Automated Deployment Method for Industrial Internet Platform Based on Domestic Operating System

Abstract

The present invention discloses an automated deployment method for an industrial Internet platform based on a domestic operating system, which relates to the technical field of automated deployment. The method includes installing an automated deployment tool; building a local software repository through the tool, where the tool automatically identifies the platform deployment mode, automatically downloads the complete platform installation package to the local and publishes it to the local software repository, and after completion, the tool automatically enables the service discovery and registration function; deploying the platform public components through the tool, where the tool automatically identifies the platform deployment mode and automatically installs the specified public components; through the service discovery and registration function of the tool, it is checked in real time whether all the platform public components are installed and ready; the tool will automatically deploy multiple business application services of the platform using a single image.

Description

Technical Field

[0001] The present invention relates to the technical field of automated deployment, in particular to an automated deployment method for an industrial Internet platform based on a domestic operating system. Background Art

[0002] With the rapid development of industrial Internet technology, the number of components of industrial Internet platforms and their implementation costs are also increasing continuously. The key to realizing the automated deployment of industrial Internet platforms lies in automatically identifying the operating system and installing the corresponding software repository service according to its version, and realizing information synchronization between servers through service discovery and registration functions, making the automated deployment work of the platform in small-scale application scenarios more intelligent and efficient, and effectively reducing the error probability and labor cost of operation and maintenance personnel.

[0003] Currently, the more popular containerized deployment tool is Kubernetes, which is an excellent container orchestration and management system, but its disadvantages are also very obvious: the high flexibility and rich function set also bring the problem of high learning cost, and it takes some time to learn and understand its architecture and working principle; the architecture is relatively complex, and for development and operation and maintenance personnel who are not familiar with it, they may face problems such as cumbersome operations and difficult problem troubleshooting in deployment and management; it needs to run complex system components, consuming relatively high resources; for small-scale application scenarios, it may bring too much complexity and management cost. Summary of the Invention

[0004] In view of the problems of high probability of human errors, high implementation cost, and poor portability in the existing technology of industrial Internet platforms, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is how to provide an automated deployment method for an industrial Internet platform based on a domestic operating system.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides an automated deployment method for an industrial Internet platform based on a domestic operating system, which includes installing an automated deployment tool. The tool automatically identifies the model and version of the domestic operating system of the current host and automatically installs the corresponding version of the basic components; builds a local software repository through the tool. The tool automatically identifies the platform deployment mode, automatically downloads the complete platform installation package to the local and publishes it to the local software repository, and after completion, the tool automatically enables the service discovery and registration function; deploys the platform public components through the tool. The tool automatically identifies the platform deployment mode and automatically installs the specified public components; through the service discovery and registration function of the tool, it checks in real time whether all the platform public components are installed and ready, and after all the checks pass, it automatically releases the front-end files, modifies the configuration parameters, and initializes the database; the tool uses a single image to automatically deploy multiple business application services of the platform.

[0008] As a preferred solution of the automated deployment method for the industrial Internet platform based on the domestic operating system of the present invention, wherein: the installation of the automated deployment tool includes the following steps: using machine learning to achieve intelligent adaptive deployment; exploring online learning and incremental deployment methods to support dynamic changes; during the deployment process, monitoring key metrics, and the model predicts the optimal solution to achieve automatic diagnosis and self-recovery of faults.

[0009] As a preferred solution of the automated deployment method for the industrial Internet platform based on the domestic operating system of the present invention, wherein: the use of machine learning to achieve intelligent adaptive deployment includes: defining the parameter vector a = [x1, x2,..., xn] of the deployment plan, and the tool randomly generates m candidate deployment plans

[0010] {b1, b2,..., bm}, defining a quality evaluation model:

[0011] Q(x) = w1x1 + w2x2 +,..., + wk×xk + c

[0012] where x is the parameter feature vector of the deployment plan, w is the weight vector, and c is the bias; using the XGBoost machine learning algorithm for model training. For new deployment requirements, the tool automatically generates multiple candidate deployment plans, inputs the plans into the model, for each candidate plan bi, inputs the vector xi into the evaluation model, and calculates the quality index:

[0013] Yi = Q(xi)

[0014] Obtain the quality indices [Y1, Y2,..., Ym] of all candidate solutions, filter out the solution with the minimum quality index. If the number of remaining solutions is less than n, randomly generate new candidate solutions xj, calculate the quality index Yj, and finally obtain n candidate deployment solutions with higher quality indices. Sort the deployment solutions according to the quality index, select the solution ranked first for deployment. If the deployment fails, select the alternative second-ranked solution for retry.

