Management method and platform of digital intelligent technology and software and hardware integration service, and storage medium

Abstract

The invention relates to the related technical field of integrated service management, in particular to a digital intelligent technology and software and hardware integrated service management method and platform and a storage medium, and the method comprises the steps: connecting an industrial internet, receiving integrated service requirements, deploying software and hardware based on specifications, fitting a service scene, setting a topological architecture conforming to the specifications, and collecting data to establish a performance model. According to the software and hardware matching topology architecture, a communication architecture is adopted, a monitoring mechanism is set, and abnormal response management is performed on each component and each service in the software and hardware matching topology architecture, so that the technical problems that a service scene is fixed and a static deployment mode is difficult to quickly respond to dynamically changing service requirements in a software and hardware integration process are solved; the technical effects that the industrial internet is connected, the integrated service demand information is received in real time, changes of service demands are flexibly coped with in the software and hardware configuration process, dynamic configuration and adjustment are carried out according to different service scenes and demands, service changes are rapidly responded, and the flexibility and adaptability of resource configuration are improved are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field related to integrated business management, and specifically to a management method, platform and storage medium for digital intelligence technology and software and hardware integrated business. Background Art

[0002] With the rapid advancement of global informatization, enterprises are facing more and more digital and intelligent challenges, including improving work efficiency and optimizing business processes. In order to meet the challenges, enterprises need to formulate digital solutions to achieve digital transformation and intelligent upgrading of their businesses. Relevant reports show that digital transformation has become an inevitable trend in enterprise development.

[0003] However, different scenarios have different requirements for servers, storage devices, and network devices. At the same time, the software level needs to adapt to various databases, middleware, and application services. This complexity makes system configuration more difficult. Conventional static deployment methods are unable to quickly respond to dynamically changing business needs, which seriously affects the company's business collaboration and decision-making efficiency, and restricts the company's digital transformation process.

[0004] In summary, in the prior art, there are technical problems in the process of software and hardware integration, the business scenarios are fixed, and the static deployment method is difficult to quickly respond to dynamically changing business needs. Summary of the invention

[0005] This application aims to solve the technical problems in the existing technology of software and hardware integration, where business scenarios are fixed and static deployment methods are difficult to quickly respond to dynamically changing business needs, by providing a management platform for digital intelligence technology and software and hardware integration services.

[0006] In view of the above problems, the technical solution to implement this application is: In a first aspect, the present application provides a management method for digital intelligence technology and software and hardware integrated business, wherein the method includes: connecting to the industrial Internet and receiving integrated business demand information, wherein the integrated business demand information includes performance demand information and interface demand information; based on the industrial Internet, combining hardware selection specifications to deploy components and fit a first set of business scenarios, wherein the hardware selection specifications include server protocols, storage device protocols, and network device protocols; based on the industrial Internet, combining software selection specifications to deploy services and fit a second set of business scenarios, wherein the software selection specifications include database protocols, middleware protocols, and application software protocols; through the first set of business scenarios and the second set of business scenarios, according to the interface requirement information in the integrated business demand information, setting a software and hardware selection topology architecture, wherein the software and hardware selection topology architecture complies with the interface management specification; collecting real-time operation data, and establishing a performance analysis model according to the performance requirement information in the integrated business demand information, wherein the performance analysis model is connected to the software and hardware selection topology architecture; using the performance analysis model, setting a performance monitoring mechanism to perform abnormal response management on each component and each service in the software and hardware selection topology architecture.

[0007] The second aspect of the present application provides a management platform for digital intelligence technology and software and hardware integrated services, which includes: a processor; a memory for storing executable instructions of the processor; wherein the processor is used to execute the management method of digital intelligence technology and software and hardware integrated services provided in the present application.

[0008] In a third aspect of the present application, a storage medium is provided on which a computer program is stored, wherein when the computer program is executed by a processor, the steps of the method for managing digital intelligence technology and software and hardware integration services described in any one of the first aspects are implemented.

[0009] In summary, one or more technical solutions provided in this application solve the technical problem that in the process of software and hardware integration, the business scenarios are fixed and the static deployment method is difficult to quickly respond to dynamically changing business needs. It realizes the connection to the industrial Internet and real-time reception of integrated business demand information, and can flexibly respond to changes in business needs during the software and hardware configuration process. It can dynamically configure and adjust according to different business scenarios and needs, quickly respond to business changes, and improve the flexibility and adaptability of resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A flowchart of a management method for digital intelligence technology and software and hardware integration business is provided for this application; Figure 2 A structural diagram of the management platform for digital intelligence technology and software and hardware integration services is provided for this application.

[0011] Description of the reference numerals: processor 21 , memory 22 , input device 23 , output device 24 . DETAILED DESCRIPTION

[0012] Embodiment 1

[0013] The present application is described in detail below with reference to the accompanying drawings. Figure 1 As shown, the present application provides a management method for digital intelligence technology and software and hardware integration services, wherein the method includes: S1: Connect to the Industrial Internet and receive integrated business demand information, which includes performance demand information and interface demand information; S2: Based on the Industrial Internet, combine hardware selection specifications to deploy components and fit a first set of business scenarios, which includes server protocols, storage device protocols, and network device protocols; S3: Based on the Industrial Internet, combine software selection specifications to deploy services and fit a second set of business scenarios, which includes database protocols, middleware protocols, and application software protocols.

[0014] Specifically, the Industrial Internet is a technology that connects various industrial equipment, systems, production processes, management and operation links through the Internet and information and communication technologies to achieve data collection, analysis, processing and decision support. It covers a technical system from physical equipment, sensors to information platforms. The Industrial Internet can provide functions such as real-time monitoring, predictive analysis and optimized control; integrated business demand information refers to the specific definition and description of system requirements of an enterprise during the software and hardware integration process, usually including performance requirement information and interface requirement information. Performance requirements refer to the system's processing power, response time, scalability and other requirements. Interface requirement information refers to the data transmission and interaction requirements between different components and services in the system.

