Data processing method and system

Abstract

The invention discloses a data processing method and system, and belongs to the technical field of data processing of secondary batteries, and the data processing method comprises the steps: determining target data of a plurality of parameters in a secondary battery, and generating a corresponding first binary tree; setting a parameter corresponding to the first binary tree as a dimension parameter, and constructing multi-dimensional data; processing the multi-dimensional data by using target data corresponding to any parameter in the multi-dimensional data, and constructing a second binary tree corresponding to the multi-dimensional data; and performing data processing on the data of the secondary battery through the first binary tree and the second binary tree. According to the method, the first binary tree is generated for each parameter, meanwhile, the parameter of each first binary tree is used as dimension data, multi-dimensional data among the multiple parameters is established, the second binary tree associated with the multiple first binary trees is generated, the parameters are associated through the binary trees, and the relevance among the data is improved; and meanwhile, the target data of each parameter is sorted in a binary tree mode, so that the data processing efficiency is improved.

Description

Technical Field

[0001] The present application belongs to the technical field of secondary battery data processing, and specifically relates to a data processing method and system. Background Art

[0002] The data of secondary batteries include voltage data, current data, temperature data and many other types of data. When studying the operating status of the battery, it is necessary to clean, sort, query and remove abnormal data. However, due to the large number of data types, the huge amount of data, and the low correlation between data, when processing data, it is often necessary to traverse all the data to obtain the required data. When the data volume is large, the data processing efficiency is low. Summary of the invention

[0003] Purpose of the invention: This application develops a data processing method and system, aiming to solve the technical problems of poor correlation between data and low data processing efficiency in the prior art.

[0004] Technical solution: In a first aspect, an embodiment of the present application provides a data processing method, which is applied to data processing of a secondary battery. The data processing method includes:

[0005] determining target data of a plurality of parameters of the secondary battery;

[0006] Based on the target data of each parameter, construct a first binary tree corresponding to each parameter;

[0007] Determine the parameter corresponding to the first binary tree as a dimensional parameter, and construct multidimensional data;

[0008] Based on the target data of any parameter in the multidimensional data and the multidimensional data, construct a second binary tree corresponding to the multidimensional data;

[0009] Data processing is performed on the secondary battery based on the first binary tree corresponding to each of the parameters and the second binary tree corresponding to the multi-dimensional data.

[0010] In some embodiments, the data processing method further includes: determining that the first binary tree is a third binary tree when constructing the first binary tree, and determining that the second binary tree is a third binary tree when constructing the second binary tree;

[0011] The step of constructing a first binary tree corresponding to each of the parameters, or the step of constructing a second binary tree corresponding to the multidimensional data includes:

[0012] constructing a fourth binary tree based on target data of any of the parameters, wherein the parameters for constructing the fourth binary tree are at least partially the same as the parameters for constructing the third binary tree;

[0013] In response to the depth of the left subtree of the root node in the fourth binary tree being greater than the depth of the right subtree of the root node, and the depth difference being greater than one, determining the root node of the third binary tree based on the depth of the fourth binary tree;

[0014] Transforming the fourth binary tree based on the root node of the third binary tree to obtain the third binary tree;

[0015] Among them, in the third binary tree and the fourth binary tree, the values ​​of all child nodes in the left subtree of any node are smaller than the value of the node, and the values ​​of all child nodes in the right subtree of the node are larger than the value of the node.

[0016] In some embodiments, the step of determining the root node of the third binary tree based on the depth of the fourth binary tree includes:

[0017] Acquire candidate nodes based on the depth of the fourth binary tree;

[0018] In response to the number of the candidate nodes being multiple, the candidate node with the largest number of child nodes is determined as the root node.

[0019] In some embodiments, the step of obtaining candidate nodes based on the depth of the fourth binary tree includes:

[0020] Taking the quotient of the depth value of the fourth binary tree and two and rounding down, a candidate number is obtained;

[0021] In the fourth binary tree, a node whose depth value is the candidate number is obtained as a candidate node.

[0022] In some embodiments, the step of performing data processing on the secondary battery based on the first binary tree corresponding to each of the parameters and the second binary tree corresponding to the multi-dimensional data includes:

[0023] Determine the data to be processed;

[0024] Determine the position of the data of each parameter in the data to be processed in the first binary tree as the first position;

[0025] Determine the position of the multidimensional data in the data to be processed in the second binary tree as the second position;

[0026] The data to be processed is processed based on the first position and the second position.

[0027] In some embodiments, the multidimensional data includes first-dimensional data and second-dimensional data, and the step of determining the position of the multidimensional data in the data to be processed in the second binary tree includes:

[0028] Start searching from the root node of the second binary tree.

