Dynamic regulation and control system for data resource integration and optimization in database

Abstract

The invention provides a dynamic regulation and control system for data resource integration and optimization in a database, and relates to the field of electric digital data processing, the dynamic regulation and control system comprises a data access module, a resource integration module, an optimization regulation and control module and an output service module, the data access module is used for accessing various structured or unstructured data resources, and the resource integration module is used for integrating and optimizing the data resources; the resource integration module is used for realizing data structure unification and content integration to form a logically consistent resource pool, the optimization regulation and control module is used for performing dynamic scheduling and structure optimization on integrated data resources, and the output service module is used for providing data support service for an upper-layer system or a terminal; according to the system, on the premise of ensuring data integrity and consistency, efficient integration and intelligent regulation and control of data resources can be realized, and the database access performance and the resource utilization rate are improved.

Description

Technical Field

[0001] This invention relates to the field of electronic digital data processing, and more specifically to a dynamic control system for data resource integration and optimization in a database. Background Technology

[0002] With the continuous development of information technology infrastructure, the amount of data that enterprises and institutions need to store and manage in their database systems is growing exponentially, and the data sources are becoming increasingly diversified, including structured, semi-structured, and unstructured data. Because different data sources differ in format, field definitions, and semantic meaning, directly integrating them into the same database can lead to problems such as inconsistent structure, high redundancy, and decreased query efficiency. Furthermore, existing database scheduling mechanisms mostly employ static or semi-static strategies, lacking the dynamic awareness of real-time access behavior and data importance. This results in significantly increased system response latency and reduced resource utilization under high-concurrency access or uneven data distribution.

[0003] Currently, common algorithms in the field of data integration and scheduling mainly include:

[0004] Field merging algorithm based on static mapping:

[0005] This method maps fields from different data sources to a unified structure by pre-defining a mapping table, making it suitable for scenarios with stable data structures and low change frequency. Its advantages are simple implementation and low operating cost, but its disadvantages include a lack of flexibility; the mapping table needs to be manually maintained when data source fields change, making it unable to cope with dynamically changing and complex environments.

[0006] Access allocation algorithm based on round-robin scheduling:

[0007] This method uses a fixed round-robin approach to distribute access requests in a multi-node database environment, ensuring load balancing among nodes. Its advantages include balanced distribution and avoiding single-node overload, but its disadvantage is that it doesn't consider the distribution and priority of data access hotspots, which may result in frequently accessed data being distributed across multiple nodes, increasing access latency.

[0008] Hot data scheduling algorithm based on cache hit rate:

[0009] This method improves access speed by monitoring data access logs, calculating the cache hit rate of each data item, and prioritizing data with high hit rates for entry into the high-speed cache. Its advantage is a significant reduction in access latency for frequently accessed data. However, its disadvantages include over-reliance on historical access records, slow response to sudden surges in hot data, and the potential for cold data to remain in the cache for extended periods, consuming valuable memory resources.

[0010] In summary, existing methods have their advantages in different scenarios, but they generally suffer from the following shortcomings: First, they lack multi-dimensional data quality verification and semantic alignment capabilities, making it difficult to guarantee data consistency and availability; second, scheduling strategies lack adaptive adjustment mechanisms, failing to optimize in real time based on access behavior and system status; and third, they lack the collaborative processing capabilities for unified integration and dynamic control of multi-source heterogeneous data. Therefore, there is an urgent need for a database system that integrates data integration and intelligent scheduling functions, and possesses dynamic control capabilities, to improve the accuracy of data management and access performance.

[0011] Many database systems have been developed. Extensive research and reference have revealed existing systems such as the one disclosed in publication number CN105677751B. These systems generally include the following methods: receiving business data and obtaining its attribute information; matching the business data against a pre-set scheduling engine database based on its attribute information to determine the corresponding business database type and transmission channel; and transmitting and storing the business data through the transmission channel in the determined business database. However, this system focuses on fixed-policy control and lacks adaptive and behavior-aware capabilities. Summary of the Invention

[0012] The purpose of this invention is to address the shortcomings by proposing a dynamic control system for data resource integration and optimization in a database.

[0013] The present invention adopts the following technical solution:

[0014] A dynamic control system for data resource integration and optimization in a database includes a data access module, a resource integration module, an optimization and control module, and an output service module.

[0015] The data access module is used to access various structured or unstructured data resources; the resource integration module is used to achieve unified data structure and content integration to form a logically consistent resource pool; the optimization and control module is used to dynamically schedule and optimize the structure of the integrated data resources; and the output service module is used to provide data support services to the upper-layer system or terminal.

