Historical and cultural information resource visualization intelligent management platform based on big data

Abstract

The invention discloses a historical and cultural information resource visualization intelligent management platform based on big data, relates to the technical field of data resource management, and solves the problem that the original data acquisition mode and sharing mode are relatively single, so that the speed is too low. The data acquisition of a plurality of data interfaces can be synchronously controlled, and the acquisition time is ensured to be consistent while the acquisition rate is ensured; meanwhile, the adjustment processing end accurately selects and determines the format through a scientific feature confirmation mode according to factors such as the rate feature and the data capacity of data format conversion, data acquisition delay is avoided, the situation of data conversion chaos is eradicated, and the data conversion efficiency is improved. Multi-dimensional factors such as the mean rate, the residual data capacity, the data type and the historical sharing record of data to be requested in the sharing process are comprehensively considered, the time required by different sharing paths is compared, and therefore the fastest data sharing logic is selected.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data resource management, in particular to a historical and cultural information resource visualized intelligent management platform based on big data. BACKGROUND

[0002] In the current situation of deep integration of cultural heritage and digital development, the digital management and sharing of historical and cultural information resources have become an important issue.

[0003] However, there are many problems in the current management of historical and cultural information resources: on the one hand, the data formats associated with different data interfaces are different, and multiple formats such as XML, JSON, CSV coexist, lacking a unified standard, which makes it difficult to achieve efficient integration during data collection, and format incompatibility and low conversion efficiency may occur during data conversion, seriously affecting the speed and accuracy of data collection; on the other hand, in the data sharing process, due to the complexity of data transmission logic, involving direct transmission, intermediate switching and other ways, and affected by data type, transmission rate, data volume and other factors, it is difficult to quickly lock the optimal sharing path, resulting in low data sharing efficiency and unable to meet the needs of all parties in a timely manner.

[0004] In addition, the existing management system has deficiencies in data collection time control and data integrity protection, and different collection process times may cause data processing delay and confusion, restricting the effective use and dissemination of historical and cultural information resources. Therefore, a system capable of efficient data collection, intelligent sharing and standardized management is needed to promote the development of historical and cultural information resource digital management. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides a historical and cultural information resource visualized intelligent management platform based on big data, which solves the problem of low speed caused by the single original data collection method and sharing method.