[0029] In the odd-numbered layers of the second binary tree, in response to the first-dimensional data being smaller than the data of the node in the odd-numbered layers, it is determined that the node corresponding to the multidimensional data in the data to be processed is located in the left subtree of the node in the odd-numbered layers; in response to the first-dimensional data being larger than the data of the node in the odd-numbered layers, it is determined that the node corresponding to the multidimensional data in the data to be processed is located in the right subtree of the node in the odd-numbered layers;

[0030] In the even layers of the second binary tree, in response to the second dimensional data being smaller than the data of the nodes in the even layers, it is determined that the nodes corresponding to the multidimensional data in the data to be processed are located in the left subtree of the nodes in the even layers; in response to the second dimensional data being larger than the data of the nodes in the even layers, it is determined that the nodes corresponding to the multidimensional data in the data to be processed are located in the right subtree of the nodes in the even layers.

[0031] In some embodiments, applied to a plurality of the secondary batteries, the data processing method further comprises:

[0032] Obtaining an identity mark of the secondary battery;

[0033] Sorting the plurality of identity tags to construct a first binary tree corresponding to the identity tag parameters;

[0034] In the data of the same secondary battery, the identity tag of the secondary battery is inserted into the node of the first binary tree corresponding to any other parameter to construct a multi-dimensional binary tree of the secondary battery.

[0035] In some embodiments, before sorting the plurality of identity tags, the data processing method further includes:

[0036] In response to the presence of letters in the identity tag, the letters are converted into numbers based on a standard code for information interchange.

[0037] In some embodiments, the plurality of parameters include a timestamp and an insulation resistance.

[0038] In a second aspect, an embodiment of the present application further provides a data processing system for use in data processing of a secondary battery, the data processing system comprising:

[0039] A data acquisition module, the data acquisition module is used to determine target data of multiple parameters of the secondary battery;

[0040] A first binary tree construction module, the first binary tree construction module is used to construct a first binary tree corresponding to each of the parameters based on the target data of each of the parameters;

[0041] A multidimensional data construction module, the multidimensional data construction module is used to determine the parameter corresponding to the first binary tree as a dimensional parameter, and construct multidimensional data;

[0042] A second binary tree construction module, the second binary tree construction module is used to construct a second binary tree corresponding to the multi-dimensional data based on the target data of any parameter in the multi-dimensional data and the multi-dimensional data;

[0043] A data processing module is used to perform data processing on the secondary battery based on the first binary tree corresponding to each of the parameters and the second binary tree corresponding to the multi-dimensional data.

[0044] Beneficial effect: Compared with the prior art, a data processing method provided in an embodiment of the present application is applied in the data processing of secondary batteries. The data processing method provided in the embodiment of the present application first determines the target data of multiple parameters in the secondary battery, and generates a corresponding first binary tree according to the target data of each parameter; then sets the parameter corresponding to the first binary tree as a dimensional parameter to construct multidimensional data; in the multidimensional data, uses the target data corresponding to any parameter in the multidimensional data to process the multidimensional data, and constructs a second binary tree corresponding to the multidimensional data; finally, the data of the secondary battery is processed through the first binary tree corresponding to each parameter and the second binary tree corresponding to the multidimensional parameter. The present application generates a first binary tree for each parameter, and uses the parameters of each first binary tree as dimensional data to establish multidimensional data between multiple parameters, and generates a second binary tree associated with multiple first binary trees, and associates the parameters through the binary tree to improve the correlation between data; at the same time, by sorting the target data of each parameter in a binary tree manner, when searching for data, the value of the data to be searched is compared with the value of the node at each layer to further determine the left and right position of the data to be searched at the current node, without traversing all the data, which can effectively shorten the data search time and improve data processing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0046] Figure 1A flowchart of the steps of the data processing method provided in the embodiment of the present application;

[0047] Figure 2 A specific flow chart for obtaining a third binary tree in the data processing method provided in an embodiment of the present application;

[0048] Figure 3 A specific flow chart of step b in the data processing method provided in an embodiment of the present application;

[0049] Figure 4 A specific flow chart of step b1 in the data processing method provided in an embodiment of the present application;

[0050] Figure 5 A specific flow chart of step 500 in the data processing method provided in an embodiment of the present application;

[0051] Figure 6 A specific flow chart of step 530 in the data processing method provided in an embodiment of the present application;

[0052] Figure 7 A specific flow chart of the data processing method provided in the embodiment of the present application applied to multiple secondary batteries;

[0053] Figure 8 A module connection diagram of a data processing system provided in an embodiment of the present application;

[0054] Fig. 9 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application;

[0055] Fig.10 A graph of the root node of a fourth binary tree constructed by using the first set of working data in the data processing method provided in an embodiment of the present application;

[0056] Fig.11 A diagram of inserting the second frame data into a fourth binary tree constructed by the first set of working data in the data processing method provided in the embodiment of the application;

[0057] Fig.12 A graph showing a fourth binary tree after construction is completed in the data processing method provided in an embodiment of the present application;

[0058] Fig.13 A graph of a third binary tree transformed from a fourth binary tree in a data processing method provided in an embodiment of the present application;

[0059] Fig.14 A diagram of a second binary tree of the multidimensional data in the data processing method provided in an embodiment of the present application;

[0060] Fig.15A schematic diagram of inserting (4,4000) multidimensional data nodes into a second binary tree corresponding to the multidimensional data in the data processing method provided in an embodiment of the present application;

[0061] Figure numerals: 10, data acquisition module; 20, first binary tree construction module; 30, multidimensional data construction module; 40, second binary tree construction module; 50, data processing module; 100, memory; 200, processor. DETAILED DESCRIPTION

[0062] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0063] The data of secondary batteries include voltage data, current data, temperature data and many other types of data. When studying the operating status of the battery, it is necessary to clean, sort, query and remove abnormal data. However, due to the large number of data types, the huge amount of data, and the low correlation between data, when processing data, it is often necessary to traverse all the data to obtain the required data. When the data volume is large, the data processing efficiency is low.