[0015] As a preferred solution of the automated deployment method for the industrial Internet platform based on domestic operating systems according to the present invention, wherein: the automated deployment tool refers to an automated script that complies with the Bash standard specification and has cross-operating system and good compatibility, written based on the Shell language and the Python language.

[0016] As a preferred solution of the automated deployment method for the industrial Internet platform based on domestic operating systems according to the present invention, wherein: the automatic identification of the tool means that by reading the key parameters in the system file / etc / os-release and using the traversal and comparison method to match them one by one with the operating system list built into the tool, the tool will automatically install the corresponding version of the basic components according to the matching results.

[0017] As a preferred solution of the automated deployment method for the industrial Internet platform based on domestic operating systems according to the present invention, wherein: the automatic identification of the deployment mode of the tool includes that if the IP address of the current host exists in the IP address set of the configuration file and is the same as the IP address of the local software repository, the tool determines that the deployment mode is the single-machine mode or the distributed master node mode, and first deploys and publishes the local software repository and enables the service discovery and registration function, and then deploys the platform public components and business applications that need to be installed on the host specified by the IP in the configuration file; if the IP address of the current host exists in the IP address set of the configuration file but is different from the IP address of the local software repository, the tool determines that the deployment mode is the distributed slave node mode, and only deploys the platform public components and business applications that need to be installed on the host specified by the IP in the configuration file; if the IP address of the current host does not exist in the IP address set of the configuration file, the tool determines that the current host has no right to install any components or services, and prompts the user and forces to exit.

[0018] As a preferred solution of the automated deployment method for the industrial Internet platform based on domestic operating systems according to the present invention, wherein: the tool uses a single image to automatically deploy multiple business application services of the platform by building a dozen platform application jar packages into a docker image. When starting the platform application container, the corresponding platform application is started according to the requirements by setting the container environment variables.

[0019] Second aspect: To further solve the problems in the prior art such as high probability of human errors, high implementation cost, and poor portability in the industrial Internet platform, the embodiments of the present invention provide an automated deployment system for an industrial Internet platform based on a domestic operating system, which includes: an automated deployment module for automatically identifying the model and version of the domestic operating system of the current host and automatically installing the basic components of the corresponding version; a service discovery and registration function module that, after enabling this function, can check in real time whether all public components of the platform are installed and ready, and automatically release the front-end files, modify the configuration parameters, and initialize the database after all checks pass; a platform public component module for automatically identifying and installing specified public components according to the platform deployment mode; and a business application service module for using a single image to automatically deploy multiple business application services of the platform.

[0020] Third aspect: The embodiments of the present invention provide a computer device, including a memory and a processor, where the memory stores a computer program, and: when the computer program is executed by the processor, it implements any step of the automated deployment method for an industrial Internet platform based on a domestic operating system as described in the first aspect of the present invention.

[0021] Fourth aspect: The embodiments of the present invention provide a computer-readable storage medium, on which a computer program is stored, and: when the computer program is executed by the processor, it implements any step of the automated deployment method for an industrial Internet platform based on a domestic operating system as described in the first aspect of the present invention.

[0022] The beneficial effects of the present invention are as follows: Through the automated deployment tool, the present invention realizes automatic adaptation to a variety of domestic operating systems, can quickly and efficiently deploy the public components and business application services of the industrial Internet platform, and effectively reduces the error probability and labor cost of operation and maintenance personnel. Description of the Drawings

[0023] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for description in the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings. Among them:

[0024] Figure 1 It is a flowchart of the automated deployment method for an industrial Internet platform based on a domestic operating system in Embodiment 1.

