Data processing method, system and device and computer readable storage medium

Abstract

The invention discloses a data processing method, system and device and a computer readable storage medium. Relates to the technical field of data processing, can be applied to the field of financial science and technology, and comprises the steps that feature data and target task features corresponding to N objects are obtained, and N is an integer larger than 1; based on the feature data corresponding to the N objects, the N objects are classified for M times through a clustering algorithm, M classification results are obtained, M is an integer larger than 1, and the category numbers corresponding to the M classification results are different; and based on the M classification results and the target task features, determining an object division mode corresponding to the target task features. Through the data classification method and device, the problem of low flexibility and accuracy of data classification caused by a single-level clustering algorithm in related technologies is solved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology and can be applied to the field of financial technology. Specifically, it relates to a data processing method, system, device and computer-readable storage medium. Background Art

[0002] As a powerful data mining tool, clustering algorithm can automatically divide samples in a data set into multiple clusters without pre-setting classification labels. Each cluster contains data points with similar features, and different clusters have significant differences. This technology provides new ideas and possibilities for the refined classification of objects.

[0003] However, in current technologies, most methods and systems that use clustering algorithms to classify objects only perform single-level classification, that is, clustering with a fixed number of categories (such as 2 or 3 categories). Although this method can perform preliminary classification based on the similarity of data points, it cannot fully explore the inherent structure of data under different numbers of categories, nor can it provide flexible and diverse solutions for different scenarios and needs. In particular, for complex and diverse application environments, single-level clustering methods may not be able to meet their diverse needs.

[0004] Currently, no effective solution has been proposed to address the problems of low flexibility and accuracy in data classification caused by single-level clustering algorithms in related technologies. Summary of the invention

[0005] Embodiments of the present invention provide a data processing method, system, device, and computer-readable storage medium to at least solve the technical problem of low flexibility and accuracy of data classification caused by a single-level clustering algorithm in the related art.

[0006] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a data processing method is provided. The method comprises: obtaining feature data and target task features corresponding to each of N objects, wherein N is an integer greater than 1; based on the feature data corresponding to each of the N objects, using a clustering algorithm to classify the N objects M times to obtain M classification results, wherein M is an integer greater than 1, and the number of categories corresponding to each of the M classification results is different; based on the M classification results and the target task features, determining the object division method corresponding to the target task features.

[0007] Optionally, obtaining feature data corresponding to each of the N objects includes: obtaining a cycle of data collection; and obtaining feature data corresponding to each of the N objects based on the cycle.

[0008] Optionally, the number of categories corresponding to each of the M classification results increases sequentially from two categories to M+1 categories.

[0009] Optionally, based on the M classification results and the target task characteristics, an object division method corresponding to the target task characteristics is determined, wherein the target task characteristics include the first task characteristics, including: when the target task characteristics are the first task characteristics, objects that match the corresponding categories in the M classification results are grouped together to obtain an object division method corresponding to the target task characteristics, wherein the gap between the feature data of the objects represented by the first task characteristics is less than a preset threshold.

[0010] Optionally, based on the M classification results and the target task characteristics, an object division method corresponding to the target task characteristics is determined, wherein the target task characteristics include the second task characteristics, including: when the target task characteristics are the second task characteristics, objects with different corresponding categories in the M classification results are grouped together to obtain an object division method corresponding to the target task characteristics, wherein the gap between the feature data of the objects represented by the second task characteristics is not less than a preset threshold.

[0011] In order to achieve the above-mentioned purpose, according to another aspect of the present application, a data processing system is provided. The system includes: an acquisition module, which is used to obtain feature data corresponding to each of N objects, wherein N is an integer greater than 1; a classification module, which is used to classify the N objects M times to obtain M classification results, wherein M is an integer greater than 1, and the number of categories corresponding to each of the M classification results is different; a storage module, which is used to store the M classification results in a database; a requirement module, which is used to obtain target task features; and a solution module, which is used to determine the object division method corresponding to the target task features based on the M classification results and the target task features.

