Heterogeneous database integration method for railway office system based on data conversion technology

Abstract

The invention discloses a method for integrating heterogeneous databases of a railway office system based on data conversion technology, and relates to the technical field of data integration. The method realizes seamless integration of heterogeneous databases in a railway office system by uniformly designing a standard model of route data and combining an AI intelligent mapping algorithm. The scheme adopts a distributed architecture to parallelly process data conversion tasks, dynamically monitors node load, conversion speed and parallel acceleration ratio, and ensures system stability under high-load scenarios. The universal adaptation layer supports the integration of multiple protocols and multiple interfaces, and effectively improves the overall performance. At the same time, the scheme introduces a multi-level security mechanism and consistency verification, and combines dynamic encryption, authority management and data redundant backup to ensure the integrity and security of data flow, and solves the problems of complex data integration, poor compatibility and performance bottlenecks.

Description

Technical Field

[0001] The invention relates to the technical field of data integration, in particular to a railway office system heterogeneous database integration method based on data conversion technology. Background Art

[0002] Railway office systems usually involve the integration of multiple heterogeneous databases, including relational databases, document databases, and graph databases. These database systems carry different data types of the railway system, such as passenger information, freight transportation records, and track equipment status. Due to the wide range of data sources and diverse formats, the integration and management of heterogeneous databases are challenging. Therefore, it is crucial to design efficient data integration solutions and real-time data synchronization mechanisms to ensure data consistency and integrity between systems, support real-time decision-making, and optimize railway operation management.

[0003] However, the heterogeneous database technology in the current railway office system has the following technical disadvantages:

[0004] 1. Low data conversion efficiency: Traditional ETL tools and conversion technologies are difficult to cope with complex heterogeneous database systems. The data conversion process is lengthy and prone to delays. Especially when facing large-scale real-time data processing, it is difficult to meet the high timeliness requirements of the scheduling system.

[0005] 2. Inconsistent data models: In existing methods, the data models of heterogeneous databases lack unified standards, resulting in inaccurate data mapping, data loss, format errors, and other problems, which affect the accuracy and consistency of dispatching and commanding;

[0006] 3. Insufficient adaptability and scalability: When integrating different databases, common solutions require a lot of customized adaptation work and poor interface compatibility. Once the database system is upgraded or expanded, the newly added system modules are difficult to integrate quickly, affecting the flexibility of the system. Summary of the invention

[0007] In view of the deficiencies of the prior art, the present invention provides a method for integrating heterogeneous databases of a railway office system based on data conversion technology, which solves the technical shortcomings of the background technology such as complex data integration, poor system compatibility and performance bottlenecks.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: A railway office system heterogeneous database integration method based on data conversion technology, comprising:

[0009] S1. Sort out the data structures of different databases, design a unified standard model for route data, and define the standard format of key data fields;

[0010] S2. Use AI-based intelligent mapping algorithms to automatically generate data conversion rules, and dynamically adjust mapping rules and the degree of automation of data conversion according to changes in data sources;

[0011] S3, using a distributed architecture to distribute data conversion tasks to multiple nodes for parallel execution, and verifying the system performance of the entire process by gradually calculating and evaluating the conversion speed index Zhzs, node load index Jdfz and parallel speedup ratio Bjsb;

[0012] S4. Develop a universal adaptation layer that integrates multiple protocol conversion functions and supports seamless integration of different database interfaces and protocols. Verify whether the adaptation layer can seamlessly integrate different protocols and interfaces, and finally evaluate the impact of the new database on the overall system performance.

[0013] S5. During the data conversion process, real-time monitoring of data flow, conversion status and performance indicators is performed, and dynamic resource scheduling is performed according to the system load, while generating real-time monitoring logs and analysis reports;

[0014] S6. Introduce multi-level security mechanisms and consistency checks in data conversion and transmission to ensure the integrity and security of data when it flows across systems, so as to avoid scheduling errors caused by data inconsistency or security vulnerabilities.

[0015] Preferably, the S1 comprises:

[0016] S11. Analyze the data structures in different databases, identify and classify key data fields, table structures and data relationships, and collect core data items in railway freight and passenger routes, including station information, route maps and dispatching rules;

[0017] S12. Based on the analysis results, design a unified route data standard model, establish a consistent data structure and field naming rules across databases, and cover all core data items involved in freight and passenger dispatching and provide mapping rules for different data sources of core data items;

[0018] S13. For core data items, develop unified standard formats and encoding rules for seamless conversion and matching of similar fields in different databases during integration, including data types, lengths, and encoding formats.

[0019] Preferably, S2 includes:

[0020] S21. Through AI-based intelligent mapping algorithms, the data structures and fields of different databases are analyzed to automatically generate data conversion rules;

[0021] S22. Monitor changes in data sources in real time, including data structure updates and field additions, and dynamically adjust mapping rules by leveraging AI algorithms.

[0022] Preferably, S3 includes:

[0023] S31. During the parallel processing, the data conversion speed of multiple nodes is monitored and evaluated in real time. After obtaining the relevant data of the nodes and communication delays through real-time logging and monitoring tools, the relevant data of the nodes and communication delays are dimensionlessly processed, and the conversion speed index Zhzs is calculated by the following formula:

[0024]

[0025] Wherein, Sxl represents the difference ratio of upstream and downstream data flow rates in the data related to the node and communication delay, Wyc represents the micro data fragmentation delay in the data related to the node and communication delay, Hcm represents the cache hit deviation rate in the data related to the node and communication delay, and Tdp represents the synchronization jitter offset in the data related to the node and communication delay;

[0026] The preset conversion speed threshold Q is compared with the conversion speed index Zhzs for evaluation. The specific contents are as follows:

[0027] If the preset conversion speed threshold Q> conversion speed index Zhzs, it means that the data conversion speed of the system is unqualified. At this time, the abnormal conversion speed result is generated and corresponding adjustments are made, including adjusting the node distribution, adjusting the cache strategy, and adjusting the synchronization mechanism;

[0028] If the preset conversion speed threshold Q ≤ conversion speed index Zhzs, it means that the data conversion speed of the system is qualified, and the node load of the system is evaluated at this time.

