Data display method and system based on multi-source data fusion

Abstract

The invention discloses a data display method and system based on multi-source data fusion, and relates to the technical field of big data. When the system runs, data feature extraction is carried out on access information of a user through a log extraction module, and a data type log information group is formed; performing type classification marking, quantity marking and data state information marking on the data type log information group through a data type marking module to obtain a data marking information group, and processing through a type level extraction module to form a first data set, a second data set and a third data set; a data cache index module analyzes and obtains a data type processing index Clzs, a data cache index is obtained after matching, an evaluation scheme is established, specific execution is carried out according to the content of the scheme, the user query access rate is dynamically evaluated, the resource utilization rate is improved for data display, and the user experience is improved. And the multi-source data fusion display effect can be displayed more effectively and quickly.

Description

Technical Field

[0001] The present invention relates to the field of big data technology, and in particular to a data display method and system based on multi-source data fusion. Background Art

[0002] Multi-source data fusion is a concept involving multiple fields and technologies. It mainly refers to the integration, consolidation and analysis of data from different data sources to extract richer and more accurate information. The purpose of multi-source data fusion is to integrate data from different sources, formats and characteristics to form a unified and complete data analysis foundation, and improve the efficiency and accuracy of data analysis and application.

[0003] In the patent with application number CN202111209444.2, a multi-source data fusion method and system are disclosed, which relates to the field of data statistics. The method includes: obtaining historical multi-source data; classifying the multi-source data according to the data meaning of the multi-source data, setting a data item for each type of data in the classification result, building an association relationship for the sub-data in the i-th type of data, and mapping the corresponding association relationship to the corresponding i-th data item, building a data fusion structure according to all data items, obtaining the input multi-source data, storing the input multi-source data in the corresponding data item category according to the data fusion structure, and by obtaining the input current multi-source data, storing the current multi-source in the corresponding data item category according to the data fusion structure, realizing real-time data classification and fusion processing of the current input business data, and orderly managing real-time multi-business data.

[0004] It is possible to classify and integrate the currently input business data in real time and manage real-time multi-business data in an orderly manner. However, in combination with the existing technology and the above application, when the statistics department obtains multi-source data from different reports from multiple departments and multiple professions and displays them, it is necessary to pre-cache a large number of quantity types or add indexes. Due to the large differences in data between various data sources, the big data that users frequently access may load slowly, which will bring certain obstacles to users' queries and searches and reduce work efficiency. If pre-caching and indexing cannot be performed according to the usage scenarios of daily queries and searches, it may lead to a waste of resources for the display of data that is not frequently viewed, while the big data resources that are frequently viewed are tight, making it difficult to achieve the expected effect of multi-source data fusion. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a data display method and system based on multi-source data fusion, which solves the problems mentioned in the background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a data display system based on multi-source data fusion, including a log extraction module, a data type marking module, a type level extraction module and a data cache index module;

[0007] The log extraction module extracts user data access logs from different report records from multiple departments and professions of the statistics department, including data type information queried by the user, data type access frequency information and data time information, and performs data feature extraction to form a data type log information group;

[0008] The data type marking module performs type classification marking, quantity marking and data status information marking according to the data type log information group, including the classification mark of the same root data type, the quantity mark of the same data type subclass and the data interaction information mark of the current data type, to form a data marking information group;

[0009] The type level extraction module counts the frequency information of the root data type, the frequency information of the sub-type data type and the data interaction amount information according to the data tag information group, and forms the first data set, the second data set and the third data set after preprocessing;

[0010] The data cache index module establishes a data type query feature analysis model for the first data set, the second data set and the third data set, obtains the data type processing index Clzs by training and statistics the data type query feature analysis model, and matches it with the preset data type query processing evaluation threshold C, obtains the data cache index to establish an evaluation plan, and specifically executes the evaluation plan according to the data cache index, including establishing a cache and indexing for the marked data types.

[0011] Preferably, the log extraction module includes a data extraction unit;

[0012] The data extraction unit extracts different report records from multiple departments and professions of the statistics department within a fixed period through preset data extraction tags, extracts data features from user data access logs, including data type information queried by users, data type access frequency information and data time information, and packages them into data type log information groups, and sends them to the data type marking module;

[0013] Among them, data types include user information, transaction information, product information, sales information, contract information and report information.