[0012] In order to achieve the above-mentioned purpose, according to another aspect of the present application, a data processing device is provided. The device includes: an acquisition module, which is used to acquire feature data and target task features corresponding to each of N objects, wherein N is an integer greater than 1; a classification module, which is used to classify the N objects M times based on the feature data corresponding to each of the N objects using a clustering algorithm to obtain M classification results, wherein M is an integer greater than 1, and the number of categories corresponding to each of the M classification results is different; a determination module, which is used to determine the object division method corresponding to the target task features based on the M classification results and the target task features.

[0013] Optionally, the acquisition module further includes: a first acquisition unit and a second acquisition unit, wherein the first acquisition unit is used to acquire a cycle of data collection; and the second acquisition unit is used to acquire characteristic data corresponding to each of the N objects based on the cycle.

[0014] Optionally, the number of categories corresponding to each of the M classification results in the classification module increases sequentially from two categories to M+1 categories.

[0015] Optionally, the determination module also includes: a first division unit, wherein the first division unit is used to, when the target task feature is a first task feature, divide objects that match the corresponding categories in M ​​classification results into a group, and obtain an object division method corresponding to the target task feature, wherein the gap between feature data of the objects represented by the first task feature is less than a preset threshold.

[0016] Optionally, the determination module also includes: a second division unit, wherein the second division unit is used to group objects with different corresponding categories in M ​​classification results when the target task feature is a second task feature, so as to obtain an object division method corresponding to the target task feature, wherein the difference between the feature data of the objects represented by the second task feature is not less than a preset threshold.

[0017] In an embodiment of the present invention, a data processing method is adopted, by obtaining feature data and target task features corresponding to each of N objects, wherein N is an integer greater than 1; based on the feature data corresponding to each of the N objects, a clustering algorithm is used to classify the N objects M times to obtain M classification results, wherein M is an integer greater than 1, and the M classification results each correspond to a different number of categories; based on the M classification results and the target task features, an object division method corresponding to the target task features is determined, thereby achieving the purpose of adjusting the object division method according to different target task features, thereby achieving the technical effect of improving the efficiency and accuracy of data processing, and further solving the technical problem of low flexibility and accuracy of data classification caused by a single-level clustering algorithm in the related art. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0019] Figure 1 It is a hardware structure block diagram of a computer terminal for implementing a data processing method;

[0020] Figure 2 is a flow chart of a data processing method provided according to an embodiment of the present application;

[0021] Figure 3 is a schematic diagram of a data processing device provided according to an embodiment of the present application;

[0022] Figure 4 It is a structural block diagram of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described 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, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0024] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] First, some nouns or terms that appear in the description of the embodiments of the present application are subject to the following explanations:

[0026] Clustering algorithm is an unsupervised learning technique in data mining and machine learning. It is used to automatically group data sets without predefined categories into multiple clusters, so that the data points within the clusters are similar to each other, while the data points between clusters are quite different. Clustering is an exploratory data analysis tool, commonly used in pattern recognition, data compression, image analysis, recommendation systems, bioinformatics, and market segmentation.

[0027] It should be noted that the collected information (including but not limited to object feature information) and data (including but not limited to data for display, analysis, etc.) involved in this application are information and data fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data are in compliance with relevant laws, regulations and standards, necessary confidentiality measures are taken, and public order and good customs are not violated, and corresponding operation entrances are provided to choose to authorize or refuse. For example, an interface is set up between this system and relevant users or institutions to provide users with corresponding operation entrances for them to choose to agree or refuse the results of automated decision-making; if you choose to refuse, you will enter the expert decision-making process.

[0028] Example 1

[0029] According to an embodiment of the present application, a data processing method embodiment is also provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0030] The method embodiment provided in the first embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 It is a hardware structure diagram of a computer terminal (or mobile device) for implementing a data processing method. Figure 1 As shown, the computer terminal 10 (or mobile device) may include one or more (102a, 102b, ..., 102n are used to illustrate) processors 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0031] It should be noted that the one or more processors 102 and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computer terminal 10 (or mobile device). As described in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0032] The memory 104 can be used to store software programs and modules of application software, such as program instructions / data storage devices corresponding to the data processing method in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, realizing the above-mentioned data processing method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal 10 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.

[0033] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0034] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 (or mobile device).