[0064] In view of this, an embodiment of the present application provides a data processing method, which is applied in the data processing of secondary batteries. The data processing method provided by the embodiment of the present application first determines the target data of multiple parameters in the secondary battery, and generates a corresponding first binary tree according to the target data of each parameter; then sets the parameter corresponding to the first binary tree as a dimensional parameter to construct multidimensional data; in the multidimensional data, uses the target data corresponding to any parameter in the multidimensional data to process the multidimensional data, and constructs a second binary tree corresponding to the multidimensional data; finally, the data of the secondary battery is processed through the first binary tree corresponding to each parameter and the second binary tree corresponding to the multidimensional parameter. The present application generates a first binary tree for each parameter, and uses the parameters of each first binary tree as dimensional data to establish multidimensional data between multiple parameters, and generates a second binary tree associated with multiple first binary trees, and associates the parameters through the binary tree to improve the correlation between data; at the same time, by sorting the target data of each parameter in a binary tree manner, when searching for data, the value of the data to be searched is compared with the value of the node at each layer to further determine the left and right position of the data to be searched at the current node, without traversing all the data, which can effectively shorten the data search time and improve data processing efficiency.

[0065] In some embodiments, see Figure 1 , Figure 1 This is a step flow chart of the data processing method provided in the embodiment of the present application. The data processing method provided in the embodiment of the present application is specifically implemented through steps 100 to 500:

[0066] Step 100: Determine target data of multiple parameters of the secondary battery.

[0067] In some embodiments, when targeting a single secondary battery, the parameters in the secondary battery that can be considered include timestamp, voltage, temperature, and insulation resistance, etc.; when targeting multiple secondary batteries, it is necessary to increase the identity tag parameters of the secondary battery to distinguish multiple secondary batteries. In this embodiment of the present application, for a single secondary battery, data processing is performed using timestamp parameters and insulation resistance parameters as examples. It can be understood that timestamp parameters and voltage parameters, timestamp parameters and temperature parameters, voltage parameters and temperature parameters, voltage parameters and insulation resistance parameters, etc. can also be used, which can be specifically determined according to the parameters considered by the data processing requirements; the number of parameters can also be increased to increase the data processing dimension, increase the number of parameters considered at the same time, further improve the correlation between target data, and improve the efficiency of data processing.

[0068] Step 200: Based on the target data of each parameter, construct a first binary tree corresponding to each parameter.

[0069] It can be understood that the present application considers the timestamp parameter and the insulation resistance parameter, and constructs the first binary tree of the timestamp based on the target data corresponding to the timestamp, and constructs the first binary tree of the insulation resistance based on the target data corresponding to the insulation resistance. When considering other parameter combinations, the corresponding first binary tree can be constructed based on the above scheme, which will not be elaborated here.

[0070] In some embodiments, when constructing the first binary tree corresponding to each parameter, the first binary tree is determined to be the third binary tree. Figure 2 , Figure 2 This is a specific flow chart for obtaining the third binary tree in the data processing method provided in the embodiment of the present application. It can be seen that the step of constructing the first binary tree corresponding to each parameter is specifically implemented through steps a to c:

[0071] Step a: construct a fourth binary tree based on target data of any parameter, wherein the parameter for constructing the fourth binary tree is at least partially the same as the parameter for constructing the third binary tree.

[0072] Step b: In response to the depth of the left subtree of the root node in the fourth binary tree being greater than the depth of the right subtree of the root node, and the depth difference being greater than one, determining the root node of the third binary tree based on the depth of the fourth binary tree.

[0073] In some embodiments, see Figure 3 , Figure 3The specific flow chart of step b in the data processing method provided in the embodiment of the present application, the method for determining the root node of the third binary tree based on the depth of the fourth binary tree is specifically implemented through steps b1 to b2:

[0074] Step b1: Obtain candidate nodes based on the depth of the fourth binary tree.

[0075] In some embodiments, see Figure 4 , Figure 4 This is a specific flow chart of step b1 in the data processing method provided in an embodiment of the present application. The method for obtaining candidate nodes based on the depth of the fourth binary tree is specifically implemented through steps b11 to b12:

[0076] Step b11: take the depth value of the fourth binary tree and 2 as the quotient and round down to obtain the candidate number.

[0077] Step b12: In the fourth binary tree, nodes whose depth values ​​are candidate numbers are obtained as candidate nodes.

[0078] Step b2: In response to the number of candidate nodes being multiple, determine the candidate node with the largest number of child nodes as the root node.

