the subassembly during the production process, the historical production data features of the subassembly during the production process can be obtained. The historical production data features of the subassembly during the production process are then matched with the early warning indicators. If the two match, it indicates that the production process of the subassembly is abnormal, and the device issues an early warning prompt message. If the two do not match, it indicates that the production process of the subassembly is normal and no early warning is currently required. In some embodiments, the historical production data can be pre-stored locally on the device and the historical production data can be obtained from the device; the historical production data can also be pre-stored on another computer device (such as a cloud server) and the historical production data can be obtained from the computer device. In other embodiments, the historical production data features of the subassembly during the production process can be pre-stored locally on the device and the historical production data features of the subassembly during the production process can be obtained from the device; the historical production data features of the subassembly during the production process can also be pre-stored on another computer device (such as a cloud server) and the historical production data features of the subassembly during the production process can be obtained from the computer device.

[0117] In the above-mentioned battery early warning method, the historical production data characteristics of the sub-component during the production process are obtained; if the historical production data characteristics of the sub-component match the early warning indicators, the equipment sub-component that actually has a potential failure risk but currently does not show relevant abnormalities is determined, and then the equipment issues an early warning prompt message. For sub-components that have already left the factory and are in use, the early warning indicators can be used to analyze the sub-components in use and accurately warn of sub-component operating failures as early as possible. Furthermore, by reminding of production anomalies of sub-components in production, the outflow of unqualified sub-components can be reduced and the safety of sub-components can be improved; early warning indicators can also be used to optimize the process steps in the sub-component production process to reduce the probability of sub-component failures.

[0118] It should be noted that the early warning indicator is created by performing a correlation analysis between sample production data and sample operational failure data based on a preset subcomponent sample, thereby obtaining production failure correlation data. The number of preset subcomponent samples is multiple. If the production failure correlation data exceeds a predetermined threshold, the early warning indicator is determined based on the sample production data corresponding to the sample operational failure data. For example, the device is an electrical energy device, the subcomponent is a battery, and the preset subcomponent sample is a preset sample battery.

[0119] The number of pre-set subassembly samples may be multiple. Correlation analysis can analyze two or more correlated variable elements to measure the degree of correlation between the two variable factors. Correlation analysis can uncover certain connections between different variable elements. Sample production data can be raw data generated during the production of any pre-set subassembly sample, such as raw data generated during the production of a pre-set sample battery or chip. Sample production data can also be production data features obtained by analyzing or extracting features from the raw data generated during the production of the pre-set subassembly sample. Sample operational failure data can include fault types and data features. Sample operational failure data can be operational data generated during the operation of a faulty subassembly sample, such as operational data of a faulty battery or chip. Operational failure data can also be sample operational failure data features obtained by analyzing or extracting features from operational failure data. Production failure correlation data is used to characterize the degree of correlation between production data and fault types. For example, production failure correlation data can be classified as complete correlation, no correlation, or incomplete correlation. It can also be a numerical value representing the degree of correlation, such as a correlation of 20%, 50%, or 70%. The fault type can be understood as the type of operational failure experienced by a faulty subcomponent sample during operation, or it can also refer to an overall device failure caused by the faulty subcomponent sample. For example, for a preset sample battery, the fault type could be at least one of excessive self-discharge and internal short circuit. Alternatively, the fault type could be at least one of difficulty starting the vehicle engine and a vehicle electrical system failure.

[0120] In some cases, by analyzing the correlation between sample production data and sample operation failure data, the production failure correlation data obtained can better reflect the actual correlation between the preset sub-component sample operation failure and the production anomaly manifested in the preset sub-component sample production process, and can realize the mutual correlation of the performance of the preset sub-component sample throughout the life cycle.

[0121] Specifically, before correlation is performed, it is necessary to obtain sample production data and sample operation failure data. The sample production data and sample operation failure data may be stored locally in advance, and the sample production data and sample operation failure data may be obtained locally; the sample production data and sample operation failure data may also be stored in advance in other computer devices (such as cloud servers), and the sample production data and sample operation failure data may be obtained from the computer device; the sample production data may also be stored locally in advance, and the operation failure data may be collected, and then the sample production data may be obtained locally and the collected sample operation failure data may be received.

[0122] It should be noted that the sample production data in the embodiments of this specification may be obtained by collecting data generated during the production process of the preset sub-component sample using data collection settings such as sensors deployed on the preset sub-component sample production line. After obtaining the sample production data and the sample operation failure data, a correlation analysis algorithm is used to mine the correlation between the obtained sample production data and the sample operation failure data to obtain production failure correlation data. In some other embodiments, after obtaining the sample production data and the sample operation failure data, feature extraction may be performed on the sample production data to obtain target production data features. Feature extraction is performed on the sample operation failure data to obtain sample operation failure data features. A correlation analysis algorithm is used to mine the correlation between the target production data features and the sample operation failure data features to obtain production failure correlation data.

[0123] After obtaining production failure correlation data based on the acquired sample production data and sample operational failure data, the production failure correlation data is compared with a predetermined threshold. If the production failure correlation data exceeds the predetermined threshold, it indicates that the production data corresponding to the production failure correlation data is likely correlated with a preset subcomponent sample operational failure. Therefore, a warning indicator can be created based on the sample production data corresponding to the production failure correlation data. For example, the sample production data corresponding to the production failure correlation data can be analyzed to obtain target production data features related to the failure type, which are used as the indicators to be created.

[0124] In some other embodiments, a number of target production data features corresponding to the production data may be stored in advance, and when the production fault correlation data is greater than a predetermined threshold, the target production data features related to the fault type may be directly used as the indicator to be created. In some embodiments, as the number of preset sub-component samples produced increases, and the number of faulty sub-component samples increases, the early warning indicators may be updated accordingly, such as modifying existing early warning indicators or adding new early warning indicators. Specifically, as the number of preset sub-component samples produced increases, the target production data features stored in the abnormal feature database will be updated, and as the number of faulty sub-component samples increases, the sample operation fault data features related to the fault type stored in the sample fault tracing database will be updated. The updated target production data features and the updated sample operation fault data features are used to perform correlation analysis to obtain new production fault correlation data. Based on the new production fault correlation data, new early warning indicators related to the fault type are determined in the updated target production data features.

[0125] In the above-mentioned indicator creation method, production failure correlation data is obtained by performing a correlation analysis based on the obtained sample production data and sample operational failure data. If the production failure correlation data is greater than a predetermined threshold, a warning indicator is determined based on the sample production data corresponding to the sample operational failure data. In the event that some products fail, the created indicator can be used to analyze the production data of these products and accurately identify products with potential failures. Furthermore, the created indicator can be used to guide the product production process to improve the quality and quality of subsequent products.

[0126] In some embodiments, the device is an electric energy device, the subassembly is a battery, and the preset subassembly sample is a preset sample battery. Referring to FIG. 2 , before performing correlation analysis on the sample production data and the sample operation failure data based on the preset subassembly sample, the following steps are further included:

[0127] S210: Acquire actual production data of a sample of a preset sample battery during the production process.

[0128] S220. According to the comparison result between the sample actual production data and the sample expected production data, abnormal production data is determined in the sample actual production data as the sample production data.

