A method and system for intelligently generating manufacturing software development solutions based on large models

Abstract

The present invention relates to the field of software construction technology, and discloses a method and system for intelligently generating manufacturing software development solutions based on a large model. The method comprises: receiving real-time constraint data and security requirement data of an industrial control system, and generating a real-time security dynamic balance table using a real-time security risk balancing algorithm; importing threat feature data and system criticality data, and forming a multi-level defense response strategy table using a biological immune hierarchical response algorithm; extracting a security defense code library and an immune memory index table through an attack pattern learning and memory algorithm; constructing a hierarchical isolation architecture diagram and a self-recovery control component set using a multi-layer isolation self-recovery algorithm; and outputting an autoimmune industrial software generation framework and a code template library. The present invention realizes the automated and intelligent generation of industrial control software by introducing a large model, thereby improving software development efficiency and quality.

Description

Technical Field

[0001] The present invention relates to the technical field of software construction, and more specifically, to a method and system for intelligently generating a manufacturing software development solution based on a large model. Background Art

[0002] Software development for industrial real-time control systems, particularly critical software systems in the manufacturing industry with stringent real-time and security requirements, faces significant challenges. In intelligent manufacturing environments, production control software must ensure millisecond-level real-time response to guarantee production accuracy and efficiency, while also defending against increasingly complex cybersecurity threats. However, these two requirements are inherently conflicting. Traditional approaches often incur additional computational overhead when enhancing security mechanisms, leading to reduced real-time performance. Conversely, overemphasizing real-time performance often sacrifices security, exposing the system to serious security risks.

[0003] Currently, the following technical issues are urgently needed in the field of industrial real-time control system software development: providing effective security protection while ensuring the real-time performance of industrial control systems; establishing a scientific trade-off mechanism between real-time performance and security to achieve optimal resource allocation; enabling the system to autonomously learn and adapt to new security threats, reducing frequent manual intervention; and ensuring that the system can quickly recover after an attack, minimizing the impact on the production process.

[0004] Furthermore, with the rapid development of artificial intelligence (AI), intelligent software generation technology holds broad promise for industrial applications. Traditional software development approaches struggle to adapt to the efficient, accurate, and personalized software generation requirements of intelligent manufacturing. There is an urgent need to explore a new AI-based intelligent software generation paradigm to achieve automated and intelligent generation of industrial control software, improve software development efficiency and quality, and accelerate the development of intelligent manufacturing. Summary of the Invention

[0005] The present invention provides a method and system for intelligently generating manufacturing software development solutions based on large models, which solves the technical problems in related technologies: enhancing security protection mechanisms usually introduces additional computing overhead, resulting in a decrease in real-time performance; while over-emphasizing real-time performance often sacrifices security, exposing the system to serious security risks.

[0006] The present invention provides a method for intelligently generating a manufacturing software development solution based on a large model, comprising:

[0007] Receive real-time constraint data and security requirement data from industrial control systems, and use real-time security risk balancing algorithms to generate a real-time security dynamic balance sheet;

[0008] Import threat signature data and system criticality data, and use the biological immune hierarchical response algorithm to form a multi-level defense response strategy table;

[0009] Extract security defense code libraries and immune memory index tables from historical security event datasets using attack pattern learning and memory algorithms;

[0010] Input system function module data and inter-module dependency data, and use a multi-layer isolation and self-recovery algorithm to build a hierarchical isolation architecture diagram and self-recovery control component set;

[0011] Output autoimmune industrial software generation framework and code template library.

[0012] In a preferred embodiment, the biological immune graded response algorithm includes a calculation formula for calculating the defense response level function as follows:

[0013] ;

[0014] in, is the defense response level function; for threats; For threats the degree of danger; Information related to system functions; The criticality of the system function; To address threats When the system function status is hour; 、 、 are the first, second and third adjustment parameters respectively.

[0015] In a preferred embodiment, the attack pattern learning and memorization algorithm includes: converting each security event into a feature vector, and the feature vector similarity calculation formula is as follows:

[0016] ;

[0017] in, is the feature vector similarity; and Represent two different security events respectively; Indicates that security incidents The transformed feature vector; Represents the feature vector and The dot product of and Represents the eigenvectors and Model.

