Early warning information processing method and apparatus, device, and storage medium

Abstract

Embodiments of the present disclosure disclose an early warning information processing method and apparatus, a device, and a storage medium. The method comprises: determining, for at least one device, first early warning information to be processed; on the basis of a first time interval, aggregating early warning information relating to a same device and a same fault type in the first early warning information, and determining first aggregated information; and when first historical early warning information having the same device and the same fault type as the first aggregated information is found in historical fault data, on the basis of the first time interval, aggregating the first aggregated information and the first historical early warning information to obtain fault early warning data.

Description

Processing method, device and equipment of early warning information and storage medium

[0001] Cross-reference to related applications

[0002] The present disclosure is based on the Chinese patent application No. 202411127097.2, filed on August 15, 2024, entitled "Processing method, device and equipment of early warning information and storage medium", and claims priority to the Chinese patent application, the whole content of which is incorporated herein by reference. TECHNICAL FIELD

[0003] The present disclosure relates to, but is not limited to, the technical field of batteries, and in particular to a processing method, device and equipment of early warning information and storage medium. BACKGROUND

[0004] With the vigorous promotion of new energy, large-scale energy storage systems are getting more and more applications. How to ensure the safe operation of the energy storage system has become a difficulty in the field of energy storage. The safe operation of the energy storage system not only needs to be designed and improved from the development of battery monomers and battery systems, but also needs to accurately identify and warn the abnormal state of the battery or the cell from the fault warning of the energy storage system, so that the equipment can be repaired as soon as possible.

[0005] At present, for the fault warning of the battery, the data analysis and algorithm decision are basically carried out on the measured point data of the battery, such as voltage, current and temperature parameters, and the algorithm result is compared with the set warning threshold to determine the abnormal battery and report the fault warning.

[0006] However, on the one hand, due to the large difference in working conditions of the battery in the energy storage system and the complexity of the electrochemical process of the battery, there is a lot of redundant warning information in the reported fault warning, which causes the repeated reporting of the warning information and reduces the processing efficiency of the fault warning. On the other hand, for some abnormal batteries, the battery cloud platform will continuously report fault warning information before maintenance, which causes the flooding of the fault warning information and reduces the processing efficiency of the fault warning. SUMMARY

[0007] The present disclosure provides a processing method, device and equipment of early warning information, which can improve the processing efficiency of the fault warning.

[0008] The technical scheme of the present disclosure embodiment is implemented as follows:

[0009] In a first aspect, the present disclosure provides a processing method of early warning information, the method comprising:

[0010] determining first early warning information to be processed for at least one device; based on the first time interval, aggregating early warning information of the same device and the same fault type in the first early warning information to determine first aggregated information; in a case where the first aggregated information is found to have the same device and the same fault type as first historical early warning information in the historical fault data, based on the first time interval, aggregating the first aggregated information and the first historical early warning information to obtain fault early warning data.

[0011] It can be understood that, on the one hand, the first early warning information to be processed indicates that there is an abnormal situation of the device in the energy storage system. There can be multiple early warning information of the same fault in the first early warning information to be processed, that is, there can be redundant early warning information in the first early warning information to be processed. By filtering early warning information with the same device and the same fault type in the first early warning information, and then aggregating the filtered first early warning information based on the first time interval, multiple early warning information of the same fault is aggregated to form aggregated information, thereby determining fault early warning data, and further reducing the repeated reporting of redundant early warning information, and effectively improving the processing efficiency of the fault early warning information. On the other hand, the first aggregated information is the first aggregated information in the current first preset time period, and the historical fault early warning data is the fault early warning data in the last first preset time period. If there is first historical early warning information with the same device and the same fault type as the first aggregated information in the historical fault early warning data, it is considered that the first aggregated information and the first historical early warning information are repeated early warning of the same fault, and the early warning information of the same fault in different first preset time periods is aggregated, thereby improving the effectiveness of the early warning information aggregation.

[0012] In some embodiments, the method further comprises: determining the first time interval according to real-time usage data of the at least one device; the usage data comprises at least one of the following: environmental temperature, usage frequency, and charge-discharge cycle.

[0013] It can be understood that the aging process of the device is nonlinear, and the aging process of the device is a complex process affected by multiple factors, mainly affected by the usage data of the device, such as usage frequency, environmental temperature, and charge-discharge cycle. Therefore, the first time interval is determined according to the usage data of the device, the first time interval is dynamically adjusted according to the actual state of the device, and the accuracy of the first time interval is effectively ensured.

[0014] In some embodiments, the method further comprises: assigning each first weight to different early warning information in the first early warning information according to the credibility of the early warning information; the credibility is positively correlated with the first weight; based on the first weight, the corresponding early warning information is weighted to aggregate early warning information of the same device and the same fault type to determine the first aggregated information.

[0015] It can be understood that the validity and accuracy of the first early warning information are judged according to the credibility of the first early warning information, and different first weights are given to different early warning information in the first early warning information according to the high and low of the credibility of different early warning information, so that more credible early warning information has a higher weight, thereby improving the effectiveness of the aggregation after the different early warning information in the first early warning information is weighted and processed.

[0016] In some embodiments, the method further comprises: using a time-based aggregation network to aggregate the early warning information of the same device and the same fault type in the first early warning information to determine the first aggregation information.

[0017] It can be understood that, according to the characteristics of the nonlinear aging of the device, the aging data of the device is predicted according to the time-based aggregation network, and the early warning information of the same device and the same fault type in the first early warning information is aggregated according to the prediction result, thereby improving the accuracy of the result of the aggregation.

[0018] In some embodiments, determining the first early warning information to be processed for at least one device comprises: performing fault correlation analysis on the initial early warning information to be processed for at least one device, fusing the early warning information having a correlation relationship in the initial early warning information, and determining the first early warning information; the fault correlation analysis is used to identify the potentially associated information in the early warning information.

[0019] It can be understood that, by performing fault correlation analysis on the early warning information to be processed in advance, the early warning information that may have a correlation is screened out as the first early warning information, thereby ensuring the effectiveness of the first early warning information and improving the accuracy of the aggregation.

[0020] In some embodiments, the method further comprises: filtering the initial early warning information to be processed for at least one device to remove the early warning information having errors therein, and determining the first early warning information.

[0021] It can be understood that there may be early warning information having errors in the initial early warning information to be processed, for example, false early warning caused by noise or false positives. Therefore, the early warning information having errors is removed in advance in the initial early warning information to be processed, thereby ensuring the effectiveness of the initial early warning information and improving the accuracy of the first early warning information.

[0022] In some embodiments, the method further comprises: processing the initial early warning information to be processed based on the multi-dimensional attribute information and the scene information of at least one device to determine the first early warning information.

[0023] It can be understood that the initial early warning information to be processed is processed by using the attribute information and the scene information of the device, and the first early warning information is formed, and the accuracy of fault aggregation by using the first early warning information is improved.

[0024] In some embodiments, the method further comprises: data cleaning and / or data filling for the initial early warning information to be processed to determine the first early warning information, wherein the data cleaning indicates deleting repeated early warning information and incorrect early warning information in the initial early warning information, and the data filling indicates adding missing early warning information in the initial early warning information.

[0025] It can be understood that the initial early warning information to be processed is pre-processed, and repeated early warning information and incorrect early warning information are deleted in the initial early warning information, which realizes the elimination or correction of incorrect early warning information, repeated early warning information and incomplete early warning information, and ensures the integrity and accuracy of the early warning information. The missing early warning information in the initial early warning information is added and filled, which reduces the influence caused by the sparseness of the initial early warning information, thereby improving the effectiveness and accuracy of the first early warning information.

[0026] In some embodiments, the multi-dimensional attribute information of the at least one device includes chemical composition and production batch of the at least one device; the multi-dimensional scene information of the at least one device includes usage scene and operation environment of the at least one device; and the initial early warning information to be processed is processed based on the multi-dimensional attribute information and the scene information of the at least one device to determine the first early warning information, including: based on at least one attribute information of the at least one device, early warning information with the same attribute information in the initial early warning information to be processed is fused to determine first fusion information; and based on at least one scene information of the at least one device, early warning information with the same scene information in the first fusion information is fused to determine the first early warning information.

[0027] It can be understood that the initial early warning information to be processed is fused by using the attribute information and the scene information of the device, and the first early warning information is formed, and the accuracy of fault aggregation by using the first early warning information is improved.

[0028] In some embodiments, the first early warning information to be processed for the at least one device is determined, including: in the case of reaching a first preset time period, increment data in the fault early warning record is acquired to obtain the first early warning information for the at least one device.

[0029] It can be understood that the increment data in the fault early warning record is read in a timing (first preset time period) processing manner, which reduces data congestion caused by continuous data reading, and ensures the throughput and low coupling of data reading.

[0030] In some embodiments, based on the first time interval, the first warning information of the same device and the same fault type is aggregated to determine the first aggregation information, including: determining at least two groups of warning information of the same device and the same fault type in the first warning information; if the plurality of warning information adjacent in time order in each group of warning information is less than the first time interval, the plurality of warning information is aggregated to determine the first aggregation information.

[0031] It can be understood that, based on the principle of the same device and the same fault type, the first warning information is grouped, and the warning information with the same device and the same fault type is grouped into the same group, which increases the convenience of subsequent aggregation judgment. In the same group, based on the first time interval, the plurality of warning information is aggregated, that is, the plurality of warning information of the same fault is aggregated to generate a first aggregation information, which reduces the repeated reporting of redundant warning information and effectively improves the efficiency of fault warning.

[0032] In some embodiments, if the plurality of warning information adjacent in time order in each group of warning information is less than the first time interval, the plurality of warning information is aggregated to determine the first aggregation information, including: if the plurality of warning information adjacent in time order in each group of warning information is less than the first time interval, the earliest occurrence time, the latest end time and the highest fault level in the plurality of warning information are determined as the fault occurrence time, the fault end time and the fault level of an aggregation information; until the plurality of aggregations in each group of warning information are completed, the first aggregation information is determined.

[0033] It can be understood that the first aggregation information indicates that the plurality of first warning information participating in the aggregation is the repeated reporting of the same fault, and by defining the fault occurrence time of the aggregation information as the earliest occurrence time in the same fault, the fault end time of the aggregation information as the latest end time in the same fault, and the fault level of the aggregation information as the highest fault level in the same fault, it is ensured that the first aggregation information can fully reflect the indication content of the plurality of warning information, and the accuracy of the first aggregation information is improved.

[0034] In some embodiments, the method further includes: in response to the viewing instruction, displaying the fault warning data and the detail entry of the first warning information on the fault management interface.

[0035] It can be understood that, due to the visual design based on the fault management interface, the fault warning data after aggregation is realized, and the entry of the details of the first warning information before aggregation is provided for viewing the warning information before aggregation, so that the diversity of information display of the warning information before and after aggregation is improved.

[0036] In some embodiments, in response to the viewing instruction, the fault early warning data and the detail entry of the first early warning information are displayed on the fault management interface, including: in response to the viewing instruction, the fault information, the fault level, the fault occurrence time, the fault end time, at least one of the device identifier and the fault state of the fault early warning data, and the detail entry of the first early warning information are displayed on the fault management interface.

