Data service multi-environment adaptation method and system

Abstract

The invention provides a data service multi-environment adaptation method and system. The objective of the invention is to solve the problem that an existing data service adaptation scheme lacks intelligent adaptive capability. The method comprises the following steps: acquiring environment information of a target environment; the environment information comprises operating system information, hardware configuration information and network condition information; determining a target adaptation scheme according to the environment information; deploying and operating a data service in the target environment according to the target adaptation scheme; wherein after the data service is deployed and operated in the target environment, the target adaptation scheme is adjusted according to the real-time performance index of the data service.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, and in particular to a data service multi-environment adaptation method and system. Background Art

[0002] With the rapid development of information technology, data services are increasingly used in various fields. Different environments have different requirements and restrictions on data services. However, traditional data service systems are often designed based on a single environment and are difficult to effectively respond to changes in requirements in multiple environments and scenarios. This leads to many challenges in cross-environment deployment and operation of data services.

[0003] Most of the existing data service adaptation solutions rely on manual configuration and static rules, lacking intelligent self-adaptation capabilities. These adaptation solutions often fail to achieve ideal adaptation effects in complex and changing environments, and may even lead to problems such as unstable operation and low efficiency of data services. Summary of the invention

[0004] In order to overcome the problems existing in the related technologies, the present disclosure provides a data service multi-environment adaptation method and system. The technical solution of the present disclosure is as follows: According to a first aspect of an embodiment of the present disclosure, a data service multi-environment adaptation method is provided, comprising: Obtaining environment information of the target environment; the environment information includes operating system information, hardware configuration information, and network condition information; Determining a target adaptation solution according to the environmental information; Deploy and run data services in the target environment according to the target adaptation solution; After the data service is deployed and operated in the target environment, the target adaptation solution is adjusted according to the real-time performance index of the data service.

[0005] Optionally, adjusting the target adaptation scheme according to the real-time performance indicator of the data service includes: Collecting performance indicators of the data service under the target adaptation scheme in real time; Analyze the change trend of the performance indicator to determine the execution effect of the target adaptation solution; Adjusting and optimizing the target adaptation scheme according to the execution effect to obtain an adjusted and optimized target adaptation scheme; The step of deploying and running the data service in the target environment according to the target adaptation solution includes: According to the adjusted and optimized target adaptation solution, the data service is deployed and operated in the target environment.

[0006] Optionally, obtain the environment information of the target environment, including: Obtaining the operating system information by reading a system file or calling a system interface; The hardware configuration information is obtained by reading a hardware information file or calling a hardware interface; the hardware configuration information represents specific parameters or configurations of the hardware; The network conditions of the target environment are determined by sending test data packets and measuring indicators such as round-trip time and packet loss rate.

[0007] Optionally, determining a target adaptation solution according to the environmental information includes: According to the environmental information, a target adaptation strategy is obtained from an adaptation strategy library using a machine learning algorithm; the adaptation strategy library includes a rule-based adaptation strategy, a machine learning-based adaptation strategy, and a genetic algorithm-based adaptation strategy; Determining, based on the environment information, adjustment information for adjusting configuration information of the data service; The adjustment information and the target adaptation strategy are evaluated to determine a target adaptation solution.

[0008] Optionally, evaluating the adjustment information and the target adaptation strategy to determine a target adaptation solution includes: Determine the adjustment sub-information of the adjustment information for adjusting each configuration item; Determine an adaptation sub-strategy for adapting each configuration item in the target adaptation strategy; Matching the adjustment sub-information and the adaptation sub-strategy for the same adaptation item; Based on the environment information, the adjustment sub-information and the adaptation sub-strategy corresponding to the same adaptation item are evaluated to determine a target adaptation strategy for the configuration item; The target adaptation strategy is determined as an adaptation sub-scheme of the target adaptation scheme for the configuration item.

[0009] Optionally, determine the adjustment information by following these steps: Determining operating system configuration parameters of the data service according to the operating system information included in the environment information; Determine a first performance tuning parameter of the data service according to the hardware configuration information included in the environment information; the first performance tuning parameter of the data service includes at least the number of concurrent connections and the cache size of the data service; Determining a second performance tuning parameter of the data service according to the network condition information included in the environmental information; the second performance tuning parameter of the data service at least includes a data transmission rate and a retransmission strategy; The adjustment information of the data service is determined according to the operating system configuration parameter, the first performance tuning parameter, and the second performance tuning parameter.

