Method and system for intelligently switching data engines and storage medium

Abstract

The invention discloses a method and system for intelligently switching data engines and a storage medium, and relates to the field of data management. The method comprises the following steps of: acquiring an available data source and determining a default data engine for the available data source; setting a performance threshold range for the data engine according to the key index of the data processing performance; determining a corresponding data source according to the data processing request, and performing data processing by adopting a default data engine of the data source; determining a performance index value of current data processing according to the key index value in the data processing process; and if the performance index value of the current data processing does not conform to the performance threshold range of the current data engine, switching the current data engine into a data engine of which the performance threshold range comprises the current performance index value. According to the method, the performance threshold range is set for each data engine according to the data processing performance, and then whether the data engine needs to be switched or not can be automatically judged according to whether the key index value of data processing conforms to the performance threshold range or not.

Description

Technical Field

[0001] The present invention relates to the field of data management, and in particular to a method, system and storage medium for intelligently switching data engines. Background Art

[0002] Data source management technology: With the development of technology, the data sources that labeling systems can manage and utilize are becoming more and more diverse, including credit data, transaction data, user behavior data, etc. These data sources provide a rich information foundation for labeling systems.

[0003] Data processing and analysis technology: With the continuous development of big data and artificial intelligence technology, labeling systems are able to process and analyze data more efficiently, thereby assessing risks more accurately.

[0004] Data bias problem: Due to the uneven quality of data sources, there may be data bias problems. This will affect the accuracy and effectiveness of the labeling system. For example, some data sources may contain misleading information, causing the system to make wrong decisions.

[0005] Therefore, when an abnormality occurs in the data source, it is necessary to switch to an appropriate data source to provide support for subsequent processes.

[0006] At present, traditional data engine switching often relies on manual judgment or fixed rules, which not only lacks flexibility and intelligence, but also requires users to have professional knowledge of multiple data engines (such as compatibility and performance differences between different data engines), making it difficult to use. Summary of the invention

[0007] In view of the defects existing in the prior art, the technical problem solved by the present invention is: how to automatically select a data engine according to the real-time situation, thereby improving work efficiency and work accuracy and reducing the difficulty of use.

[0008] To achieve the above objectives, in a first aspect, an embodiment of the present application provides a method for intelligently switching a data engine, the method comprising the following steps:

[0009] Obtain available data sources, determine the default data engine for them based on the information of the data sources; set the performance threshold range for the data engine based on the key indicators of data processing performance;

[0010] Determine the corresponding data source according to the data processing request, and use the default data engine of the data source to process the data; determine the performance indicator value of the current data processing according to the key indicator value in the data processing process;

[0011] If the performance indicator value of the current data processing does not meet the performance threshold range of the current data engine, the current data engine is switched to a data engine whose performance threshold range includes the current performance indicator value.

[0012] In conjunction with the first aspect, in one implementation, the process of setting a performance threshold range for a data engine according to a key indicator of data processing performance includes:

[0013] Set the corresponding weight for each key indicator and determine the upper and lower limits of each key indicator in the data engine;

[0014] The upper limit of each key indicator is multiplied by the weight and then added to obtain the lower limit of the performance threshold range; the lower limit of each key indicator is multiplied by the weight and then added to obtain the upper limit of the performance threshold range;

[0015] The process of determining the performance indicator value of the current data processing by the key indicator value in the data processing process includes: multiplying each key indicator value by the corresponding weight and then adding them to obtain the performance indicator value of the current data processing.

[0016] In combination with the first aspect, in one implementation, the method further includes the following steps: after the data engine is switched, replacing the default data engine of the data source being used with the switched data engine.

[0017] In combination with the first aspect, in an implementation, it is characterized in that the process of obtaining the available data source includes: setting a corresponding data processing tag according to the available data source information;

[0018] The process of determining the corresponding data source according to the data processing request includes: determining a data processing tag according to the data processing request, and determining the data source with the same data tag as the data source corresponding to the current data processing request.