[0035] Under the above operating environment, this application provides Figure 2 The data processing method shown. Figure 2 It is a flowchart of a data processing method provided according to an embodiment of the present application.

[0036] Step S201, obtaining feature data and target task features corresponding to each of N objects, where N is an integer greater than 1.

[0037] In this step, the feature data corresponding to each of the N objects refers to the multi-dimensional attributes or indicators of each object in the data set. Each object (or individual, sample) can be described by a series of features. These features cover the information of the object in different dimensions and are the basis for data analysis and clustering algorithm execution. The target task feature refers to the feature set directly related to the realization of a specific goal or task when dividing objects in data processing, which directly affects the effectiveness of the division results and the efficiency of goal realization. In different application scenarios, the specific content and importance of the target task features will vary, but they play a role in guiding the direction of division, optimizing decisions and improving the effectiveness of strategy implementation. For example, in the application scenario of employee construction, the N objects can be N employees, and the feature data can be information that characterizes the personal attributes, work performance and degree of integration of employees, which can include professional lines, length of service, rank, overtime hours, contact production time, daily email reception, frequency of participation in party, government, trade union and other activities, and the target task feature can be the target task assigned to employees, which can include the allocation of overtime guest rooms, the allocation of employee dormitories, company team building groups, talent echelon construction, employee exchange seminars, etc.

[0038] Step S202 , based on the feature data corresponding to each of the N objects, a clustering algorithm is used to classify the N objects M times to obtain M classification results, where M is an integer greater than 1, and the M classification results each correspond to a different number of categories.

[0039] In this step, a suitable clustering algorithm can be selected, such as K-means, hierarchical clustering, DBSCAN, etc. Each type of algorithm has its own unique applicable scenarios and parameter requirements. The clustering algorithm will perform M different classifications based on the feature data of N objects. For example, the number of target categories for classification can start from 2 and gradually increase to M, where M is an integer greater than 1. N objects are divided into two categories based on their feature data, which may reveal two more obvious behavioral tendencies in the group. Based on the first classification, the algorithm divides N objects into three categories, which can further refine the division of the group, and may identify a group between the first two categories, or emphasize more subtle differences within the group. As the number of categories increases, the algorithm divides N objects into M categories, so that the division of objects can be more personalized and refined, meeting the data processing strategies under different scenarios and requirements. After each classification, the generated classification results are usually presented in the form of labels or category identifiers, and each object will be assigned to a specific category. Each of the M classification results obtained has its own unique number and composition of categories, reflecting the group structure at different levels.

[0040] Step S203: Based on the M classification results and the target task characteristics, determine the object division method corresponding to the target task characteristics.

[0041] In this step, the objects are clustered into different categories M times by applying a clustering algorithm. Each clustering will divide the objects into different categories according to the number of categories. For example, object 1 may be classified into category 2-1 in the first clustering, and into 3-1 in the second clustering, until M clustering results are obtained. Each result has a different number of categories, reflecting the structure of the data at different granularities. The above classification results can be analyzed to compare the categories corresponding to different objects in each classification result. Through this analysis process, it is possible to identify which categories best reflect the characteristics of the target task, thereby pointing to a better way to divide the objects.

[0042] The data processing method provided in the embodiment of the present application, by using a clustering algorithm to classify objects multiple times, can flexibly adjust the object division method according to different target task characteristics, thereby improving the efficiency and accuracy of data processing. In practical applications, this method can significantly improve the analysis and management capabilities of large data sets, especially in scenarios where classification standards need to be dynamically adjusted according to specific needs, and can quickly respond and provide effective solutions, thereby providing strong support for business decision-making.

[0043] Optionally, in the data processing method provided in the embodiment of the present application, obtaining feature data corresponding to each of N objects includes: obtaining a cycle of data collection; and based on the cycle, obtaining feature data corresponding to each of the N objects.