[0014] Preferably, the data type marking module includes a classification unit;

[0015] The classification unit classifies and marks the data according to the data type log information group, including identifying data with the same root data type by analyzing the data type log information group, synchronously marking the same data type subclass quantity mark and the current data interaction information mark of the current data type, and then classifying the data with the same root data type according to its root data type to form a type classification of the data, and then marking it as belonging to the same root data type classification mark to form a data marking information group;

[0016] Among them, in the process of classification and labeling, multi-level classification is involved, that is, data with the same root data type is further subdivided into subcategories of data types.

[0017] Preferably, the type level extraction module includes a statistical unit and a processing unit;

[0018] The statistical unit calculates the frequency information of the root data type, the frequency information of the sub-type data type and the data interaction amount information of the current data type according to the data tag information group;

[0019] The root data type frequency information includes the maximum data type frequency and the average data type frequency;

[0020] The subclass data type frequency information includes the maximum subclass data frequency, the average subclass data frequency, and the average subclass level access value;

[0021] Data interaction information includes data update times, data type upload and download times, and data type uplink and downlink traffic values;

[0022] The processing unit pre-processes the root data type frequency information, the sub-class data type frequency information and the data interaction amount information, and forms the processed root data type frequency information into a first data set, forms the sub-class data type frequency information into a second data set, and forms the data interaction amount information into a third data set;

[0023] The first data set includes: the maximum frequency of the root data type Gzd and the average frequency of the root data type Gpj;

[0024] The second data set includes: the maximum sub-category data frequency Zzd, the average sub-category data Zpj and the average sub-category level access level value Cpj;

[0025] The third data set includes: data update times Gxz, upload and download times Sxz and uplink and downlink traffic average Lpj.

[0026] Preferably, the data cache index module includes a modeling unit and a solution matching unit;

[0027] The modeling unit establishes a data type query feature analysis model for the first data set, the second data set, and the third data set by using deep learning technology, and trains and counts the data type query feature analysis model to obtain a type selection fluctuation factor Xzyz and a type operation factor Czyz, and then fits the type selection fluctuation factor Xzyz and the type operation factor Czyz to obtain a data type processing index Clzs;

[0028] The scheme matching unit matches the preset relevant information with the required comparison value, including matching the preset data type query processing evaluation threshold C with the data type processing index Clzs, obtaining the data cache index to establish an evaluation scheme, and establishing specific execution of the evaluation scheme content according to the data cache index, including establishing a cache and index for the marked data type.

[0029] Preferably, the data type processing index Clzs is obtained by the following calculation formula:

[0030] ;

[0031] Where Clzs represents the data type processing index, Xzyz represents the type selection fluctuation factor, Czyz represents the type operation factor, c1 and c2 represent the proportional coefficients of the type selection fluctuation factor Xzyz and the type operation factor Czyz respectively;

[0032] in, , ,and , D represents the correction constant.

[0033] Preferably, the type selection fluctuation factor Xzyz is obtained by the following calculation formula:

[0034] ;

[0035] In the formula, Xzyz represents the type selection fluctuation factor, Gzd represents the maximum frequency of the root data type, Gpj represents the average frequency of the root data type, Zzd represents the maximum frequency of the subclass data, Zpj represents the average frequency of the subclass data, and Cpj represents the average level value of the subclass level access, which is used to obtain the fluctuation performance of the user access query data type.

[0036] Preferably, the type operation factor Czyz is obtained by the following calculation formula:

[0037] ;

[0038] Where Czyz represents the type operation factor, n represents the total number of subclasses of the root data type, i represents the i-th subclass data type, Gxz and Respectively represent the data update times and the preset data update active times, Sxz and Respectively represent the upload and download times and the preset upload and download active times, Lpj and Respectively represent the average value of uplink and downlink traffic and the active average preset value of uplink and downlink traffic.