[0029] Preferably, S3 further includes:

[0030] S32. By combining the system monitoring tool with the node's local resource monitoring plug-in, resource usage data and node load-related data are collected regularly, including the node CPU utilization Cly, the node memory utilization Nsy, and the node IO throughput Ttl; after extracting the node CPU utilization Cly, the node memory utilization Nsy, and the node IO throughput Ttl and performing dimensionless processing, the node load index Jdfz is calculated using the following formula:

[0031]

[0032] By comparing the preset node load threshold W with the node load index Jdfz, the specific evaluation content is as follows:

[0033] If the node load threshold W ≥ the node load index Jdfz, it means that the node load is normal and no load adjustment is required. The processing efficiency of parallel execution is further evaluated.

[0034] If the node load threshold W is less than the node load index Jdfz, it means that the node load is abnormal and there is a resource overload problem. At this time, measures should be taken immediately to adjust the load, including task reallocation, node resource expansion or load balancing adjustment.

[0035] Preferably, S3 further includes:

[0036] S33. By combining system logs, node monitoring and timestamp analysis, data related to parallel execution processing is collected. Then, the data related to parallel execution processing is dimensionlessly processed and the parallel speedup ratio Bjsb is calculated by the following formula:

[0037]

[0038] Wherein, Lwr represents the microtask switching frequency in the processing-related data executed in parallel, Lhc represents the cache prefetch error rate in the processing-related data executed in parallel, Lsj represents the data sharding conflict rate in the processing-related data executed in parallel, and Ljd represents the inter-node data flow offset in the processing-related data executed in parallel;

[0039] The preset parallel acceleration ratio threshold E is compared with the parallel acceleration ratio Bjsb for evaluation. The specific evaluation contents are as follows:

[0040] If the parallel acceleration ratio threshold E ≤ the parallel acceleration ratio Bjsb, it means that the parallel processing effect of the system is qualified;

[0041] If the parallel acceleration ratio threshold E>parallel acceleration ratio Bjsb, it means that the parallel processing effect of the system is unsatisfactory. At this time, further analysis of parallel execution processing related data is performed, including analysis of task switching frequency and cache prefetch error.

[0042] Preferably, S4 includes:

[0043] S41. After assigning data conversion tasks to multiple nodes and verifying the system performance when executing in parallel, conduct an in-depth analysis of the different database protocols used in the system and generate analysis results; design the universal adaptation layer into a modular structure, and each module corresponds to one or several protocol conversion functions; finally, based on the design plan, actually develop the universal adaptation layer.

[0044] Preferably, S42, by actually integrating the universal adaptation layer with different database systems, executing pre-designed test cases, and recording relevant data of problems and abnormal situations in the test process; secondly, extracting relevant data of problems and abnormal situations in the test process, including protocol parsing time offset Jpy, protocol adaptation delay jitter rate Ddl, cross-protocol data packet reconstruction failure rate Sbl and interface compatibility conflict frequency Cpl, and after dimensionless processing of the above data, calculating and obtaining the seamless integration capability index Wfnz by the following formula:

[0045]

[0046] By comparing and evaluating the preset seamless integration capability threshold R with the seamless integration capability index Wfnz, the following evaluation contents are generated:

[0047] If the seamless integration capability threshold R < the seamless integration capability index Wfnz, it indicates that the seamless integration capability of the adaptation layer is qualified, supporting the seamless integration of different database systems while running stably in the current multi-protocol, multi-interface environment. At this time, the new database is connected to the system through the universal adaptation layer;

[0048] If the seamless integration capability threshold R ≥ the seamless integration capability index Wfnz, it indicates that the seamless integration capability of the adaptation layer is unqualified and the adaptation layer is unstable or inefficient in some protocol conversions or interface operations. Further adjustments should be made at this time.

[0049] S43. After the new database is connected to the adaptation layer, a performance test is performed. Performance-related data is obtained by reading log records, cache monitoring, and transaction management tools. After dimensionless processing of the performance-related data, the first performance index Xnzs1 before the new database is added and the second performance index Xnzs2 after the new database is added are obtained by calculation. The first performance index Xnzs1 and the second performance index Xnzs2 are compared and calculated to obtain the performance change value Bhxn. The specific formula is as follows:

[0050]

[0051]

[0052]

[0053] Where Dpc represents the dynamic connection pool response deviation rate in performance-related data, Cxt represents the query cache degradation rate in performance-related data, and Ddp represents the asynchronous task waiting jitter offset in performance-related data;

[0054] Dct represents the multi-database query path conflict rate in performance-related data, Fxt represents the distributed transaction coordination failure rate in performance-related data, Kyc represents the cross-node index reconstruction delay rate in performance-related data, 1.5 is the adjustment factor, and the 1.5 power is to amplify the impact of the multi-database query path conflict rate Dct and the distributed transaction coordination failure rate Fxt on the overall performance index;

[0055] The preset performance change threshold T is compared with the performance change value Bhxn for evaluation. The specific contents are as follows:

[0056] If the performance change value Bhxn ≤ the performance change threshold T, it means that the impact of the addition of the new database on the system performance is within an acceptable range, and the system seamlessly integrates the new database under the current configuration without the need for additional optimization;

[0057] If the performance change value Bhxn>performance change threshold T, it means that the impact of the addition of the new database on system performance exceeds the acceptable range. At this time, the database integration strategy is adjusted, including adjusting the adaptation layer, improving resource allocation efficiency, or redesigning the load balancing mechanism.

