A multi-source data-oriented big data application development platform

Abstract

The application provides a big data application development platform for multi-source data, comprising: a data receiving module, configured to receive multi-source data uploaded by a client based on a target receiving end; a learning module, configured to input the multi-source data into a big data network for learning to obtain a target decision; a data analysis module, configured to analyze the multi-source data uploaded by the client based on the target decision to generate an analysis report, and meanwhile, to perform qualified verification on the analysis report; and a storage module, configured to visually store the multi-source data uploaded by the client and the analysis report when the analysis report is qualified. The application facilitates accurate and reliable management of multi-source data, mines data with potential value, thereby improving the utilization rate of multi-source data, and meanwhile, improves the efficiency and accuracy of multi-source data processing through analysis and processing of multi-source data by the big data application development platform, and guarantees the management effect of multi-source data.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a big data application development platform for multi-source data. Background Technology

[0002] Currently, big data application development platforms are platforms designed to analyze massive amounts of data using big data technology. Through these platforms, massive amounts of data can be processed efficiently, valuable data can be extracted, and multi-source data can be managed in a reasonable manner.

[0003] However, in existing technologies, data development platforms can often only process one type of data at a time, resulting in single-type data processing and low utilization, which in turn reduces data processing efficiency and is not conducive to the management of different types of data.

[0004] Therefore, in order to overcome the above problems, the present invention provides a big data application development platform for multi-source data. Summary of the Invention

[0005] This invention provides a big data application development platform for multi-source data, which analyzes and integrates multi-source data through big data, facilitating accurate and reliable management of multi-source data, and mining data with potential value, thereby improving the utilization rate of multi-source data. At the same time, by analyzing and processing multi-source data through the big data application development platform, the efficiency and accuracy of multi-source data processing are improved, ensuring the management effect of multi-source data.

[0006] This invention provides a big data application development platform for multi-source data, comprising:

[0007] The data receiving module is used to receive multi-source data uploaded by the client based on the target receiving end;

[0008] The learning module is used to input multi-source data into a big data network for learning and to obtain target decisions.

[0009] The data analysis module is used to analyze multi-source data uploaded by the client based on target decisions, generate analysis reports, and verify the quality of the analysis reports.

[0010] The storage module is used to visualize and store the multi-source data and analysis report uploaded by the client when the analysis report is qualified.

[0011] Preferably, a big data application development platform for multi-source data includes a data receiving module, comprising:

[0012] The request generation unit is used to generate a data upload request based on the client. The data upload request includes the client address and the data type of the multi-source data to be uploaded.

[0013] The verification unit is used to perform authorization verification at the target receiving end based on the client-side address and the data type of the multi-source data to be uploaded.

[0014] The data upload unit is used to receive multi-source data uploaded by the client based on the target receiving end when the verification is successful.

[0015] Preferably, a big data application development platform for multi-source data includes a verification unit comprising:

[0016] The request processing subunit is used to obtain access permissions to the information management database in the target receiving end, determine the request input standard based on the access permissions, transform the data upload request based on the request input standard to obtain the target data upload request, and determine the request type of the target data upload request.

[0017] The matching subunit is used to input the target data upload request into the information management database based on the request type for the first matching, and to obtain the set of authorized client addresses and the set of authorized data types that match the request type;

[0018] The verification subunit is used to perform a second match between the client address in the target data upload request and the authorized client address set, and a third match between the data type of the multi-source data to be uploaded in the target data upload request and the authorized data type set. Based on the second and third matches, it determines whether the target data upload request passes the authorization verification.

[0019] Preferably, a big data application development platform for multi-source data includes a verification subunit, comprising:

[0020] Obtain the matching result of the second match. If there is an authorized client address in the authorized client address set that matches the client address, and obtain the matching result of the third match, if there is an authorized data type in the authorized data type set that matches the data type of the multi-source data to be uploaded, then the authorization verification is deemed to have passed; otherwise, the authorization verification is deemed to have failed.

[0021] Preferably, a big data application development platform for multi-source data includes a learning module comprising:

[0022] The classification unit is used to classify multi-source data according to data dimensions, generate multiple sub-data segments, determine the segment identifier corresponding to each sub-data segment, and retrieve the segment identifier corresponding to each sub-data segment with each network node in the big data network to determine the target network node corresponding to each sub-data segment.