[0037] It can be understood that after receiving the viewing instruction, the fault early warning data is displayed through the fault management interface, and through the fault management interface, the fault information, the fault level, the fault occurrence time, the fault end time, the device identifier and the fault state in the fault early warning data are more intuitively obtained, which helps to quickly view the fault problem and improves the display effect and intuitiveness and the visibility of the fault early warning. In addition, the entry of the details of the first early warning information before aggregation is provided for viewing the early warning information before aggregation. Therefore, the diversity of information display of the early warning information before and after aggregation is improved.

[0038] In some embodiments, the method further includes: in response to a triggering operation of the detail entry, displaying fault detail information of the first early warning information and a viewing entry of historical information on the fault analysis interface; the fault detail information includes: the fault occurrence time and the fault level of the first early warning information; in response to a selection operation of the first early warning information, displaying the selected first early warning information in a first manner on the fault analysis interface; and in response to a viewing operation of the historical information, displaying fault parameters of a fault type to which the selected first early warning information belongs in a drop-down page of the fault analysis interface.

[0039] It can be understood that in response to the triggering operation of the detail entry of the fault management interface, the fault analysis interface is jumped from the fault management interface, the fault occurrence time and the fault level of the first early warning information participating in forming the fault early warning data are displayed through the fault analysis interface, the same early warning information of different times and different fault levels aggregated in the same reported fault can be more intuitively obtained, and the visualization and diversity of the fault early warning are enhanced; through the viewing operation of the historical information, the fault parameters of the fault type to which the selected first early warning information belongs are displayed in the drop-down page of the fault analysis interface, which facilitates positioning and analyzing the fault cause through the fault parameters. In the fault early warning process, the effectiveness of providing the fault information is enhanced.

[0040] In some embodiments, in response to the viewing operation of the historical information, the fault parameters of the fault type to which the selected first early warning information belongs are displayed in the drop-down page of the fault analysis interface, including: in response to the viewing operation of the historical information, different dimensions of the fault parameters of the same device parameter at different times under the fault type to which the selected first early warning information belongs are displayed in the drop-down page of the fault analysis interface.

[0041] It can be understood that in the pull-down page of the fault analysis interface, the fault parameters of the first early warning information at different time points are displayed. The time span of the different time points is from the fault occurrence time of the first early warning information to the fault end time, and the fault parameters in the whole time period of the first early warning information are covered. By obtaining the fault parameters from different dimensions, the effectiveness and accuracy of the fault information provided in the fault early warning are improved.

[0042] In some embodiments, based on the first time interval, the first aggregated information is aggregated with the first historical early warning information to obtain the fault early warning data, including: based on the first time interval, the earliest occurrence time, the latest end time and the highest fault level in the first aggregated information and the first historical early warning information are determined as the fault occurrence time, the fault end time and the fault level of the fault early warning data.

[0043] It can be understood that since the first historical early warning information is aggregated with the first aggregated information based on the first time interval, it indicates that the first historical early warning information and the first aggregated information are reports of the same fault. The earliest occurrence time and the latest end time of the same fault are taken as the fault duration of the same fault, i.e. the fault duration of the fault early warning data. And the highest fault level in the first historical early warning information and the first aggregated information is taken as the fault level of the same fault. Therefore, the accuracy of the early warning time and the fault level of the fault early warning data is improved.

[0044] In a second aspect, the embodiments of the present disclosure provide a processing device for early warning information. The device includes: a device for determining the early warning information to be processed for at least one device; and based on a first time interval, aggregating the early warning information of the same battery and the same fault type in the first early warning information to determine the first aggregated information; and if the first historical early warning information with the same device and the same fault type as the first aggregated information is determined from the historical fault early warning data, then based on the first time interval, the first aggregated information is aggregated with the first historical early warning information to obtain the fault early warning data.

[0045] In a third aspect, the embodiments of the present disclosure provide a processing device for early warning information, including a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the program, the steps in the above method are implemented.

[0046] In a fourth aspect, the embodiments of the present disclosure provide a computer readable storage medium having a computer program stored thereon. When the processor executes the program, the steps in the above method are implemented. BRIEF DESCRIPTION OF DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings needed to be used in the embodiments description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present disclosure, and for those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0048] Fig. 1 is an optional flowchart of a processing method of early warning information according to an embodiment of the present disclosure;

[0049] Fig. 2a and Fig. 2b are optional aggregation diagrams of a processing method of early warning information according to an embodiment of the present disclosure;

[0050] Fig. 3 is an optional flowchart of a processing method of early warning information according to an embodiment of the present disclosure;

[0051] Fig. 4 is an optional flowchart of a processing method of early warning information according to an embodiment of the present disclosure;

[0052] Fig. 5 is an optional fault analysis page diagram of a processing method of early warning information according to an embodiment of the present disclosure;

[0053] Fig. 6 is an optional selection history information viewing page diagram of a processing method of early warning information according to an embodiment of the present disclosure;

[0054] Fig. 7 is an optional freezing diagram of a processing method of early warning information according to an embodiment of the present disclosure;

[0055] Fig. 8 is an optional overdue closing diagram of a processing method of early warning information according to an embodiment of the present disclosure;

[0056] Fig. 9 is an optional structural diagram of a processing method of early warning information according to an embodiment of the present disclosure;

[0057] Fig. 10 is an exemplary flowchart of a processing method of early warning information according to an embodiment of the present disclosure;

[0058] Fig. 11 is a structural diagram of a processing device of early warning information according to an embodiment of the present disclosure;

[0059] Fig. 12 is a structural diagram of a processing device of early warning information according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0060] In order to make the purposes, technical solutions and advantages of the present disclosure clearer, the technical solutions of the present disclosure are further described in detail below in combination with the drawings and embodiments, and the described embodiments should not be regarded as limitations of the present disclosure. All other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present disclosure.

[0061] In the following description, "some embodiments" are related to a subset of all possible embodiments, but it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict.

[0062] It should be noted that the terms "first", "second", "third" involved in the embodiments of the present disclosure are only to distinguish similar objects, and do not represent a specific order of the objects. It can be understood that "first", "second", "third" can be interchanged in a specific order or sequence as allowed, so that the embodiments of the present disclosure described herein can be implemented in an order other than that illustrated or described herein.

[0063] Those skilled in the art can understand that, unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meaning as generally understood by those of ordinary skill in the art to which the embodiments of the present disclosure belong. It should also be understood that terms such as those defined in a general dictionary should be understood to have meanings consistent with those in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as such herein.

[0064] The embodiments of the present disclosure provide a processing method for early warning information, as shown in FIG. 1, which can include S101-S103:

[0065] S101, determining first early warning information to be processed for at least one device.

[0066] In the embodiments of the present disclosure, in order to detect whether the device is running normally, the processing device for early warning information or the processing device for early warning information, i.e. the device cloud platform, can detect the reported or collected measuring point data or device data to determine whether the device has problems such as hidden dangers, abnormalities or failures, and the embodiments of the present disclosure do not limit the type of device.

[0067] It should be noted that the device can be a battery or a cell, or a component of a mobile device, and the embodiments of the present disclosure do not limit the device.

[0068] In the following embodiments, the battery in the energy storage system is taken as the device, and the battery cloud platform is taken as the device cloud platform.

[0069] In the embodiments of the present disclosure, when the device end of the energy storage system detects a battery abnormality or triggers a predefined alarm condition, the device end generates a pre-warning information (i.e., alarm data) of the battery. The pre-warning information of the battery is transmitted to the battery cloud platform of the energy storage system through data. The battery cloud platform processes the pre-warning information of the battery. The device end can be a sensor, a monitoring device, a server, or the like, and the embodiments of the present disclosure do not limit the device end. The battery cloud platform is a system or a server responsible for receiving and processing the pre-warning information.

[0070] It should be noted that the pre-warning information received by the battery cloud platform can have repeated reporting of the same fault, and the repeated reporting of the pre-warning information needs to be aggregated.

[0071] In the embodiments of the present disclosure, the battery cloud platform reads the incremental pre-warning information from the fault pre-warning record in a timing reading manner according to a first preset time period, for subsequent pre-warning processing of data. That is, the battery cloud platform reads the incremental pre-warning information in the fault pre-warning record when the first preset time period is reached. The incremental pre-warning information is determined as the first pre-warning information.

[0072] It can be understood that the incremental data in the fault pre-warning record is read in a timing (first preset time period) processing manner, which reduces the data congestion caused by continuous reading of data and ensures the throughput and low coupling of data reading.

[0073] In the embodiments of the present disclosure, the first pre-warning information can include pre-warning information of a plurality of devices (e.g., faulty batteries) or a plurality of pre-warning information of different types of the same device. The battery cloud platform obtains the first pre-warning information to be processed for at least one device within the first preset time period by timing reading of the first pre-warning information.

[0074] In some embodiments, the first pre-warning information can include fault information, fault level, fault occurrence time, fault end time, device identifier, and the like. The fault information includes a fault code and a fault name. The fault code in the fault information is used to distinguish different fault types, and the pre-warning information with the same fault code has the same fault type. The fault level can include a plurality of levels and a plurality of forms.

[0075] For example, the fault level is divided into three types: prompt, general, and serious. The fault level can be represented by L1 for prompt, L2 for general, and L3 for serious.

[0076] Exemplarily, the first preset time period is 3 days, the battery cloud platform acquires the warning information in the fault warning record at 2022-2-3 16:00:23 (the fault occurrence time), and acquires the warning information in the fault warning record from 2022-2-3 16:00:23 to 2022-2-6 16:00:23 (the fault end time) at 2022-2-6 16:00:23 (the fault end time), and determines the warning information in the time range from 2022-2-3 16:00:23 to 2022-2-6 16:00:23 as the first warning information. In the first warning information, there can be a piece of warning information with a fault code 601, a fault name of component fault alarm, a fault level of serious, a fault occurrence time of 2022-2-4 16:00:23, a fault end time of 2022-2-4 17:00:23, and a device identifier 00110.

[0077] In S102, based on the first time interval, the battery cloud platform aggregates the warning information of the same device and the same fault type in the first warning information to determine the first aggregated information.

[0078] In the embodiment of the present disclosure, when the battery cloud platform reads the first warning information, the warning information with the same device and the same fault type is divided into a group. The battery cloud platform determines the warning information with the same device and the same fault type as the aggregation subject, and each group can contain multiple aggregation subjects. The first time interval is used as a judgment condition for whether the aggregation subject can be aggregated. The battery cloud platform aggregates the multiple aggregation subjects in the same group that meet the aggregation condition based on the first time interval to form the first aggregated information.

[0079] In the embodiment of the present disclosure, when the multiple aggregation subjects meet the first time interval condition, the battery cloud platform considers that the faults to which the multiple aggregation subjects belong are the same fault, and therefore, the battery cloud platform aggregates the multiple warning information indicating the same fault to obtain the first aggregated information.

[0080] It should be noted that the result of aggregating the multiple warning information can be aggregation success or aggregation failure. In the case of aggregation success, the first aggregated information is the warning information after aggregation; in the case of aggregation failure, the first aggregated information is the first warning information participating in aggregation.