[0058] Preferably, S5 includes:

[0059] The system uses the monitored data to identify potential performance bottlenecks or abnormal resource consumption in the operation of the overall conversion process in real time. Next, by analyzing the monitored data, the system dynamically adjusts the resource allocation strategy according to the current load situation, including scheduling more computing resources to high-load nodes or reallocating task priorities.

[0060] The system uses an adaptive load balancing algorithm to dynamically schedule resources. When some nodes are overloaded or face resource bottlenecks, the system will automatically migrate some tasks to other idle nodes. At the same time, an early warning mechanism is set up. Once it is detected that the resource utilization rate exceeds the set threshold or the conversion efficiency fluctuates abnormally, the system will trigger an automatic adjustment process for pre-regulation.

[0061] Preferably, S6 includes:

[0062] In the data conversion and transmission process, by designing a multi-level security mechanism, combined with data encryption, access control and authority management, the data is dynamically encrypted before transmission, a unique key is generated for verification, and multi-level authority authentication is set; at the same time, full-process monitoring of data transmission is introduced to monitor the integrity and transmission status of data packets to avoid security risks caused by network attacks, transmission errors or unauthorized access;

[0063] In terms of data consistency verification, the system adopts a distributed consistency algorithm; before and after data conversion, the system performs consistency verification, including hash value comparison, transaction locking and version control methods; after the verification, the system records the data status change and performs 1-3 redundant backups of key data segments to cope with sudden abnormal situations. Beneficial Effects

[0064] (1) A railway office system heterogeneous database integration method based on data conversion technology. The railway office system heterogeneous database integration method based on data conversion technology solves the problems of complex data integration and poor system compatibility. First, in view of the differences in data structures of different databases, a unified standard model is designed and combined with an AI intelligent mapping algorithm to achieve seamless conversion and integration of data structures. In particular, when designing the standard model, standard format and mapping rules for route data, it not only improves the efficiency of data integration, but also lays the foundation for the system to achieve cross-database scheduling. This solution can not only cope with the conversion needs of multi-source heterogeneous data, but also dynamically adapt to changes in data sources to ensure the accuracy and consistency of data integration.

[0065] (2) The heterogeneous database integration method of the railway office system based on data conversion technology solves the performance bottleneck problem of the system under high-load scenarios. The distributed architecture executes data conversion tasks in parallel, and the intelligent monitoring system collects and evaluates key indicators such as node load, conversion speed and parallel acceleration in real time, ensuring the smooth operation of the system during peak load. Especially in the process of node load and parallel acceleration ratio evaluation, the complex formula design fully considers multiple influencing factors, avoids the limitations of a single indicator on the overall evaluation, and improves the accuracy of system resource scheduling. At the same time, through the seamless integration of the universal adaptation layer with multiple protocols and multiple interfaces, the system effectively supports the parallel operation of different databases and further optimizes the overall performance.

[0066] (3) The railway office system heterogeneous database integration method based on data conversion technology introduces a multi-level security mechanism and consistency verification design, which fundamentally solves the security and consistency problems when data flows across systems. Dynamic encryption, permission management and full-process monitoring measures work together to ensure the integrity and security of data during transmission and prevent data leakage and scheduling errors. In addition, through consistency algorithms, hash value comparisons and redundant backups, the system greatly reduces the risk of scheduling failures caused by inconsistent data. The overall solution effectively guarantees data security and system reliability while taking into account performance optimization, providing robust technical support for the integration of heterogeneous databases in railway office systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1The present invention is a schematic flow chart of the steps of the railway office system heterogeneous database integration method based on data conversion technology. DETAILED DESCRIPTION

[0068] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Example 1

[0069] See also Figure 1 ,The railway office system heterogeneous database integration method based on data conversion technology includes the following steps:

[0070] S1. Sort out the data structures of different databases, design a unified standard model for route data, and define the standard format of key data fields;

[0071] S2. Use AI-based intelligent mapping algorithms to automatically generate data conversion rules, and dynamically adjust mapping rules and the degree of automation of data conversion according to changes in data sources;

[0072] S3, using a distributed architecture to distribute data conversion tasks to multiple nodes for parallel execution, and verifying the system performance of the entire process by gradually calculating and evaluating the conversion speed index Zhzs, node load index Jdfz and parallel speedup ratio Bjsb;

[0073] S4. Develop a universal adaptation layer that integrates multiple protocol conversion functions and supports seamless integration of different database interfaces and protocols. Verify whether the adaptation layer can seamlessly integrate different protocols and interfaces, and finally evaluate the impact of the new database on the overall system performance.

[0074] S5. During the data conversion process, real-time monitoring of data flow, conversion status and performance indicators is performed, and dynamic resource scheduling is performed according to the system load, while generating real-time monitoring logs and analysis reports;

[0075] S6. Introduce multi-level security mechanisms and consistency checks in data conversion and transmission to ensure the integrity and security of data when it flows across systems, so as to avoid scheduling errors caused by data inconsistency or security vulnerabilities.