[0006] To achieve the above purpose, the present application realizes the following technical scheme: a historical and cultural information resource visualized intelligent management platform based on big data, comprising: An adjustment processing end confirms different to-be-collected data associated with different data interfaces in a set collection period, confirms the characteristics of each group of to-be-collected data, locks the optimal collection logic, and executes it through a data collection end, in particular: Confirm the data formats that the management platform can accept, and record the confirmed data formats as to-be-determined formats; Based on the set collection period, the collection period is a preset period, based on the data protocol associated with different data interfaces, confirm the to-be-collected data that needs to be collected in the corresponding collection period, different data interfaces correspond to different to-be-collected data; Based on the corresponding acquisition cycle associated with a plurality of groups of data to be collected, different data belonging to different data formats are classified from the plurality of groups of data to be collected, and different classified data sets are confirmed; The data format associated with the classified data set is recorded as a format to be converted, the conversion record of data conversion from the different format to be converted to the different format to be determined is identified from the historical data, and the associated conversion rate is processed by averaging from the corresponding conversion record, and the rate characteristics between the format to be converted and the format to be determined are confirmed; Randomly select a group of formats to be determined as selected formats, and confirm the data capacity of different classified data sets, and mark as R i , wherein i represents different classified data sets, and based on the rate characteristics associated between different formats to be converted and formats to be determined, the corresponding classified formats associated with the format to be converted and the format to be determined of this classified data set are confirmed, and the associated rate characteristics are marked as V, using: R i ÷V=T i Confirm the feature time T associated with this classified data set i , and confirm the different feature times T associated with different classified data sets of this selected format i , and process a plurality of feature times T i to confirm the mean value feature and the variance feature by performing variance processing again, using: Total feature = mean value feature × C1 + variance feature × C2 to confirm the total feature associated with this selected format, wherein C1 and C2 are both preset fixed coefficient factors; In turn, different formats to be determined are selected as selected formats, different total features associated with different selected formats are confirmed, the minimum value is locked from the associated different total features, the total feature associated with the minimum value is recorded as the determined feature, the selected format associated with the determined feature is recorded as the determined format, and the determined format locked in the current acquisition cycle is transmitted to the data acquisition end for data acquisition, format conversion, and transmission of the converted acquisition data to the cloud database for storage; The data sharing center confirms the stored acquisition data from the cloud database based on the data request instructions of different requesters, locks the optimal sharing logic based on the sharing process, and executes the locked optimal sharing logic through the execution end, in the following manner: Based on the confirmed data request instruction, lock the data to be requested from the cloud database; Identify whether the data to be requested is in a data end being shared, if so, record the corresponding data end as a data end to be determined, if not, directly share the data to be requested to the specified data end, and execute the data sharing process by the execution end; Based on the determined pending data end, the associated sharing rate of the pending data in the sharing process is identified, and the mean value is processed to confirm the mean rate. Then, according to the remaining data capacity of the pending data, the pending time of the sharing completion is confirmed, which is the remaining data capacity ÷ the mean rate. Then, the data end associated with the data request instruction is recorded as the pending sharing data end. The data type of the pending data is identified, and the sharing record of the pending data end and the pending sharing data end is confirmed from the historical interaction data. The sharing rate associated with the corresponding data type is confirmed from the sharing record, and the associated multiple groups of different sharing rates are processed by mean value to lock the processing rate. Based on the classification data associated with different data types in the pending data and the corresponding processing rate, the sharing time associated with the corresponding classification data is confirmed, which is the classification data capacity ÷ the corresponding processing rate. The multiple groups of confirmed sharing times are summed to lock the total time, and the pending time and the total time are summed to confirm the first feature time. From the historical data, the sharing rate associated with different data types of the management end is identified, which is the mean value of the rate associated with the historical sharing process. Based on the data capacity of different classification data, the single feature time associated with different classification data is confirmed, which is the data capacity of different classification data ÷ the corresponding sharing rate. Then, the multiple groups of single feature times are summed to determine the total feature time and mark it as the second feature time. Based on the confirmed first feature time and second feature time, the minimum value is selected therefrom. If the first feature time is the minimum value, the optimal sharing logic is formulated, which is: the management end-pending data end-pending sharing data end. If the second feature time is the minimum value, the optimal sharing logic is: the management end-pending sharing data end.

[0007] Preferably, the data collection end collects information data associated with different historical and cultural information resource data interfaces, and transmits different information data associated with different data interfaces to the cloud database for storage.

[0008] Preferably, the cloud database stores the collected data after conversion processing in a specified collection period, and provides data sharing for the data sharing center.

[0009] Preferably, the execution end shares the associated pending data according to the formulated optimal sharing logic, including: If the optimal sharing logic is: the management end-pending data end-pending sharing data end, the pending data is preferentially shared into the pending data end, and then shared from the pending data end to the pending sharing data end. If the optimal sharing logic is: the management end-to-be shared data end, the data to be requested is directly shared into the to-be-shared data end.

[0010] The application provides a big data-based historical and cultural information resource visualized intelligent management platform. The application can synchronously control data collection of multiple data interfaces by locking optimal collection logic, ensure consistent collection time while ensuring collection rate, and accurately select and determine format according to factors such as data format conversion rate and data capacity by using scientific feature confirmation mode of the adjustment processing end, thereby avoiding data collection delay and eliminating data conversion confusion, greatly improving data collection efficiency and accuracy, and laying a solid foundation for subsequent data processing. Based on data request instructions, the optimal sharing logic is locked through complex and scientific calculation and analysis, the mean rate of data to be requested in the sharing process, the remaining data capacity, the data type and the historical sharing records are comprehensively considered, the required time of different sharing paths is compared, and the fastest data sharing logic is selected, so that the speed of data sharing is greatly improved, the data can timely and efficiently meet the needs of different requesters, and the value of historical and cultural information resources is fully played. BRIEF DESCRIPTION OF DRAWINGS

[0011] Figure 1 It is a schematic diagram of the principle framework of the application. DETAILED DESCRIPTION