[0016] This system employs a multi-module design to ensure effective management and optimization of data at every stage, from access to final output. By organically combining different modules, the system can cope with increasingly complex data environments, enabling dynamic data scheduling and efficient integration, thereby improving overall system performance and response speed.

[0017] The data access module includes an interface access unit, a data parsing unit, and a data verification unit. The interface access unit supports access methods such as databases, API interfaces, and file systems. The data parsing unit is used to standardize data of different formats. The data verification unit is used to verify the field integrity and format validity of the accessed data.

[0018] In the data access module, the interface access unit acts as an external data access bridge for the system, supporting multiple access methods such as databases, API interfaces, and file systems, further enhancing the system's flexibility and scalability. Through the combined efforts of the data parsing unit and the data verification unit, the system can ensure the consistency of the accessed data in terms of format and perform necessary integrity checks, avoiding potential errors and inconsistencies caused by data format issues.

[0019] The resource integration module includes a metadata management unit, a data alignment unit, and a redundancy identification and merging unit. The metadata management unit is used to record metadata information such as data source, format, and update time. The data alignment unit implements field standardization and semantic alignment based on rules or mapping tables. The redundancy identification and merging unit is used to identify duplicate or similar data entries and perform aggregation processing.

[0020] As one of the core modules, the resource integration module is responsible for the unified processing and integration of access data. The metadata management unit provides comprehensive monitoring of all data resources, ensuring the system can track key information such as data source, format, and update time. The data alignment unit standardizes data through rules or mapping tables, resolving semantic inconsistencies between different data sources and creating conditions for subsequent data aggregation and integration. The redundancy identification and merging unit uses an intelligent deduplication mechanism to identify and merge duplicate or similar entries, thereby reducing redundant data storage and improving resource pool utilization.

[0021] The optimization and control module includes a control strategy engine unit, a resource scheduling unit, and a structure optimization unit. The control strategy engine unit defines control strategies based on access frequency and data importance. The resource scheduling unit implements real-time loading, caching, and migration of data according to the strategies. The structure optimization unit dynamically optimizes the integrated data structure.

[0022] The optimization and control module plays a crucial role in the system. It ensures optimal storage and scheduling of integrated data and dynamically adjusts strategies based on different data usage scenarios. The control strategy engine unit formulates the most suitable control strategy based on access frequency and data importance, providing a basis for system resource scheduling. Guided by the strategy, the resource scheduling unit performs real-time loading, caching, and migration of data. Through this dynamic scheduling, the system maximizes data access efficiency and minimizes resource waste. The structure optimization unit dynamically optimizes the integrated data structure to ensure high efficiency in data storage, querying, and access.

[0023] The output service module includes a query service unit, a response optimization unit, and a permission control unit. The query service unit provides standardized SQL or API interfaces to the outside world. The response optimization unit preloads hot data based on query history to improve response speed. The permission control unit filters accessible data resources based on user permissions.

[0024] The output service module enables the system to provide stable and reliable data support for upper-layer applications or terminals. The query service unit ensures that data query operations can be completed easily and efficiently through standardized SQL or API interfaces. The response optimization unit preloads hot data using query history information; this mechanism significantly improves the system's response speed, especially in large-scale data query scenarios, effectively reducing response time. The access control unit, through granular access management, ensures that users can only access the data they are authorized to view, guaranteeing data security and compliance.

[0025] Furthermore, the regulation strategy engine unit includes a strategy definition processor, a behavior analysis processor, and a strategy adjustment processor. The strategy definition processor is used to configure rule-based data regulation strategies, the behavior analysis processor is used to analyze user access behavior and generate a data access popularity model, and the strategy adjustment processor is used to dynamically adjust strategy parameters according to the system operating status.

[0026] This design further enhances the system's flexibility and intelligence. The policy definition processor enables personalized control of different data resources through rule-based configuration. The behavior analysis processor generates a data access frequency model through in-depth analysis of user access behavior. This model accurately reflects which data is accessed frequently, thus providing a scientific basis for data scheduling and optimization. The policy adjustment processor dynamically adjusts policy parameters, enabling the system to adjust resource scheduling strategies in real time according to its operating status, ensuring efficient and stable system operation.