[0025] Figure 2 It is an authentication prompt diagram for accessing the local software repository through a browser in Embodiment 1.

[0026] Figure 3This is the docker container information diagram in Example 1.

[0027] Figure 4 The following are statistical data diagrams of the two construction methods in Example 2 when applied on platforms of different orders of magnitude. DETAILED DESCRIPTION

[0028] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0029] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0030] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0031] Example 1

[0032] Reference Figures 1 to 3 , which is the first embodiment of the present invention, and provides an automated deployment method for an industrial Internet platform based on a domestic operating system, comprising the following steps:

[0033] S1: Install the automated deployment tool. The tool automatically identifies the domestic operating system model and version of the current host and automatically installs the basic components of the corresponding version, including file download services and container engine services.

[0034] S1.1: Use machine learning to enable intelligent adaptive deployment.

[0035] Preferably, historical deployment log data is collected, and deployment parameters, operation indicators, etc. are extracted as training features; a deployment quality assessment model is constructed to predict the quality index of the deployment plan. Specifically: a parameter vector a=[x1,x2,...,xn] of the deployment plan is defined, including server configuration, network bandwidth, etc. The tool randomly generates m candidate deployment plans {b1,b2,...,bm}; a quality assessment model is defined:

[0036] Q(x)=w1x1+w2x2+,...,+wk×xk+c

[0037] Among them, x is the parameter feature vector of the deployment plan, w is the weight vector, and c is the bias.

[0038] Use the XGBoost machine learning algorithm for model training. For new deployment requirements, the tool automatically generates multiple candidate deployment plans, inputs the plans into the model, calculates the quality index Q of each plan, and selects the optimized plan with the highest quality index for intelligent deployment.

[0039] Furthermore, the tool automatically generates multiple candidate deployment plans, and inputting the plans into the model to calculate the quality index Q of each plan includes the following steps:

[0040] For each candidate plan bi, input the vector xi into the evaluation model to calculate the quality index:

[0041] Yi = Q(xi)

[0042] Obtain the quality indices [Y1, Y2,..., Ym] of all candidate plans, filter out the plan with the smallest quality index. If the number of remaining plans is less than n, randomly generate new candidate plans xj, calculate the quality index Yj, and finally obtain n candidate deployment plans with higher quality indices. Sort the deployment plans according to the quality index, select the plan ranked first for deployment. If the deployment fails, select the alternative second-ranked plan for retry.

[0043] S1.2: Explore online learning and incremental deployment methods to support dynamic changes.

[0044] Specifically, during the deployment process, collect operation metrics and feedback them to the online learning model. The model is incrementally updated to continuously optimize the deployment strategy; when there are new deployment requirements, perform incremental training to adjust the model. The tool supports describing deployment changes in an orchestration language, automatically generates an incremental execution process, parses the change description, extracts information such as involved modules and parameters, and based on the extracted information, automatically locates the components that need to be adjusted to generate an incremental upgrade execution deployment plan to achieve dynamic changes.

[0045] S1.3: During the deployment process, monitor key metrics, the model predicts the optimal plan, and realizes automatic diagnosis and self-recovery from failures.

[0046] Specifically, an LSTM neural network is used as the evaluation model. The input is the historical metric time series data monitored during the deployment process, and the output is the predicted metric performance for a future time period. The LSTM captures the long-term dependence characteristics of the metrics to judge the abnormal interaction process of the metrics: set the normal fluctuation range for each metric. When the metric exceeds the fluctuation range, perform an anomaly check: prompt the metric anomaly situation and request the user to confirm. The user can view the time period of the metric anomaly. The user can confirm whether the metric is abnormal or normal, and the user can modify the normal fluctuation range of the metric; collect the user's confirmation feedback, mark the metric data, and use the marked data to train the model to judge metric anomalies; when the metric is abnormal, input the real-time metric into the model to evaluate the performance F of the current solution, generate the metric data of the alternative deployment solutions, calculate the performance scores output by the model for each alternative solution, select the candidate solution with the highest score as the current optimal solution, automatically redeploy the optimal solution to replace the problem solution, collect the problem and replacement solution samples, incrementally adjust the model, repeatedly monitor the metrics, and continuously optimize the deployment solution. When the metric returns to normal, the self-recovery is completed.