[0129] The actual production data of the sample may be data generated by the preset sample battery in the production process steps, and the actual production data of the sample includes but is not limited to the preset sample battery front weight, preset sample battery back weight, injection volume, current I during the formation process, and the like. HC , voltage V HC , Capacity CAP HC , temperature T HC , the current during charging I CD , voltage V CD , Capacity CAP CD , temperature T CD , the current I during the capacity division process FR , voltage V FR CAP FR , temperature T FR The actual production data of the sample corresponds to the expected production data of the sample. The expected production data of the sample can be the expected production data considered when designing the preset sample battery, or it can be the production data that is more in line with actual production needs after the preset sample battery is put into production based on the sample production data of a large number of preset sample batteries.

[0130] In some cases, the preset sample battery production process steps cannot maintain the sample actual production data of each preset sample battery at a constant ideal value, and the preset sample battery production process steps may cause the sample actual production data of the preset sample battery to be abnormal due to the preset sample battery itself or the production environment.

[0131] Specifically, based on the data generated by the process steps during the production of the preset sample battery, sample actual production data of the preset sample battery during the production process can be obtained. The sample actual production data of the preset sample battery is compared with the sample expected production data of the preset sample battery to obtain a comparison result between the sample actual production data and the sample expected production data. Based on the comparison result between the sample actual production data and the sample expected production data, it can be determined that there is abnormal production data in the sample actual production data of the preset sample battery, i.e., abnormal production data, which is used as sample production data.

[0132] For example, the expected sample production data for different models of preset sample batteries may differ. Therefore, a relational database can be established between each model of preset sample battery and the expected sample production data for each model of preset sample battery. After obtaining the actual sample production data of the preset sample battery during production, the model of the preset sample battery can be used to search the relational database. The expected sample production data of the preset sample battery found can be compared with the actual sample production data of the preset sample battery. Based on the comparison results, abnormal production data in the actual sample production data can be identified as the sample production data.

[0133] In this embodiment, the sample production data obtained may be abnormal production data, and a correlation analysis may be performed based on the abnormal production data and the sample operation fault data to obtain production fault correlation data. The production fault correlation data may be used to characterize the degree of correlation between the abnormal production data and the fault type.

[0134] In the above embodiment, actual production data of a sample of a preset sample battery during the production process is obtained. Then, based on the comparison results between the actual production data and the expected production data, abnormal production data is identified within the actual production data, and finally, the abnormal production data is used as the sample production data. This indicates that by comparing the actual production data with the expected production data, abnormal production data can be identified and used as the sample production data, narrowing the data scope for correlation analysis. Specifically, identifying abnormal production data can reduce the scope of data mining for the target production data features.

[0135] In some embodiments, determining abnormal production data includes at least one of the following steps:

[0136] Determine the actual production data of the sample that is different from the expected production data of the sample as abnormal production data;

[0137] The actual production data of the sample whose difference with the expected production data of the sample exceeds the preset difference threshold range is determined as abnormal production data.

[0138] Among them, the preset difference threshold range can be set in combination with actual conditions, and different preset difference threshold ranges can be set according to different models of preset sample batteries. Since different process steps in the production process of the preset sample batteries will cause different degrees of errors, the preset difference threshold range can be set according to the error data allowed to exist in different process production steps during the production process of the preset sample batteries. For example: the battery capacity in the expected production data of the preset sample battery is generally set to 100%. In the process steps of the preset sample battery production process, the preset sample batteries with a capacity of 99.8% to 100% can all be regarded as preset sample batteries of a normal batch. Therefore, the preset difference threshold range of the preset sample battery capacity can be set to 0% to 0.2%.

[0139] Specifically, in some embodiments, the sample actual production data may be compared with the sample expected production data. If there is a difference between the two, the sample actual production data with the difference may be determined as abnormal production data. It is understood that if the sample actual production data is the same as the sample expected production data, the corresponding sample actual production data is regarded as normal production data.

[0140] In yet other embodiments, the actual production data of the sample may be compared with the expected production data of the sample. When a difference exists between the two, the difference is further compared with a preset difference threshold range. Specifically, by comparing the actual production data of the sample with the expected production data of the sample, difference data between the actual production data of the sample and the expected production data of the sample is determined, and the difference data is compared with a preset difference threshold range. If the difference data is within the preset difference threshold range, the corresponding actual production data of the sample may be considered normal production data. If the difference data exceeds the preset difference threshold range, the corresponding actual production data of the sample may be determined as abnormal production data.

[0141] In the above-described embodiment, abnormal production data is determined to be actual production data that differs from the expected production data, or abnormal production data is determined to be actual production data that differs from the expected production data by a predetermined difference threshold. This method not only utilizes multiple methods to determine abnormal production data, but also utilizes a predetermined difference threshold to enhance the flexibility of comparing the actual production data with the expected production data, thereby enabling more accurate determination of abnormal production data.

[0142] In some embodiments, referring to FIG. 3 , a correlation analysis is performed based on sample production data and sample operation failure data of a predetermined subassembly sample, including:

[0143] S310: Extract features of abnormal production data to obtain target production data features.

[0144] Among them, the situation where the abnormal production data deviates from the expected production data can be obtained by feature extraction, and the deviation is used as the target production data feature. The target production data feature is used to characterize the situation where the actual production data of the battery deviates from the expected production data of the battery. The target production data feature can be understood as the abnormal production data feature that occurs during the battery production process. For example: if the voltage of the battery is lower than the expected voltage data, the target production data feature can be the voltage deviation value between the battery voltage and the expected voltage data; if the current of the battery is lower than the expected current data, the target production data feature can be the current deviation value between the battery current and the expected current data. For another example, during the production and charging process of the battery, the voltage should normally show an upward trend. If the actual production charging voltage of the battery shows a downward trend during the production and charging process, it indicates that there is a production abnormality. Therefore, the target production data feature can be that the battery terminal voltage decreases when there is a charging current during the production and charging process.

[0145] In some cases, the operating conditions of the electrochemical process treatment involved in the production process of the preset sample battery are smooth and single, the preset sample battery is in a quasi-steady state, and the various parameters of the preset sample battery are determined at this stage. Therefore, the production process data of the preset sample battery can better reflect the initial quality of the preset sample battery. The abnormal characteristics of the preset sample battery in the production data may be the root cause of the failure of the preset sample battery during operation. Therefore, it is necessary to determine the abnormal target production data characteristics existing in the production process of the preset sample battery.

[0146] To uncover the correlation between battery operational failures and battery production data, we first need to obtain the target production data features from the battery production process, as well as the operational failure data features associated with the failure type during the operation of the faulty battery. Specifically, by extracting features from abnormal production data, we can determine how the abnormal production data deviates from the expected production data for the battery. This deviation from the expected production data can then be used to determine the target production data features.

[0147] For example, the actual production data of the battery during the production process can be compared with the expected data of the design to obtain the actual production data with anomalies. Feature extraction is performed based on the actual production data with anomalies to obtain the target production data features of the battery during the production process.

[0148] S320: Perform correlation analysis on the target production data characteristics and the sample operation failure data to obtain production failure correlation data.

[0149] Correlation analysis can analyze two or more correlated variable elements to measure the closeness of the relationship between the two variable factors. Correlation analysis can uncover certain connections between different variable elements. Production failure correlation data is used to characterize the correlation between target production data characteristics and failure types. For example, production failure correlation data can be categorized as completely correlated, uncorrelated, or partially correlated.