[0015] The hardware selection specification is the technical standards and protocol requirements for the configuration of hardware components, including: Server protocol: Configuration requirements of the server in the network environment, such as supported communication protocols (such as TCP / IP), performance specifications (such as CPU, memory, storage, etc.), Storage device protocol: The interaction protocol between the data storage system and other components or systems, such as RAID protocol, SATA, SAS, NAS protocol, etc., Network device protocol: Communication protocol between network devices, including protocols used by routers, switches and other devices, such as Ethernet protocol, IP protocol, etc.

[0016] Software selection specifications refer to the selection and deployment specifications for application software that are matched with the configuration of software and hardware resources, including: database protocols (communication protocols and operating standards between database systems and application systems or other databases, such as SQL protocols, NoSQL protocols, etc.), middleware protocols (interaction protocols between application software and databases, operating systems, etc., common ones are JMS, SOAP, REST, etc.), application software protocols (configuration requirements for specific application software, such as HTTP protocols for Web applications, distributed computing frameworks such as Hadoop protocols, etc.).

[0017] Execution steps: Establish a connection with the Industrial Internet. The Industrial Internet connects different hardware and different software systems to form an efficient and dynamic business operation platform. Through the Industrial Internet, the system can obtain and receive demand information from different business scenarios in real time. The demand information includes performance demand information (for example, a business scenario needs to process millions of requests per second, requiring high throughput and low latency) and interface demand information (for example, the system needs to support data exchange with external ERP systems, requiring a RESTful API interface).

[0018] After receiving the integration requirement information, hardware deployment will be carried out according to different business scenarios based on the hardware selection specifications. First, select the appropriate server protocol, storage device protocol and network device protocol to ensure that all hardware components meet business requirements and make reasonable selections. Further, server protocol: select a server that can handle high concurrency according to requirements. For example, if the performance requirement is high-throughput data processing, select a server protocol that supports high concurrency (such as multi-core CPU, SSD storage); storage device protocol: select a data storage protocol that meets business requirements. For example, if high availability and redundant storage are required, select RAID protocol; network device protocol: in order to ensure high-speed data flow, it is necessary to deploy switches and router protocols that support high-speed network standards.

[0019] After the hardware deployment is completed, the software level deployment is carried out. Specifically, according to the needs of the second business scenario, select the appropriate database, application middleware and application software, and ensure that the software components comply with the predetermined protocol specifications. Furthermore, database protocol: select a suitable database, such as SQL or NoSQL, and configure it according to actual needs. For example, if you need to process a large amount of unstructured data, you may choose NoSQL database protocols such as MongoDB; middleware protocol: select a middleware protocol that supports high-concurrency requests and distributed transactions, such as Apache Kafka (for message queues), Redis (for caching), etc.; application software protocol: according to business needs, select an application protocol that matches the business process, such as Web applications may use HTTP / HTTPS protocols.

[0020] According to the deployment of the first and second business scenario sets, set up a hardware and software selection topology architecture that complies with the interface management specifications to ensure that different hardware and software components can work together efficiently and seamlessly. For example, at the hardware level, ensure that servers, storage devices, and network devices can connect and communicate correctly; at the software level, ensure that databases, middleware, application software and other components can effectively interact with each other and provide business support.

[0021] S4: through the first business scenario set and the second business scenario set, according to the interface requirement information in the integrated business requirement information, set the software and hardware matching topology architecture, and the software and hardware matching topology architecture complies with the interface management specification; S5: collect real-time operation data, and establish a performance analysis model according to the performance requirement information in the integrated business requirement information, and the performance analysis model is connected to the software and hardware matching topology architecture; S6: using the performance analysis model, set a performance monitoring mechanism to perform abnormal response management on each component and each service in the software and hardware matching topology architecture.

[0022] Specifically, the hardware and software optional topology architecture refers to the structural layout of the hardware and software system designed to meet specific business needs, including the connection and interaction between hardware devices (such as servers, storage devices, network devices) and software systems (such as databases, middleware, applications), ensuring that the hardware and software systems can collaborate efficiently and comply with interface management specifications; interface management specifications refer to the standards and requirements for data interaction between different components or services in the hardware and software system, for example, interface protocols (such as RESTful API, SOAP), data formats (such as JSON, XML), transmission protocols (such as HTTP, TCP / IP), etc.; the performance analysis model is a tool that collects real-time operation data and combines it with performance requirement information to quantitatively analyze the operation status. The performance analysis model can predict and evaluate system performance, such as response time, throughput, resource utilization, etc.; the performance monitoring mechanism is used to monitor the operation status of components and services in the hardware and software optional topology architecture, detect anomalies and provide response measures; abnormal response management is an emergency response mechanism for abnormal problems that may occur during operation (such as resource overload, component failure), by locating, recording, analyzing anomalies and generating response strategies.

[0023] Execution steps: Design the hardware and software selection topology architecture according to the first business scenario set (hardware configuration) and the second business scenario set (software configuration), combined with the interface requirements in the integrated business requirement information; ensure that different components can work together seamlessly through interface management specifications. For example, the hardware part connects multiple servers and storage devices through network devices, and the software part implements the interaction between the application and the database through the API interface. Furthermore, use system architecture design tools (such as Visio or Draw.io) to draw the topology diagram; automate the deployment of hardware and software through configuration management tools (such as Ansible and Terraform). For example, if it is necessary to support high concurrent user access, the hardware architecture design includes load balancers, application server clusters, database servers, and distributed storage; in the software architecture, the API layer uses the RESTful interface, the database uses MySQL, and the cache layer uses Redis.