[0079] Step c: transform the fourth binary tree based on the root node of the third binary tree to obtain the third binary tree.

[0080] Among them, in the third binary tree and the fourth binary tree, the values ​​of all child nodes in the left subtree of any node are smaller than the value of the node, and the values ​​of all child nodes in the right subtree of the node are larger than the value of the node.

[0081] For example, taking the construction of the fourth binary tree in step a as an example, taking the secondary battery with the identity mark i d1 as an example, the process of constructing the fourth binary tree based on the timestamp in step a is exemplarily described:

[0082] First, the first set of working data of the secondary battery with multiple i d1 is obtained, see Table 1:

[0083] Table 1: The first set of working data

[0084]

[0085] See also Fig.10 , Fig.10 A graph of the root node of the fourth binary tree constructed by the first set of working data in the data processing method provided in an embodiment of the present application, wherein the first frame data is processed into a dictionary to store the parameters selected from the root node as identity information and a timestamp, wherein the identity information is used to characterize the secondary battery to which the frame data belongs, and the timestamp is used as a sorting basis to generate a fourth binary tree.

[0086] When inserting the second frame of data, refer to Fig.11 , Fig.11 The figure of inserting the second frame data when the fourth binary tree is constructed by the first set of working data in the data processing method provided in the embodiment of the present application is based on the principle that "the values ​​of all child nodes in the left subtree of any node are less than the value of the node, and the values ​​of all child nodes in the right subtree of the node are greater than the value of the node". With the timestamp as the judgment basis, it can be seen that 3 is less than 5, and the second frame data should be inserted into the left subtree of the root node of the fourth binary tree.

[0087] In some embodiments, if the same timestamp exists, the frame data is deleted to achieve the purpose of data cleaning and deleting duplicate data.

[0088] In some embodiments, if the identity tag of the inserted frame is different from the identity tag in the fourth binary tree, another fourth binary tree is created to store the frame with the different identity tag. It can be understood that each fourth binary tree represents data corresponding to an identity tag, that is, each fourth binary tree is a binary tree for a certain parameter in a secondary battery.

[0089] Sequentially obtain the data of subsequent frames to construct the fourth binary tree. See Fig.12 , Fig.12 This is a diagram after the fourth binary tree is constructed in the data processing method provided in the embodiment of the present application. It can be seen that when it is necessary to find data with a timestamp of 1, it is necessary to first find 5, then find 3, then find 2, and then find 1.

[0090] In some embodiments, in order to improve the number search efficiency, the present application further transforms the fourth binary tree so that the structure of the fourth binary tree is as symmetrical as possible to obtain a third binary tree with higher search efficiency.

[0091] Specifically, see Fig.12 , Fig.12 This is the graph after the construction of the fourth binary tree is completed, and its depth is 3. First, determine whether the fourth subtree needs to be transformed: the depth of the left subtree of its root node is 2, and the depth of the left subtree of the root node is 0. The depth difference is greater than 1. It can be seen that the fourth subtree needs to be transformed to make the structure of the fourth binary tree as symmetrical as possible. Then determine the root node of the transformed subtree, which is the third subtree: take the quotient of the tree height 3 and 2, and round down to get the candidate number 1. In the fourth binary tree, determine the node with a depth value of 1 as the candidate node, that is, the two nodes in the second layer of the fourth binary tree are candidate nodes; after confirming the candidate node, if there is one candidate node, then the candidate node is the root node of the third binary tree. If there are multiple candidate nodes, determine the candidate node with the largest number of child nodes as the root node. Please refer to Fig.12 , Fig.12This is a graph after the fourth binary tree is constructed. The number of child nodes of the left node in the second layer is 3, and the number of child nodes of the right node is 0. It can be seen that the left node is the root node of the third binary tree. Then, based on the principle that "the values ​​of all child nodes in the left subtree of any node are smaller than the value of the node, and the values ​​of all child nodes in the right subtree of the node are larger than the value of the node", the fourth binary tree is transformed into the third binary tree. Please refer to Fig.13 , Fig.13 The diagram of the third binary tree transformed from the fourth binary tree in the data processing method provided in the embodiment of the present application shows that when it is necessary to find a node with a timestamp of 1, it is only necessary to find 2 from 3, and then find 1 from 2. Compared with the fourth binary tree, the search efficiency of the third binary tree is significantly improved.

[0092] Step 300: Determine the parameters corresponding to the first binary tree as dimension parameters, and construct multidimensional data.

[0093] Step 400: constructing a second binary tree corresponding to the multidimensional data based on target data of any parameter in the multidimensional data and the multidimensional data;

[0094] In some embodiments, when constructing a second binary tree corresponding to the multidimensional data, the second binary tree is determined to be a third binary tree. Figure 2 , Figure 2 This is a specific flow chart of obtaining the third binary tree in the data processing method provided in the embodiment of the present application. It can be seen that the step of constructing the second binary tree corresponding to the multidimensional data is specifically implemented through steps a to c:

[0095] Step a: construct a fourth binary tree based on target data of any parameter, wherein the parameter for constructing the fourth binary tree is at least partially the same as the parameter for constructing the third binary tree.

