Algorithm scheduling method and device, equipment and storage medium

Abstract

The invention relates to the technical field of batteries, in particular to an algorithm scheduling method, device and equipment and a storage medium, and discloses an algorithm scheduling method which comprises the following steps: querying algorithm configuration information in a corresponding service scene according to a triggered service processing instruction; and determining a to-be-called comprehensive algorithm and a basic algorithm corresponding to each input parameter in the comprehensive algorithm according to the algorithm configuration information, and generating service processing results under different service processing scenes by combining the battery data, the basic algorithm and the comprehensive algorithm. The technical problem of unified algorithm scheduling in each battery service processing scene can be solved, and the algorithm scheduling efficiency is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of battery technology, and in particular to an algorithm scheduling method, device, equipment and storage medium. Background Art

[0002] Battery operation requires the collaboration of multiple algorithms, and because the algorithm input and output in the battery field are complex and difficult to normalize, user input data is generally converted into a standard protocol through a packaged built-in processor, and then the input data is calculated. The same processor is difficult to meet the algorithm scheduling requirements of different business scenarios. Summary of the invention

[0003] The embodiments of the present invention provide an algorithm scheduling method, device, equipment and storage medium, aiming to solve the technical problem that the prior art cannot realize unified algorithm scheduling in various battery service processing scenarios.

[0004] In a first aspect, an embodiment of the present application proposes an algorithm scheduling method, the algorithm scheduling method comprising:

[0005] In response to the triggered service processing instruction, obtaining battery data representing the operating condition of the battery;

[0006] querying algorithm configuration information according to the business processing instruction;

[0007] Determine the comprehensive algorithm to be called and the basic algorithm corresponding to each input parameter in the comprehensive algorithm according to the algorithm configuration information;

[0008] A business processing result is generated according to the battery data, the basic algorithm and the comprehensive algorithm.

[0009] This embodiment proposes an algorithm scheduling method, which includes: in response to a triggered business processing instruction, obtaining battery data characterizing the battery operating condition; querying algorithm configuration information according to the business processing instruction; determining the comprehensive algorithm to be called and the basic algorithm corresponding to each input parameter in the comprehensive algorithm according to the algorithm configuration information; generating a business processing result according to the battery data, the basic algorithm and the comprehensive algorithm. By querying the algorithm configuration information in the corresponding business scenario according to the triggered business processing instruction, and then determining the comprehensive algorithm to be called and the basic algorithm corresponding to each input parameter in the comprehensive algorithm according to the algorithm configuration information, and then combining the battery data, the basic algorithm and the comprehensive algorithm to generate business processing results in different business processing scenarios, it can meet the technical problem of unified scheduling of algorithms in various battery business processing scenarios, and effectively improve the efficiency of algorithm scheduling.

[0010] In some embodiments, generating a business processing result according to the battery data, the basic algorithm, and the comprehensive algorithm includes:

[0011] Calling the basic class algorithm based on the battery data to generate target battery data;

[0012] The comprehensive algorithm is called based on the target battery data to generate a business processing result.

[0013] In this embodiment, the steps of generating business processing results according to the battery data, the basic class algorithm and the comprehensive class algorithm specifically include: calling the basic class algorithm based on the battery data to generate target battery data; calling the comprehensive class algorithm based on the target battery data to generate business processing results, by first calling the basic class algorithm to generate target battery data corresponding to the battery data, and then calling the comprehensive class algorithm to process the target battery data, thereby generating a business processing result corresponding to the target battery data, avoiding the problem of algorithm operation errors and low business processing efficiency due to parameter mapping conflicts during the algorithm scheduling process, and at the same time meeting the necessary conditions for the operation of the comprehensive class algorithm.

[0014] In some embodiments, the calling the basic class algorithm based on the battery data to generate target battery data includes:

[0015] Associating the battery data with the input parameters of the basic class algorithm according to a first parameter mapping relationship, wherein the first parameter mapping relationship is a preset parameter mapping relationship between the battery data and the input parameters of the basic class algorithm;

[0016] The basic algorithm is called to generate target battery data corresponding to the input parameters of the basic algorithm.

[0017] In this embodiment, the basic class algorithm is called based on the battery data to generate the target battery data, including: associating the battery data with the input parameters of the basic class algorithm according to a first parameter mapping relationship, wherein the first parameter mapping relationship is a parameter mapping relationship between the preset battery data and the input parameters of the basic class algorithm; calling the basic class algorithm to generate target battery data corresponding to the input parameters of the basic class algorithm, and presetting the parameter mapping relationship between the battery data and the input parameters of the basic class algorithm so that when the basic class algorithm is called, the input parameters of the basic class algorithm have correct input, thereby generating accurate target battery data and improving the accuracy of the operation of the basic class algorithm.

[0018] In some embodiments, associating the battery data with the input parameters of the basic class algorithm according to the first parameter mapping relationship includes:

[0019] Determining identifiers and attribute names of input parameters in the base class algorithm;

[0020] The battery data is loaded into the algorithm configuration table according to the identifier and the attribute name to associate the battery data with input parameters of the base class algorithm.

[0021] In this embodiment, the step of associating the battery data with the input parameters of the basic class algorithm according to the first parameter mapping relationship includes: determining the identifier and attribute name of the input parameter in the basic class algorithm; loading the battery data into the algorithm configuration table according to the identifier and the attribute name to associate the battery data with the input parameters of the basic class algorithm, and by obtaining the identifier and attribute name of the input parameter in the basic class algorithm, when loading the battery data, the battery data can be associated and loaded based on the identifier and attribute name of the input parameter in the basic class algorithm, thereby avoiding the problem of parameter mapping errors in the calculation process of the basic class algorithm, thereby outputting erroneous business processing results.

[0022] In some embodiments, calling the comprehensive algorithm based on the target battery data to generate a business processing result includes:

[0023] Associating the target battery data with the input parameters of the comprehensive algorithm according to a second parameter mapping relationship, wherein the second parameter mapping relationship is a preset mapping relationship between the output parameters of the basic algorithm and the input parameters of the comprehensive algorithm;

[0024] The comprehensive algorithm is called to generate a business processing result corresponding to the input parameters of the comprehensive algorithm.