[0024] After the deployment is completed, the operation data is collected in real time. The data includes CPU utilization, memory usage, network traffic, database query time, etc., which will become the basis for establishing a performance analysis model. Furthermore, monitoring tools (such as Prometheus, Nagios, Zabbix) are used to collect real-time operation data; log analysis tools (such as ELKStack) are used to analyze the operation logs of applications and services.

[0025] Based on the collected operating data and performance requirement information, a performance analysis model is established using statistical methods or machine learning technology. The performance analysis model analyzes the current system performance data, predicts potential bottlenecks and evaluates whether the system meets performance requirements. For example, a comprehensive analysis is performed based on indicators such as throughput, latency, and error rate. Furthermore, a performance model is constructed using statistical analysis tools (such as R, MATLAB) or machine learning frameworks (such as TensorFlow, Scikit-learn); a time series database (such as InfluxDB) is used to store and analyze performance indicators.

[0026] Integrate the performance analysis model into the performance monitoring tool to form a performance monitoring mechanism. The performance monitoring mechanism can evaluate the system status in real time and trigger an alarm when an abnormality occurs. For example, when the CPU utilization exceeds 80% or the database query time exceeds the set threshold, an early warning is issued. Furthermore, an automated monitoring platform (such as Grafana combined with Prometheus) is used to create a real-time monitoring dashboard; define alarm rules and policies. For example, the real-time number of requests and latency distribution can be displayed through the Grafana dashboard; when the number of requests to the application server surges and causes the latency to exceed the set value, the expansion operation is triggered.

[0027] When the performance monitoring mechanism detects an anomaly, the exception response process is automatically triggered. Specifically, the source of the anomaly is located (such as a specific component or service) and an exception report is generated. Fault recovery operations are performed according to the exception response strategy, such as restarting the service, allocating more resources, or sending an alarm notification. Furthermore, automated operation and maintenance tools (such as RunDeck and SaltStack) are used to handle the exception response. The exception information is stored in a knowledge base (such as Confluence) for subsequent analysis and optimization. For example, if a node fails, the exception response management process can automatically remove the node from the load balancer and start a backup node to ensure service continuity. At the same time, the failure is recorded in the exception response knowledge base for future optimization of node health monitoring.

[0028] Through the above steps, starting from the deployment of software and hardware, the topology architecture is designed in combination with the interface requirement information, and the performance analysis model is established using real-time operation data. After connecting with the software and hardware optional topology architecture, a performance monitoring mechanism is set up to implement dynamic monitoring and exception response management. For example, in an e-commerce platform, the architecture design needs to ensure service stability under high concurrency, and the performance monitoring mechanism uses real-time evaluation indicators (such as latency and throughput) to detect potential problems in advance and quickly resolve faults through an automated response mechanism, thereby ensuring the reliability and flexibility of system operation. This not only solves the problem that traditional static deployment is difficult to cope with dynamic business needs, but also improves the efficiency and adaptability of system resource configuration.

[0029] Furthermore, based on the industrial Internet, components are deployed in combination with hardware selection specifications to fit the first business scenario set. The application method includes: Based on the manufacturing process chain, response efficiency constraints are added; through the response efficiency constraints, when deploying components, multiple components that support the response efficiency constraints are selected to complete the initialization configuration of the first business scenario set; after completing the initialization configuration, communication tests are performed between components, and performance feedback tuning is performed based on the communication test results to fit the first business scenario set.

[0030] Specifically, the Industrial Internet is used to realize the digitization, intelligence and networking of industrial production. For example, in intelligent manufacturing, the Industrial Internet can connect sensors, robots, production lines and other equipment to achieve data sharing and optimize production. Hardware selection specifications are guiding principles for hardware selection and configuration, including server protocols, storage device protocols and network device protocols. For example, server protocols specify hardware performance (such as CPU model, memory size), storage device protocols define storage methods (such as RAID level, SSD / HDD type), and network device protocols involve bandwidth and switch performance. Response efficiency constraints are performance requirements for the selection and deployment of hardware components, such as system response time, data transmission rate or computing power, which are used to ensure that components can meet the timeliness requirements in specific business scenarios. Communication testing is the process of verifying the data transmission performance between components. By simulating actual usage scenarios, the interaction efficiency and stability of each component, such as network latency, throughput and error rate, are tested. Performance feedback tuning refers to adjusting component configuration and parameters based on communication test results to optimize overall performance, for example, replacing inefficient components or modifying network configuration parameters to improve data transmission efficiency.

[0031] Execution steps: In the industrial Internet environment, according to the requirements of the manufacturing process chain, define response efficiency constraints. These constraints can be specific performance indicators. For example, the production system must complete the processing of work orders within 5 seconds or the data transmission rate must reach more than 100Mbps. The response efficiency constraints serve as the core reference conditions for component deployment. Furthermore, use process modeling tools (such as BPMN modeling tools, Visio) to analyze the manufacturing process chain and extract key performance requirements.

[0032] According to the defined response efficiency constraints, select the components that meet the requirements from the hardware selection specifications. For example, to meet the needs of high-performance computing, you can choose a server with high computing power (such as a server with a multi-core CPU and a large memory configuration) and match it with a high-throughput network device (such as a 10 Gigabit Ethernet switch). Furthermore, use configuration management tools (such as Puppet and Chef) to automatically deploy hardware; combine the component library and performance database to select the best hardware.

[0033] Deploy the selected hardware components and build the initial system environment, including server installation and network connection, storage device configuration, etc.; after completing the basic hardware deployment, load the relevant software and middleware to complete the initialization of the first set of business scenarios, and further use automated deployment tools (such as Ansible) to batch deploy hardware components and initialize the configuration; use virtualization technology (such as VMware, Docker) to simulate the business environment, illustratively, deploy high-performance storage arrays for real-time processing of logistics data, and set up routers and switches to ensure stable data flow.

