A data mining method, system, medium and device

Abstract

A data mining method, system, medium and device, relating to the field of data mining technology. The method comprises: obtaining designated data and associated data customized by a user on a visual configuration interface; generating data mining rules based on the designated data and the associated data; when receiving a target business that the user needs to mine, parsing the data mining rules to obtain a mining path; performing data mining on the target business based on the mining path to obtain target mining data. Implementing the technical solution provided by the present application can improve the effectiveness of data mining.

Description

Technical Field

[0001] The present application relates to the field of data mining technology, and in particular to a data mining method, system, medium and device. Background Art

[0002] With the rapid growth of data volume, data mining technology has been widely used in various industries to mine useful data. How to effectively extract relevant data from multi-source heterogeneous big data has always been a core issue in the field of data science. Now data mining technology is becoming an important means of extracting information from big data.

[0003] The current traditional data mining model is to mine in a fixed way, that is, fixed relationships, fixed scopes and fixed operations. According to the above traditional data mining model, when users want to obtain the target data they want, the fixed method often generates some data attributes that are not of concern to the user himself, that is, redundant data is generated, resulting in poor effectiveness of data mining. Summary of the invention

[0004] The present application provides a data mining method, system, medium and device, which can improve the effectiveness of data mining.

[0005] In a first aspect, the present application provides a data mining method, the method comprising:

[0006] Obtain the specified data and associated data customized by the user on the visual configuration interface;

[0007] Generate data mining rules based on the specified data and the associated data;

[0008] When receiving the target business that the user needs to mine, parsing the data mining rules to obtain a mining path;

[0009] Data mining is performed on the target business based on the mining path to obtain target mining data.

[0010] By adopting the above technical solution, by obtaining the specified data and related data customized by the user on the visual configuration interface, and generating data mining rules based on the specified data and related data, a user-defined and business-customized data mining process is realized. When receiving a business that the user needs to mine, the data mining rules can be parsed to obtain the mining path for the business, and then data mining is performed based on the mining path to obtain target mining data that meets user needs. Compared with the existing data mining technology that uses a fixed mode, this solution realizes the application of a visual configuration interface, provides user-defined configuration, and generates targeted rules to customize and dynamically adjust data mining for different business scenarios, avoiding the output of redundant and invalid data and enhancing the accuracy and effectiveness of mining data.

[0011] Optionally, before obtaining the specified data and related data customized by the user on the visualization configuration interface, the method also includes: obtaining the main area selected by the user on the visualization configuration interface, wherein the main area includes multiple independent areas; obtaining the header information of each of the independent areas and performing attribute binding to obtain the binding information of each of the independent areas; generating an initial template based on the binding information of each of the independent areas, and finalizing the initial template to obtain a target template.

[0012] By adopting the above technical solution, the main data mining area selected by the user in the visual interface is obtained, and the main area contains multiple independent areas. The header information of each independent area is analyzed and extracted, and the corresponding binding relationship between the header and the attribute field is defined to obtain the binding information of the independent area. After obtaining the binding information of all independent areas, an initial template is generated according to the binding information. The template contains all the header information and corresponding attribute fields of the main area, and finally the target template is formed after finalization.

[0013] Optionally, the obtaining of header information of each of the independent areas and performing attribute binding to obtain binding information of each of the independent areas includes: obtaining header information set by a user for each of the independent areas; for a current independent area corresponding to any one of the header information: obtaining object attributes set by a user for the current independent area, and obtaining a plurality of associated attributes associated with the object attributes based on the object attributes; obtaining a target associated attribute selected by the user from the plurality of associated attributes; performing attribute binding of the target associated attribute with the current independent area to obtain binding information corresponding to the current independent area; obtaining a binding completion instruction to obtain binding information of each of the independent areas.

[0014] By adopting the above technical solution, the header information set by the user for each business sub-area is first obtained to establish a corresponding relationship between the header and the attribute field. Then, for any current independent area, the object attribute field set by the user is obtained, and based on the object attribute, its multiple associated attribute fields are obtained to associate the object attribute with the associated attribute. Then, the target associated attribute field selected by the user in these associated attributes is obtained, that is, the final associated field of the object attribute is determined, and the target associated attribute field is attribute-bound with the header of the current business sub-area to form the binding information of the current independent area. Finally, the mapping binding information between each sub-area and the database field is obtained to provide support for the subsequent generation of business data mining templates.