[0025] In this embodiment, the step of calling the comprehensive algorithm based on the target battery data to generate a business processing result includes: associating the target battery data with the input parameters of the comprehensive algorithm according to a second parameter mapping relationship, wherein the second parameter mapping relationship is a pre-set mapping relationship between the output parameters of the basic algorithm and the input parameters of the comprehensive algorithm; calling the comprehensive algorithm to generate a business processing result corresponding to the input parameters of the comprehensive algorithm, and pre-setting the parameter mapping relationship between the target battery data and the input parameters of the comprehensive algorithm so that when the comprehensive algorithm is called, the input parameters of the comprehensive algorithm have correct input, generate accurate business processing results, improve the accuracy of the operation of the comprehensive algorithm, and realize an efficient algorithm scheduling process under different business scenarios.

[0026] In some embodiments, before calling the comprehensive algorithm based on the target battery data to generate a business processing result, the method further includes:

[0027] Check whether the basic class algorithms have all been called;

[0028] If the basic algorithms have all been called, the input parameters of the comprehensive algorithms are configured according to the preset parameter mapping relationship and the basic data.

[0029] In this embodiment, before calling the comprehensive algorithm, it is checked whether all the basic algorithms have been called and have corresponding output parameters. When all the basic algorithms have been called, the comprehensive algorithm is called so that the input of the comprehensive algorithm has parameter inputs to prevent the comprehensive algorithm from running with errors.

[0030] In some embodiments, the step of determining the comprehensive algorithm to be called and the basic algorithm corresponding to each input parameter in the comprehensive algorithm according to the algorithm configuration information includes:

[0031] Query the comprehensive algorithms and basic algorithms corresponding to the algorithm configuration information;

[0032] An algorithm association relationship between the comprehensive algorithm and the basic algorithm is established according to the algorithm configuration information, wherein the algorithm association relationship is a parameter mapping relationship between an output parameter of the basic algorithm and an input parameter of the comprehensive algorithm;

[0033] The comprehensive algorithm to be called and the basic algorithm corresponding to each input parameter in the comprehensive algorithm are determined according to the parameter mapping relationship, the comprehensive algorithm and the basic algorithm.

[0034] In this embodiment, the comprehensive algorithms and basic algorithms existing in the algorithm configuration information are queried, and the parameter mapping relationship between the output parameters of the basic algorithms and the input parameters of the comprehensive algorithms is constructed through the algorithm configuration information, so as to make the algorithm scheduling process continuous, reduce the impact caused by the repetition of the underlying independent variables of the algorithm, and improve the parameter mapping accuracy of the basic algorithms and the comprehensive algorithms.

[0035] In some embodiments, the querying of the comprehensive algorithms and basic algorithms corresponding to the algorithm configuration information includes:

[0036] Querying the algorithm to be called contained in the algorithm configuration information;

[0037] Obtaining identification information of the algorithm to be called;

[0038] The algorithms to be called are classified according to the identification information to obtain comprehensive algorithms and basic algorithms.

[0039] In this embodiment, all the algorithms to be called contained in the algorithm configuration information are queried, and the algorithms to be called are classified according to the identification information of each algorithm to be called, so as to distinguish comprehensive algorithms from basic algorithms, facilitate the establishment of parameter mapping, and ensure the smoothness of algorithm operation.

[0040] In some embodiments, before querying the algorithm configuration information according to the business processing instruction, the method further includes:

[0041] Display algorithm configuration charts for various business processing scenarios through preset display pages;

[0042] Counting the algorithm configuration instructions input by the user based on the algorithm configuration chart;

[0043] Algorithm configuration information is set according to the algorithm configuration instruction.

[0044] In this embodiment, the algorithm configuration icons for each business scenario are displayed through a preset display page, and the algorithm configuration instructions input by the user based on the algorithm configuration icons are counted, so as to realize the pre-setting of the algorithm configuration information according to the algorithm configuration instructions, so that when facing different business processing environments in the future, the relevant algorithm configuration information can be directly queried, thereby improving the efficiency of algorithm scheduling.

[0045] In some embodiments, the setting of algorithm configuration information according to the algorithm configuration instruction further includes:

[0046] The algorithm to be called is determined according to the algorithm configuration instruction;

[0047] configuring a mapping relationship between the battery data and the parameters of the algorithm to be called based on the algorithm configuration instruction;

[0048] Algorithm configuration information is generated according to the algorithm to be called and the parameter mapping relationship.

[0049] In this embodiment, the parameter mapping relationship between the algorithm to be called and the battery data is set according to the algorithm configuration instruction input by the user, thereby obtaining the algorithm configuration information without temporary configuration, thereby improving the efficiency of business processing.

[0050] In some embodiments, the algorithm configuration instruction includes: a parameter mapping instruction and an algorithm association instruction, the algorithm to be called includes a basic algorithm and a comprehensive algorithm, and the parameter mapping relationship includes a first parameter mapping relationship and a second parameter mapping relationship;

[0051] The step of constructing a mapping relationship between the battery data and the parameters of the algorithm to be called based on the algorithm configuration instruction includes:

[0052] Constructing a first parameter mapping relationship between battery data and input parameters of the basic class algorithm based on the parameter mapping instruction;

[0053] A second mapping relationship between the output parameters of the basic algorithm and the input parameters of the comprehensive algorithm is constructed based on the algorithm association instruction.

[0054] In this embodiment, the first parameter mapping relationship between the battery data and the input parameters of the basic class algorithm and the second mapping relationship between the output parameters of the basic class algorithm and the input parameters of the comprehensive class algorithm are set respectively to achieve the setting of the parameter mapping relationship between the battery data and the algorithm to be called, thereby avoiding the problem of parameter mapping errors during the operation of the algorithm.

[0055] In some embodiments, the generating algorithm configuration information according to the algorithm to be called and the parameter mapping relationship includes:

[0056] Determine the business processing scenario corresponding to the algorithm configuration instruction and the algorithm configuration chart corresponding to the business processing scenario;

[0057] The algorithm to be called and the parameter mapping relationship are loaded into the algorithm configuration chart to generate algorithm configuration information.

[0058] In this embodiment, the step of generating algorithm configuration information based on the algorithm to be called and the parameter mapping relationship includes: determining the business processing scenario corresponding to the algorithm configuration instruction and the algorithm configuration chart corresponding to the business processing scenario; loading the algorithm to be called and the parameter mapping relationship into the algorithm configuration chart to generate algorithm configuration information, by determining the business processing scenario corresponding to the algorithm configuration instruction input by the user and the corresponding algorithm configuration chart, and then loading the parameter mapping relationship set above into the algorithm configuration chart to generate algorithm configuration information for algorithm scheduling, thereby improving the algorithm scheduling efficiency and facilitating the user's subsequent mapping relationship modification or algorithm scheduling adjustment.