[0027] Furthermore, the behavior analysis processor calculates the popularity index H of each resource data according to the following formula:

[0028]

[0029] Among them, f (t) f represents the access frequency of resource data in the current period. (t-i) The frequency of access to resource data before the i-th period is represented by m, the number of memory periods is δ, the decay rate is α, the current period coefficient is β, the recent period coefficient is γ, the historical period coefficient is t, and the current period number is H. (t) This indicates the current period's popularity index.

[0030] The popularity index calculation formula is one of the core functions of the behavior analysis processor. It dynamically calculates the popularity index H for each piece of resource data based on multiple dimensions such as access frequency, memory cycles, and decay rate. This popularity index not only reflects the current popularity of the data but also weighs the importance of the data based on historical access behavior. This calculation mechanism enables the system to more intelligently identify hot data, thereby enabling more precise resource scheduling.

[0031] Furthermore, the policy adjustment processor updates the priority threshold θ according to the following formula:

[0032]

[0033] Where, θ t θ is the priority threshold for the current period. t+1 λ is the priority threshold for the next cycle, λ is the sensitivity adjustment factor, and σH is the standard deviation of all heat values. This represents the average popularity during the current period.

[0034] The policy adjustment processor enables the system to adjust the priority of data resources in real time by dynamically updating priority thresholds, ensuring that frequently accessed data receives timely responses. By adjusting the priority thresholds, the system can flexibly optimize resource allocation based on real-time conditions, ensuring efficient data access.

[0035] Furthermore, the resource scheduling unit includes a data loading scheduler, a cache management processor, and a migration control processor. The data loading scheduler is used to control the loading order and priority of data. The cache management processor is used to cache hot data locally to reduce access latency. The migration control processor is used to schedule data storage locations among multiple nodes.

[0036] The data loading scheduler calculates the scheduling priority P of each piece of data according to the following formula:

[0037]

[0038] Where ρ is the read / write pressure coefficient of resource data;

[0039] The higher the scheduling priority, the earlier the loading order, and the earlier it will be scheduled to the local cache and the node with high access efficiency.

[0040] By calculating scheduling priorities, the data loading scheduler can prioritize various data resources, ensuring that the system can efficiently complete data loading, caching, and scheduling tasks when processing large amounts of data.

[0041] The beneficial effects achieved by this invention are:

[0042] This system achieves intelligent management of the entire process from data access and resource integration to optimized scheduling and service output by introducing a multi-module collaborative dynamic control mechanism into the database. Through technologies such as heat analysis, dynamic threshold adjustment, and priority scheduling, it can adaptively allocate storage and computing resources based on real-time access behavior and data importance, reducing data loading latency and improving system response speed. The output service module supports standardized SQL and API interfaces, and combined with access control and hotspot preloading strategies, it improves the security and efficiency of data access.

[0043] To further understand the features and technical content of the present invention, please refer to the following detailed description and drawings of the present invention. However, the drawings provided are for reference and illustration only and are not intended to limit the present invention. Attached Figure Description

[0044] Figure 1 This is a schematic diagram of the overall structural framework of the present invention;

[0045] Figure 2 This is a schematic diagram of the data access module of the present invention;

[0046] Figure 3 This is a schematic diagram of the resource integration module of the present invention;

[0047] Figure 4 This is a schematic diagram of the optimized control module of the present invention;

[0048] Figure 5 This is a schematic diagram of the output service module of the present invention;

[0049] Figure 6 This is a comparison chart of the response time performance of the present invention and a conventional system;

[0050] Figure 7 This is a comparison chart showing the data loading power efficiency of the present invention and a conventional system under different scenarios. Detailed Implementation

[0051] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can understand the advantages and effects of the present invention from the content disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of the present invention. Furthermore, the accompanying drawings of the present invention are for simple illustrative purposes only and are not depictions of actual dimensions; this is stated beforehand. The following embodiments will further describe the relevant technical content of the present invention in detail, but the disclosed content is not intended to limit the scope of protection of the present invention.

[0052] Example 1.

[0053] This embodiment provides a dynamic control system for data resource integration and optimization in a database, combined with... Figure 1 It includes a data access module, a resource integration module, an optimization and control module, and an output service module;

[0054] The data access module is used to access various structured or unstructured data resources; the resource integration module is used to achieve unified data structure and content integration to form a logically consistent resource pool; the optimization and control module is used to dynamically schedule and optimize the structure of the integrated data resources; and the output service module is used to provide data support services to the upper-layer system or terminal.