[0039] Preferably, the data cache index establishment evaluation scheme is obtained by the following matching method:

[0040] The data type processing index Clzs is less than the data type query processing evaluation threshold C, and a qualified solution for the user query access rate of multiple data sources is obtained, and multiple data sources are not cached or indexed;

[0041] The data type processing index Clzs ≥ the data type query processing evaluation threshold C, obtain the user query access multi-data source rate unqualified solution, execute multi-data source cache creation or multi-data source index creation;

[0042] When the data type processing index Clzs ≥ twice the data type query processing evaluation threshold C, the user query access multi-data source rate warning plan is obtained, and the multi-data source cache establishment and multi-data source index establishment are executed.

[0043] A data display method based on multi-source data fusion includes the following steps:

[0044] Step 1: The log extraction module extracts user data access logs from different report records from multiple departments and professions of the statistics department, including data type information queried by users, data type access frequency information and data time information, and extracts data features to form a data type log information group;

[0045] Step 2: The data type marking module performs type classification marking, quantity marking and data status information marking according to the data type log information group, including the classification mark belonging to the same root data type, the quantity mark of the same data type subclass and the data interaction information mark of the current data type, to form a data marking information group;

[0046] Step 3: The type level extraction module counts the frequency information of the root data type, the frequency information of the sub-type data type and the data interaction amount according to the data tag information group, and forms the first data set, the second data set and the third data set after preprocessing;

[0047] Step 4: The data cache index module establishes a data type query feature analysis model for the first data set, the second data set, and the third data set, obtains the data type processing index Clzs by training and statistics the data type query feature analysis model, and matches it with the preset data type query processing evaluation threshold C, obtains the data cache index to establish an evaluation plan, and implements the evaluation plan according to the data cache index, including caching and indexing the marked data types.

[0048] The present invention provides a data display method and system based on multi-source data fusion, which has the following beneficial effects:

[0049] (1) When the system is running, the log extraction module extracts data features from the user's access information to form a data type log information group. The data type marking module performs type classification marking, quantity marking and data status information marking on the data type log information group to obtain a data marking information group. The type hierarchy extraction module processes the data to form a first data set, a second data set and a third data set. The data cache index module analyzes the data to obtain the data type processing index Clzs. After matching, the data cache index is obtained to establish an evaluation plan, and the plan is executed according to the content, including establishing a cache and indexing for the marked data type, so as to achieve dynamic evaluation of the user query access rate, and execute the corresponding optimization plan according to the result, effectively improving the stability and reliability of the system, while improving the efficiency of user query and search, and improving the resource utilization of data display, so that the effect of multi-source data fusion display is more effective and faster.

[0050] (2) The type selection fluctuation factor Xzyz and the type operation factor Czyz are calculated and fitted to obtain the data type processing index Clzs, which is then matched to obtain the data cache index to establish an evaluation plan, thereby optimizing the data access method based on the data type processing index Clzs and improving the user's query and access experience. If the data type processing index Clzs is lower than the threshold, the system can avoid unnecessary cache or index establishment and reduce resource waste; if the data type processing index Clzs is higher than the threshold, the system can establish a cache or index according to demand to improve the efficiency of data access and thus enhance the user experience.

[0051] (3) Through steps 1 to 4, the log information of users accessing multiple data sources is extracted and processed, so as to extract, mark and hierarchically extract data from different data sources, realize the unified management of the display of multiple data sources, and make the data easier to understand and use. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1This is a schematic diagram of a data display system block diagram based on multi-source data fusion according to the present invention;

[0053] Figure 2 The present invention is a schematic diagram of the steps of a data display method based on multi-source data fusion. DETAILED DESCRIPTION

[0054] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0055] Example 1

[0056] The present invention provides a data display system based on multi-source data fusion, please refer to Figure 1 , including a log extraction module, a data type marking module, a type level extraction module and a data cache index module;

[0057] The log extraction module extracts user data access logs from different report records from multiple departments and professions of the statistics department, including data type information queried by the user, data type access frequency information and data time information, and performs data feature extraction to form a data type log information group;

[0058] The data type marking module performs type classification marking, quantity marking and data status information marking according to the data type log information group, including the classification mark of the same root data type, the quantity mark of the same data type subclass and the data interaction information mark of the current data type, to form a data marking information group;

