Heterogeneous platform data mining and calling and data analysis processing method and device, equipment and medium

Abstract

The invention provides a heterogeneous platform data mining calling and data analysis processing method and device, equipment and a medium, and the method comprises the steps: generating a data mining strategy and a data analysis strategy based on multi-modal live features; executing the data mining strategy, and calling mined target data; based on the data analysis strategy, performing data analysis on the target data; and carrying out adaptive processing on the target data based on the data analysis result. According to the heterogeneous platform data mining calling and data analysis processing method, a data mining strategy and a data analysis strategy are generated on the basis of multi-modal live features, the data mining strategy is executed, target data is mined, data analysis is conducted on the target data on the basis of the data analysis strategy, and data mining calling and data analysis processing are conducted on the basis of a data analysis result. According to the method, the target data is adaptively processed, the method is suitable for the situation that the data source, the data type, the data use purpose and the like of the heterogeneous platform are frequently changed, the mining and analysis accuracy is improved, and the service capacity of the heterogeneous platform is improved.

Description

Technical Field

[0001] The present invention relates to the field of computer data processing technology, and in particular to methods, devices, equipment and media for data mining, retrieval and data analysis on heterogeneous platforms. Background Art

[0002] Heterogeneous platforms have become an important tool for handling large-scale data mining and analysis tasks. However, traditional heterogeneous platforms often use fixed mining and analysis strategies when conducting data mining and analysis. For example, for the same type of data mining scenarios, the same data mining method is used for data mining. However, the data sources, data types, and data usage purposes of heterogeneous platforms often change. If fixed mining and analysis strategies are still used, the accuracy of mining and analysis may be greatly reduced, and the business capabilities of heterogeneous platforms may be further reduced.

[0003] Therefore, a solution is urgently needed. Summary of the invention

[0004] One of the purposes of the present invention is to provide a data mining, retrieval and data analysis processing method for a heterogeneous platform, generate data mining strategies and data analysis strategies based on multimodal real-time features, execute data mining strategies, mine target data, perform data analysis on the target data based on the data analysis strategy, and adaptively process the target data based on the data analysis results. The method is suitable for situations where the data source, data type and data usage purpose of the heterogeneous platform often change, thereby improving the accuracy of mining and analysis and enhancing the business capabilities of the heterogeneous platform.

[0005] The heterogeneous platform data mining, retrieval and data analysis processing method provided in the embodiment of the present invention is applied to the heterogeneous platform, including:

[0006] Generate data mining and data analysis strategies based on multimodal real-time features;

[0007] Execute data mining strategies and retrieve the mined target data;

[0008] Based on the data analysis strategy, perform data analysis on the target data;

[0009] Based on the data analysis results, the target data is adaptively processed.

[0010] Preferably, the generating of data mining strategy and data analysis strategy based on multimodal real-time features includes:

[0011] A feature type set for parsing multimodal ground truth features;

[0012] Matching the feature type set with a plurality of standard feature type sets respectively to obtain a plurality of first matching degrees;

[0013] Acquire a plurality of levels of standard feature sets associated with the standard feature type set having the maximum first matching degree;

[0014] Traverse each standard feature set in order from small to large levels;

[0015] Each time the multimodal actual situation feature is traversed, the multimodal actual situation feature is matched with the multiple standard features in the traversed standard feature set respectively. When the matched standard feature meets the feature joint trigger condition, the matched standard feature is used as the target feature, and the description feature set is constructed based on the target feature obtained in the current traversal and the standard feature set traversed in the past; wherein the matching means that the second matching degree between the standard feature and the sub-feature of the multimodal actual situation feature is 100%;

[0016] Determine the strategy generation knowledge corresponding to the description feature set from the strategy generation knowledge base;

[0017] Based on the strategy-generated knowledge, determine the data mining strategy and data analysis strategy;

[0018] The feature joint triggering conditions include one or more of the following combinations:

[0019] The sum of the triggering degrees of the matching standard features is greater than or equal to the triggering degree and the threshold;

[0020] The number of features that match the standard features is greater than or equal to the number threshold corresponding to the level of the traversed standard feature set;

[0021] There is at least one feature association relationship between the matching standard features.