[0055] The data access module includes an interface access unit, a data parsing unit, and a data verification unit. The interface access unit supports access methods such as databases, API interfaces, and file systems. The data parsing unit is used to standardize data of different formats. The data verification unit is used to verify the field integrity and format validity of the accessed data.

[0056] The resource integration module includes a metadata management unit, a data alignment unit, and a redundancy identification and merging unit. The metadata management unit is used to record metadata information such as data source, format, and update time. The data alignment unit implements field standardization and semantic alignment based on rules or mapping tables. The redundancy identification and merging unit is used to identify duplicate or similar data entries and perform aggregation processing.

[0057] The optimization and control module includes a control strategy engine unit, a resource scheduling unit, and a structure optimization unit. The control strategy engine unit defines control strategies based on access frequency and data importance. The resource scheduling unit implements real-time loading, caching, and migration of data according to the strategies. The structure optimization unit dynamically optimizes the integrated data structure.

[0058] The output service module includes a query service unit, a response optimization unit, and a permission control unit. The query service unit provides standardized SQL or API interfaces to the outside world. The response optimization unit preloads hot data based on query history to improve response speed. The permission control unit filters accessible data resources based on user permissions.

[0059] The control strategy engine unit includes a strategy definition processor, a behavior analysis processor, and a strategy adjustment processor. The strategy definition processor is used to configure rule-based data control strategies. The behavior analysis processor is used to analyze user access behavior and generate a data access popularity model. The strategy adjustment processor is used to dynamically adjust strategy parameters according to the system operating status.

[0060] The behavior analysis processor calculates the popularity index H of each piece of resource data according to the following formula:

[0061]

[0062] Among them, f (t) f represents the access frequency of resource data in the current period. (t-i) The frequency of access to resource data before the i-th period is represented by m, the number of memory periods is δ, the decay rate is α, the current period coefficient is β, the recent period coefficient is γ, the historical period coefficient is t, and the current period number is H. (t) This indicates the current period's popularity index.

[0063] The strategy adjustment processor updates the priority threshold θ according to the following formula:

[0064]

[0065] Where, θ t θ is the priority threshold for the current period. t+1 λ is the priority threshold for the next cycle, λ is the sensitivity adjustment factor, and σH is the standard deviation of all heat values. This represents the average popularity during the current period.

[0066] The resource scheduling unit includes a data loading scheduler, a cache management processor, and a migration control processor. The data loading scheduler is used to control the loading order and priority of data. The cache management processor is used to cache hot data locally to reduce access latency. The migration control processor is used to schedule data storage locations among multiple nodes.

[0067] The data loading scheduler calculates the scheduling priority P of each piece of data according to the following formula:

[0068]

[0069] Where ρ is the read / write pressure coefficient of resource data;

[0070] The higher the scheduling priority, the earlier the loading order, and the earlier it will be scheduled to the local cache and the node with high access efficiency.

[0071] Example 2.

[0072] This embodiment includes all the contents of Embodiment 1, and provides a dynamic control system for data resource integration and optimization in a database, including a data access module, a resource integration module, an optimization control module and an output service module;

[0073] The data access module is used to access various structured or unstructured data resources; the resource integration module is used to achieve unified data structure and content integration to form a logically consistent resource pool; the optimization and control module is used to dynamically schedule and optimize the structure of the integrated data resources; and the output service module is used to provide data support services to the upper-layer system or terminal.

[0074] Combination Figure 2 The data access module includes an interface access unit, a data parsing unit, and a data verification unit. The interface access unit supports access methods such as databases, API interfaces, and file systems. The data parsing unit is used to standardize data of different formats. The data verification unit is used to verify the field integrity and format validity of the accessed data.

[0075] Combination Figure 3 The resource integration module includes a metadata management unit, a data alignment unit, and a redundancy identification and merging unit. The metadata management unit is used to record metadata information such as data source, format, and update time. The data alignment unit implements field standardization and semantic alignment based on rules or mapping tables. The redundancy identification and merging unit is used to identify duplicate or similar data entries and perform aggregation processing.

[0076] Combination Figure 4 The optimization and control module includes a control strategy engine unit, a resource scheduling unit, and a structure optimization unit. The control strategy engine unit defines control strategies based on access frequency and data importance. The resource scheduling unit implements real-time loading, caching, and migration of data according to the strategies. The structure optimization unit dynamically optimizes the integrated data structure.

[0077] Combination Figure 5 The output service module includes a query service unit, a response optimization unit, and a permission control unit. The query service unit is used to provide standardized SQL or API interfaces to the outside world. The response optimization unit preloads hot data based on query history to improve response speed. The permission control unit filters accessible data resources based on user permissions.