[0096] In some embodiments, when constructing a second binary tree corresponding to multidimensional data, any one-dimensional parameter in the multidimensional data is selected to process the multidimensional data to obtain a second binary tree of the multidimensional data. Specifically, the multidimensional parameters in the present application include a timestamp parameter and an insulation resistance parameter, and the timestamp parameter or the insulation resistance parameter can be selected as a construction parameter of the fourth binary tree. The embodiment of the present application takes the timestamp parameter as an example to process the multidimensional data and construct the fourth binary tree.

[0097] Step b: In response to the depth of the left subtree of the root node in the fourth binary tree being greater than the depth of the right subtree of the root node, and the depth difference being greater than one, determining the root node of the third binary tree based on the depth of the fourth binary tree.

[0098] In some embodiments, see Figure 3 , Figure 3The specific flow chart of step b in the data processing method provided in the embodiment of the present application, the method for determining the root node of the third binary tree based on the depth of the fourth binary tree is specifically implemented through steps b1 to b2:

[0099] Step b1: Obtain candidate nodes based on the depth of the fourth binary tree.

[0100] In some embodiments, see Figure 4 , Figure 4 This is a specific flow chart of step b1 in the data processing method provided in an embodiment of the present application. The method for obtaining candidate nodes based on the depth of the fourth binary tree is specifically implemented through steps b11 to b12:

[0101] Step b11: take the depth value of the fourth binary tree and 2 as the quotient and round down to obtain the candidate number.

[0102] Step b12: In the fourth binary tree, nodes whose depth values ​​are candidate numbers are obtained as candidate nodes.

[0103] Step b2: In response to the number of candidate nodes being multiple, determine the candidate node with the largest number of child nodes as the root node.

[0104] Step c: transform the fourth binary tree based on the root node of the third binary tree to obtain the third binary tree.

[0105] Among them, in the third binary tree and the fourth binary tree, the values ​​of all child nodes in the left subtree of any node are smaller than the value of the node, and the values ​​of all child nodes in the right subtree of the node are larger than the value of the node.

[0106] Exemplarily, taking the multidimensional data constructed by the timestamp parameter and the insulation resistance parameter as an example, a second binary tree is constructed, and the first binary tree corresponding to the timestamp parameter and the first binary tree corresponding to the insulation resistance parameter are combined through the second binary tree to construct a multidimensional combined binary tree structure to improve the correlation between the parameters. Specifically, please refer to Table 2, which is the second set of working data of the secondary battery with the identity mark i d1:

[0107] Table 2: The second set of working data

[0108]

[0109] In each frame of data, the targets corresponding to the timestamp parameter and the insulation resistance parameter are combined to obtain multiple multidimensional data in multiple frames of data: (5,8000), (10,9000), (7,5000), (8,2000), (3,11000). In multiple multidimensional data, the timestamp parameter or the insulation resistance parameter is used as a construction parameter. This application takes the timestamp parameter as an example as a construction parameter to process multiple multidimensional data, and constructs a second binary tree corresponding to the multidimensional data based on step 400 to associate the first binary tree corresponding to the timestamp parameter and the first binary tree corresponding to the insulation resistance through the second binary tree corresponding to the multidimensional data. The detailed construction process can refer to the construction process of the first binary tree above. Since the construction process is similar, it will not be repeated here. Please refer to Fig.14 , Fig.14 A diagram of a second binary tree of multidimensional data in the data processing method provided in an embodiment of the present application.

[0110] Step 500: Based on the first binary tree corresponding to each parameter and the second binary tree corresponding to the multi-dimensional data, data processing is performed on the secondary battery.

[0111] In some embodiments, see Figure 5 , Figure 5 This is a specific flow chart of step 500 in the data processing method provided in the embodiment of the present application. The method for processing the data of the secondary battery is specifically implemented through steps 510 to 540:

[0112] Step 510: Determine data to be processed.

[0113] Step 520: Determine the position of the data of each parameter in the data to be processed in the first binary tree, which is the first position.

[0114] Step 530: Determine the position of the multidimensional data in the data to be processed in the second binary tree as the second position.

[0115] In some embodiments, see Figure 6 , Figure 6 This is a specific flow chart of step 530 in the data processing method provided in an embodiment of the present application. The method for determining the position of the multidimensional data in the data to be processed in the second binary tree is specifically implemented through steps 531 and 532:

[0116] Step 531: In the odd layers of the second binary tree, in response to the first dimensional data being smaller than the data of the nodes in the odd layers, it is determined that the nodes corresponding to the multidimensional data in the data to be processed are located in the left subtree of the nodes in the odd layers; in response to the first dimensional data being larger than the data of the nodes in the odd layers, it is determined that the nodes corresponding to the multidimensional data in the data to be processed are located in the right subtree of the nodes in the odd layers.