[0034] After the initial configuration is completed, the communication performance between components is comprehensively tested. For example, the data transmission rate, delay and error rate between the server and the storage device are tested. These tests are used to verify whether the system meets the requirements of response efficiency constraints. Furthermore, network performance testing tools (such as iperf and PingPlotter) are used to test network connection performance. Data generators (such as ApacheJMeter) are used to simulate actual load tests to test the communication between components. Commonly, in industrial automation environments, iperf is used to test whether the throughput of the network switch reaches 10Gbps to verify whether it meets the real-time data transmission requirements.

[0035] Optimize component configuration based on feedback from communication tests. For example, when data transmission latency is high, replace network switches that support higher rates or adjust network topology. For inefficient storage devices, adjust cache policies or optimize the RAID level of storage arrays. Furthermore, use performance analysis tools (such as Perfmon and Grafana) to monitor component operation status. Use parameter tuning tools (such as KubernetesHPA) to dynamically adjust resource allocation. For example, use Grafana to monitor the IOPS (input and output operations per second) of the storage system. If performance bottlenecks are found, replace storage devices or adjust read-write optimization policies.

[0036] After the tuning is completed, the operating performance of each component of the business system is stable, and the first set of business scenarios is fitted. For example, in the manufacturing scenario, the various production equipment can collaborate efficiently to meet the time requirements of the process flow. Furthermore, simulation tools (such as Simulink and Arena) are used to verify the feasibility and performance of the first set of business scenarios.

[0037] By gradually deploying components in the industrial Internet environment in combination with hardware selection specifications, starting from adding response efficiency constraints, ensure that the hardware selection meets business needs; after completing the component deployment, verify the system performance through communication testing, and perform feedback and tuning based on the test results; the first business scenario set is optimized to meet the performance requirements of the manufacturing process chain, ensure efficient collaboration between various industrial equipment connected by the Industrial Internet, and achieve high responsiveness and high adaptability of the manufacturing process chain.

[0038] Furthermore, after completing the initialization configuration, a communication test between components is performed, and the method of the present application further includes: Based on the industrial Internet, an online resource sharing platform is connected, and the online resource sharing platform is communicated with multiple user terminals; after the online resource sharing platform receives a user access request, a CDN acceleration service is configured based on the software and hardware selection topology architecture; based on the CDN acceleration service, balanced distribution optimization is performed on the online resource sharing platform to determine the user access mode.

[0039] Specifically, the online resource sharing platform is a collaborative platform based on the industrial Internet, which is used to manage and share resources (such as data, services or hardware capabilities). For example, a cloud service platform or an enterprise's internal resource scheduling platform provides users with a unified access interface through the network; user terminals refer to the devices used by users to access the online resource sharing platform, such as PCs, smart phones, industrial control terminals, IoT devices, etc. Users send access requests through these terminals to interact with the sharing platform; CDN (Content Delivery Network) acceleration service is a service that speeds up access by distributing cache nodes and copying resources to the vicinity of users. For example, AWS CloudFront, Akamai, etc. are mainstream CDN service providers; balanced distribution optimization is a technology that reasonably distributes access traffic to different nodes through a load balancing algorithm to ensure efficient use of server resources and rapid response to user requests. Common methods include polling, minimum number of connections, and weight-based allocation; user access patterns are abstract descriptions of user behavior characteristics, including access frequency, access time distribution, types of accessed resources, and request volume. By analyzing user access patterns, user needs can be predicted and resource allocation can be optimized.

[0040] Execution steps: After completing the component initialization configuration and communication test, access the online resource sharing platform. The online resource sharing platform realizes the collaborative communication of multiple terminal devices through the industrial Internet to ensure resource sharing and unified scheduling. Furthermore, use platform management tools (such as Kubernetes cluster management tools) to realize dynamic resource allocation; connect the user terminal and the platform through the API interface.

[0041] The user sends an access request to the online resource sharing platform through the terminal, such as requesting to download a file or calling a service; the online resource sharing platform uses the CDN acceleration service to quickly distribute resources to cache nodes near the user based on the user's geographical location and request content. Furthermore, the CDN management tool (such as AWS CloudFront or Azure CDN) is used to configure the node and set the resource caching strategy.

[0042] After the configuration is completed, the online resource sharing platform will balance the traffic according to the user request volume and node load. For example, it uses polling to evenly distribute requests to multiple nodes, or dynamically adjusts the distribution weight according to the current node load. Furthermore, it uses load balancers (such as Nginx and HAProxy) to distribute traffic; it uses monitoring tools (such as Grafana and Prometheus) to monitor the health status of nodes in real time. For example, in the smart manufacturing scenario, when a supplier queries the bill of materials, the platform distributes the request to different data centers through the Nginx load balancer to avoid delays due to overload of a single node.

[0043] Based on the user's access log, analyze their access behavior and identify commonly used resources and high-frequency request patterns. For example, by counting the number of accesses and time distribution of a resource, determine whether it needs to be cached first or the allocation strategy is optimized. Furthermore, use log analysis tools (such as ELKStack: Elasticsearch, Logstash, Kibana) to analyze user access logs; use machine learning models (such as clustering algorithms) to extract user access features.

[0044] After completing the initialization configuration of the components, access the online resource sharing platform through the industrial Internet to ensure that the system can communicate with multiple user terminals; after the user issues an access request, the CDN acceleration service is configured to distribute the resources to the cache node closest to the user to improve access efficiency. At the same time, load balancing is used to optimize the distribution of user requests to avoid overloading of a single node, and user access patterns are analyzed to further optimize resource allocation strategies and improve overall response speed and stability.

