Data processing method, device and terminal equipment based on text data set

Abstract

The present invention provides a data processing method, device and terminal equipment based on a text data set. The method is applied to the field of data processing, including: obtaining the registration source data of a target specific group population monitoring system, and extracting valid keywords in each registration source data to obtain a keyword group corresponding to each registration source data; generating a behavioral ability index representation corresponding to each keyword group based on the valid keywords in each keyword group and a preset weight comparison table; classifying each registration source data according to the behavioral ability index representation corresponding to each keyword group to obtain multiple data classes, and storing each data class in blocks. The data processing method, device and terminal equipment based on a text data set provided by the present invention can effectively classify and process the registration source data in a specific group population monitoring system, thereby improving the subsequent data processing speed.

Description

Technical Field

[0001] The present invention belongs to the field of data processing technology, and more specifically, relates to a data processing method, device and terminal equipment based on text data set. Background Art

[0002] It is known that a large amount of registration source data is stored in a population monitoring system for a specific group. How to effectively classify and process the large amount of registration source data has become an urgent problem that technical personnel in this field need to solve. Summary of the invention

[0003] The purpose of the present invention is to provide a data processing method, device and terminal equipment based on text data set to realize the classification processing of registration source data in a population monitoring system for a specific group.

[0004] A first aspect of an embodiment of the present invention provides a data processing method based on a text data set, wherein the data processing method based on a text data set is applied to a population monitoring system for a specific group, and the method comprises:

[0005] Acquire the registration source data of the population monitoring system of the target specific group, and extract the effective keywords in each registration source data to obtain the keyword group corresponding to each registration source data;

[0006] Generate a behavioral ability index representation corresponding to each keyword group based on the valid keywords in each keyword group and a preset weight comparison table; wherein the weight comparison table is a comparison table of each valid keyword and the behavioral ability index corresponding to each valid keyword;

[0007] Each registered source data is classified according to the behavioral ability index representation quantity corresponding to each keyword group to obtain multiple data classes, and each data class is stored in blocks.

[0008] A second aspect of an embodiment of the present invention provides a data processing device based on a text data set, wherein the data processing device based on a text data set is applied to a population monitoring system for a specific group, and is characterized by comprising:

[0009] The data acquisition module is used to acquire the registration source data of the population monitoring system of the target specific group, and extract the effective keywords in each registration source data to obtain the keyword group corresponding to each registration source data;

[0010] An index determination module, for generating a behavioral ability index representation corresponding to each keyword group based on the valid keywords in each keyword group and a preset weight comparison table; wherein the weight comparison table is a comparison table of each valid keyword and the behavioral ability index corresponding to each valid keyword;

[0011] The classification storage module is used to classify each registered source data according to the behavioral ability index representation quantity corresponding to each keyword group, obtain multiple data classes, and store each data class in blocks.

[0012] According to a third aspect of an embodiment of the present invention, a terminal device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned data processing method based on a text data set when executing the computer program.

[0013] According to a fourth aspect of an embodiment of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned data processing method based on a text data set are implemented.

[0014] The beneficial effects of the data processing method, device and terminal device based on text data set provided by the embodiments of the present invention are:

[0015] The present invention performs data classification processing based on a text data set of a target specific group population monitoring system. Compared with a solution that performs classification processing directly based on data table fields, the data classification processing method based on a text data set provided by the present invention can take into account the personalized text description of the registration source data in the target specific group population monitoring system, thereby more accurately classifying the data of the target specific group population monitoring system. Based on the above concept, the present invention provides a specific implementation scheme, namely, extracting effective keywords from the registration source data, and classifying each registration source data according to the behavioral ability index corresponding to the effective keywords, thereby achieving a more accurate data classification processing scheme for the target specific group population monitoring system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 A flowchart of a data processing method based on a text data set provided by an embodiment of the present invention;

[0018] Figure 2 A structural block diagram of a data processing device based on a text data set provided by an embodiment of the present invention;

[0019] Figure 3 A schematic block diagram of a terminal device provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0021] Please refer to Figure 1 , Figure 1 A flowchart of a data processing method based on a text data set provided by an embodiment of the present invention, the method comprising:

[0022] S101: Acquire the registration source data of the population monitoring system of the target specific group, and extract the effective keywords in each registration source data to obtain the keyword group corresponding to each registration source data.