[0023] The evaluation unit is used to read the policy information data stored in the target network node, determine the policy features corresponding to the policy information data, obtain the data structure of the sub-data segment corresponding to the policy information data, and perform a fourth match between the policy features and the data structure. Based on the matching result of the fourth match, the executability of the policy information data to analyze the corresponding sub-data segment is evaluated.

[0024] The decision unit is used for:

[0025] Obtain the executability threshold and compare it with the executability of the corresponding sub-data segment analyzed by the strategy information data to determine whether the strategy information data is qualified.

[0026] When the executability of the strategy information data for analyzing the corresponding sub-data segment is equal to or greater than the executability threshold, the strategy information data is deemed qualified, and an analysis strategy for analyzing the corresponding sub-data segment is determined based on the strategy information data.

[0027] Otherwise, the strategy information data is deemed unqualified, and the target network node is re-matched in the big data network.

[0028] Preferably, an evaluation unit for a big data application development platform oriented towards multi-source data includes:

[0029] The sub-unit is used to read the data structure, determine the data type, total data volume, and data logic corresponding to the sub-data segment, and at the same time, read the strategy characteristics to determine the strategy analysis type, total analysis volume, and strategy analysis logic corresponding to the strategy information data, and perform a fourth match between the data structure and the strategy information data.

[0030] The fourth matching unit is used for:

[0031] Perform a first sub-match between the data type corresponding to the sub-data segment and the strategy analysis type corresponding to the strategy information data to obtain the first correlation between the sub-data segment and the strategy information data. At the same time, determine the first matching weight corresponding to the first sub-match.

[0032] The total amount of data corresponding to the sub-data segment is matched with the total amount of analysis corresponding to the strategy information data to obtain the second correlation between the sub-data segment and the strategy information data. At the same time, the second matching weight corresponding to the second sub-match is determined.

[0033] Perform a third sub-match between the data logic corresponding to the sub-data segment and the strategy analysis logic corresponding to the strategy information data to obtain the third correlation between the sub-data segment and the strategy information data. At the same time, determine the third matching weight corresponding to the third sub-match.

[0034] The calculation subunit is used to calculate the first correlation degree, the first matching weight, the second correlation degree, the second matching weight, the third correlation degree, and the third matching weight, and to determine the executability of the strategy information data to analyze the corresponding sub-data segment based on the calculation results.

[0035] Preferably, a big data application development platform for multi-source data includes a data analysis module, comprising:

[0036] The data analysis unit is used to analyze multi-source data based on target decisions and obtain analysis results from the multi-source data.

[0037] The report generation unit is used to generate analysis reports based on target decisions, multi-source data, and analysis results.

[0038] Preferably, a big data application development platform for multi-source data includes a report generation unit comprising:

[0039] The data attribute acquisition subunit is used to acquire the data attributes of multi-source data, retrieve the first file list from the preset document management library based on the data attributes of multi-source data, and automatically enter the multi-source data into the first file list to obtain the first report;

[0040] The first copying subunit is used to perform a first copy of the first report, and add a strategy column based on the first copying result to generate a second file list, and automatically enter the target strategy into the second file list to obtain a second report;

[0041] The second copying subunit is used to perform a second copy of the second report. At the same time, it adds a result column based on the second copying result to generate a third file list, and automatically enters the analysis results into the third file list to generate a third report, which is an analysis report.

[0042] Preferably, a big data application development platform for multi-source data includes a data analysis module, comprising:

[0043] The identification unit is used to determine the analysis results of multi-source data based on the analysis report;

[0044] The data reading unit is used to read multi-source data and determine the range of analysis results based on the data characteristics of the multi-source data.

[0045] The conformity verification unit is used for:

[0046] Compare the analysis results with the range of analysis results to determine whether the analysis report is qualified;

[0047] If the analysis results are within the acceptable range, the analysis report is deemed acceptable.

[0048] Otherwise, the analysis report will be deemed unqualified.

[0049] Preferably, a big data application development platform for multi-source data includes a storage module comprising:

[0050] A storage visual window creation unit is used to acquire data characteristics of multi-source data and create a storage visual window based on the data characteristics of multi-source data.