[0081] In the embodiments of the present disclosure, the first time interval is a condition for judging whether the aggregation subject can be aggregated. If the reporting time interval of the early warning information of the aggregation subject is less than the first time interval, it is considered that the faults to which the plurality of aggregation subjects belong are the reporting of the same fault, and the plurality of aggregation subjects need to be aggregated, and it is determined as one of the first aggregation information. If the reporting time interval of the early warning information of the aggregation subject exceeds the first time interval, it is considered that the faults to which the plurality of aggregation subjects belong are the reporting of different faults, and the plurality of aggregation subjects are not aggregated, and the plurality of aggregation subjects are directly determined as one of the first aggregation information.

[0082] It should be noted that the early warning reporting time can be the fault occurrence time or the fault end time.

[0083] Exemplarily, the first time interval is 3 days, if there is a first early warning information with a warning reporting time of 2022-07-12 12:00:20, and a first early warning information with a warning reporting time of 2022-07-13 12:00:20, and the two early warning information have the same device and the same fault type, that is, the two early warning information are two aggregation subjects. Since the time interval of the two aggregation subjects is less than 3 days, the two aggregation subjects can be aggregated, and the first aggregation information is obtained.

[0084] S103, in the case of querying the first historical early warning information with the same device and the same fault type as the first aggregation information in the historical fault data, the first aggregation information is aggregated with the first historical early warning information based on the first time interval, and the fault early warning data is obtained.

[0085] In the embodiments of the present disclosure, the battery cloud platform reads the incremental early warning information from the fault early warning record every first preset time period, and the early warning information read in the current first preset time period is determined as the first early warning information. The first aggregation information is obtained by aggregating the first early warning information. In the last first preset time period, the fault early warning data determined by the first aggregation information is the historical fault early warning data. The first aggregation information in the current first preset time period is aggregated with the historical fault early warning data to determine the fault early warning data.

[0086] Among them, the first early warning information before aggregation is stored in the fault detail table, and the fault early warning data after aggregation is stored in the fault result table.

[0087] It should be noted that the early warning information generated by the device is transmitted to the battery cloud platform through a communication mechanism. The communication mechanism can be a network connection. Illustratively, a message queue telemetry transmission protocol (MQTT) or a hypertext transfer protocol (HTTP). The communication mechanism can also be other special protocols. Illustratively, a Modbus communication protocol (Modbus) or a simple network management protocol (SNMP).

[0088] It should be noted that the fault detail table and the fault result table are designed in a twin structure, which can be independently optimized, expanded or modified without affecting each other, facilitating the writing of the aggregated first early warning information.

[0089] Illustratively, the fault detail table can be as shown in Table 1. The fault detail table can include field name, comment and data type.

[0090] Table 1

[0091] In the embodiments of the present disclosure, a first preset time period is set, and the incremental early warning information is read from the fault early warning record in a timing reading manner, and the fault early warning data generated in the first preset time period is stored in the fault result table. Therefore, in the current first preset time period, the fault result table contains a plurality of historical fault early warning data in the historical first preset time period.

[0092] In the embodiments of the present disclosure, when the first preset time period is reached, the battery cloud platform reads the incremental early warning information in the fault early warning record, and obtains the first aggregated information through the aggregation rule. After obtaining the first aggregated information, the battery cloud platform queries the historical fault data in the fault result table, i.e. the historical fault data in the historical first preset time period. If there is first historical early warning information with the same battery and the same fault type as the first aggregated information, i.e. there is an aggregation subject between the first historical early warning information and the first aggregated information. The battery cloud platform aggregates the aggregation subject based on the judgment condition of the first time interval to form the fault early warning data.

[0093] It should be noted that the battery cloud platform aggregates multiple early warning information indicating the same fault to obtain first aggregation information. The result of aggregating the multiple early warning information can be successful aggregation or failed aggregation. In the case of successful aggregation, the first aggregation information is the early warning information after successful aggregation. In the case of failed aggregation, the first aggregation information is the first early warning information participating in aggregation.

[0094] In some embodiments, in the aggregation rule defined by the battery cloud platform, the fault occurrence time of the aggregated early warning information is the earliest fault occurrence time among the multiple aggregation subjects; the fault end time of the aggregated early warning information is the latest fault end time among the multiple aggregation subjects; and the fault level of the aggregated early warning information is the highest fault level among the multiple aggregation subjects.

[0095] In the embodiments of the present disclosure, if the first historical early warning information with the same device and the same fault type as the first aggregation information is determined from the historical fault early warning data, the earliest occurrence time in the first aggregation information and the first historical early warning information is determined as the fault occurrence time of the fault early warning data based on the first time interval; the latest end time in the first aggregation information and the first historical early warning information is determined as the fault end time of the fault early warning data; and the highest fault level in the first aggregation information and the first historical early warning information is determined as the fault level of the fault early warning data.

[0096] It should be noted that the reporting time of the fault can be the occurrence time of the fault or the end time of the fault.

[0097] Exemplarily, as shown in FIGS. 2a and 2b, five pieces of fault early warning data are displayed in the fault management interface. In the drop-down page of the fault early warning data with the first fault code ***********06629, it is shown that the fault early warning data is aggregated by three aggregation subjects. The three columnar bodies respectively represent the three aggregation subjects. Among them, the first aggregation subject has the earliest fault reporting time, and the fault reporting time is 2024-2-15 17:01:03; the third aggregation subject has the latest fault reporting time, and the fault reporting time is 2024-2-19 09:24:08; and the fault level of the second aggregation subject is the highest, which is serious (L3). Therefore, based on the aggregation rule, the fault occurrence time of the aggregated fault early warning data is 2024-2-15 17:01:03; the fault end time of the fault early warning data is 2024-2-19 09:24:08; and the fault level of the fault early warning data is serious (L3).

[0098] It can be understood that, on the one hand, the first early warning information to be processed indicates that there is an equipment abnormality in the energy storage system. There can be multiple early warning information of the same fault in the first early warning information to be processed, that is, there can be redundant early warning information in the first early warning information to be processed. By filtering the early warning information with the same equipment and the same fault type in the first early warning information, and then based on the first time interval, the filtered first early warning information is aggregated. That is, multiple early warning information of the same fault is aggregated to form aggregated information, so as to determine the fault early warning data, thereby reducing the repeated reporting of redundant early warning information and effectively improving the processing efficiency of the fault early warning information. On the other hand, the first aggregated information is the first aggregated information in the current first preset time period, and the historical fault early warning data is the fault early warning data in the last first preset time period. If there is first historical early warning information with the same equipment and the same fault type in the first aggregated information in the historical fault early warning data, it is considered that the first aggregated information and the first historical early warning information are repeated early warning of the same fault, and the early warning information of the same fault in different first preset time periods is aggregated, thereby improving the effectiveness of the early warning information aggregation.

[0099] In some embodiments, the method further comprises: determining the first time interval according to real-time usage data of the at least one device; the usage data comprises at least one of the following: ambient temperature, usage frequency, and charge-discharge cycle.

[0100] In the embodiments of the present disclosure, the first time interval can be adjusted according to real-time usage data of the device, and the manner of adjusting the first time interval in the embodiments of the present disclosure is not limited. The adjustment can be to extend the first time interval as needed, or to shorten the first time interval as needed.

[0101] In some embodiments, the adjustment of the first time interval can be adjusted by using time series analysis, for example, a neural network analysis method, etc. The data of the aging of the device is predicted by using the time series analysis, so as to adjust the first time interval.

[0102] In the embodiments of the present disclosure, the real-time usage data of the device is not limited, which can be the ambient temperature, the usage frequency, or the charge-discharge cycle.

[0103] In the embodiments of the present disclosure, the first time interval should be continuously adjusted according to the actual aging process of the device. The aging process of the device is nonlinear, and the nonlinear process is affected by factors such as the ambient temperature, the usage frequency, and the charge-discharge cycle of the device. Therefore, the first time interval is adjusted according to the real-time usage data of the device.

[0104] It can be understood that the aging process of the device is nonlinear, and the aging process of the device is a complex process affected by multiple factors, mainly affected by the use data of the device, for example, the use frequency of the device, the environmental temperature, and the charge and discharge cycle. Therefore, according to the use data of the device, the first time interval is determined, the first time interval is dynamically adjusted according to the actual state of the device, and the accuracy of the first time interval is effectively ensured.

[0105] In some embodiments, the method further comprises: assigning respective first weights to different pieces of first early warning information according to the credibility of the early warning information; the credibility is positively correlated with the first weight; and based on the first weight, the corresponding early warning information is processed to aggregate early warning information of the same device and the same fault type, and determine first aggregated information.

[0106] In the embodiments of the present disclosure, the manner in which the battery cloud platform judges the credibility of the early warning information is not limited, and in general, it is a manner that can score the credibility of the early warning information.

[0107] In some embodiments, based on the battery cloud platform, an evaluation model for the credibility of the early warning information can be trained, and the evaluation model is used to score the credibility of the first early warning information.

[0108] In the embodiments of the present disclosure, the manner in which the battery cloud platform processes the early warning information is not limited. In some possible embodiments, the battery cloud platform can use a simple weighted sum method or a more complex aggregation algorithm method, such as cluster analysis, to calculate the first aggregated information.

[0109] In the embodiments of the present disclosure, since the first early warning information can be affected by noise, resulting in inaccurate first early warning information, before aggregating the early warning information of the same device and the same fault type in the first early warning information, the battery cloud platform first processes the multiple early warning information to be aggregated in the first early warning information, and gives higher weights to more credible early warning information to be aggregated. Then, according to the first time interval, the early warning information of the same device and the same fault type in the first early warning information is aggregated to determine the first aggregated information.

[0110] It can be understood that according to the credibility of the first early warning information, the effectiveness and accuracy of the first early warning information are judged, and different first weights are given to different early warning information according to the credibility of different early warning information in the first early warning information, so that more credible early warning information has a higher weight. Therefore, after the different early warning information in the first early warning information is processed, the effectiveness of the aggregation is improved.

[0111] In some embodiments, the method further comprises: using a time-based aggregation network to aggregate the early warning information of the same device and the same fault type in the first early warning information to determine first aggregation information.

[0112] In the embodiments of the present disclosure, the time-based aggregation network is not limited, and in general, it is capable of performing time series analysis on the first early warning information, for example, a neural network model.

[0113] In some embodiments, the battery cloud platform arranges the first early warning information in chronological order to form time series data, and the battery cloud platform applies an aggregation network model to model each time series data. Based on the characteristics of device aging, the battery cloud platform uses the model to predict the early warning information of the device in the future time, compares the current actual generated first early warning information, and aggregates the first early warning information that meets the prediction result according to the aggregation principle of the same device and the same fault type.

[0114] It can be understood that the battery cloud platform predicts the aging data of the device according to the time-based aggregation network based on the characteristics of the nonlinear aging of the device, and aggregates the early warning information of the same device and the same fault type in the first early warning information according to the prediction result, thereby improving the accuracy of the aggregated result.

[0115] In some embodiments, the method further comprises: performing fault correlation analysis on the initial early warning information to be processed of at least one device, fusing the early warning information having a correlation relationship in the initial early warning information to determine the first early warning information; and the fault correlation analysis is used to identify the potentially correlated information in the early warning information.

[0116] In the embodiments of the present disclosure, there is a fault correlation between the initial early warning information to be processed, and based on this, the battery cloud platform needs to perform fault correlation analysis on the initial early warning information to be processed before aggregating the initial early warning information to be processed, and filter out the early warning information that may have a correlation as the first early warning information.