[0023] In this embodiment, the registration source data may be a text data set describing the current status of the behavioral capabilities of people in the population monitoring system of the target specific group.

[0024] S102: Generate a behavioral ability index representation corresponding to each keyword group based on the valid keywords in each keyword group and a preset weight comparison table.

[0025] In this embodiment, the weight comparison table is a comparison table of each valid keyword and the behavior capability index corresponding to each valid keyword.

[0026] In this embodiment, the behavioral ability index representation corresponding to each keyword group can be determined based on the behavioral ability index of each valid keyword in each keyword group. The behavioral ability index representation is used to describe the behavioral ability of the person corresponding to each registration source data.

[0027] S103: Classify each registered source data according to the behavioral ability index representation corresponding to each keyword group to obtain multiple data classes, and store each data class in blocks.

[0028] In this embodiment, the block storage of each data class can be described in detail as follows:

[0029] Each data class is sorted according to its average access frequency, and the sorting order of each data class is determined as the storage order of each data class in the target data block (the earlier the sorting order, the earlier the storage position). The same data class is stored in a continuous storage space.

[0030] The average access frequency of a data class is the average number of accesses to each registered source data in the data class.

[0031] From the above, it can be concluded that the present invention performs data classification processing based on the text data set of the target specific group population monitoring system. Compared with the scheme of directly performing classification processing based on the data table fields, the data classification processing method based on the text data set provided by the present invention can take into account the personalized text description of the registration source data in the target specific group population monitoring system, thereby more accurately classifying the data of the target specific group population monitoring system. On the basis of the above concept, the present invention provides a specific implementation scheme, that is, extracting the effective keywords of the registration source data, and classifying each registration source data according to the behavioral ability index corresponding to the effective keywords, thereby realizing a more accurate data classification processing scheme for the target specific group population monitoring system.

[0032] Optionally, as a specific implementation of the data processing method based on the text data set provided by an embodiment of the present invention, for a certain registration source data, a method for extracting effective keywords in the registration source data is:

[0033] An approximate search is performed in a preset keyword library to extract candidate keywords in the registration source data and target keywords corresponding to each candidate keyword in the keyword library.

[0034] Each candidate keyword and a target keyword corresponding to each candidate keyword are input into a preset matching model to determine a valid keyword corresponding to the registration source data.

[0035] In this embodiment, a candidate keyword is a keyword similar to a target keyword in the keyword library. For example, a target keyword is "inability to act", and the registration source data contains a phrase "lack of ability to act" or "x action x ability" (where x represents a character or word). In the approximate search, "inability to act" will become the target keyword corresponding to "lack of ability to act" or "x action x ability" (where x represents a character or word). In other words, the approximate search is to search for certain target keywords that have overlapping phrases with the candidate keywords from the keyword library. It is worth noting that the candidate keyword is a keyword that has already corresponded to the target keyword. If there is a keyword in the registration source data that does not match any target keyword in the keyword library, then the keyword cannot be called a candidate keyword.

[0036] Optionally, as a specific implementation of the data processing method based on the text data set provided by an embodiment of the present invention, an approximate search is performed in a preset keyword library to extract candidate keywords in the registration source data and target keywords corresponding to each candidate keyword in the keyword library, including:

[0037] An approximate search is performed in a preset keyword library. If the similarity between a keyword in the registration source data and a keyword in the keyword library is greater than a preset value, the keyword is used as a candidate keyword, and the keyword in the keyword library corresponding to the keyword is used as a target keyword.

[0038] In this embodiment, the similarity between a certain keyword in the registration source data and a certain keyword in the keyword library refers to the phrase overlap between the certain keyword in the registration source data and the certain keyword in the keyword library.

[0039] Optionally, as a specific implementation of the data processing method based on the text data set provided in an embodiment of the present invention, the preset matching model is a probabilistic neural network model.

[0040] Inputting each candidate keyword and the target keyword corresponding to each candidate keyword into a preset matching model to determine the valid keyword corresponding to the registration source data includes:

[0041] S41: Select any candidate keyword, input the candidate keyword and the target keyword corresponding to the candidate keyword into a preset matching model, and obtain the matching probability between the candidate keyword and the target keyword.

[0042] S42: If the matching probability is greater than the preset probability value, the target keyword is used as a valid keyword. If the matching probability is not greater than the preset probability value, the candidate keyword is deleted.