[0076] In this embodiment, by designing a unified route data standard model, data consistency and compatibility between different databases are ensured, and conflicts and errors during data conversion are reduced; the AI-based intelligent mapping algorithm can automatically generate and adjust data conversion rules to adapt to changes in different data sources, thereby improving the automation and efficiency of data conversion; the AI-based intelligent mapping algorithm can automatically generate and adjust data conversion rules to adapt to changes in different data sources, thereby improving the automation and efficiency of data conversion; a distributed architecture is used to process data conversion tasks in parallel, and by quantifying conversion speed, node load and processing efficiency, multi-dimensional evaluation parameters are used to ensure that the system can still run stably under high load, thereby solving the performance bottleneck problem that is prone to occur in traditional systems; a universal adaptation layer is developed to integrate multi-protocol and multi-interface conversion functions to ensure that the system can adapt to the seamless integration of different databases, thereby improving the flexibility and scalability of the system; by monitoring data flow and performance indicators, combined with a dynamic resource scheduling mechanism, the system load distribution is optimized in real time to avoid resource bottlenecks and improve the response efficiency of the system during peak hours. Example 2

[0077] S11. Analyze the data structures in different databases, identify and classify key data fields, table structures and data relationships, and collect core data items in railway freight and passenger routes, including station information, route maps and dispatching rules;

[0078] S12. Based on the analysis results, design a unified route data standard model and establish consistent data structure and field naming rules across databases; the route data standard model covers all core data items involved in freight and passenger dispatching and provides mapping rules for different data sources of core data items;

[0079] S13. For core data items, develop unified standard formats and encoding rules for seamless conversion and matching of similar fields in different databases during integration, including data types, lengths, and encoding formats.

[0080] In this embodiment, in the initial stage of database integration, it is first necessary to conduct an in-depth analysis of the data structures of different databases, identify and classify key data fields, table structures and data relationships, especially core data items closely related to railway freight and passenger routes, such as station information, route maps, dispatching rules, etc. This step lays the foundation for the subsequent design of a unified data model by combing the similarities and differences of each database. Then, based on the classification results, a unified route data standard model is designed, which not only covers all key data items involved in freight and passenger dispatching and command, but also establishes a consistent data structure and field naming rules across databases. In order to ensure the seamless flow of data in different systems, it is also necessary to formulate a unified standard format and encoding rules for each core data field, including the data type, length and encoding format of the field, to ensure that accurate matching and integration can be achieved when converting data between heterogeneous systems. At the same time, these rules and mapping rules provide a reliable basis for subsequent automated data conversion, and realize efficient compatibility of different databases in the integration process. Example 3

[0081] S21. Through AI-based intelligent mapping algorithms, the data structures and fields of different databases are analyzed to automatically generate data conversion rules;

[0082] S22. Real-time monitoring of data source changes, including data structure updates and field additions, and dynamic adjustment of mapping rules by using AI algorithms;

[0083] In this embodiment, through the AI-based intelligent mapping algorithm, the data structure and fields in different databases are first comprehensively analyzed to automatically generate conversion rules applicable to multi-source data. Next, the system continuously monitors changes in data sources, especially focusing on updates to data structures and the addition of new fields. Combined with real-time monitoring information, the AI ​​algorithm dynamically adjusts the mapping rules so that the data conversion process can adapt to changes and ensure efficient and accurate cross-database data conversion. In addition, the system also optimizes the mapping strategy based on real-time feedback to improve the automation and accuracy of data conversion. Example 4

[0084] S31. During the parallel processing, the data conversion speed of multiple nodes is monitored and evaluated in real time. After obtaining the relevant data of the nodes and communication delays through real-time logging and monitoring tools, the relevant data of the nodes and communication delays are dimensionlessly processed, and the conversion speed index Zhzs is calculated by the following formula:

[0085]

[0086] Wherein, Sxl represents the difference ratio of upstream and downstream data flow rates in the data related to the node and communication delay, Wyc represents the micro data fragmentation delay in the data related to the node and communication delay, Hcm represents the cache hit deviation rate in the data related to the node and communication delay, and Tdp represents the synchronization jitter offset in the data related to the node and communication delay;

[0087] The preset conversion speed threshold Q is compared with the conversion speed index Zhzs for evaluation. The specific contents are as follows:

[0088] If the preset conversion speed threshold Q> conversion speed index Zhzs, it means that the data conversion speed of the system is unqualified. At this time, the abnormal conversion speed result is generated and corresponding adjustments are made, including adjusting the node distribution, adjusting the cache strategy, and adjusting the synchronization mechanism;

[0089] If the preset conversion speed threshold Q ≤ conversion speed index Zhzs, it means that the data conversion speed of the system is qualified, and the node load of the system is evaluated at this time;

[0090] S32. By combining the system monitoring tool with the node's local resource monitoring plug-in, resource usage data and node load-related data are collected regularly, including the node CPU utilization Cly, the node memory utilization Nsy, and the node IO throughput Ttl; after extracting the node CPU utilization Cly, the node memory utilization Nsy, and the node IO throughput Ttl and performing dimensionless processing, the node load index Jdfz is calculated using the following formula:

[0091]

[0092] By comparing the preset node load threshold W with the node load index Jdfz, the specific evaluation content is as follows:

[0093] If the node load threshold W ≥ the node load index Jdfz, it means that the node load is normal and no load adjustment is required. The processing efficiency of parallel execution is further evaluated.