[0010] Optionally, according to the target adaptation solution, deploying and running a data service in the target environment includes: Generate a corresponding deployment file according to the target adaptation solution; the deployment file is a deployment script or a configuration file; Deploy the deployment files to the target environment; Based on the deployment file, the data service is started.

[0011] According to a second aspect of an embodiment of the present disclosure, a data service multi-environment adaptation system is provided, including: An environment perception module is used to obtain environment information of a target environment; the environment information includes operating system information, hardware configuration information, and network condition information; An intelligent decision-making module, used to determine a target adaptation solution according to the environmental information; A service execution module, configured to deploy and run data services in the target environment according to the target adaptation solution; After the data service is deployed and operated in the target environment, the target adaptation solution is adjusted according to the real-time performance index of the data service.

[0012] Optionally, it also includes: The performance monitoring and feedback module is used to collect the performance indicators of the data service under the target adaptation scheme in real time; and feed back the performance indicators to the intelligent decision-making module so that the intelligent decision-making module adjusts and optimizes the target adaptation scheme based on the performance indicators.

[0013] Optionally, it also includes: A data service configuration module, used to determine adjustment information for adjusting configuration information of the data service according to the environment information; The adaptation database is used to store multiple adaptation strategies so that the intelligent decision-making module adopts a machine learning algorithm to obtain a target adaptation strategy from the adaptation strategy library; the multiple adaptation strategies include rule-based adaptation strategies, machine learning-based adaptation strategies, and genetic algorithm-based adaptation strategies.

[0014] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the steps of the data service multi-environment adaptation method as described in the first aspect are implemented.

[0015] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the data service multi-environment adaptation method described in the first aspect are implemented.

[0016] According to a fifth aspect of an embodiment of the present disclosure, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps of the data service multi-environment adaptation method as described in the first aspect are implemented.

[0017] By acquiring the environmental information of the target environment, the present disclosure can fully understand the characteristics of the target environment and determine the most suitable adaptation scheme for the environment, thereby ensuring that the data service can run stably in the environment and reducing compatibility issues caused by environmental differences. The present disclosure obtains environmental information and determines the adaptation scheme in an automated manner, which simplifies the deployment process of the data service. When the target environment changes, the environmental information can be re-acquired and the adaptation scheme can be quickly adjusted to adapt to the new environmental requirements. Since the adaptation method is automatically determined based on the environmental information, the data service can be more easily migrated and deployed between different environments, improving the portability of the data service. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings required for use in the description of the embodiments of the present disclosure will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 It is a schematic diagram of the steps of a data service multi-environment adaptation method shown in an embodiment of the present disclosure; Figure 2 is a schematic diagram of a data service multi-environment adaptation system shown in an embodiment of the present disclosure; Figure 3 is a schematic diagram of a workflow of a data service multi-environment adaptation system shown in an embodiment of the present disclosure; Figure 4 It is a schematic diagram of an electronic device shown in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, rather than all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0021] The terms "first", "second", etc. in the specification and claims of the present disclosure are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable when appropriate, so that the embodiments of the present disclosure can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are generally of the same type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0022] In the prior art, although there are some data service adaptation solutions, most of them have problems such as low adaptation efficiency, poor adaptability, and high maintenance costs. For example, some data service adaptation solutions achieve data service adaptation through manual configuration, which is not only time-consuming and labor-intensive, but also prone to errors; other data service adaptation solutions use hard coding, which improves the adaptation efficiency to a certain extent, but lacks flexibility and is difficult to adapt to changing application scenarios.

[0023] In order to solve the problems existing in the related art, the present disclosure proposes a data service multi-environment adaptation method, which can adaptively adapt data services based on environmental information.

[0024] Figure 1 Schematic diagram of the steps of a data service multi-environment adaptation method shown in an embodiment of the present disclosure. Figure 1 As shown, the method may specifically include the following steps: Step S11: Acquire the environment information of the target environment; the environment information includes operating system information, hardware configuration information and network condition information.

[0025] The operating system information may indicate the type and version information of the operating system, etc. The types of operating systems include Windows, Linux, macOS, etc.

[0026] Hardware configuration information is information related to hardware. For example, for CPU (Central Processing Unit), you can determine the type, number, and speed of the CPU. For memory, you can determine information such as the size of the memory. For storage devices, you can check the type and capacity of the storage device.

[0027] Network condition information is information related to network performance. For example, the bandwidth and delay of the network can be determined through testing.

[0028] Step S12: Determine a target adaptation solution according to the environmental information.