[0015] Optionally, obtaining the specified data and associated data customized by the user on the visual configuration interface includes: obtaining the object fields and object associated fields of each of the independent areas of the user on the target template; using the data corresponding to the object fields as the specified data, and using the data corresponding to the object associated fields as the associated data.

[0016] By adopting the above technical solution, after generating the target template, the object fields set by the user in each business sub-area of ​​the template and the object-associated fields associated with the object fields are obtained. Then the data corresponding to the object fields are used as the key designated data, and the data corresponding to the object-associated fields associated with the object fields are used as the associated data of the designated data. Through such a process, the user can flexibly configure the object fields and associated fields for different business sub-areas based on the generated target template, so as to finally obtain the designated data and associated data configured by the user.

[0017] Optionally, when the target business that the user needs to mine is received, the data mining rules are parsed to obtain a mining path, including: when the target business that the user needs to mine is received, the data mining rules selected by the user are obtained, and the data mining rules are parsed to obtain a mining plan, wherein the mining plan includes data attributes and associated data attributes that need to be mined; and the mining plan is converted into a mining path recognized by code.

[0018] By adopting the above technical solution, when receiving a target business that the user needs to perform data mining, the corresponding data mining rules defined by the user are obtained, and the rules are parsed to obtain a specific mining solution. The mining solution here includes the data attribute fields and associated data attribute fields that the user configures to be mined. The parsed mining solution is further converted into a mining path that can be identified and executed by code, and the user-defined business data mining rules can be automatically converted into a specific, code-level mining path. When receiving the user's mining needs, the mining path code can be directly called, thereby guiding the system to accurately locate and mine the target business data that the user is concerned about from the massive database in a targeted manner.

[0019] Optionally, data mining is performed on the target business based on the mining path to obtain target mining data, including: extracting business data in the target business, and performing data preprocessing on each business data information to obtain standard business data; identifying characteristic attributes in the standard business data; mining each characteristic attribute in the target business based on the mining path to obtain target mining data corresponding to the characteristic attribute, the target mining data including target data corresponding to the object attributes and target associated data corresponding to the associated object attributes.

[0020] By adopting the above technical solution, the original business data is extracted from the target business, and the data is preprocessed to obtain standardized business data. Then the characteristic attribute fields in the standard business data are identified. Based on the generated mining path, the system strictly locks these characteristic attribute fields and their configured associated attribute fields, and can use SQL statements to conduct targeted data mining on the target business. Finally, the data corresponding to the characteristic attributes and their associated attributes are accurately mined according to the user configuration as the key target mining data results. Based on the mining path, the template data required by the user can be accurately located and mined from the massive and messy business data automatically, thereby improving the effectiveness of data mining.

[0021] Optionally, mining each of the characteristic attributes in the target business based on the mining path to obtain target mining data corresponding to the characteristic attributes includes: mining each of the characteristic attributes in the target business based on the mining path to obtain a mining result; judging whether there is data in the mining result; if there is no data in the mining result, taking the empty data as the target mining data; if there is data in the mining result, converting the data in the mining result into a preset visualization format to obtain the target mining data.

[0022] By adopting the above technical solution, based on the generated mining path, the characteristic attributes in the target business are targeted for mining to obtain the corresponding mining results, and then it is determined whether the result data set is empty. If it is empty, the empty data is directly output to represent that no business data is obtained. If it is not empty, the mined data set is converted into a standard format preset by the visualization configuration platform, and unified formatting is performed for subsequent visualization. By judging and processing the mining results, the final results are output in a unified visualization format, and feedback is given on the situation where the data cannot be obtained, so as to achieve standardized output and feedback confirmation of the mining results.

[0023] In a second aspect of the present application, a data mining system is provided, the system comprising:

[0024] The data configuration module is used to obtain the specified data and associated data customized by the user on the visual configuration interface;

[0025] A mining rule generating module, used for generating data mining rules based on the specified data and the associated data;

[0026] A mining path generation module, used for parsing the data mining rules to obtain a mining path when receiving the target business that the user needs to mine;

[0027] The data mining module is used to perform data mining on the target business based on the mining path to obtain target mining data.

[0028] In a third aspect of the present application, a computer storage medium is provided, wherein the computer storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing the above method steps.

