An industrial intelligent control method and device based on a central variable system and a storage medium

Abstract

The application relates to the technical field of industrial automation control, in particular to an industrial intelligent control method, equipment and storage medium based on a central variable system. The method comprises the following steps: establishing a central variable management system, constructing an intelligent rule engine with anti-jitter, integrating multiple brands of PLCs through a three-layer driving architecture, realizing expression analysis, barcode intelligent identification, self-defined picture configuration, message protocol analysis and MES system data uploading, and increasing user permission management and control and global exception self-recovery capability. The application can unify industrial field data management, improve equipment compatibility and automation level, reduce development and maintenance cost, improve system operation stability, and is suitable for various industrial intelligent control scenes.

Description

Technical Field

[0001] This invention relates to the technical field of industrial automation control, specifically to an industrial intelligent control method, device, and storage medium based on a central variable system. Background Technology

[0002] Industrial automation control systems have become a core support for modern manufacturing. Currently, industrial sites commonly suffer from data silos, lacking a unified variable management mechanism between different functional modules, leading to complex data interaction. Differences in protocols and address encoding among different PLC brands make equipment integration difficult. Barcode recognition solutions have poor scalability and weak message protocol compatibility, making MES system integration cumbersome. Furthermore, the systems lack intelligent rule engines, interface customization is difficult, automation levels and operational stability are insufficient, and development and maintenance costs are high. Summary of the Invention

[0003] The purpose of this invention is to provide an industrial intelligent control method, device, and storage medium based on a central variable system to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: an industrial intelligent control method based on a central variable system, comprising the following steps: S1: establishing a central variable management system, adopting the X numbering rule of industrial PLC style, supporting multiple data types and dual-format variable references; S2: Build an intelligent rule engine with a debouncing mechanism to monitor variable changes, file status and timed trigger conditions in real time and execute corresponding actions; S3: Achieve multi-brand PLC device integration through a three-layer drive architecture, and establish bidirectional data synchronization between PLC addresses and system variables; S4: Start the expression parsing engine, identify and replace variables in the expression, and complete calculations and nested function calls; S5: Performs intelligent barcode parsing and verification, identifies barcode types through pattern matching, and performs duplicate detection and intelligent pattern learning; S6: Generates custom screens based on configuration files, binds screen elements to system variables, and refreshes them in real time; S7: Runs the message recognition engine to perform protocol recognition, parsing, and variable mapping on communication messages; S8: Synchronize variable data and rule execution results to the MES system via HTTP interface, and use a dual verification mechanism to ensure reliable upload; S9: Implement user permissions and security control, establish a multi-user, multi-role permission system, and achieve hierarchical permission isolation and operation security verification; S10: Performs global exception handling and system self-healing, and performs unified capture, classification, processing and automatic recovery of system exceptions.

[0005] Preferably, S3 further includes: performing heartbeat keep-alive on the PLC, automatic reconnection after disconnection, real-time evaluation of communication quality, concurrent acquisition and scheduling of multiple PLCs, communication data caching, register read / write debouncing, and automatic circuit breaking in case of abnormal communication.

[0006] Preferably, step S7 further includes: performing packet fragmentation and reassembly, checksum verification, automatic packet format correction, protocol version compatibility, packet mirroring and forwarding, abnormal packet filtering, and parsing result caching.

[0007] Preferably, S8 further includes: using an upload message queue, failure index backoff retry, breakpoint resume, data compression, upload rate limiting, response timeout protection, upload whitelist verification, and automatic alarm for upload failure.

[0008] Preferably, S9 specifically includes: establishing a four-level isolation mechanism of menu permissions, operation permissions, variable permissions, and screen permissions; performing secondary confirmation on key operations; distinguishing variable read and write permissions; and recording and alarming abnormal logins and unauthorized operations.

[0009] Preferably, S10 specifically includes: globally capturing exceptions, automatically classifying exceptions, automatically retrying and resetting recoverable exceptions, saving the current state, gracefully degrading and safely exiting for unrecoverable exceptions, and outputting exception logs with context and automatic alarms.