[0019] In combination with the first aspect, in one implementation, the process of obtaining available data sources includes: collecting and updating data source information in real time.

[0020] In combination with the first aspect, in one implementation, the process of acquiring key indicator values ​​in the data processing process includes: acquiring original data in the data processing process, converting the original data into a recognizable format, and extracting key indicator values ​​therein.

[0021] In combination with the first aspect, in one implementation, the key indicators include: system load rate, data transmission speed and data flow.

[0022] In a second aspect, an embodiment of the present application provides a system for intelligently switching data engines, the system being used to implement the steps of the method provided in the first aspect.

[0023] In conjunction with the second aspect, in one implementation, the system includes a data engine management module, a data processing module, a data monitoring module, an intelligent switching module, and a learning optimization module;

[0024] The data engine management module is used to: obtain available data sources, determine the default data engine for it based on the information of the data source; set the performance threshold range for the data engine based on the key indicators of data processing performance;

[0025] The data processing module is used to: determine the corresponding data source from the data engine management module according to the data processing request, and use the default data engine of the data source to process the data; obtain the original data during the data processing process, convert the original data into a recognizable format, extract the key indicator values ​​therein and send them to the data monitoring module;

[0026] The data monitoring module is used to: determine the performance index value of the current data processing according to the key index value in the data processing process and send it to the intelligent switching module;

[0027] The intelligent switching module is used to: determine whether the performance index value of data processing belongs to the performance threshold range of the data engine being used, and if so, switch the current data engine to a data engine whose performance threshold range includes the current performance index value; otherwise, continue to use the current data engine;

[0028] The learning optimization module is used to: in the data engine management module, replace the default data engine of the data source currently in use with the data engine switched by the intelligent switching module.

[0029] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, on which a program of an intelligent switching data engine is stored, wherein when the program of the intelligent switching data engine is executed, the steps of the method provided in the first aspect are implemented.

[0030] Compared with the prior art, the advantages of the present invention are:

[0031] The present invention sets a performance threshold range for each data engine according to the data processing performance, and can automatically determine whether the data engine needs to be switched according to whether the key indicator value of the data processing meets the performance threshold range.

[0032] Therefore, on the basis of selecting the corresponding data source for data processing needs, the present invention can automatically select the most appropriate data engine according to actual conditions (for example, when processing large-scale data, a distributed data engine with higher performance can be selected; when processing real-time data, a memory data engine with faster response speed can be selected), thereby significantly improving the flexibility, efficiency and accuracy of data processing; in addition, the present invention automatically calculates performance indicator values ​​and automatically switches data engines, does not require users to have complex professional knowledge, and reduces the difficulty of use. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0034] Figure 1 A schematic diagram of a flow chart of a method for intelligently switching data engines in an embodiment of the present application;

[0035] Figure 2 This is a schematic diagram of the architecture of a system for intelligently switching data engines in an embodiment of the present application;

[0036] Figure 3 This is a schematic diagram of the hardware structure of the device of the intelligent switching data engine involved in the embodiment of the present application. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0038] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0039] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0040] In a first aspect, an embodiment of the present application provides a method for intelligently switching a data engine, the method comprising:

[0041] Step A: Obtain available data sources, and determine the default data engine for them based on their information (address, type, quality, etc.); set performance threshold ranges for the data engine based on key indicators of data processing performance (such as data size, processing requirements, system load, etc.); this step is a preset process.

[0042] Specifically, the process of obtaining available data sources in step A includes: collecting and / or updating data source information in real time; and ensuring accurate understanding of the current available data source status by updating the data source information in real time. Therefore, step A is a preset process plus a real-time update process, which will be executed before step B and will also be executed in real time in step B and subsequent processes.

[0043] Step B: Determine the corresponding data source according to the data processing request, and use the default data engine of the data source to perform data processing.