[0094] If the node load threshold W is less than the node load index Jdfz, it means that the node load is abnormal and there is a resource overload problem. At this time, measures should be taken immediately to adjust the load, including task reallocation, node resource expansion or load balancing adjustment;

[0095] S33. Collect the processing related data of parallel execution by combining system log, node monitoring and timestamp analysis; then, perform dimensionless processing on the processing related data of parallel execution, and calculate the parallel speedup ratio Bjsb by the following formula:

[0096]

[0097] Wherein, Lwr represents the microtask switching frequency in the processing-related data executed in parallel, Lhc represents the cache prefetch error rate in the processing-related data executed in parallel, Lsj represents the data sharding conflict rate in the processing-related data executed in parallel, and Ljd represents the inter-node data flow offset in the processing-related data executed in parallel;

[0098] The preset parallel acceleration ratio threshold E is compared with the parallel acceleration ratio Bjsb for evaluation. The specific evaluation contents are as follows:

[0099] If the parallel acceleration ratio threshold E ≤ the parallel acceleration ratio Bjsb, it means that the parallel processing effect of the system is qualified;

[0100] If the parallel acceleration ratio threshold E>parallel acceleration ratio Bjsb, it means that the parallel processing effect of the system is unsatisfactory. In this case, further analysis of the processing-related data of parallel execution is performed, including analysis of task switching frequency and cache prefetch error.

[0101] In this embodiment, the upstream and downstream data flow rate difference ratio Sxl reflects the difference in data flow rate before and after conversion. A large difference may mean that there is a bottleneck or delay in the conversion process, affecting the overall conversion efficiency; the micro data sharding delay Wyc represents the time offset caused by a small delay in the data sharding process, which may accumulate during large-scale parallel processing and affect the overall speed; the cache hit deviation rate Hcm measures the accuracy deviation of data hits in the cache. The lower the cache hit rate, the higher the frequency of the system reading data from the main memory or remote nodes, thereby extending the data conversion time; the synchronization jitter offset Tdp represents the time offset caused by unstable jitter in the data synchronization process. This jitter may cause inconsistency or delay in the data flow, affecting the continuity and efficiency of the conversion process;

[0102] The node CPU utilization Cly reflects the CPU resource usage of the node during the data conversion process. A higher CPU utilization usually means that the node is processing a large number of computing tasks and the load is heavy, which may lead to performance bottlenecks or task delays. The node memory usage Nsy represents the proportion of node memory resources. A high memory usage may indicate that the data processing process requires a large amount of cache and temporary storage space. Too high an occupancy rate may cause memory overflow or frequent memory swapping, affecting the data conversion speed and stability. The node IO throughput Ttl is used to measure the efficiency of the node in data reading and writing operations during the data conversion process. The higher the IO throughput, the faster the node can process a large amount of data. However, if it is overloaded or the bandwidth is insufficient, it may cause a data transmission bottleneck, affecting the overall conversion performance.

[0103] The microtask switching frequency Lwr reflects the frequency of the system switching between different tasks in parallel processing. A higher switching frequency may bring additional overhead, resulting in an increase in the cost of task context switching, which in turn affects the overall efficiency of parallel processing. The cache prefetch error rate Lhc indicates the accuracy of the system's prefetching of cached data during parallel processing. The higher the prefetch error rate, the lower the match between the data obtained from the cache and the actual required data, which will lead to more cache misses, increase data access latency, and reduce parallel processing performance. The data shard conflict rate Lsj measures the competition and conflict between different nodes for the same data shard during parallel processing. A high conflict rate may cause nodes to wait for resources or reallocate tasks, thereby extending the overall processing time and affecting the effect of parallel processing. The inter-node data flow offset Ljd reflects the synchronization of inter-node data flows during parallel execution. A larger offset may mean that data is out of sync or delayed when it is transferred between nodes, affecting the overall coordination of the system and the parallel acceleration effect. Example 5

[0104] S41. After verifying the system performance when the data conversion tasks are assigned to multiple nodes and executed in parallel, conduct an in-depth analysis of the different database protocols used in the system and generate analysis results; design the universal adaptation layer into a modular structure, and each module corresponds to one or several protocol conversion functions; finally, based on the design plan, actually develop the universal adaptation layer;

[0105] S42. By actually integrating the universal adaptation layer with different database systems, executing the pre-designed test cases, and recording the relevant data of problems and abnormal situations in the test process; secondly, extracting the relevant data of problems and abnormal situations in the test process, including the protocol parsing time offset Jpy, the protocol adaptation delay jitter rate Ddl, the cross-protocol data packet reconstruction failure rate Sbl and the interface compatibility conflict frequency Cpl, and after dimensionless processing of the above data, the seamless integration capability index Wfnz is calculated by the following formula:

[0106]

[0107] By comparing and evaluating the preset seamless integration capability threshold R with the seamless integration capability index Wfnz, the following evaluation contents are generated:

[0108] If the seamless integration capability threshold R < the seamless integration capability index Wfnz, it indicates that the seamless integration capability of the adaptation layer is qualified, supporting the seamless integration of different database systems while running stably in the current multi-protocol, multi-interface environment. At this time, the new database is connected to the system through the universal adaptation layer;

[0109] If the seamless integration capability threshold R ≥ the seamless integration capability index Wfnz, it indicates that the seamless integration capability of the adaptation layer is unqualified and the adaptation layer is unstable or inefficient in some protocol conversions or interface operations. Further adjustments should be made at this time.