[0010] Preferably, the three-layer driver architecture in S3 consists of: an abstract base class layer that provides a unified read / write interface, a protocol driver layer that implements Modbus TCP / RTU communication, and a brand adaptation layer that completes address conversion for different PLC brands.

[0011] Preferably, the message recognition engine in S7 supports text and binary message parsing, initiates a self-learning mode for unknown protocols, automatically extracts features and generates parsing rules, and maps the parsing results to system variables.

[0012] An industrial intelligent control device based on a central variable system includes a processor and a memory, wherein the memory stores a computer program.

[0013] A computer-readable storage medium having a computer program stored thereon.

[0014] Compared with the prior art, the beneficial effects of the present invention are: This invention achieves unified management of industrial data through a central variable system, eliminating data silos and improving data consistency and reliability. Multi-brand PLC compatibility and integration reduce equipment integration difficulty, while a rule engine and expression parsing enable automated logic configuration. Intelligent barcode and message learning reduce on-site debugging workload, customizable screens enhance interface adaptability, and MES dual verification ensures stable uploads. Newly added access control and anomaly self-healing enhance system security and stability, significantly improving overall development efficiency and greatly reducing maintenance costs, meeting the long-term stable operation requirements of industrial sites. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the overall system architecture in an embodiment of the present invention; Figure 2 This is a system startup flowchart in an embodiment of the present invention; Figure 3 This is a flowchart of the variable management system in an embodiment of the present invention; Figure 4 This is a flowchart of PLC data synchronization in an embodiment of the present invention; Figure 5 This is a flowchart illustrating the execution process of the rule engine in an embodiment of the present invention. Figure 6 This is a flowchart illustrating the data upload process in an embodiment of the present invention; Figure 7 This is a flowchart of the exception handling process in an embodiment of the present invention; Figure 8 This is a flowchart illustrating the user authentication process in an embodiment of the present invention. Figure 9 This is a flowchart of the data processing algorithm in an embodiment of the present invention; Figure 10 This is a flowchart of the real-time data synchronization algorithm in an embodiment of the present invention; Figure 11 This is a flowchart of the rule engine algorithm in an embodiment of the present invention; Figure 12 This is a flowchart of the exception handling algorithm in an embodiment of the present invention; Figure 13 This is a flowchart of the performance optimization algorithm in an embodiment of the present invention; Figure 14 This is a flowchart of the custom screen engine in an embodiment of the present invention; Figure 15 This is a flowchart of the message recognition engine in an embodiment of the present invention; Figure 16 This is a flowchart illustrating the screen configuration parsing process in an embodiment of the present invention; Figure 17 This is a flowchart illustrating the message protocol parsing process in an embodiment of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Please see Figures 1 to 17 This invention provides a technical solution: an industrial intelligent control method, device, and storage medium based on a central variable system, wherein the control method includes the following steps: S1: Establish a central variable management system, adopting the X numbering rule of industrial PLCs, supporting multiple data types and dual-format variable references. Specifically: define variable numbering rules, adopting the industrial PLC-style numbering format of X0001, X0002, ..., X9999; establish a mapping table from variable to number, supporting access to variable values ​​by variable name or variable number; implement a real-time synchronization mechanism, automatically notifying all observers when variable values ​​change to ensure data consistency; use an SQLite database for persistent storage, supporting power outage recovery and historical data query.

[0018] S2: Build an intelligent rule engine with a debouncing mechanism to monitor variable changes, file status and timed trigger conditions in real time and execute corresponding actions.

[0019] S3: Achieves integration of multi-brand PLC devices through a three-layer driver architecture, establishing bidirectional data synchronization between PLC addresses and system variables; S3 also includes heartbeat keep-alive for PLCs, automatic reconnection after disconnection, real-time communication quality assessment, concurrent acquisition and scheduling of multiple PLCs, communication data caching, register read / write debouncing, and automatic circuit breaking in case of abnormal communication; the three-layer driver architecture in S3 is as follows: an abstract base class layer that provides a unified read / write interface, a protocol driver layer that implements Modbus TCP / RTU communication, and a brand adaptation layer that completes address conversion for different PLC brands.

[0020] S4: Start the expression parsing engine, identify and replace variables in the expression, and complete calculations and nested function calls.