[0043] S43: If all candidate keywords have been traversed, each valid keyword obtained in step S42 is used as a valid keyword corresponding to the registration source data.

[0044] If not all candidate keywords have been traversed, the process returns to step S41.

[0045] In this embodiment, the probabilistic neural network model is used to receive a candidate keyword and a target keyword corresponding to the candidate keyword, and output the matching probability of the two. If the matching probability of the two is not greater than the preset probability value, it indicates that the matching fails and the candidate keyword is deleted. If the matching probability of the two is greater than the preset probability value, it indicates that the matching is successful and the target keyword is used as a valid keyword representing the candidate keyword to facilitate subsequent calculations.

[0046] Optionally, as a specific implementation of the data processing method based on a text data set provided by an embodiment of the present invention, for a certain keyword group, generating a behavioral ability index representation corresponding to the keyword group based on valid keywords in the keyword group and a preset weight comparison table includes:

[0047] A preset weight comparison table is searched to determine the behavior ability index corresponding to each valid keyword in the keyword group, and the behavior ability index representation quantity corresponding to the keyword group is determined based on the behavior ability index corresponding to each valid keyword in the keyword group.

[0048] In this embodiment, the sum of the behavior ability indexes corresponding to the effective keywords in a certain keyword group can be directly used as the behavior ability index representation amount corresponding to the keyword group.

[0049] Optionally, as a specific implementation of the data processing method based on the text data set provided by the embodiment of the present invention, each registered source data is classified according to the behavioral ability index representation corresponding to each keyword group to obtain multiple data classes, including:

[0050] Each registered source data is classified based on the parameter range to which the behavioral ability index representation quantity corresponding to each keyword group belongs, and a plurality of data classes are obtained.

[0051] In this embodiment, if the behavior ability index characterization amount corresponding to a certain keyword group belongs to the parameter range (0, r1), the registration source data corresponding to the keyword group is divided into the first data class, if the behavior ability index characterization amount corresponding to a certain keyword group belongs to the parameter range (r1, r2), the registration source data corresponding to the keyword group is divided into the second data class, and the behavior ability index characterization amount corresponding to a certain keyword group belongs to the parameter range (r2, +∞), the registration source data corresponding to the keyword group is divided into the third data class. Among them, r1, r2, and r3 are all greater than zero and can be preset according to actual needs.

[0052] Corresponding to the data processing method based on the text data set in the above embodiment, Figure 2 This is a structural block diagram of a data processing device based on a text data set provided by an embodiment of the present invention. For ease of explanation, only the parts related to the embodiment of the present invention are shown. Figure 2 The data processing device 20 based on the text data set includes: a data acquisition module 21, an index determination module 22, and a classification storage module 23.

[0053] The data acquisition module 21 is used to acquire the registration source data of the population monitoring system of the target specific group, and extract the effective keywords in each registration source data to obtain the keyword group corresponding to each registration source data.

[0054] The index determination module 22 is used to generate the behavior ability index representation corresponding to each keyword group based on the valid keywords in each keyword group and a preset weight comparison table. The weight comparison table is a comparison table of each valid keyword and the behavior ability index corresponding to each valid keyword.

[0055] The classification storage module 23 is used to classify each registered source data according to the behavioral ability index representation quantity corresponding to each keyword group, obtain multiple data categories, and store each data category in blocks.

[0056] Optionally, as a specific implementation of the data processing device based on the text data set provided by an embodiment of the present invention, for a certain registration source data, a method for extracting effective keywords from the registration source data is:

[0057] An approximate search is performed in a preset keyword library to extract candidate keywords in the registration source data and target keywords corresponding to each candidate keyword in the keyword library.

[0058] Each candidate keyword and a target keyword corresponding to each candidate keyword are input into a preset matching model to determine a valid keyword corresponding to the registration source data.

[0059] Optionally, as a specific implementation of the data processing device based on the text data set provided by an embodiment of the present invention, an approximate search is performed in a preset keyword library to extract candidate keywords in the registration source data and target keywords corresponding to each candidate keyword in the keyword library, including:

[0060] An approximate search is performed in a preset keyword library. If the similarity between a keyword in the registration source data and a keyword in the keyword library is greater than a preset value, the keyword is used as a candidate keyword, and the keyword in the keyword library corresponding to the keyword is used as a target keyword.