[0044] Step C: Determine the performance indicator value of the current data processing based on the key indicator value in the data processing process; if the performance indicator value of the current data processing does not meet the performance threshold range of the current data engine, switch the current data engine to a data engine whose performance threshold range includes the current performance indicator value.

[0045] It can be seen from this that the present invention sets a performance threshold range for each data engine according to the data processing performance, and can automatically determine whether the data engine needs to be switched according to whether the performance indicator value of the data processing meets the performance threshold range.

[0046] Therefore, on the basis of selecting the corresponding data source for data processing needs, the present invention can automatically select the most appropriate data engine according to actual conditions (for example, when processing large-scale data, a distributed data engine with higher performance can be selected; when processing real-time data, a memory data engine with faster response speed can be selected), thereby significantly improving the flexibility, efficiency and accuracy of data processing; in addition, the present invention automatically calculates performance indicator values ​​and automatically switches data engines, does not require users to have complex professional knowledge, and reduces the difficulty of use.

[0047] Steps A to C are described in detail below.

[0048] How to determine the corresponding data source based on the data processing request:

[0049] In one embodiment, the process of obtaining available data sources in step A also includes: setting a corresponding data processing tag according to each available data source information (the specific implementation method is conventional technology in the art), and the process of determining the corresponding data source according to the data processing request in step B includes: determining the data processing tag according to the data processing request, and determining the data source with the same data tag as the data source corresponding to the current data processing request.

[0050] For calculation of performance threshold range and performance indicator value:

[0051] In one embodiment, the process of setting a performance threshold range for a data engine based on key indicators of data processing performance in step A includes: setting a corresponding weight for each key indicator, determining the upper and lower limits of each key indicator corresponding to the data engine; multiplying the upper limit of each key indicator by the weight and adding the results to obtain the lower limit of the performance threshold range; multiplying the lower limit of each key indicator by the weight and adding the results to obtain the upper limit of the performance threshold range.

[0052] Similarly, the process of determining the performance indicator value of the current data processing according to the key indicator value in the data processing process in step C includes: multiplying each key indicator value by the corresponding weight and then adding them to obtain the performance indicator value of the current data processing.

[0053] In one embodiment, the process of obtaining the key indicator values ​​in step C includes: obtaining original data during data processing, converting the original data into a recognizable format, and extracting the key indicator values ​​therein.

[0054] In one embodiment, the key indicators in step A and step C include: system load rate, data transmission speed and data flow; in actual applications, the key indicators and their corresponding weights can be set according to different requirements.

[0055] In one embodiment, step C further includes the following steps: replacing the default data engine of the current data source with the switched data engine, so as to achieve the effect of continuously optimizing the degree of adaptation between the data engine and data processing by analyzing actual data.

[0056] The above method is described below through a specific embodiment.

[0057] 1. Demand Analysis

[0058] Determine application scenarios: Identify application scenarios for the intelligent switching data engine, such as large-scale data processing and real-time data analysis in industries such as finance, e-commerce, and life sciences.

[0059] Analyze data characteristics: Analyze the characteristics of the data to be processed, including data size, data type, processing requirements, etc., to determine the most suitable data engine.

[0060] 2. Technology selection

[0061] Select the appropriate data engine: Based on the results of the demand analysis, select a variety of appropriate data engines, such as Hive or Greenplum database, distributed computing framework, etc.

[0062] Evaluate performance and compatibility: Evaluate the performance and compatibility of various data engines to ensure that they can meet application requirements and work together.

[0063] 3. Intelligent switching logic design

[0064] Design switching strategies: Design intelligent switching strategies for data engines based on real-time conditions, such as data scale, processing requirements, system load, etc. For example, when processing large-scale data, you can choose a higher-performance distributed data engine; when processing real-time data, you can choose a faster-response memory data engine.

[0065] Implement a switching mechanism: Implement an intelligent switching mechanism through programming so that the system can automatically select the most suitable data engine. This can include writing switching algorithms, configuring data sources, and managing the start and stop of data engines.