[0045] Furthermore, based on the CDN acceleration service, balanced allocation optimization is performed on the online resource sharing platform to determine the user access mode. The method of the present application includes: On the online resource sharing platform, a hotspot caching strategy is set; based on multiple user terminals, user access logs are obtained, and high-frequency access resources are identified according to the user access logs; through the hotspot caching strategy, the high-frequency access resources are actively loaded into the CDN node, and the CDN acceleration service is set.

[0046] Specifically, the hotspot cache strategy is a technical strategy that preferentially caches frequently accessed resources to CDN nodes, which improves user access speed by reducing the load on the source server and optimizing the resource access path. For example, dynamic web page content or popular files will automatically enter the cache list based on the number of visits; user access logs are used to record log data generated when users access online resource sharing platforms, including access time, access frequency, access resource type, user terminal IP address and other information, which is used to analyze user behavior patterns and optimize resource allocation; frequently accessed resources refer to resources that users request multiple times in a short period of time, such as a file download, interface call or data query result. Identifying these resources can help optimize platform performance; CDN nodes are the basic components of CDN services. They are servers distributed in different geographical locations and are used to cache and distribute resources to improve user access speed and reduce server load.

[0047] Execution steps: Configure the hotspot cache strategy in the online resource sharing platform. The core of the hotspot cache strategy is to monitor user access logs and dynamically adjust the cache priority of resources based on the access frequency and the hotness of the accessed resources. Furthermore, use the cache management system (such as Redis, Memcached) and CDN console (such as Cloudflare) to define resource caching rules. You can set thresholds. For example, resources with more than 100 visits are marked as hotspot resources.

[0048] Collect user access data from the log system of the online resource sharing platform, including access time, resource type, number of visits, and other information. Based on this data, calculate the access frequency of each resource and identify the most popular resources. Further, use log analysis tools (such as ELKStack: Elasticsearch, Logstash, Kibana) to analyze access logs in real time.

[0049] Aggregate the data in the user access log to identify frequently accessed resources, such as certain file download links or video streaming playback requests. These resources usually have the characteristics of large request volume and high response demand in a short period of time. Furthermore, use data processing frameworks (such as Apache Spark or Flink) for real-time data aggregation and pattern recognition; you can also use machine learning models to predict hot spots of access data.

[0050] Based on the identified high-frequency resource list, these resources are proactively loaded to the CDN node closest to the user so that when the user makes a request, it can be responded directly from the cache without accessing the source server. Furthermore, the cache preheating function of the CDN service is used to copy the high-frequency resources to the designated node. For example, the online education platform loads the PDF files of popular test papers to the CDN node during the exam preparation period to ensure that students can download them quickly.

[0051] Configure CDN acceleration services to ensure that the cache strategy for high-frequency resources remains effective, including cache time, expiration rules, and dynamic refresh mechanisms. Dynamically adjust node load according to user request distribution to optimize access speed. Further, use the CDN console to adjust cache parameters. Further optimize traffic distribution through a load balancer (such as Nginx or HAProxy).

[0052] On the online resource sharing platform, hotspot caching strategies and access log analysis can be used to dynamically identify high-frequency resources with the largest number of user requests, such as frequently accessed API interfaces. After analyzing access behavior using tools such as ELK or cloud platform log services, these high-frequency resources are preferentially loaded to the CDN nodes closest to the user. CDN acceleration services can be used to optimize user access experience. For example, on an e-commerce platform, product images and videos of new products will be preheated to the main nodes when they are released, thereby avoiding overloading of the source server. By adjusting the caching strategy in real time, access speed can be improved, node load can be balanced, and resource response delay can be reduced.

[0053] Furthermore, by using the hotspot cache strategy, the high-frequency access resources are actively loaded into the CDN node, and the CDN acceleration service is set. The present application method includes: Based on the hotspot cache strategy, the user request volume and node health status are obtained; the traffic peak window is extracted through the user request volume and node health status; based on the traffic peak window, the load situation and resource utilization efficiency are monitored, and the first abnormal response management strategy is set based on the resource download success rate as the judgment condition.

[0054] Specifically, the hotspot cache strategy is used to prioritize frequently accessed resources to CDN nodes, optimizing user experience by shortening resource access paths and reducing server loads. For example, videos or images with high traffic are placed in cache servers. User request volume refers to the frequency of user access to a resource as recorded by the online resource sharing platform. It can be obtained by analyzing access logs or traffic monitoring tools and is usually used to evaluate resource popularity. Node health status is an evaluation of the performance indicators of CDN nodes, including parameters such as node response time, CPU usage, memory usage, and bandwidth utilization, which are used to determine whether the node can provide services normally. Traffic peak window refers to the time interval within a certain period of time when user access volume reaches a peak, which is used to predict resource demand and allocate resources in advance. Resource download success rate refers to the proportion of successful user requests for resources, which is used to measure the resource distribution efficiency of the platform and is a key indicator for judging platform performance. Abnormal response management strategy is a management mechanism for dealing with abnormal situations (such as high load, node failure, resource unavailability, etc.).

[0055] Execution steps: Model user access behavior and analyze system performance bottlenecks during peak hours. If the database query time is too long, it is necessary to further optimize the database index or add a read-write separation mechanism. Specifically, extract user request volume data from the access log of the online resource sharing platform and the CDN monitoring system, and obtain the health status of each CDN node, including CPU load, bandwidth usage, response time and other information. Furthermore, use log analysis tools (such as ELKStack) to obtain user request volume data; use the node monitoring function (AWSCloudFrontHealthCheck) provided by the CDN service to evaluate the node status.