[0059] The type level extraction module counts the frequency information of the root data type, the frequency information of the sub-type data type and the data interaction amount information according to the data tag information group, and forms the first data set, the second data set and the third data set after preprocessing;

[0060] The data cache index module establishes a data type query feature analysis model for the first data set, the second data set and the third data set, obtains the data type processing index Clzs by training and statistics the data type query feature analysis model, and matches it with the preset data type query processing evaluation threshold C, obtains the data cache index to establish an evaluation plan, and specifically executes the evaluation plan according to the data cache index, including establishing a cache and indexing for the marked data types.

[0061] In this embodiment, data features of user access information are extracted through a log extraction module to form a data type log information group. The data type log information group is marked by type classification, quantity and data status information through a data type marking module to obtain a data marking information group, which is processed through a type hierarchy extraction module to form a first data set, a second data set and a third data set. The data cache index module is used to analyze and obtain a data type processing index Clzs, and the index is matched with a preset data type query processing evaluation threshold C. The data cache index is obtained to establish an evaluation plan, and the evaluation plan is specifically executed according to the data cache index, including establishing a cache and an index for the marked data type, so as to achieve a dynamic evaluation of the user query access rate, and execute the corresponding optimization plan according to the result, thereby effectively improving the stability and reliability of the system, and at the same time improving the efficiency of user query and search, and improving resource utilization for data display, so that the effect of multi-source data fusion display is more effective and quick.

[0062] Example 2

[0063] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the log extraction module includes a data extraction unit;

[0064] The data extraction unit extracts different report records from multiple departments and professions of the statistics department within a fixed period through preset data extraction tags, extracts data features from user data access logs, including data type information queried by users, data type access frequency information and data time information, and packages them into data type log information groups, and sends them to the data type marking module;

[0065] Among them, data types include user information, transaction information, product information, sales information, contract information and report information.

[0066] The data type marking module includes a classification unit;

[0067] The classification unit classifies and marks the data according to the data type log information group, including identifying data with the same root data type by analyzing the data type log information group, synchronously marking the same data type subclass quantity mark and the current data interaction information mark of the current data type, and then classifying the data with the same root data type according to its root data type to form a type classification of the data, and then marking it as belonging to the same root data type classification mark to form a data marking information group;

[0068] Among them, in the process of classification and labeling, multi-level classification is involved, that is, data with the same root data type is further subdivided into subcategories of data types.

[0069] Example 3

[0070] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the type level extraction module includes a statistical unit and a processing unit;

[0071] The statistical unit calculates the frequency information of the root data type, the frequency information of the sub-type data type and the data interaction amount information of the current data type according to the data tag information group;

[0072] The root data type frequency information includes the maximum data type frequency and the average data type frequency;

[0073] The subclass data type frequency information includes the maximum subclass data frequency, the average subclass data frequency, and the average subclass level access value;

[0074] Data interaction information includes data update times, data type upload and download times, and data type uplink and downlink traffic values;

[0075] The processing unit pre-processes the root data type frequency information, the sub-class data type frequency information and the data interaction amount information, and forms the processed root data type frequency information into a first data set, forms the sub-class data type frequency information into a second data set, and forms the data interaction amount information into a third data set;

[0076] The first data set includes: the maximum frequency of the root data type Gzd and the average frequency of the root data type Gpj;

[0077] The second data set includes: the maximum sub-category data frequency Zzd, the average sub-category data Zpj and the average sub-category level access level value Cpj;

[0078] The third data set includes: data update times Gxz, upload and download times Sxz and uplink and downlink traffic average Lpj.

[0079] The data cache index module includes a modeling unit and a solution matching unit;

[0080] The modeling unit establishes a data type query feature analysis model for the first data set, the second data set, and the third data set by using deep learning technology, and trains and counts the data type query feature analysis model to obtain a type selection fluctuation factor Xzyz and a type operation factor Czyz, and then fits the type selection fluctuation factor Xzyz and the type operation factor Czyz to obtain a data type processing index Clzs;

[0081] The scheme matching unit matches the preset relevant information with the required comparison value, including matching the preset data type query processing evaluation threshold C with the data type processing index Clzs, obtaining the data cache index to establish an evaluation scheme, and establishing specific execution of the evaluation scheme content according to the data cache index, including establishing a cache and index for the marked data type.