[0066] 4. System integration and testing

[0067] Integrate multiple data engines: Integrate the selected data engines into the system to ensure that they can function properly and work together.

[0068] Test the smart switching function: Test the smart switching function to verify whether it can automatically select the optimal data engine based on the real-time situation. This can include unit testing, integration testing, stress testing, etc.

[0069] 5. Deployment and Operation and Maintenance

[0070] Deployment system: Deploy the integrated and tested system to the production environment to ensure that it can run normally and meet application requirements.

[0071] Monitoring and tuning: The method and device for intelligently switching data engines are monitored in real time through a monitoring system, and are tuned as needed. This may include monitoring the performance of the data engine, adjusting the switching strategy, optimizing resource allocation, etc.

[0072] 6. Expansion and maintenance

[0073] Support new data engines: With the development of technology and the emergence of new data engines, the system should be able to easily add new data engines to adapt to new needs.

[0074] Regular maintenance: Regularly maintain the system to ensure the stable operation of the data engine and the normal operation of the intelligent switching function.

[0075] 7. Evaluation and Optimization

[0076] Performance evaluation: Regularly evaluate the performance of the system, including data processing speed, response time, resource utilization, etc., to ensure that the system can meet application requirements.

[0077] Optimization strategy: Based on the results of performance evaluation, the intelligent switching strategy is optimized to improve the performance and efficiency of the system.

[0078] Through the above technical implementation plan, it can be ensured that a method and device for intelligently switching data engines can play their advantages in practical applications and bring better user experience and economic benefits.

[0079] On this basis, see Figure 1 As shown, the process of this embodiment includes:

[0080] S0: Collect and update available data source information in real time, and set corresponding data processing tags according to each data source information; determine the default data engine for it based on the data source information, and set the performance threshold range for the data engine based on key indicators of data processing performance (system load rate, data transmission speed and data flow).

[0081] S1: After receiving a data processing request, determine a data processing tag according to the data processing request, determine a data source with the same data tag as the data source corresponding to the current data processing request; and use the default data engine of the data source to perform data processing.

[0082] S2: Obtain the original data during the data processing process, convert the original data into a recognizable format, and extract the key indicator values.

[0083] S3: Determine the performance indicator value of the current data processing according to the key indicator value.

[0084] S4: Determine whether the performance indicator value of the current data processing falls within the performance threshold range of the current data engine. If so, switch the current data engine to a data engine whose performance threshold range includes the current performance indicator value, and replace the default data engine of the current data source with the switched data engine; otherwise, continue to use the current data engine.

[0085] In a second aspect, an embodiment of the present application further provides a system for intelligently switching data engines, the system being used to implement the method mentioned in the first aspect.

[0086] See also Figure 2 As shown, the system specifically includes a data engine management module, a data processing module, a data monitoring module, an intelligent switching module and a learning optimization module.

[0087] The data engine management module is used to: obtain available data sources, determine the default data engine for it based on the information of the data source; and set the performance threshold range for the data engine based on the key indicators of data processing performance.

[0088] The data processing module is used to: determine the corresponding data source from the data engine management module according to the data processing request, and use the default data engine of the data source to process the data; obtain the original data during the data processing process, convert the original data into a recognizable format, extract the key indicator values ​​​​therein and send them to the data monitoring module.

[0089] The data monitoring module is used to determine the performance indicator value of the current data processing according to the key indicator value in the data processing process and send it to the intelligent switching module.

[0090] The intelligent switching module is used to determine whether the performance indicator value of data processing falls within the performance threshold range of the data engine currently being used. If so, the current data engine is switched to a data engine whose performance threshold range includes the current performance indicator value; otherwise, the current data engine continues to be used.

[0091] The learning optimization module is used to: in the data engine management module, replace the default data engine of the data source currently in use with the data engine switched by the intelligent switching module.

[0092] The functional implementation of each of the above modules corresponds to the steps in the above method embodiment, and their functions and implementation processes will not be repeated here one by one.