[0047] Furthermore, the process of calculating the performance scores output by the model is as follows: For the candidate deployment solution A, its metric data is: [xA1, xA2,..., xAn]; input the metric data of solution A into the linear regression model to obtain FA; perform the same processing for other candidate solutions B, C, etc., and calculate their model output values FB, FC; compare the model output values of each candidate solution: If FA > FB and FA > FC, then the performance score of solution A is the highest.

[0048] Furthermore, the automated deployment tool refers to an automated script that complies with the Bash standard specification and is written based on the Shell language and Python language, which is cross-operating system and has good compatibility, including: an automated script for installing and uninstalling the automated deployment tool, which is used to install and uninstall the httpd file download service and the docker container engine service. Httpd is used to publish the local software repository and provide service discovery and registration functions, and docker is used to host all containers of the platform public components and business applications; an automated script for publishing the installation packages in the local software repository, which realizes the online download of the installation packages of the platform public components and business applications through the wget instruction, releases and publishes them to the local software repository; an automated script for the service discovery and registration function, which responds to the service registration requests sent by the platform public components and business applications after deployment and the service information query requests before the initialization work by listening to specific keyword combinations in the httpd access log; an automated script for platform deployment and uninstallation, which is used to install and uninstall the platform public components, release the front-end files, modify the configuration parameters, initialize the database, and install and assist the platform business applications.

[0049] The tool automatically identifies the domestic operating system model and version of the current host, which means that by reading the key parameters in the system file / etc / os-release and using the traversal and comparison method to match them one by one with the operating system list built into the tool, the tool will automatically install the corresponding version of the basic components according to the matching results.

[0050] Specifically, when the automated deployment tool is installed, it first reads the key parameters in the system file / etc / os-release and compares and matches them with the built-in configuration file to accurately identify the operating system model and version of the current host to confirm whether it is supported. If the tool does not support this operating system, relevant prompts will be made and it will be forced to exit to avoid compatibility issues.

[0051] When it is confirmed that the operating system is supported, the tool will further check the host environment to confirm whether the httpd and docker services have been installed and whether the relevant ports and deployment directories are occupied or already exist, to prevent operation and maintenance accidents caused by overwriting installations.

[0052] When the host environment check passes, the tool will install the httpd and docker services corresponding to the operating system model and version, and verify whether these two services are installed successfully and can work properly after the installation. If the installation of the httpd and docker services fails, the tool will make relevant prompts and be forced to exit to avoid subsequent service deployment failures.

[0053] S2: Build a local software repository through the tool. The tool automatically identifies the platform deployment mode, automatically downloads the complete platform installation package to the local and publishes it to the local software repository, and after completion, the tool automatically enables the service discovery and registration function.

[0054] S2.1: When the automated deployment tool builds a local software repository, it first obtains the IP address of the host and compares and matches it with the built-in configuration file, and automatically identifies the deployment mode of the platform.

[0055] When the tool identifies that the deployment mode is the single-machine mode or the distributed master node mode and the httpd service has been installed, the tool allows building a local software repository.

[0056] S2.2: After the tool identifies the deployment mode and passes the verification, it will automatically check whether the offline software installation package specified in the configuration file already exists in the local resource directory; if the offline installation package does not exist, the tool will automatically access the cloud software repository and try to download the specified software installation package.

[0057] S2.3: After all software installation packages pass the detection, the tool will automatically release the files in the installation package to the corresponding positions in the software repository, including platform public components and business applications, etc.

[0058] S2.4: After the tool finishes building the local software repository, it will automatically enable the service discovery and registration functions.

[0059] S2.5: The local software repository enables user access authentication by default, providing high network security protection for local area network file downloads and service discovery and registration functions. The authentication prompt for accessing the local software repository through a browser is as Figure 2 shown.