[0110] S43. After the new database is connected to the adaptation layer, a performance test is performed. Performance-related data is obtained by reading log records, cache monitoring, and transaction management tools. After dimensionless processing of the performance-related data, the first performance index Xnzs1 before the new database is added and the second performance index Xnzs2 after the new database is added are obtained by calculation. The first performance index Xnzs1 and the second performance index Xnzs2 are compared and calculated to obtain the performance change value Bhxn. The specific formula is as follows:

[0111]

[0112]

[0113]

[0114] Where Dpc represents the dynamic connection pool response deviation rate in performance-related data, Cxt represents the query cache degradation rate in performance-related data, and Ddp represents the asynchronous task waiting jitter offset in performance-related data;

[0115] Dct represents the multi-database query path conflict rate in performance-related data, Fxt represents the distributed transaction coordination failure rate in performance-related data, Kyc represents the cross-node index reconstruction delay rate in performance-related data, 1.5 is the adjustment factor, and the 1.5 power is to amplify the impact of the multi-database query path conflict rate Dct and the distributed transaction coordination failure rate Fxt on the overall performance index;

[0116] The preset performance change threshold T is compared with the performance change value Bhxn for evaluation. The specific contents are as follows:

[0117] If the performance change value Bhxn ≤ the performance change threshold T, it means that the impact of the addition of the new database on the system performance is within an acceptable range, and the system seamlessly integrates the new database under the current configuration without the need for additional optimization;

[0118] If the performance change value Bhxn>performance change threshold T, it means that the impact of the addition of the new database on system performance exceeds the acceptable range. At this time, the database integration strategy is adjusted, including adjusting the adaptation layer, improving resource allocation efficiency, or redesigning the load balancing mechanism.

[0119] In this embodiment, when evaluating the seamless integration capability index Wfnz, the protocol parsing time offset Jpy indicates the time deviation of different protocols during the parsing process. A large time offset may cause a decrease in the protocol conversion efficiency, affecting the real-time performance and seamless integration effect of the overall system; the protocol adaptation delay jitter rate Ddl measures the stability of the delay during the protocol adaptation process. A high delay jitter rate indicates that the delay fluctuations during the adaptation process are large, which may cause unstable data transmission and affect the integration effect of the system; the cross-protocol data packet reconstruction failure rate Sbl reflects the frequency of failures between different protocols during data packet reconstruction. A high failure rate means that the data integrity during cross-protocol data transmission is difficult to guarantee, which directly affects the accuracy of the data and the reliability of the integration; the interface compatibility conflict frequency Cpl measures the frequency of conflicts between different interfaces during the integration process. A high conflict frequency indicates poor compatibility between interfaces, which may cause problems in data interaction between different databases or protocols in the system, affecting the smoothness of the integration;

[0120] When evaluating the first performance index Xnzs1 before the new database is added, the dynamic connection pool response deviation rate Dpc is collected to indicate the deviation between the actual response and the expected response when the database connection pool processes dynamic loads. A large deviation rate will cause inconsistent responses under high concurrent requests, affecting the stability of the overall performance. The query cache degradation rate Cxt is collected to measure the performance degradation of the database query cache after the load increases or the time is extended. A high degradation rate indicates that the cache hit rate decreases, resulting in slower query speeds, which affects system performance, especially during frequent query operations. The asynchronous task waiting jitter offset Ddp is collected to reflect the time jitter and offset that occurs when the asynchronous task is waiting for execution. A large jitter offset will cause delays and instability in task execution, thus affecting the overall performance of the system and the efficiency of task scheduling.

[0121] When evaluating the second performance index Xnzs2 after the new database is added, the multi-database query path conflict rate Dct is collected to reflect the frequency of conflicts between multiple database paths when executing cross-database queries. A high conflict rate will lead to reduced query efficiency and increased response delays, especially when it is necessary to coordinate data access from multiple databases, which will have a negative impact on the overall performance of the system; the distributed transaction coordination failure rate Fxt is collected to measure the frequency of coordination failures between nodes when processing distributed transactions. Transaction coordination failures may lead to data inconsistencies, frequent rollback operations and other problems, thereby affecting the reliability and performance of the system, especially in complex business scenarios with high concurrency; the cross-node index reconstruction delay rate Kyc is collected to indicate the delay in updating or reconstructing indexes in a multi-node distributed environment. A high delay rate indicates low index synchronization efficiency, which may lead to slower query speeds and increased data access delays, thereby affecting the overall performance of the system when processing cross-node data.

[0122] The first performance index Xnzs1 focuses on the core performance dimensions of the database in the existing environment, such as dynamic connection pool, cache efficiency and asynchronous task scheduling, while the second performance index Xnzs2 focuses on complex scenarios such as cross-database query paths, distributed transaction coordination and index management involved after the new database is connected. By collecting different parameters, the core performance of each stage can be evaluated more specifically. Example 6

[0123] S5 includes:

[0124] The system uses the monitored data to identify potential performance bottlenecks or abnormal resource consumption in the operation of the overall conversion process in real time. Next, by analyzing the monitored data, the system dynamically adjusts the resource allocation strategy according to the current load situation, including scheduling more computing resources to high-load nodes or reallocating task priorities.

[0125] The system uses an adaptive load balancing algorithm to dynamically schedule resources. When some nodes are overloaded or face resource bottlenecks, the system will automatically migrate some tasks to other idle nodes. At the same time, an early warning mechanism is set up. Once it is detected that the resource utilization rate exceeds the set threshold or the conversion efficiency fluctuates abnormally, the system will trigger an automatic adjustment process for pre-regulation.

[0126] In this embodiment, the system dynamically adjusts resource allocation through an adaptive load balancing algorithm based on monitoring data to ensure that each node avoids overload when running efficiently. The benefit of this mechanism is that it improves the overall conversion efficiency and system stability. The specific process includes: real-time analysis of the load status of each node, automatic migration of tasks from high-load nodes to idle nodes, and triggering early warnings and automatic adjustments based on preset resource usage thresholds; at the same time, the system can flexibly adjust task priorities according to the current load conditions, optimize resource utilization, and ensure the continuity and consistency of the data conversion process. Example 7

[0127] S6 includes:

[0128] In the data conversion and transmission process, by designing a multi-level security mechanism, combined with data encryption, access control and authority management, the data is dynamically encrypted before transmission, a unique key is generated for verification, and multi-level authority authentication is set; at the same time, full-process monitoring of data transmission is introduced to monitor the integrity and transmission status of data packets to avoid security risks caused by network attacks, transmission errors or unauthorized access;

[0129] In terms of data consistency verification, the system adopts a distributed consistency algorithm; before and after data conversion, the system performs consistency verification, including hash value comparison, transaction locking and version control methods; after the verification, the system records the data status change and performs 1-3 redundant backups of key data segments to cope with sudden abnormal situations.