[0150] In some cases, by analyzing the correlation between the target production data characteristics and the operation failure data characteristics, the production failure correlation data obtained can better reflect the true correlation between battery operation failures and production anomalies manifested in the battery production process, and can realize the correlation between the battery's performance throughout its life cycle.

[0151] Specifically, by using a correlation analysis algorithm to mine the correlation between the target production data features and the sample operation fault data, the production fault correlation data between the target production data features and the fault type can be obtained to quantify the degree of correlation between the target production data features and the fault type.

[0152] In the above implementation, by determining target production data features and performing correlation analysis using the target production data features and sample operation fault data, the data mining scope of target production data features related to fault types can be reduced and the data processing speed can be improved.

[0153] In some embodiments, referring to FIG. 4a , after extracting features from abnormal production data, the following steps are further included:

[0154] S410: Extract features of abnormal production data to obtain features of the abnormal production data.

[0155] Specifically, based on the data generated during the process steps of a preset sample battery production process, abnormal production data for the preset sample battery during the production process can be obtained. By extracting features from the abnormal production data, the production conditions corresponding to the abnormal production data can be obtained, and the characteristics of the abnormal production data can be obtained based on the production conditions corresponding to the abnormal production data. Feature extraction methods include but are not limited to derivation, threshold range, and other methods.

[0156] For example, during the production charging process of a preset sample battery, the voltage of the preset sample battery should consistently show an upward trend under normal charging conditions. Referring to Figure 4b , by taking the derivative of the voltage curve for the charging portion of Figure 4b , the location of the zero point in the curve can be determined. If a zero point exists, it can be assumed that the voltage first increased and then decreased, or first decreased and then increased, during the charging process of the preset sample battery. This indicates that the preset sample battery is abnormal. Both 410 and 420 in Figure 4b have zero points. The abnormal production data characteristic of the preset sample battery can be represented by: during the production charging process, the terminal voltage of the preset sample battery decreases when a charging current is flowing. Therefore, data features 410 and 420 belong to classification abnormal data feature M. It should be noted that a negative current indicates that the preset sample battery is charging. 430 in Figure 4b indicates that charging of the preset sample battery has stopped. The voltage decreases because the voltage of the preset sample battery drops after disconnecting the charging power supply. The voltage drop of the preset sample battery after stopping charging is within a reasonable and normal range.

[0157] For example, a voltage difference is considered reasonable within a certain range, so a preset threshold range for the voltage difference is set. Referring to Figure 4b , the voltage at 04:30 in Figure 4b is calculated as a difference from the voltage at time A in Figure 4b . If this value exceeds the preset threshold range, the preset sample battery is considered abnormal. The abnormal production data characteristic of the preset sample battery can be represented by a voltage difference exceeding the preset threshold range.

[0158] For example, during the charging process of a preset sample battery, the temperature of the preset sample battery can fluctuate within a certain range. Therefore, a preset threshold range is set for the preset sample battery temperature. Referring to Figure 4c , during the charging process of the preset sample battery, if the temperature at position 440 in Figure 4c exceeds the preset threshold range, the preset sample battery is considered abnormal. The abnormal production data characteristic of the preset sample battery can be represented by: the battery temperature is too high when the charging current is flowing during the production charging process.

[0159] For example, the capacity of the preset sample battery is reasonable within a certain range, so a preset threshold range for the capacity of the preset sample battery is set. Referring to FIG. 4d , during the charging process of the preset sample battery, if the capacity of the preset sample battery reaches position 450 in FIG. 4d and stops increasing, and this value is not within the preset threshold range, then the preset sample battery is considered abnormal. The abnormal production data characteristic of the preset sample battery can be represented as: the capacity of the preset sample battery does not reach the calibrated battery capacity.

[0160] S420: Filter out abnormal production data features that meet a first preset screening condition as target production data features.

[0161] The first preset screening condition may be the true correlation and credibility between the abnormal production data characteristics and the battery failure.

[0162] In some cases, not all abnormal production data features can be stored in the abnormal feature database. Therefore, the first preset screening conditions are used as the storage conditions for the abnormal feature database to ensure the authenticity, relevance and credibility of the production abnormality features stored in the abnormal feature database as much as possible. If the first preset screening conditions are not followed and data is stored in the database, there will be risks such as early warning errors.

[0163] Specifically, by analyzing the abnormal production data characteristics, it can be determined whether the abnormal production data characteristics meet the first preset screening conditions, and the production data characteristics that meet the first preset screening conditions are used as target production data characteristics, and the target production data characteristics are stored in the abnormal characteristics database.

[0164] In some other embodiments, the abnormal production data feature corresponds to a second cause, which is the fault cause that causes the abnormal production data feature. The second cause can be a cause that can lead to the occurrence of the abnormal production data feature and is manually derived based on relevant knowledge and experience.

[0165] In some cases, not all production abnormality data features can be stored in the abnormality feature database. Instead, the second generation cause is used as the entry condition for the abnormality feature database to ensure the true relevance and credibility of the production abnormality features stored in the abnormality feature database as much as possible. If the data are not entered into the database according to the second generation cause, there will be risk events such as early warning errors. Specifically, by performing a cause analysis on the abnormal production data feature, the cause of the abnormal production data feature, that is, the second generation cause, can be determined. In actual applications, by verifying the correlation between the second generation cause and the abnormal production data feature, it is determined whether the second generation cause is the true cause of the abnormal production data feature. If the second generation cause is the true cause, it can be considered that the second generation cause meets the first preset screening condition, and the abnormal production data feature can be stored in the abnormality feature database.

[0166] For example, please continue to refer to FIG4b. The abnormal production data feature of the preset sample battery is represented by a drop in the terminal voltage of the preset sample battery when a charging current is present during the production charging process. A cause analysis is performed on the abnormal production data feature of the preset sample battery. It can be considered that the reason for the abnormal production data feature of the preset sample battery is that, during the production process of the preset sample battery, dust adheres to the electrode or leaks liquid, causing a slight internal short circuit inside the preset sample battery and a decrease in internal resistance. The true correlation and accuracy between the slight internal short circuit caused by dust adheres to the electrode or leaks liquid during the production process of the preset sample battery and a decrease in internal resistance and the drop in the terminal voltage of the preset sample battery when a charging current is present during the production charging process are verified. If a true and credible correlation exists between the two, it can be considered that the second cause meets the first preset screening condition, and the production data feature of the drop in the terminal voltage of the preset sample battery when a charging current is present during the production charging process can be used as the target production data feature that needs to be stored in the abnormal feature database.

[0167] For example, an abnormal production data characteristic of a preset sample battery is represented by an excessively high temperature of the preset sample battery during a production charging process with a charging current. A cause analysis of the production data characteristic of this preset sample battery reveals that the abnormal production data characteristic of the preset sample battery is caused by a problem with the transmission characteristics of the preset sample battery itself, resulting in an internal circuit abnormality. The true correlation and accuracy between the problem with the transmission characteristics of the preset sample battery itself, the internal circuit abnormality, and the excessively high temperature of the preset sample battery during a production charging process with a charging current are verified. If a true and credible correlation exists between the two, it can be determined that the second cause meets the first preset screening condition, and the production data characteristic of excessively high battery temperature during a production charging process with a charging current can be selected as a target production data characteristic to be stored in the abnormal characteristic database.