[0059] In some embodiments, the algorithm scheduling method further includes:

[0060] The parameters of the additional configuration items in the algorithm configuration chart are adjusted according to the algorithm configuration instruction.

[0061] In this embodiment, by adjusting the parameters of the additional configuration items in the algorithm configuration chart, the algorithm configuration in different business scenarios can be implemented, thereby improving the comprehensiveness and adaptability of the algorithm scheduling application scenarios.

[0062] In some embodiments, the algorithm scheduling method further includes:

[0063] The algorithm name and rule name of the algorithm configuration chart are text-edited according to the algorithm configuration instruction.

[0064] In this embodiment, by editing the algorithm name and rule name in the algorithm configuration chart according to the algorithm configuration instruction, it is possible to adapt to the user's business processing plan and save the cost of manpower focusing on algorithm scheduling.

[0065] In a second aspect, an embodiment of the present invention further provides an algorithm scheduling device, the algorithm scheduling device comprising:

[0066] An acquisition module, configured to acquire battery data representing a battery operating condition in response to a triggered service processing instruction;

[0067] A query module, used to query algorithm configuration information according to the business processing instruction;

[0068] A determination module, used to determine the comprehensive algorithm to be called and the basic algorithm corresponding to each input parameter in the comprehensive algorithm according to the algorithm configuration information;

[0069] The scheduling module is used to generate a business processing result according to the battery data, the basic algorithm and the comprehensive algorithm.

[0070] The algorithm scheduling device in this embodiment obtains the battery operating condition data of the battery to be detected within a preset time period through the acquisition module, and performs feature extraction on the battery operating condition data through the extraction module to obtain the trend change characteristics of the battery to be detected; compares the trend change characteristics with the preset rules through the comparison module to obtain a comparison result, wherein the preset rule is the trend change of the battery under a normal state; and issues an early warning to the battery to be detected according to the comparison result through the early warning module, so that the trend change characteristics of the battery to be detected can be monitored in real time, so as to determine whether the battery to be detected has an abnormality according to the trend change characteristics, and timely issue an early warning before the battery to be detected fails, thereby effectively avoiding the occurrence of battery failure.

[0071] In a third aspect, an embodiment of the present invention further proposes an algorithm scheduling platform, which is used to execute the algorithm scheduling method as described above.

[0072] In a fourth aspect, an embodiment of the present invention further proposes a storage medium, on which an algorithm scheduling program is stored, and when the algorithm scheduling program is executed by a processor, the algorithm scheduling method as described above is implemented.

[0073] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0075] Figure 1 A schematic diagram of a flow chart of an embodiment of an algorithm scheduling method proposed in an embodiment of the present invention;

[0076] Figure 2 Schematic diagram of a business scenario example of an embodiment of the algorithm scheduling method proposed in an embodiment of the present invention

[0077] Figure 3 A schematic diagram of a flow chart of another embodiment of the algorithm scheduling method proposed in an embodiment of the present invention;

[0078] Figure 4 This is a schematic diagram of an example of parameter mapping of an embodiment of an algorithm scheduling method according to an embodiment of the present invention;

[0079] Figure 5 A schematic diagram of a flow chart of another embodiment of the algorithm scheduling method proposed in an embodiment of the present invention;

[0080] Figure 6 A schematic diagram of the algorithm association relationship configuration of an embodiment of the algorithm scheduling method proposed in an embodiment of the present invention;

[0081] Figure 7 A schematic diagram of a flow chart of another embodiment of the algorithm scheduling method proposed in an embodiment of the present invention;

[0082] Figure 8 A schematic diagram of the overall algorithm configuration of an embodiment of the algorithm scheduling method proposed in an embodiment of the present invention;

[0083] Fig. 9 This is a structural diagram of an embodiment of an algorithm scheduling device proposed in an embodiment of the present invention.

[0084] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0085] The following embodiments of the technical solution of the present invention are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are therefore only used as examples, and cannot be used to limit the protection scope of the present invention.

[0086] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which the present invention belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention; the terms "including" and "having" in the specification and claims of the present invention and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0087] In the description of the embodiments of the present invention, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present invention, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0088] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0089] In the description of the embodiments of the present invention, the term "and / or" is only a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0090] In the description of the embodiments of the present invention, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0091] In the description of the embodiments of the present invention, the technical terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the embodiments of the present invention.

[0092] In the description of the embodiments of the present invention, unless otherwise clearly specified and limited, technical terms such as "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the embodiments of the present invention can be understood according to specific circumstances.

[0093] At present, the algorithm scheduling of business processing in the battery field is generally carried out by parsing the business data to be executed, and uniformly encapsulating the business data into the algorithm processor. The algorithm processor performs business processing according to the task execution process and feeds back the results to the user after the processing. In this process, the user cannot independently configure the called algorithm. In addition, in the battery field, some algorithms need to rely on other algorithms to be successfully executed. In the face of complex business processing environments, there may be parameter conflicts and algorithm chain conflicts, which require overall maintenance, resulting in low business processing efficiency.

[0094] Based on the above considerations, in order to improve the effect of algorithm scheduling in different business processing scenarios, after in-depth research, an algorithm scheduling method, device, equipment and storage medium are proposed, which are suitable for various scenarios of algorithm scheduling, battery health detection and battery fault judgment during actual use of the battery. For example: collecting battery voltage, current, output power and other data during battery operation, and performing fault judgment through a fault judgment model constructed by multiple algorithms.

[0095] In some embodiments of the present invention, reference Figure 1 , an embodiment of the present invention proposes an algorithm scheduling method, which may include:

[0096] Step S10: In response to the triggered service processing instruction, battery data representing the battery operating condition is acquired.

[0097] It should be noted that the business processing instructions can be used to judge or detect the battery in different business scenarios, including but not limited to: battery health detection, battery fault judgment and battery safety detection, etc. This embodiment does not impose specific restrictions on this.