[0018] In a preferred embodiment, the multi-layer isolation self-recovery algorithm includes: dividing the system functional modules into a core control layer, a basic functional layer, an auxiliary functional layer, and a non-critical functional layer according to their criticality; calculating the dependency strength between modules, and reducing strong cross-layer dependencies by minimizing the isolation index. The calculation formula is as follows:

[0019] ;

[0020] in, is the isolation index; A graph structure representing the functional modules of the system; Representation diagram The set of edges in ; is the number of edges; Indicates the connection module and modules edge; Represents an edge The weight of and Represents modules respectively and modules The level where Representation Module and modules The absolute value of the level difference.

[0021] In a preferred embodiment, the restoration priority of a module in the restoration priority table is calculated as follows:

[0022] ;

[0023] in, Representation Module Recovery priority; Represents a specific module; Representation Module Level Representation Module The number of dependencies; Representation Module Criticality score; 、 、 Represent the first, second and third weight coefficients respectively.

[0024] In a preferred embodiment, the process of integrating, processing and outputting an autoimmune industrial software generation framework includes: collecting output components; establishing data exchange channels between components to form a unified information flow network; generating a layered code template library, including basic function layer templates, security protection layer templates, self-learning layer templates, self-recovery layer templates and coordination control layer templates; creating a code optimization rule set; and building a self-assessment and continuous optimization engine.

[0025] In a preferred embodiment, the contextual conditions in the real-time security dynamic balance table include: network environment security level; production task criticality; historical attack frequency; resource load status; and production continuity requirements.

[0026] In a preferred embodiment, in the process of generating a security defense code library, when generating corresponding efficient defense code for each attack mode, it also includes: analyzing the key features of the attack mode, extracting the core attack behavior and intrusion path; optimizing the computing resources of the generated defense code; and verifying the functional effectiveness and operational security of the defense code.

[0027] In a preferred embodiment, an incremental learning mechanism is also implemented to update the attack pattern set and defense code library based on newly collected security event data. The calculation formula is as follows:

[0028] ;

[0029] ;

[0030] in, Indicates time The updated attack pattern set; Indicates time The attack pattern set when Is a function used to collect security events based on the newly collected About time Attack pattern set Update to get time Attack pattern set ; Represents a newly collected security event set; Indicates time Updated defensive code base; Indicates time defensive code base when Is a function used to update the attack pattern set About time Defensive code base Update to get time Defensive code base .

[0031] The present invention provides a large-scale model-based intelligent generation system for manufacturing software development solutions, comprising:

[0032] A real-time security risk balancing module is used to receive real-time constraint data and security requirement data from the industrial control system and generate a real-time security dynamic balance table using a real-time security risk balancing algorithm;

[0033] The immune system hierarchical response module is used to import threat signature data and system criticality data, and use the biological immune hierarchical response algorithm to form a multi-level defense response strategy table;

[0034] The security self-learning and memory module is used to extract the security defense code library and immune memory index table from the historical security event data set through the attack pattern learning and memory algorithm;

[0035] The fault self-recovery module is used to input system function module data and inter-module dependency data, and uses a multi-layer isolation self-recovery algorithm to build a hierarchical isolation architecture diagram and a self-recovery control component set;

[0036] The system integration module is used for component integration processing and outputs the autoimmune industrial software generation framework and code template library.

[0037] The beneficial effects of the present invention are:

[0038] Real-time Security Risk Balance Model: This model breaks through the limitations of traditional static security configurations and establishes a unified mathematical framework for real-time constraints and security requirements. This eliminates the trade-off between the two and instead seeks an optimal balance in a dynamic context. Compared to existing technologies, this model goes beyond simply weighing real-time performance against security. Instead, it uses a risk-utility function to quantify the dynamic balance between the two in different scenarios.

[0039] Immune system hierarchical response mechanism: Innovatively draws on the hierarchical response principle of the biological immune system to transform traditional single-intensity security protection into multi-level adaptive protection. It dynamically adjusts the protection intensity according to the threat level and consumes more computing resources only when necessary, fundamentally resolving the contradiction between security and real-time performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a flow chart of a method for intelligently generating a manufacturing software development solution based on a large model according to the present invention. DETAILED DESCRIPTION

[0041] The subject matter described herein will now be discussed with reference to example embodiments. It should be understood that these embodiments are discussed solely to enable those skilled in the art to better understand and implement the subject matter described herein, and that the functions and arrangements of the elements discussed may be varied without departing from the scope of this specification. Various examples may omit, substitute, or add various processes or components as needed. Furthermore, features described in some examples may be combined in other examples.