[0168] In the above embodiment, by constructing an abnormal feature database, the abnormal production data features that meet the first preset screening conditions are stored in the abnormal feature database, which not only ensures the true correlation and credibility of the abnormal production data features stored in the abnormal feature database with the battery failure as much as possible, but also can precipitate the production abnormal data features of the preset sample battery, and provide a data source for the optimization of the preset sample battery production process or process.

[0169] In some embodiments, before performing correlation analysis on the sample production data based on the preset subcomponent sample and the sample operation failure data, the method may further include: obtaining sample operation failure data features from a sample failure tracing database as the sample operation failure data.

[0170] The sample operation fault data feature is used to characterize abnormalities in the actual operation data of a preset sample battery. For example, during charging, the preset sample battery experiences a voltage rise that is faster than that of other preset sample batteries of the same model, and the terminal voltage of the preset sample battery reaches the cutoff voltage before the other preset sample batteries. Therefore, the sample operation fault data feature can be that the charging voltage of the preset sample battery rises too quickly and reaches the cutoff voltage before normal preset sample batteries.

[0171] In some cases, when the preset sample batteries are put into use, different battery failures may occur according to the usage of the batteries in different vehicles. Therefore, it is necessary to use the sample operation failure data characteristics to describe the abnormal data conditions existing in the operation process of the batteries in use.

[0172] Specifically, a sample fault tracing database is provided in advance, and a number of sample operation fault data features are stored in the sample fault tracing database. The sample operation fault data features can be obtained from the sample fault tracing database as the sample operation fault data.

[0173] In the above implementation, by obtaining sample operation fault data features from the sample fault tracing database, correlation analysis can be performed between the sample operation fault data features and the target production data, thereby reducing the data mining scope of the target production data features related to the fault type and improving the data processing speed.

[0174] In some embodiments, a method for constructing a sample fault tracing database includes: obtaining operating data characteristics of a faulty battery during operation; screening out operating data characteristics that meet a second preset screening condition as sample operating fault data characteristics; and storing the sample operating fault data characteristics in the sample fault tracing database.

[0175] The faulty battery is a preset sample battery that has a fault during operation. The second preset screening condition may be the true correlation and credibility between the operating data feature and the battery fault.

[0176] In some cases, not all operational data features can be stored in the fault tracing database. Therefore, the second preset screening condition is used as the entry condition for the sample fault tracing database to ensure the true relevance and credibility of the operational data features stored in the sample fault tracing database as much as possible. If the second preset screening condition is not followed and the operational data features are entered into the database, there will be risks such as early warning errors.

[0177] Specifically, by analyzing the operation data characteristics, it can be determined whether the operation data characteristics meet the second preset screening conditions, and the operation data characteristics that meet the second preset screening conditions are used as sample operation fault data characteristics, and the sample operation fault data characteristics are stored in the sample fault tracing database.

[0178] In some other embodiments, the operating data feature corresponds to a first cause, which is the fault cause that causes the operating data feature. The first cause can be a cause that can cause the operating data feature to appear and is manually derived based on relevant knowledge and experience.

[0179] In some cases, not all operating data features can be stored in the fault tracing database. Instead, the first cause of occurrence is used as the entry condition for the sample fault tracing database to ensure the true relevance and credibility of the operating data features stored in the sample fault tracing database to the battery fault. If the data is not entered based on the first cause of occurrence, there will be risks such as early warning errors.

[0180] Specifically, by analyzing the cause of the operating data feature, the cause of the operating data feature, namely the first cause, can be determined. In practical applications, the correlation between the first cause and the operating data feature is verified to determine whether the first cause is the true cause of the operating data feature. If the first cause is the true cause, it can be considered that the first cause meets the fault feature screening criteria, and the operating data feature can be stored in the sample fault tracing database.

[0181] For example, the operational data characteristic of a preset sample battery is represented by the battery voltage rising too quickly during charging and being the first to reach the cutoff voltage. A cause analysis of the operational data characteristic of this preset sample battery indicates that the reason for this operational data characteristic is that this preset sample battery has a lower internal resistance than other preset sample battery cells. The true correlation and accuracy between the lower internal resistance of the preset sample battery and the fact that the preset sample battery voltage rises too quickly during charging and is the first to reach the cutoff voltage is verified. If a true and credible correlation exists between the two, it can be determined that the first cause meets the fault characteristic screening criteria, and the operational data characteristic of the preset sample battery voltage rising too quickly during charging and being the first to reach the cutoff voltage can be included as a sample operational fault data characteristic that needs to be stored in the sample fault tracing database.

[0182] In the above embodiment, by constructing a sample fault tracing database, the operating data features that meet the second preset screening condition are stored in the sample fault tracing database. This not only ensures the true correlation and credibility of the operating data features stored in the sample fault tracing database with the battery fault as much as possible, but also can precipitate the operating data features of the faulty battery, providing an accurate data source for battery fault analysis.

[0183] In some embodiments, referring to FIG. 5 a , obtaining the operating data characteristics of a faulty battery during operation may include the following steps:

[0184] S510: Acquire initial operating data of the faulty battery during operation.

[0185] S520: Clean the initial operation data to obtain target operation data.

[0186] The initial operation data may include but is not limited to the current I of the single battery. Cell , voltage V Cell CAP Cell , battery state of charge SOC, temperature T Cell .

[0187] In some cases, the initial operating data of a faulty battery during operation may contain some interference data and incomplete data. Therefore, data cleaning is used to reduce the impact of noise or inaccurate data on the extraction of operating data features and improve the accuracy of operating data features.

[0188] Specifically, the vehicle with the faulty battery sends initial operating data of the faulty battery to the cloud server via the vehicle-side Tbox. The cloud server then cleans the initial operating data to obtain the target operating data.

[0189] S530: Extract features from the target operation data to obtain operation data features.

[0190] The operating data feature is used to characterize the abnormality of target operating data of the faulty battery during operation.

[0191] Specifically, by extracting features from the target operating data, we can obtain abnormal operating conditions of the target operating data during the operation of the faulty battery. Based on these abnormal operating conditions, we can obtain operating data features related to the operation process. Feature extraction methods include but are not limited to derivation, threshold range, and other methods.

[0192] For example, please refer to Figure 5b, which is a diagram of the vehicle battery cell voltage sent by the vehicle-side Tbox and after data cleaning. In Figure 5b, there is an operating data feature 310 in which the voltage rise rate of a certain battery is higher than that of other batteries in the same period and the charging terminal voltage reaches the cut-off voltage first. This operating data feature 310 belongs to the classification fault data feature Y. The voltage of this battery can be compared with the data of the voltage of batteries of the same model. Based on the threshold range of the battery voltage, the location of the voltage anomaly of the battery and the manifestation of the voltage anomaly can be extracted. The operating data feature of the battery can then be expressed as the battery voltage rising too fast during charging and reaching the cut-off voltage first.