[0029] Comprehensively analyze the collected operating system, hardware configuration, and network condition information. Identify any potential issues that may affect the deployment and operation of data services. Based on the analysis results, determine the best way to deploy data services in the target environment.

[0030] Step S13: deploying and running data services in the target environment according to the target adaptation solution.

[0031] Install the data service into the target environment. Start the data service and verify its normal operation, and implement monitoring mechanisms to track the performance and stability of the data service.

[0032] Step S14: After the data service is deployed and operated in the target environment, the target adaptation solution is adjusted according to the real-time performance indicators of the data service.

[0033] When the data service is deployed and operated in the target environment according to the target adaptation scheme, the real-time performance indicators of the data service will be obtained in real time. The real-time performance indicators of the data service will have a certain impact on the target adaptation scheme, causing the target adaptation scheme to be adjusted to obtain an adjusted target adaptation scheme. After obtaining the adjusted target adaptation scheme, the data service will be re-deployed and operated in the target environment based on the adjusted target adaptation scheme.

[0034] By using the embodiments of the present disclosure, by accurately perceiving environmental characteristics, more appropriate and efficient adaptation solutions can be formulated for the unique needs and limitations of different environments, thereby significantly improving the adaptability and stability of data services in various environments. Based on the acquired environmental information, the configuration parameters and resource allocation strategies that best suit the current environment can be intelligently selected. Through in-depth analysis of environmental information, potential performance bottlenecks and failure points can be identified, so that preventive measures can be taken in advance to reduce the probability of failure.

[0035] Among them, in an optional embodiment, the target adaptation scheme is adjusted according to the real-time performance indicators of the data service, including: real-time collection of the performance indicators of the data service under the target adaptation scheme; analyzing the changing trend of the performance indicators to determine the execution effect of the target adaptation scheme; adjusting and optimizing the target adaptation scheme according to the execution effect to obtain the adjusted and optimized target adaptation scheme; deploying and running the data service in the target environment according to the target adaptation scheme includes: executing the deployment and operation of the data service in the target environment according to the adjusted and optimized target adaptation scheme.

[0036] Performance monitoring tools can be deployed or integrated in the target environment to collect performance indicator data in real time and accurately. Performance indicators that need to be monitored can be determined based on the characteristics of data services and business requirements, such as response time, throughput, error rate, resource utilization, etc. The frequency of performance indicator collection can be set based on the sensitivity of performance changes and monitoring requirements, thereby balancing the real-time nature of monitoring and system overhead.

[0037] After the performance indicators are collected, they can be preprocessed to ensure the reliability of the performance indicators.

[0038] Based on the trend of performance indicators and threshold settings, evaluate the execution effect of the target adaptation solution to determine whether the expected performance goals and business requirements are met. If the execution effect is not good, you can locate the performance bottleneck or the root cause of the problem by deeply analyzing the performance indicator data and system logs. According to the problem location results, formulate an adjustment and optimization strategy, apply the adjustment and optimization strategy to the target adaptation solution, and obtain the adjusted and optimized target adaptation solution.

[0039] According to the adjusted and optimized target adaptation plan, the data service is deployed and operated in the target environment. After deployment, the performance indicators are collected in real time, and the next round of performance monitoring, effect evaluation and adjustment and optimization cycle is entered to form a closed-loop continuous optimization process.

[0040] By adopting the embodiments of the present disclosure, by collecting the performance indicators of the data service under the target adaptation scheme in real time, the running status of the service can be continuously monitored, which helps to timely discover performance bottlenecks or abnormal fluctuations, so as to take corresponding measures for tuning and ensure that the data service always maintains the best running state. Real-time performance monitoring and adjustment optimization mechanism helps to ensure that the data service always provides stable and efficient service quality.

[0041] Among them, in an optional embodiment, obtaining the environmental information of the target environment includes: obtaining the operating system information by reading a system file or calling a system interface; obtaining the hardware configuration information by reading a hardware information file or calling a hardware interface; the hardware configuration information characterizes the specific parameters or configuration of the hardware; and determining the network conditions of the target environment by sending a test data packet and measuring indicators such as round-trip time and packet loss rate.

[0042] The operating system usually stores its version information in a specific location, and the operating system type can be identified by reading these system files. For example, for Windows systems, the operating system version and version number can be obtained by reading the registry or using the winver command.

[0043] Different operating systems provide corresponding APIs (Application Programming Interface) to directly query the type and version of the operating system. You can obtain system information by calling these APIs. For example, for Windows systems, you can use the GetVersionEx function or the SystemInfo API to obtain the operating system version and other related information.