[0042] At least one embodiment of the present invention discloses a method for intelligently generating a manufacturing software development solution based on a large model, such as Figure 1 As shown, the following steps are included:

[0043] Step 100: receiving real-time constraint data and security requirement data of the industrial control system, and generating a real-time security dynamic balance table using a real-time security risk balancing algorithm;

[0044] Step 101: Receive the following set of real-time constraint parameters of the industrial control system:

[0045] ;

[0046] in, represents a set of real-time constraint parameters; 、 、 Respectively represent 、 、 Constraint parameters; Indicates the total number of system real-time constraint parameters.

[0047] At the same time, the security requirement parameter set received is as follows:

[0048] ;

[0049] in, Represents a set of security requirement parameters; 、 、 Respectively represent 、 、 security requirements; Indicates the total number of security requirement parameters.

[0050] Step 102: Obtain the following system operating environment context parameter set:

[0051] ;

[0052] in, Represents the system operating environment context information parameter set; 、 、 Respectively represent 、 、 System operating environment context information parameters; Indicates the number of system operating environment context information parameters.

[0053] Production task criticality: reflects the importance of the production task to the entire production process. Tasks with high criticality have stricter requirements for real-time response and higher standards for safety protection.

[0054] Historical attack frequency: reflects the frequency of attacks on the system in the past. A higher historical attack frequency means that the system faces greater security risks and requires more powerful security protection measures.

[0055] Step 103: The risk utility function calculation formula is as follows:

[0056] ;

[0057] in, represents the risk utility function; Represents real-time constraints; Express security needs; Represents the system operating environment context information; is a context-dependent weight function; Indicates that real-time constraints are met Performance benefit function; is a context-dependent weight function; Satisfy security requirements The safety benefit function of is the context-dependent weight function Indicates that real-time constraints are met at the same time and security requirements The resource cost function.

[0058] Step 104: The risk utility function calculation formula is as follows:

[0059] ;

[0060] in, represents the risk utility function; and In a given context Runtime Constraint Set and security requirements set selected from is a mathematical operator that finds the function Get the maximum value of the independent variable; Representing real-time constraints Express security needs; Represents the system operating environment context information; is the risk utility function.

[0061] The output of this step is a real-time security dynamic balance table, which contains the optimal real-time constraint parameter and security requirement parameter combinations under different contextual conditions. This balance table is a multidimensional data structure, where rows represent different contextual conditions and columns represent the corresponding optimal real-time parameters and security parameters. This balance table can be used to dynamically adjust the system's real-time performance and security protection strength in subsequent steps.

[0062] Step 200: Import threat signature data and system criticality data, and use a bio-immune hierarchical response algorithm to form a multi-level defense response strategy table;

[0063] Step 201: Import the threat signature data parameter set as follows:

[0064] ;

[0065] in, Represents a set of threat signature data parameters; 、 、 Respectively represent 、 、 Threat signature data parameters; Indicates the number of threat signature data parameters.

[0066] At the same time, the key data parameter sets of the system are imported as follows:

[0067] ;

[0068] in, Indicates the system's critical data parameter set; 、 、 Respectively represent 、 、 Key data parameters of the system; Indicates the number of system-critical data parameters.

[0069] In step 202, the defense response level function calculation formula is as follows:

[0070] ;

[0071] in, represents the defense response level function; Represents threat signature data; Indicates system critical data; Expressing a threat the degree of danger; In the context The criticality of the system function; In the context Downward defensive threat The performance overhead required; 、 、 are the first, second and third adjustment parameters respectively.

[0072] Step 203: Based on the calculated response level, a set of hierarchical defense measures parameters is generated as follows:

[0073] ;

[0074] in, Represents a set of hierarchical defense measures parameters; 、 、 Respectively represent 、 、 Parameters of graded defense measures; Indicates the number of hierarchical defense measures parameters.

[0075] Level 1 (Basic Defense): Lightweight security check instructions that barely impact real-time performance.

[0076] Level 2 (enhanced monitoring): monitoring frequency enhancement instructions, slightly affecting real-time performance;

[0077] Level 3 (Active Defense): Active defense mechanism instructions, moderate impact on real-time performance;

[0078] Level 4 (Full Protection): All safety mechanisms are activated, significantly affecting real-time performance but providing the strongest protection.

[0079] In step 204, a dynamic defense strategy selection table is created based on the response level function, and the optimal defense measure calculation formula is automatically determined as follows:

[0080]

[0081] ;

[0082] in, represents the optimal defense measure; Indicates the number of threat signatures; Indicates system critical data; Represents a hierarchical set of defense measures The a defensive measure; Represents a hierarchical set of defense measures: Indicates the number of graded defense measures; Represents the defense response level function.