[0044] The cycle of data collection can be determined by analyzing the characteristics of the target task. For example, in the application scenario of employee construction, weekly or monthly data collection may be more appropriate than daily updates for the target task of allocating dormitories, because the relevant employee data usually changes over a longer time scale. In addition, the stability of the data can be evaluated to understand the law of changes in feature data over time. If the data changes frequently, a shorter collection cycle may be required to capture the latest status. Conversely, if the data is relatively stable, a longer cycle can be set. It is also possible to evaluate whether the existing technical capabilities support the set collection cycle. For example, a real-time data stream processing system may support minute-level data collection, while a traditional data warehouse may be more suitable for daily, weekly or monthly cycles. Reasonable determination of the data collection cycle and effective acquisition and processing of feature data of N objects can significantly improve the accuracy and practicality of data analysis and provide strong support for decision making.

[0045] Optionally, in the data processing method provided in an embodiment of the present application, the number of categories corresponding to each of the M classification results increases successively from two categories to M+1 categories.

[0046] The data set may contain complex group relationships and hierarchical structures. By starting with two categories and gradually increasing to M+1 categories, these structures can be gradually discovered. The initial two-class classification may reveal the most obvious group differences, and as the number of categories increases, more subtle group characteristics can be identified, thereby deepening the understanding of the diversity of the data. Directly using M+1 clustering may overfit the data due to excessive category segmentation, that is, the model focuses too much on the details of the training data and ignores the broader patterns. By starting with two categories and gradually increasing, the main group patterns can be identified and understood first, and then gradually refined, which helps to balance the complexity and generalization ability of the model. The incremental setting of the number of categories can not only deeply explore and understand the multi-level structure of the data, but also flexibly adapt to different decision-making needs, optimize resource allocation, and reduce the risk of overfitting, thereby achieving higher efficiency and effectiveness in practical applications. For example, in the application scenario of employee construction, N objects, that is, N employees, can be classified three times, that is, from 2 categories to 4 categories. Table 1 is an optional employee classification result. As shown in Table 1, each employee corresponds to a different category in the three classifications.

[0047] 2. Classification 3. Classification 4 Categories Employee A 2-1 3-1 4-1 Employee B 2-1 3-1 4-1 Employee C 2-1 3-2 4-2 Employee D 2-2 3-2 4-3 Employee E 2-2 3-3 4-4 Employee F 2-2 3-3 4-4

[0048] Table 1 Result of an optional employee classification

[0049] Optionally, in the data processing method provided in the embodiment of the present application, based on M classification results and target task characteristics, an object division method corresponding to the target task characteristics is determined, wherein the target task characteristics include a first task characteristic, including: when the target task characteristic is the first task characteristic, objects that match the corresponding categories in the M classification results are grouped together to obtain an object division method corresponding to the target task characteristics, wherein the gap between the feature data of the objects represented by the first task characteristic is less than a preset threshold.

[0050] The first task feature may be a target task feature that requires similar objects to be grouped together. For each classification result obtained by clustering, the feature data gap belonging to each object is evaluated. If the gap is less than a preset threshold, it means that the corresponding objects are sufficiently similar in key features and can be regarded as a group. Therefore, in the M classification results, objects belonging to the same category can be divided into one or more groups according to the first task feature and the preset threshold. This division method is intended to ensure that the objects in the group have high consistency in features, so as to better meet specific goals. For example, in the application scenario of employee construction, the first task feature may be the allocation of employee dormitories, so similar employees need to be grouped together. For example, in Table 1 above, employee A and employee B have the same corresponding categories in 2 categories, 3 categories, and 4 categories, indicating that the gap between the feature data of A and B is less than the preset threshold, and the two can be regarded as similar objects and can be grouped together.

[0051] Optionally, in the data processing method provided in the embodiment of the present application, based on M classification results and target task characteristics, an object division method corresponding to the target task characteristics is determined, wherein the target task characteristics include a second task characteristic, including: when the target task characteristic is the second task characteristic, objects with different corresponding categories in the M classification results are grouped together to obtain an object division method corresponding to the target task characteristics, wherein the gap between the feature data of the objects represented by the second task characteristic is not less than a preset threshold.