[0051] The storage visual sub-window creation unit is used to add storage visual sub-windows according to the corresponding visual storage window;

[0052] The visualization storage unit is used to store multi-source data to the storage visualization window and the analysis report to the storage visualization sub-window. Based on the storage visualization window and the storage visualization sub-window, the visualization storage of multi-source data and analysis reports uploaded by the client is completed.

[0053] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0054] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0055] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0056] Figure 1 This is a structural diagram of a big data application development platform for multi-source data in an embodiment of the present invention;

[0057] Figure 2 This is a structural diagram of a data receiving module in a big data application development platform for multi-source data, as described in an embodiment of the present invention.

[0058] Figure 3 This is a structural diagram of the learning module in a big data application development platform for multi-source data, as described in an embodiment of the present invention. Detailed Implementation

[0059] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0060] Example 1:

[0061] This embodiment provides a big data application development platform for multi-source data, such as... Figure 1 As shown, it includes:

[0062] The data receiving module is used to receive multi-source data uploaded by the client based on the target receiving end;

[0063] The learning module is used to input multi-source data into a big data network for learning and to obtain target decisions.

[0064] The data analysis module is used to analyze multi-source data uploaded by the client based on target decisions, generate analysis reports, and verify the quality of the analysis reports.

[0065] The storage module is used to visualize and store the multi-source data and analysis report uploaded by the client when the analysis report is qualified.

[0066] In this embodiment, the target receiving end can be an intelligent device capable of analyzing and processing multi-source data, specifically a computer or the like.

[0067] In this embodiment, the client can be a smart terminal that can upload data and generate data to be analyzed, specifically a mobile phone or other device.

[0068] In this embodiment, multi-source data can be data from multiple different sources, and the data types are not unique.

[0069] In this embodiment, the target decision can be a data analysis method corresponding to different sources of data obtained from a big data network, and the same source data corresponds to one decision.

[0070] In this embodiment, the analysis report is used to record the analysis results of multi-source data, thereby facilitating the effective management of multi-source data.

[0071] In this embodiment, visual storage can store analysis reports and multi-source data uploaded by the client, and the storage results are visible to the user. The user can retrieve and view the stored analysis reports and multi-source data according to their own viewing needs.

[0072] The beneficial effects of the above technical solution are: by analyzing and integrating multi-source data through big data, it is easier to manage multi-source data accurately and reliably, and to mine data with potential value, thereby improving the utilization rate of multi-source data. At the same time, by analyzing and processing multi-source data through a big data application development platform, the efficiency and accuracy of multi-source data processing are improved, ensuring the management effect of multi-source data.

[0073] Example 2:

[0074] Based on Example 1, this example provides a big data application development platform for multi-source data, such as... Figure 2 As shown, the data receiving module includes:

[0075] The request generation unit is used to generate a data upload request based on the client. The data upload request includes the client address and the data type of the multi-source data to be uploaded.

[0076] The verification unit is used to perform authorization verification at the target receiving end based on the client-side address and the data type of the multi-source data to be uploaded.

[0077] The data upload unit is used to receive multi-source data uploaded by the client based on the target receiving end when the verification is successful.

[0078] In this embodiment, the data upload request is generated by the client and is used to convey data upload information to the target receiving end.

[0079] In this embodiment, authorization verification can be performed by the target receiving end to verify the client's data upload permissions, thereby facilitating the assurance of data security and reliability.

[0080] The beneficial effects of the above technical solution are: by determining the client's data upload request and analyzing the data upload request through the target receiving end, it is convenient to upload the client's multi-source data to the target receiving end after the client's authorization verification is passed, thus ensuring the reliability of data upload and providing a guarantee for the analysis and processing of multi-source data through a big data application development platform.

[0081] Example 3:

[0082] Based on Example 2, this example provides a big data application development platform for multi-source data, including a verification unit:

[0083] The request processing subunit is used to obtain access permissions to the information management database in the target receiving end, determine the request input standard based on the access permissions, transform the data upload request based on the request input standard to obtain the target data upload request, and determine the request type of the target data upload request.

[0084] The matching subunit is used to input the target data upload request into the information management database based on the request type for the first matching, and to obtain the set of authorized client addresses and the set of authorized data types that match the request type;

[0085] The verification subunit is used to perform a second match between the client address in the target data upload request and the authorized client address set, and a third match between the data type of the multi-source data to be uploaded in the target data upload request and the authorized data type set. Based on the second and third matches, it determines whether the target data upload request passes the authorization verification.