[0117] In some embodiments, the battery cloud platform first performs fault correlation analysis and processing on the initial early warning information to be processed of at least one device, and then fuses the processed information based on the multi-dimensional attribute information and scene information of the at least one device to determine the first early warning information.

[0118] In some embodiments, the battery cloud platform performs fault correlation analysis and processing on the initial early warning information to be processed of at least one device, and at the same time, the battery cloud platform fuses the initial early warning information to be processed based on the multi-dimensional attribute information and scene information of the at least one device. The battery cloud platform determines the first early warning information according to the results of the fault correlation analysis and the fusion results.

[0119] It can be understood that, by performing fault correlation analysis on the to-be-processed early warning information in advance, early warning information that may be associated is screened out as the first early warning information, the effectiveness of the first early warning information is ensured, and the aggregation accuracy is improved.

[0120] In some embodiments, the method further includes: filtering the initial early warning information to-be-processed of the at least one device, eliminating incorrect early warning information, and determining the first early warning information.

[0121] In the embodiments of the present disclosure, the filtering manner is not limited, and in general, it is a manner that can filter incorrect early warning information in the initial early warning information.

[0122] In some embodiments, the filtering manner can be a filtering algorithm based on a statistical rule, or a filtering algorithm based on a machine learning model.

[0123] In the embodiments of the present disclosure, the battery cloud platform filters the initial early warning information to-be-processed of the at least one device according to the filtering algorithm, eliminates incorrect early warning information in the initial early warning information, for example, early warning information that does not match the actual situation of the device due to noise influence or false positives.

[0124] It can be understood that, by eliminating incorrect early warning information in the initial early warning information to-be-processed in advance, the effectiveness of the initial early warning information is ensured, and the accuracy of the first early warning information is improved.

[0125] In some embodiments, the method further includes: processing the initial early warning information to-be-processed based on multi-dimensional attribute information and scene information of the at least one device, and determining the first early warning information.

[0126] In the embodiments of the present disclosure, the multi-dimensional attribute information of the at least one device includes chemical components and production batches of the at least one device; and the multi-dimensional scene information of the at least one device includes a use scene and an operation environment of the at least one device.

[0127] In the embodiments of the present disclosure, after determining the early warning information to-be-processed of the at least one device, the battery cloud platform processes the early warning information to-be-processed based on the multi-dimensional attribute information and the scene information, and determines the processed early warning information as the first early warning information.

[0128] In some embodiments, based on the multi-dimensional attribute information and scene information of the at least one device, the processing of the to-be-processed early warning information is performed to determine the first early warning information, including: based on at least one attribute information of the at least one device, the early warning information with the same attribute information in the to-be-processed early warning information is fused to determine the first fusion information; based on at least one scene information of the at least one device, the early warning information with the same scene information in the first fusion information is fused to determine the first early warning information.

[0129] In the embodiments of the present disclosure, the to-be-processed early warning information contains at least one early warning information with the same attribute information (chemical composition information and production batch information), and the battery cloud platform fuses the early warning information with the same attribute information. According to the scene information of the fused early warning information, the early warning information with the same scene information in the fused early warning information is fused again, and based on this, the to-be-processed early warning information is fused to obtain at least one first early warning information with the same attribute information and scene information.

[0130] It can be understood that the initial early warning information to be processed is fused by using the attribute information and scene information of the device to form the first early warning information, which improves the accuracy of fault aggregation by using the first early warning information.

[0131] In some embodiments, the method further includes: data cleaning and / or data filling are performed on the initial early warning information to be processed to determine the first early warning information, wherein the data cleaning indicates that the repeated early warning information and the incorrect early warning information in the initial early warning information are deleted, and the data filling indicates that the missing early warning information in the initial early warning information is added.

[0132] In the embodiments of the present disclosure, the initial early warning information to be processed may contain repeated early warning information and incorrect early warning information different from the actual situation of the device, and the battery cloud platform deletes such early warning information.

[0133] In the embodiments of the present disclosure, the initial early warning information to be processed may be partially missing or have less early warning information, and therefore the battery cloud platform performs data filling on such early warning information.

[0134] In some embodiments, the data cleaning can include eliminating or correcting the incorrect early warning information, the repeated early warning information and the incomplete early warning information, to ensure the integrity and accuracy of the initial early warning information to be processed.

[0135] In some embodiments, the data filling can use an interpolation method, a regression method or a machine learning method to fill the missing early warning information, to reduce the influence of the sparsity of the early warning information.

[0136] It can be understood that the initial early warning information to be processed is subjected to data preprocessing, and repeated early warning information and incorrect early warning information are deleted from the initial early warning information, so that incorrect early warning information, repeated early warning information and incomplete early warning information are eliminated or corrected, and the completeness and accuracy of the early warning information are ensured. The missing early warning information in the initial early warning information is added and supplemented, the influence caused by the sparseness of the initial early warning information is reduced, and thus the effectiveness and accuracy of the first early warning information are improved.

[0137] In some embodiments, as shown in FIG. 3, S102 can include S201-S202:

[0138] S201, determining at least two groups of early warning information of the same fault device and the same fault type in the first early warning information.

[0139] In the embodiments of the present disclosure, the battery cloud platform queries the first early warning information, and divides the first early warning information with the same device and the same fault type into a group. Since the first early warning information contains early warning information of multiple devices, in the first early warning information, there are at least two groups of early warning information.

[0140] It should be noted that the battery cloud platform is provided with an aggregation rule, and the aggregation subject in the aggregation rule is the first early warning information with the same device and the same fault type. The aggregation subject determines whether to aggregate based on the time interval in the aggregation rule.

[0141] In some embodiments, after the plurality of first early warning information is aggregated, the battery cloud platform updates the fault occurrence time, the fault end time and the fault level of the aggregated early warning information according to the aggregation rule. The fault occurrence time is the earliest occurrence time in the plurality of first early warning information; the fault end time is the latest end time in the plurality of first early warning information; and the fault level is the highest fault level in the plurality of first early warning information.

[0142] S202, if the plurality of early warning information adjacent in time in each group of early warning information is less than the first time interval in time sequence, the plurality of early warning information is aggregated to determine the first aggregated information.

[0143] It should be noted that when the battery cloud platform groups the first early warning information, the first early warning information is arranged in each group according to the fault occurrence time. The fault occurrence time arrangement can be ascending arrangement or descending arrangement, which is not limited in the embodiments of the present disclosure.

[0144] In the embodiments of the present disclosure, the battery cloud platform defines a first time interval in the aggregation rule, and determines whether to aggregate the early warning information based on the first time interval. In each group of early warning information, the battery cloud platform arranges the first early warning information based on the fault occurrence time of the first early warning information. The battery cloud platform starts from the first early warning information in the group and sequentially queries, queries the time interval of a plurality of early warning information adjacent in time, when the time interval of the plurality of early warning information is less than the first time interval, the battery cloud platform regards the plurality of early warning information as a repeated report of the same fault, and the battery cloud platform aggregates the plurality of early warning information.

[0145] That is, in the embodiments of the present disclosure, the battery cloud platform queries the early warning information of the fault detail table, and divides the first early warning information with the same fault code and the same device code into the same group, and the battery cloud platform includes at least one aggregation subject. In the same group, the aggregation subject is arranged according to the fault occurrence time. The battery cloud platform aggregates the aggregation subject according to the aggregation rule based on the first time interval, and the first aggregated information after aggregation is stored in the fault result table.

[0146] In some embodiments, when the time interval of the plurality of early warning information is greater than the first time interval, the battery cloud platform regards the plurality of early warning information as a report of different faults, and the battery cloud platform does not aggregate the plurality of early warning information.

[0147] In the embodiments of the present disclosure, if the aggregation subjects in the same group meet the first time interval, that is, the aggregation subjects meeting the first time interval belong to the report of the same fault, the aggregation subjects are aggregated to form the early warning information after aggregation. If the aggregation subjects do not meet the first time interval, the aggregation subjects belong to different faults. At this time, the aggregation subjects are not aggregated, and the battery cloud platform confirms the aggregation subjects exceeding the first time interval as the report of a new fault to form a new early warning information. The battery cloud platform displays the early warning information through the upper computer interface (fault management interface).

[0148] It should be noted that if the fault end time of a certain aggregation subject traversed by the battery cloud platform and the fault occurrence time of the next aggregation subject differ by less than the first time interval, the two aggregation subjects meet the first time interval, and the two aggregation subjects are aggregated. If the fault end time of a certain aggregation subject traversed by the battery cloud platform and the fault occurrence time of the next aggregation subject differ by more than the first time interval, the two aggregation subjects do not meet the first time interval, and the battery cloud platform does not aggregate the two aggregation subjects. Moreover, for the aggregation subject with the later fault occurrence time in the two aggregation subjects, the battery cloud platform confirms it as a new fault report.

[0149] It should be noted that when the battery cloud platform determines whether the aggregation subject meets the first time interval, the aggregation subject needs to be traversed in sequence in the same group. The early warning information of the aggregation subject includes the fault occurrence time and the fault end time.

[0150] In the embodiments of the present disclosure, the early warning information in the fault management interface includes the early warning information aggregated by the aggregation subject and new early warning information. By clicking the detail entry of the aggregated early warning information in the fault management interface, the early warning information of each aggregated aggregation subject can be queried.

[0151] It can be understood that, based on the principle of the same battery and the same fault type, the first early warning information is grouped, and the early warning information with the same battery and the same fault type is grouped into the same group, which increases the convenience of subsequent aggregation judgment. In the same group, based on the first time interval, the multiple early warning information is aggregated, that is, the multiple early warning information belonging to the same fault is aggregated to generate a first aggregation information, which reduces the repeated reporting of redundant early warning information and effectively improves the efficiency of fault early warning.

[0152] In some embodiments, as shown in FIG. 4, S202 can include S301 and S302:

[0153] S301, if the multiple early warning information adjacent in time in each group of early warning information is less than the first time interval, the earliest occurrence time, the latest end time and the highest fault level in the multiple early warning information are determined as the fault occurrence time, the fault end time and the fault level of an aggregation information;

[0154] S302, until the multiple aggregations in each group of early warning information are completed, the first aggregation information is determined.

[0155] It should be noted that the first aggregation information is aggregated by multiple first early warning information. The multiple first early warning information participating in the first aggregation information is the repeated reporting of the same fault.

[0156] In the embodiments of the present disclosure, in each group of early warning information, the early warning information is arranged in ascending order or descending order of fault occurrence time, and the battery cloud platform starts from the first early warning information or the last early warning information in the group to query the time interval of the multiple early warning information adjacent in time. If the time interval of the early warning information adjacent in time is less than the first time interval, that is, the multiple early warning information is the repeated reporting of the same fault, the multiple early warning information is aggregated. The result of aggregation is determined as the first aggregation information. The fault occurrence time of the first aggregation information is the earliest occurrence time in the multiple early warning information; the fault end time of the first aggregation information is the latest end time in the multiple early warning information; and the fault level of the first aggregation information is the highest fault level in the multiple early warning information.