[0052] Unlike the first task feature that emphasizes similarity, the second task feature emphasizes diversity or heterogeneity. When the gap between objects in feature data is not less than this threshold, they are considered to have sufficient differences in the feature and can be divided into the same group to promote the realization of the second task feature. For example, in the application scenario of employee construction, the second task feature can be employee communication, so different types of employees need to be divided into one group. For example, in Table 1 above, employee A and employee D have different corresponding categories in classification 2, classification 3, and classification 4, indicating that the gap between the feature data of A and B is greater than the preset threshold, and the two can be regarded as different types of objects and can be divided into one group. Based on the above embodiment, it can be ensured that each group of objects has sufficient differences in the second task feature, so as to achieve more efficient, innovative and adaptive management or decision-making, which is particularly suitable for scenarios that need to cross traditional boundaries, promote cross-domain cooperation, or improve resource utilization efficiency.

[0053] The data processing method provided in the embodiment of the present application obtains feature data and target task features corresponding to N objects, where N is an integer greater than 1; based on the feature data corresponding to the N objects, a clustering algorithm is used to classify the N objects M times to obtain M classification results, where M is an integer greater than 1, and the M classification results each correspond to a different number of categories; based on the M classification results and the target task features, an object division method corresponding to the target task features is determined, thereby solving the problem of low flexibility and accuracy of data classification caused by a single-level clustering algorithm in the related art, thereby achieving the effect of improving the efficiency and accuracy of data processing.

[0054] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0055] As an optional embodiment, a method for employee building organization based on the above data processing method may also be provided, the method comprising:

[0056] S1: Collect non-privacy information of all employees in the data center (professional line, length of service, rank, overtime hours, time spent in production, amount of emails received daily, frequency of participation in party, government, trade union and union activities, etc.).

[0057] S2: Classify the data collected in step S1 using a clustering algorithm, and perform 2-classification, 3-classification, 4-classification to N-classification in sequence, and mark the category corresponding to each employee in each classification.

[0058] S3: Save the classification results of S2 to the database and send them to HR via email.

[0059] S4: Collect employee development needs, such as allocating overtime rooms, allocating employee dormitories, company team building groups, talent team building and employee exchange seminars, etc.

[0060] S5: Provide a solution based on the classification result information saved in step S3 and the employee development needs collected in step S4. For example, when the classification result is as shown in Table 1 above, and the needs collected in step S4 are to allocate overtime guest rooms and employee dormitories, similar employees should be allocated to teams, such as A and B are grouped together, E and F are grouped together, and C and D can be matched with other employees in the employee pool; if the needs collected in step S4 are company team building groups, talent echelon construction, and employee exchange seminars, different types of employees should be allocated to teams to promote employee exchanges and strengthen talent echelon construction, such as A and D are grouped together, B and E are grouped together, and C and F are grouped together.

[0061] Example 2

[0062] The embodiment of the present application also provides a data processing system. It should be noted that the data processing system of the embodiment of the present application can apply the data processing method provided in the embodiment of the present application. The data processing system provided in the embodiment of the present application is introduced below.

[0063] According to an embodiment of the present application, a system for implementing the above data processing method is also provided, the system comprising:

[0064] The acquisition module is used to obtain feature data corresponding to each of N objects, where N is an integer greater than 1.

[0065] The classification module is used to classify N objects M times to obtain M classification results, where M is an integer greater than 1, and the M classification results each correspond to a different number of categories.

[0066] The saving module is used to save the M classification results in the database.

[0067] The requirement module is used to obtain the target task characteristics.

[0068] The scheme module is used to determine the object division method corresponding to the target task characteristics based on the M classification results and the target task characteristics.

[0069] Optionally, the system may also include a push module, wherein the push module is used to transmit information among the above modules. For example, the push module can push the classification results of the classification module to the saving module, and can also push the target task features obtained by the requirement module to the solution module, and can also push the classification results of the classification module to the data management personnel (for example, in the employee development scenario, it can be pushed to the human resources specialist by email).

[0070] The design of the above data processing system provides a flexible and efficient method for processing and analyzing complex data. The acquisition module ensures the comprehensiveness of the data, the classification module provides a multi-level clustering perspective, the preservation module ensures the reusability of the results, the demand module focuses on the target task, and the solution module generates the optimal partitioning solution based on this information. This systematic approach not only simplifies the data processing process, but also improves the accuracy and efficiency of decision-making. It is especially suitable for scenarios that require interdisciplinary knowledge, innovative thinking or sophisticated market analysis. By dynamically adjusting the value of M and the granularity of classification, the system can adapt to changing needs and provide customized data analysis and decision support.