[0082] Example 4

[0083] This embodiment is explained in Example 1, please refer to Figure 1 Specifically: the data type processing index Clzs is obtained by the following calculation formula

[0084] ;

[0085] Where Clzs represents the data type processing index, Xzyz represents the type selection fluctuation factor, Czyz represents the type operation factor, c1 and c2 represent the proportional coefficients of the type selection fluctuation factor Xzyz and the type operation factor Czyz respectively;

[0086] in, , ,and , D represents the correction constant.

[0087] The type selection fluctuation factor Xzyz is obtained by the following calculation formula

[0088] ;

[0089] In the formula, Xzyz represents the type selection fluctuation factor, Gzd represents the maximum frequency of the root data type, Gpj represents the average frequency of the root data type, Zzd represents the maximum frequency of the subclass data, Zpj represents the average frequency of the subclass data, and Cpj represents the average level value of the subclass level access, which is used to obtain the fluctuation performance of the user access query data type.

[0090] The type operation factor Czyz is obtained by the following calculation formula

[0091] ;

[0092] Where Czyz represents the type operation factor, n represents the total number of subclasses of the root data type, i represents the i-th subclass data type, Gxz and Respectively represent the data update times and the preset data update active times, Sxz and Respectively represent the upload and download times and the preset upload and download active times, Lpj and Respectively represent the average value of uplink and downlink traffic and the active average preset value of uplink and downlink traffic.

[0093] The data cache index establishment evaluation scheme is obtained by the following matching method

[0094] The data type processing index Clzs is less than the data type query processing evaluation threshold C, and a qualified solution for the user query access rate of multiple data sources is obtained, and multiple data sources are not cached or indexed;

[0095] The data type processing index Clzs ≥ the data type query processing evaluation threshold C, obtain the user query access rate of multiple data sources unqualified plan, execute the multi-data source cache or multi-data source index, and dynamically adjust the cache content and range according to the characteristics and access mode of the data to improve the cache hit rate and usage efficiency;

[0096] When the data type processing index Clzs ≥ twice the data type query processing evaluation threshold C, obtain the user query access multi-data source rate warning plan, execute multi-data source cache establishment and multi-data source index establishment, and after executing cache and index establishment, monitor the system performance and resource utilization, and adjust the cache and index configuration in time to ensure the stability and reliability of the system;

[0097] Among them, cache is a technology for temporarily storing data to improve data access speed and reduce the load on the back-end data storage system. During the data access process, frequently accessed data will be cached in memory or other fast storage media so that the data can be obtained faster the next time it is accessed, without having to read it again from the back-end data storage system. The cache can store various types of data, including query results, calculation results, and static resources, to improve data access speed and responsiveness;

[0098] Index: A data structure used to quickly locate and retrieve data. It is often used for data access and query in database management systems. By associating the key fields of the data with the actual data, a fast query data structure is established to accelerate data retrieval and query operations. It is designed and optimized according to different data characteristics and query requirements, including single-column indexes, multi-column indexes and unique indexes to improve data retrieval efficiency and query performance.

[0099] In this embodiment, the type selection fluctuation factor Xzyz and the type operation factor Czyz are calculated, and the data type processing index Clzs is obtained after fitting, and the data cache index is obtained by matching to establish an evaluation plan, so as to optimize the data access method according to the data type processing index Clzs and improve the user's query and access experience. If the data type processing index Clzs is lower than the threshold, the system can avoid unnecessary cache or index establishment and reduce resource waste; if the data type processing index Clzs is higher than the threshold, the system can establish a cache or index according to demand to improve the efficiency of data access, thereby enhancing the user experience.