[0157] In the embodiments of the present disclosure, the battery cloud platform sequentially queries all the early warning information in the group from the first early warning information in the group, and when the early warning information meeting the aggregation rule is queried, the early warning information is aggregated. After the aggregation is completed, the battery cloud platform continues to query in the group, and then the aggregated early warning information and the currently queried early warning information are aggregated. The battery cloud platform can perform multiple aggregations in the same group until all the early warning information in the group is queried. At this time, there is no early warning information in the same group that can be aggregated. The battery cloud platform determines the early warning information aggregated in the same group as a first aggregation information.

[0158] It should be noted that the battery cloud platform can process in batches between different groups until each group completes the aggregation processing and obtains the first aggregation information.

[0159] It can be understood that the first aggregation information indicates that the multiple first early warning information participating in the aggregation is the repeated reporting of the same fault, and the fault occurrence time of the aggregation information is defined as the earliest occurrence time in the same fault, the fault end time of the aggregation information is the latest end time in the same fault, and the fault level of the aggregation information is the highest fault level in the same fault, so as to ensure that the first aggregation information can fully reflect the indication content of the multiple early warning information and improve the accuracy of the first aggregation information.

[0160] In some embodiments, the method can further include: in response to the viewing instruction, displaying, in the fault management interface, the fault early warning data and the detail entry of the first early warning information.

[0161] In the embodiments of the present disclosure, the battery cloud platform displays, in the fault management interface, at least one of the fault information, the fault level, the fault occurrence time, the fault end time, the device identifier and the fault state of the fault early warning data and the detail entry of the first early warning information in response to the viewing instruction.

[0162] In the embodiments of the present disclosure, in order to visualize the early warning information, after the early warning information is aggregated to form the fault early warning data, the battery cloud platform can display the fault early warning data in the fault management interface in response to the viewing operation. The fault early warning interface can be a graphic text, which displays at least one of the fault information, the fault level, the fault occurrence time, the fault end time, the device identifier and the fault state of the fault early warning data and the detail entry of the first early warning information. In addition, the detail entry of the first early warning information can also be displayed in any one of the fault early warning data in the fault management interface, and the detail entry is used to view the multiple first early warning information participating in the aggregation of the fault early warning data. The display of the detail entry can be an icon display control or a text display control, and the embodiments of the present disclosure do not make specific limitations on the display mode of the detail entry.

[0163] Exemplarily, the fault management interface of the battery cloud platform is shown in FIG. 2a and FIG. 2b. The condition controls included in the fault management interface are fault number, fault code, fault name, fault level, fault occurrence time, fault confirmation time, fault end time, device type, device number, fault status, and operation. Among them, the fault early warning data displayed in the fault management interface is: the fault number is ***********06629, the fault code is 602, the fault name is battery abuse type alarm, the fault level is L3-severe, the fault occurrence time is 2024-02-15 17:01:03, the fault end time is 2024-02-19 19:24:08, the device type is vehicle frame code number VIN, the device number is *********00241, the fault status is active_unconfirmed, and the operation is to view details (i.e. the details entry). The fault management interface displays 5 fault early warning data, and each of the fault early warning data contains a details entry. The details entry is used to view the multiple first early warning information participating in the aggregation of the fault early warning data.

[0164] It should be noted that the battery cloud platform obtains the first aggregation information by aggregating the first early warning information, and then obtains the fault early warning data according to the first aggregation information. That is, the fault early warning data is obtained by participating in multiple first early warning information.

[0165] It can be understood that, on the one hand, the first early warning information indicates that there is an abnormal situation of the device in the energy storage system. There may be multiple early warning information of the same fault in the first early warning information, that is, there may be redundant early warning information in the first early warning information. By filtering the early warning information with the same device and the same fault type in the first early warning information, and then aggregating the filtered first early warning information based on the first time interval, the multiple early warning information of the same fault is aggregated to form the aggregation information, so as to determine the fault early warning data, thereby reducing the repeated reporting of redundant early warning information and effectively improving the processing efficiency of the fault early warning information. On the other hand, based on the visual design on the fault management interface, the fault early warning data after aggregation is realized, and the entry of the details of the first early warning information before aggregation is provided for viewing the early warning information before aggregation. Therefore, the diversity of information display before and after aggregation of the early warning information is improved.

[0166] In some embodiments, the method can further include S401 to S403:

[0167] S401, in response to the triggering operation of the details entry, displaying the fault detail information of the first early warning information and the viewing entry of the historical information in the fault analysis interface; the fault detail information includes the fault occurrence time and the fault level of the first early warning information.

[0168] In some embodiments, the fault early warning data and a detail entry of the first early warning information are displayed on the fault management interface. By entering the detail entry, a plurality of first early warning information participating in the aggregation of the fault early warning data can be displayed. The battery cloud platform can respond to a triggering operation of the detail entry of any fault early warning data.

[0169] In the embodiments of the present disclosure, in the case of needing to view the plurality of first early warning information participating in the aggregation of the fault early warning data, the battery cloud platform responds to the triggering operation of the detail entry to jump from the fault management interface to the fault analysis interface, in which the fault detail information of the first early warning information and a viewing entry of historical information are displayed. The fault detail information of the first early warning information can be displayed in a table or in a graph, and the display mode of the fault detail information is not limited in the embodiments of the present disclosure.

[0170] In the embodiments of the present disclosure, the fault detail information can include the fault occurrence time and the fault level of the first early warning information. The historical information is the fault parameter of the first early warning information at different historical time.

[0171] It should be noted that the start time of the different historical time is the fault occurrence time of the first early warning information, and the end time of the different historical time is the fault end time of the first early warning information.

[0172] Exemplarily, the battery cloud platform responds to the triggering operation of the detail entry to jump from the fault management interface to the fault analysis interface. As shown in FIG. 5, the fault analysis interface includes a fault reason (pressure difference too large warning) control, a first early warning information control, and a viewing historical information control (a viewing entry of historical information). In the first early warning information control, the fault levels (L3-severe, L2-general, and L1-prompt) of the 11 first early warning information with the fault reporting times of 2023-06-11 04:35:28, 2023-06-11 04:35:58, 2023-06-11 04:50:13, 2023-06-11 07:20:29, 2023-06-11 07:51:12, 2023-06-11 08:30:23, 2023-06-11 14:00:58, 2023-06-11 14:02:39, 2023-06-11 15:18:48, 2023-06-11 17:07:14, and 2023-06-11 21:01:36 are displayed respectively. The first early warning information control is displayed in a column chart, and each column corresponds to each aggregated first early warning information. By selecting the first early warning information 611 and selecting the viewing historical information control, the detailed information of the first early warning information (a plurality of early warning information) before aggregation can be displayed.

[0173] S402, in response to the selection operation of the first early warning information, displaying the selected first early warning information in the fault analysis interface in a first manner.

[0174] In the embodiments of the present disclosure, the fault analysis interface contains a plurality of first early warning information, and the battery cloud platform can display the selected first early warning information in the fault analysis interface in a first manner in response to the selection operation of any one of the first early warning information.

[0175] It should be noted that the first manner can be highlighted display or frame selection display, and the embodiments of the present disclosure do not limit the type of the first manner.

[0176] Exemplarily, as shown in FIG. 6, in the first early warning information control of the fault analysis interface, the columnar body of the first early warning information with the fault occurrence time of 2023-06-1121:01:36 is clicked, and a dashed line frame is displayed outside the columnar body with the fault occurrence time of 2023-06-1121:01:36, which indicates that the early warning information has been selected. The battery cloud platform responds to the first early warning information selection and displays the fault parameters of the selected first early warning information.

[0177] S403, in response to the viewing operation of the historical information, displaying the fault parameters of the fault type to which the selected first early warning information belongs in the drop-down page of the fault analysis interface.

[0178] In the embodiments of the present disclosure, the battery cloud platform displays another drop-down page below the fault analysis interface in response to the viewing operation of the historical information. In the drop-down page, the fault parameters of the first early warning information at different times are displayed. The time span of the different times is from the fault occurrence time of the first early warning information to the fault end time, which covers the fault parameters of the whole time period of the first early warning information. The fault parameters are different dimensions of the same device parameter at different times.

[0179] It should be noted that the display manner of the fault parameters of the fault type to which the first early warning information belongs can be a data table display or a diagram, and the embodiments of the present disclosure do not make specific limitations.

[0180] It can be understood that, in response to the triggering operation of the detail entry of the fault management interface, the jump is triggered from the fault management interface to the fault analysis interface, the fault occurrence time and the fault level of the first early warning information participating in forming the fault early warning data are displayed through the fault analysis interface, the multiple early warning information of different time and different fault level aggregated in the same reported fault can be more intuitively obtained, and the visualization and diversity of the fault early warning are enhanced; through the viewing operation of the historical information, the fault parameters of the fault type to which the selected first early warning information belongs are displayed in the drop-down page of the fault analysis interface, so that the fault reason can be located and analyzed through the fault parameters. In the fault early warning process, the effectiveness of providing fault information is enhanced.

[0181] In some embodiments of the present disclosure, in response to the viewing operation of the historical information, in the drop-down page of the fault analysis interface, the fault parameters of different dimensions of the same device parameter at different times under the fault type to which the selected first early warning information belongs are displayed.

[0182] Exemplarily, as shown in FIG. 6, in the viewing historical information control of the fault analysis interface, through the selection of the first early warning information with the fault occurrence time of 2023-06-11 21:01:36, a drop-down page appears in the fault analysis page, and the drop-down page displays the voltage of the early warning information with the fault occurrence time of 2023-06-11 21:01:36 at different times under three sampling modes. The voltage of the early warning information at different times under the three sampling modes corresponds to three curves respectively. Among them, the display range of different times is 2023-09-26 22:27:44 to 2023-09-26 22:42:14; the display range of the voltage is 4211 to 4380, and the unit of the voltage is millivolt (mV); the three sampling modes are the maximum value of the voltage (voltage max), the average value of the voltage (voltage mean), and the minimum value of the voltage (voltage min) respectively.

[0183] It can be understood that, in the drop-down page of the fault analysis interface, the fault parameters of the first early warning information at different times are displayed. Among them, the time span of different times is from the fault occurrence time of the first early warning information to the fault end time, which covers the fault parameters of the whole time period of the first early warning information, and the effectiveness and accuracy of providing fault information in the fault early warning are improved by obtaining the fault parameters from different dimensions.

[0184] In some embodiments, if there is no first historical early warning information with the same battery and the same fault type as the first aggregated information from the historical fault early warning data, the first aggregated information is determined as the fault early warning data.

[0185] In the embodiments of the present disclosure, if the first historical warning information with the same device and the same fault type as the first aggregation information does not exist in the historical fault warning data, that is, the first aggregation information and the first historical warning information are different fault warnings, the fault warning data is determined for different fault warnings, and the effectiveness of the warning information is improved.

[0186] It can be understood that the first historical warning information with the same device and the same fault type as the first aggregation information does not exist in the historical fault warning data, that is, the first aggregation information and the first historical warning information are different fault warnings, and the fault warning data is determined for different fault warnings, thereby improving the effectiveness of the warning information.

[0187] In some embodiments, if the time interval between each two adjacent pieces of warning information in the time sequence exceeds the first time interval, the second warning information in which the fault occurs earliest among the two pieces of warning information is frozen, and the third warning information in which the fault occurs latest among the two pieces of warning information is determined as one of the aggregation information in the first aggregation information.