[0193] For example, different battery discharge rates may lead to voltage differences between single cells. During battery discharge, the battery releases stored charge to supply the current load, and at different discharge rates, the speed of the chemical reaction inside the battery and the rate of energy release will also be different. Please refer to Figure 5c, which is a vehicle battery cell voltage diagram. According to the formula self-discharge rate = ((V1-V2) / (t-t0))*100%, it can be obtained that the self-discharge rates of different single cells in Figure 5c are different, and there is an operating data feature 510 in Figure 5c that the self-discharge rate of a certain single cell is obviously different from that of other single cells. This operating data feature 510 belongs to the classification fault data feature Z. The battery self-discharge rate is different, and the battery cells in the vehicle-end battery pack will have voltage differences. The accumulation of differences will cause faults such as limited battery pack discharge SOC. The operating data feature of the battery can be expressed as different battery self-discharge rates and voltage differences between the battery cells in the vehicle-end battery pack. Wherein, V1 is the battery voltage at the initial moment, V2 is the battery voltage after a period of time t, t0 is the initial moment, that is, the time before the self-discharge measurement starts, and t is the elapsed time.

[0194] In the above embodiment, the initial operating data of the faulty battery during operation is obtained and the initial operating data is cleaned to obtain the target operating data; the impact of noise or inaccurate data on the extraction of operating data features is reduced through data cleaning, thereby improving the accuracy of the operating data features.

[0195] In some embodiments, referring to FIG6 , after obtaining the target production data characteristics, the following steps may be included:

[0196] S610: Classify the target production data features to obtain classified abnormal data features.

[0197] After obtaining the sample operation fault data characteristics, including:

[0198] S620: Classify the sample operation fault data features to obtain classified fault data features.

[0199] S630: Perform correlation analysis on the classified abnormality data features and the classified fault data features to obtain production fault correlation data.

[0200] In some cases, by classifying the target production data features and the sample operation failure data features separately and performing correlation analysis based on the classified features, rather than randomly and aimlessly conducting correlation mining on the target production data features and the sample operation failure data features, targeted correlation analysis can be performed, thereby improving the accuracy and credibility of the correlation analysis results to a certain extent.

[0201] Specifically, by classifying the target production data features, we can obtain classified anomaly data features. By classifying the sample operational fault data features, we can obtain classified fault data features. Correlation analysis is performed on the classified anomaly data features and the classified fault data features, mining the correlation between the classified anomaly data features and the classified fault data features to obtain production fault correlation data that describes the degree of correlation between the target production data features and the fault type.

[0202] In the above implementation, correlation analysis is performed in combination with the classified abnormal data features and the classified fault data features. The obtained correlation data can better reflect the true correlation between battery operation failures and production anomalies manifested in the battery production process, and realize the correlation between the performance of the battery throughout its life cycle.

[0203] In some embodiments, the analysis method for performing correlation analysis to obtain production failure correlation data includes at least one of clustering, variance analysis, and proportion statistics.

[0204] Specifically, by using at least one of clustering, variance analysis, and proportion statistics, a correlation analysis can be performed on the acquired production data and operation fault data. Based on the results of the correlation analysis, production fault correlation data describing the degree of correlation between the target production data characteristics and the fault type can be obtained.

[0205] In this embodiment, the acquired production data can be understood as classified abnormal data features, and the operational fault data can be understood as classified fault data features. Thus, at least one of clustering, variance analysis, and percentage statistics can be used to perform a correlation analysis on the classified abnormal data features and the classified fault data features to obtain production fault correlation data. Using at least one of clustering, variance analysis, and percentage statistics, a correlation analysis can be performed on the classified abnormal data features and the classified fault data features. Based on the results of the correlation analysis, production fault correlation data describing the degree of correlation between the target production data features and the fault type can be obtained.

[0206] For example, a correlation analysis of the classified abnormal data features and the classified fault data features is performed using a percentage statistical method to obtain production fault correlation data. Referring to FIG7 , for example, the number of preset battery samples is 10,000. The corresponding vehicles on which the preset battery samples are installed and operated are sample vehicles, and the batteries in the preset battery samples that have the classified abnormal data feature M are target batteries. Among the 10,000 sample vehicles, the proportion of faulty vehicles with the classified fault data feature Y is 0.734%. Among the vehicles equipped with the target battery, the proportion of faulty vehicles with the classified fault data feature Y is 20.86%. The proportion of faulty vehicles in vehicles with battery production data abnormality features, 20.86%, is much higher than the proportion of faulty vehicles in the total number of vehicles, 0.734%. Therefore, it can be considered that the correlation between the production data abnormality feature and vehicle battery failure is significant. The proportion data between the two can be used to confirm whether there is a significant difference in the proportion data based on the difference in order of magnitude.

[0207] For example, cluster analysis can usually group elements with significant correlation into the same cluster. Cluster analysis is performed on the classification anomaly data features and classification fault data features. If classification anomaly data feature A and classification fault data feature B are grouped into the same cluster C through cluster analysis, it can be considered that there is a potential correlation between classification anomaly data feature A and classification fault data feature B, and further analysis is needed to obtain the corresponding production fault correlation data.

[0208] For example, categorical anomaly data features and categorical fault data features serve as independent variables, and fault type serves as the dependent variable. Within-group variation can be captured using these features, while between-group variation can be captured using the independent variables, categorical anomaly data features and categorical fault data features, and the dependent variable, fault type. Analyzing the significant impact of within-group and between-group variation can identify the data that plays a decisive role.

[0209] In the aforementioned method for creating early warning indicators, correlation analysis is performed using at least one of clustering, variance analysis, and percentage statistics to generate production failure correlation data. This production failure correlation data better reflects the true correlation between battery operational failures and production anomalies exhibited during the battery production process, enabling correlation between battery performance throughout its lifecycle.

[0210] For example, referring to FIG8 , determining the production early warning indicator may include the following steps:

[0211] S810. Determine target related data that meets preset related screening conditions in the production failure related data.

[0212] S820. Use the target production data characteristics corresponding to the target-related data as production early warning indicators.

[0213] Among them, there are many degrees of correlation between the target production data characteristics included in the production fault correlation data and the fault type. Therefore, preset relevant screening conditions are set to evaluate the correlation between the target production data characteristics and the fault type, and to screen out the target relevant data whose correlation between the target production data characteristics and the fault type reaches the expected level.

[0214] Specifically, the production fault correlation data is filtered according to preset relevant filtering conditions to obtain target relevant data that meets the preset relevant filtering conditions, so that the target production data characteristics corresponding to the target relevant data are used as production early warning indicators.

[0215] In the above embodiment, target-related data that meets preset relevant screening conditions is determined, and the corresponding target production data characteristics are used as production warning indicators. By presetting relevant screening conditions, abnormal production data characteristics that are significantly correlated with battery failure are ensured as much as possible as production warning indicators, thereby improving the accuracy of production warning indicators.

[0216] The embodiment of this specification provides an electric energy device early warning method applied to a network device, wherein the electric energy device includes a battery. Referring to FIG9 , the electric energy device early warning method may include the following steps:

[0217] S910. Obtain historical production data characteristics of the battery during the production process.

[0218] The early warning indicator is obtained according to the method for creating the early warning indicator in any of the above embodiments. The historical production data feature is the situation where the historical production data of the battery in the production process step deviates from the expected production data.

[0219] Specifically, by extracting features from the operating data of the battery on the electric energy device during operation, the operating data features of the battery on the electric energy device during operation can be obtained. If the operating data features indicate that the battery has an operating fault data feature related to the fault type during operation. In some embodiments, the historical production data features of the battery during the production process can be pre-stored in a storage unit of the electric energy device, so that the historical production data features of the battery during the production process can be directly obtained from the storage unit. In other embodiments, actual production data of the battery during production is obtained, and feature extraction is performed on the actual production data to obtain the historical production data features.