[0130] In this embodiment, the security mechanism ensures the integrity and security of data during the conversion and transmission process through multi-level protection measures. Specifically, the system performs dynamic encryption before data transmission and generates a unique key for verification. At the same time, multi-level authority authentication is set up to prevent unauthorized access, monitor the integrity and transmission status of data packets throughout the process, and effectively avoid network attacks and transmission errors. The data consistency check uses a distributed consistency algorithm to perform hash value comparison, transaction locking and version control to ensure that the data remains consistent before and after conversion, and redundant backup of key data is performed to enhance the system's ability to respond to abnormal situations.

[0131] 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 railway office system heterogeneous database integration method based on data conversion technology, characterized in that: include: S1. Sort out the data structures of different databases, design a unified standard model for route data, and define the standard format of key data fields; S2. Use AI-based intelligent mapping algorithms to automatically generate data conversion rules, and dynamically adjust mapping rules and the degree of automation of data conversion according to changes in data sources; S3, using a distributed architecture, assigning data conversion tasks to multiple nodes for parallel execution, and verifying the system performance of the entire process by gradually calculating and evaluating the conversion speed index Zhzs, node load index Jdfz and parallel acceleration ratio Bjsb, among which, S31. During the parallel processing, the data conversion speed of multiple nodes is monitored and evaluated in real time. After obtaining the relevant data of the nodes and communication delays through real-time logging and monitoring tools, the relevant data of the nodes and communication delays are dimensionlessly processed, and the conversion speed index Zhzs is calculated by the following formula: Wherein, Sxl represents the difference ratio of upstream and downstream data flow rates in the data related to the node and communication delay, Wyc represents the micro data fragmentation delay in the data related to the node and communication delay, Hcm represents the cache hit deviation rate in the data related to the node and communication delay, and Tdp represents the synchronization jitter offset in the data related to the node and communication delay; The preset conversion speed threshold Q is compared with the conversion speed index Zhzs for evaluation. The specific contents are as follows: If the preset conversion speed threshold Q> conversion speed index Zhzs, it means that the data conversion speed of the system is unqualified. At this time, the abnormal conversion speed result is generated and corresponding adjustments are made, including adjusting the node distribution, adjusting the cache strategy and adjusting the synchronization mechanism; If the preset conversion speed threshold Q ≤ conversion speed index Zhzs, it means that the data conversion speed of the system is qualified, and the node load of the system is evaluated at this time; S4. Develop a universal adaptation layer that integrates multiple protocol conversion functions to support seamless integration of different database interfaces and protocols. Verify whether the adaptation layer can seamlessly integrate different protocols and interfaces. Finally, evaluate the impact of the new database on the overall system performance. S5. During the data conversion process, real-time monitoring of data flow, conversion status and performance indicators is performed, and dynamic resource scheduling is performed according to the system load, while generating real-time monitoring logs and analysis reports; S6. Introduce multi-level security mechanisms and consistency checks in data conversion and transmission.

2. The railway office system heterogeneous database integration method based on data conversion technology according to claim 1 is characterized in that: The S1 includes: S11. Analyze the data structures in different databases, identify and classify key data fields, table structures and data relationships, and collect core data items in railway freight and passenger routes, including station information, route maps and dispatching rules; S12. Based on the analysis results, design a unified route data standard model, establish a consistent data structure and field naming rules across databases, and cover all core data items involved in freight and passenger dispatching and provide mapping rules for different data sources of core data items; S13. For core data items, develop unified standard formats and encoding rules for seamless conversion and matching of similar fields in different databases during integration, including data types, lengths, and encoding formats.

3. The railway office system heterogeneous database integration method based on data conversion technology according to claim 1 is characterized in that: The S2 includes: S21. Through AI-based intelligent mapping algorithms, the data structures and fields of different databases are analyzed to automatically generate data conversion rules; S22. Monitor changes in data sources in real time, including data structure updates and field additions, and dynamically adjust mapping rules by leveraging AI algorithms.

4. The railway office system heterogeneous database integration method based on data conversion technology according to claim 1 is characterized in that: The S3 further includes: S32. By combining the system monitoring tool with the node's local resource monitoring plug-in, resource usage data and node load-related data are collected regularly, including the node CPU utilization Cly, the node memory utilization Nsy, and the node IO throughput Ttl; after extracting the node CPU utilization Cly, the node memory utilization Nsy, and the node IO throughput Ttl and performing dimensionless processing, the node load index Jdfz is calculated using the following formula: By comparing the preset node load threshold W with the node load index Jdfz, the specific evaluation content is as follows: If the node load threshold W ≥ the node load index Jdfz, it means that the node load is normal and no load adjustment is required. The processing efficiency of parallel execution is further evaluated. If the node load threshold W is less than the node load index Jdfz, it means that the node load is abnormal and there is a resource overload problem. At this time, measures should be taken immediately to adjust the load, including task reallocation, node resource expansion or load balancing adjustment.