[0022] Preferably, after the target data is adaptively processed based on the data analysis result, the method further includes:

[0023] Obtaining a processing record of adaptively processing different target data within a recent first time period;

[0024] Analyze and process the recorded visualization requirements;

[0025] When the visualization requirement value is greater than or equal to the requirement threshold, a visualization model is generated based on the visualization model template and the processing records;

[0026] Obtaining platform operation information performed by users who log in to the heterogeneous platform within the most recent second time period;

[0027] Based on the platform operation information, the visualization model is configured in a targeted manner;

[0028] The configured visualization model is displayed to the user.

[0029] Preferably, the visualization requirement value of the analysis and processing record includes:

[0030] Determine whether there is at least one sub-record in the processing record whose object weight of the graph object corresponding to the demand graph is greater than or equal to a weight threshold;

[0031] When the answer is yes, the visualization requirement value of the processing record is calculated as the target value; otherwise, based on the quantitative conversion table, the sum of the object weights of the graph objects corresponding to each record of the processing record in the demand graph is quantitatively converted to obtain the visualization requirement value of the processing record;

[0032] Among them, the target value is greater than or equal to the demand threshold.

[0033] Preferably, the targeted configuration of the visualization model based on the platform operation information includes:

[0034] A set of information types for parsing platform operation information;

[0035] Determine the regional search rule corresponding to the information type set from the regional search rule library;

[0036] Based on the region search rule, determine the feature extraction region from the visualization model;

[0037] Based on the first characterization processing template, the feature extraction region is subjected to characterization processing to obtain a regional feature set;

[0038] Determine a second characterization processing template corresponding to the regional feature set from the characterization processing template library;

[0039] Based on the second characterization processing template, the platform operation information is characterized to obtain an information feature set;

[0040] Obtain multiple sets of one-to-one corresponding standard feature combination pairs and configuration strategies;

[0041] Determine a target combination pair from the standard feature combination pairs; the regional feature set and the information feature set each contain a standard feature of the standard feature combination pair;

[0042] Based on the configuration strategy corresponding to the target combination, the visualization model is configured in a targeted manner.

[0043] The heterogeneous platform data mining, retrieval and data analysis processing device provided in the embodiment of the present invention is applied to the heterogeneous platform, and includes:

[0044] A generation module, used to generate data mining strategies and data analysis strategies based on multimodal real-time features;

[0045] The retrieval module is used to execute the data mining strategy and retrieve the mined target data;

[0046] An analysis module, used to perform data analysis on target data based on a data analysis strategy;

[0047] The processing module is used to perform adaptive processing on the target data based on the data analysis results.

[0048] Preferably, the generation module generates a data mining strategy and a data analysis strategy based on multimodal real-time features, including:

[0049] A feature type set for parsing multimodal ground truth features;

[0050] Matching the feature type set with a plurality of standard feature type sets respectively to obtain a plurality of first matching degrees;

[0051] Acquire a plurality of levels of standard feature sets associated with the standard feature type set having the maximum first matching degree;

[0052] Traverse each standard feature set in order from small to large levels;

[0053] Each time the multimodal actual situation feature is traversed, the multimodal actual situation feature is matched with the multiple standard features in the traversed standard feature set respectively. When the matched standard feature meets the feature joint trigger condition, the matched standard feature is used as the target feature, and the description feature set is constructed based on the target feature obtained in the current traversal and the standard feature set traversed in the past; wherein the matching means that the second matching degree between the standard feature and the sub-feature of the multimodal actual situation feature is 100%;

[0054] Determine the strategy generation knowledge corresponding to the description feature set from the strategy generation knowledge base;

[0055] Based on the strategy-generated knowledge, determine the data mining strategy and data analysis strategy;

[0056] The feature joint triggering conditions include one or more of the following combinations:

[0057] The sum of the triggering degrees of the matching standard features is greater than or equal to the triggering degree and the threshold;

[0058] The number of features that match the standard features is greater than or equal to the number threshold corresponding to the level of the traversed standard feature set;

[0059] There is at least one feature association relationship between the matching standard features.