[0078] The interface access unit includes a database connection processor, an API request manager, and a file access manager. The database connection processor is used to establish connections with mainstream databases, the API request manager is used to manage API data request and response processing, and the file access manager is used to implement access to and reading of the local file system.

[0079] The data parsing unit includes a format recognition processor, a structure conversion processor, and a type adaptation processor. The format recognition processor is used to automatically identify the data format, the structure conversion processor is used to convert metadata into a unified data model defined by the system, and the type adaptation processor is used to automatically match and convert the data types of the fields.

[0080] The data verification unit includes a field integrity detector, a format validity validator, and an anomaly record processor. The field integrity detector is used to determine whether the data is missing key fields. The format validity validator is used to verify whether the values ​​of the data fields conform to the rules. The anomaly record processor is used to record all abnormal fields and data entries.

[0081] The metadata management unit includes a metadata collector, a metadata storage unit, and a data version manager. The metadata collector is used to collect information related to the data source, the metadata storage unit is used to store the collected metadata in a structured manner, and the data version manager records the version information of the same data at different times.

[0082] The data alignment unit includes a field mapping processor, a semantic parsing processor, and a unified model processor. The field mapping processor is used to convert field names and structures according to a configured mapping table. The semantic parsing processor is used to parse the consistency of field semantic implementation logic. The unified model converter is used to convert the processed data into a unified data structure model.

[0083] The redundancy identification and merging unit includes a similarity calculator, an aggregation strategy manager, and a conflict resolution processor. The similarity calculator is used to identify data items with similar content through a fuzzy matching algorithm. The aggregation strategy manager is used to define the merging method for multiple duplicate data. The conflict resolution processor is used to provide automatic correction suggestions for conflict fields.

[0084] The control strategy engine unit includes a strategy definition processor, a behavior analysis processor, and a strategy adjustment processor. The strategy definition processor is used to configure rule-based data control strategies. The behavior analysis processor is used to analyze user access behavior and generate a data access popularity model. The strategy adjustment processor is used to dynamically adjust strategy parameters according to the system operating status.

[0085] The behavior analysis processor calculates the popularity index H of each piece of resource data according to the following formula:

[0086]

[0087] Among them, f (t) f represents the access frequency of resource data in the current period. (t-i) The frequency of access to resource data before the i-th period is represented by m, the number of memory periods is δ, the decay rate is α, the current period coefficient is β, the recent period coefficient is γ, the historical period coefficient is t, and the current period number is H. (t) This indicates the current period's popularity index;

[0088] The strategy adjustment processor updates the priority threshold θ according to the following formula:

[0089]

[0090] Where, θ t θ is the priority threshold for the current period. t+1 λ is the priority threshold for the next cycle, λ is the sensitivity adjustment factor, and σH is the standard deviation of all heat values. This represents the average popularity over the current period.

[0091] The resource scheduling unit includes a data loading scheduler, a cache management processor, and a migration control processor. The data loading scheduler is used to control the loading order and priority of data. The cache management processor is used to cache hot data locally to reduce access latency. The migration control processor is used to schedule data storage locations among multiple nodes.

[0092] The data loading scheduler calculates the scheduling priority P of each piece of data according to the following formula:

[0093]

[0094] Where ρ is the read / write pressure coefficient of resource data;

[0095] The higher the scheduling priority, the earlier the loading order, and the earlier it will be scheduled to the local cache and the node with high access efficiency.

[0096] The structure optimization unit includes an index optimization processor, a data partition manager, and a redundant structure cleaner. The index optimization processor is used to automatically recommend and create the optimal index based on the query log. The data partition manager is used to split the large table according to multiple dimensions such as time and region and manage the dimension information. The redundant structure cleaner is used to identify and clean up invalid indexes, empty table duplicate structures.

[0097] The query service unit includes an interface definition processor, a semantic translation processor, and a query scheduling processor. The interface definition processor is used to configure standard API interface information. The semantic translation processor is used to convert user natural language or simplified queries into standard SQL. The query scheduling processor selects the optimal query path based on the data distribution.

[0098] The response optimization unit includes a hotspot identification processor, a preloading processor, and a latency analysis processor. The hotspot identification processor is used to count frequently accessed data fields and table entries. The preloading processor is used to load hotspot data in advance during system idle time. The latency analysis processor is used to analyze interface response time and feed it back to the structure optimization unit.

