Data grouping method and device based on dynamic competition of multi-dimensional indexes, and medium

Abstract

A data grouping method and device based on the dynamic competition of multi-dimensional indexes, and a medium, which relate to the field of electronic data processing. The method comprises: acquiring a plurality of data samples needing to be subjected to data grouping (S101); according to indexes included in a scenario requirement corresponding to the present data grouping, determining index values corresponding to the data samples under the indexes, and index weights; obtaining a comprehensive index matrix according to an initial index matrix and an index weight matrix, and merging the comprehensive index matrix with the initial index matrix, so as to obtain an integrated index matrix (S103); and combining a first grouping index matrix with a second grouping index matrix, so as to obtain a combined index matrix, and updating the index values. During a grouping process, the dominant role of comprehensive indexes is determined macroscopically by means of performing sorting in a descending order according to the comprehensive indexes, and a comprehensive index and single index dual-driven index balancing method is utilized, thus taking into consideration the balance of each single index.

Description

A data grouping method, device and medium based on dynamic competition of multi-dimensional indicators

[0001] This application claims priority to the Chinese patent application filed with the China Patent Office on June 19, 2023, with application number 202310720624.X and invention name “A data grouping method, device and medium based on dynamic competition of multi-dimensional indicators”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of electronic digital data processing, and in particular to a data grouping method, device and medium based on dynamic competition of multi-dimensional indicators. Background Art

[0003] With the development of technology, more and more data are aggregated in the enterprise's server, which requires grouping and processing the data in order to increase data processing efficiency.

[0004] Generally speaking, for large data samples with multiple evaluation indicators, a grouping method based on comprehensive indicators is generally used. However, traditional grouping methods based on comprehensive indicators ignore the balance of individual indicators, which can easily lead to significant differences in certain individual indicators between groups, causing imbalances in individual indicators between groups.

[0005] Summary of the Invention

[0006] To solve the above problems, this application proposes a data grouping method based on dynamic competition of multi-dimensional indicators, including:

[0007] Acquire multiple data samples that need to be grouped;

[0008] According to the indicators included in the scenario requirements corresponding to this data grouping, determine the indicator values ​​corresponding to the data samples under the indicators and the indicator weights corresponding to the indicators, establish an initial indicator matrix based on the indicator values, and establish an indicator weight matrix based on the indicator weights;

[0009] Obtaining a comprehensive indicator matrix based on the initial indicator matrix and the indicator weight matrix, and merging the comprehensive indicator matrix with the initial indicator matrix to obtain an integrated indicator matrix;

[0010] Determining a corresponding number of samples according to the scenario requirements, and selecting first specified data samples corresponding to the number of samples in the integrated indicator matrix to obtain a first grouping indicator matrix including the first specified data samples;

[0011] A second specified data sample corresponding to the sample quantity is selected in the integrated indicator matrix to obtain a second grouping indicator matrix containing the second specified data sample, and the first grouping indicator matrix and the second grouping indicator matrix are combined to obtain a combined indicator matrix, so as to complete this round of grouping according to the combined indicator matrix and update the indicator value.

[0012] On the other hand, the present application also proposes a data grouping device based on dynamic competition of multi-dimensional indicators, including:

[0013] at least one processor; and,

[0014] a memory communicatively coupled to the at least one processor;

[0015] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the following operations:

[0016] Acquire multiple data samples that need to be grouped;

[0017] According to the indicators included in the scenario requirements corresponding to this data grouping, determine the indicator values ​​corresponding to the data samples under the indicators and the indicator weights corresponding to the indicators, establish an initial indicator matrix based on the indicator values, and establish an indicator weight matrix based on the indicator weights;

[0018] Obtaining a comprehensive indicator matrix based on the initial indicator matrix and the indicator weight matrix, and merging the comprehensive indicator matrix with the initial indicator matrix to obtain an integrated indicator matrix;

[0019] Determining a corresponding number of samples according to the scenario requirements, and selecting first specified data samples corresponding to the number of samples in the integrated indicator matrix to obtain a first grouping indicator matrix including the first specified data samples;

[0020] A second specified data sample corresponding to the sample quantity is selected in the integrated indicator matrix to obtain a second grouping indicator matrix containing the second specified data sample, and the first grouping indicator matrix and the second grouping indicator matrix are combined to obtain a combined indicator matrix, so as to complete this round of grouping according to the combined indicator matrix and update the indicator value.