[0060] Preferably, after the processing module performs adaptive processing on the target data based on the data analysis result, it further comprises:

[0061] Visualization modules for:

[0062] Obtaining a processing record of adaptively processing different target data within a recent first time period;

[0063] Analyze and process the recorded visualization requirements;

[0064] When the visualization requirement value is greater than or equal to the requirement threshold, a visualization model is generated based on the visualization model template and the processing records;

[0065] Obtaining platform operation information performed by users who log in to the heterogeneous platform within the most recent second time period;

[0066] Based on the platform operation information, the visualization model is configured in a targeted manner;

[0067] The configured visualization model is displayed to the user.

[0068] An electronic device provided by an embodiment of the present invention includes a memory, a processor, and a computer program stored and executable on the memory. When the processor executes the program, the steps of the heterogeneous platform data mining retrieval and data analysis processing method as described in any one of the above items are implemented.

[0069] A computer-readable storage medium provided by an embodiment of the present invention stores a computer program, which, when executed by a processor, implements the steps of any of the above-mentioned heterogeneous platform data mining, retrieval and data analysis processing methods.

[0070] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0071] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0073] Figure 1 A schematic diagram of a method for data mining, retrieval and data analysis and processing on a heterogeneous platform in an embodiment of the present invention;

[0074] Figure 2 Schematic diagram of a heterogeneous platform data mining, retrieval and data analysis processing device in an embodiment of the present invention. DETAILED DESCRIPTION

[0075] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0076] The embodiment of the present invention provides a heterogeneous platform data mining and retrieval and data analysis processing method, which is applied to heterogeneous platforms, such as Figure 1 As shown, including:

[0077] S1. Generate data mining strategies and data analysis strategies based on multimodal real-time features;

[0078] S2, execute data mining strategy and retrieve the mined target data;

[0079] S3. Perform data analysis on target data based on data analysis strategy;

[0080] S4. Based on the data analysis results, adaptively process the target data.

[0081] In the above technical scheme, the multimodal real-time features include: the scene type to be data mined, the purpose of using the mined data, etc.; based on the multimodal real-time features, a data mining strategy and a data analysis strategy are generated, the data mining strategy is a strategy for instructing the system how to perform data mining, and the data analysis strategy is a strategy for instructing the system how to perform data analysis on the mined target data; the data mining strategy is executed to mine the target data; based on the data analysis strategy, the target data is analyzed; finally, based on the data analysis results, the target data is adaptively processed, for example: the data analysis results indicate that the data quality of the target data is low, and when the adaptive processing is performed on it, it is invalidated.

[0082] This application generates data mining strategies and data analysis strategies based on multimodal real-time features, executes data mining strategies, mines target data, performs data analysis on the target data based on the data analysis strategies, and adaptively processes the target data based on the data analysis results. It is suitable for situations where the data sources, data types, and data usage purposes of heterogeneous platforms often change, which improves the accuracy of mining and analysis and enhances the business capabilities of heterogeneous platforms.

[0083] In one embodiment, the generating of data mining strategy and data analysis strategy based on multimodal real-time features includes:

[0084] A feature type set for parsing multimodal ground truth features;

[0085] Matching the feature type set with a plurality of standard feature type sets respectively to obtain a plurality of first matching degrees;

[0086] Acquire a plurality of levels of standard feature sets associated with the standard feature type set having the maximum first matching degree;

[0087] Traverse each standard feature set in order from small to large levels;

[0088] Each time the multimodal actual situation feature is traversed, the multimodal actual situation feature is matched with the multiple standard features in the traversed standard feature set respectively. When the matched standard feature meets the feature joint trigger condition, the matched standard feature is used as the target feature, and the description feature set is constructed based on the target feature obtained in the current traversal and the standard feature set traversed in the past; wherein the matching means that the second matching degree between the standard feature and the sub-feature of the multimodal actual situation feature is 100%;

[0089] Determine the strategy generation knowledge corresponding to the description feature set from the strategy generation knowledge base;

[0090] Based on the strategy-generated knowledge, determine the data mining strategy and data analysis strategy;

[0091] The feature joint triggering conditions include one or more of the following combinations:

[0092] The sum of the triggering degrees of the matching standard features is greater than or equal to the triggering degree and the threshold;

[0093] The number of features that match the standard features is greater than or equal to the number threshold corresponding to the level of the traversed standard feature set;

[0094] There is at least one feature association relationship between the matching standard features.