[0012] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0013] First embodiment Please refer to Figure 1 The application provides a big data-based historical and cultural information resource visualized intelligent management platform, which comprises a data collection end, an adjustment processing end, a cloud database, a data sharing center and an execution end. The data collection end and the adjustment processing end are bidirectionally connected, the data collection end, the cloud database and the data sharing center are electrically connected from an output node to an input node in sequence, and the data sharing center is electrically connected with an input node of the execution end. The data collection end collects information data associated with different historical and cultural information resource data interfaces, and transmits different information data associated with different data interfaces to the cloud database for storage. The adjustment processing end confirms different data to be collected associated with different data interfaces in the set collection period, performs feature confirmation on each group of data to be collected, locks the optimal collection logic, and executes through the data collection end. Specifically, the so-called optimal collection logic is to synchronize the data of multiple data interfaces in the collection process, which not only guarantees the collection rate, but also guarantees the consistency of the related time of collection, so as to achieve a more optimal collection execution processing effect. The specific way of feature confirmation is as follows: Confirm the data format that the management platform can accept (such data formats are all preset formats, preset in the corresponding management platform), and record the confirmed data format as a pending format; Based on the set collection period, the collection period is a preset period, and its specific value is determined by the operator according to experience, which is generally determined according to the data generation period of several data interfaces. Based on the data protocol associated with different data interfaces, confirm the data to be collected in the corresponding collection period. Different data interfaces correspond to different data to be collected. Based on the multiple groups of data to be collected associated with the corresponding collection period, classify different data belonging to different data formats from the multiple groups of data to be collected, confirm different classification data sets (the data format of each classification data set is the same, from different data to be collected), and in the classification process, the corresponding classification data all have different classification marks, which will be used for data integration in the subsequent process. Record the data format associated with the classification data set as a pending format, identify the conversion records of different pending formats to different pending formats from historical data, and from the corresponding conversion records, process the associated conversion rates to confirm the rate characteristics between the pending format and the pending format. Specifically, the existing pending formats A, B, and C have associated pending formats D and F. In the conversion process, the conversion records of A-D and A-F exist, and their conversion records are associated with different conversion rates. In the mean value processing process, the conversion rates of A-D and A-F can confirm the rate characteristics associated with A-D and A-F. Similarly, B-D and B-F, C-D and C-F also have associated rate characteristics, which are confirmed and processed in turn to lock the corresponding feature parameters. Randomly select a group of pending formats as selected formats, and confirm the data capacity of different classification data sets, and mark it as R iwherein i represents different classification data sets, and based on the rate characteristics associated between the to-be-converted format and the to-be-determined format, the to-be-converted format and the to-be-determined format associated with the classification format and the selected format of the classification data set are confirmed (the to-be-converted format is consistent with the classification format, and the to-be-determined format is consistent with the selected format), and the associated rate characteristics are marked as V, using: R i ÷V=T i Confirm the feature time T associated with the classification data set i Confirm the different feature times T associated with the selected format for different classification data sets i And confirm the mean feature by averaging several groups of feature times T i And confirm the variance feature by again processing the variance, using: Total feature = mean feature × C1 + variance feature × C2 Confirm the total feature associated with the selected format, wherein C1 and C2 are both preset fixed coefficient factors, and their specific values are determined by the operator according to experience, and C1 generally takes a value of 0.546, and C2 generally takes a value of 0.454; In turn, different to-be-determined formats are taken as selected formats, and different total features associated with different selected formats are confirmed, and the minimum value is locked from the different total features associated, and the total feature associated with the minimum value is recorded as the determined feature, and the selected format associated with the determined feature is recorded as the determined format, and the determined format locked in the current collection period is transmitted to the data collection end for data collection, format conversion, and transmission of the converted collection data to the cloud database for storage; For example, after the classification data set is determined, different to-be-determined formats are taken in turn as different selected formats, and data conversion is performed, the classification data set is associated with different classification formats, and when the classification format is converted to the selected format, there are corresponding rate characteristics, and the time characteristics are confirmed in detail through the data capacity associated with the corresponding classification data set, and the comprehensive feature of time (that is, the total feature) is confirmed according to the confirmed time characteristics. In the confirmed total feature, the smaller the mean feature and the variance feature associated, the smaller the total value associated, and vice versa, the larger the total feature associated. The smaller the total feature associated with different selected formats, the closer the time characteristics associated, and the time associated with each processing process is more consistent, so that the data conversion process associated with each classification data set is more consistent, and the data processing process of the next period will not be delayed, so that the data conversion disorder will not occur.

[0014] Among them, the cloud database stores the converted collection data in the specified collection period, and provides data sharing for the data sharing center.