[0044] Hardware configuration information can include CPU information, memory information, hard disk information, etc. The CPU model, number of cores, frequency, and other information can be read through the interface. Memory information includes the total amount of system memory and available memory. The memory size and type will affect data service performance. Hard disk information can include the storage capacity, type, and available space of the disk. Disk types include HDD (Hard Disk Drive) or SSD (Solid State Disk or Solid State Drive).

[0045] Network conditions can be determined by measuring key indicators such as network bandwidth, latency, packet loss rate, etc., and determining network condition information based on the performance indicators corresponding to the network test.

[0046] By adopting the embodiments of the present disclosure, by fully understanding the various parameters of the target environment, including the operating system, hardware configuration and network conditions, it is possible to accurately grasp the data service requirements and restrictions under different operating environments. By obtaining environmental information such as the operating system, hardware configuration and network conditions, the configuration of the data service can be dynamically adjusted according to the characteristics of the environment to improve the adaptability of the system. By obtaining and analyzing various parameters of the target environment in real time, the performance and operational stability of the data service can be improved, and the negative impact caused by environmental changes can be reduced.

[0047] Among them, in an optional embodiment, a target adaptation scheme is determined according to the environmental information, including: according to the environmental information, using a machine learning algorithm to obtain a target adaptation strategy from an adaptation strategy library; the adaptation strategy library includes rule-based adaptation strategies, machine learning-based adaptation strategies, and genetic algorithm-based adaptation strategies; according to the environmental information, adjustment information for adjusting the configuration information of the data service is determined; the adjustment information and the target adaptation strategy are evaluated to determine the target adaptation scheme.

[0048] Based on the collected environmental information, a machine learning algorithm is used to select the most suitable strategy for the current environment from the predefined adaptation strategy library. Based on the current environmental information, a suitable machine learning algorithm is selected, and the environmental information is used as input to predict the best adaptation strategy, and the adaptation strategy is selected from the adaptation strategy library.

[0049] The adaptation strategies included in the adaptation strategy library are pre-set. The adaptation strategies include rule-based adaptation strategies, machine learning-based adaptation strategies, and genetic algorithm-based adaptation strategies.

[0050] Rule-based adaptation strategies are suitable for environments with clear rule constraints. For example, operating system type, hardware configuration, network bandwidth, etc. can be mapped to specific rules.

[0051] Adaptation strategies based on machine learning are suitable for scenarios that require prediction and self-optimization based on historical data, and are particularly suitable for complex and changing environments. Machine learning models automatically adjust strategies based on historical data.

[0052] The adaptation strategy based on genetic algorithm is suitable for selecting the optimal adaptation strategy from multiple alternatives by optimizing search, and is especially suitable for complex adaptation scenarios that cannot be described by simple rules.

[0053] Determine the configuration information of the data service based on the environment information. Adjusting the information will affect the operating parameters of the data service to ensure that the service can run efficiently in the target environment.

[0054] After selecting the target adaptation strategy and determining the adjustment information, you need to evaluate the adjustment information and the target adaptation strategy to ensure that these adjustments can optimize the operation of the data service and will not cause negative impacts. The evaluation can include performance evaluation, stability evaluation, and resource consumption evaluation. Performance evaluation can analyze whether the adjusted configuration can improve the performance of the data service, which can be specifically reflected in whether it can increase throughput, reduce response time and other key performance indicators. Stability evaluation can evaluate whether the adjusted configuration will introduce system instability, such as memory overflow, CPU overload and other problems. Resource consumption evaluation can analyze whether the adjustment reasonably utilizes the hardware resources of the target environment and whether there is resource waste or bottlenecks.

[0055] Based on the evaluation results, the target adaptation plan is finally determined.

[0056] By adopting the embodiments of the present disclosure and utilizing a machine learning algorithm, a target adaptation strategy is intelligently selected from an adaptation strategy library based on current environmental information, which can ensure that under different environmental conditions, the adaptation strategy that best suits the current situation is selected, thereby improving the adaptability and flexibility of the system. Based on the environmental information, adjustment information for adjusting the configuration information of the data service can be determined to ensure that the configuration of the data service can meet current needs to the greatest extent possible and improve the performance and efficiency of the service. After obtaining the target adaptation strategy and adjustment information, the method will also conduct a comprehensive evaluation of these two parts of information to determine the final target adaptation solution, which can ensure that the selected solution is optimal overall, both meeting the requirements of the current environment and maximizing the potential of the data service.