[0060] S3: Deploy the platform public components through the tool. The tool automatically identifies the platform deployment mode and automatically installs the specified public components, including middleware, cache, configuration center, relational database, object storage, message queue, and computing engine.

[0061] S3.1: When the automated deployment tool installs the platform public components, it first matches the list of components to be installed on the current host according to the built-in configuration file.

[0062] S3.2: After the tool completes the environment check of the current host, it will create the component instance directory item by item in the orchestration order, and then modify the installation configuration files of each component.

[0063] Before executing the sub-installation program of each component, the tool will also check whether the instance directory, service port, container application name, etc. of the component are occupied to prevent operation and maintenance accidents caused by overwriting installations.

[0064] S3.3: When the component installation is completed, the tool will check whether the component has been successfully registered in the service discovery and registration function list. At the same time, the service discovery and registration function running in the background will receive service registration or information query requests and make responses.

[0065] It should be noted that the tool automatic recognition platform deployment mode in S2 and S3 refers to a method of determining the deployment mode by comparing the IP address of the current host with the IP address set in the configuration file according to specific rules, including: when the IP address of the current host exists in the IP address set of the configuration file and is the same as the IP address of the local software repository, the tool determines the deployment mode as "single-server" or "multi-server-master", and first deploys and publishes the local software repository and enables the service discovery and registration function, and then deploys the platform public components and business applications that need to be installed on the host specified by this IP in the configuration file; when the IP address of the current host exists in the IP address set of the configuration file but is different from the IP address of the local software repository, the tool determines the deployment mode as "multi-server-nodes", and only deploys the platform public components and business applications that need to be installed on the host specified by this IP in the configuration file; when the IP address of the current host does not exist in the IP address set of the configuration file, the tool determines that the current host has no right to install any components or services, prompts the user and forces to exit.

[0066] S4: Through the service discovery and registration function of the tool, the tool checks in real time whether all platform public components are installed and ready, and automatically releases the front-end files, modifies the configuration parameters, and initializes the database after all checks pass.

[0067] S4.1: After the public components on the current host are installed, the automated deployment tool will automatically detect the registration information of all platform public components in the component service discovery and registration function to confirm that all platform public components have been installed and registered successfully.

[0068] If some components are not registered successfully, the tool will enter the waiting state and detect the registration status of this component again every 10 seconds until the component is registered successfully and then continue with the subsequent operations, or timeout and force the installation process to end.

[0069] S4.2: When all components are registered successfully, the tool will start to release the front-end files, initialize the database, and modify the relevant configuration parameters of the platform in the database.

[0070] Preferably, the service discovery and registration function refers to a method of responding to the service registration request and service information query request sent by the platform public components and business applications after deployment by listening to specific keyword combinations in the httpd access log in real time.

[0071] S5: After all initialization operations are completed, the tool will use a single image to automatically deploy multiple business application services of the platform, including background applications in industrial fields such as system management, real-time monitoring, factory modeling, and parameter alarm.

[0072] The tool will use a single image to automatically deploy multiple business application services of the platform, which means that by building more than a dozen platform application jar packages into a single docker image, when starting the platform application container, the corresponding platform applications are started according to requirements by setting the container environment variables.

[0073] S5.1: When the file or data initialization jobs of all public components are completed, the automated deployment tool will automatically start to deploy the platform business application modules.

[0074] S5.2: Since there are dozens of platform business application modules, if they are packaged as independent docker image installation packages using traditional building methods, the files will occupy a large amount of space, and the operating efficiency of operations such as compression, release, upload, and download of the installation packages will be severely affected in actual application scenarios.

[0075] To solve the drawbacks of traditional building methods, we adopt an integrated building method to package the platform business application modules. The specific processing method is: build all platform business application modules into the same docker image package and add a startup script with the function of automatically identifying the application name. In actual usage scenarios, the automated deployment tool will build and start the container instances corresponding to the business application names by specifying the docker container environment variables. S5.3: This automated deployment method for the industrial Internet platform based on domestic operating systems has been verified through real applications at more than a dozen project sites, and can complete the offline deployment of a complete set of platforms with 30 public components and business applications within 10 minutes. In the automated deployment tool, the running time of building the software repository module and deploying public components and business application modules accounts for more than 98% of the total running time.