[0056] Based on time series analysis, identify the time period with the highest user access volume, and determine whether the resource distribution strategy needs to be adjusted in combination with the node health status. Furthermore, use data analysis tools (such as Python's pandas and matplotlib libraries) to perform time series analysis on the request volume data; automatically extract traffic peak windows through the cloud platform's traffic reporting function; monitor the resource load of CDN nodes in real time, including indicators such as CPU, memory, and bandwidth utilization, and dynamically adjust resource distribution in combination with traffic peak windows. Furthermore, use load monitoring tools (such as Prometheus and Zabbix) to track the load situation; adjust resource allocation rules, such as setting larger bandwidth limits or adding spare nodes.

[0057] Dynamically determine whether to trigger an exception response management strategy based on the success rate of user resource requests. When the download success rate is lower than the preset threshold (such as 90%), trigger an exception response, including quickly switching to backup nodes, adjusting cache priorities, etc. Specifically, use anomaly detection tools (such as Grafana combined with Prometheus) to monitor the resource download success rate in real time; write automated scripts (such as using Python or Shell) to configure backup strategies, such as reallocating traffic to healthy nodes.

[0058] Based on the hotspot cache strategy, we analyze the user request volume and the health status of CDN nodes to identify traffic peak windows where high loads may occur. We use tools such as ELK and Prometheus to monitor these time periods in detail, dynamically adjust the load distribution of nodes, and monitor the resource download success rate in real time. When the download success rate is lower than the threshold, we immediately enable abnormal response strategies, such as switching nodes or expanding cache resources. This deployment method can effectively avoid the risk of node overload.

[0059] Furthermore, by using the performance analysis model, a performance monitoring mechanism is set to perform abnormal response management on each component and each service in the software and hardware selection topology architecture. The present application method includes: An exception response threshold is defined based on the resource download success rate as a judgment condition. When the exception response threshold is exceeded, an exception response process is triggered; fault location is performed based on the exception response process, and an exception identifier is added, where the exception identifier is used to mark problem components and problem services; the processing progress is tracked through the exception identifier, and a processing report is generated; the processing report is recorded in an exception response knowledge base; based on the exception response knowledge base, the performance analysis model is iteratively optimized, and the first exception response management strategy is updated.

[0060] Specifically, the performance analysis model is used to predict and evaluate the performance of the software and hardware system, including indicators such as resource utilization efficiency, response time, and failure rate. The performance analysis model can be established through machine learning algorithms (such as decision trees or neural networks) to analyze the operating status of components and services. The performance monitoring mechanism includes real-time monitoring, data collection and analysis, alarm triggering and other functions. For example, the system health status can be judged by monitoring CPU occupancy and bandwidth utilization. The abnormal response threshold is a reference standard for measuring whether the system is in an abnormal state. It is usually set based on performance indicators (such as download success rate and response time). When the actual value exceeds (or is lower than) the set value, the system will consider that an abnormality has occurred. The abnormal identification is used to mark the label information of the specific component or service where the abnormality occurs, including the abnormality type, occurrence time, impact range, etc., for subsequent fault tracking and management. The abnormal response knowledge base is a database that centrally records and manages abnormal response cases and solutions for fault analysis, learning and performance optimization. The abnormal response knowledge base can be analyzed based on historical records and help formulate optimization strategies. The first abnormal response management strategy refers to the processing strategy executed when an abnormality is detected, including fault location, load transfer, node switching, etc., to reduce the impact of abnormalities on the industrial Internet.

[0061] Execution steps: According to historical data and performance baselines, set the minimum acceptable value for resource download success rate. For example, the threshold is set to 95%. If it is lower than this value, an abnormal response is triggered. Furthermore, use data analysis tools (such as Python) to analyze the distribution of download success rate and set a reasonable threshold range. Use monitoring tools (such as Prometheus and Grafana) to monitor the download success rate in real time and set alarm rules.

[0062] When the download success rate is lower than the set threshold, the exception response process is automatically triggered, including real-time log collection, analysis of exception sources (such as hardware failure or service bottleneck), and locating faulty components or services. Furthermore, distributed tracing tools (such as Jaeger or Zipkin) are used to locate the location where the exception occurs; log analysis tools (such as ELKStack) are used to view error information and the time period when the exception occurs; then, exception identifiers are added, such as "CPU high usage", "network latency is too high", etc., and related components and services are associated for subsequent processing. Furthermore, event management tools (such as ServiceNow or JIRA) are used to record exceptions and generate tags.

[0063] Track the progress of problem handling through exception identification, record the time, handling method and results of each step, generate a processing report, and further use ticket management tools (such as Zendesk or Opsgenie) to track the progress of problem solving; automatically generate reporting tools (such as Tableau or PowerBI) to summarize processing data; then, upload the processing report to the knowledge base, and use a knowledge management system (such as Confluence) to store and classify and manage the archive, including problem description, cause analysis, solution and preventive measures.

[0064] Based on the historical cases in the knowledge base, optimize the prediction accuracy and response strategy of the performance analysis model. For example, adjust the model parameters, update the priority order in the exception response process, and further use machine learning tools (such as Scikit-learn or TensorFlow) to retrain the performance analysis model; update the exception response strategy file, and deploy and test it in the software and hardware optional topology architecture.

[0065] Through the performance analysis model, the exception response threshold is defined, and the resource download success rate is used as the judgment standard to monitor the operating status of the software and hardware optional topology architecture in real time; when the monitoring indicator is lower than the set threshold, the exception response process is triggered, the problem component or service is located, and an exception mark is added to it; the processing progress is automatically tracked, all operations are recorded and detailed reports are generated and stored in the exception response knowledge base, and the performance analysis model and response strategy are iteratively optimized.