[0029] In a fourth aspect of the present application, an electronic device is provided, comprising: a processor and a memory; wherein the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the above-mentioned method steps.

[0030] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0031] 1. This application implements a user-defined and business-customized data mining process by obtaining the specified data and related data customized by the user on the visual configuration interface, and generating data mining rules based on the specified data and related data. When a business that the user needs to mine is received, the data mining rules can be parsed to obtain the mining path for the business, and then data mining is performed based on the mining path to obtain target mining data that meets user needs. Compared with existing data mining technologies that use fixed modes, this solution implements the application of a visual configuration interface, provides user-defined configurations, and generates targeted rules to customize and dynamically adjust data mining for different business scenarios, avoiding the output of redundant and invalid data and enhancing the accuracy and effectiveness of mined data;

[0032] 2. This application obtains the data mining main area selected by the user in the visual interface. The main area contains multiple independent areas. The header information of each independent area is analyzed and extracted, and the corresponding binding relationship between the header and the attribute field is defined to obtain the binding information of the independent area. After obtaining the binding information of all independent areas, an initial template is generated according to the binding information. The template contains all the header information and corresponding attribute fields of the main area, and then the target template is finally formed after finalization;

[0033] 3. When receiving a target business that the user needs to perform data mining on, the application will obtain the corresponding data mining rules defined by the user and parse the rules to obtain a specific mining plan. The mining plan here includes the data attribute fields and associated data attribute fields that the user configures to be mined. The parsed mining plan is further converted into a mining path that can be identified and executed by code, and the user-defined business data mining rules can be automatically converted into a specific, code-level mining path, so that when the user's mining needs are received, the mining path code can be directly called, thereby guiding the system to accurately locate and mine the target business data that the user is concerned about from the massive database in a targeted manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a flowchart of a data mining method provided by an embodiment of the present application;

[0035] Figure 2 It is a schematic diagram of a data mining system module provided by an embodiment of the present application;

[0036] Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0037] Description of reference numerals: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0038] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0039] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0040] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0041] The following will provide a clear and complete description of the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0042] Please refer to Figure 1 , a flow chart of a data mining method is proposed, the method can be implemented by a computer program, can be implemented by a single-chip microcomputer, or can be run on a data mining system, the computer program can be integrated in an intelligent terminal, or can be run as an independent tool application, specifically, the method includes steps 10 to 40, the above steps are as follows:

[0043] As an optional embodiment, before the step of obtaining the specified data and associated data customized by the user on the visual configuration interface, a template configuration process is also included, which specifically includes the following steps:

[0044] Step 101: obtaining a main area selected by a user on a visual configuration interface, wherein the main area includes a plurality of independent areas;

[0045] Specifically, in the visual configuration interface, users are allowed to select a main area for subsequent data mining customization. Users select a main area by dragging or clicking. The main area is the main part of the template and is a large configurable area, such as an editable table with 10 rows and 10 columns, which is used to configure all fields and areas. Each template has only one main area. Users can further divide the main area into multiple relatively independent independent areas. Each independent area represents a component module or attribute concept of the business, which realizes the preliminary work of block customization according to business logic.

[0046] Step 102: Obtain the header information of each independent area and perform attribute binding to obtain binding information of each independent area;

[0047] After the user selects the main area, the user can set the header for each independent area in the visual interface. Based on the actual needs of each business sub-module, the user can enter customized header information to identify the business significance of each independent area, which achieves the purpose of structuring data management according to business knowledge. After obtaining the header information of each independent area, the user needs to bind attributes to each area, that is, select an object attribute and select one of the multiple candidate associated attributes bound to the object attribute as the associated attribute.

[0048] Specifically, for the current independent area corresponding to any header information, the user first sets an object attribute. The visual interface will obtain the existing data association relationship library and provide multiple candidate association attributes related to the object attribute. The user selects one or more related target association attributes and binds each target association attribute to the current independent area. For example, after setting the header, the column of the independent area corresponding to the header will carry the field attribute bound to it. In this way, the association between objects from a data perspective can be established in advance. Repeat the selection and binding process until the object attributes of each independent area are set to complete the association attribute binding. Finally, the user submits the binding completion instruction, and the visual interface records the header information and corresponding attribute binding information of each independent area.

[0049] Step 103: Generate an initial template based on the binding information of each independent area, and finalize the initial template to obtain a target template.