[0015] Second embodiment The data sharing center confirms the stored collection data from the cloud database based on the data request instruction of different requesters, locks the optimal sharing logic based on the sharing process, and executes the locked optimal sharing logic through the execution end, and the specific way of locking the optimal sharing logic is: Lock the to-be-requested data from the cloud database based on the confirmed data request instruction; Identify whether the to-be-requested data exists in a data end that is being shared (that is, a data end associated with a corresponding data interface), if it exists, mark the corresponding data end as a pending data end, if it does not exist, directly share the to-be-requested data to the specified data end, and execute the data sharing process by the execution end; Based on the determined pending data end, identify the sharing rate associated with the to-be-requested data in the sharing process, and perform mean value processing to confirm the mean rate, and then determine the pending time of sharing completion according to the remaining data capacity of the to-be-requested data, which is the remaining data capacity ÷ the mean rate, and then mark the data end associated with the data request instruction as a to-be-shared data end; Identify the data type of the to-be-requested data, and from the historical interaction data, confirm the sharing record of the pending data end and the to-be-shared data end, confirm the sharing rate associated with the corresponding data type from the sharing record, and perform mean value processing on the associated multiple groups of different sharing rates to lock the to-be-processed rate (different data types correspond to different to-be-processed rates), based on the classification data associated with different data types in the to-be-requested data and the corresponding to-be-processed rate, confirm the sharing time associated with the corresponding classification data, which is the classification data capacity ÷ the corresponding to-be-processed rate, and sum the confirmed multiple groups of sharing times to lock the total time, and sum the pending time and the total time to confirm the first feature time; From the historical data, identify the sharing rate associated with different data types by the management end, which is the rate mean value associated with the historical sharing process, and based on the data capacity of different classification data, confirm the single feature time associated with different classification data, which is the data capacity of different classification data ÷ the corresponding sharing rate, and then sum multiple groups of single feature times to determine the total feature time and label it as the second feature time; Based on the confirmed first feature time and second feature time, select the minimum value from them, if the first feature time is the minimum value, then the optimal sharing logic is determined, which is: the management end-pending data end-to-be-shared data end, if the second feature time is the minimum value, then the optimal sharing logic is: the management end-to-be-shared data end.

[0016] Specifically, corresponding data in the transmission process, generally exist a plurality of different data transmission logic, can be directly transmitted by the corresponding management end, also can be by the corresponding intermediate end for transmission, intermediate end can be other data end, using the fastest data sharing logic, to realize the fast sharing process of data.

[0017] The execution end, according to the optimal sharing logic, shares the associated data to be requested; If the optimal sharing logic is: this management end - pending data end - data to be shared, the data to be requested is shared to the pending data end first, and then the pending data end shares the data to be requested to the data to be shared. If the optimal sharing logic is: this management end - data to be shared, the data to be requested is directly shared to the data to be shared.

[0018] Some of the data in the above formula are dimensionless numerical calculations, and the contents not described in detail in the specification belong to the prior art known to those skilled in the art.

[0019] The above examples are only used to illustrate the technical method of the present application and are not limited. Although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical method of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical method of the present application.

Claims

1. A big data-based visualized intelligent management platform for historical and cultural information resources, characterized in that: include: Adjust the processing end, confirm the different data to be collected associated with different data interfaces within the set collection period, confirm the characteristics of each group of data to be collected, lock the optimal collection logic, and execute it through the data collection end. The data sharing center, based on the data request instructions from different requesters, confirms the collected data stored in the cloud database, locks the optimal sharing logic based on the sharing process, and executes the locked optimal sharing logic through the execution terminal.

2. The big data-based intelligent management platform for historical and cultural information resources, as described in claim 1, is characterized in that... The data acquisition terminal collects information data associated with different historical and cultural information resource data interfaces, and transmits different information data associated with different data interfaces to the cloud database for storage.