[0061] Optionally, as a specific implementation of the data processing device based on the text data set provided by an embodiment of the present invention, the preset matching model is a probabilistic neural network model.

[0062] Inputting each candidate keyword and the target keyword corresponding to each candidate keyword into a preset matching model to determine the valid keyword corresponding to the registration source data includes:

[0063] S41: Select any candidate keyword, input the candidate keyword and the target keyword corresponding to the candidate keyword into a preset matching model, and obtain the matching probability between the candidate keyword and the target keyword.

[0064] S42: If the matching probability is greater than the preset probability value, the target keyword is used as a valid keyword. If the matching probability is not greater than the preset probability value, the candidate keyword is deleted.

[0065] S43: If all candidate keywords have been traversed, each valid keyword obtained in step S42 is used as a valid keyword corresponding to the registration source data.

[0066] If not all candidate keywords have been traversed, the process returns to step S41.

[0067] Optionally, as a specific implementation of the data processing device based on the text data set provided by an embodiment of the present invention, for a certain keyword group, based on the valid keywords in the keyword group and a preset weight comparison table, a behavioral ability index representation corresponding to the keyword group is generated, including:

[0068] A preset weight comparison table is searched to determine the behavior ability index corresponding to each valid keyword in the keyword group, and the behavior ability index representation quantity corresponding to the keyword group is determined based on the behavior ability index corresponding to each valid keyword in the keyword group.

[0069] Optionally, as a specific implementation of the data processing device based on the text data set provided by the embodiment of the present invention, each registered source data is classified according to the behavioral ability index representation corresponding to each keyword group to obtain multiple data classes, including:

[0070] Each registered source data is classified based on the parameter range to which the behavioral ability index representation quantity corresponding to each keyword group belongs, and a plurality of data classes are obtained.

[0071] See also Figure 3 , Figure 3 A schematic block diagram of a terminal device provided by an embodiment of the present invention. Figure 3 The terminal 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303 and one or more memories 304. The processors 301, input devices 302, output devices 303 and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to perform the following operations to perform the functions of the modules / units in the above-mentioned device embodiments, such as Figure 2 The functions of modules 21 to 23 are shown.

[0072] It should be understood that in the embodiment of the present invention, the processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0073] The input device 302 may include a touch panel, a fingerprint sensor (for collecting the user's fingerprint information and fingerprint direction information), a microphone, etc., and the output device 303 may include a display (LCD, etc.), a speaker, etc.

[0074] The memory 304 may include a read-only memory and a random access memory, and provide instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information about the device type.

[0075] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiment of the present invention can execute the implementation methods described in the first and second embodiments of the data processing method based on the text data set provided in the embodiment of the present invention, and can also execute the implementation methods of the terminal described in the embodiment of the present invention, which will not be repeated here.

[0076] In another embodiment of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, wherein the program instructions are executed by a processor to implement all or part of the processes in the above-mentioned embodiment method, and may also be completed by instructing the relevant hardware through the computer program, wherein the computer program may be stored in a computer-readable storage medium, and wherein the computer program may be executed by the processor to implement the steps of the above-mentioned various method embodiments. Among them, the computer program includes computer program code, and the computer program code may be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the contents contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electric carrier signal and telecommunication signal.

[0077] The computer-readable storage medium may be an internal storage unit of the terminal of any of the foregoing embodiments, such as a hard disk or memory of the terminal. The computer-readable storage medium may also be an external storage device of the terminal, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the terminal. Further, the computer-readable storage medium may also include both an internal storage unit of the terminal and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the terminal. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0078] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0079] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the terminals and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

[0080] In the several embodiments provided in the present application, it should be understood that the disclosed terminals and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or it can be an electrical, mechanical or other form of connection.

[0081] 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 embodiments of the present invention.

[0082] In addition, each functional unit in each embodiment of the present invention 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.