[0021] S5: Performs intelligent barcode parsing and verification, identifies barcode types through pattern matching, and performs duplicate detection and intelligent pattern learning.

[0022] S6: Generates custom screens based on configuration files, binds screen elements to system variables, and refreshes them in real time.

[0023] S7: Run the message recognition engine to perform protocol recognition, parsing, and variable mapping on communication messages; S7 also includes performing message fragmentation and reassembly, checksum verification, automatic message format correction, protocol version compatibility, message mirroring and forwarding, abnormal message filtering, and caching of parsing results; the message recognition engine in S7 supports text and binary message parsing, starts a self-learning mode for unknown protocols, automatically extracts features and generates parsing rules, and maps the parsing results to system variables.

[0024] S8: Synchronize variable data and rule execution results to the MES system via HTTP interface, and use a dual verification mechanism to ensure reliable upload; S8 also includes uploading message queue, failure index backoff retry, breakpoint resume, data compression, upload rate limiting, response timeout protection, upload whitelist verification, and automatic alarm for upload failure.

[0025] S9: Implement user permissions and security control, establish a multi-user, multi-role permission system, and realize hierarchical permission isolation and operation security verification; S9 specifically includes establishing a four-level isolation mechanism of menu permissions, operation permissions, variable permissions, and screen permissions, performing secondary confirmation on key operations, distinguishing variable read and write permissions, and recording and alarming abnormal logins and unauthorized operations.

[0026] S10: Perform global exception handling and system self-healing, uniformly capture, classify and process system exceptions and automatically recover them; S10 specifically includes globally capturing exceptions, automatically classifying exceptions, automatically retrying and resetting recoverable exceptions, saving the current state, gracefully degrading and safely exiting for unrecoverable exceptions, and outputting exception logs with context and automatic alarms.

[0027] Please see Figure 1 and Figure 2 , Figure 1 The system architecture shown illustrates a four-layer structure: user interface layer, core business layer, data storage layer, and external system. These layers interact with each other through standard interfaces, achieving modularity and scalability. The central variable system executes the following steps upon startup: Step 1: Hardware license verification; The system first checks the hardware license status. If the hardware is not licensed, an unlock dialog box is displayed, prompting the user to enter the license code.

[0028] Step 2: Database initialization; Create and initialize the SQLite database table structure, including variable tables, rule tables, log tables, etc.

[0029] Step 3: Configuration loading; Load JSON format configuration files such as global rule configuration, PLC configuration, and upload interface configuration.

[0030] Step 4: User authentication; Display the login interface, verify the user's identity and permissions, and load the user configuration.

[0031] Step 5: Module initialization; Initialize core modules such as the variable management system, rule engine, and PLC integration.

[0032] Step 6: Algorithm initialization; Initialize core algorithm components such as data processing, synchronization, and rule execution.

[0033] Step 7: Interface display; Create and display the main window, enter the event loop, and begin receiving user operations.

[0034] Please see Figure 3 The specific implementation method of the variable management system is as follows: The variable manager provides a unified interface for creating, reading, updating, and deleting variables. When a variable is created, the system automatically assigns a unique number in the format X0001-X9999 and establishes a two-way mapping relationship between the variable name and the number. When a variable value changes, the system notifies all modules that have subscribed to the variable through the observer pattern to ensure real-time data synchronization. Variable data is stored in an SQLite database and supports power outage recovery and historical data query functions.

[0035] Please see Figure 4 The specific implementation method for PLC device integration is as follows: The system adopts a three-layer drive architecture to achieve unified management of PLCs from multiple brands; The first layer is the abstract base class layer, which defines a unified driver interface, including methods for connection, disconnection, reading, and writing. The second layer is the protocol driver layer, which implements the low-level communication of Modbus TCP and Modbus RTU protocols, and handles four types of inputs: coils, discrete inputs, holding registers, and input registers. The third layer is the brand adaptation layer, which implements address conversion for different PLC brands, such as the D register of a Delta PLC corresponding to the Modbus address 4096+N.

[0036] The system creates a serial port pool manager and uses a reference counting mechanism to achieve serial port sharing, allowing multiple PLC stations to share the same serial port connection.