[0076] S54: After all components of the industrial Internet platform are installed, the tool will print out all docker container information, such as Figure 3 shown.

[0077] This embodiment also provides an automated deployment system for the industrial Internet platform based on domestic operating systems, including: an automated deployment module for automatically identifying the domestic operating system model and version of the current host and automatically installing the corresponding version of the basic components; a service discovery and registration function module, after the tool enables this function, it can check in real time whether all public components of the platform are installed and ready, and automatically release the front-end files, modify configuration parameters, and initialize the database after all checks pass; a platform public component module for automatically identifying and installing specified public components according to the platform deployment mode; a business application service module for the tool to use a single image to automatically deploy multiple business application services of the platform.

[0078] This embodiment also provides a computer device, which is applicable to the scenario of the automated deployment method of the industrial Internet platform based on a domestic operating system, and includes: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the automated deployment method of the industrial Internet platform based on a domestic operating system as proposed in the above embodiment.

[0079] The computer device can be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, a touchpad, or a mouse, etc.

[0080] This embodiment also provides a storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the automated deployment method of the industrial Internet platform based on a domestic operating system as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM for short), Electrically Erasable Programmable Read-Only Memory (EEPROM for short), Erasable Programmable Read-Only Memory (EPROM for short), Programmable Read-Only Memory (PROM for short), Read-Only Memory (ROM for short), magnetic memory, flash memory, a magnetic disk, or an optical disc.

[0081] In summary, the present invention realizes automatic adaptation to multiple domestic operating systems through an automated deployment tool, can quickly and efficiently deploy the common components and business application services of the industrial Internet platform, and effectively reduces the error probability and labor cost of operation and maintenance personnel.

[0082] Example 2

[0083] Reference Figure 4 , which is the second embodiment of the present invention. To further verify the present invention, simulation data of the automated deployment method of the industrial Internet platform based on the domestic operating system is provided.

[0084] The present invention constructs all business application modules of the platform into the same docker image package and adds a startup script with the function of automatically identifying the application name. In the actual use scenario, the automated deployment tool will build and start the container instance corresponding to the business application name by specifying the docker container environment variables. Compared with the traditional method, the integrated construction method can greatly reduce the total size of the installation package.

[0085] The number of applications in a typical industrial Internet platform is between 10 and 30. The integrated construction method will save 70% - 80% of the storage space, transmission traffic, and the corresponding operation and maintenance time cost compared with the traditional method. Figure 4 It is the comparison of the statistical data of the two construction methods under the condition of platform applications with different orders of magnitude.

[0086] It can be seen that the present invention has made great progress compared with the prior art in terms of saving the transmission space and the proportion of transmission traffic, which further shows that the present invention can quickly and efficiently deploy the public components and business application services of the industrial Internet platform, and effectively reduce the error probability and labor cost of the operation and maintenance personnel.