5. The railway office system heterogeneous database integration method based on data conversion technology according to claim 4 is characterized in that: The S3 further includes: S33. By combining system logs, node monitoring and timestamp analysis, data related to parallel execution processing is collected. Then, the data related to parallel execution processing is dimensionlessly processed and the parallel speedup ratio Bjsb is calculated by the following formula: Wherein, Lwr represents the microtask switching frequency in the processing-related data executed in parallel, Lhc represents the cache prefetch error rate in the processing-related data executed in parallel, Lsj represents the data sharding conflict rate in the processing-related data executed in parallel, and Ljd represents the inter-node data flow offset in the processing-related data executed in parallel; The preset parallel acceleration ratio threshold E is compared with the parallel acceleration ratio Bjsb for evaluation. The specific evaluation contents are as follows: If the parallel acceleration ratio threshold E ≤ the parallel acceleration ratio Bjsb, it means that the parallel processing effect of the system is qualified; If the parallel acceleration ratio threshold E>parallel acceleration ratio Bjsb, it means that the parallel processing effect of the system is unsatisfactory. At this time, further analysis of parallel execution processing related data is performed, including analysis of task switching frequency and cache prefetch error.

6. The railway office system heterogeneous database integration method based on data conversion technology according to claim 1 is characterized in that: The S4 includes: S41. After assigning data conversion tasks to multiple nodes and verifying the system performance when executing in parallel, conduct an in-depth analysis of the different database protocols used in the system and generate analysis results. Design the universal adaptation layer into a modular structure, with each module corresponding to one or several protocol conversion functions. Finally, based on the design plan, actually develop the universal adaptation layer.

7. The railway office system heterogeneous database integration method based on data conversion technology according to claim 6 is characterized in that: The S4 further comprises: S42. By actually integrating the universal adaptation layer with different database systems, executing pre-designed test cases, and recording relevant data of problems and abnormal situations during the test, secondly, extracting relevant data of problems and abnormal situations during the test, including protocol parsing time offset Jpy, protocol adaptation delay jitter rate Ddl, cross-protocol data packet reconstruction failure rate Sbl and interface compatibility conflict frequency Cpl, and after dimensionless processing of the above data, the seamless integration capability index Wfnz is calculated by the following formula: By comparing and evaluating the preset seamless integration capability threshold R with the seamless integration capability index Wfnz, the following evaluation contents are generated: If the seamless integration capability threshold R < the seamless integration capability index Wfnz, it indicates that the seamless integration capability of the adaptation layer is qualified, supporting the seamless integration of different database systems while running stably in the current multi-protocol, multi-interface environment. At this time, the new database is connected to the system through the universal adaptation layer; If the seamless integration capability threshold R ≥ the seamless integration capability index Wfnz, it indicates that the seamless integration capability of the adaptation layer is unqualified and the adaptation layer is unstable or inefficient in some protocol conversions or interface operations. Further adjustments should be made at this time. S43. After the new database is connected to the adaptation layer, a performance test is performed. Performance-related data is obtained by reading log records, cache monitoring, and transaction management tools. After dimensionless processing of the performance-related data, the first performance index Xnzs1 before the new database is added and the second performance index Xnzs2 after the new database is added are obtained by calculation. The first performance index Xnzs1 and the second performance index Xnzs2 are compared and calculated to obtain the performance change value Bhxn. The specific formula is as follows: Where Dpc represents the dynamic connection pool response deviation rate in performance-related data, Cxt represents the query cache degradation rate in performance-related data, and Ddp represents the asynchronous task waiting jitter offset in performance-related data; Dct represents the multi-database query path conflict rate in performance-related data, Fxt represents the distributed transaction coordination failure rate in performance-related data, Kyc represents the cross-node index reconstruction delay rate in performance-related data, 1.5 is the adjustment factor, and the 1.5 power is to amplify the impact of the multi-database query path conflict rate Dct and the distributed transaction coordination failure rate Fxt on the overall performance index; The preset performance change threshold T is compared with the performance change value Bhxn for evaluation. The specific contents are as follows: If the performance change value Bhxn ≤ the performance change threshold T, it means that the impact of the addition of the new database on the system performance is within an acceptable range, and the system seamlessly integrates the new database under the current configuration without the need for additional optimization; If the performance change value Bhxn>performance change threshold T, it means that the impact of the addition of the new database on system performance exceeds the acceptable range. At this time, the database integration strategy is adjusted, including adjusting the adaptation layer, improving resource allocation efficiency, or redesigning the load balancing mechanism.

8. The railway office system heterogeneous database integration method based on data conversion technology according to claim 1 is characterized in that: The S5 includes: The system uses the monitored data to identify potential performance bottlenecks or abnormal resource consumption in the operation of the overall conversion process in real time. Next, by analyzing the monitored data, the system dynamically adjusts the resource allocation strategy according to the current load situation, including scheduling more computing resources to high-load nodes or reallocating task priorities. The system uses an adaptive load balancing algorithm to dynamically schedule resources. When some nodes are overloaded or face resource bottlenecks, the system will automatically migrate some tasks to other idle nodes. At the same time, an early warning mechanism is set up. Once it is detected that the resource utilization rate exceeds the set threshold or the conversion efficiency fluctuates abnormally, the system will trigger an automatic adjustment process for pre-regulation.

9. The railway office system heterogeneous database integration method based on data conversion technology according to any one of claims 1 to 8, characterized in that: The S6 includes: In the data conversion and transmission process, by designing a multi-level security mechanism, combined with data encryption, access control and authority management, the data is dynamically encrypted before transmission, a unique key is generated for verification, and multi-level authority authentication is set; at the same time, full-process monitoring of data transmission is introduced to monitor the integrity and transmission status of data packets to avoid security risks caused by network attacks, transmission errors or unauthorized access; In terms of data consistency verification, the system adopts a distributed consistency algorithm; before and after data conversion, the system performs consistency verification, including hash value comparison, transaction locking and version control methods; after the verification, the system records the data status change and performs 1-3 redundant backups of key data segments to cope with sudden abnormal situations.

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