[0093] The applicant used the above system statistical data and found that compared with traditional methods, its performance in processing big data tasks was improved by 30%, while reducing operating costs by 20%.

[0094] In summary, the advantages of the above method and system are:

[0095] Improve user experience: The intelligent switching data engine device can ensure service continuity and stability, and reduce user complaints and dissatisfaction caused by service interruptions.

[0096] By improving data processing efficiency and accuracy, the system can respond to user requests faster and provide more accurate results, thereby improving user experience.

[0097] Enhanced system reliability: The multi-data source management and intelligent switching technology in the patented solution can ensure that the system can still work normally when facing a single data source failure, improving the system's reliability and fault tolerance.

[0098] By optimizing data transmission and processing processes and introducing advanced data processing algorithms and engines, the system can better cope with various complex scenarios and challenges, further enhancing the reliability of the system.

[0099] Promote business innovation and development: The intelligent switching data engine device provides new solutions and technical support for various industries, promoting business innovation and development.

[0100] By integrating multiple data sources and providing efficient data processing capabilities, the system can provide richer and more comprehensive data support to organizations in various industries, thereby promoting their business innovation and development.

[0101] In a third aspect, an embodiment of the present application provides a device for intelligently switching a data engine. The device for intelligently switching a data engine may be a personal computer (PC), a laptop computer, a server, or other device having a data processing function.

[0102] Reference Figure 3 , Figure 3 The hardware structure diagram of the intelligent switching data engine device involved in the embodiment of the present application is shown in FIG. In the embodiment of the present application, the intelligent switching data engine device may include a processor, a memory, a communication interface, and a communication bus.

[0103] The communication bus may be of any type and is used to interconnect the processor, the memory, and the communication interface.

[0104] The communication interface includes input / output (I / O) interfaces, physical interfaces, and logical interfaces, which are used to interconnect devices within the device of the intelligent switching data engine, and interfaces used to interconnect the device of the intelligent switching data engine with other devices (such as other computing devices or user devices). The physical interface can be an Ethernet interface, a fiber optic interface, an ATM interface, etc.; the user device can be a display, a keyboard, etc.

[0105] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0106] The processor may be a general-purpose processor, and the general-purpose processor may call the program of the intelligent switching data engine stored in the memory and execute the method of the intelligent switching data engine provided in the embodiment of the present application. For example, the general-purpose processor may be a central processing unit (CPU). The method executed when the program of the intelligent switching data engine is called may refer to the various embodiments of the method of the intelligent switching data engine of the present application, which will not be repeated here.

[0107] Those skilled in the art will understand that Figure 3The hardware structure shown in the figure does not constitute a limitation on the present application, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0108] In a fourth aspect, an embodiment of the present application also provides a computer-readable storage medium.

[0109] The computer-readable storage medium of the present application stores a program of an intelligent switching data engine, wherein when the program of the intelligent switching data engine is executed by a processor, the steps of the method of the intelligent switching data engine as described above are implemented.

[0110] Among them, the method implemented when the program of the intelligent switching data engine is executed can refer to the various embodiments of the method of the intelligent switching data engine of the present application, and will not be repeated here.

[0111] It should be noted that the serial numbers of the above-mentioned embodiments of the present application are only for description and do not represent the advantages or disadvantages of the embodiments.

[0112] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD) as described above, and includes a number of instructions for a terminal device to execute the methods described in each embodiment of the present application.

[0113] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit "first", "second" and "third" to different types.

[0114] In the description of the embodiments of the present application, "exemplary", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "for example" or "for example" is intended to present related concepts in a specific way.

[0115] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; the “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.

[0116] In some processes described in the embodiments of the present application, multiple operations or steps that appear in a specific order are included, but it should be understood that these operations or steps may not be executed in the order in which they appear in the embodiments of the present application or in parallel, and the sequence number of the operation is only used to distinguish the different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed in sequence or in parallel, and these operations or steps may be combined.

[0117] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD) as described above, and includes a number of instructions for a terminal device to execute the methods described in each embodiment of the present application.