[0117] Step 532: In the even layers of the transformed binary tree, in response to the second dimensional data being smaller than the data of the nodes in the even layers, it is determined that the nodes corresponding to the multidimensional data in the data to be processed are located in the left subtree of the nodes in the even layers; in response to the second dimensional data being larger than the data of the nodes in the even layers, it is determined that the nodes corresponding to the multidimensional data in the data to be processed are located in the right subtree of the nodes in the even layers.

[0118] In some embodiments, see 15, Fig.15 A schematic diagram of inserting (4,4000) multidimensional data nodes into the second binary tree corresponding to the multidimensional data in the data processing method provided in an embodiment of the present application, wherein the parameters of the data of each dimension in the multidimensional data are arbitrarily matched according to the data processing requirements, wherein the data of the timestamp parameter is used as the first dimensional data, and the data of the insulation impedance parameter is used as the second dimensional data, and the search starts from the root node of the second binary tree, and the first layer where the root node (7,5000) is located is an odd layer, and the timestamp parameter is used as the second dimensional data. Compare the quantity 4 with 7, 4 is less than 7, then the (4,4000) multidimensional data node is in the left subtree of the root node (7,5000); enter the root node (5,8000) of the left subtree of the root node (7,5000), the root node of the left subtree is an even layer in the second layer, compare the insulation resistance parameter 4000 with 8000, 4000 is less than 8000, then (4,4000) is located on the left side of (5,8000), now the leaf node is found and the search is completed.

[0119] Step 540: Process the data to be processed based on the first position and the second position.

[0120] In some embodiments, when the data processing method provided in this application is applied to multiple secondary batteries, please refer to Figure 7 , Figure 7 This is a specific flow chart of the data processing method provided in the embodiment of the present application being applied to multiple secondary batteries. The data processing method provided in the present application being applied to multiple secondary batteries is specifically implemented through steps 610 to 630:

[0121] Step 610: Obtain the identity tag of the secondary battery.

[0122] Step 620: Sort the multiple identity tags and construct a first binary tree corresponding to the identity tag parameters.

[0123] In some embodiments, before sorting the plurality of identity tags, it is determined whether there are letters in each identity tag, and if there are letters, the letters are converted into numbers based on the information exchange standard code.

[0124] Step 630: In the data of the same secondary battery, insert the identity tag of the secondary battery into the node of the first binary tree corresponding to any other parameter to construct a multi-dimensional binary tree of the secondary battery.

[0125] It can be understood that the data processing method provided in the embodiment of the present application is applied in the data processing of secondary batteries. The data processing method provided in the embodiment of the present application first determines the target data of multiple parameters in the secondary battery, and generates a corresponding first binary tree according to the target data of each parameter; then sets the parameter corresponding to the first binary tree as a dimensional parameter to construct multidimensional data; in the multidimensional data, the multidimensional data is processed using the target data corresponding to any parameter in the multidimensional data to construct a second binary tree corresponding to the multidimensional data; finally, the data of the secondary battery is processed through the first binary tree corresponding to each parameter and the second binary tree corresponding to the multidimensional parameter. The present application generates a first binary tree for each parameter, and uses the parameters of each first binary tree as dimensional data to establish multidimensional data between multiple parameters, and generates a second binary tree associated with multiple first binary trees, and associates the parameters through the binary tree to improve the correlation between data; at the same time, by sorting the target data of each parameter in a binary tree manner, when searching for data, the value of the data to be searched is compared with the value of the node at each layer to further determine the left and right position of the data to be searched at the current node, without traversing all the data, which can effectively shorten the data search time and improve data processing efficiency.

[0126] Accordingly, in some embodiments, the embodiments of the present application also provide a data processing system, see Figure 8 , Figure 8 This is a module connection diagram of a data processing system provided in an embodiment of the present application. The data processing system provided in an embodiment of the present application is applied to data processing of a secondary battery. The data processing system includes:

[0127] A data acquisition module 10, the data acquisition module 10 is used to determine target data of multiple parameters of the secondary battery;

[0128] A first binary tree construction module 20, the first binary tree construction module 20 is used to construct a first binary tree corresponding to each parameter based on the target data of each parameter;

[0129] The multidimensional data construction module 30 is used to determine the parameters corresponding to the first binary tree as dimension parameters and construct multidimensional data.

[0130] A second binary tree construction module 40, the second binary tree construction module 40 is used to construct a second binary tree corresponding to the multidimensional data based on target data of any parameter in the multidimensional data and the multidimensional data;

[0131] The data processing module 50 is used to process the data of the secondary battery based on the first binary tree corresponding to each parameter and the second binary tree corresponding to the multi-dimensional data.

[0132] In some embodiments, the first binary tree construction module 20 and the second binary tree construction module 40 are specifically used to:

[0133] When constructing the first binary tree, the first binary tree is determined to be the third binary tree, and when constructing the second binary tree, the second binary tree is determined to be the third binary tree;

[0134] constructing a fourth binary tree based on target data of any parameter, wherein the parameter for constructing the fourth binary tree is at least partially the same as the parameter for constructing the third binary tree;

[0135] In response to the depth of the left subtree of the root node in the fourth binary tree being greater than the depth of the right subtree of the root node, and the depth difference being greater than one, determining the root node of the third binary tree based on the depth of the fourth binary tree;

[0136] Transform the fourth binary tree based on the root node of the third binary tree to obtain the third binary tree;

[0137] Among them, in the third binary tree and the fourth binary tree, the values ​​of all child nodes in the left subtree of any node are smaller than the value of the node, and the values ​​of all child nodes in the right subtree of the node are larger than the value of the node.