[0083] Step 205: The safety performance balance optimization calculation formula is as follows:

[0084] ;

[0085] in, Indicates a safety-performance balance; Represents the set of all protection strategies; Representation Strategy Safety protection effect; Representation Strategy The performance loss caused.

[0086] The output of this step is a multi-level defense response strategy table. This table maps different threat types and system states to corresponding defense response levels, forming a dynamic query table structure. This table contains three dimensions: threat type, system state, and corresponding defense strategy, allowing subsequent steps to quickly query applicable defense measures at runtime.

[0087] Step 300: extracting a security defense code library and an immune memory index table from the historical security event data set through an attack pattern learning and memory algorithm;

[0088] Step 301: Extract the intelligent defense code library from the historical security event data set through the attack pattern learning and memory algorithm; import the historical security event parameter set as follows:

[0089] ;

[0090] in, Represents the parameter set of historical security event dataset; 、 、 Respectively represent 、 、 Historical security event dataset parameters; Indicates the number of parameters in the historical security event dataset.

[0091] In step 302, an attack pattern clustering method is used to classify historical security events into attack pattern parameter sets based on feature vector similarity calculations as follows:

[0092] ;

[0093] in, Represents a set of attack patterns; 、 、 Respectively represent 、 、 attack mode; Indicates the total number of attack patterns.

[0094] Step 303: for each identified attack pattern , generate corresponding efficient defense code snippets , the set of parameters that make up the defense code library is as follows:

[0095] ;

[0096] in, Represents a collection of defensive code libraries; 、 、 Respectively represent 、 、 A defense code; Indicates the total number of defense codes.

[0097] Defensive code generation includes the following operations;

[0098] Analyze the key features of attack patterns, extract core attack behaviors and intrusion paths; generate detection and interception codes for these specific attack behaviors; optimize computing resources for the generated defense codes to reduce execution overhead; verify the functional effectiveness and operational security of the defense codes; construct an immune memory index table to establish a mapping relationship between the identified attack patterns and their corresponding defense codes, supporting the system's rapid retrieval and response when encountering similar attacks.

[0099] Step 304: construct an immune memory index table to establish a mapping relationship between the identified attack patterns and their corresponding defense codes. The defense code corresponding to a new security event is calculated as follows:

[0100]

[0101] ;

[0102] in, Indicates new security incidents The corresponding defense code; Represents emerging security incidents; Indicates the first A defensive code snippet; Indicates the number of security events used to calculate new security events. and attack mode similarity; represents the similarity threshold; Indicates the attack pattern set attack mode; Represents the set of all attack patterns.

[0103] Step 305: Implement an incremental learning module that can update the attack pattern set calculation formula based on newly collected security event data as follows:

[0104] ;

[0105] in, Indicates time The updated attack pattern set; Indicates time The attack pattern set when Represents a newly collected security event set; Represents a function that is used to calculate the time Attack pattern set and newly collected security event collections .

[0106] The formula for calculating the defensive code base is as follows:

[0107] ;

[0108] in, Indicates time Updated defense code base; Indicates time defensive code base when Represents the updated attack pattern set mentioned above; Represents a function that is used to calculate the time Defensive code base and updated attack pattern set .

[0109] The outputs of this step are a security defense code library and an immune memory index table. The defense code library contains optimized defense code snippets for various attack patterns, while the immune memory index table provides a mapping from attack patterns to defense code. Together, these two constitute the system's "immune memory," enabling subsequent steps to quickly invoke appropriate defense measures when threats are detected.

[0110] Step 400: Input system function module data and inter-module dependency data, use a multi-layer isolation and self-recovery algorithm to build a hierarchical isolation architecture diagram and a self-recovery control component set; output an autoimmune industrial software generation framework and a code template library;

[0111] Step 401: Receive industrial control software function module data, classify them according to their criticality, and form a function hierarchy structure with the following parameters:

[0112] ;

[0113] in, Represents a set of function hierarchy parameters; 、 、 Respectively represent 、 、 Functional hierarchy parameters; Indicates the number of function hierarchy parameters.

[0114] Step 402: Receive inter-module dependency data and generate a functional module dependency graph using the following calculation formula:

[0115] ;

[0116] in, Represents the functional module dependency graph; Represents a collection of functional modules; Represents a collection of dependencies.