[0086] In this embodiment, the information management database is pre-set and used to store access permission information corresponding to different clients.

[0087] In this embodiment, the requested data standard may be a representation of the data format requirements and upload methods of different clients when uploading multi-source data to the target receiving end.

[0088] In this embodiment, the target data upload request can be the specific data upload information obtained by analyzing the data upload request through the request input standard, including the upload method and type, etc.

[0089] In this embodiment, the first matching may be to match the obtained target data upload request with the access permissions of each client stored in the information management database.

[0090] In this embodiment, the authorized client address set can be the set of all client addresses in the information management database that match the client's target data upload request.

[0091] In this embodiment, the authorized data type set can be the set of all uploadable data types in the information management database that are consistent with the target data upload request.

[0092] In this embodiment, the second matching may be to match the client address in the target data upload request with the obtained set of authorized client addresses, thereby realizing permission verification of the client's address.

[0093] In this embodiment, the third matching may be to match the data type of the multi-source data to be uploaded with the authorized data type set, thereby verifying the upload permission of the data type.

[0094] The beneficial effects of the above technical solution are: by parsing the client's data upload request, the client's address and the data type of the data to be uploaded in the data upload request are verified for permissions, thereby ensuring that the client has data upload permissions, guaranteeing the reliability and security of multi-source data upload, and providing convenience and guarantee for accurate and reliable analysis of multi-source data.

[0095] Example 4:

[0096] Based on Example 3, this example provides a big data application development platform for multi-source data, including a verification subunit:

[0097] Obtain the matching result of the second match. If there is an authorized client address in the authorized client address set that matches the client address, and obtain the matching result of the third match, if there is an authorized data type in the authorized data type set that matches the data type of the multi-source data to be uploaded, then the authorization verification is deemed to have passed; otherwise, the authorization verification is deemed to have failed.

[0098] The beneficial effects of the above technical solution are: by authorizing and verifying the client's terminal address and data type respectively, the legitimacy of the client is guaranteed, providing security for efficient analysis of multi-source data through big data application platforms.

[0099] Example 5:

[0100] Based on Example 1, this example provides a big data application development platform for multi-source data, such as... Figure 3 As shown, the learning module includes:

[0101] The classification unit is used to classify multi-source data according to data dimensions, generate multiple sub-data segments, determine the segment identifier corresponding to each sub-data segment, and retrieve the segment identifier corresponding to each sub-data segment with each network node in the big data network to determine the target network node corresponding to each sub-data segment.

[0102] The evaluation unit is used to read the policy information data stored in the target network node, determine the policy features corresponding to the policy information data, obtain the data structure of the sub-data segment corresponding to the policy information data, and perform a fourth match between the policy features and the data structure. Based on the matching result of the fourth match, the executability of the policy information data to analyze the corresponding sub-data segment is evaluated.

[0103] The decision unit is used for:

[0104] Obtain the executability threshold and compare it with the executability of the corresponding sub-data segment analyzed by the strategy information data to determine whether the strategy information data is qualified.

[0105] When the executability of the strategy information data for analyzing the corresponding sub-data segment is equal to or greater than the executability threshold, the strategy information data is deemed qualified, and an analysis strategy for analyzing the corresponding sub-data segment is determined based on the strategy information data.

[0106] Otherwise, the strategy information data is deemed unqualified, and the target network node is re-matched in the big data network.

[0107] In this embodiment, the data dimensions are pre-defined, with one data type corresponding to one dimension, thereby enabling accurate and effective classification of the obtained multi-source data.

[0108] In this embodiment, the sub-data segment can be a collection of different types of data obtained after classifying the multi-source data.

[0109] In this embodiment, the segment identifier can be a tag that marks different sub-data segments, which can quickly distinguish the sub-data segments.

[0110] In this embodiment, the network node can be a tool in the big data network used to store policy information data corresponding to different types of data, and it is not unique.

[0111] In this embodiment, the target network node can be a network node in the big data network that matches the segment identifier, and each sub-data segment corresponds to one target network node.

[0112] In this embodiment, the strategy information data may be data such as rules and methods for analyzing multi-source data.