[0098] The battery data characterizing the battery operating condition may be data of the battery to be tested collected in real time, or may be battery data stored in a historical log or database, wherein the battery data includes but is not limited to voltage, current, temperature, output power, etc., and the battery to be tested may include a battery pack consisting of one battery pack or multiple battery packs, and the battery pack includes multiple battery cells. The battery to be tested may also be multiple battery cells. This embodiment does not impose specific restrictions on this. The type of battery to be tested can be set according to the user's business processing requirements.

[0099] Step S20: querying algorithm configuration information according to the business processing instruction.

[0100] It should be understood that the algorithm configuration information is used to map the algorithms and parameters in various business scenarios. The algorithm configuration information may be in the form of an algorithm configuration chart or a configuration table, or other representational forms, and this embodiment does not impose any specific restrictions on this.

[0101] In a specific implementation, the process of querying algorithm configuration information according to a business processing instruction may be to determine the business processing scenario corresponding to the business processing instruction and the feature identifier corresponding to the business processing scenario, and then query the algorithm configuration information corresponding to the feature identifier from a pre-allocated database. For example, in the battery health detection scenario, the keyword or identifier corresponding to the battery health detection scenario may be determined first, so that the algorithm for collecting the battery health status and the algorithm for comparing the battery health status may be obtained by querying the pre-allocated database. The input parameters of the algorithm for collecting the battery health status may be mapped to the output parameters of algorithms such as battery capacity calculation, battery internal resistance calculation, battery power calculation, and battery peak power calculation, and then the battery health status may be collected through a comprehensive method using various basic calculation algorithms, thereby improving the accuracy of the battery health status detection judgment.

[0102] Step S30: determining the comprehensive algorithm to be called and the basic algorithm corresponding to each input parameter in the comprehensive algorithm according to the algorithm configuration information.

[0103] Basic algorithms are used to process the raw data of batteries, for example, denoising, shaping and screening battery data, where shaping operations include numerical addition, subtraction, multiplication and division operations or numerical rounding operations. Comprehensive algorithms rely on the output parameters of each basic algorithm to perform further data calculations. In short, basic algorithms do not need to rely on other algorithms that also require calculations, and can directly process the raw data. At least one input of a comprehensive algorithm is associated with the output of other basic algorithms.

[0104] For example: Reference Figure 2, when performing numerical query, the output of X after the first basic class algorithm is X>1, the output of X after the second basic class algorithm is X<3, and the output of X after the third basic class algorithm is X is an integer. The outputs of these three basic class algorithms are used as the input of the comprehensive class algorithm to get X=2. In the above, the original data is X, and the three basic class algorithms are all direct processing of the original data, while the input of the comprehensive class algorithm corresponds to the output of the basic class algorithm and depends on the output results of the three basic class algorithms, otherwise it cannot run.

[0105] It should be understood that in order to realize business processing in various business scenarios, this embodiment determines the comprehensive algorithm to be called, and then determines the input parameters of the comprehensive algorithm, and determines the basic algorithm associated with the input parameters of the comprehensive algorithm according to the preset algorithm association relationship. Different algorithms can achieve the same purpose and improve the reliability and adaptability of data processing. Taking the stored power in the battery fault judgment process as an example, the no-load power loss value X1 of the battery per unit time and the theoretical battery loss value X2 stored in the database are calculated by the basic algorithm, and then the comprehensive algorithm is used to compare and judge whether there is leakage. In the comprehensive algorithm, its input values ​​are the actual battery data Y1 and the theoretical battery data Y2 involved in the comparison. In the actual comparison process, the actual battery data Y1 can be associated with the no-load power loss value X1 in the above text, and the theoretical battery data Y2 can be associated with the theoretical battery loss value X2 in the above text database. Then, the associated actual battery data Y1 and theoretical battery data Y2 are compared, that is, the battery fault judgment can be realized, and the integrity of its original data will not be affected, and the subsequent battery data processing will not be interfered.

[0106] Of course, in the actual battery fault judgment process, fault judgment is not only performed based on the amount of electricity stored in the battery, but also based on data such as the battery internal resistance, charge and discharge power, and heating status. This embodiment does not impose specific restrictions on this.

[0107] Step S40: Generate a business processing result according to the battery data, the basic algorithm and the comprehensive algorithm.

[0108] In a specific implementation, in order to execute business processing instructions, this embodiment can associate battery data with the input of the basic class algorithm, call the basic class algorithm to calculate the target battery data corresponding to the battery data, and then associate the target battery data with the input of the comprehensive class algorithm, and finally call the comprehensive class algorithm to calculate the business processing result corresponding to the target battery data, thereby realizing an algorithm scheduling process with strong adaptability and anti-interference, which is suitable for different business processing environments.

[0109] This embodiment proposes an algorithm scheduling method, which includes: in response to a triggered business processing instruction, obtaining battery data characterizing the battery operating condition; querying algorithm configuration information according to the business processing instruction; determining the comprehensive algorithm to be called and the basic algorithm corresponding to each input parameter in the comprehensive algorithm according to the algorithm configuration information; generating a business processing result according to the battery data, the basic algorithm and the comprehensive algorithm. By querying the algorithm configuration information in the corresponding business scenario according to the triggered business processing instruction, and then determining the comprehensive algorithm to be called and the basic algorithm corresponding to each input parameter in the comprehensive algorithm according to the algorithm configuration information, and then combining the battery data, the basic algorithm and the comprehensive algorithm to generate business processing results in different business processing scenarios, it can meet the technical problem of unified scheduling of algorithms in various battery business processing scenarios, and effectively improve the efficiency of algorithm scheduling.

[0110] In some embodiments, reference Figure 3 The steps of generating a business processing result according to the battery data, the basic algorithm and the comprehensive algorithm include:

[0111] Step S401: calling the basic class algorithm based on the battery data to generate target battery data.

[0112] It is understandable that due to the dependency of the comprehensive algorithm, this embodiment may first call the basic algorithm to process the collected battery data to meet the input of the comprehensive algorithm, and then call the comprehensive algorithm to process the output of the basic algorithm to generate a business processing result.