[0117] Step 403: Apply a dependency optimization algorithm to calculate and reduce strong cross-level dependencies and generate an optimized isolation interface. The isolation index is calculated as follows:

[0118] ;

[0119] in, represents the isolation index; Represents the functional module dependency graph; Represents a set of dependencies the number of median edges; Representing a collection An edge in ; Representation Module For modules the degree of dependence; Representation Module The level at which you are located; Representation Module The level at which you are located; Representation Module With module The absolute value of the difference at the level.

[0120] Step 404: Generate a self-recovery control component based on the optimized dependency graph, including:

[0121] Health status monitoring table: defines the normal status parameter range and abnormal judgment threshold of each functional module;

[0122] Isolation control rule set: for each module Define fault isolation rules, which are executed when the module is attacked or fails. The isolation calculation formula is as follows:

[0123] ;

[0124] in, Indicates the module Perform isolation operations; Indicates the module that needs to be isolated; Indicates that the module is disabled With module connections between; For modules All adjacent modules .

[0125] Recovery priority table: Based on the module hierarchy and dependencies, the recovery priority calculation formula for each module is as follows:

[0126] ;

[0127] in, Representation Module Recovery priority; Represents a specific module; Representation Module Level Representation Module The number of dependencies; Representation Module Criticality score; Represent the first, second and third weight coefficients respectively.

[0128] Progressive recovery sequence: Pre-generates a recovery operation sequence from high to low. The calculation formula is as follows:

[0129] ;

[0130] in, Represents a sequence of recovery operations; represents the sorting function, 、 、 Respectively represent the 、 、 modules; Indicates the number of modules in the system; Representation Module The recovery priority.

[0131] Step 405: Create a flexible resource allocation table to define resource allocation plans at each level during the system attack period. The calculation formula is as follows:

[0132] ;

[0133] in, Representation level The amount of resources allocated during the system attack; Represents a level in the system; Representation level Basic resource allocation; Representation level Resource enhancement factor in emergency situations; Indicates the severity of the attack.

[0134] The outputs of this step are a hierarchical isolation architecture diagram and a self-recovery control component set. The hierarchical isolation architecture diagram describes the hierarchical relationships of the system's functional modules and the optimized dependency interfaces. The self-recovery control component set includes a health status monitoring table, an isolation control rule set, a recovery priority table, and a resource allocation table. Together, these components support fault isolation and self-recovery in the event of an attack.

[0135] Step 406: Using the components generated in each step as input, processing them through the autoimmune system integration algorithm, and outputting a complete autoimmune industrial software generation framework and code template library;

[0136] Context Information Bus: Creates a unified data sharing channel so that each component can obtain system status information. The calculation formula is as follows:

[0137] ;

[0138] in, Represents the context information bus; Indicates real-time status information; Indicates current environment information; Indicates the latest security status information.

[0139] Decision coordination matrix: Generates decision coordination rules between components to ensure consistency of system behavior. The calculation formula for the comprehensive decision result is as follows:

[0140]

[0141]

[0142] ;

[0143] in, Indicates the comprehensive decision result; represents the decision arbitration function; represents risky decision making; Indicates response decision; Represents memory decision; Represents a recovery decision.

[0144] Step 407: Generate a hierarchical code template library, including the following levels of code templates:

[0145] Basic function layer template: contains the basic function implementation template of industrial control system;

[0146] Security protection layer template: a protection code template generated based on a multi-level defense response strategy table;

[0147] Self-learning layer template: a self-learning function template based on the immune memory index table;

[0148] Self-recovery layer template: A template for fault self-recovery based on a hierarchical isolation architecture;

[0149] Coordination control layer template: a control logic template responsible for communication and resource allocation between various layers;

[0150] Step 408: Create a code optimization rule set for optimizing the performance of the generated code. The calculation formula is as follows:

[0151]

[0152] ;

[0153] in, Indicates the optimized code; Represents a code optimizer; Indicates the original code; Indicates platform constraints; Indicates resource limits.

[0154] The optimization goal is to maximize code execution efficiency while meeting real-time constraints and security requirements.

[0155] Step 409: Build a self-assessment and continuous optimization engine to form a closed-loop optimization mechanism. The indicator set calculation formula is as follows:

[0156]

[0157] ;

[0158] in, Represents a set of indicators; Indicates real-time performance; Indicates safety and effectiveness; Indicates resource utilization; Recovery speed and other indicators.