[0057] Among them, in an optional embodiment, the adjustment information and the target adaptation strategy are evaluated to determine the target adaptation scheme, including: determining the adjustment sub-information for adjusting each configuration item by the adjustment information; determining the adaptation sub-strategy for adapting each configuration item in the target adaptation strategy; matching the adjustment sub-information and the adaptation sub-strategy for the same adaptation item; based on environmental information, evaluating the adjustment sub-information and the adaptation sub-strategy corresponding to the same adaptation item to determine the target adaptation strategy for the configuration item; determining the target adaptation strategy as the adaptation sub-scheme of the target adaptation scheme for the configuration item.

[0058] Adjustment information is the system's modification suggestions for data service configuration, involving multiple configuration items, such as concurrency, cache size, transmission rate, etc. The adjustment sub-information details the specific adjustment requirements and scope of each configuration item.

[0059] Parse the adjustment information determined in the environment information. Determine the configuration items that need to be adjusted in the data service, as well as the possible adjustment ranges or values ​​of these configuration items. The adjustment content is refined into adjustment sub-information, each of which targets a specific configuration item. For example, at the operating system level, specific system resource allocation policies may need to be adjusted according to different versions of the operating system.

[0060] Extract the adaptation sub-strategies corresponding to each configuration item from the target adaptation strategy. The adaptation sub-strategy is a specific component of the target adaptation strategy. Each sub-strategy targets a specific configuration item and provides a specific method for applying the adaptation strategy on the configuration item.

[0061] Matching each adjustment sub-information with the corresponding adaptation sub-strategy ensures that the adjustment sub-information of each configuration item is consistent with the adaptation sub-strategy, thereby avoiding configuration conflicts or inconsistencies.

[0062] After the matching is completed, the adjustment sub-information and adaptation sub-strategy of each adaptation item are evaluated based on the current environment information. According to the evaluation results, the target adaptation strategy of each configuration item can be determined. After determining the target adaptation strategy corresponding to each configuration item, the target adaptation strategies of all configuration items are integrated together to form a complete target adaptation solution.

[0063] By adopting the embodiments of the present disclosure, by determining the adjustment sub-information in the adjustment information and the adaptation sub-strategy in the target adaptation strategy, accurate matching of the same adaptation item is achieved, ensuring a close association between the adjustment measures and the adaptation strategy, thereby improving the accuracy and effectiveness of the adaptation. Based on the current environmental information, the adjustment sub-information and the adaptation sub-strategy corresponding to the same adaptation item are evaluated, fully considering the changes in environmental factors, and ensuring that the selected adaptation strategy is optimal in the current environment.

[0064] Among them, in an optional embodiment, the adjustment information is determined according to the following steps: determining the operating system configuration parameters of the data service based on the operating system information included in the environmental information; determining the first performance tuning parameters of the data service based on the hardware configuration information included in the environmental information; the first performance tuning parameters of the data service include at least the concurrency and cache size of the data service; determining the second performance tuning parameters of the data service based on the network condition information included in the environmental information; the second performance tuning parameters of the data service include at least the data transmission rate and the retransmission strategy; determining the adjustment information of the data service based on the operating system configuration parameters, the first performance tuning parameters and the second performance tuning parameters.

[0065] Different operating systems have different mechanisms and optimization methods in terms of system resource management, thread scheduling, memory management, etc. Therefore, determine the corresponding configuration files or adjustment parameters of the data service according to the type of the target operating system. For example, for Windows systems, you may need to configure specific memory cache settings and thread pool configuration-related optimization parameters. Adjust the configuration related to the operating system to ensure that the data service can maximize the use of the characteristics of the operating system.

[0066] The hardware configuration directly affects the concurrent processing capability, cache management, and resource allocation of the data service. Adjusting the first performance tuning parameter corresponding to the hardware configuration can ensure that the data service runs efficiently under the constraints of hardware resources. For example, for the hardware configuration information of the CPU, the number of CPU cores can be determined based on the hardware configuration information of the CPU. The number of CPU cores can affect concurrent threads or processes. More cores can support more concurrent threads or processes. Therefore, the number of concurrent data services can be adjusted based on the number of CPU cores. For the hardware configuration information of the memory, the cache size of the data service can be adjusted based on the total memory and available memory. Machines with larger memory can be configured with larger caches to increase the speed of data reading and writing.