[0188] In the embodiments of the present disclosure, the warning information is arranged in ascending order of the occurrence time of the warning information in each group. The warning information includes the fault occurrence time and the fault end time. The battery cloud platform queries the fault occurrence time and the fault end time of the two warning information adjacent in time in the same group. Among the two pieces of warning information, the warning information with the earliest fault occurrence time is the second warning information, and the warning information with the latest fault occurrence time is the third warning information. If the fault end time of the second warning information and the fault occurrence time of the third warning information exceed the first time interval, the battery cloud platform freezes the second warning information, that is, the second warning information does not participate in aggregation, and the battery cloud platform determines the third warning information as one of the aggregation information, and the third warning information can participate in the aggregation of the first aggregation information.

[0189] It should be noted that the third warning information is one of the aggregation information and participates in the aggregation according to the aggregation rule. After the aggregation judgment, the third warning information may meet the aggregation rule and can be aggregated with other aggregation subjects to form the first aggregation information, or the third warning information may not meet the aggregation rule and cannot be aggregated with other aggregation subjects.

[0190] In some embodiments, the battery cloud platform displays the frozen second warning information on the fault information detail page. The warning information displayed in the fault information detail page includes the frozen second warning information and the first aggregation information. In the fault information detail page, the fault codes of the frozen second warning information and the first aggregation information are different.

[0191] Exemplarily, as shown in FIG. 7, the early warning information with fault codes ***********00001 and ***********00002 is contained in the fault management interface. The early warning information with fault code ***********00001 has a fault start time of 2022-10-06 09:28:53, a fault end time of 2022-10-07 13:29:53, a fault code of 601, a fault level of serious (L3), a fault name of component fault alarm, and a device code of 1110. The early warning information with fault code ***********00002 has a fault start time of 2022-10-10 14:28:53, a fault end time of 2022-10-10 13:29:53, a fault code of 601, a fault level of serious (L3), a fault name of component fault alarm, and a device code of 1110.

[0192] The early warning information with fault codes ***********00001 and ***********00002 has the same fault code (601) and device code (1110), and therefore, the two early warning information with fault codes ***********00001 and ***********00002 belong to two aggregation subjects. The fault end time of the aggregation subject with fault code ***********00001 is 2022-10-07 13:29:53, and until 2022-10-10 14:28:53, the aggregation subject with fault code ***********00002 reports a fault in the battery cloud platform. Since the time interval between 2022-10-07 13:29:53 and 2022-10-10 14:28:53 exceeds the first time interval (3 days), the two aggregation subjects belong to different fault reports, and the aggregation subjects are not aggregated.

[0193] It can be understood that, beyond the first time interval, the second early warning information with the earliest fault in each pair of early warning information is frozen, the fault end time of the frozen second early warning information is no longer updated, and only the third early warning information that is not frozen is queried and updated for the fault end time, which improves the resource utilization and thus improves the early warning efficiency.

[0194] In some embodiments, the method further includes S501 and S502:

[0195] S501, if the fault early warning data is frozen and the fault state of the fault early warning data is a first preset state, it is determined that the fault early warning data is overdue early warning information if a second preset time period is passed. The first preset state indicates that the fault early warning data is in a to-be-processed state.

[0196] S502, closing the overdue warning information.

[0197] It should be noted that the fault state of the fault warning data can include at least one of a first preset state, a second preset state and a third preset state. The first preset state is an active state, indicating that the fault to which the fault warning data belongs is pending processing. The second preset state is a clear unconfirmed state, indicating that the fault to which the fault warning data belongs is not processed and will not be processed. The third preset state is a clear confirmed state, indicating that the fault to which the fault warning data belongs has been processed and will not be processed.

[0198] In the embodiments of the present disclosure, the second preset time period is the pending processing time of the fault warning data. If the frozen fault warning data is still not processed after the second preset time period, the battery cloud platform determines that the fault warning data is overdue warning information. The battery cloud platform closes the overdue warning information.

[0199] It can be understood that the frozen fault warning data with the first preset state of the fault state indicates that the fault to which the fault warning data belongs is in a pending processing state. After the second preset time period, if the frozen fault warning data with the first preset state of the fault state is still in the pending processing state, it is considered that the fault to which the fault warning data belongs has been long pending processing. In the case that the warning system continuously reports new fault warning information, the overdue warning information is closed, reducing the complexity of the warning information and improving the manageability of the warning information.

[0200] The measures for the battery cloud platform to close the overdue warning information are as follows:

[0201] In response to the closing instruction of the overdue warning information, the battery cloud platform displays, on the fault management interface, that the fault state of the overdue warning information is updated from the first preset state to the second preset state, and the second preset state is clear unconfirmed, indicating that the overdue warning information is closed.

[0202] In some embodiments, the fault state of the fault warning data can include active confirmation, active unconfirmed, clear confirmation and clear unconfirmed. The active confirmation and active unconfirmed states indicate that the warning result is pending processing. The clear confirmation state indicates that the warning result has been processed and can be closed. The clear unconfirmed state indicates that the warning result has not been processed and can be closed.

[0203] It should be noted that the first preset state can be an active confirmation state or an active unconfirmed state. The second preset state is a clear unconfirmed state.

[0204] It can be understood that in the fault management interface, the overdue warning information is closed, and the update of the fault state of the overdue warning information is updated from the first preset state to the second preset state, and the second preset state indicates that the overdue warning information is closed, so as to intuitively show the closing condition of the warning information, and improve the visibility of the warning information in different scenes. At the same time, other operations and processing can be realized for the update of the fault state, thereby improving the manageability of the warning information.

[0205] In some embodiments, the method further comprises S601 and S602:

[0206] S601, after generating the fault warning data, continuously monitoring the equipment indicated by the fault warning data;

[0207] S602, if the parameters indicated by the first warning information to which the equipment belongs return to normal, updating the fault state of the fault warning data from the first preset state to the third preset state in the fault management interface, and the third preset state indicates that the fault warning data is in a cleared state.

[0208] In the embodiments of the present disclosure, when the fault warning data of the equipment is generated, the battery cloud platform continuously detects the state of the equipment. The equipment may be in a continuous fault state, at which time the parameters of the equipment are fault parameters; the equipment may also return to a normal operating state after repair, at which time the parameters of the equipment are normal parameters. If the battery cloud platform detects that the parameters of the equipment return to normal parameters from fault parameters, i.e., the fault of the equipment is removed, then in the fault management interface, the battery cloud platform updates the fault state of the fault warning data from the first preset state to the third preset state, i.e., the battery cloud platform removes the fault warning data generated by the equipment.

[0209] Exemplarily, as shown in FIG. 8, in the fault management interface, there are six overdue warning information with fault codes of ***********48519, ***********47369, ***********49462, ***********37556, ***********48373 and ***********37567. The battery cloud platform updates the fault states of the six overdue warning information in the fault state control, and updates and displays the condition control of the fault state from the active state (the first preset state) to the cleared_unconfirmed state (the third preset state). Among them, the cleared_unconfirmed state indicates that the overdue warning information is in a closed state.

[0210] It can be understood that the parameters of the equipment return to normal, indicating that the fault problem of the equipment has been handled. The clearing of the fault warning data for the equipment that has returned to normal improves the timeliness and accuracy of the fault warning.

[0211] The processing method of the early warning information will be described below in combination with an application embodiment of a battery cloud platform. However, it should be noted that the embodiment is only used to better illustrate the present disclosure and does not constitute an improper limitation on the present disclosure.

[0212] In the embodiments of the present disclosure, the battery cloud platform is a system for processing early warning information, and the device is a battery in an energy storage system. In the battery cloud platform, the implementation flowchart of fault aggregation is shown in FIG. 9:

[0213] The battery cloud platform serves as a data receiving end and receives alarm data in an abnormal condition of the battery. After being analyzed and processed, the alarm data enters the alarm center background of the cloud platform, and through the data receiving and storage module, the rule engine processing module and the alarm pushing unit in the alarm center background, the aggregation of the fault early warning of the battery cloud platform is completed, and the aggregated alarm information is pushed to the customer system.

[0214] The battery cloud platform includes an algorithm result unit, an analysis center unit, a report unit and an alarm center background. The algorithm result unit is configured to send algorithm results to the alarm center background. The analysis center unit and the report unit are configured to send alarm information to the alarm center background.

[0215] The alarm center background includes a data receiving and storage module, a rule engine processing module and an alarm pushing unit.

[0216] The data receiving and storage module includes a kafka (message queue system) 1 unit, a kafka 2 unit, a rule unit, a nifi (data processing and distribution system) unit, a hive (data tool warehouse) fault detail table unit and a Mysql (relational database management system) fault result table. The algorithm results are sent to the rule unit through the kafka 1 unit. The rule unit sends alarm events to the kafka 2 unit. The kafka 2 unit sends the alarm events to the hive fault detail table unit through the nifi unit, and at the same time, the kafka 2 unit sends the alarm events to the data receiving and storage module. The data receiving and storage module is configured to generate alarm events after processing the data in the algorithm results, and send the alarm events to the hive fault detail table unit and the data receiving and storage module.

[0217] The rule engine processing module includes a flink (open source stream processing framework) alarm aggregation unit and a kafka 3 unit. The flink alarm aggregation unit sends alarm information to the kafka unit and the Mysql fault result table unit, respectively. The rule engine processing module is configured to aggregate the alarm events according to the rule engine to generate alarm information.

[0218] The alarm pushing unit is configured to push the alarm information to the customer system.

[0219] The alarm center page sends information to the Mysql fault result table unit, the hive fault detail table unit, and the knowledge base through the alarm front-end API (application programming interface) unit, to confirm whether the fault is handled and manually configure constant parameters. The customer system sends information to the Mysql fault result table through the alarm OpenApi unit, to eliminate the alarm. The system configuration page sends information to the push aggregation rule unit through the alarm aggregation configuration unit, and the push aggregation rule unit sends the information to the flink alarm aggregation unit, to push the formulated fault aggregation rules. The system configuration page sends information to the push configuration unit through the alarm push configuration unit, and the push configuration unit sends the information to the alarm push unit, to push the subscription, suppression, and other rules other than the fault aggregation rules.

[0220] Embodiments of the present disclosure provide a processing method for early warning information, which can include S1001 to S1006:

[0221] S1001, the device end collects first early warning information.

[0222] In some embodiments of the present disclosure, when the battery fails, the device end generates first early warning information when monitoring the abnormal situation of the battery or triggering a predefined early warning condition. The first early warning information is transmitted to the battery cloud platform, and the algorithm result unit of the battery cloud platform is transmitted to the early warning center background for early warning.

[0223] It should be noted that the first early warning information can include fault level, fault occurrence time, fault end time, device identifier, and other parameters.

[0224] Exemplarily, the device end can be a sensor, a monitoring device, or a server, and the embodiments of the present disclosure are not limited.

[0225] S1002, the battery cloud platform receives and stores the first early warning information.

[0226] It should be noted that after the battery cloud platform receives the first early warning information from the device end, it is parsed and processed, including verifying data integrity, extracting key information, and storing data for subsequent processing.

[0227] In some embodiments of the present disclosure, the first early warning information is transmitted to the data receiving and storage module of the early warning center background through the algorithm result unit of the battery cloud platform, to verify, extract, and store the first early warning information. The first early warning information before aggregation is stored in the fault detail table, and the aggregated fault early warning data is stored in the fault result table.