3. The big data-based visualized intelligent management platform for historical and cultural information resources according to claim 1, characterized in that, The specific method by which the adjustment processing terminal performs feature verification on each group of data to be collected is as follows: Confirm the data formats that this management platform can accept, and record the confirmed data formats as pending formats; Based on the set collection period, which is a preset period, and based on the data protocol associated with different data interfaces, the data to be collected within the corresponding collection period is confirmed. Different data interfaces correspond to different data to be collected. Based on multiple sets of data to be collected within the corresponding collection period, different data belonging to different data formats are classified from the multiple sets of data to be collected to identify different classification datasets. The data format associated with the classification dataset is denoted as the format to be converted. The conversion records of different formats to be converted to different formats to be determined are identified from the historical data. The average of several associated conversion rates in the corresponding conversion records is then processed to confirm the rate characteristics between the format to be converted and the format to be determined. Randomly select a set of undetermined formats as the chosen formats, determine the data capacity of different classification datasets, and label them as R0. i Where i represents different classification datasets, and based on the rate features associated with different formats to be converted and formats to be determined, the corresponding classification format and the selected format associated with this classification dataset are identified as the format to be converted and the format to be determined, and the associated rate features are labeled as V, using: R i ÷V=T i Identify the feature time T associated with this classification dataset i And confirm that this selected format is for the different feature times T associated with different classification datasets. i and several sets of characteristic times T i Perform mean processing to confirm the mean characteristics, and then perform variance processing again to confirm the variance characteristics. Use the formula: Total characteristics = Mean characteristics × C1 + Variance characteristics × C2 to confirm the total characteristics associated with this selected format, where C1 and C2 are preset fixed coefficient factors.

4. The big data-based visualized intelligent management platform for historical and cultural information resources according to claim 3, characterized in that, The specific method by which the adjustment processing terminal locks the optimal acquisition logic is as follows: Different pending formats are selected sequentially, and the different total features associated with the different selected formats are confirmed. Then, the minimum value is locked from the different total features associated with it, and the total feature associated with the minimum value is recorded as the determined feature. The selected format associated with the determined feature is recorded as the determined format. The determined format locked in this collection cycle is transmitted to the data collection terminal for data collection and format conversion. The collected data after conversion is transmitted to the cloud database for storage.

5. The big data-based visualized intelligent management platform for historical and cultural information resources according to claim 4, characterized in that, The cloud database stores the processed data collected within a specified collection period and provides it for data sharing with the data sharing center.

6. The big data-based visualized intelligent management platform for historical and cultural information resources according to claim 1, characterized in that, The specific method by which the data sharing center locks the optimal sharing logic is as follows: Based on the confirmed data request instruction, the requested data is retrieved from the cloud database; The system identifies whether the requested data exists on a data end that is being shared. If it does, the corresponding data end is marked as a pending data end. If it does not exist, the requested data is directly shared to the specified data end, and the execution end executes the data sharing process. Based on the identified pending data endpoint, identify the sharing rate associated with the requested data in the sharing process, perform averaging, confirm the average rate, and then, based on the remaining data capacity of the requested data, confirm the pending time for sharing to be completed. The pending time = remaining data capacity ÷ average rate. Then, record the data endpoint associated with the data request instruction as the data endpoint to be shared. Identify the data type of the data to be requested, and confirm the sharing records between the pending data end and the data to be shared from historical interaction data. Confirm the sharing rate associated with the corresponding data type from the sharing records, and average the multiple sets of different sharing rates associated with each other to lock the processing rate. Based on the classification data associated with different data types in the data to be requested and the corresponding processing rates, confirm the sharing time associated with the corresponding classification data. The sharing time = classification data capacity ÷ corresponding processing rate. Sum the confirmed multiple sets of sharing times to lock the total time. Sum the pending time with the total time to confirm the first characteristic time. From historical data, identify the sharing rate associated with different data types in this management terminal. The sharing rate is the average rate associated in the historical sharing process. Based on the data capacity of different categories of data, confirm the single feature time associated with different categories of data. The single feature time = data capacity of different categories of data ÷ corresponding sharing rate. Then sum up multiple sets of single feature times to determine the total feature time and mark it as the second feature time. Based on the confirmed first characteristic time and second characteristic time, the minimum value is selected. If the first characteristic time is the minimum value, the optimal sharing logic is proposed as follows: this management terminal - pending data terminal - pending data terminal. If the second characteristic time is the minimum value, the optimal sharing logic is as follows: this management terminal - pending data terminal.

7. The big data-based visualized intelligent management platform for historical and cultural information resources according to claim 6, characterized in that, The execution end performs sharing processing on the associated requested data according to the proposed optimal sharing logic.

8. The big data-based visualized intelligent management platform for historical and cultural information resources according to claim 7, characterized in that, The execution process of the execution terminal includes: If the optimal sharing logic is: this management terminal - pending data terminal - pending data terminal, the data to be requested is first shared to the pending data terminal, and then the pending data terminal shares the data to be requested to the pending data terminal. If the optimal sharing logic is: This management terminal - the data terminal to be shared, directly share the data to be requested to the data terminal to be shared.

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