[0118] The above are only specific implementations of the embodiments of the present invention, but the protection scope of the embodiments of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the embodiments of the present invention, and these modifications or replacements should be included in the protection scope of the embodiments of the present invention. Therefore, the protection scope of the embodiments of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for intelligently switching data engines, characterized in that: The method comprises the following steps: Obtain available data sources, determine the default data engine for them based on the information of the data sources; set the performance threshold range for the data engine based on the key indicators of data processing performance; Determine the corresponding data source according to the data processing request, and use the default data engine of the data source to process the data; determine the performance indicator value of the current data processing according to the key indicator value in the data processing process; If the performance indicator value of the current data processing does not meet the performance threshold range of the current data engine, the current data engine is switched to a data engine whose performance threshold range includes the current performance indicator value.

2. The method for intelligently switching data engines according to claim 1, characterized in that: The process of setting a performance threshold range for a data engine according to key indicators of data processing performance includes: Set the corresponding weight for each key indicator and determine the upper and lower limits of each key indicator in the data engine; The upper limit of each key indicator is multiplied by the weight and then added to obtain the lower limit of the performance threshold range; the lower limit of each key indicator is multiplied by the weight and then added to obtain the upper limit of the performance threshold range; The process of determining the performance indicator value of the current data processing by the key indicator value in the data processing process includes: multiplying each key indicator value by the corresponding weight and then adding them to obtain the performance indicator value of the current data processing.

3. The method for intelligently switching data engines according to claim 1, characterized in that: The method further includes the following steps: after the data engine is switched, replacing the default data engine of the data source being used with the switched data engine.

4. The method for intelligently switching data engines according to any one of claims 1 to 3, characterized in that: The process of obtaining the available data source includes: setting a corresponding data processing tag according to the available data source information; The process of determining the corresponding data source according to the data processing request includes: determining a data processing tag according to the data processing request, and determining the data source with the same data tag as the data source corresponding to the current data processing request.

5. The method for intelligently switching data engines according to any one of claims 1 to 3, characterized in that: The process of obtaining available data sources includes: collecting and updating data source information in real time.

6. The method for intelligently switching data engines according to any one of claims 1 to 3, characterized in that: The process of obtaining the key indicator values ​​in the data processing process includes: obtaining original data in the data processing process, converting the original data into a recognizable format, and extracting the key indicator values ​​therein.

7. The method for intelligently switching data engines according to any one of claims 1 to 3, characterized in that: The key indicators include: system load rate, data transmission speed and data flow.

8. A system for intelligently switching data engines, characterized in that: The system is used to: implement the steps of the method for intelligently switching data engines as described in any one of claims 1 to 8.

9. The system for intelligently switching data engines as claimed in claim 8, characterized in that: The system includes a data engine management module, a data processing module, a data monitoring module, an intelligent switching module and a learning optimization module; The data engine management module is used to: obtain available data sources, determine the default data engine for it based on the information of the data source; set the performance threshold range for the data engine based on the key indicators of data processing performance; The data processing module is used to: determine the corresponding data source from the data engine management module according to the data processing request, and use the default data engine of the data source to process the data; obtain the original data during the data processing process, convert the original data into a recognizable format, extract the key indicator values ​​therein and send them to the data monitoring module; The data monitoring module is used to: determine the performance index value of the current data processing according to the key index value in the data processing process and send it to the intelligent switching module; The intelligent switching module is used to: determine whether the performance indicator value of data processing belongs to the performance threshold range of the data engine being used, and if so, switch the current data engine to a data engine whose performance threshold range includes the current performance indicator value; Otherwise continue to use the current data engine; The learning optimization module is used to: in the data engine management module, replace the default data engine of the data source currently in use with the data engine switched by the intelligent switching module.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program of an intelligent switching data engine, wherein when the program of the intelligent switching data engine is executed, the steps of the method of the intelligent switching data engine according to any one of claims 1 to 8 are implemented.

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