[0228] S1003, the rule engine of the battery cloud platform processes.

[0229] It should be noted that the rule engine is a component for defining and executing rules, which is used for processing and analyzing the first early warning information.

[0230] In some embodiments of the present disclosure, the battery cloud platform comprises a rule engine processing module, which is used for aggregating the first early warning information to generate first aggregated information. The aggregation subject and aggregation rules are defined in the rule engine processing module, and the first early warning information is aggregated according to the rule engine.

[0231] It should be noted that the rule engine can also comprise a data processing module, which is used for preprocessing the first early warning information before aggregation, and the purpose of preprocessing is to ensure the consistency and accuracy of the first early warning information.

[0232] In the rule engine processing of the battery cloud platform, S1031 to S1036 can be included, as shown in FIG. 10.

[0233] S1031, query the first early warning information of the fault detail table.

[0234] It should be noted that the first early warning information is stored in the fault detail table, which is located in the data receiving and storage module of the early warning center background.

[0235] In an embodiment of the present disclosure, the battery cloud platform queries the first early warning information in the fault detail table based on time increment. The time increment is the ascending order of the fault occurrence time of the early warning information. The battery cloud platform traverses the first early warning information in ascending order of the fault occurrence time of the early warning information.

[0236] It should be noted that the first early warning information in the fault detail table is queried based on the time increment, which is prepared for subsequent early warning information aggregation.

[0237] S1032, grouping based on the aggregation subject, and aggregating the first early warning information in each group based on the aggregation rule.

[0238] It should be noted that the aggregation subject is defined in the rule engine, that is, the first early warning information with the same fault code and the same device code is the aggregation subject.

[0239] In an embodiment of the present disclosure, the aggregation rule is defined in the battery cloud platform, and the first time interval can be defined in the aggregation rule. Based on the first time interval, it is judged whether the aggregation subject in each group is the report of the same fault or the report of a new fault.

[0240] In the embodiment of the present disclosure, the aggregation subject needs to update the first aggregation information according to the aggregation rule when the first aggregation information is aggregated. In the aggregation rule, the failure occurrence time of the first aggregation information is the earliest failure occurrence time in the aggregated subjects; the failure end time of the first aggregation information is the latest failure end time in the aggregated subjects; and the failure level of the first aggregation information is the highest failure level in the aggregated subjects.

[0241] S1033, query whether the first early warning information of the same aggregation subject exists in the fault result table, if yes, go to S1034, and if no, go to S1035.

[0242] In the embodiment of the present disclosure, for a large number of early warning information, the battery cloud platform adopts a timing processing mode. The battery cloud platform defines a first preset time period, and the timing task manager starts a timing task every first preset time period to read the incremental early warning information of the fault detail table. The incremental early warning information is the early warning information reported within the first preset time period. The first aggregation information is obtained by aggregating the incremental early warning information.

[0243] In the embodiment of the present disclosure, the battery cloud platform queries the historical fault early warning data in the fault result table, and judges whether the first historical early warning information with the same aggregation subject as the first aggregation information exists in the historical fault early warning data.

[0244] S1034, whether the aggregation subjects in the fault detail table and the fault result table meet the aggregation rule, if yes, go to S1036, and if no, go to S1035.

[0245] In the embodiment of the present disclosure, the battery cloud platform queries the historical fault early warning data in the fault result table, and determines that the first historical early warning information with the same aggregation subject as the first aggregation information exists in the historical fault early warning data. The battery cloud platform judges whether the time interval between the first historical early warning information and the first aggregation information is less than the first time interval, if the time interval between the two is less than the first time interval, the first historical early warning information and the first aggregation information meet the aggregation rule; and if the time interval between the two is greater than the first time interval, the first historical early warning information and the first aggregation information do not meet the aggregation rule.

[0246] S1036, aggregate the aggregation subjects in the fault detail table and the fault result table.

[0247] In the embodiment of the present disclosure, if the battery cloud platform judges that the first historical early warning information and the first aggregation information meet the aggregation rule, the first aggregation information in the fault detail table and the first historical early warning information in the fault result table are aggregated.

[0248] S1035, insert the early warning information in the fault detail table into the fault result table.

[0249] In the embodiment of the present disclosure, if the battery cloud platform determines that the first historical early warning information does not conform to the aggregation rule with the first aggregated information, that is, the first aggregated information in the fault detail table and the first historical early warning information in the fault result table belong to different fault reports, the first aggregated information in the fault detail table is inserted into the fault result table as a newly added early warning result in the fault result table.

[0250] It should be noted that the first aggregated information in the fault detail table and the first historical early warning information in the fault result table belong to different fault reports, and there is no other historical early warning information with the same aggregation subject as the first aggregated information in the fault result table. Therefore, there is no fault report with the same first aggregated information in the fault result table.

[0251] S1004, generate and push an early warning notification.

[0252] It should be noted that the early warning information is aggregated to generate fault early warning data. The fault early warning data contains the related information of the aggregated early warning information, such as fault occurrence time, fault end time, fault level, fault state, etc.

[0253] In the embodiment of the present disclosure, when the early warning information is aggregated to generate the fault early warning data, the battery cloud platform can generate an early warning notification based on the fault early warning data, and set the early warning level and associate other related information. Wherein, the early warning level is, for example: emergency, serious, warning, which is not limited in the embodiment of the present disclosure.

[0254] In the embodiment of the present disclosure, the generated early warning notification is pushed to the predetermined target. So that the relevant personnel can receive the early warning notification in time and take corresponding measures to troubleshoot or respond to emergencies.

[0255] Exemplarily, the predetermined target can be the mobile phone of the operation and maintenance personnel, and can also be a workstation, an email or other communication channels, which are not limited in the embodiment of the present disclosure.

[0256] S1005, release and update the early warning result.

[0257] In the embodiment of the present disclosure, after the early warning result is generated, the battery cloud platform continues to monitor the fault battery state corresponding to the early warning result. When it is monitored that the fault parameter of the fault battery returns to normal, the battery cloud platform will update the fault state of the early warning result in the fault management interface from the active state to the clear state. That is, the release of the early warning result is completed.

[0258] It should be noted that when the fault state of the early warning result is in the active state, it indicates that the early warning result is in the to-be-processed state; when the fault state of the early warning result is in the clear state, it indicates that the early warning result can not be processed.

[0259] S1006, overdue closing of the early warning result.

[0260] In the embodiments of the present disclosure, an overdue closing time can be preset, and the battery cloud platform can perform overdue closing on the fault early warning data that has been frozen and reaches the overdue closing time without being closed loop.

[0261] In the embodiments of the present disclosure, the overdue closing of the fault early warning data by the battery cloud platform is that the battery cloud platform updates the fault state of the fault early warning data to be overdue closed in the fault management interface, and updates the fault state to a new cleared unconfirmed state. That is, the overdue closing of the fault early warning data is completed.

[0262] In the embodiments of the present disclosure, on the one hand, the first early warning information indicates that there is an equipment abnormality in the energy storage system. There can be multiple early warning information of the same fault in the first early warning information, that is, there can be redundant early warning information in the first early warning information. The early warning information with the same equipment and the same fault type in the first early warning information is filtered, and then the filtered first early warning information is aggregated based on the first time interval. That is, multiple early warning information of the same fault is aggregated to determine the fault early warning data, which reduces the repeated reporting of redundant early warning information and effectively improves the processing efficiency of fault early warning.

[0263] The embodiments of the present disclosure provide a processing device 1100 of early warning information, as shown in FIG. 11, the device comprises:

[0264] A determination module 1101 is configured to determine first early warning information to be processed for at least one equipment.

[0265] An aggregation module 1102 is configured to aggregate early warning information of the same equipment and the same fault type in the first early warning information based on a first time interval to determine first aggregation information, and in a case where first historical early warning information with the same equipment and the same fault type as the first aggregation information is queried in historical fault data, aggregate the first aggregation information and the first historical early warning information based on the first time interval to obtain fault early warning data.

[0266] In some embodiments, the determination module 1101 is further configured to determine the first time interval according to real-time use data of the at least one equipment.

[0267] In some embodiments, the determination module 1101 is further configured to assign respective first weights to different early warning information in the first early warning information according to the credibility of the early warning information.

[0268] The aggregation module 1102 is further configured to perform weighted processing on the early warning information corresponding to the first weight based on the first weight, so as to aggregate the early warning information of the same equipment and the same fault type to determine the first aggregation information.

[0269] In some embodiments, the aggregation module 1102 is further configured to aggregate the early warning information of the same device and the same fault type in the first early warning information by using a time-based aggregation network.

[0270] In some embodiments, the early warning information processing apparatus 1100 further comprises a fusion module 1103 configured to perform fault correlation analysis on the initial early warning information of the at least one device to be processed, fuse the early warning information having a correlation relationship in the initial early warning information, and determine the first early warning information.

[0271] In some embodiments, the early warning information processing apparatus 1100 further comprises a filtering module 1104 configured to filter the initial early warning information of the at least one device to be processed, eliminate the early warning information having errors therein, and determine the first early warning information.

[0272] In some embodiments, the determination module 1101 is further configured to process the initial early warning information to be processed based on the multi-dimensional attribute information and the scene information of the at least one device, and determine the first early warning information.

[0273] In some embodiments, the fusion module 1103 is further configured to fuse the early warning information having the same attribute information in the initial early warning information to be processed based on the at least one attribute information of the at least one device, determine the first fusion information, and fuse the early warning information having the same scene information in the first fusion information based on the at least one scene information of the at least one device, and determine the first early warning information.

[0274] In some embodiments, the determination module 1101 is further configured to perform data cleaning and / or data filling on the initial early warning information to be processed, and determine the first early warning information.

[0275] In some embodiments, the determination module 1101 is further configured to, if a first preset time period is reached, acquire the incremental data in the fault early warning record, and obtain the first early warning information of the at least one device.

[0276] In some embodiments, the determination module 1101 is further configured to determine at least two groups of early warning information of the same device and the same fault type in the first early warning information.

[0277] The aggregation module 1102 is further configured to, if the number of the early warning information adjacent in time in each group of early warning information is less than a first time interval in a time sequence, aggregate the early warning information, and determine the first aggregation information.

[0278] In some embodiments, the determining module 1101 is further configured to, if the plurality of pieces of early warning information that are adjacent in time in the order of time in each group of early warning information is less than the first time interval, determine the earliest occurrence time, the latest end time and the highest fault level in the plurality of pieces of early warning information as the fault occurrence time, the fault end time and the fault level of one piece of aggregated information; and determine the first aggregated information when the aggregation of the plurality of pieces of early warning information in each group is completed.

[0279] In some embodiments, the early warning information processing apparatus 1100 further includes a display module 1105, configured to, in response to a viewing instruction, display the fault early warning data and a detail entry of the first piece of early warning information on a fault management interface.

[0280] In some embodiments, the display module 1105 is further configured to, in response to the viewing instruction, display at least one of the fault information, the fault level, the fault occurrence time, the fault end time, the device identifier and the fault state of the fault early warning data and the detail entry of the first piece of early warning information on the fault management interface.