[0067] Network condition information directly affects the transmission efficiency, reliability and stability of data services. Therefore, the second performance tuning parameter can be dynamically adjusted according to the network condition information, and the second performance tuning parameter includes transmission rate, retransmission strategy, etc. to cope with different network conditions. In a specific implementation, when the bandwidth is high, the data transmission rate in the data service can be set higher, thereby accelerating the transmission of data. When it is determined according to the network condition information that the packet loss rate of the current network is high, the retransmission mechanism can be enhanced to ensure the reliability of the data. For example, the number of retransmissions can be adjusted or a suitable retransmission mechanism can be selected according to the packet loss rate.

[0068] After adjusting various parameters of the data service according to the operating system, hardware configuration and network conditions, the various parameters of the data service are integrated to determine the adjustment information.

[0069] By adopting the embodiments of the present disclosure, various parameters of the data service can be intelligently adjusted based on different operating systems, hardware configurations and network conditions, so as to ensure that the data service can run efficiently and stably under different operating systems, hardware configurations and network conditions, thereby improving the overall performance and adaptability of the system and eliminating the compatibility issues of the traditional system when running across platforms and environments. The data service configuration module can automatically adjust the configuration according to the environmental information provided by the environmental perception module, reducing the need for manual configuration, which not only improves the level of automation, but also greatly reduces the management cost of the data service during deployment, operation and maintenance.

[0070] Among them, in an optional embodiment, according to the target adaptation scheme, the data service is deployed and run in the target environment, including: generating a corresponding deployment file according to the target adaptation scheme; the deployment file is a deployment script or configuration file; deploying the deployment file to the target environment; and starting the data service based on the deployment file.

[0071] After determining the target adaptation solution, generate a configuration file corresponding to the current target environment. The configuration file can be in the form of a deployment script or a configuration file.

[0072] If it is in the form of a deployment script, then the deployment script has corresponding types depending on the target environment. For example, for a Windows environment, a PowerShell script needs to be generated. According to the configuration parameters determined in the adaptation solution, these parameters are automatically inserted into the deployment script to ensure that the deployment script contains the required operating system configuration parameters, the first performance tuning parameters, and the second performance tuning parameters.

[0073] If it is in the form of a configuration file, a configuration file specific to the target environment can also be generated according to the target adaptation solution.

[0074] After generating the deployment script or configuration file, you need to check it to ensure that the deployment fails due to incorrect configuration. For example, check whether the file path, permission settings, environment variables, etc. are correct.

[0075] After obtaining the deployment script or configuration file, deploy the deployment script or configuration file to the target environment and start the data service based on the deployment script or configuration file.

[0076] By adopting the embodiments of the present disclosure, by generating a deployment file, the automation of data service deployment can be realized, the complexity of manual configuration and deployment can be reduced, and the accuracy and efficiency of deployment can be improved. Using a deployment file for deployment can ensure that each deployment is repeatable, that is, each deployment follows the same steps and configuration, thereby producing consistent results, which helps to maintain consistency between development, test and production environments.

[0077] Based on the same technical concept, the present disclosure provides a data service multi-environment adaptation system, and the data service multi-environment adaptation method provided by the present disclosure is implemented through the system. Figure 2 Schematic diagram of a data service multi-environment adaptation system shown in an embodiment of the present disclosure. Figure 2 As shown, the system comprises: An environment perception module is used to obtain environment information of a target environment; the environment information includes operating system information, hardware configuration information, and network condition information; An intelligent decision-making module, used to determine a target adaptation solution according to the environmental information; A service execution module, used to deploy and run data services in the target environment according to the target adaptation solution; After the data service is deployed and operated in the target environment, the target adaptation solution is adjusted according to the real-time performance index of the data service.

[0078] The system further comprises: The performance monitoring and feedback module is used to collect the performance indicators of the data service under the target adaptation scheme in real time; and feed back the performance indicators to the intelligent decision-making module so that the intelligent decision-making module adjusts and optimizes the target adaptation scheme based on the performance indicators.

[0079] The system further comprises: A data service configuration module, used to determine adjustment information for adjusting configuration information of the data service according to the environment information; The adaptation database is used to store multiple adaptation strategies so that the intelligent decision-making module adopts a machine learning algorithm to obtain a target adaptation strategy from the adaptation strategy library; the multiple adaptation strategies include rule-based adaptation strategies, machine learning-based adaptation strategies, and genetic algorithm-based adaptation strategies.

[0080] The data service multi-environment adaptation system disclosed in the present invention can significantly improve the speed of deployment through automated adaptation and deployment, especially in large-scale environments, reducing the long period of time that may be required for traditional manual configuration. Through the intelligent decision-making module, the system can make the best adaptation decision when facing different operating systems, hardware platforms and network environments. The intelligent decision-making module can dynamically adjust the adaptation plan based on the feedback information of the performance monitoring and feedback module, and the system can promptly discover and solve performance bottlenecks during operation to continuously optimize the performance of the service.