[0113] In a specific implementation, due to the coding habits of the coder or the limited number of letters during the algorithm compilation process, the algorithm codes pre-stored in the database may have the same letters, but the meanings they represent are completely different. In order to avoid algorithm operation errors caused by the same letters referring to different parameters when the algorithms are nested, this embodiment associates the parameters through mapping to avoid direct parameter nesting. For example, in the electrical field, P can be the output power of the battery, but P can also refer to the pressure value of the battery. Both are conventional references in this field. If P is directly nested in the comprehensive algorithm, the same letter will refer to two different variables, which will interfere with the algorithm operation. Therefore, when the comprehensive algorithm needs to use these two parameters, the input parameter 1 of the comprehensive algorithm can be associated with the output power P mentioned above, and the input parameter 2 of the comprehensive algorithm can be associated with the pressure value P mentioned above. In this way, during the operation of the comprehensive algorithm, there will be no situation where the same function refers to two variables, thereby improving the efficiency and accuracy of the algorithm scheduling process.

[0114] For another example, as mentioned above, in order to realize the judgment of specific battery faults, after the no-load power loss value X1 of the battery per unit time and the theoretical battery loss value X2 stored in the database are calculated through the basic algorithm, if the variables used in the battery health detection scenario are also X1 and X2, then conflicts will occur when the program is running, which will affect the running results. In this embodiment, each basic algorithm and comprehensive algorithm is packaged and run separately. During scheduling, the input of each algorithm is realized by reading the mapping relationship of its input parameters, so that the normal operation of the algorithm is realized. The variables between different algorithms are not directly related and will not affect each other.

[0115] The target battery data refers to the output data after various basic algorithms are used to perform data processing such as denoising, shaping and screening on the original battery data.

[0116] Step S402: calling the comprehensive algorithm based on the target battery data to generate a business processing result.

[0117] The input parameters of the comprehensive algorithm include but are not limited to the output parameters of each basic algorithm and other additional configuration parameters. The other additional configuration parameters may be pre-set constants or standard parameters defined in the art, etc. This embodiment does not impose any specific restrictions on this.

[0118] In this embodiment, the step of generating a business processing result according to the battery data, the basic class algorithm and the comprehensive class algorithm includes: calling the basic class algorithm based on the battery data to generate target battery data; calling the comprehensive class algorithm based on the target battery data to generate a business processing result, by first calling the basic class algorithm to generate target battery data corresponding to the battery data, and then calling the comprehensive class algorithm to process the target battery data, thereby generating a business processing result, avoiding parameter mapping conflicts during the algorithm scheduling process, resulting in algorithm operation errors and affecting the efficiency of business processing, and at the same time satisfying the runtime dependencies of the comprehensive class algorithm.

[0119] In some embodiments, the step of calling the base class algorithm based on the battery data to generate target battery data includes:

[0120] Associating the battery data with the input parameters of the basic class algorithm according to a first parameter mapping relationship, wherein the first parameter mapping relationship is a preset parameter mapping relationship between the battery data and the input parameters of the basic class algorithm;

[0121] The basic algorithm is called to generate target battery data corresponding to the input parameters of the basic algorithm.

[0122] Presetting the parameter mapping relationship between battery data and the input parameters of the basic algorithm can improve algorithm scheduling and business processing. For example, if you need to calculate the average temperature in a battery pack, the collected battery data is the single cell temperature of all battery cells in the battery pack. Before calculating the average value, you can filter the collected single cell temperature to remove the erroneous data, and then calculate the average value based on the single cell temperature of each battery cell. In the process of filtering through the basic algorithm, several consecutive identical temperatures may appear. At this time, if the collected temperature array is mapped with the input of the basic algorithm, even if the filtering is wrong, it will not affect the original data. If it is loaded into the algorithm in a nested form, if it is a misjudgment, some of the correct temperatures will be eliminated. In order to ensure the accuracy of the temperature average calculation, it is necessary to re-collect the temperature of each single chip of the battery pack or re-establish the communication connection and load the temperature array, wasting a lot of resources.

[0123] In this embodiment, the parameter mapping relationship between the battery data and the input parameters of the basic class algorithm is pre-set so that when the basic class algorithm is called, the input parameters of the basic class algorithm have correct input, generate accurate target battery data, and improve the accuracy of the operation of the basic class algorithm.

[0124] In some embodiments, associating the battery data with the input parameters of the basic class algorithm according to the first parameter mapping relationship includes:

[0125] Determining identifiers and attribute names of input parameters in the base class algorithm;

[0126] The battery data is loaded into the algorithm configuration table according to the identifier and the attribute name to associate the battery data with input parameters of the base class algorithm.

[0127] refer to Figure 4 , Figure 4 This is an example diagram of parameter mapping in this embodiment. The identifier is generally the commonly used English identifier of the attribute name in this field. The attribute name includes but is limited to battery data and battery properties that characterize the battery operating conditions. The battery data can be information such as battery voltage, battery current, and output power. The battery properties can be the manufacturer of the battery cell, the characteristic curves of each cell, and the battery pack number. This embodiment does not make specific restrictions on this.

[0128] In this embodiment, by obtaining the identifier and attribute name of the input parameter in the basic class algorithm, when loading the battery data, the battery data can be associated and loaded based on the identifier and attribute name of the input parameter in the basic class algorithm, thereby avoiding the problem of parameter mapping errors in the calculation process of the basic class algorithm, thereby outputting erroneous business processing results.

[0129] In some embodiments, calling the comprehensive algorithm based on the target battery data to generate a business processing result includes:

[0130] Associating the target battery data with the input parameters of the comprehensive algorithm according to a second parameter mapping relationship, wherein the second parameter mapping relationship is a preset mapping relationship between the output parameters of the basic algorithm and the input parameters of the comprehensive algorithm;

[0131] The comprehensive algorithm is called to generate a business processing result corresponding to the input parameters of the comprehensive algorithm.

[0132] Presetting the output parameters of the basic algorithm and the input parameters of the comprehensive algorithm can improve the efficiency of algorithm scheduling and business processing. As shown in the example above: P can be the output power of the battery, but P can also refer to the pressure value of the battery. Both are conventional references in this field. If P is directly nested in the comprehensive algorithm, the same letter will refer to two different variables, which will interfere with the operation of the algorithm. If parameter mapping is used, the data involved in the calculation are the input parameters of the comprehensive algorithm, not the output parameters of the basic algorithm.

[0133] In this embodiment, the parameter mapping relationship between the target battery data and the input parameters of the comprehensive algorithm is pre-set so that when the comprehensive algorithm is called, the input parameters of the comprehensive algorithm are correctly input, accurate business processing results are generated, the accuracy of the operation of the comprehensive algorithm is improved, and an efficient algorithm scheduling process is achieved in different business scenarios.