[0071] As an optional embodiment, an employee organization system based on the above data processing system may also be provided, including: a collection module, a classification module, a push module, an employee construction demand module and a solution providing module.

[0072] The collection module is used to collect various types of information corresponding to N employees in the target department and push them to the classification module.

[0073] The classification module is used to classify the information counted by the collection module multiple times, with a different number of categories each time, and pass it to the push module.

[0074] The push module is used to push the results of the classification module to HR and save them in the database.

[0075] The employee development demand module is used to collect employee development needs and push them to the solution provision module.

[0076] The solution providing module is used to provide employee development solutions based on the requirements collected by the employee development requirements module.

[0077] Example 3

[0078] The embodiment of the present application also provides a data processing device. It should be noted that the data processing device of the embodiment of the present application can be used to execute the data processing method provided by the embodiment of the present application. The data processing device provided by the embodiment of the present application is introduced below.

[0079] According to an embodiment of the present application, a device for implementing the above data processing method is also provided. Figure 3 is a schematic diagram of a data processing device provided according to an embodiment of the present application, such as Figure 3 As shown, the device comprises:

[0080] The acquisition module 31 is used to acquire feature data and target task features corresponding to each of N objects, where N is an integer greater than 1.

[0081] The acquisition module 31 can collect feature data of N objects, where N is any positive integer, which means that the system can process data sets ranging from small to very large. At the same time, it also obtains the target task features, which is the core guiding principle of the entire analysis and ensures the pertinence and effectiveness of subsequent processing.

[0082] The classification module 32 is connected to the acquisition module 31, and is used to classify the N objects M times based on the feature data corresponding to each of the N objects using a clustering algorithm to obtain M classification results, wherein M is an integer greater than 1, and the M classification results each correspond to a different number of categories.

[0083] The classification module 32 uses a clustering algorithm to perform M classifications, with a different number of categories each time, which provides multiple perspectives to examine the data set, and the classification results from coarse-grained to fine-grained help identify the internal structure and pattern of the data set. Through incremental cluster analysis, the device can more accurately capture the similarities and differences between objects, providing a basis for more in-depth analysis and decision-making.

[0084] The determination module 33 is connected to the classification module 32, and is used to determine the object division method corresponding to the target task characteristics based on the M classification results and the target task characteristics.

[0085] The determination module 33 determines the object division method that best matches the target task characteristics based on the M classification results and the target task characteristics. This goal-driven approach ensures that the analysis results directly serve the decision-making needs and can effectively guide the strategic planning in specific scenarios such as resource allocation, team building, and market segmentation.

[0086] The data processing device provided in the embodiment of the present application is used to obtain the feature data and target task features corresponding to each of N objects through the acquisition module 31, wherein N is an integer greater than 1; the classification module 32 is used to classify the N objects M times based on the feature data corresponding to each of the N objects using a clustering algorithm to obtain M classification results, wherein M is an integer greater than 1, and the M classification results each correspond to a different number of categories; the determination module 33 is used to determine the object division method corresponding to the target task features based on the M classification results and the target task features, thereby solving the problem of low flexibility and accuracy of data classification caused by the single-level clustering algorithm in the related technology, thereby achieving the effect of improving the efficiency and accuracy of data processing.

[0087] Optionally, in the data processing device provided in the embodiment of the present application, the acquisition module 31 also includes: a first acquisition unit and a second acquisition unit, wherein the first acquisition unit is used to acquire the cycle of data collection; the second acquisition unit is used to acquire the feature data corresponding to each of the N objects based on the cycle.

[0088] Optionally, in the data processing device provided in the embodiment of the present application, the number of categories corresponding to each of the M classification results in the classification module 32 increases sequentially from two categories to M+1 categories.

[0089] Optionally, in the data processing device provided in the embodiment of the present application, the determination module 33 also includes: a first division unit, wherein the first division unit is used to, when the target task feature is the first task feature, divide the objects that match the corresponding categories in the M classification results into a group, and obtain an object division method corresponding to the target task feature, wherein the gap between the feature data of the objects represented by the first task feature is less than a preset threshold.