[0113] In this embodiment, the strategy features can be key data that can characterize the type and characteristics of different strategy information data.

[0114] In this embodiment, the data structure can be a data type that can represent the sub-data segments and the relationships between the data in the sub-data segments.

[0115] In this embodiment, the fourth matching can be to match policy features with data structures, thereby enabling the executability of analyzing different sub-data segments of policy information data.

[0116] In this embodiment, the executability is used to characterize the executability of the sub-data segment when analyzing the strategy information data. The larger the value, the better the strategy information data fits the sub-data segment.

[0117] In this embodiment, the executability threshold is pre-set and is used to characterize the minimum value of executability, i.e., the minimum standard.

[0118] The beneficial effects of the above technical solution are as follows: First, the multi-source data is classified according to data dimensions, and the segment identifiers corresponding to different sub-data segments obtained after classification are determined. Then, the target network nodes are matched with the big data network, and the policy information data contained in the target network nodes is read. Second, the executability of the policy information data for the sub-data segments is analyzed based on the reading results, thereby ensuring the correspondence between the policy information data and the sub-data segments. Finally, the analysis strategies corresponding to different sub-data segments are determined through the policy information data, achieving accurate and reliable analysis of the sub-data segments. This ensures the efficiency and accuracy of multi-source data analysis through the big data application platform, thereby achieving effective management of multi-source data.

[0119] Example 6:

[0120] Based on Example 5, this example provides a big data application development platform for multi-source data, including an evaluation unit:

[0121] The sub-unit is used to read the data structure, determine the data type, total data volume, and data logic corresponding to the sub-data segment, and at the same time, read the strategy characteristics to determine the strategy analysis type, total analysis volume, and strategy analysis logic corresponding to the strategy information data, and perform a fourth match between the data structure and the strategy information data.

[0122] The fourth matching unit is used for:

[0123] Perform a first sub-match between the data type corresponding to the sub-data segment and the strategy analysis type corresponding to the strategy information data to obtain the first correlation between the sub-data segment and the strategy information data. At the same time, determine the first matching weight corresponding to the first sub-match.

[0124] The total amount of data corresponding to the sub-data segment is matched with the total amount of analysis corresponding to the strategy information data to obtain the second correlation between the sub-data segment and the strategy information data. At the same time, the second matching weight corresponding to the second sub-match is determined.

[0125] Perform a third sub-match between the data logic corresponding to the sub-data segment and the strategy analysis logic corresponding to the strategy information data to obtain the third correlation between the sub-data segment and the strategy information data. At the same time, determine the third matching weight corresponding to the third sub-match.

[0126] The calculation subunit is used to calculate the first correlation degree, the first matching weight, the second correlation degree, the second matching weight, the third correlation degree, and the third matching weight, and to determine the executability of the strategy information data to analyze the corresponding sub-data segment based on the calculation results.

[0127] In this embodiment, determining the executability of analyzing the corresponding sub-data segment based on the calculation results includes:

[0128] The executability of analyzing the corresponding sub-data segments based on the strategy information data is calculated using the following formula;

[0129] δ=ρ1*ω1+ρ2*ω2+ρ3*ω3;

[0130] Where δ represents the executability of analyzing the corresponding sub-data segment using strategy information data; ρ1 represents the first degree of relevance; ω1 represents the first matching weight; ρ2 represents the second degree of relevance; ω2 represents the second matching weight; ρ3 represents the third degree of relevance; and ω3 represents the third matching weight.

[0131] In this embodiment, the data logic can be the relationship between the data in the sub-data segments.

[0132] In this embodiment, the strategy analysis logic can be the data analysis order and logical coherence order when the strategy information data is analyzed on the sub-data segments.

[0133] In this embodiment, the first sub-match can be to match the data type corresponding to the sub-data segment with the strategy analysis type corresponding to the strategy information data, thereby verifying the degree of type compatibility between the two.

[0134] In this embodiment, the first correlation degree is used to characterize the degree of correlation between the sub-data segment and the policy information data. The larger the value, the more matched the two are.

[0135] In this embodiment, the first matching weight can be a representation of the importance of the first sub-match in determining executability.

[0136] In this embodiment, the second sub-match can be a match between the total amount of data corresponding to the sub-data segment and the total amount of analysis corresponding to the strategy information data.