[0050] Specifically, after completing the attribute binding of all independent areas, the visual configuration interface obtains the header information of each independent area and the attribute binding information of the corresponding object and the associated object. Based on these customized structured information, the visual configuration interface can automatically generate an initial template that conforms to the business knowledge structure and requirements, which contains the defined independent business areas and attribute relationships. The user can make further modifications and optimizations on the initial template, such as adjusting the relative position and size of the area, modifying some attribute relationships, etc., to better meet the user's understanding of the business knowledge structure. After the user's customized adjustment, the initial template is finalized, that is, a stereotyped target template is formed, which reflects the user's customized understanding and configuration of the business knowledge module. Subsequently, the specified data and associated data of each independent area can be extracted based on the customized template to achieve customized data mining for this specific business field.

[0051] Step 10: Obtain the specified data and associated data customized by the user on the visual configuration interface.

[0052] The embodiments of the present application can be applied but not limited to the field of business data analysis, and can allow business analysts to customize the business data indicators and associations that need to be mined through a visual interface, and perform personalized data mining on user behavior data, transaction data, experimental data, etc.

[0053] In the embodiment of the present application, the visual configuration interface refers to a human-computer interaction interface that allows users to customize data mining. Through this interface, users can select and plan the configurations required for data mining. The visual interface realizes user-defined driven business mining customization.

[0054] Designated data refers to the key business data fields that users customize in the visual configuration interface to be mined, which reflects the user's personalized focus and key information needs for target business mining. Related data refers to other business data fields that are related to designated data, which reflects the relationship between multiple related business data customized by the user.

[0055] Specifically, the visualization configuration interface in this embodiment can provide a drag-and-drop operation, and the user can select the business area of ​​interest for visualization planning. Based on the understanding of the business, the user sets the table header and binds the attributes on the interface to generate a business template, thereby completing the preliminary work of customized business mining. After obtaining the customized template, the user can further specify the object data fields that need to be mined in the template according to his or her understanding of key business indicators, as well as other business fields related to the object data as associated fields, such as specifying the data field as "product name" and selecting the associated field as "product code". These specified data and associated data reflect the user's personalized needs for business mining.

[0056] Furthermore, after completing the template customization of the business knowledge structure through the visual interface, you can enter each independent business area to perform specific configuration of the data field. For each area, the user can select an object data field of interest. The data corresponding to this object field is the designated data of the area. At the same time, the data corresponding to the object association field related to the object field is used as the associated data of the designated data. Repeat the configuration process in each independent area in turn, and finally the visual interface obtains the corresponding relationship between the designated data and the associated data in each area. Then, when business data flows in, the designated data and the associated data can be accurately extracted according to the configuration correspondence between the designated data and the associated data in each area, as an important input for generating personalized mining rules for the business, guiding the direction of subsequent intelligent mining.

[0057] Step 20: Generate data mining rules based on the specified data and associated data.

[0058] Specifically, after obtaining the specified data and associated data of each business area customized by the user in the visualization interface, these key business data and their relationships become important inputs for personalized intelligent mining. The business template generated by the previous visualization configuration interface already contains the specified data fields and associated data fields in each business area customized by the user. In order to accurately mine these specified data that meet the user's concerns from the massive business data, it is necessary to generate specific executable data mining rules. Call the intelligent algorithm, take the business template generated by visualization as input, learn the relationship and linkage mode between the specified data fields in the template and its corresponding associated data fields, and finally form an integrated background executable data mining rule, which is used to accurately extract the data fields specified by the template from the original business data, that is, to conduct targeted mining on massive business data and output the business data results specified by the user-defined template. In this way, while ensuring that the mining results meet the personalized business needs of users, unnecessary data is minimized to make the mining process more efficient and accurate.

[0059] Step 30: When receiving the target business that the user needs to mine, parse the data mining rules to obtain the mining path.

[0060] Specifically, when the user needs to perform data mining, the system needs to start the data mining process by submitting the target business that needs to be mined for business intelligence. In order to ensure that the subsequent mining strictly follows the user's personalized configuration requirements for the specified business, it is necessary to first parse the customized data mining rules generated by the visualization of the corresponding field of the business. These rules integrate the user's configuration of key business specified data fields and associated data fields. Through parsing, the rules can be converted into specific business data access paths, that is, from which original target business data the data of the specified field is extracted, and the data of other associated business fields needs to be added for the associated impact analysis. In this way, the subsequent mining process can be guided to strictly follow the user-defined business scenario requirements, effectively lock the mining scope, and make the results more accurate in line with the user's definition of key business information. Finally, the business data mining path that reflects user needs is parsed.