[0090] Optionally, in the data processing device provided in the embodiment of the present application, the determination module 33 also includes: a second division unit, wherein the second division unit is used to, when the target task feature is the second task feature, divide objects having different corresponding categories in the M classification results into a group, and obtain an object division method corresponding to the target task feature, wherein the difference between the feature data of the objects represented by the second task feature is not less than a preset threshold.

[0091] It should be noted that the acquisition module 31, classification module 32 and determination module 33 correspond to steps S201 to S203 in Example 1, and the three modules and the corresponding steps implement the same examples and application scenarios, but are not limited to the contents disclosed in the above-mentioned Example 1. It should be noted that the above-mentioned modules or units may be hardware components or software components stored in a memory (e.g., memory 104) and processed by one or more processors (e.g., processors 102a, 102b, ..., 102n), and the above-mentioned modules may also be part of the device and may be run in the computer terminal 10 provided in Example 1.

[0092] Through the collaborative work of the acquisition module 31, the classification module 32 and the determination module 33, the device not only processes the raw data, but also deeply explores the insights behind the data, providing high-value information for decision makers. Since the classification module 32 provides M classification results, comprehensive classification can be performed based on multiple classification results instead of relying on a single classification result. This helps to reduce allocation bias, avoid misjudgment caused by the limitations of the clustering algorithm or data noise, and improve the reliability and effectiveness of data processing.

[0093] Example 4

[0094] An embodiment of the present application may provide an electronic device, Figure 4 is a structural block diagram of an electronic device provided according to an embodiment of the present application. Figure 4 As shown, the electronic device may include: one or more ( Figure 4 (only one is shown) processor 402, memory 404, storage controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module and display.

[0095] Among them, the memory can be used to store software programs and modules, such as program instructions / modules corresponding to the methods and devices in the embodiments of the present application, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, realizing the above-mentioned method. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the terminal 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.

[0096] The processor can call the information and application programs stored in the memory through the transmission device to perform the following steps: obtain the feature data and target task features corresponding to each of the N objects, where N is an integer greater than 1; based on the feature data corresponding to each of the N objects, use a clustering algorithm to classify the N objects M times to obtain M classification results, where M is an integer greater than 1, and the M classification results each correspond to a different number of categories; based on the M classification results and the target task features, determine the object division method corresponding to the target task features.

[0097] The processor can also call the information and application programs stored in the memory through the transmission device to perform the following steps: obtaining characteristic data corresponding to each of the N objects, including: obtaining the cycle of data collection; based on the cycle, obtaining characteristic data corresponding to each of the N objects.

[0098] The processor may also call the information and application program stored in the memory through the transmission device to perform the following steps: the number of categories corresponding to each of the M classification results increases from two categories to M+1 categories in sequence.

[0099] The processor can also call the information and application programs stored in the memory through the transmission device to perform the following steps: based on M classification results and target task characteristics, determine the object division method corresponding to the target task characteristics, wherein the target task characteristics include the first task characteristics, including: when the target task characteristics are the first task characteristics, group the objects that match the corresponding categories in the M classification results to obtain the object division method corresponding to the target task characteristics, wherein the gap between the feature data of the objects represented by the first task characteristics is less than a preset threshold.

[0100] The processor can also call the information and application programs stored in the memory through the transmission device to perform the following steps: based on the M classification results and the target task characteristics, determine the object division method corresponding to the target task characteristics, wherein the target task characteristics include the second task characteristics, including: when the target task characteristics are the second task characteristics, group objects with different corresponding categories in the M classification results into a group to obtain the object division method corresponding to the target task characteristics, wherein the gap between the feature data of the objects represented by the second task characteristics is not less than a preset threshold.

[0101] According to an embodiment of the present application, a data processing method is provided. By acquiring feature data and target task features corresponding to each of N objects, where N is an integer greater than 1; based on the feature data corresponding to each of the N objects, a clustering algorithm is used to classify the N objects M times to obtain M classification results, where M is an integer greater than 1, and the number of categories corresponding to each of the M classification results is different; based on the M classification results and the target task features, an object division method corresponding to the target task features is determined, thereby achieving the purpose of adjusting the object division method according to different target task features, thereby solving the technical problem of low flexibility and accuracy of data classification caused by a single-level clustering algorithm in the related art.