[0100] Example 5

[0101] A data display method based on multi-source data fusion, please refer to Figure 2 , specifically: including the following steps:

[0102] Step 1: The log extraction module extracts user data access logs from different report records from multiple departments and professions of the statistics department, including data type information queried by users, data type access frequency information and data time information, and extracts data features to form a data type log information group;

[0103] Step 2: The data type marking module performs type classification marking, quantity marking and data status information marking according to the data type log information group, including the classification mark belonging to the same root data type, the quantity mark of the same data type subclass and the data interaction information mark of the current data type, to form a data marking information group;

[0104] Step 3: The type level extraction module counts the frequency information of the root data type, the frequency information of the sub-type data type and the data interaction amount according to the data tag information group, and forms the first data set, the second data set and the third data set after preprocessing;

[0105] Step 4: The data cache index module establishes a data type query feature analysis model for the first data set, the second data set, and the third data set, obtains the data type processing index Clzs by training and statistics the data type query feature analysis model, and matches it with the preset data type query processing evaluation threshold C, obtains the data cache index to establish an evaluation plan, and implements the evaluation plan according to the data cache index, including caching and indexing the marked data types.

[0106] In this embodiment, through steps 1 to 4, the log information of users accessing multiple data sources is extracted and processed, so as to extract, mark and hierarchically extract data from different data sources, realize the unified management of the display of multiple data sources, and make the data easier to understand and use.

[0107] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A data display system based on multi-source data fusion, characterized by: It includes a log extraction module, a data type marking module, a type level extraction module and a data cache index module; The log extraction module extracts user data access logs from different report records from multiple departments and professions of the statistics department, including data type information queried by the user, data type access frequency information and data time information, and performs data feature extraction to form a data type log information group; The data type marking module performs type classification marking, quantity marking and data status information marking according to the data type log information group, including the classification mark of the same root data type, the quantity mark of the same data type subclass and the data interaction information mark of the current data type, to form a data marking information group; The type level extraction module counts the frequency information of the root data type, the frequency information of the sub-type data type and the data interaction amount information according to the data tag information group, and forms the first data set, the second data set and the third data set after preprocessing; The data cache index module establishes a data type query feature analysis model for the first data set, the second data set and the third data set, obtains the data type processing index Clzs by training and statistics the data type query feature analysis model, and matches it with the preset data type query processing evaluation threshold C, obtains the data cache index to establish an evaluation plan, and specifically executes the evaluation plan according to the data cache index, including establishing a cache and indexing for the marked data types.

2. A data display system based on multi-source data fusion according to claim 1, characterized in that: The log extraction module includes a data extraction unit; The data extraction unit extracts different report records from multiple departments and multiple professions of the statistics department within a fixed period through preset data extraction tags, and extracts data features from user data access logs, including data type information queried by users, data type access frequency information and data time information, and packages them into data type log information groups, and sends them to the data type marking module.

3. The data display system based on multi-source data fusion according to claim 1, characterized in that: The data type marking module includes a classification unit; The classification unit classifies and marks the data according to the data type log information group, including identifying data with the same root data type by analyzing the data type log information group, synchronously marking the same data type subclass quantity mark and the current data interaction information mark of the current data type, and then classifying the data with the same root data type according to its root data type to form a type classification of the data, and then marking it as belonging to the same root data type classification mark to form a data marking information group.

4. The data display system based on multi-source data fusion according to claim 1, characterized in that: The type level extraction module includes a statistical unit and a processing unit; The statistical unit calculates the frequency information of the root data type, the frequency information of the sub-type data type and the data interaction amount information of the current data type according to the data tag information group; The root data type frequency information includes the maximum data type frequency and the average data type frequency; The sub-category data type frequency information includes the maximum sub-category data frequency, the average sub-category data frequency, and the average sub-category level access value; Data interaction information includes data update times, data type upload and download times, and data type uplink and downlink traffic values; The processing unit pre-processes the root data type frequency information, the sub-class data type frequency information and the data interaction amount information, and forms the processed root data type frequency information into a first data set, forms the sub-class data type frequency information into a second data set, and forms the data interaction amount information into a third data set; The first data set includes: the maximum frequency of the root data type Gzd and the average frequency of the root data type Gpj; The second data set includes: the maximum sub-category data frequency Zzd, the average sub-category data Zpj and the average sub-category level access level value Cpj; The third data set includes: data update times Gxz, upload and download times Sxz and uplink and downlink traffic average Lpj.