[0137] In this embodiment, the second correlation degree can be used to characterize the degree of correlation between the data volume of the sub-data segment and the strategy information data. The larger the value, the more matched the two are.

[0138] In this embodiment, the second matching weight can be a characterization of the importance of the second sub-match in determining executability.

[0139] In this embodiment, the data logic corresponding to the third sub-matching sub-data segment is matched with the strategy analysis logic corresponding to the strategy information data.

[0140] In this embodiment, the third correlation degree can be used to characterize the logical correlation between the sub-data segment and the policy information data. The larger the value, the better the match between the two.

[0141] In this embodiment, the third matching weight can be a representation of the importance of the third sub-match in determining executability.

[0142] The beneficial effects of the above technical solution are as follows: by determining the data type, total amount of data, and data logic corresponding to the sub-data segments and the strategy analysis type, total amount of analysis, and strategy analysis logic corresponding to the strategy information data, a one-to-one correspondence can be achieved between the two. This enables an accurate and effective assessment of the feasibility of analyzing the corresponding sub-data segments using the strategy information data, ensuring the accuracy and efficiency of multi-source data analysis and improving the management effect of multi-source data through the big data application platform.

[0143] Example 7:

[0144] Based on Example 1, this example provides a big data application development platform for multi-source data, including a data analysis module:

[0145] The data analysis unit is used to analyze multi-source data based on target decisions and obtain analysis results from the multi-source data.

[0146] The report generation unit is used to generate analysis reports based on target decisions, multi-source data, and analysis results.

[0147] The beneficial effects of the above technical solution are: by analyzing multi-source data through target decision-making, it ensures that valuable data in the multi-source data is mined and organized. Finally, the analysis results, target decisions, and multi-source data are used to generate corresponding analysis reports, which are convenient for users to query in a timely manner, thereby achieving effective management of multi-source data.

[0148] Example 8:

[0149] Based on Example 7, this example provides a big data application development platform for multi-source data, including a report generation unit:

[0150] The data attribute acquisition subunit is used to acquire the data attributes of multi-source data, retrieve the first file list from the preset document management library based on the data attributes of multi-source data, and automatically enter the multi-source data into the first file list to obtain the first report;

[0151] The first copying subunit is used to perform a first copy of the first report, and add a strategy column based on the first copying result to generate a second file list, and automatically enter the target strategy into the second file list to obtain a second report;

[0152] The second copying subunit is used to perform a second copy of the second report. At the same time, it adds a result column based on the second copying result to generate a third file list, and automatically enters the analysis results into the third file list to generate a third report, which is an analysis report.

[0153] In this embodiment, the data attribute can be a key data segment that can characterize the data type and data volume of multi-source data.

[0154] In this embodiment, the preset document management library is pre-set and used to store different file templates.

[0155] In this embodiment, the first file list can be a file template that records multi-source data.

[0156] In this embodiment, the first report may be an initial report obtained after inputting multi-source data into a first file list, and this report only contains multi-source data items.

[0157] In this embodiment, the first copy can be a copy of the first report, thereby generating a second file list by adding a policy column (recording the target policy) to the first report.

[0158] In this embodiment, the second file list can be based on the first file list, recording file templates of the target strategy.

[0159] In this embodiment, the second report may be a report containing multi-source data and the target policy after the target policy has been added to the second file list.

[0160] In this embodiment, the second copy can be to copy the second report, thereby adding a results column (recording the analysis results) to the second report and generating a third file list.

[0161] In this embodiment, the third file list can be a template used to record analysis results based on the second file list.

[0162] In this embodiment, the results column is used to record the analysis results of the target strategy on the corresponding sub-data segments.

[0163] The beneficial effects of the above technical solution are: by recording the target decision, multi-source data and analysis results in the report template respectively, the analysis report can be obtained, which makes it easier for users to accurately and reliably understand the analysis of multi-source data based on the analysis report, and ensures the analysis effect of the big data application platform on multi-source data.

[0164] Example 9:

[0165] Based on Example 1, this example provides a big data application platform for multi-source data, including a data analysis module:

[0166] The identification unit is used to determine the analysis results of multi-source data based on the analysis report;

[0167] The data reading unit is used to read multi-source data and determine the range of analysis results based on the data characteristics of the multi-source data.