[0081] In order to explain the workflow of the data service multi-environment adaptation system in detail, the present disclosure provides the following embodiments. Figure 3 Provide explanation. Figure 3 It is a schematic diagram of the workflow of a data service multi-environment adaptation system shown in an embodiment of the present disclosure.

[0082] Step 1: The environment perception module obtains the environmental information of the target environment by reading system files, calling system APIs, reading hardware information files, calling hardware interfaces, and sending test data packets. The environmental information includes operating system information, hardware configuration information, and network condition information.

[0083] Step 2: The data service configuration module intelligently generates or adjusts the configuration information of the data service according to the environmental information obtained by the environmental perception module to obtain the adjustment information. Specifically, the operating system configuration parameters of the data service can be determined according to the operating system information; the first performance tuning parameters of the data service can be determined according to the hardware configuration information, and the first performance tuning parameters include the number of concurrent connections and cache size of the data service; the second performance tuning parameters of the data service can be determined according to the network condition information, and the second performance tuning parameters include the data transmission rate and retransmission strategy.

[0084] Step 3: The intelligent decision module screens and sorts the adaptation strategies in the adaptation strategy library through a machine learning algorithm to determine the target adaptation strategy. The intelligent decision module receives the adjustment information determined by the data service configuration module, and further evaluates and compares the target adaptation strategy through the adjustment information to obtain the target adaptation solution.

[0085] The adaptation strategy library stores a variety of adaptation strategies, including rule-based adaptation strategies, machine learning-based adaptation strategies, and genetic algorithm-based adaptation strategies. The corresponding adaptation strategy can be selected and used according to different application scenarios and requirements.

[0086] Step 4: The service execution module generates a corresponding deployment script or configuration file according to the target adaptation solution determined by the intelligent decision-making module, deploys the deployment script or configuration file to the target environment, and starts the data service.

[0087] Step 5: After starting the data service, the performance monitoring and feedback module monitors the execution effect of the data service in real time, and adjusts and optimizes the adaptation plan according to the execution effect. The performance indicators of the data service, such as response time, throughput, etc., can be collected in real time. Based on the collected performance indicators, the change trend of the performance indicators is analyzed to determine the execution effect of the data service. When it is determined that there is a potential problem at present through the change trend of the performance indicators, the monitoring and feedback module feeds back the performance indicators of the current data service to the intelligent decision-making module, so that the intelligent decision-making module adjusts and optimizes the target adaptation plan based on the performance indicators of the data service.

[0088] The present disclosure also provides an electronic device, referring to Figure 4 , Figure 4 is a schematic diagram of an electronic device shown in an embodiment of the present disclosure. Figure 4 As shown, the electronic device 400 includes: a memory 410 and a processor 420. The memory 410 and the processor 420 are connected via a bus communication. A computer program is stored in the memory 410. The computer program can be run on the processor 420 to implement the steps in the data service multi-environment adaptation method disclosed in the embodiment of the present disclosure.

[0089] The embodiment of the present disclosure further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps in the data service multi-environment adaptation method disclosed in the embodiment of the present disclosure are implemented.

[0090] The embodiment of the present disclosure further provides a computer program product, including a computer program. When the computer program is executed by a processor, the steps in the data service multi-environment adaptation method disclosed in the embodiment of the present disclosure are implemented.

[0091] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0092] It should be understood by those skilled in the art that the embodiments of the present disclosure may be provided as methods, devices or computer program products. Therefore, the embodiments of the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the embodiments of the present disclosure may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0093] The embodiments of the present disclosure are described with reference to the flowcharts and / or block diagrams of the methods, apparatuses, electronic devices, and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0094] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0095] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0096] Although some embodiments of the present disclosure have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiment and all changes and modifications that fall within the scope of the present disclosure.

[0097] The above is a detailed introduction to a data service multi-environment adaptation method and system provided by the present disclosure. Specific examples are used in this article to illustrate the principles and implementation methods of the present disclosure. The description of the above embodiments is only used to help understand the method of the present disclosure and its core idea. At the same time, for general technical personnel in this field, according to the idea of ​​the present disclosure, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present disclosure.