[0061] Based on the above embodiment, as an optional embodiment, when receiving a target business that a user needs to mine, the step of parsing data mining rules to obtain a mining path may also include the following steps:

[0062] Step 301: When receiving a target business that the user needs to mine, obtain the data mining rules selected by the user, and parse the data mining rules to obtain a mining plan, which includes the data attributes and associated data attributes that need to be mined.

[0063] Specifically, when receiving a target business that needs customized intelligent mining submitted by a user, the corresponding data mining rules selected by the user for the target business are first obtained. Then the specific content of the data mining rules is parsed, and the specified data fields and associated data fields customized by the user in each business area are extracted from the data mining rules to form a specific and directly executable mining plan. The mining plan clearly points out which fields need to be extracted from which original business data sets as key specified data when mining in this business field, and which fields need to be added as associated influencing data of the specified data, so as to guide subsequent mining to accurately obtain the key business information required by the user from massive business data. In this way, customized mining can be carried out strictly in accordance with the user's personalized configuration requirements.

[0064] Step 302: Convert the mining solution into a mining path identified by the code.

[0065] Specifically, after obtaining an executable mining solution that reflects the user's business needs, it needs to be converted into a technical path that the computer can recognize and execute. Specifically, the business data extraction requirements specified in the solution are converted into technical access path codes for data sources, table names, field names, associations, etc. This allows the computer to directly locate the user-specified business data from a huge database based on the code and automatically execute the mining solution. At the same time, the code can also be called repeatedly to mine new business data. Converting the mining solution into a code path realizes the automatic conversion from business semantics to technical access, improving mining efficiency.

[0066] Step 40: Perform data mining on the target business based on the mining path to obtain target mining data.

[0067] Specifically, according to the data source, table name, field information, etc. in the mining path code, the business key data configured by the user is accurately located from the database of a large amount of business data. The mined target data is combined with the impact-related data configured, and a table of the corresponding attribute fields in the target template is automatically generated, and the target mining data is finally displayed in the form of a table in the target template. According to the mining path code and strictly in accordance with the user's personalized configuration requirements, the target mining data that meets the business needs is accurately mined from a large amount of data. The target mining data includes the target data corresponding to the object attributes and the target associated data corresponding to the associated object attributes, achieving the purpose of automatically, efficiently and accurately obtaining the key data in the business field that the user is concerned about.

[0068] Based on the above embodiment, as an optional embodiment, the step of performing data mining on the target business based on the mining path to obtain the target mining data may also include the following steps:

[0069] Step 401: extract the business data in the target business, and perform data preprocessing on each business data information to obtain standard business data.

[0070] Specifically, the original business data extracted from the target business may have problems such as inconsistent formats and null values. In order for the subsequent algorithms to match and process efficiently, the extracted data needs to be preprocessed to obtain business data in a standardized format. Specifically, the system scans the data and converts the same business entities represented in different formats into a defined standard format, such as date, time, address, etc. For fields with null values, you can choose to delete or fill in default values ​​as needed. Duplicate and erroneous data will also be filtered out. After the above preprocessing, business data from different sources are converted into a structured standard form, which improves data quality and provides unified input for subsequent mining calculations. Finally, standard business data that can be directly analyzed by the algorithm is obtained.

[0071] Step 402: Identify characteristic attributes in standard business data.

[0072] Step 403: mining each characteristic attribute in the target business based on the mining path to obtain target mining data corresponding to the characteristic attribute, the target mining data including target data corresponding to the object attribute and target associated data corresponding to the associated object attribute.

[0073] Specifically, after obtaining standardized business data, it is necessary to lock the key business feature attributes, which is determined by the data fields specified by the user when visually configuring different business areas. The system matches and finds the feature attributes pre-set in the target template in the standard business data, such as product name, product code, etc. Then, according to the generated mining path code, strictly lock the data table and field information where these feature attributes are located, and use SQL query statements for targeted extraction. At the same time, the associated attribute data is also extracted according to the associated configuration. Finally, the business data corresponding to the feature attributes and their associated attributes are extracted as the key target mining data results to avoid returning a large amount of useless information. Finally, the target data and target associated data required by the user are output to complete precise mining.