[0168] The conformity verification unit is used for:

[0169] Compare the analysis results with the range of analysis results to determine whether the analysis report is qualified;

[0170] If the analysis results are within the acceptable range, the analysis report is deemed acceptable.

[0171] Otherwise, the analysis report will be deemed unqualified.

[0172] In this embodiment, the range of analysis results can be the theoretical range of values ​​corresponding to the analysis results after analyzing multi-source data through the analysis strategy.

[0173] The beneficial effects of the above technical solution are: by comparing the analysis results of multi-source data in the analysis report with the range of theoretical analysis results corresponding to the multi-source data, the qualification of the analysis report can be accurately verified, thereby facilitating the assurance of the rationality of the obtained analysis report and improving the accuracy of multi-source data analysis and management effectiveness.

[0174] Example 10:

[0175] Based on Example 1, this example provides a big data application platform for multi-source data, including a storage module comprising:

[0176] A storage visual window creation unit is used to acquire data characteristics of multi-source data and create a storage visual window based on the data characteristics of multi-source data.

[0177] The storage visual sub-window creation unit is used to add storage visual sub-windows according to the corresponding visual storage window;

[0178] The visualization storage unit is used to store multi-source data to the storage visualization window and the analysis report to the storage visualization sub-window. Based on the storage visualization window and the storage visualization sub-window, the visualization storage of multi-source data and analysis reports uploaded by the client is completed.

[0179] In this embodiment, the data characteristics can be the data type of the multi-source data and the value range of the multi-source data, etc.

[0180] In this embodiment, the storage view window can be a window for storing multi-source data.

[0181] In this embodiment, the storage visual sub-window can be a window for storing analysis reports.

[0182] The beneficial effects of the above technical solution are: by constructing a storage visual window based on the data characteristics of multi-source data, the multi-source data and analysis reports can be stored separately through the storage visual window, thus achieving reliable storage of analysis results and multi-source data. This facilitates users to query the data and analysis results according to their own needs, ensuring the analysis and management effects of multi-source data.

[0183] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A big data application development platform for multi-source data, characterized in that, include: The data receiving module is used to receive multi-source data uploaded by the client based on the target receiving end; The learning module is used to input multi-source data into a big data network for learning and to obtain target decisions. The data analysis module is used to analyze multi-source data uploaded by the client based on target decisions, generate analysis reports, and verify the quality of the analysis reports. The storage module is used to visualize and store the multi-source data and analysis report uploaded by the client when the analysis report is qualified. The learning modules include: The classification unit is used to classify multi-source data according to data dimensions, generate multiple sub-data segments, determine the segment identifier corresponding to each sub-data segment, and retrieve the segment identifier corresponding to each sub-data segment with each network node in the big data network to determine the target network node corresponding to each sub-data segment. The evaluation unit is used to read the policy information data stored in the target network node, determine the policy features corresponding to the policy information data, obtain the data structure of the sub-data segment corresponding to the policy information data, and perform a fourth match between the policy features and the data structure. Based on the matching result of the fourth match, the executability of the policy information data to analyze the corresponding sub-data segment is evaluated. The decision unit is used for: Obtain the executability threshold and compare it with the executability of the corresponding sub-data segment analyzed by the strategy information data to determine whether the strategy information data is qualified. When the executability of the strategy information data for analyzing the corresponding sub-data segment is equal to or greater than the executability threshold, the strategy information data is deemed qualified, and an analysis strategy for analyzing the corresponding sub-data segment is determined based on the strategy information data. Otherwise, the strategy information data is deemed unqualified, and the target network node is re-matched in the big data network. The evaluation unit includes: The sub-unit is used to read the data structure, determine the data type, total data volume, and data logic corresponding to the sub-data segment, and at the same time, read the strategy characteristics to determine the strategy analysis type, total analysis volume, and strategy analysis logic corresponding to the strategy information data, and perform a fourth match between the data structure and the strategy information data. The fourth matching unit is used for: Perform a first sub-match between the data type corresponding to the sub-data segment and the strategy analysis type corresponding to the strategy information data to obtain the first correlation between the sub-data segment and the strategy information data. At the same time, determine the first matching weight corresponding to the first sub-match. The total amount of data corresponding to the sub-data segment is matched with the total amount of analysis corresponding to the strategy information data to obtain the second correlation between the sub-data segment and the strategy information data. At the same time, the second matching weight corresponding to the second sub-match is determined. Perform a third sub-match between the data logic corresponding to the sub-data segment and the strategy analysis logic corresponding to the strategy information data to obtain the third correlation between the sub-data segment and the strategy information data. At the same time, determine the third matching weight corresponding to the third sub-match. The calculation subunit is used to calculate the first correlation degree, the first matching weight, the second correlation degree, the second matching weight, the third correlation degree, and the third matching weight, and to determine the executability of the strategy information data to analyze the corresponding sub-data segment based on the calculation results.