[0021] On the other hand, the present application also proposes a non-volatile computer storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured as follows:

[0022] Acquire multiple data samples that need to be grouped;

[0023] According to the indicators included in the scenario requirements corresponding to this data grouping, determine the indicator values ​​corresponding to the data samples under the indicators and the indicator weights corresponding to the indicators, establish an initial indicator matrix based on the indicator values, and establish an indicator weight matrix based on the indicator weights;

[0024] Obtaining a comprehensive indicator matrix based on the initial indicator matrix and the indicator weight matrix, and merging the comprehensive indicator matrix with the initial indicator matrix to obtain an integrated indicator matrix;

[0025] Determining a corresponding number of samples according to the scenario requirements, and selecting first specified data samples corresponding to the number of samples in the integrated indicator matrix to obtain a first grouping indicator matrix including the first specified data samples;

[0026] A second specified data sample corresponding to the sample quantity is selected in the integrated indicator matrix to obtain a second grouping indicator matrix containing the second specified data sample, and the first grouping indicator matrix and the second grouping indicator matrix are combined to obtain a combined indicator matrix, so as to complete this round of grouping according to the combined indicator matrix and update the indicator value.

[0027] The data grouping method based on dynamic competition of multi-dimensional indicators proposed in this application can bring the following beneficial effects:

[0028] During the grouping process, the comprehensive indicator matrix takes the dominant position as the foundation. The selected grouping indicator matrix is ​​used to expand the scope of consideration for individual indicators, thus achieving an indicator balancing method driven by both comprehensive and individual indicators. This method takes into account the balance of individual indicators and achieves a comprehensive consideration of multi-dimensional indicators. Compared with traditional algorithms that group big data based solely on comprehensive indicators, this method can ensure the balance of comprehensive indicators between groups after grouping. Furthermore, the strategy of multiple rounds of grouping maximizes the balance between comprehensive and individual indicators between groups. At a macro level, the method arranges data in descending order of comprehensive indicators, minimizing the impact of the final round of grouping on imbalanced comprehensive indicators. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0030] FIG1 is a flow chart of a data grouping method based on dynamic competition of multi-dimensional indicators in an embodiment of the present application;

[0031] FIG2 is a flow chart of a data grouping method based on dynamic competition of multi-dimensional indicators in a scenario according to an embodiment of the present application;

[0032] FIG3 is a schematic diagram of a data grouping device based on dynamic competition of multi-dimensional indicators in an embodiment of the present application. DETAILED DESCRIPTION

[0033] To make the purpose, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the specific embodiments of this application and the corresponding drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0034] The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0035] As shown in FIG1 , an embodiment of the present application provides a data grouping method based on dynamic competition of multi-dimensional indicators, including:

[0036] S101: Acquire multiple data samples that need to be grouped.

[0037] FIG2 is a flow chart of a data grouping method based on dynamic competition of multi-dimensional indicators in a scenario in an embodiment of the present application, which will be explained below in conjunction with FIG1 and FIG2 .

[0038] Data samples can be of different types, such as images and text. They can be collected from an enterprise's ERP system or obtained from the internet. Data samples are typically stored and processed on the enterprise's server.

[0039] S102: According to the indicators contained in the scenario requirements corresponding to this data grouping, determine the indicator values ​​corresponding to the data samples under the indicators, and the indicator weights corresponding to the indicators, and establish an initial indicator matrix according to the indicator values, and establish an indicator weight matrix according to the indicator weights.

[0040] Scenario requirements are set based on the needs of the enterprise. For example, if the data sample is customer transaction data, the scenario requirement may be to classify the collected text data according to different indicators such as customer level, transaction type, and transaction amount to ensure that the data in each category is more similar. Alternatively, if the data sample is enterprise production data, it can be classified according to indicators such as product type, production efficiency, and product value.

[0041] Specifically, after determining the indicators corresponding to the scenario requirements, each data sample is manually labeled with the indicator value under each indicator and the indicator weight corresponding to each indicator. For example, after collecting a customer's transaction data, the corresponding customer level is marked as 1 (the higher the number, the higher the level), the transaction type is marked as 1 (a total of 8 transaction types are set, each type corresponds to a different tag value, and adjacent tag values ​​have a higher degree of similarity), and the transaction amount is set based on the actual amount.

[0042] Establish the initial indicator matrix A according to the indicator value mn , establish the indicator weight matrix P according to the indicator weight.

[0043] in, P T =[λ1 λ2 … λ j … λ n ];

[0044] Among them, A mn is an m×n-order indicator matrix, P is an n×1-order weight matrix, m is the number of samples, n is the number of indicators, α ij is the index value corresponding to the i-th data sample in the j-th index; j is the indicator weight corresponding to the jth indicator; P T is the transposed matrix of the indicator weight matrix P.