[0037] Please see Figure 5 and Figure 11 The specific implementation of the intelligent rule engine is as follows: The rule engine defines rules through a JSON configuration file, including parameters such as rule ID, name, trigger conditions, and action type. The rule engine supports three trigger types: variable change trigger, file monitoring trigger, and timed trigger. The system employs a debouncing mechanism, setting a 500-millisecond stabilization period to prevent the same rule from being triggered repeatedly within a short period. When the trigger condition is met, the rule engine parses the condition expression, performs variable reference substitution, and evaluates the condition. After the condition is met, the system executes the corresponding operation based on the action type, including pop-up alerts, HTTP requests, file operations, and variable writing.

[0038] The specific implementation of the expression parsing engine is as follows: The system recognizes expressions in<X####> For formatted variable references, the system retrieves the actual value from the variable manager and replaces it. For nested function calls, the system uses regular expressions to identify the innermost function, executes it, and replaces it with the result value, iterating until there are no more function calls. The system supports algorithms such as MD5, SHA256, BASE64, and TIMESTAMP for data encryption and signature calculation.

[0039] The specific implementation method of the barcode intelligent parsing module is as follows: The system identifies barcode types through regular expression pattern matching, distinguishing between fixture codes and product codes. It maintains four data areas for duplicate detection: the current scan group, the pre-scan queue, the processing group, and the historical completed group. An intelligent pattern learning algorithm extracts features from sample barcodes, analyzes character type distribution, and automatically generates regular expression patterns. The system calculates a confidence score to evaluate the learning effect: confidence score = base score 60 + sample size bonus + length consistency bonus + verification success rate.

[0040] Please see Figure 14 and Figure 16 The specific implementation method of the custom screen system is as follows: The system defines the screen structure through a JSON configuration file, including screen ID, name, layout method, and list of interface elements. Screen elements support various types, including labels, buttons, input boxes, and charts. After reading the configuration, the system uses a configuration parsing algorithm to parse the screen definition and an element parsing algorithm to identify various components. The system dynamically creates interface elements using a component construction algorithm, setting attributes such as position, style, and size according to the configuration. The system uses a variable binding algorithm to establish a connection between screen elements and system variables, achieving two-way data binding. When the bound variable value changes, the system uses a status monitoring algorithm to detect the change and a UI update algorithm to refresh the display. The system uses an event binding algorithm to handle user interactions, supporting event types such as clicks, input, and selections. After a user action triggers an event, the system uses an interaction processing algorithm to execute the corresponding action, such as modifying variable values ​​or calling functions.

[0041] Please see Figure 15 and Figure 17 The specific implementation of the message recognition engine is as follows: The system defines the protocol structure through a JSON configuration file, including protocol ID, name, type, format, and field definitions. After receiving message data, the system uses a format recognition algorithm to determine the data format type, such as text or binary format. The system uses a protocol analysis algorithm to identify the communication protocol type, distinguishing between standard protocols and custom protocols. For standard protocols, the system uses predefined parsing rules to extract data fields. For custom protocols, the system initiates a protocol learning mode and uses a feature analysis algorithm to analyze message features. The system uses a rule generation algorithm to automatically generate parsing rules based on the analysis results, enabling automatic identification of unknown protocols. The system uses a field extraction algorithm to extract key data fields from the message and uses a data verification algorithm to check data integrity. The system uses a variable mapping algorithm to map the extracted data to corresponding system variables, triggering variable change events.

[0042] Please see Figure 6 The specific implementation method for MES system integration is as follows: The system synchronizes data to the MES system via an HTTP interface, supporting request methods such as POST, PUT, and GET. The system parses variable references in the URL, replaces them with actual variable values, and constructs a complete request address. The system parses variable references and function calls in the request headers and body to generate the final request data. After sending the HTTP request, the system uses a dual-validation mechanism to ensure successful data upload. The first validation checks if the HTTP status code equals the expected value; for example, 200 indicates success. The second validation uses JSONPath to extract specified fields from the response content and compares them with the expected value. After successful validation, the system performs post-processing actions, including deleting the trigger file and writing feedback variables.