[0095] There are multiple feature types of multimodal real-life features in the feature type set; a standard feature type set is preset, and the standard feature type set is associated with standard feature sets of different levels. The greater the first matching degree between the feature type set and the standard feature type set, the more suitable the multimodal real-life features are for screening out the descriptive feature set for determining the strategy generation knowledge using the standard feature sets of different levels associated with the corresponding standard feature type set; therefore, multiple levels of standard feature sets associated with the standard feature type set with the maximum first matching degree are obtained; the larger the level of the standard feature set, the more priority it needs to be traversed. Each time it is traversed, the multimodal real-life features are matched with the multiple standard features in the traversed standard feature set respectively. When the matched standard features meet the feature joint triggering conditions, it means that the strategy generation knowledge can be determined at present, and the matching symbols are The combined standard features are used as target features, and a descriptive feature set is constructed based on the target features obtained during the current traversal and the historical traversal of the standard feature set; the strategy generation knowledge base contains strategy generation knowledge corresponding to different descriptive feature sets, and the data mining strategy and data analysis strategy are determined based on the strategy generation knowledge; specifically, for example: the feature types in the standard feature type set are data source and data usage purpose, and the standard features in the associated standard feature sets of different levels are data source as shopping platform and data usage purpose as analysis of user shopping habits. When the standard feature meets the feature joint triggering conditions, it is used as the target feature, and the corresponding strategy generation knowledge after forming the descriptive feature set is that the data mining strategy is to crawl the frequency of users purchasing the same type of goods on the shopping platform, and the data analysis strategy is to analyze the average value, mode, etc. of the frequency.

[0096] In the feature joint triggering condition, the trigger degree represents the standard feature that can be represented after matching with the standard feature and can be used to determine the basis for deriving strategy generation knowledge, that is, the degree to which the feature set is described. It can be pre-selected by technical personnel and set according to actual needs; the level of the standard feature set corresponds to a preset quantity threshold. When the number of features of the matched standard features is greater than or equal to the quantity threshold corresponding to the level of the traversed standard feature set, it represents that the standard features can be used to determine the basis for deriving strategy generation knowledge, that is, the feature set is described; the feature association relationship can be elements that can belong to the same event, etc.

[0097] The embodiment of the present invention introduces a standard feature type set and standard feature sets of different levels associated with the standard feature type set when generating data mining strategies and data analysis strategies based on multimodal real-time features, so as to facilitate the rapid determination of the basis for deriving strategy generation knowledge, namely, the descriptive feature set, and greatly improves the accuracy and efficiency of the generation of data mining strategies and data analysis strategies.

[0098] In one embodiment, after the target data is adaptively processed based on the data analysis result, the method further includes:

[0099] Obtaining a processing record of adaptive processing of different target data within a recent first time period; the recent first time period may be the latest 0.5 days;

[0100] Analyze the visualization requirement value of the processing record; the visualization requirement value represents the degree of the requirement for visualization of the processing record;

[0101] When the visualization requirement value is greater than or equal to the requirement threshold, a visualization model is generated based on the visualization model template according to the processing record; the requirement threshold may be 8; when the visualization requirement value is greater than or equal to the requirement threshold, a visualization model is generated based on the visualization model template according to the processing record; the visualization model template is a template for generating a visualization model from the processing record, which may be set in advance by a technician;

[0102] Obtaining platform operation information performed by a user who logs in to the heterogeneous platform within a recent second time period; the recent second time period may be 1000 seconds; the platform operation information includes the type of operation, operation time, and operation object performed by the user on the heterogeneous platform;

[0103] Based on the platform operation information, the visualization model is configured in a targeted manner; the heterogeneous platform is configured in a targeted manner to make the visualization model more suitable for display to users;

[0104] The configured visualization model is displayed to the user.