[0045] In order to measure indicators of different dimensions, each indicator value is normalized. Normalization can be done by using linear function conversion, logarithmic function conversion, inverse cotangent function conversion, Z-SCORE, etc. Taking linear function conversion as an example, the process of normalizing the j-th indicator value is as follows:

[0046] Among them, max{α ij |i=1,2,3,…,m} is the maximum value of the jth index of m data samples, min{α ij |i=1,2,3,…,m} is the minimum value of the jth indicator of m data samples.

[0047] Based on this, the index values ​​in the initial index matrix are normalized in turn to obtain the normalized initial index matrix B mn ;

[0048] in,

[0049] Among them, β ij is the index value corresponding to the i-th data sample in the j-th index after normalization.

[0050] S103: Obtain a comprehensive indicator matrix according to the initial indicator matrix and the indicator weight matrix, and merge the comprehensive indicator matrix with the initial indicator matrix to obtain an integrated indicator matrix.

[0051] Specifically, the normalized initial indicator matrix and the indicator weight matrix are multiplied to obtain the comprehensive indicator matrix C;

[0052] in,

[0053] The comprehensive indicator matrix is ​​combined with the initial indicator matrix to obtain the integrated indicator matrix D.

[0054] in,

[0055] The integrated indicator matrix D can take into account both comprehensive indicators and single-item indicators, and it is an m×(n+1)-order matrix.

[0056] The integrated indicator matrix is ​​divided into ij Arrange the sizes of in descending order to obtain the integrated indicator matrix E in descending order;

[0057] in,

[0058] Among them, γ ij To convert β ij After sorting in descending order according to size, the value corresponding to the i-th row and j+1-th column in the integrated indicator matrix E.

[0059] S104: Determine the corresponding number of samples according to the scenario requirements, and select first specified data samples corresponding to the number of samples in the integrated indicator matrix to obtain a first grouping indicator matrix containing the first specified data samples.

[0060] The above has completed the construction of the integrated indicator matrix and unified the data samples. Now it is time to start initial grouping.

[0061] Specifically, the number k of groups to be grouped included in the scenario requirement is determined, where k is manually set and refers to the number of groups to be obtained this time.

[0062] Selecting the first k first designated data samples from the integrated indicator matrix arranged in descending order to obtain a first grouping indicator matrix F containing the first designated data samples;

[0063] in,

[0064] S105: Select a second specified data sample corresponding to the sample quantity in the integrated indicator matrix to obtain a second grouping indicator matrix containing the second specified data sample, and combine the first grouping indicator matrix and the second grouping indicator matrix to obtain a combined indicator matrix, so as to complete this round of grouping according to the combined indicator matrix and update the indicator value.

[0065] In the integrated indicator matrix, the k+1th to 2kth data samples (the number of data samples is also k at this time) are selected as the corresponding second designated data samples to obtain a second grouping indicator matrix H containing the second designated data samples.

[0066] in,

[0067] For the first grouping indicator matrix F, the minimum value in the first grouping indicator matrix F is selected, and the minimum value is used as the main evaluation indicator of this round, so as to arrange the first grouping indicator matrix in descending order according to the main evaluation indicator to obtain the first grouping indicator matrix G after descending order.

[0068] For the second grouping indicator matrix H, the minimum value is used as the main evaluation indicator of this round, and the second grouping indicator matrix is ​​arranged in ascending order according to the main evaluation indicator to obtain the second grouping indicator matrix I after ascending order.

[0069] From this we get

[0070] Among them, δ ij γ ij After sorting in descending or ascending order, the index value of the corresponding position.

[0071] Add the first grouping indicator matrix G after descending order and the second grouping indicator matrix I after ascending order, iteratively update the indicator values ​​of each group to obtain the combined indicator matrix J;

[0072] in,

[0073] The index values ​​are updated using the index values ​​contained in the combined index matrix J thus obtained. At this point, the grouping of this round has been completed, and the data samples contained in each row are the data samples contained in a single group.

[0074] For the remaining data samples in the integrated indicator matrix, the next round of grouping is performed based on the updated indicator values, and the main evaluation indicators are reselected. Since the indicator values ​​have been updated, the reselected main evaluation indicators may change. At this time, this process can be called a dynamic competition process, in which different indicators dynamically compete to select the main evaluation indicators. The corresponding second grouping indicator matrix is ​​regenerated based on the main evaluation indicators and then sorted in ascending order. The regenerated second grouping indicator matrix is ​​added to the combined indicator matrix after being sorted in ascending order until all data samples are added to the combined indicator matrix.