[0134] In some embodiments, before calling the comprehensive algorithm based on the target battery data to generate a business processing result, the method further includes:

[0135] Check whether the basic class algorithms have all been called;

[0136] If the basic algorithms have all been called, the input parameters of the comprehensive algorithms are configured according to the preset parameter mapping relationship and the basic data.

[0137] Since the comprehensive algorithm needs to rely on other algorithms when it is running, that is, the input parameters of the comprehensive algorithm are associated with the output parameters of the basic algorithm. If the basic algorithm is not called and the corresponding output parameters are not output, the input parameters of the comprehensive algorithm will be missing, affecting the normal operation of the algorithm. Therefore, this embodiment needs to detect whether all the basic algorithms in the current configuration information have been called and have corresponding outputs. If so, the step of configuring the input parameters of the comprehensive algorithm according to the preset parameter mapping relationship and the basic data is executed. If not, the business processing flow is terminated and the business processing instruction is received again to execute the next algorithm scheduling.

[0138] In this embodiment, before calling the comprehensive algorithm, it is checked whether all the basic algorithms have been called and have corresponding output parameters. When all the basic algorithms have been called, the comprehensive algorithm is called so that the input of the comprehensive algorithm has parameter inputs to prevent the comprehensive algorithm from running with errors.

[0139] In some embodiments, reference Figure 5 Specifically, the step of determining the comprehensive algorithm to be called and the basic algorithm corresponding to each input parameter in the comprehensive algorithm according to the algorithm configuration information includes:

[0140] Step S301: query the comprehensive algorithms and basic algorithms corresponding to the algorithm configuration information.

[0141] In a specific implementation process, the algorithm configuration information may be an algorithm configuration chart, or other forms of algorithm configuration information, which is not specifically limited in this embodiment.

[0142] Step S302: constructing an algorithm association relationship between the comprehensive algorithm and the basic algorithm according to the algorithm configuration information, wherein the algorithm association relationship is a parameter mapping relationship between the output parameters of the basic algorithm and the input parameters of the comprehensive algorithm.

[0143] Configure algorithm association relationships, which mainly include the architectural relationship between basic algorithms and comprehensive algorithms. At the same time, configure the input parameter mapping relationship of the basic algorithm under a comprehensive algorithm.

[0144] In the specific implementation, refer to Figure 6 , you can edit the parameter mapping relationship between the output parameters of the basic class algorithm and the input parameters of the comprehensive class algorithm by clicking the "Edit Mapping Relationship" operation, so as to facilitate the subsequent business processing process without the need to edit the algorithm code in real time, and can adapt to different business processing environments.

[0145] Step S303: Determine the comprehensive algorithm to be called and the basic algorithm corresponding to each input parameter in the comprehensive algorithm according to the parameter mapping relationship, the comprehensive algorithm and the basic algorithm.

[0146] In this embodiment, the comprehensive algorithms and basic algorithms existing in the algorithm configuration information are queried, and the parameter mapping relationship between the output parameters of the basic algorithms and the input parameters of the comprehensive algorithms is constructed through the algorithm configuration information, so as to make the algorithm scheduling process continuous, reduce the impact caused by the repetition of the underlying independent variables of the algorithm, and improve the parameter mapping accuracy of the basic algorithms and the comprehensive algorithms.

[0147] In some embodiments, the querying of the comprehensive algorithms and basic algorithms corresponding to the algorithm configuration information includes:

[0148] Querying the algorithm to be called contained in the algorithm configuration information;

[0149] Obtaining identification information of the algorithm to be called;

[0150] The algorithms to be called are classified according to the identification information to obtain comprehensive algorithms and basic algorithms.

[0151] When querying the comprehensive algorithms and basic algorithms corresponding to the algorithm configuration information, you can first query all the algorithms involved in the algorithm configuration information, and then classify the comprehensive algorithms and basic algorithms according to their execution identifiers (execution IDs).

[0152] The identification information may be the name, number, and other unique codes of the algorithm, for example, a convolutional neural network algorithm (CNN), which is not specifically limited in this embodiment.

[0153] In this embodiment, all the algorithms to be called contained in the algorithm configuration information are queried, and the algorithms to be called are classified according to the identification information of each algorithm to be called, so as to distinguish comprehensive algorithms from basic algorithms, facilitate the establishment of parameter mapping, and ensure the smoothness of algorithm operation.

[0154] In some embodiments, reference Figure 7 , before the step of querying the algorithm configuration information according to the business processing instruction, it also includes:

[0155] Step S110: displaying the algorithm configuration chart for each business processing scenario through a preset display page.

[0156] The preset display page may be a web page or an application page on the display terminal, and this embodiment does not impose any specific limitation on this.

[0157] refer to Figure 8 The algorithm configuration icon includes the rules of the business processing scenario, the algorithm name and operation information, where the operation information includes but is not limited to parameter mapping and configuration editing. In addition, the algorithm configuration table can also be configured through drop-down selection or text input.

[0158] Step S120: Counting the algorithm configuration instructions input by the user based on the algorithm configuration chart.

[0159] Algorithm configuration instructions include but are not limited to parameter mapping instructions, algorithm association instructions, and text editing instructions.

[0160] Step S130: setting algorithm configuration information according to the algorithm configuration instruction.

[0161] In this embodiment, the algorithm configuration icons for each business scenario are displayed through a preset display page, and the algorithm configuration instructions input by the user based on the algorithm configuration icons are counted, so as to realize the pre-setting of the algorithm configuration information according to the algorithm configuration instructions, so that when facing different business processing environments in the future, the relevant algorithm configuration information can be directly queried, thereby improving the efficiency of algorithm scheduling.

[0162] In some embodiments, the setting of algorithm configuration information according to the algorithm configuration instruction further includes:

[0163] The algorithm to be called is determined according to the algorithm configuration instruction;

[0164] configuring a mapping relationship between the battery data and the parameters of the algorithm to be called based on the algorithm configuration instruction;

[0165] Algorithm configuration information is generated according to the algorithm to be called and the parameter mapping relationship.

[0166] Configuring parameter mapping relationships refers to setting parameter mapping relationships between battery data and input parameters of basic algorithms, and associating parameter mapping relationships between output parameters of basic algorithms and input parameters of comprehensive algorithms. In addition, it is also necessary to associate the architectural relationship between basic algorithms and comprehensive algorithms to facilitate direct calling during business processing without temporary configuration, thereby improving business processing efficiency.