[0138] In some embodiments, the first binary tree construction module 20 and the second binary tree construction module 40 are specifically used to:

[0139] Obtain candidate nodes based on the depth of the fourth binary tree;

[0140] In response to the number of candidate nodes being multiple, a candidate node with the largest number of child nodes is determined as a root node.

[0141] In some embodiments, the first binary tree construction module 20 and the second binary tree construction module 40 are specifically used to:

[0142] The depth value of the fourth binary tree is divided by two and the result is rounded down to obtain the candidate number.

[0143] In the fourth binary tree, nodes whose depth values ​​are candidate numbers are obtained as candidate nodes.

[0144] In some embodiments, the data processing module 50 is specifically used to:

[0145] Determine the data to be processed;

[0146] Determine the position of the data of each parameter in the data to be processed in the first binary tree, which is the first position;

[0147] Determine the position of the multidimensional data in the data to be processed in the second binary tree as the second position;

[0148] The data to be processed is processed based on the first position and the second position.

[0149] In some embodiments, the data processing module 50 is specifically used to:

[0150] Start searching from the root node of the second binary tree.

[0151] In the odd-numbered layers of the second binary tree, in response to the first-dimensional data being smaller than the data of the node in the odd-numbered layers, it is determined that the node corresponding to the multidimensional data in the data to be processed is located in the left subtree of the node in the odd-numbered layers; in response to the first-dimensional data being larger than the data of the node in the odd-numbered layers, it is determined that the node corresponding to the multidimensional data in the data to be processed is located in the right subtree of the node in the odd-numbered layers;

[0152] In the even layers of the transformed binary tree, in response to the second dimensional data being smaller than the data of the nodes in the even layers, it is determined that the nodes corresponding to the multidimensional data in the data to be processed are located in the left subtree of the nodes in the even layers; in response to the second dimensional data being larger than the data of the nodes in the even layers, it is determined that the nodes corresponding to the multidimensional data in the data to be processed are located in the right subtree of the nodes in the even layers.

[0153] In some embodiments, the data processing system is further configured to:

[0154] Obtaining an identity tag of a secondary battery;

[0155] Sorting multiple identity tags to construct a first binary tree corresponding to the identity tag parameters;

[0156] In the data of the same secondary battery, the identity tag of the secondary battery is inserted into the node of the first binary tree corresponding to any other parameter to construct a multi-dimensional binary tree of the secondary battery.

[0157] In some embodiments, the data processing system is further configured to:

[0158] In response to the presence of letters in the identity tag, the letters are converted into numbers based on a standard code for information interchange.

[0159] Accordingly, an embodiment of the present application further provides an electronic device, see Fig. 9 , Fig. 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device provided in an embodiment of the present application includes:

[0160] The memory 100 and the processor 200 are connected to each other in communication, the memory 100 stores computer instructions, and the processor 200 executes the computer instructions to perform the data processing method provided in the embodiment of the present application. Specifically, the memory 100 and the processor 200 are connected via a communication bus.

[0161] In some embodiments, the processor 200 may be a central processing unit (CPU). The processor 200 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above chips.

[0162] In some embodiments, the memory 100 is a non-transitory computer-readable storage medium that can be used to store non-transitory software programs, non-transitory computer executable programs and modules, such as program instructions / modules corresponding to the methods in the method embodiments of the present application. The processor 200 executes various functional applications and data processing of the processor 200 by running the non-transitory software programs, instructions and modules stored in the memory 100, that is, implementing the methods in the above method embodiments.

[0163] In some embodiments, the memory 100 may include a program storage area and a data storage area, wherein the program storage area may store an application required for operating the device or at least one function; the data storage area may store data created by the processor 200, etc. In addition, the memory 100 may include a high-speed random access memory 100, and may also include a non-volatile memory 100, such as at least one disk memory 100, a flash memory device, or other non-volatile solid-state memory 100. In some embodiments, the memory 100 may optionally include a memory 100 remotely arranged relative to the processor 200, and these remote memories 100 may be connected to the processor 200 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0164] In some embodiments, one or more modules are stored in the memory 100 and when executed by the processor 200, the methods in the above method embodiments are performed.

[0165] In some embodiments, the specific details of the above-mentioned electronic device can be understood by referring to the corresponding related descriptions and effects in the above-mentioned method embodiments, and will not be repeated here.