[0220] S920: Match the historical production data characteristics with the early warning indicators. If the match is successful, send a corresponding early warning signal to the electric energy equipment, so that the electric energy equipment sends an early warning prompt information according to the early warning signal.

[0221] The method for creating the early warning indicator includes: performing a correlation analysis based on sample production data and sample operational failure data of a preset sample battery to obtain production failure correlation data; the number of preset sample batteries is multiple. If the production failure correlation data exceeds a predetermined threshold, the early warning indicator is determined based on the sample production data corresponding to the sample operational failure data.

[0222] Specifically, after acquiring historical production data characteristics, early warning indicators related to the fault type can be obtained from the storage unit of the power equipment, or from a cloud server. Furthermore, the acquired historical production data characteristics are matched with the early warning indicators related to the fault type. If the two match, indicating that the production process of the battery on the power equipment is abnormal, a corresponding early warning signal is issued to the power equipment, causing the power equipment to issue an early warning prompt information based on the early warning signal. If the two do not match, indicating that the production process of the battery on the power equipment is normal, no early warning is currently required.

[0223] It should be noted that the number of electrical energy devices in this embodiment can be one or more. When there are multiple electrical energy devices, batch warnings can be issued for multiple devices, achieving high-accuracy batch warnings. In the above-mentioned electrical energy device warning method, historical production data characteristics of batteries during the production process are obtained; if the historical production data characteristics match the warning indicators related to the fault type, a warning prompt message is issued. By combining data from the entire life cycle of the battery warning, the accuracy of the electrical energy device battery warning is improved.

[0224] The embodiment of this specification provides an early warning method applied to an electric device end, the electric device includes a battery, and the method may include: receiving an early warning signal issued by a network device, and issuing corresponding early warning prompt information according to the early warning signal; the early warning signal is determined by the network device based on the matching of the historical production data characteristics of the battery and the early warning indicators.

[0225] Among them, the method for creating early warning indicators includes: performing correlation analysis on sample production data and sample operation failure data based on preset sample batteries to obtain production failure correlation data; the number of preset sample batteries is multiple; if the production failure correlation data is greater than a predetermined threshold, determining the early warning indicators based on the sample production data corresponding to the sample operation failure data.

[0226] The electric device can be any of a variety of battery-equipped devices, including vehicles, drones, ships, aircraft, thermostats, computers, and mobile phones. Specifically, the network device determines the characteristics of the battery's historical production data, matches these characteristics with production warning indicators, and generates a warning signal based on the matching results. The network device then transmits the warning signal to the electric device. Upon receiving the warning signal, the electric device issues a corresponding warning notification message based on the warning signal.

[0227] In this early warning method, a warning signal is sent to the electric equipment based on the matching of historical battery production data characteristics with production warning indicators. This warning signal then generates corresponding warning information. By combining data from the entire life cycle, the accuracy of warnings on the electric equipment side is improved.

[0228] The implementation method of this specification provides an early warning method applied to an electric device end, where the electric device includes a battery, and the electric device stores historical production data characteristics of the battery. The method includes: receiving early warning indicators issued by a network device; matching the historical production data characteristics with the early warning indicators, and if the match is successful, issuing corresponding early warning prompt information.

[0229] Among them, the method for creating early warning indicators includes: performing correlation analysis on sample production data and sample operation failure data based on preset sample batteries to obtain production failure correlation data; the number of preset sample batteries is multiple; if the production failure correlation data is greater than a predetermined threshold, determining the early warning indicators based on the sample production data corresponding to the sample operation failure data.

[0230] The electric device can be any of a variety of battery-equipped devices, including vehicles, drones, ships, aircraft, thermostats, computers, and mobile phones. Specifically, the network device sends production warning indicators to the electric device. The electric device receives the production warning indicators from the network device and matches the locally stored historical battery production data features with the production warning indicators. If a match is found, a corresponding warning message is issued.

[0231] In the above-mentioned early warning method, by receiving production early warning indicators and issuing corresponding early warning information based on the matching of historical production data characteristics with production early warning indicators, the accuracy of early warning on the electric equipment side is improved by combining early warning with data from the entire life cycle.

[0232] An embodiment of this specification provides a network device, including a memory and a processor. The memory stores a computer program, and the processor implements the method in any of the above embodiments when executing the computer program.

[0233] An embodiment of this specification provides an electric energy device, including a memory and a processor. The memory stores a computer program, and the processor implements the method in any of the above embodiments when executing the computer program.

[0234] In some embodiments, the electrical energy device is a vehicle, a watercraft, or an aircraft.

[0235] An embodiment of this specification provides a computer-readable storage medium. When instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device can execute the method in any of the above embodiments.

[0236] The embodiments of this specification provide a computer program product, including a computer program. When the computer program is executed by a processor, the method in any one of the above embodiments is implemented.

[0237] This specification provides a vehicle warning method, as shown in FIG10 , which may include the following steps:

[0238] S1010. If the power battery on the vehicle has an operation fault data feature related to the target fault type during operation, obtain historical production data features during the production process of the power battery and a target production warning indicator related to the target fault type.

[0239] In some cases, if only the operating data generated by the power battery during operation is used for early warning, the abnormal characteristics of the battery in the production data will be ignored, which is the root cause of the battery failure during operation. Therefore, in this embodiment, the operating data generated by the power battery during operation and the production data generated by the battery during production are combined to provide vehicle early warning from the perspective of the entire life cycle, combining the operating data and production data of the entire life cycle.

[0240] Specifically, operational fault data features related to the target fault type can be pre-set. By extracting features from the vehicle's power battery's operational data, operational data features related to the vehicle's power battery during operation can be obtained. If these operational data features indicate that the power battery exhibits operational fault data features related to the target fault type during operation, in order to provide early warning for the vehicle from a full lifecycle perspective, historical production data features of the power battery during production can be further obtained, along with target production early warning indicators related to the target fault type.

[0241] In this embodiment, the production warning indicator can be stored in a storage unit on the vehicle side, and the production warning indicator can be directly obtained from the storage unit on the vehicle side. The production warning indicator can be obtained according to the method for creating the warning indicator in any of the above embodiments.

[0242] In this embodiment, the historical production data features can be stored in a vehicle-side storage unit, and the historical production data features can be directly obtained from the vehicle-side storage unit. Alternatively, the historical production data of the power battery can be stored in the vehicle-side storage unit, and the historical production data of the power battery can be obtained from the vehicle-side storage unit. Feature extraction is performed based on the historical production data of the power battery to obtain the historical production data features.

[0243] In some cases, considering the computing power and storage space of the vehicle side, the operating fault data characteristics are determined on the vehicle side based on the operating data of the power battery during operation. The historical production data can be stored in server A, and server B obtains the historical production data of the power battery from server A. Server B extracts features from the historical production data of the power battery to obtain historical production data characteristics. Server B can also store production warning indicators, and server B determines the target production warning indicators related to the target fault type. Server B sends the historical production data characteristics and the target production warning indicators related to the target fault type to the vehicle side, and the vehicle side obtains the historical production data characteristics and the target production warning indicators. It should be noted that servers A and B can be the same server or different servers.