[0105] In one embodiment, the visualization requirement value of the parsing process record includes:

[0106] Determine whether there is at least one sub-record in the processing record whose object weight of the corresponding graph object in the demand graph is greater than or equal to the weight threshold; there are graph objects corresponding to different sub-records in the demand graph, and the object weight of the graph object represents the degree to which the processing record needs to be visualized. The technician can set the demand graph in advance according to actual needs; the weight threshold can be 8;

[0107] When it is yes, the visualization requirement value of the processing record is counted as the target value; otherwise, based on the quantitative conversion table, the sum of the object weights of the map objects corresponding to the respective records of the processing records in the demand map is quantitatively converted to obtain the visualization requirement value of the processing record; when the object weight of the map object corresponding to a word record in the demand map is greater than or equal to the weight threshold, it is sufficient to represent that the processing record needs to be visualized, and the visualization requirement value of the processing record is counted as the target value; there are visualization requirement values ​​corresponding to the sums of different object weights in the quantitative conversion table, and based on the quantitative conversion table, the sum of the object weights of the map objects corresponding to the respective records of the processing records in the demand map is quantitatively converted to obtain the visualization requirement value of the processing record;

[0108] Among them, the target value is greater than or equal to the demand threshold.

[0109] In one embodiment, the step of configuring the visualization model in a targeted manner based on the platform operation information includes:

[0110] An information type set for parsing platform operation information; the information type set includes multiple information types of platform operation information;

[0111] Determine the regional search rules corresponding to the information type set from the regional search rule library; the regional search rule library contains regional search rules corresponding to different information type sets, the information type set represents a situation where a feature extraction regional search is required, and the regional search rule is a rule that instructs the system to search and determine the feature extraction region from the visualization model in this situation, for example: the information type in the information type set is that the user selects to view the historical network user shopping habit data, and the corresponding regional search rule is to search out the data display area related to the shopping habit in the visualization model;

[0112] Based on the region search rule, determine the feature extraction region from the visualization model;

[0113] Based on the first characterization processing template, the feature extraction region is subjected to characterization processing to obtain a regional feature set; the regional feature set includes a plurality of regional features of the feature extraction region;

[0114] Determine a second characterization processing template corresponding to the regional feature set from the characterization processing template library; the characterization processing template library contains second characterization processing templates corresponding to different regional feature sets, and the regional features in the regional feature set reflect the features required for targeted configuration of the visualization model, thereby characterizing the platform operation information to extract corresponding information features to form an information feature set, for example: the regional features in the regional feature set are shopping habit data of elderly network users, and the corresponding characterization processing template is to extract features that may reflect the user's interest in the shopping habit data of elderly network users;

[0115] Based on the second characterization processing template, the platform operation information is characterized to obtain an information feature set;

[0116] Obtain multiple sets of one-to-one corresponding standard feature combination pairs and configuration strategies;

[0117] Determine a target combination pair from the standard feature combination pairs; the regional feature set and the information feature set each contain one standard feature of the standard feature combination pair; preset the standard feature combination pair, when; the regional feature set and the information feature set each contain one standard feature of the standard feature combination pair, it indicates that the corresponding configuration strategy can be used to perform targeted configuration on the visualization model;

[0118] Based on the configuration strategy corresponding to the target combination, the visualization model is configured in a targeted manner.

[0119] This application configures the visualization model in a targeted manner based on the platform operation information, so that users can quickly, intuitively and specifically understand the data mining and data analysis process of the platform when using heterogeneous platforms, thereby improving the applicability of the system and making it more intelligent and user-friendly.

[0120] The heterogeneous platform data mining, retrieval and data analysis processing device provided by the embodiment of the present invention is applied to heterogeneous platforms, such as Figure 2 As shown, including:

[0121] A generation module 1 is used to generate data mining strategies and data analysis strategies based on multimodal real-time features;

[0122] Retrieval module 2, used to execute data mining strategy and retrieve the mined target data;

[0123] Analysis module 3, used for performing data analysis on target data based on data analysis strategy;

[0124] The processing module 4 is used to perform adaptive processing on the target data based on the data analysis results.

[0125] The generation module generates data mining strategies and data analysis strategies based on multimodal real-time features, including:

[0126] A feature type set for parsing multimodal ground truth features;

[0127] Matching the feature type set with a plurality of standard feature type sets respectively to obtain a plurality of first matching degrees;

[0128] Acquire a plurality of levels of standard feature sets associated with the standard feature type set having the maximum first matching degree;

[0129] Traverse each standard feature set in order from small to large levels;