[0167] In this embodiment, the parameter mapping relationship between the algorithm to be called and the battery data is set according to the algorithm configuration instruction input by the user, thereby obtaining the algorithm configuration information without temporary configuration, thereby improving the efficiency of business processing.

[0168] In some embodiments, the algorithm configuration instruction includes: a parameter mapping instruction and an algorithm association instruction, the algorithm to be called includes a basic algorithm and a comprehensive algorithm, and the parameter mapping relationship includes a first parameter mapping relationship and a second parameter mapping relationship;

[0169] The step of constructing a mapping relationship between the battery data and the parameters of the algorithm to be called based on the algorithm configuration instruction includes:

[0170] Constructing a first parameter mapping relationship between battery data and input parameters of the basic class algorithm based on the parameter mapping instruction;

[0171] A second mapping relationship between the output parameters of the basic algorithm and the input parameters of the comprehensive algorithm is constructed based on the algorithm association instruction.

[0172] The parameter mapping instruction is used to set the parameter mapping relationship between battery data and the input parameters of the basic class algorithm. The algorithm association instruction is used to associate the architectural relationship between the basic class algorithm and the comprehensive class algorithm, and to associate the parameter mapping relationship between the output parameters of the basic class algorithm and the input parameters of the comprehensive class algorithm.

[0173] In this embodiment, the first parameter mapping relationship between the battery data and the input parameters of the basic class algorithm and the second mapping relationship between the output parameters of the basic class algorithm and the input parameters of the comprehensive class algorithm are set respectively to achieve the setting of the parameter mapping relationship between the battery data and the algorithm to be called, thereby avoiding the problem of parameter mapping errors during the operation of the algorithm.

[0174] In some embodiments, the generating algorithm configuration information according to the algorithm to be called and the parameter mapping relationship includes:

[0175] Determine the business processing scenario corresponding to the algorithm configuration instruction and the algorithm configuration chart corresponding to the business processing scenario;

[0176] The algorithm to be called and the parameter mapping relationship are loaded into the algorithm configuration chart to generate algorithm configuration information.

[0177] There is an algorithm configuration chart corresponding to each business processing scenario, which is used for algorithm scheduling when executing business processing. The algorithm configuration chart of each business processing scenario can be pre-set and stored in a divided area in the non-volatile memory to facilitate subsequent direct calls, reduce algorithm editing in actual application scenarios, and improve business processing efficiency.

[0178] For example: for the three business processing scenarios of battery health detection, battery fault judgment and battery safety detection, the battery health detection is provided with an algorithm configuration chart A1, the battery fault judgment is provided with an algorithm configuration chart A2, and the battery safety detection is provided with an algorithm configuration chart A3. The algorithm configuration chart A1, the algorithm configuration chart A2 and the algorithm configuration chart A3 may include algorithms of the same type, for example: basic algorithms for collecting battery power, internal resistance or charging and discharging power, etc.

[0179] In this embodiment, by determining the business processing scenario and the corresponding algorithm configuration chart corresponding to the algorithm configuration instruction input by the user, and then loading the parameter mapping relationship set above into the algorithm configuration chart, the algorithm configuration information for algorithm scheduling is generated, thereby improving the algorithm scheduling efficiency, and also facilitating the user's subsequent mapping relationship modification or algorithm scheduling adjustment.

[0180] In some embodiments, the algorithm scheduling method further includes:

[0181] The parameters of the additional configuration items in the algorithm configuration chart are adjusted according to the algorithm configuration instruction.

[0182] Additional configuration items include but are not limited to the algorithm architecture and constants of basic and comprehensive algorithms, which are used to form the underlying operating logic of the algorithm.

[0183] In this embodiment, by adjusting the parameters of the additional configuration items in the algorithm configuration chart, the algorithm configuration in different business scenarios can be implemented, thereby improving the comprehensiveness and adaptability of the algorithm scheduling application scenarios.

[0184] In some embodiments, the algorithm scheduling method further includes:

[0185] The algorithm name and rule name of the algorithm configuration chart are text-edited according to the algorithm configuration instruction.

[0186] In order to realize algorithm scheduling for various business scenarios, this embodiment can perform text editing on the algorithm names and rule names in the algorithm configuration chart. For example, the business scenarios belonging to fault judgment can be subdivided into fault level judgment, fault occurrence time judgment, and fault location detection, etc., and then the rule names of these three algorithm configurations can be added to the algorithm configuration chart, and the corresponding algorithm names and scheduling relationships can be added to adapt to the user's business processing plan and improve the user's usage experience.

[0187] In this embodiment, by editing the algorithm name and rule name in the algorithm configuration chart according to the algorithm configuration instruction, it is possible to adapt to the user's business processing plan and save the cost of manpower focusing on algorithm scheduling.

[0188] To achieve the above purpose, refer to Fig. 9 The embodiment of the present invention further provides an algorithm scheduling device, the algorithm scheduling device comprising:

[0189] The acquisition module 10 is used to acquire battery data representing the operating condition of the battery in response to the triggered service processing instruction.

[0190] The query module 20 is used to query the algorithm configuration information according to the business processing instruction.

[0191] The determination module 30 is used to determine the comprehensive algorithm to be called and the basic algorithm corresponding to each input parameter in the comprehensive algorithm according to the algorithm configuration information.

[0192] The scheduling module 40 is used to generate a business processing result according to the battery data, the basic algorithm and the comprehensive algorithm.

[0193] To achieve the above objectives, an embodiment of the present invention further proposes an algorithm scheduling platform, which is used to execute the algorithm scheduling method as described above.

[0194] To achieve the above objective, an embodiment of the present invention further proposes a storage medium, on which an algorithm scheduling program is stored. When the algorithm scheduling program is executed by a processor, the algorithm scheduling method as described above is implemented.

[0195] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. All equivalent structural changes made using the contents of the present invention's specification and drawings, or directly / indirectly applied in other related technical fields, are included in the patent protection scope of the present invention.

Claims

1. An algorithm scheduling method, characterized in that: The algorithm scheduling method comprises: In response to the triggered service processing instruction, obtaining battery data representing the operating condition of the battery; querying algorithm configuration information according to the business processing instruction; Determine the comprehensive algorithm to be called and the basic algorithm corresponding to each input parameter in the comprehensive algorithm according to the algorithm configuration information; A business processing result is generated according to the battery data, the basic algorithm and the comprehensive algorithm.