[0244] In some cases, to further reduce computing pressure and storage space usage on the vehicle side, the vehicle sends the battery's operating data to the server. The server determines operational fault data characteristics based on this data. As mentioned above, the extraction of historical production data characteristics also occurs on the server and will not be further described here.

[0245] S1020. If the historical production data characteristics match the target production warning indicators, a warning prompt message is issued.

[0246] Specifically, the acquired historical production data features are matched with the target production warning indicators. If the two match, it indicates that the production process of the vehicle's power battery is abnormal, and a warning message is issued. If the two do not match, it indicates that the production process of the vehicle's power battery is normal and no warning is currently required.

[0247] In the aforementioned vehicle early warning method, if the power battery on a vehicle exhibits operational fault data characteristics related to a target fault type during operation, the historical production data characteristics of the power battery during production are obtained, along with target production early warning indicators related to the target fault type. If the historical production data characteristics match the target production early warning indicators, an early warning message is issued. By combining production and operational data from the battery's entire life cycle to perform vehicle early warnings, the accuracy of early warnings is improved.

[0248] The embodiment of this specification also provides a method for creating an early warning indicator, wherein the operating fault data features are stored in a fault tracing database; the target production data features are stored in an abnormal feature database. For example, referring to Figure 11, the method for creating an early warning indicator may include the following steps:

[0249] S1102: Acquire initial operating data of the faulty battery during operation.

[0250] S1104: Clean the initial operation data to obtain target operation data.

[0251] S1106: Extract features from the target operation data to obtain operation data features related to the operation process.

[0252] Among them, the operating data feature corresponds to the first cause.

[0253] S1108: The operation data feature of the first generation cause that meets the fault feature screening condition is used as the operation fault data feature that needs to be stored in the fault tracing database.

[0254] S1110. Acquire actual production data of the battery during the production process.

[0255] Among them, the actual production data corresponds to the expected production data.

[0256] S1112. Establish a production database based on actual production data generated during the process steps of the battery production process.

[0257] S1114: Determine the actual production data, whose difference from the expected production data exceeds a preset difference threshold range, as abnormal production data.

[0258] S1116. Extract features from abnormal production data to obtain production data features.

[0259] Among them, the production data characteristics correspond to the second cause.

[0260] S1118. The production data feature whose second generation cause meets the abnormal feature screening condition is used as the target production data feature that needs to be stored in the abnormal feature database.

[0261] The target production data feature is used to characterize a situation where the actual production data of the battery deviates from the expected production data of the battery.

[0262] S1120 , obtaining classified abnormal data features obtained by classifying target production data features, and classified fault data features obtained by classifying operation fault data features.

[0263] S1122. Use at least one of clustering, variance analysis, and proportion statistics to perform correlation analysis on the classified abnormal data features and the classified fault data features to obtain production fault correlation data.

[0264] Among them, the production failure correlation data is used to characterize the degree of correlation between the target production data characteristics and the failure type.

[0265] S1124. Determine target related data that meets preset related screening conditions in the production failure related data.

[0266] S1126. Use the target production data characteristics corresponding to the target-related data as production early warning indicators.

[0267] The embodiment of this specification provides a device 1200 for creating a warning indicator. Please refer to Figure 12. The device 1200 for creating a warning indicator includes: a data feature acquisition module 1210, a correlation analysis module 1220, and a warning indicator determination module 1230.

[0268] The data feature acquisition module 1210 is used to acquire target production data features during the production process of the battery, and operational fault data features related to the fault type during the operation of the faulty battery;

[0269] The correlation analysis module 1220 is used to perform correlation analysis using the target production data characteristics and the operational fault data characteristics to obtain production fault correlation data; wherein the production fault correlation data is used to characterize the degree of correlation between the target production data characteristics and the fault type;

[0270] The early warning indicator determination module 1230 is used to determine the production early warning indicator related to the fault type in the target production data characteristics according to the production fault correlation data.

[0271] The embodiment of this specification provides a battery warning device 1300 , as shown in FIG13 . The battery warning device 1300 includes: a production feature acquisition module 1310 and a warning prompt issuing module 1320 .

[0272] The production feature acquisition module 1310 is used to obtain actual production data features of the battery to be evaluated during the production process;

[0273] The warning prompt issuing module 1320 is used to issue a warning prompt message when the actual production data characteristics of the battery to be evaluated match the warning indicator. The method for creating the warning indicator includes:

[0274] Performing correlation analysis based on sample production data and sample operation failure data of preset sample batteries to obtain production failure correlation data; the number of preset sample batteries is multiple;

[0275] If the production fault correlation data is greater than a predetermined threshold, an early warning indicator is determined based on the sample production data corresponding to the sample operation fault data.

[0276] The embodiment of this specification provides a vehicle warning device 1400 , please refer to FIG. 14 . The vehicle warning device 1400 includes: a production feature acquisition module 1410 and a warning prompt issuing module 1420 .

[0277] The production feature acquisition module 1410 is configured to acquire historical production data features of the power battery during production if the power battery on the vehicle has an operation fault data feature related to the fault type during operation;

[0278] The warning prompt issuing module 1420 is used to issue a warning prompt message if the historical production data characteristics match the production warning index related to the fault type; wherein the production warning index is obtained according to the method described in any of the above embodiments.

[0279] The embodiment of this specification provides a vehicle warning device 1500 , as shown in FIG15 . The vehicle warning device 1500 includes: a characteristic indicator acquisition module 1510 and a warning prompt issuing module 1520 .

[0280] The characteristic indicator acquisition module 1510 is configured to acquire historical production data characteristics during the production process of the power battery and target production warning indicators related to the target fault type if the power battery on the vehicle exhibits operational fault data characteristics related to the target fault type during operation;

[0281] The early warning prompt issuing module 1520 is used to issue an early warning prompt message if the historical production data characteristics match the target production early warning index.

[0282] The embodiment of this specification provides a battery warning device 1600 , as shown in FIG16 . The battery warning device 1600 includes: a production feature acquisition module 1610 and a warning prompt issuing module 1620 .

[0283] The production feature acquisition module 1610 is used to obtain actual production data features of the battery to be evaluated during the production process;

[0284] The warning prompt issuing module 1620 is used to issue a warning prompt message when the actual production data characteristics of the battery to be evaluated match the production warning index; wherein the production warning index is obtained according to the method described in any of the above embodiments.

[0285] The embodiment of this specification provides a vehicle warning device 1700 , wherein the vehicle includes a power battery. Referring to FIG. 17 , the vehicle warning device 1700 includes a production feature acquisition module 1710 and a warning prompt issuing module 1720 .

[0286] The production feature acquisition module 1710 is used to obtain historical production data features during the production process of the power battery;

[0287] The warning prompt issuing module 1720 is used to match the historical production data characteristics with the production warning indicators. If the match is successful, a corresponding warning signal is issued to the vehicle, so that the vehicle issues a warning prompt information according to the warning signal. The method for creating the warning indicator includes:

[0288] Performing correlation analysis based on sample production data and sample operation failure data of preset sample batteries to obtain production failure correlation data; the number of preset sample batteries is multiple;

[0289] If the production fault correlation data is greater than a predetermined threshold, sample production data corresponding to the sample operation fault data is run.