[0075] At this time, for the combined indicator matrix, the data samples contained in each row are regarded as a single group in the grouping result.

[0076] As shown in FIG3 , an embodiment of the present application further provides a data grouping device based on dynamic competition of multi-dimensional indicators, including:

[0077] at least one processor; and,

[0078] a memory communicatively coupled to the at least one processor;

[0079] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the following operations:

[0080] Acquire multiple data samples that need to be grouped;

[0081] According to the indicators included in the scenario requirements corresponding to this data grouping, determine the indicator values ​​corresponding to the data samples under the indicators and the indicator weights corresponding to the indicators, establish an initial indicator matrix based on the indicator values, and establish an indicator weight matrix based on the indicator weights;

[0082] Obtaining a comprehensive indicator matrix based on the initial indicator matrix and the indicator weight matrix, and merging the comprehensive indicator matrix with the initial indicator matrix to obtain an integrated indicator matrix;

[0083] Determining a corresponding number of samples according to the scenario requirements, and selecting first specified data samples corresponding to the number of samples in the integrated indicator matrix to obtain a first grouping indicator matrix including the first specified data samples;

[0084] A second specified data sample corresponding to the sample quantity is selected in the integrated indicator matrix to obtain a second grouping indicator matrix containing the second specified data sample, and the first grouping indicator matrix and the second grouping indicator matrix are combined to obtain a combined indicator matrix, so as to complete this round of grouping according to the combined indicator matrix and update the indicator value.

[0085] The embodiment of the present application further provides a non-volatile computer storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured to:

[0086] Acquire multiple data samples that need to be grouped;

[0087] According to the indicators included in the scenario requirements corresponding to this data grouping, determine the indicator values ​​corresponding to the data samples under the indicators and the indicator weights corresponding to the indicators, establish an initial indicator matrix based on the indicator values, and establish an indicator weight matrix based on the indicator weights;

[0088] Obtaining a comprehensive indicator matrix based on the initial indicator matrix and the indicator weight matrix, and merging the comprehensive indicator matrix with the initial indicator matrix to obtain an integrated indicator matrix;

[0089] Determining a corresponding number of samples according to the scenario requirements, and selecting first specified data samples corresponding to the number of samples in the integrated indicator matrix to obtain a first grouping indicator matrix including the first specified data samples;

[0090] A second specified data sample corresponding to the sample quantity is selected in the integrated indicator matrix to obtain a second grouping indicator matrix containing the second specified data sample, and the first grouping indicator matrix and the second grouping indicator matrix are combined to obtain a combined indicator matrix, so as to complete this round of grouping according to the combined indicator matrix and update the indicator value.

[0091] The various embodiments in this application are described in a progressive manner. Similar portions between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the device and medium embodiments are generally similar to the method embodiments, so their descriptions are relatively simple. For relevant portions, refer to the descriptions of the method embodiments.

[0092] The devices and media provided in the embodiments of the present application correspond one-to-one to the methods. Therefore, the devices and media also have similar beneficial technical effects to their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0093] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0094] The present application is described with reference to the flow chart and / or block diagram of the method, device (system), and computer program product according to the embodiment of the present application. It should be understood that each flow process and / or box in the flow chart and / or block diagram and the combination of the flow process and / or box in the flow chart and / or block diagram can be realized by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processing machine or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for realizing the function specified in one flow chart flow or multiple flows and / or one box or multiple boxes of the block diagram.