[0099] The access control unit includes an identity authentication processor, an access rule manager, and an access log recorder. The identity authentication processor is used to access the authentication mechanism and verify the identity of the visitor. The access rule manager is used to set access rules based on roles, organizations, or resource attributes. The access log recorder is used to record all access behaviors for easy auditing and tracking.

[0100] The 'i' mentioned above is used to indicate the ordinal number of the sequence number and has no actual meaning.

[0101] The following is a portion of the code for this system:

[0102]

[0103]

[0104]

[0105] We will now use test samples to conduct experimental tests on this system and a general system, obtain response time and loading success rate data, and organize them into... Figure 6 and Figure 7 The comparison chart.

[0106] The content disclosed above is only a preferred and feasible embodiment of the present invention, and is not intended to limit the scope of protection of the present invention. Therefore, all equivalent technical changes made based on the content of the present invention specification and drawings are included within the scope of protection of the present invention. Furthermore, the elements therein can be updated as technology develops.

Claims

1. A dynamic regulation system for data resource integration optimization in a database, characterized in that, The system comprises a data access module, a resource integration module, an optimization control module and an output service module. The data access module is configured to access various structured or unstructured data resources, the resource integration module is configured to realize data structure unification and content integration to form a logically consistent resource pool, the optimization control module is configured to dynamically schedule and optimize the integrated data resources, and the output service module is configured to provide data support services for upper-layer systems or terminals. The data access module comprises an interface access unit, a data analysis unit and a data verification unit, the interface access unit is configured to support database, API interface and file system access modes, the data analysis unit is configured to standardize different format data, and the data verification unit is configured to verify the field integrity and format legality of the accessed data. The resource integration module comprises a metadata management unit, a data alignment unit and a redundancy identification and merging unit, the metadata management unit is configured to record metadata such as data source, format and update time, the data alignment unit is configured to realize field standardization and semantic alignment based on rules or mapping tables, and the redundancy identification and merging unit is configured to identify duplicate or similar data entries and perform aggregation processing. The optimization control module comprises a control strategy engine unit, a resource scheduling unit and a structure optimization unit, the control strategy engine unit is configured to define control strategies according to access frequency and data importance, the resource scheduling unit is configured to realize real-time loading, caching and migration of data according to the strategies, and the structure optimization unit is configured to dynamically optimize the integrated data structure. The output service module comprises a query service unit, a response optimization unit and a permission control unit, the query service unit is configured to provide standardized SQL or API interfaces to the outside, the response optimization unit is configured to pre-load hot data to improve response speed according to query history, and the permission control unit is configured to filter accessible data resources according to user permissions.

2. The dynamic regulation system for data resource integration optimization in a database of claim 1, wherein, The control strategy engine unit comprises a strategy definition processor, a behavior analysis processor and a strategy adjustment processor, the strategy definition processor is configured to configure rule-based data control strategies, the behavior analysis processor is configured to analyze user access behaviors and generate a data access popularity model, and the strategy adjustment processor is configured to dynamically adjust strategy parameters according to system running states.

3. The dynamic regulation system for data resource integration optimization in a database according to claim 2, wherein, The behavior analysis processor calculates the popularity index H of each resource data according to the following formula: Wherein, f (t) represents the access frequency of the current period resource data, f (t-i) represents the access frequency of the resource data before the i th period, m is the number of memory periods, δ is the decay rate, α is the current period coefficient, β is the recent period coefficient, γ is the historical period coefficient, t is the current period number, H (t) represents the heat index of the current period.

4. The dynamic regulation system for data resource integration optimization in a database of claim 3, wherein, The strategy adjustment processor updates the priority threshold θ according to the following formula: where θ t is the priority threshold for the current cycle, θ t+1 is the priority threshold for the next cycle, λ is the adjustment sensitivity factor, and σH is the standard deviation of all heat values, is the average heat value for the current cycle.

5. The dynamic regulation system for data resource integration optimization in a database according to claim 4, wherein, The resource scheduling unit comprises a data loading scheduler, a cache management processor and a migration control processor, the data loading scheduler is configured to control the loading order and priority of data, the cache management processor is configured to locally cache hot data to reduce access delay, and the migration control processor is configured to schedule data storage locations among multiple nodes. The data loading scheduler calculates the scheduling priority P of each data according to the following formula: Wherein, ρ is the read-write pressure coefficient of resource data. The higher the scheduling priority is, the earlier the loading order is, and the data is scheduled to local cache and nodes with high access efficiency first.

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