2. The algorithm scheduling method according to claim 1, characterized in that: The generating of the business processing result according to the battery data, the basic algorithm and the comprehensive algorithm includes: Calling the basic class algorithm based on the battery data to generate target battery data; The comprehensive algorithm is called based on the target battery data to generate a business processing result.

3. The algorithm scheduling method according to claim 2, characterized in that: The calling the basic class algorithm based on the battery data to generate target battery data includes: Associating the battery data with the input parameters of the basic class algorithm according to a first parameter mapping relationship, wherein the first parameter mapping relationship is a preset parameter mapping relationship between the battery data and the input parameters of the basic class algorithm; The basic algorithm is called to generate target battery data corresponding to input parameters of the basic algorithm.

4. The algorithm scheduling method according to claim 3, characterized in that: The associating the battery data with the input parameters of the basic algorithm according to the first parameter mapping relationship includes: Determining identifiers and attribute names of input parameters in the base class algorithm; The battery data is loaded into the algorithm configuration table according to the identifier and the attribute name to associate the battery data with input parameters of the base class algorithm.

5. The algorithm scheduling method according to claim 2, characterized in that: The calling of the comprehensive algorithm based on the target battery data to generate a business processing result includes: Associating the target battery data with the input parameters of the comprehensive algorithm according to a second parameter mapping relationship, wherein the second parameter mapping relationship is a preset mapping relationship between the output parameters of the basic algorithm and the input parameters of the comprehensive algorithm; The comprehensive algorithm is called to generate a business processing result corresponding to the input parameters of the comprehensive algorithm.

6. The algorithm scheduling method according to claim 2, characterized in that: Before calling the comprehensive algorithm based on the target battery data to generate a business processing result, the method further includes: Check whether the basic class algorithms have all been called; If the basic algorithms have all been called, the input parameters of the comprehensive algorithms are configured according to the preset parameter mapping relationship and the basic data.

7. The algorithm scheduling method according to any one of claims 1 to 6, characterized in that: The step of determining the comprehensive algorithm to be called and the basic algorithm corresponding to each input parameter in the comprehensive algorithm according to the algorithm configuration information includes: Query the comprehensive algorithms and basic algorithms corresponding to the algorithm configuration information; An algorithm association relationship between the comprehensive algorithm and the basic algorithm is established according to the algorithm configuration information, wherein the algorithm association relationship is a parameter mapping relationship between an output parameter of the basic algorithm and an input parameter of the comprehensive algorithm; The comprehensive algorithm to be called and the basic algorithm corresponding to each input parameter in the comprehensive algorithm are determined according to the parameter mapping relationship, the comprehensive algorithm and the basic algorithm.

8. The algorithm scheduling method according to claim 7, characterized in that: The querying of the comprehensive algorithms and basic algorithms corresponding to the algorithm configuration information includes: Querying the algorithm to be called contained in the algorithm configuration information; Obtaining identification information of the algorithm to be called; The algorithms to be called are classified according to the identification information to obtain comprehensive algorithms and basic algorithms.

9. The algorithm scheduling method according to any one of claims 1 to 8, characterized in that: Before querying the algorithm configuration information according to the business processing instruction, the method further includes: Display algorithm configuration charts for various business processing scenarios through preset display pages; Counting the algorithm configuration instructions input by the user based on the algorithm configuration chart; Algorithm configuration information is set according to the algorithm configuration instruction.

10. The algorithm scheduling method according to claim 9, characterized in that: The setting of algorithm configuration information according to the algorithm configuration instruction further includes: The algorithm to be called is determined according to the algorithm configuration instruction; configuring a mapping relationship between the battery data and the parameter of the algorithm to be called based on the algorithm configuration instruction; Algorithm configuration information is generated according to the algorithm to be called and the parameter mapping relationship.

11. The algorithm scheduling method according to claim 10, characterized in that: The algorithm configuration instruction includes: a parameter mapping instruction and an algorithm association instruction, the algorithm to be called includes a basic algorithm and a comprehensive algorithm, and the parameter mapping relationship includes a first parameter mapping relationship and a second parameter mapping relationship; The step of constructing a mapping relationship between the battery data and the parameters of the algorithm to be called based on the algorithm configuration instruction includes: Constructing a first parameter mapping relationship between battery data and input parameters of the basic class algorithm based on the parameter mapping instruction; A second mapping relationship between the output parameters of the basic algorithm and the input parameters of the comprehensive algorithm is constructed based on the algorithm association instruction.

12. The algorithm scheduling method according to claim 10, characterized in that: The generating algorithm configuration information according to the algorithm to be called and the parameter mapping relationship includes: Determine the business processing scenario corresponding to the algorithm configuration instruction and the algorithm configuration chart corresponding to the business processing scenario; The algorithm to be called and the parameter mapping relationship are loaded into the algorithm configuration chart to generate algorithm configuration information.

13. The algorithm scheduling method according to claim 12, characterized in that: The algorithm scheduling method further includes: The parameters of the additional configuration items in the algorithm configuration chart are adjusted according to the algorithm configuration instruction.

14. The algorithm scheduling method according to claim 12, characterized in that: The algorithm scheduling method further includes: The algorithm name and rule name of the algorithm configuration chart are text-edited according to the algorithm configuration instruction.

15. An algorithm scheduling device, characterized in that: The algorithm scheduling device comprises: An acquisition module, configured to acquire battery data representing a battery operating condition in response to a triggered service processing instruction; A query module, used to query algorithm configuration information according to the business processing instruction; A determination module, used to determine the comprehensive algorithm to be called and the basic algorithm corresponding to each input parameter in the comprehensive algorithm according to the algorithm configuration information; The scheduling module is used to generate a business processing result according to the battery data, the basic algorithm and the comprehensive algorithm.

16. An algorithm scheduling device, characterized in that: The algorithm scheduling device comprises: a memory, a processor, and an algorithm scheduling program stored in the memory and executable on the processor, wherein the algorithm scheduling program is configured to implement the algorithm scheduling method according to any one of claims 1 to 14.

17. A storage medium, characterized in that: The storage medium stores an algorithm scheduling program, and when the algorithm scheduling program is executed by the processor, the algorithm scheduling method according to any one of claims 1 to 14 is implemented.

Images