[0083] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A data processing method based on a text data set, characterized in that: The data processing method based on text data set is applied to a specific population monitoring system, and the method comprises: Acquire the registration source data of the population monitoring system of the target specific group, and extract the effective keywords in each registration source data to obtain the keyword group corresponding to each registration source data; For a certain registration source data, the method of extracting effective keywords in the registration source data is: Performing an approximate search in a preset keyword library to extract candidate keywords in the registration source data and target keywords corresponding to each candidate keyword in the keyword library; Input each candidate keyword and the target keyword corresponding to each candidate keyword into a preset matching model to determine the valid keyword corresponding to the registration source data; The preset matching model is a probabilistic neural network model; The step of inputting each candidate keyword and a target keyword corresponding to each candidate keyword into a preset matching model to determine a valid keyword corresponding to the registration source data includes: S41: Select a candidate keyword, input the candidate keyword and a target keyword corresponding to the candidate keyword into a preset matching model, and obtain a matching probability between the candidate keyword and the target keyword; S42: If the matching probability is greater than a preset probability value, the target keyword is used as a valid keyword; if the matching probability is not greater than the preset probability value, the candidate keyword is deleted; S43: If all candidate keywords have been traversed, each valid keyword obtained in step S42 is used as a valid keyword corresponding to the registration source data; If all candidate keywords have not been traversed, return to step S41; Generate a behavioral ability index representation corresponding to each keyword group based on the valid keywords in each keyword group and a preset weight comparison table; wherein the weight comparison table is a comparison table of each valid keyword and the behavioral ability index corresponding to each valid keyword; Each registered source data is classified according to the behavioral ability index representation quantity corresponding to each keyword group to obtain multiple data classes, and each data class is stored in blocks.

2. The data processing method based on text data set according to claim 1, characterized in that: The method of performing an approximate search in a preset keyword library to extract candidate keywords in the registration source data and target keywords corresponding to each candidate keyword in the keyword library includes: An approximate search is performed in a preset keyword library. If the similarity between a keyword in the registration source data and a keyword in the keyword library is greater than a preset value, the keyword is used as a candidate keyword, and the keyword in the keyword library corresponding to the keyword is used as a target keyword.

3. The data processing method based on text data set according to claim 1, characterized in that: For a certain keyword group, based on the valid keywords in the keyword group and a preset weight comparison table, a behavioral ability index representation corresponding to the keyword group is generated, including: A preset weight comparison table is searched to determine the behavior ability index corresponding to each valid keyword in the keyword group, and the behavior ability index representation quantity corresponding to the keyword group is determined based on the behavior ability index corresponding to each valid keyword in the keyword group.

4. The data processing method based on text data set according to claim 1, characterized in that: The registration source data are classified according to the behavioral ability index representation corresponding to each keyword group to obtain multiple data categories, including: Each registered source data is classified based on the parameter range to which the behavioral ability index representation quantity corresponding to each keyword group belongs, and a plurality of data classes are obtained.

5. A data processing device based on a text data set, wherein the data processing device based on a text data set is applied to a population monitoring system for a specific group, characterized in that: include: The data acquisition module is used to acquire the registration source data of the population monitoring system of the target specific group, and extract the effective keywords in each registration source data to obtain the keyword group corresponding to each registration source data; For a certain registration source data, the method of extracting effective keywords in the registration source data is: Performing an approximate search in a preset keyword library to extract candidate keywords in the registration source data and target keywords corresponding to each candidate keyword in the keyword library; Input each candidate keyword and the target keyword corresponding to each candidate keyword into a preset matching model to determine the valid keyword corresponding to the registration source data; The preset matching model is a probabilistic neural network model; The step of inputting each candidate keyword and a target keyword corresponding to each candidate keyword into a preset matching model to determine a valid keyword corresponding to the registration source data includes: S41: Select a candidate keyword, input the candidate keyword and a target keyword corresponding to the candidate keyword into a preset matching model, and obtain a matching probability between the candidate keyword and the target keyword; S42: If the matching probability is greater than a preset probability value, the target keyword is used as a valid keyword; if the matching probability is not greater than the preset probability value, the candidate keyword is deleted; S43: If all candidate keywords have been traversed, each valid keyword obtained in step S42 is used as a valid keyword corresponding to the registration source data; If all candidate keywords have not been traversed, return to step S41; An index determination module, for generating a behavioral ability index representation corresponding to each keyword group based on the valid keywords in each keyword group and a preset weight comparison table; wherein the weight comparison table is a comparison table of each valid keyword and the behavioral ability index corresponding to each valid keyword; The classification storage module is used to classify each registered source data according to the behavioral ability index representation quantity corresponding to each keyword group, obtain multiple data classes, and store each data class in blocks.

6. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

Images