[0281] In some embodiments, the display module 1105 is further configured to, in response to a triggering operation of the detail entry, display the fault detail information of the first piece of early warning information and a viewing entry of historical information on a fault analysis interface; and, in response to a selection operation of the first piece of early warning information, display the selected first piece of early warning information in a first manner on the fault analysis interface; and, in response to a viewing operation of the historical information, display the fault parameters of the same device parameter in different dimensions at different time points under the fault type to which the selected first piece of early warning information belongs in a drop-down page of the fault analysis interface.

[0282] In some embodiments, the display module 1105 is further configured to, in response to the viewing operation of the historical information, display the fault parameters of the same device parameter in different dimensions at different time points under the fault type to which the selected first piece of early warning information belongs in a drop-down page of the fault analysis interface.

[0283] In some embodiments, the determining module 1101 is further configured to determine the earliest occurrence time, the latest end time and the highest fault level in the first aggregated information and the first historical early warning information as the fault occurrence time, the fault end time and the fault level of the fault early warning data based on the first time interval.

[0284] In some embodiments, the determining module 1101 is further configured to, if the time interval of each two pieces of early warning information that are adjacent in time in the order of time in each group of early warning information exceeds the first time interval, freeze the second piece of early warning information in which the fault occurs earliest among the two pieces of early warning information, and determine the third piece of early warning information in which the fault occurs latest among the two pieces of early warning information as one piece of aggregated information in the first aggregated information.

[0285] It can be understood that, on the one hand, the to-be-processed early warning information indicates that there is an equipment abnormality in the energy storage system. There can be multiple early warning information of the same fault in the to-be-processed early warning information, that is, there can be redundant early warning information in the to-be-processed early warning information. By fusing the early warning information with the same attribute information and scene information in the to-be-processed early warning information, the first early warning information is obtained, and by filtering the early warning information with the same equipment and the same fault type in the first early warning information, the filtered first early warning information is aggregated based on the first time interval. That is, multiple early warning information of the same fault is aggregated to form aggregated information, so as to determine the fault early warning data, thereby reducing the repeated reporting of redundant early warning information and effectively improving the processing efficiency of the fault early warning information. On the other hand, based on the visual design on the fault management interface, the aggregated fault early warning data is realized, and an entry for details of the first early warning information before aggregation is also provided for viewing the early warning information before aggregation. Therefore, the diversity of information display before and after aggregation of the early warning information is improved.

[0286] The above device embodiments are similar to the above method embodiments in description, and have similar beneficial effects as the method embodiments. In some embodiments, the device provided by the embodiments of the present disclosure has functions or includes modules that can be used to execute the methods described in the above method embodiments. For technical details not disclosed in the device embodiments of the present disclosure, please refer to the description of the method embodiments of the present disclosure for understanding.

[0287] It should be noted that, in the embodiments of the present disclosure, if the above method is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present disclosure can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of the embodiments of the present disclosure. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a magnetic disk or an optical disk, and various media that can store program codes. Thus, the embodiments of the present disclosure are not limited to any particular hardware, software or firmware, or any combination of hardware, software and firmware.

[0288] The embodiments of the present disclosure provide a processing device for early warning information, as shown in FIG. 12, the hardware entity of the processing device for early warning information 1200 includes a processor 1201 and a memory 1203, the memory stores a computer program executable on the processor, and the processor implements the steps in the processing method applied to early warning information when executing the program.

[0289] It should be noted that the processor 1201 generally controls the overall operation of the early warning information processing device 1200. The memory 1203 is configured to store instructions and applications executable by the processor 1201, and can also cache data (for example, image data, audio data, voice communication data, and video communication data) to be processed or having been processed by the processor 1201 and various modules in the early warning information processing device 1200, and can be implemented by a FLASH or a Random Access Memory (RAM). The early warning information processing device can further include a communication interface 1202, which can enable the early warning information processing device to communicate with other terminals or servers through a network. The processor 1201, the communication interface 1202, and the memory 1203 can perform data transmission through the bus 1204.

[0290] Based on the foregoing embodiments, the modules included in the early warning information processing device and the units included in the modules, etc. according to the embodiments of the present disclosure can be implemented by a processor in a computer device. Of course, they can also be implemented by a specific logic circuit. In the implementation process, the processor can be a Central Processing Unit (CPU), a Microprocessor Unit (MPU), a Digital Signal Processor (DSP), or a Field Programmable Gate Array (FPGA), etc.

[0291] The embodiments of the present disclosure provide a computer readable storage medium having a computer program stored thereon, and the computer program, when executed by a processor, implements all steps of the early warning information processing method. The computer readable storage medium can be transitory or non-transitory.

[0292] The above is only an implementation manner of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present disclosure, which should be covered by the protection scope of the present disclosure.

Claims

1. A method for processing early warning information, the method comprising: determining first early warning information to be processed for at least one device; based on a first time interval, aggregating early warning information of the same device and the same fault type in the first early warning information to determine first aggregated information; in a case where first historical early warning information of the same device and the same fault type as the first aggregated information is queried in historical fault data, based on the first time interval, aggregating the first aggregated information and the first historical early warning information to obtain the fault early warning data.

2. The method of claim 1, wherein, The method further comprises: determining the first time interval according to real-time usage data of at least one device; the usage data comprises at least one of the following: ambient temperature, usage frequency, and charge and discharge cycle.

3. The method of claim 1, wherein, The method further comprises: assigning respective first weights to different early warning information in the first early warning information according to the reliability of the early warning information; the reliability is positively correlated with the first weight; based on the first weight, performing weighted processing on the early warning information corresponding to the first weight to aggregate early warning information of the same device and the same fault type to determine the first aggregated information.

4. The method of claim 1, wherein, The method further comprises: using a time-based aggregation network to aggregate early warning information of the same device and the same fault type in the first early warning information to determine the first aggregated information.

5. The method according to any one of claims 1 to 4, wherein, The determining of the first early warning information for at least one device comprises: performing fault correlation analysis on initial early warning information to be processed for the at least one device, fusing early warning information having a correlation relationship in the initial early warning information to determine the first early warning information; the fault correlation analysis is used to identify potentially correlated information in the early warning information.

6. The method according to any one of claims 1 to 4, wherein, The determining of the first early warning information for at least one device comprises: filtering the initial early warning information to be processed for the at least one device to remove erroneous early warning information to determine the first early warning information.

7. The method according to any one of claims 1 to 4, wherein, The determining of the first early warning information for at least one device comprises: based on at least one of attribute information and scene information of the at least one device, processing the initial early warning information to be processed to determine the first early warning information.

8. The method of claim 7, wherein, The processing of the initial early warning information to be processed based on at least one of attribute information and scene information of the at least one device to determine the first early warning information comprises: based on at least one of attribute information and scene information of the at least one device, fusing early warning information having the same attribute information and / or the same scene information in the initial early warning information to be processed to determine the first early warning information; wherein the attribute information of the at least one device comprises chemical composition and production batch of the at least one device; and the scene information of the at least one device comprises usage scene and operating environment of the at least one device.

9. The method according to any one of claims 1 to 4, wherein, The determining of the first early warning information for at least one device comprises: The method comprises the following steps: performing data cleaning and / or data filling on initial early warning information to be processed to determine the first early warning information; wherein the data cleaning indicates deleting repeated early warning information and incorrect early warning information in the initial early warning information, and the data filling indicates adding or correcting incorrect early warning information for missing early warning information in the initial early warning information.

10. The method according to any one of claims 1 to 9, wherein, The determining the first early warning information to be processed for at least one device comprises: In the case that a first preset time period is reached, increment data in the fault early warning record is acquired to obtain the first early warning information for the at least one device.

11. The method according to any one of claims 1 to 10, wherein, The aggregating, based on a first time interval, early warning information of the same device and the same fault type in the first early warning information to determine first aggregated information comprises: Determining at least two groups of early warning information of the same device and the same fault type in the first early warning information; If, in each group of early warning information, a plurality of early warning information adjacent in time according to a time sequence is less than the first time interval, the plurality of early warning information is aggregated to determine the first aggregated information.

12. The method of claim 11, wherein, The aggregating, based on a first time interval, early warning information of the same device and the same fault type in the first early warning information to determine first aggregated information comprises: If, in each group of early warning information, a plurality of early warning information adjacent in time according to a time sequence is less than the first time interval, the earliest occurrence time, the latest end time and the highest fault level in the plurality of early warning information are determined as the fault occurrence time, the fault end time and the fault level of an aggregated information; Until the plurality of aggregations in each group of early warning information are completed, the first aggregated information is determined.

13. The method according to any one of claims 1 to 12, wherein, The method further comprises: In response to a viewing instruction, displaying the fault early warning data and a detail entry of the first early warning information on a fault management interface.

14. The method of claim 13, wherein, The displaying the fault early warning data and the detail entry of the first early warning information on the fault management interface in response to the viewing instruction comprises: In response to the viewing instruction, displaying at least one of fault information, a fault level, a fault occurrence time, a fault end time, a device identifier and a fault state of the fault early warning data and the detail entry of the first early warning information on the fault management interface.

15. The method of claim 13 or 14, wherein, The method further comprises: In response to a triggering operation of the detail entry, displaying fault detail information of the first early warning information and a viewing entry of historical information on a fault analysis interface; the fault detail information comprises a fault occurrence time and a fault level of the first early warning information; In response to a selection operation of the first early warning information, displaying the selected first early warning information in a first mode on a fault analysis interface; In response to a viewing operation of the historical information, displaying fault parameters of a fault type to which the selected first early warning information belongs in a drop-down page of the fault analysis interface.

16. The method of claim 15, wherein, The displaying fault parameters of a fault type to which the selected first early warning information belongs in a drop-down page of the fault analysis interface in response to the viewing operation of the historical information comprises: In response to the viewing operation of the historical information, in a drop-down page of the fault analysis interface, fault parameters of different dimensions of the same device parameter at different time instants are displayed under the fault type to which the selected first early warning information belongs.

17. The method of any one of claims 1 to 16, wherein, The aggregating the first aggregated information and the first historical early warning information based on the first time interval comprises: The aggregating the first aggregated information and the first historical early warning information based on the first time interval comprises:

18. The method of claim 11, wherein, The method further comprises: If the time interval between two pieces of early warning information adjacent in time order in the groups of early warning information exceeds the first time interval, the second early warning information of the two pieces of early warning information in which the fault occurs earliest is frozen, and the third early warning information of the two pieces of early warning information in which the fault occurs latest is determined as one of the aggregated information in the first aggregated information.

19. An apparatus for processing early warning information, the apparatus comprising: a determining module configured to determine first early warning information to be processed for at least one device; an aggregating module configured to aggregate early warning information of the same device and the same fault type in the first early warning information based on a first time interval to determine first aggregated information; and in a case where first historical early warning information of the same device and the same fault type as the first aggregated information is queried in historical fault data, the aggregating module is configured to aggregate the first aggregated information and the first historical early warning information based on the first time interval to obtain the fault early warning data.

20. A processing device for early warning information, comprising a memory and a processor, the memory storing a computer program executable on the processor, and the processor implements the steps in the method of any one of claims 1 to 18 when executing the program.

21. A computer storage medium storing a computer program, the computer program being executable by a processor to implement the steps in the method of any one of claims 1 to 18.

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