[0166] The present application has introduced a data processing method and system provided by the embodiments of the present application in detail above. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A data processing method, characterized in that: Applied to data processing of secondary batteries, the data processing method comprises: determining target data of a plurality of parameters of the secondary battery; Based on the target data of each parameter, construct a first binary tree corresponding to each parameter; Determine the parameter corresponding to the first binary tree as a dimensional parameter, and construct multidimensional data; Based on the target data of any parameter in the multidimensional data and the multidimensional data, construct a second binary tree corresponding to the multidimensional data; Data processing is performed on the secondary battery based on the first binary tree corresponding to each of the parameters and the second binary tree corresponding to the multi-dimensional data.

2. The data processing method according to claim 1, characterized in that: The data processing method further includes: determining that the first binary tree is a third binary tree when constructing the first binary tree, and determining that the second binary tree is a third binary tree when constructing the second binary tree; The step of constructing a first binary tree corresponding to each parameter, or the step of constructing a second binary tree corresponding to the multidimensional data includes: constructing a fourth binary tree based on target data of any of the parameters, wherein the parameters for constructing the fourth binary tree are at least partially the same as the parameters for constructing the third binary tree; In response to the depth of the left subtree of the root node in the fourth binary tree being greater than the depth of the right subtree of the root node, and the depth difference being greater than one, determining the root node of the third binary tree based on the depth of the fourth binary tree; Transforming the fourth binary tree based on the root node of the third binary tree to obtain the third binary tree; Among them, in the third binary tree and the fourth binary tree, the values ​​of all child nodes in the left subtree of any node are smaller than the value of the node, and the values ​​of all child nodes in the right subtree of the node are larger than the value of the node.

3. The data processing method according to claim 2, characterized in that: The step of determining the root node of the third binary tree based on the depth of the fourth binary tree comprises: Acquire candidate nodes based on the depth of the fourth binary tree; In response to the number of the candidate nodes being multiple, the candidate node with the largest number of child nodes is determined as the root node.

4. The data processing method according to claim 3, characterized in that: The step of acquiring candidate nodes based on the depth of the fourth binary tree comprises: Taking the quotient of the depth value of the fourth binary tree and two and rounding down, a candidate number is obtained; In the fourth binary tree, a node whose depth value is the candidate number is obtained as a candidate node.

5. The data processing method according to claim 2, characterized in that: The step of performing data processing on the secondary battery based on the first binary tree corresponding to each of the parameters and the second binary tree corresponding to the multi-dimensional data comprises: Determine the data to be processed; Determine the position of the data of each parameter in the data to be processed in the first binary tree as the first position; Determine the position of the multidimensional data in the data to be processed in the second binary tree as the second position; The data to be processed is processed based on the first position and the second position.

6. The data processing method according to claim 5, characterized in that: The multidimensional data includes first-dimensional data and second-dimensional data, and the step of determining the position of the multidimensional data in the data to be processed in the second binary tree includes: Start searching from the root node of the second binary tree. In the odd-numbered layers of the second binary tree, in response to the first-dimensional data being smaller than the data of the node in the odd-numbered layers, it is determined that the node corresponding to the multidimensional data in the data to be processed is located in the left subtree of the node in the odd-numbered layers; in response to the first-dimensional data being larger than the data of the node in the odd-numbered layers, it is determined that the node corresponding to the multidimensional data in the data to be processed is located in the right subtree of the node in the odd-numbered layers; In the even layers of the second binary tree, in response to the second dimensional data being smaller than the data of the nodes in the even layers, it is determined that the nodes corresponding to the multidimensional data in the data to be processed are located in the left subtree of the nodes in the even layers; in response to the second dimensional data being larger than the data of the nodes in the even layers, it is determined that the nodes corresponding to the multidimensional data in the data to be processed are located in the right subtree of the nodes in the even layers.

7. The data processing method according to claim 1, characterized in that: Applied to a plurality of the secondary batteries, the data processing method further comprises: Obtaining an identity mark of the secondary battery; Sorting the plurality of identity tags to construct a first binary tree corresponding to the identity tag parameters; In the data of the same secondary battery, the identity tag of the secondary battery is inserted into the node of the first binary tree corresponding to any other parameter to construct a multi-dimensional binary tree of the secondary battery.

8. The data processing method according to claim 7, characterized in that: Before sorting the plurality of identity tags, the data processing method further comprises: In response to the presence of letters in the identity tag, the letters are converted into numbers based on a standard code for information interchange.

9. The data processing method according to claim 1, characterized in that: The plurality of parameters include a time stamp and an insulation resistance value.

10. A data processing system, characterized in that: Applied to data processing of secondary batteries, the data processing system comprises: A data acquisition module, the data acquisition module is used to determine target data of multiple parameters of the secondary battery; A first binary tree construction module, the first binary tree construction module is used to construct a first binary tree corresponding to each of the parameters based on the target data of each of the parameters; A multidimensional data construction module, the multidimensional data construction module is used to determine the parameter corresponding to the first binary tree as a dimensional parameter, and construct multidimensional data; A second binary tree construction module, the second binary tree construction module is used to construct a second binary tree corresponding to the multidimensional data based on target data of any parameter in the multidimensional data and the multidimensional data; A data processing module is used to perform data processing on the secondary battery based on the first binary tree corresponding to each of the parameters and the second binary tree corresponding to the multi-dimensional data.

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