Claims

1. A data service multi-environment adaptation method, characterized in that: include: Obtaining environmental information of the target environment; The environmental information includes operating system information, hardware configuration information and network condition information; Determining a target adaptation solution according to the environmental information; Deploy and run data services in the target environment according to the target adaptation solution; After the data service is deployed and operated in the target environment, the target adaptation solution is adjusted according to the real-time performance index of the data service.

2. The method according to claim 1, characterized in that According to the real-time performance indicator of the data service, the target adaptation scheme is adjusted, including: Collecting performance indicators of the data service under the target adaptation scheme in real time; Analyze the change trend of the performance indicator to determine the execution effect of the target adaptation solution; Adjusting and optimizing the target adaptation scheme according to the execution effect to obtain an adjusted and optimized target adaptation scheme; The step of deploying and running the data service in the target environment according to the target adaptation solution includes: According to the adjusted and optimized target adaptation solution, the data service is deployed and operated in the target environment.

3. The method according to claim 1, characterized in that Get the target environment's environment information, including: Obtaining the operating system information by reading a system file or calling a system interface; The hardware configuration information is obtained by reading a hardware information file or calling a hardware interface; the hardware configuration information represents specific parameters or configurations of the hardware; The network conditions of the target environment are determined by sending test data packets and measuring indicators such as round-trip time and packet loss rate.

4. The method according to claim 1, characterized in that: Determine a target adaptation solution according to the environmental information, including: According to the environmental information, a target adaptation strategy is obtained from an adaptation strategy library using a machine learning algorithm; the adaptation strategy library includes a rule-based adaptation strategy, a machine learning-based adaptation strategy, and a genetic algorithm-based adaptation strategy; Determining, based on the environment information, adjustment information for adjusting configuration information of the data service; The adjustment information and the target adaptation strategy are evaluated to determine a target adaptation solution.

5. The method according to claim 4, characterized in that The adjustment information and the target adaptation strategy are evaluated to determine a target adaptation solution, including: Determine the adjustment sub-information of the adjustment information for adjusting each configuration item; Determine an adaptation sub-strategy for adapting each configuration item in the target adaptation strategy; Matching the adjustment sub-information and the adaptation sub-strategy for the same adaptation item; Based on the environment information, the adjustment sub-information and the adaptation sub-strategy corresponding to the same adaptation item are evaluated to determine a target adaptation strategy for the configuration item; The target adaptation strategy is determined as an adaptation sub-scheme of the target adaptation scheme for the configuration item.

6. The method according to claim 4 or 5, characterized in that: Follow these steps to determine the adjustment information: Determining operating system configuration parameters of the data service according to the operating system information included in the environment information; Determine a first performance tuning parameter of the data service according to the hardware configuration information included in the environment information; the first performance tuning parameter of the data service includes at least the number of concurrent connections and the cache size of the data service; Determining a second performance tuning parameter of the data service according to the network condition information included in the environmental information; the second performance tuning parameter of the data service at least includes a data transmission rate and a retransmission strategy; The adjustment information of the data service is determined according to the operating system configuration parameter, the first performance tuning parameter, and the second performance tuning parameter.

7. The method according to claim 1, characterized in that According to the target adaptation solution, deploying and running data services in the target environment includes: Generate a corresponding deployment file according to the target adaptation solution; the deployment file is a deployment script or a configuration file; Deploy the deployment files to the target environment; Based on the deployment file, the data service is started.

8. A data service multi-environment adaptation system, characterized in that: include: Environmental perception module, used to obtain environmental information of the target environment; The environmental information includes operating system information, hardware configuration information and network condition information; An intelligent decision-making module, used to determine a target adaptation solution according to the environmental information; A service execution module, used to deploy and run data services in the target environment according to the target adaptation solution; After the data service is deployed and operated in the target environment, the target adaptation solution is adjusted according to the real-time performance index of the data service.

9. The system according to claim 8, characterized in that Also includes: The performance monitoring and feedback module is used to collect the performance indicators of the data service under the target adaptation scheme in real time; and feed back the performance indicators to the intelligent decision-making module so that the intelligent decision-making module adjusts and optimizes the target adaptation scheme based on the performance indicators.

10. The system according to claim 8, characterized in that Also includes: A data service configuration module, used to determine adjustment information for adjusting configuration information of the data service according to the environment information; The adaptation database is used to store multiple adaptation strategies so that the intelligent decision-making module adopts a machine learning algorithm to obtain a target adaptation strategy from the adaptation strategy library; the multiple adaptation strategies include rule-based adaptation strategies, machine learning-based adaptation strategies, and genetic algorithm-based adaptation strategies.

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