[0087] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not restrictive. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. An automated deployment method for an industrial Internet platform based on a domestic operating system, characterized in that: Including: Install an automated deployment tool. The tool automatically identifies the domestic operating system model and version of the current host and automatically installs the corresponding version of the basic components. Build a local software repository through the tool. The tool automatically identifies the platform deployment mode, automatically downloads the complete platform installation package to the local and publishes it to the local software repository. After completion, the tool automatically enables the service discovery and registration function. Deploy the platform common components through the tool. The tool automatically identifies the platform deployment mode and automatically installs the specified common components. Through the service discovery and registration function of the tool, check in real time whether all platform common components are installed and ready. After all checks pass, automatically release the front-end files, modify the configuration parameters, and initialize the database. The tool will use a single image to automatically deploy multiple platform business application services. The steps for installing the automated deployment tool are as follows: Use machine learning to achieve intelligent adaptive deployment. Explore online learning and incremental deployment methods to support dynamic changes. During the deployment process, monitor key metrics, and the model predicts the optimal solution to achieve automatic diagnosis and self-recovery of faults. The use of machine learning to achieve intelligent adaptive deployment includes: Define the parameter feature vector x of the deployment plan as x = [x1,..., x n , and the tool randomly generates m candidate deployment plans {b1,..., b m}, and each candidate plan b j (j = 1, 2,..., m) corresponds to a parameter feature vector, denoted as Define the quality evaluation model: Q(x) = w1x1 + w2x2 +... + w n x n + c where x is the parameter feature vector of the deployment scheme, w is the weight vector, w = [w1, w2,..., w n , and c is the bias; Use the XGBoost machine learning algorithm for model training. For new deployment requirements, the tool automatically generates multiple candidate deployment plans, inputs the plans into the model, and for each candidate plan b j , input its corresponding parameter feature vector into the evaluation model to calculate the quality index: Obtain the quality indices [Y1, Y2,..., Y m of all candidate solutions, filter out the solution with the minimum quality index. If the number of remaining solutions is less than k, randomly generate new candidate solution b t and its corresponding parameter feature vector x t , calculate the quality index Y t . Finally, obtain k candidate deployment solutions with relatively high quality indices, sort the deployment solutions according to the quality indices, select the solution ranked first for deployment. If the deployment fails, select the alternative second-ranked solution for retry.

2. The automated deployment method of the industrial Internet platform based on domestic operating system according to claim 1, characterized in that: The automated deployment tool refers to an automated script that complies with the Bash standard specification and has cross-operating system and good compatibility, written based on the Shell language and Python language.

3. The automated deployment method of the industrial Internet platform based on domestic operating system according to claim 2, wherein: The tool's automatic identification means that by reading the key parameters in the system file / etc / os-release and using a traversal and comparison method to match them one by one with the operating system list built into the tool, the tool will automatically install the corresponding version of the basic components according to the matching results.

4. The automated deployment method of the industrial Internet platform based on domestic operating system according to claim 3, characterized in that: The tool's automatic identification of the platform deployment mode includes If the IP address of the current host exists in the IP address set of the configuration file and is the same as the IP address of the local software repository, the tool determines that the deployment mode is the single-machine mode or the distributed master node mode. First, deploy and publish the local software repository and enable the service discovery and registration function, and then deploy the platform common components and business applications that the host specified by this IP in the configuration file needs to install. If the IP address of the current host exists in the IP address set of the configuration file but is different from the IP address of the local software repository, the tool determines that the deployment mode is the distributed slave node mode, and only deploys the platform common components and business applications that the host specified by this IP in the configuration file needs to install. If the IP address of the current host does not exist in the IP address set of the configuration file, the tool determines that the current host has no right to install any components or services, prompts the user, and forces the exit.

5. The automated deployment method of the industrial Internet platform based on domestic operating system according to claim 4, characterized in that: The tool will use a single image to automatically deploy multiple platform business application services by building more than a dozen platform application jar packages into a docker image. When starting the platform application container, start the corresponding platform application as needed by setting the container environment variables.

6. An automated deployment system for an industrial Internet platform based on a domestic operating system, based on the automated deployment method for an industrial Internet platform based on a domestic operating system according to any one of claims 1 to 5, characterized in that: Including An automated deployment module for automatically identifying the domestic operating system model and version of the current host and automatically installing the corresponding version of the basic components. Service discovery and registration function module. After enabling this function, the tool can check in real time whether all public components of the platform are installed and ready, and automatically release front-end files, modify configuration parameters, and initialize the database after all checks pass; Platform public component module, which is used to automatically identify and install specified public components according to the platform deployment mode; Business application service module, which is used for the tool to automatically deploy multiple business application services of the platform using a single image.

7. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the automated deployment method of the industrial Internet platform based on domestic operating systems according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the automated deployment method of the industrial Internet platform based on domestic operating systems according to any one of claims 1 to 5.

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