[0043] Please see Figure 7 and Figure 12 The specific implementation methods for exception handling are as follows: The system implements a global exception handler to catch all unhandled exceptions and prevent program crashes. The system uses an exception classification algorithm to categorize exceptions, including connection exceptions, data exceptions, permission exceptions, and system exceptions. For recoverable exceptions, the system uses a retry strategy algorithm to retry, employing an exponential backoff algorithm to avoid frequent retries. For unrecoverable exceptions, the system uses a graceful degradation algorithm to save the system state and a safe exit algorithm to terminate the program. The system records all exception information in a log file for easy troubleshooting and system optimization.

[0044] Please see Figure 9 , Figure 10 and Figure 13 The specific implementation methods for system performance optimization are as follows: The system uses performance monitoring algorithms to monitor metrics such as CPU utilization, memory usage, and I / O operation frequency in real time. It uses bottleneck analysis algorithms to identify performance bottlenecks and employs different optimization strategies for different types of bottlenecks. For scenarios with high memory usage, the system uses cache management algorithms to optimize memory allocation and release unused resources in a timely manner. For scenarios with high CPU usage, the system uses task scheduling algorithms to optimize task allocation and avoid CPU resource contention. For scenarios with frequent I / O operations, the system uses batch processing algorithms to reduce the number of I / O operations and improve processing efficiency.

[0045] The specific implementation methods of the data processing algorithm include: Data parsing algorithms are responsible for converting raw data into a standard format; data validation algorithms check data integrity and consistency; data cleaning algorithms identify and process abnormal data; data repair algorithms attempt to repair damaged data; and data standardization algorithms convert data into a unified format for easier subsequent processing and storage.

[0046] The specific implementation methods of the real-time data synchronization algorithm include: The change detection algorithm detects changes in variable values ​​and PLC status in real time and adds the change events to a queue; the event scheduling algorithm sorts events according to priority, and the concurrent processing algorithm supports processing multiple events simultaneously; the consistency verification algorithm verifies data consistency after synchronization is completed to ensure the correctness of the synchronization operation.

[0047] Example 1: In a temperature monitoring system at a chemical plant, the system monitors 10 temperature sensors connected to 2 PLC devices. The system is configured with automatic alarm rules for exceeding temperature limits, and data is automatically uploaded to the MES system. Data accuracy is ensured through data processing algorithms, and a real-time synchronization algorithm guarantees a response time of less than 5 seconds. The system achieves 24-hour unattended monitoring, significantly reducing labor costs.

[0048] Example 2: In a glue-filling production line of an electronics factory, a USB barcode scanner is integrated and connected to three PLC devices. The system is configured to automatically trigger data upload rules upon completion of barcode scanning, and production output is automatically calculated. Automation control is achieved through a rule engine algorithm, and processing efficiency is improved through performance optimization algorithms. The system increases production efficiency by 25% and achieves 100% data accuracy.

[0049] Example 3: In a quality traceability system at an automotive parts factory, the system integrates multiple PLC stations to monitor production parameters in real time. The system is configured with automatic alarm rules for quality anomalies, and data traceability is fully recorded. Anomaly handling algorithms ensure system stability, while data processing algorithms guarantee data quality. The system achieves 100% accuracy in quality traceability and 99.9% availability.

[0050] This invention also provides an industrial intelligent control device based on a central variable system. The device includes at least one processor and at least one memory. The memory stores computer-executed instructions, and the processor and memory are connected via a communication bus. During operation, the processor calls and executes the computer instructions stored in the memory, sequentially completing the entire process of central variable management, rule engine execution, PLC device communication, expression parsing, barcode recognition, screen rendering, message parsing, MES data uploading, access control, and exception handling, thereby achieving stable and reliable automated control and data collaborative processing in the industrial field.

[0051] This invention also provides a computer-readable storage medium storing an executable computer program. When the computer program is loaded and run by a processor, it can fully execute an industrial intelligent control method based on a central variable system, completing all operations such as unified variable management, multi-device communication, intelligent rule triggering, data parsing and verification, dynamic interface generation, and data uploading. This storage medium is suitable for program deployment and persistent storage in industrial control equipment, ensuring that the system can quickly recover its operating state and execute control logic normally after a power outage and restart.