[0290] For a detailed description of the warning indicator creation device, the battery warning device, and the vehicle warning device, please refer to the description of the indicator creation method, the warning indicator creation method, the battery warning method, and the vehicle warning method above, which will not be repeated here. Each module in the above-mentioned device can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0291] An embodiment of this specification provides a server including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method in any of the above embodiments when executing the computer program.

[0292] An embodiment of this specification provides a vehicle including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method in any one of the above embodiments are implemented.

[0293] It should be noted that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic device), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or otherwise processing it in a suitable manner if necessary, and then storing it in a computer memory.

Claims

1. A device early warning method, the device comprising subcomponents, the early warning method comprising: Obtaining historical production data characteristics of the subassembly during the production process; If the historical production data characteristics of the subcomponent match the warning indicator, a warning prompt message is issued; Among them, the early warning indicator is created by obtaining production failure correlation data based on sample production data and sample operation failure data of preset sub-component samples; if the production failure correlation data is greater than a predetermined threshold, the early warning indicator is determined according to the sample production data corresponding to the sample operation failure data; wherein the number of the preset sub-component samples is multiple.

2. The method according to claim 1, wherein: If the historical production data characteristics of the subcomponent match the warning index, a warning prompt message is issued to the subcomponent.

3. The method according to claim 1, wherein: The device is an electric energy device, the subassembly is a battery, and the preset subassembly sample is a preset sample battery; Before the correlation analysis is performed between the sample production data based on the preset sub-assembly sample and the sample operation failure data, the method further includes: Acquire sample actual production data of the preset sample battery during the production process; wherein the sample actual production data corresponds to sample expected production data; According to the comparison result between the sample actual production data and the sample expected production data, abnormal production data is determined in the sample actual production data as the sample production data.

4. The method according to claim 3, wherein: The method of determining the abnormal production data includes at least one of the following steps: determining the sample actual production data different from the sample expected production data as the abnormal production data; The sample actual production data whose difference data with the sample expected production data exceeds a preset difference threshold range is determined as the abnormal production data.

5. The method according to claim 3, wherein: The correlation analysis of the sample production data and the sample operation failure data based on the preset sub-assembly samples includes: Extracting features of the abnormal production data to obtain target production data features; A correlation analysis is performed on the target production data features and the sample operation fault data to obtain the production fault correlation data.

6. The method according to claim 5, wherein: After extracting features from the abnormal production data, the method further includes: Extracting features from the abnormal production data to obtain features of the abnormal production data; The abnormal production data features that meet the first preset screening condition are screened out as the target production data features.

7. The method according to claim 5 or 6, wherein: Before performing correlation analysis on the sample production data based on the preset subassembly sample and the sample operation failure data, the method further includes: The sample operation fault data features are obtained from the sample fault tracing database as the sample operation fault data.

8. The method according to claim 7, wherein: The method for constructing the sample fault tracing database includes: Acquiring operation data characteristics of a faulty battery during operation; the faulty battery is the preset sample battery that fails during operation; Filtering out the operation data features that meet the second preset screening condition as the sample operation fault data features; The sample operation fault data features are stored in the sample fault tracing database.

9. The method according to claim 8, wherein: The step of obtaining the operating data characteristics of the faulty battery during operation includes: Acquiring initial operation data of the faulty battery during operation; Performing data cleaning on the initial operation data to obtain target operation data; Feature extraction is performed on the target operation data to obtain the operation data feature.

10. The method according to claim 7, wherein: After obtaining the target production data characteristics, the following steps are included: Classified abnormal data features obtained by classifying the target production data features; After obtaining the sample operation fault data characteristics, the method includes: Classified fault data features obtained by classifying the sample operation fault data features; A correlation analysis is performed on the classified abnormal data features and the classified fault data features to obtain the production fault correlation data.

11. The method according to any one of claims 1 to 6, 8 to 10, wherein: The analysis method of performing correlation analysis to obtain the production failure correlation data includes at least one of clustering, variance analysis, and proportion statistics.

12. An electric energy device early warning method applied to a network device, the electric energy device comprising a battery, the method comprising: Obtaining historical production data characteristics of the battery during the production process; Matching the historical production data characteristics with the early warning indicators, and if the match is successful, sending a corresponding early warning signal to the electric energy equipment, so that the electric energy equipment sends out early warning prompt information according to the early warning signal; The method for creating the early warning indicator includes: Performing correlation analysis on sample production data and sample operation failure data of preset sample batteries to obtain production failure correlation data; the number of the preset sample batteries is multiple; If the production fault correlation data is greater than a predetermined threshold, a warning indicator is determined according to the sample production data corresponding to the sample operation fault data.

13. An early warning method applied to an electric energy device, the electric energy device comprising a battery, the method comprising: Receive warning signals from network devices, and issue corresponding warning prompt information according to the warning signals; The warning signal is determined by the network device according to the matching of the historical production data characteristics of the battery and the warning indicator. The method for creating the warning indicator includes: Performing correlation analysis on sample production data and sample operation failure data of preset sample batteries to obtain production failure correlation data; the number of the preset sample batteries is multiple; If the production fault correlation data is greater than a predetermined threshold, a warning indicator is determined according to the sample production data corresponding to the sample operation fault data.

14. An early warning method applied to an electric device, the electric device comprising a battery, the electric device storing historical production data characteristics of the battery, the method comprising: Receive early warning indicators from network devices; Matching the historical production data characteristics with the early warning indicators, and if the match is successful, issuing corresponding early warning prompt information; The method for creating the early warning indicator includes: Performing correlation analysis on sample production data and sample operation failure data of preset sample batteries to obtain production failure correlation data; the number of the preset sample batteries is multiple; If the production fault correlation data is greater than a predetermined threshold, a warning indicator is determined according to the sample production data corresponding to the sample operation fault data.

15. A network device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 12 when executing the computer program.

16. An electric energy device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the method according to claim 13 or 14 when executing the computer program.

17. The electrical energy device according to claim 16, wherein: The electric energy device is a vehicle, a ship or an aircraft.

18. A computer-readable storage medium, when instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the method according to any one of claims 1 to 14.

19. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the method according to any one of claims 1 to 14 is implemented.

20. A battery warning device, comprising: A production feature acquisition module is used to obtain actual production data features of the battery to be evaluated during the production process; The early warning prompt issuing module is used to issue an early warning prompt message when the actual production data characteristics of the battery to be evaluated match the early warning index; wherein the method for creating the early warning index includes: Performing correlation analysis on sample production data and sample operation failure data of preset sample batteries to obtain production failure correlation data; the number of the preset sample batteries is multiple; If the production fault correlation data is greater than a predetermined threshold, a warning indicator is determined according to the sample production data corresponding to the sample operation fault data.

21. A vehicle warning device, the vehicle comprising a power battery, the device comprising: A production feature acquisition module, used to acquire historical production data features during the production process of the power battery; An early warning prompt issuing module is used to match the historical production data characteristics with the production early warning indicators, and if the match is successful, a corresponding early warning signal is issued to the vehicle, so that the vehicle issues early warning prompt information according to the early warning signal; The method for creating the early warning indicator includes: Performing correlation analysis on sample production data and sample operation failure data of preset sample batteries to obtain production failure correlation data; the number of the preset sample batteries is multiple; If the production fault correlation data is greater than a predetermined threshold, a warning indicator is determined according to the sample production data corresponding to the sample operation fault data.

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