[0074] Based on the above embodiment, as an optional embodiment, the step of mining each characteristic attribute in the target business based on the mining path to obtain the target mining data corresponding to the characteristic attribute may also include the following steps:

[0075] Step 4031: Mining each characteristic attribute in the target business based on the mining path to obtain mining results.

[0076] Step 4032: Determine whether there is data in the mining results.

[0077] Step 4033: If there is no data in the mining result, the empty data is used as the target mining data.

[0078] Step 4034: If there is data in the mining result, the data in the mining result is converted into a preset visualization format to obtain target mining data.

[0079] Specifically, after conducting targeted mining of the characteristic attributes of the target business according to the mining path, it is necessary to judge the mining results to determine whether the business data required by the user has been obtained. The amount of data in the mining results can be counted. If the data set in the mining results is empty or the amount of data is zero, it is determined that no valid business data has been obtained in this mining, and the empty data is used as the target mining data. The user can also be prompted to reconfigure the mining path. If the mining result is not empty and contains a certain amount of business data, it is determined that the data required by the user has been successfully mined. The format conversion module is called to convert the mined business data into a standard format pre-defined by the visualization configuration platform. The standard format is a visualization format that can be rendered and displayed in the end, and is used for subsequent analysis and display through the visualization interface.

[0080] See also Figure 2 , is a schematic diagram of a data mining system module provided in an embodiment of the present application, the data mining system may include: a data configuration module, a mining rule generation module, a mining path generation module and a data mining module, wherein:

[0081] The data configuration module is used to obtain the specified data and associated data customized by the user on the visual configuration interface;

[0082] A mining rule generating module, used for generating data mining rules based on the specified data and the associated data;

[0083] A mining path generation module, used for parsing the data mining rules to obtain a mining path when receiving the target business that the user needs to mine;

[0084] The data mining module is used to perform data mining on the target business based on the mining path to obtain target mining data.

[0085] Optionally, a data mining system also includes a template configuration module, which is used to obtain a main area selected by a user on a visual configuration interface, wherein the main area includes multiple independent areas; obtain header information of each of the independent areas and perform attribute binding to obtain binding information of each of the independent areas; generate an initial template based on the binding information of each of the independent areas, and finalize the initial template to obtain a target template.

[0086] Optionally, the template configuration module is also used to obtain header information set by the user for each of the independent areas; for the current independent area corresponding to any one of the header information: obtain the object attribute set by the user for the current independent area, and obtain multiple associated attributes associated with the object attribute based on the object attribute; obtain the target associated attribute selected by the user from the multiple associated attributes; perform attribute binding of the target associated attribute with the current independent area to obtain binding information corresponding to the current independent area; obtain a binding completion instruction to obtain binding information for each of the independent areas.

[0087] Optionally, the data configuration module is also used to obtain the object fields and object-related fields of each independent area of ​​the user on the target template; use the data corresponding to the object fields as designated data, and use the data corresponding to the object-related fields as associated data.

[0088] Optionally, the mining path generation module is also used to obtain the data mining rules selected by the user when receiving the target business that the user needs to mine, and parse the data mining rules to obtain a mining plan, wherein the mining plan includes data attributes and associated data attributes that need to be mined; and convert the mining plan into a mining path recognized by the code.

[0089] Optionally, the data mining module is also used to extract business data from the target business, and perform data preprocessing on each business data information to obtain standard business data; identify characteristic attributes in the standard business data; and mine each characteristic attribute in the target business based on the mining path to obtain target mining data corresponding to the characteristic attribute, wherein the target mining data includes target data corresponding to the object attributes and target associated data corresponding to the associated object attributes.

[0090] Optionally, the data mining module is also used to mine each of the characteristic attributes in the target business based on the mining path to obtain a mining result; determine whether there is data in the mining result; if there is no data in the mining result, use the empty data as the target mining data; if there is data in the mining result, convert the data in the mining result into a preset visualization format to obtain the target mining data.

[0091] It should be noted that: when the system provided in the above embodiment realizes its functions, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0092] An embodiment of the present application also provides a computer storage medium, which can store multiple instructions. The instructions are suitable for being loaded by a processor and executing a data mining method of the above embodiment. The specific execution process can be found in the specific description of the above embodiment, which will not be repeated here.