[0130] Each time the multimodal actual situation feature is traversed, the multimodal actual situation feature is matched with the multiple standard features in the traversed standard feature set respectively. When the matched standard feature meets the feature joint trigger condition, the matched standard feature is used as the target feature, and the description feature set is constructed based on the target feature obtained in the current traversal and the standard feature set traversed in the past; wherein the matching means that the second matching degree between the standard feature and the sub-feature of the multimodal actual situation feature is 100%;

[0131] Determine the strategy generation knowledge corresponding to the description feature set from the strategy generation knowledge base;

[0132] Based on the strategy-generated knowledge, determine the data mining strategy and data analysis strategy;

[0133] The feature joint triggering conditions include one or more of the following combinations:

[0134] The sum of the triggering degrees of the matching standard features is greater than or equal to the triggering degree and the threshold;

[0135] The number of features that match the standard features is greater than or equal to the number threshold corresponding to the level of the traversed standard feature set;

[0136] There is at least one feature association relationship between the matching standard features.

[0137] After the processing module performs adaptive processing on the target data based on the data analysis result, it also includes:

[0138] Visualization modules for:

[0139] Obtaining a processing record of adaptively processing different target data within a recent first time period;

[0140] Analyze and process the recorded visualization requirements;

[0141] When the visualization requirement value is greater than or equal to the requirement threshold, a visualization model is generated based on the visualization model template and the processing records;

[0142] Obtaining platform operation information performed by users who log in to the heterogeneous platform within the most recent second time period;

[0143] Based on the platform operation information, the visualization model is configured in a targeted manner;

[0144] The configured visualization model is displayed to the user.

[0145] An electronic device provided by an embodiment of the present invention includes a memory, a processor, and a computer program stored and executable on the memory. When the processor executes the program, the steps of the heterogeneous platform data mining retrieval and data analysis processing method as described in any one of the above items are implemented.

[0146] A computer-readable storage medium provided by an embodiment of the present invention stores a computer program, which, when executed by a processor, implements the steps of any of the above-mentioned heterogeneous platform data mining, retrieval and data analysis processing methods.

[0147] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for data mining, retrieval and data analysis and processing on a heterogeneous platform, applied to a heterogeneous platform, characterized in that: include: Generate data mining and data analysis strategies based on multimodal real-time features; Execute data mining strategies and retrieve the mined target data; Based on the data analysis strategy, perform data analysis on the target data; Based on the data analysis results, the target data is adaptively processed.

2. The heterogeneous platform data mining, retrieval and data analysis processing method according to claim 1, characterized in that: The method of generating data mining strategies and data analysis strategies based on multimodal real-time features includes: A set of feature types for parsing multimodal ground truth features; Matching the feature type set with a plurality of standard feature type sets respectively to obtain a plurality of first matching degrees; Acquire a plurality of levels of standard feature sets associated with the standard feature type set having the maximum first matching degree; Traverse each standard feature set in order from small to large levels; Each time the multimodal actual situation feature is traversed, the multimodal actual situation feature is matched with the multiple standard features in the traversed standard feature set respectively. When the matched standard feature meets the feature joint trigger condition, the matched standard feature is used as the target feature, and the description feature set is constructed based on the target feature obtained in the current traversal and the standard feature set traversed in the past; wherein the matching means that the second matching degree between the standard feature and the sub-feature of the multimodal actual situation feature is 100%; Determine the strategy generation knowledge corresponding to the description feature set from the strategy generation knowledge base; Based on the strategy-generated knowledge, determine the data mining strategy and data analysis strategy; The feature joint triggering conditions include one or more of the following combinations: The sum of the triggering degrees of the matching standard features is greater than or equal to the triggering degree and the threshold; The number of features that match the standard features is greater than or equal to the number threshold corresponding to the level of the traversed standard feature set; There is at least one feature association relationship between the matching standard features.

3. The heterogeneous platform data mining, retrieval and data analysis processing method according to claim 1, characterized in that: After the target data is adaptively processed based on the data analysis result, the method further includes: Obtaining a processing record of adaptively processing different target data within a recent first time period; Analyze and process the recorded visualization requirements; When the visualization requirement value is greater than or equal to the requirement threshold, a visualization model is generated based on the visualization model template and the processing records; Obtaining platform operation information performed by users who log in to the heterogeneous platform within the most recent second time period; Based on the platform operation information, the visualization model is configured in a targeted manner; The configured visualization model is displayed to the user.