[0102] It can be understood by those skilled in the art that Figure 4 The structure shown is for illustration only, and the electronic device may also be a smart phone (such as an Android phone, an iOS phone, etc.), a tablet computer, a PDA, a mobile Internet device (Mobile Internet Devices, MID), a PAD, and other terminal devices. Figure 4 The structure of the electronic device is not limited. Figure 4 More or fewer components (such as network interfaces, display devices, etc.) shown in, or having Figure 4 Different configurations shown.

[0103] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a computer-readable storage medium, and the storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0104] Example 5

[0105] The embodiment of the present application further provides a storage medium. Optionally, in this embodiment, the storage medium can be used to store the program code executed by the data processing method provided in the first embodiment.

[0106] Optionally, in this embodiment, the above storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0107] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing the program of the data processing method steps.

[0108] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0109] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0110] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0111] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0112] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0113] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0114] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A data processing method, characterized in that: include: Obtain feature data and target task features corresponding to each of N objects, where N is an integer greater than 1; Based on the feature data corresponding to each of the N objects, a clustering algorithm is used to classify the N objects M times to obtain M classification results, where M is an integer greater than 1, and the M classification results each correspond to a different number of categories; Based on the M classification results and the target task characteristics, an object division method corresponding to the target task characteristics is determined.

2. The method according to claim 1, characterized in that The obtaining of feature data corresponding to each of the N objects includes: Get the cycle of data collection; Based on the period, characteristic data corresponding to each of the N objects is obtained.

3. The method according to claim 1, characterized in that The number of categories corresponding to each of the M classification results increases sequentially from two categories to M+1 categories.

4. The method according to claim 1, characterized in that The determining, based on the M classification results and the target task feature, an object division method corresponding to the target task feature, wherein the target task feature includes a first task feature, including: When the target task feature is the first task feature, objects that match the corresponding categories in the M classification results are grouped together to obtain the object division method corresponding to the target task feature, wherein the difference between the feature data of the objects represented by the first task feature is less than a preset threshold.

5. The method according to claim 1, characterized in that The determining, based on the M classification results and the target task feature, an object division method corresponding to the target task feature, wherein the target task feature includes a second task feature, comprises: In the case where the target task feature is the second task feature, objects in different corresponding categories in the M classification results are grouped together to obtain the object division method corresponding to the target task feature, wherein the difference between the feature data of the objects represented by the second task feature is not less than a preset threshold.

6. A data processing system, characterized in that: The data processing method according to any one of claims 1 to 5 is applied, comprising: A collection module, used to obtain feature data corresponding to each of N objects, where N is an integer greater than 1; A classification module is used to classify the N objects M times to obtain M classification results, where: M is an integer greater than 1, and the M classification results each correspond to a different number of categories; A saving module, used for saving the M classification results in a database; Requirements module, used to obtain target task characteristics; A solution module is used to determine an object division method corresponding to the target task characteristics based on the M classification results and the target task characteristics.

7. A data processing device, characterized in that: include: An acquisition module, used to acquire feature data and target task features corresponding to each of N objects, where N is an integer greater than 1; A classification module, configured to classify the N objects M times using a clustering algorithm based on the feature data corresponding to each of the N objects, to obtain M classification results, wherein M is an integer greater than 1, and the M classification results each correspond to a different number of categories; A determination module is used to determine an object division method corresponding to the target task characteristics based on the M classification results and the target task characteristics.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored executable program, wherein when the executable program is run, the device where the computer-readable storage medium is located is controlled to execute the data processing method according to any one of claims 1 to 5.

9. An electronic device, characterized in that: include: A memory storing an executable program; A processor, used to run the program, wherein the program executes the data processing method according to any one of claims 1 to 5 when running.

10. A computer program product comprising computer instructions, characterized in that: When the computer instructions are executed by a processor, the steps of the data processing method according to any one of claims 1 to 5 are implemented.

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