[0093] Please refer to Figure 3 The application also discloses an electronic device. Figure 3 The electronic device 300 may include: at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .

[0094] The communication bus 302 is used to realize the connection and communication between these components.

[0095] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0096] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0097] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0098] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally also be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 , the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program of a data mining method.

[0099] exist Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call an application program storing a data mining method in the memory 305, and when executed by one or more processors 301, the electronic device 300 executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0100] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0101] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. 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. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0102] 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.

[0103] 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.

[0104] 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 memory. Based on this understanding, the technical solution of the present application, 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, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0105] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0106] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A data mining method, characterized in that: The method comprises: Acquire a main area selected by a user on a visual configuration interface, wherein the main area includes a plurality of independent areas; Obtaining header information set by the user for each of the independent areas; For any current independent area corresponding to the header information: obtaining an object attribute set by a user for the current independent area, and obtaining a plurality of associated attributes associated with the object attribute based on the object attribute; Acquire a target associated attribute selected by the user from among the multiple associated attributes; Binding the target associated attribute with the current independent area to obtain binding information corresponding to the current independent area; Obtaining a binding completion instruction to obtain binding information of each of the independent areas; Generate an initial template based on the binding information of each of the independent areas, and finalize the initial template to obtain a target template; obtain the specified data and associated data customized by the user on the visual configuration interface; Generate data mining rules based on the specified data and the associated data; When receiving the target business that the user needs to mine, parsing the data mining rules to obtain a mining path; Data mining is performed on the target business based on the mining path to obtain target mining data.

2. The data mining method according to claim 1, characterized in that: The obtaining of the specified data and associated data customized by the user on the visual configuration interface includes: Obtaining the object fields and object-related fields of each of the independent areas of the user on the target template; The data corresponding to the object field is used as designated data, and the data corresponding to the object association field is used as associated data.

3. The data mining method according to claim 1, characterized in that: When receiving the target business that the user needs to mine, parsing the data mining rules to obtain a mining path includes: When receiving the target business that the user needs to mine, obtaining the data mining rules selected by the user, parsing the data mining rules, and obtaining a mining plan, the mining plan includes data attributes that need to be mined and associated data attributes; The mining scheme is converted into a mining path identified by the code.

4. The data mining method according to claim 1, characterized in that: The performing data mining on the target business based on the mining path to obtain target mining data includes: Extracting the business data in the target business, and performing data preprocessing on each business data information to obtain standard business data; Identifying characteristic attributes in the standard business data; Mining each of the characteristic attributes in the target business based on the mining path to obtain target mining data corresponding to each of the characteristic attributes, wherein the target mining data includes target data corresponding to the object attribute and target associated data corresponding to the associated object attribute.

5. The data mining method according to claim 4, characterized in that: The mining of each of the characteristic attributes in the target business based on the mining path to obtain target mining data corresponding to the characteristic attributes includes: Mining each of the characteristic attributes in the target business based on the mining path to obtain a mining result; Determining whether there is data in the mining result; If there is no data in the mining result, the empty data is used as the target mining data; If there is data in the mining result, the data in the mining result is converted into a preset visualization format to obtain the target mining data.

6. A data mining system, characterized in that: The system comprises: A data configuration module is used to obtain a main area selected by a user on a visual configuration interface, wherein the main area includes multiple independent areas; obtain header information set by a user for each of the independent areas; for a current independent area corresponding to any of the header information: obtain an object attribute set by a user for the current independent area, and based on the object attribute, obtain multiple associated attributes associated with the object attribute; obtain a target associated attribute selected by the user from the multiple associated attributes; attribute-bind the target associated attribute with the current independent area to obtain binding information corresponding to the current independent area; obtain a binding completion instruction to obtain binding information for each of the independent areas; generate an initial template based on the binding information for each of the independent areas, and finalize the initial template to obtain a target template; obtain specified data and associated data customized by the user on the visual configuration interface; A mining rule generating module, used for generating data mining rules based on the specified data and the associated data; A mining path generation module, used for parsing the data mining rules to obtain a mining path when receiving the target business that the user needs to mine; The data mining module is used to perform data mining on the target business based on the mining path to obtain target mining data.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the method according to any one of claims 1 to 5.

8. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method as described in any one of claims 1-5.

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