[0066] Furthermore, based on the abnormal response knowledge base, the performance analysis model is iteratively optimized to update the first abnormal response management strategy. The method of the present application includes: A bidirectional fault tracing function is established through the first business scenario set and the first exception response management strategy, and the second business scenario set and the second exception response management strategy; the bidirectional fault tracing function: ;in, It is a set of abnormal response management strategies under the optimized performance monitoring mechanism. is the expected performance of the ith component or service, is the actual performance of the ith component or service, is the failure probability of the ith component or service, is the abnormal response threshold, is the weight coefficient, which is used to balance the impact of performance differences and failure probability on the optimization strategy; ={ }, where each It comprises a fault monitoring grid, a fault response grid and a fault processing grid which are mapped one by one to each other, and the fault monitoring grid, the fault response grid and the fault processing grid serve as components of the performance analysis model.

[0067] Specifically, the first exception response management strategy is a response measure and strategy designed for a specific exception type or problem in a business scenario corresponding to any one of the first business scenario set, including troubleshooting steps, fault recovery plans, load balancing adjustments, etc.; corresponding to the first exception response management strategy is a business scenario corresponding to any one of the second business scenario set, which is used for optimization when a failure occurs at the software level or in a specific network or storage environment, including service recovery (by dynamically adjusting network bandwidth and allocating more computing resources) and failover.

[0068] The bidirectional fault tracing function is a mathematical model used to correlate the abnormal response management strategy with the actual performance of specific components or services, helping to analyze and track the sources, impacts and effectiveness of response measures of faults in the system. The bidirectional fault tracing function takes into account both performance differences and failure probabilities to optimize the fault response process; the fault monitoring grid, fault response grid and fault handling grid are components of the performance analysis model, among which the fault monitoring grid is used to monitor and identify anomalies (such as performance indicators exceeding thresholds); the fault response grid is used to decide how to respond (such as whether to start backup components) based on detected faults; and the fault handling grid is used to resolve and repair identified faults (such as restarting services or replacing hardware).

[0069] Execution step: Establish a bidirectional fault tracing function through the first business scenario set and the first exception response management strategy, and the second business scenario set and the second exception response management strategy: ,in, It is a set of abnormal response management strategies under the optimized performance monitoring mechanism. is the expected performance of the ith component or service, is the actual performance of the ith component or service, is the failure probability of the ith component or service, is the abnormal response threshold, is a weight coefficient used to balance the impact of performance differences and failure probability on the optimization strategy.

[0070] Build a fault monitoring grid, a fault response grid, and a fault processing grid. Each grid is a logical unit and is used for operations at different stages. The fault monitoring grid is responsible for monitoring performance data, identifying anomalies, and transmitting anomaly information to the next stage; the fault response grid is used to automatically select appropriate response measures based on the type of fault, such as enabling a backup server or performing load balancing; the fault processing grid is used to perform actual fault repair work, such as replacing damaged hardware, restarting services, etc. Furthermore, an event-driven architecture (such as Kafka) is used to transmit fault information in real time and trigger corresponding operations; use automated operation and maintenance tools (such as Ansible, Kubernetes) to configure and manage the fault monitoring grid, fault response grid, and fault processing grid.

[0071] By building a two-way fault tracking function, the exception response strategy can be dynamically adjusted according to the actual performance, expected performance and fault probability, and the performance analysis model can be optimized on this basis; by combining the fault monitoring grid, response grid and processing grid, various fault situations can be responded to quickly, and fault handling efficiency can be improved through automated adjustments; then, by feeding historical data and experience back into the knowledge base, the response strategy and performance monitoring model are continuously optimized, thereby improving the overall stability and processing capabilities, and effectively avoiding service interruptions caused by traffic overload.

[0072] In summary, the beneficial effects of the embodiments of the present application are: 1. By combining hardware and software selection specifications, dynamically fitting a set of business scenarios, and based on the exception response knowledge base and iterative optimization mechanism, quickly respond to integrated business needs, and continuously optimize the performance analysis model to improve the overall system reliability and stability.

[0073] 2. Through hotspot caching strategies and CDN acceleration services, active allocation of high-frequency resources is achieved, traffic load is balanced, and performance analysis models and monitoring mechanisms are used to conduct real-time performance tracking and exception management of components and services in the software and hardware topology architecture, thereby optimizing resource allocation and utilization.

[0074] 3. Due to the adoption of the first business scenario set and the first exception response management strategy, the second business scenario set and the second exception response management strategy, a two-way fault tracing function is established; the two-way fault tracing function: ;in, It is a set of abnormal response management strategies under the optimized performance monitoring mechanism. is the expected performance of the ith component or service, is the actual performance of the ith component or service, is the failure probability of the ith component or service, is the abnormal response threshold, is the weight coefficient, which is used to balance the impact of performance differences and failure probability on the optimization strategy; ={ }, where each It includes a fault monitoring grid, a fault response grid, and a fault processing grid that are mapped one to one with each other. The fault monitoring grid, the fault response grid, and the fault processing grid are components of the performance analysis model. By constructing a two-way fault tracking function, the abnormal response strategy can be dynamically adjusted according to the actual performance, expected performance, and failure probability, and the performance analysis model can be optimized on this basis. By combining the fault monitoring grid, the response grid, and the processing grid, various fault situations can be responded to quickly, and the fault processing efficiency can be improved through automated adjustments. Then, by feeding back historical data and experience into the knowledge base, the response strategy and performance monitoring model are continuously optimized, thereby improving the overall stability and processing capabilities, and effectively avoiding service interruptions caused by traffic overload.

[0075] Embodiment 2

[0076] like Figure 2 As shown, it is a structural diagram of the management platform of digital intelligence technology and software and hardware integrated services provided in Example 2 of the present application, which is a block diagram of the exemplary digital intelligence technology and software and hardware integrated services management platform suitable for implementing the implementation mode of the present invention. Figure 2 The management platform of digital intelligence technology and software and hardware integration services shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention. Figure 2 As shown, the management platform for digital intelligence technology and software and hardware integration services includes a processor 21, a memory 22, an input device 23, and an output device 24; the number of processors 21 in the management platform for digital intelligence technology and software and hardware integration services can be one or more. Figure 2 Taking a processor 21 as an example, the processor 21, memory 22, input device 23 and output device 24 in the management platform of digital intelligence technology and software and hardware integration services can be connected through a bus or other means. Figure 2 The example of connecting through bus is taken in the following.