[0095] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device that implements the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0096] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0097] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0098] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0099] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0100] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0101] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A data grouping method based on dynamic competition of multi-dimensional indicators, wherein: include: Acquire a plurality of data samples that need to be grouped, where the types of the data samples include: image data and text data; According to the indicators included in the scenario requirements corresponding to this data grouping, determine the indicator value corresponding to the data sample under the indicator and the indicator weight corresponding to the indicator, and establish an initial indicator matrix according to the indicator value, and establish an indicator weight matrix according to the indicator weight; wherein, the data sample is transaction data, and the indicators included in the scenario requirements include customer level, transaction type, and transaction amount. The higher the number of the customer level mark, the higher the level. Different transaction types correspond to different mark values, and the transaction amount is set based on the actual amount; According to the initial indicator matrix and the indicator weight matrix, a comprehensive indicator matrix is ​​obtained, and the comprehensive indicator matrix is ​​combined with the initial indicator matrix to obtain an integrated indicator matrix; Determine the corresponding number of samples according to the scenario requirements, and select first specified data samples corresponding to the number of samples in the integrated indicator matrix to obtain a first grouping indicator matrix containing the first specified data samples; Selecting second designated data samples corresponding to the number of samples in the integrated indicator matrix to obtain a second grouping indicator matrix containing the second designated data samples, and combining the first grouping indicator matrix and the second grouping indicator matrix to obtain a combined indicator matrix, so as to complete this round of grouping according to the combined indicator matrix and update the indicator value; Establishing an initial indicator matrix according to the indicator values, and establishing an indicator weight matrix according to the indicator weights, specifically includes: Establish an initial indicator matrix A based on the indicator values mn , establish an indicator weight matrix P according to the indicator weights; in, P T =[λ1 λ2 … λ j … l n ]; Among them, m is the number of samples, n is the number of indicators, α ij is the index value corresponding to the i-th data sample in the j-th index; j is the indicator weight corresponding to the jth indicator; P T is the transposed matrix of the indicator weight matrix P; After establishing the initial indicator matrix according to the indicator values, the method further includes: The index values ​​in the initial index matrix are normalized to obtain a normalized initial index matrix B mn ; in, Among them, β ij is the index value corresponding to the i-th data sample in the j-th index after normalization; According to the initial indicator matrix and the indicator weight matrix, a comprehensive indicator matrix is ​​obtained, and the comprehensive indicator matrix is ​​merged with the initial indicator matrix to obtain an integrated indicator matrix, which specifically includes: Multiplying the normalized initial indicator matrix and the indicator weight matrix to obtain a comprehensive indicator matrix C; in, Merging the comprehensive indicator matrix with the initial indicator matrix to obtain an integrated indicator matrix D; in, The integrated indicator matrix is ​​divided into ij Arrange the sizes in descending order to obtain the integrated indicator matrix E in descending order; in, Among them, γ ij To ij After arranging in descending order according to size, the value corresponding to the i-th row and j+1-th column in the integrated indicator matrix E; Determining the corresponding number of samples according to the scenario requirements, and selecting first specified data samples corresponding to the number of samples in the integrated indicator matrix for multiple times to obtain a first grouping indicator matrix containing the first specified data samples, specifically includes: Determine the number k of groups to be grouped included in the scenario requirements; In the integrated indicator matrix arranged in descending order, the first k data samples are selected as first designated data samples to obtain a first grouping indicator matrix F containing the first designated data samples; in, Selecting second designated data samples corresponding to the number of samples in the integrated indicator matrix to obtain a second grouping indicator matrix containing the second designated data samples specifically includes: In the integrated indicator matrix, select the k+1th to 2kth data samples as the corresponding second designated data samples to obtain a second grouping indicator matrix H including the second designated data samples; in, Combining the first grouping indicator matrix and the second grouping indicator matrix to obtain a combined indicator matrix, and updating the indicator value according to the combined indicator matrix, specifically includes: For the first grouping indicator matrix F, select the minimum value in the first grouping indicator matrix F, and use the minimum value as the main evaluation indicator of this round, so as to arrange the first grouping indicator matrix in descending order according to the main evaluation indicator, and obtain the first grouping indicator matrix G after descending order; in, For the second grouping indicator matrix H, the minimum value is used as the main evaluation indicator of this round, so as to arrange the second grouping indicator matrix in ascending order according to the main evaluation indicator, and obtain the second grouping indicator matrix I after ascending order; in, Add the first grouping index matrix G after the descending order and the second grouping index matrix I after the ascending order to obtain a combined index matrix J; in, According to each indicator value included in the combined indicator matrix J, the indicator value is updated, and according to the combined indicator matrix J, the remaining number of samples in the integrated indicator matrix is ​​grouped.

2. The method according to claim 1, wherein: The method further comprises: For the remaining data samples in the integrated indicator matrix, the main evaluation index is reselected according to the updated index value, and the corresponding second grouping indicator matrix is ​​regenerated according to the main evaluation index, so that the regenerated second grouping indicator matrix is ​​arranged in ascending order and added to the combined indicator matrix until all data samples are added to the combined indicator matrix; For the combined indicator matrix, the data samples contained in each row are taken as a single group in the grouping result.

3. A data grouping device based on dynamic competition of multi-dimensional indicators, wherein: include: at least one processor; as well as, a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor. Executed by the at least one processor so that the at least one processor can execute the data grouping method based on multi-dimensional indicator dynamic competition as described in claim 1.

4. A non-volatile computer storage medium storing computer executable instructions, wherein: The computer executable instructions are configured as: the data grouping method based on dynamic competition of multi-dimensional indicators as described in claim 1.

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