[0052] This invention proposes an industrial intelligent control method, device, and storage medium based on a central variable system. With unified variable management as its core, it constructs a complete control system covering data acquisition, device communication, rule execution, interface configuration, protocol parsing, and data uploading. The system is compatible with multiple PLC brands through a three-layer driver architecture, achieves automated triggering through a rule engine, supports intelligent barcode recognition and message protocol self-learning, can dynamically configure the human-machine interface, and stably connects to MES systems. It also adds capabilities such as access control, anomaly self-healing, and performance optimization, forming a highly compatible, highly reliable, and easily maintainable industrial intelligent control solution.

[0053] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An industrial intelligent control method based on a central variable system, characterized in that, Includes the following steps: S1: Establish a central variable management system, adopt the X numbering rule of industrial PLC, and support multiple data types and dual-format variable references; S2: Build an intelligent rule engine with a debouncing mechanism to monitor variable changes, file status and timed trigger conditions in real time and execute corresponding actions; S3: Achieve multi-brand PLC device integration through a three-layer drive architecture, and establish bidirectional data synchronization between PLC addresses and system variables; S4: Start the expression parsing engine, identify and replace variables in the expression, and complete calculations and nested function calls; S5: Performs intelligent barcode parsing and verification, identifies barcode types through pattern matching, and performs duplicate detection and intelligent pattern learning; S6: Generates custom screens based on configuration files, binds screen elements to system variables, and refreshes them in real time; S7: Runs the message recognition engine to perform protocol recognition, parsing, and variable mapping on communication messages; S8: Synchronize variable data and rule execution results to the MES system via HTTP interface, and use a dual verification mechanism to ensure reliable upload; S9: Implement user permissions and security control, establish a multi-user, multi-role permission system, and achieve hierarchical permission isolation and operation security verification; S10: Performs global exception handling and system self-healing, and performs unified capture, classification, processing and automatic recovery of system exceptions.

2. The industrial intelligent control method based on a central variable system according to claim 1, characterized in that: The S3 also includes: performing heartbeat keep-alive on the PLC, automatic reconnection after disconnection, real-time evaluation of communication quality, concurrent acquisition and scheduling of multiple PLCs, communication data caching, register read / write debouncing, and automatic circuit breaking in case of abnormal communication.

3. The industrial intelligent control method based on a central variable system according to claim 1, characterized in that: The S7 also includes: performing packet fragmentation and reassembly, checksum verification, automatic packet format correction, protocol version compatibility, packet mirroring and forwarding, abnormal packet filtering, and parsing result caching.

4. The industrial intelligent control method based on a central variable system according to claim 1, characterized in that: The S8 also includes: uploading message queue, failure index backoff retry, breakpoint resume, data compression, upload rate limiting, response timeout protection, upload whitelist verification, and automatic alarm for upload failure.

5. The industrial intelligent control method based on a central variable system according to claim 1, characterized in that: Specifically, S9 includes: establishing a four-level isolation mechanism for menu permissions, operation permissions, variable permissions, and screen permissions; performing secondary confirmation for critical operations; distinguishing variable read and write permissions; and recording and alerting for abnormal logins and unauthorized operations.

6. The industrial intelligent control method based on a central variable system according to claim 1, characterized in that: S10 specifically includes: globally capturing exceptions, automatically classifying exceptions, automatically retrying and resetting recoverable exceptions, saving the current state, gracefully degrading and safely exiting for unrecoverable exceptions, and outputting exception logs with context and automatic alarms.

7. The industrial intelligent control method based on a central variable system according to claim 1, characterized in that: The three-layer driver architecture in S3 consists of: an abstract base class layer that provides a unified read / write interface, a protocol driver layer that implements Modbus TCP / RTU communication, and a brand adaptation layer that completes address conversion for different PLC brands.

8. The industrial intelligent control method based on a central variable system according to claim 1, characterized in that: The message recognition engine in S7 supports text and binary message parsing, initiates a self-learning mode for unknown protocols, automatically extracts features and generates parsing rules, and maps the parsing results to system variables.

9. An industrial intelligent control device based on a central variable system, characterized in that, It includes a processor and a memory; the memory stores a computer program, and the processor executes the computer program to implement the method of any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 8.

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