5. The data display system based on multi-source data fusion according to claim 1, characterized in that: The data cache index module includes a modeling unit and a solution matching unit; The modeling unit establishes a data type query feature analysis model for the first data set, the second data set, and the third data set by using deep learning technology, and trains and counts the data type query feature analysis model to obtain a type selection fluctuation factor Xzyz and a type operation factor Czyz, and then fits the type selection fluctuation factor Xzyz and the type operation factor Czyz to obtain a data type processing index Clzs; The scheme matching unit matches the preset relevant information with the required comparison value, including matching the preset data type query processing evaluation threshold C with the data type processing index Clzs, obtaining the data cache index to establish an evaluation scheme, and establishing specific execution of the evaluation scheme content according to the data cache index, including establishing a cache and index for the marked data type.

6. The data display system based on multi-source data fusion according to claim 1, characterized in that: The data type processing index Clzs is obtained by the following calculation formula ； Where Clzs represents the data type processing index, Xzyz represents the type selection fluctuation factor, Czyz represents the type operation factor, c1 and c2 represent the proportional coefficients of the type selection fluctuation factor Xzyz and the type operation factor Czyz respectively; in, , ,and , D represents the correction constant.

7. The data display system based on multi-source data fusion according to claim 5, characterized in that: The type selection fluctuation factor Xzyz is obtained by the following calculation formula ； In the formula, Xzyz represents the type selection fluctuation factor, Gzd represents the maximum frequency of the root data type, Gpj represents the average frequency of the root data type, Zzd represents the maximum frequency of the subclass data, Zpj represents the average frequency of the subclass data, and Cpj represents the average level value of the subclass level access, which is used to obtain the fluctuation performance of the user access query data type.

8. The data display system based on multi-source data fusion according to claim 5, characterized in that: The type operation factor Czyz is obtained by the following calculation formula ； Where Czyz represents the type operation factor, n represents the total number of subclasses of the root data type, i represents the i-th subclass data type, Gxz and Respectively represent the data update times and the preset data update active times, Sxz and Respectively represent the upload and download times and the preset upload and download active times, Lpj and Respectively represent the average value of uplink and downlink traffic and the active average preset value of uplink and downlink traffic.

9. The data display system based on multi-source data fusion according to claim 1, characterized in that: The data cache index establishment evaluation scheme is obtained by the following matching method The data type processing index Clzs is less than the data type query processing evaluation threshold C, and a qualified solution for the user query access rate of multiple data sources is obtained, and multiple data sources are not cached or indexed; The data type processing index Clzs ≥ the data type query processing evaluation threshold C, obtain the user query access multi-data source rate unqualified solution, execute multi-data source cache creation or multi-data source index creation; When the data type processing index Clzs ≥ twice the data type query processing evaluation threshold C, the user query access multi-data source rate warning plan is obtained, and the multi-data source cache establishment and multi-data source index establishment are executed.

10. A data display method based on multi-source data fusion, comprising a data display system based on multi-source data fusion as claimed in any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: The log extraction module extracts user data access logs from different report records from multiple departments and professions of the statistics department, including data type information queried by users, data type access frequency information and data time information, and extracts data features to form a data type log information group; Step 2: The data type marking module performs type classification marking, quantity marking and data status information marking according to the data type log information group, including the classification mark belonging to the same root data type, the quantity mark of the same data type subclass and the data interaction information mark of the current data type, to form a data marking information group; Step 3: The type level extraction module counts the frequency information of the root data type, the frequency information of the sub-type data type and the data interaction amount according to the data tag information group, and forms the first data set, the second data set and the third data set after preprocessing; Step 4: The data cache index module establishes a data type query feature analysis model for the first data set, the second data set, and the third data set, obtains the data type processing index Clzs by training and statistics the data type query feature analysis model, and matches it with the preset data type query processing evaluation threshold C, obtains the data cache index to establish an evaluation plan, and implements the evaluation plan according to the data cache index, including caching and indexing the marked data types.

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