[0077] The memory 22, as a computer-readable storage medium, can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the management method of digital intelligence technology and software and hardware integrated services in the embodiment of the present application. The processor 21 executes various functional applications and data processing of the computer device by running the software programs, instructions and modules stored in the memory 22, that is, realizes the management method of the digital intelligence technology and software and hardware integrated services.

[0078] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0079] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0080] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A management method for digital intelligence technology and software and hardware integration services, characterized in that: The method comprises: Connecting to the industrial Internet and receiving integrated business demand information, wherein the integrated business demand information includes performance demand information and interface demand information; Based on the industrial Internet, components are deployed in combination with hardware selection specifications to fit a first set of business scenarios, where the hardware selection specifications include server protocols, storage device protocols, and network device protocols; Based on the industrial Internet, service deployment is performed in combination with software selection specifications to fit a second set of business scenarios, wherein the software selection specifications include database protocols, middleware protocols, and application software protocols; By using the first business scenario set and the second business scenario set, according to the interface requirement information in the integrated business requirement information, a software and hardware selection topology architecture is set, and the software and hardware selection topology architecture complies with an interface management specification; Collecting real-time operation data, and establishing a performance analysis model according to the performance requirement information in the integrated business requirement information, wherein the performance analysis model is connected with the software and hardware selection topology architecture; The performance analysis model is used to set a performance monitoring mechanism to perform abnormal response management on each component and each service in the software and hardware selection topology architecture.

2. The management method of digital intelligence technology and software and hardware integration business according to claim 1, characterized in that: Based on the industrial Internet, components are deployed in combination with hardware selection specifications to fit a first business scenario set, and the method includes: Based on the manufacturing process chain, add response efficiency constraints; By using the response efficiency constraint, when deploying components, multiple components that support the response efficiency constraint are selected to complete the initialization configuration of the first business scenario set; After completing the initialization configuration, a communication test is performed between components, and performance feedback tuning is performed based on the communication test results to fit the first business scenario set.

3. The management method of digital intelligence technology and software and hardware integration business according to claim 2, characterized in that: After completing the initialization configuration, a communication test between components is performed, and the method further includes: Based on the industrial Internet, connect to an online resource sharing platform, wherein the online resource sharing platform is communicatively connected with a plurality of user terminals; After the online resource sharing platform receives a user access request, the CDN acceleration service is configured based on the software and hardware selection topology architecture; Based on the CDN acceleration service, balanced distribution optimization is performed on the online resource sharing platform to determine the user access mode.

4. The management method of digital intelligence technology and software and hardware integration business according to claim 3, characterized in that: Based on the CDN acceleration service, balanced allocation optimization is performed on the online resource sharing platform to determine the user access mode, and the method includes: On the online resource sharing platform, setting a hotspot cache strategy; Based on multiple user terminals, obtain user access logs and identify frequently accessed resources based on the user access logs; Through the hot spot cache strategy, the high-frequency access resources are actively loaded into the CDN node, and the CDN acceleration service is set.

5. The management method of digital intelligence technology and software and hardware integration business according to claim 4, characterized in that: By using the hotspot cache strategy, the high-frequency access resources are actively loaded into the CDN node, and a CDN acceleration service is set. The method includes: Based on the hotspot cache strategy, obtain user request volume and node health status; Extracting the traffic peak window according to the user request volume and node health status; Based on the traffic peak window, the load situation and resource utilization efficiency are monitored, and the first abnormal response management strategy is set based on the resource download success rate as a judgment condition.

6. The management method of digital intelligence technology and software and hardware integration business according to claim 5, characterized in that: By using the performance analysis model, a performance monitoring mechanism is set to perform abnormal response management on each component and each service in the software and hardware matching topology architecture. The method includes: Taking the resource download success rate as a judgment condition, defining an abnormal response threshold, and triggering an abnormal response process when the abnormal response threshold is exceeded; Perform fault location based on the abnormal response process and add an abnormal identification, where the abnormal identification is used to mark the problem component and problem service; Tracking the processing progress and generating a processing report through the abnormal identification; Recording the processing report into an abnormal response knowledge base; Based on the exception response knowledge base, the performance analysis model is iteratively optimized to update the first exception response management strategy.

7. The management method of digital intelligence technology and software and hardware integration business according to claim 6, characterized in that: Based on the abnormal response knowledge base, the performance analysis model is iteratively optimized to update the first abnormal response management strategy, the method comprising: Establishing a bidirectional fault tracing function through the first business scenario set and the first exception response management strategy, and the second business scenario set and the second exception response management strategy; The bidirectional fault tracing function: ； in, It is a set of abnormal response management strategies under the optimized performance monitoring mechanism. is the expected performance of the ith component or service, is the actual performance of the ith component or service, is the failure probability of the ith component or service, is the abnormal response threshold, is the weight coefficient, which is used to balance the impact of performance differences and failure probability on the optimization strategy; ={ }, where each It comprises a fault monitoring grid, a fault response grid and a fault processing grid which are mapped one by one to each other, and the fault monitoring grid, the fault response grid and the fault processing grid serve as components of the performance analysis model.

8. A management platform for digital intelligence technology and software and hardware integration services, characterized by: include: A memory for storing executable instructions; A processor, used to implement the management method of digital intelligence technology and software and hardware integration business as described in any one of claims 1 to 7 when executing the executable instructions stored in the memory.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, it implements the management method of digital intelligence technology and software and hardware integration business as described in any one of claims 1 to 7.

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