4. The heterogeneous platform data mining, retrieval and data analysis processing method according to claim 3, characterized in that: The visualization requirement value of the analysis and processing record includes: Determine whether there is at least one sub-record in the processing record whose object weight of the graph object corresponding to the demand graph is greater than or equal to a weight threshold; When the answer is yes, the visualization requirement value of the processing record is calculated as the target value; otherwise, based on the quantitative conversion table, the sum of the object weights of the graph objects corresponding to each record of the processing record in the demand graph is quantitatively converted to obtain the visualization requirement value of the processing record; Among them, the target value is greater than or equal to the demand threshold.

5. The heterogeneous platform data mining and data analysis processing method according to claim 3, characterized in that: The targeted configuration of the visualization model based on the platform operation information includes: A set of information types for parsing platform operation information; Determine the regional search rule corresponding to the information type set from the regional search rule library; Based on the region search rule, determine the feature extraction region from the visualization model; Based on the first characterization processing template, the feature extraction region is subjected to characterization processing to obtain a regional feature set; Determine a second characterization processing template corresponding to the regional feature set from the characterization processing template library; Based on the second characterization processing template, the platform operation information is characterized to obtain an information feature set; Obtain multiple sets of one-to-one corresponding standard feature combination pairs and configuration strategies; Determine a target combination pair from the standard feature combination pairs; the regional feature set and the information feature set each contain a standard feature of the standard feature combination pair; Based on the configuration strategy corresponding to the target combination, the visualization model is configured in a targeted manner.

6. A heterogeneous platform data mining and data analysis processing device, applied to a heterogeneous platform, characterized in that: include: A generation module, used to generate data mining strategies and data analysis strategies based on multimodal real-time features; The retrieval module is used to execute the data mining strategy and retrieve the mined target data; An analysis module, used to perform data analysis on target data based on a data analysis strategy; The processing module is used to perform adaptive processing on the target data based on the data analysis results.

7. The heterogeneous platform data mining, retrieval and data analysis processing device according to claim 6, characterized in that: The generation module generates data mining strategies and data analysis strategies based on multimodal real-time features, including: A set of feature types for parsing multimodal ground truth features; Matching the feature type set with a plurality of standard feature type sets respectively to obtain a plurality of first matching degrees; Acquire a plurality of levels of standard feature sets associated with the standard feature type set having the maximum first matching degree; Traverse each standard feature set in order from small to large levels; Each time the multimodal actual situation feature is traversed, the multimodal actual situation feature is matched with the multiple standard features in the traversed standard feature set respectively. When the matched standard feature meets the feature joint trigger condition, the matched standard feature is used as the target feature, and the description feature set is constructed based on the target feature obtained in the current traversal and the standard feature set traversed in the past; wherein the matching means that the second matching degree between the standard feature and the sub-feature of the multimodal actual situation feature is 100%; Determine the strategy generation knowledge corresponding to the description feature set from the strategy generation knowledge base; Based on the strategy-generated knowledge, determine the data mining strategy and data analysis strategy; The feature joint triggering conditions include one or more of the following combinations: The sum of the triggering degrees of the matching standard features is greater than or equal to the triggering degree and the threshold; The number of features that match the standard features is greater than or equal to the number threshold corresponding to the level of the traversed standard feature set; There is at least one feature association relationship between the matching standard features.

8. The heterogeneous platform data mining, retrieval and data analysis processing device according to claim 6, characterized in that: After the processing module performs adaptive processing on the target data based on the data analysis result, it also includes: Visualization modules for: Obtaining a processing record of adaptively processing different target data within a recent first time period; Analyze and process the recorded visualization requirements; When the visualization requirement value is greater than or equal to the requirement threshold, a visualization model is generated based on the visualization model template and the processing records; Obtaining platform operation information performed by users who log in to the heterogeneous platform within the most recent second time period; Based on the platform operation information, the visualization model is configured in a targeted manner; The configured visualization model is displayed to the user.

9. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable, characterized in that: When the processor executes the program, the steps of the heterogeneous platform data mining retrieval and data analysis processing method as described in any one of claims 1-5 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the program is executed by a processor, the steps of the heterogeneous platform data mining, retrieval and data analysis processing method as described in any one of claims 1 to 5 are implemented.

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