2. The big data application development platform for multi-source data according to claim 1, characterized in that, The data receiving module includes: The request generation unit is used to generate a data upload request based on the client. The data upload request includes the client address and the data type of the multi-source data to be uploaded. The verification unit is used to perform authorization verification at the target receiving end based on the client-side address and the data type of the multi-source data to be uploaded. The data upload unit is used to receive multi-source data uploaded by the client based on the target receiving end when the verification is successful.

3. The big data application development platform for multi-source data according to claim 2, characterized in that, The verification unit includes: The request processing subunit is used to obtain access permissions to the information management database in the target receiving end, determine the request input standard based on the access permissions, transform the data upload request based on the request input standard to obtain the target data upload request, and determine the request type of the target data upload request. The matching subunit is used to input the target data upload request into the information management database based on the request type for the first matching, and to obtain the set of authorized client addresses and the set of authorized data types that match the request type; The verification subunit is used to perform a second match between the client address in the target data upload request and the authorized client address set, and a third match between the data type of the multi-source data to be uploaded in the target data upload request and the authorized data type set. Based on the second and third matches, it determines whether the target data upload request passes the authorization verification.

4. The big data application development platform for multi-source data according to claim 3, characterized in that, The verification subunit includes: Obtain the matching result of the second match. If there is an authorized client address in the authorized client address set that matches the client address, and obtain the matching result of the third match, if there is an authorized data type in the authorized data type set that matches the data type of the multi-source data to be uploaded, then the authorization verification is deemed to have passed; otherwise, the authorization verification is deemed to have failed.

5. A big data application development platform for multi-source data according to claim 1, characterized in that, The data analysis module includes: The data analysis unit is used to analyze multi-source data based on target decisions and obtain analysis results from the multi-source data. The report generation unit is used to generate analysis reports based on target decisions, multi-source data, and analysis results.

6. A big data application development platform for multi-source data according to claim 5, characterized in that, The report generation unit includes: The data attribute acquisition subunit is used to acquire the data attributes of multi-source data, retrieve the first file list from the preset document management library based on the data attributes of multi-source data, and automatically enter the multi-source data into the first file list to obtain the first report; The first copying subunit is used to perform a first copy of the first report, add a strategy column based on the first copy result to generate a second file list, and automatically enter the target strategy into the second file list to obtain a second report; The second copying subunit is used to perform a second copy of the second report. At the same time, it adds a result column based on the second copying result to generate a third file list, and automatically enters the analysis results into the third file list to generate a third report, which is an analysis report.

7. A big data application development platform for multi-source data according to claim 1, characterized in that, The data analysis module includes: The identification unit is used to determine the analysis results of multi-source data based on the analysis report; The data reading unit is used to read multi-source data and determine the range of analysis results based on the data characteristics of the multi-source data. The conformity verification unit is used for: Compare the analysis results with the range of analysis results to determine whether the analysis report is qualified; If the analysis results are within the acceptable range, the analysis report is deemed acceptable. Otherwise, the analysis report will be deemed unqualified.

8. A big data application development platform for multi-source data according to claim 1, characterized in that, Storage module, including: A storage visual window creation unit is used to acquire data characteristics of multi-source data and create a storage visual window based on the data characteristics of multi-source data. The storage visual sub-window creation unit is used to add storage visual sub-windows according to the corresponding visual storage window; The visualization storage unit is used to store multi-source data into the storage visualization window and the analysis report into the storage visualization sub-window. Based on the storage visualization window and the storage visualization sub-window, the visualization storage of multi-source data and analysis reports uploaded by the client is completed.

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