[0159] The output of this step is a self-immune industrial software generation framework, which includes a component integration structure diagram, data exchange specifications, a layered code template library, a code optimization rule set, and a self-assessment engine. This framework can receive industrial software requirements and automatically generate industrial software code with real-time security balancing capabilities, security self-learning capabilities, and fault self-recovery capabilities, while also supporting continuous optimization and evolution of the code.

[0160] At the same time, on this basis, we further introduce intelligent software generation technology based on large models to achieve the automated and intelligent generation of industrial control software. The specific steps include:

[0161] Receive natural language requirements descriptions of industrial scenarios, use large models for semantic understanding, extract key information, and build a structured requirements model. The requirements model includes multiple dimensions such as functional requirements, performance requirements, and resource constraints, and is used to guide subsequent software generation.

[0162] The structured requirements model is fed into a pre-trained code generation model to automatically generate a software design that meets the requirements. This design includes system architecture, module division, interface definition, and other aspects, while also considering quality attributes such as real-time performance, security, and reliability. During the generation process, the code generation model integrates industry domain knowledge and best practices, continuously iterating and optimizing to achieve the optimal design.

[0163] Convert design plans into executable code templates, automatically generating a code framework containing core logic. Code templates support multiple common programming languages ​​for industrial control systems, significantly reducing the coding workload for developers.

[0164] Evaluate the quality of generated software and collect feedback data for continuous training and optimization of the code generation model. Through self-supervised learning and reinforcement learning techniques, the code generation model can continuously learn and evolve, generating industrial control software of increasingly higher quality.

[0165] Experimental verification shows that the intelligent software generation technology introduced in this step can increase industrial control software development efficiency by 6.5 times. Key quality indicators such as real-time performance, security, and reliability of the generated software are superior to those of manually developed software. For example, the control software generated using this method for a chemical plant's DCS control system achieved a 58% reduction in control response time, an 83% reduction in the number of security vulnerabilities, and a 91% reduction in code defect rate compared to manually developed software.

[0166] This implementation creatively resolves the inherent contradiction between real-time performance and security in industrial software by introducing the principles of the biological immune system, achieving the following technical effects:

[0167] Optimal balance between security protection and real-time performance: The system dynamically adjusts security protection strength based on the real-time environment and threat conditions, providing full security while minimizing the impact on real-time performance. Traditional security hardening methods typically result in a 20-50% performance degradation. This system activates full-strength defenses only in high-threat situations, keeping performance overhead to within 58% under normal circumstances.

[0168] Enhanced system security adaptability: Through security self-learning and immune memory mechanisms, the system continuously learns from security incidents and optimizes its defense code, continuously improving its ability to identify and defend against new attacks. Experimental data shows that the system has an 87% success rate in defending against unseen variant attacks, while traditional rule-based defense systems typically have a success rate of less than 40%.

[0169] Significantly improved attack response speed: Leveraging an immune memory mechanism, the system can quickly identify known attack patterns, reducing response time from seconds with traditional methods to milliseconds, an improvement of nearly two orders of magnitude. Especially for repetitive attacks, the defense code can initiate defenses before the attack is complete, providing true preventative protection.

[0170] Improved system resilience and availability: Through a hierarchical isolation architecture with self-recovery, core functions remain operational and automatically recover from compromised functionality even when some system components are attacked. Tests have shown that even under severe attacks, the system's core function availability remains above 99.7%, and recovery time is reduced by 83%.

[0171] Maintenance costs have been significantly reduced: The system's self-learning and self-recovery capabilities reduce the need for manual intervention, reducing the workload for security maintenance personnel by approximately 65%. Furthermore, through a continuously optimized code generation mechanism, system performance and security performance automatically improve over time, eliminating the need for frequent manual updates and patch deployments.

[0172] Improved software development efficiency: This implementation provides a unified self-immune industrial software generation framework. Developers do not need to design real-time control logic and security protection mechanisms separately. Large models can automatically generate code that balances real-time and security based on this framework, shortening the development cycle of industrial software and reducing development time by an average of 47%.

[0173] To summarize, this implementation method achieves a dynamic balance between real-time performance and security by innovatively applying the principles of the biological immune system to the field of industrial software development, and establishes a closed-loop system of self-learning, self-defense, and self-recovery. It not only breaks through the technical bottlenecks faced by traditional industrial software development, but also provides a new solution for the security resilience of industrial control systems.

[0174] The key parameters of high-precision CNC machine tool control systems are shown in the following table:

[0175]

[0176] In this CNC machine tool control system case, we collected the system's real-time constraint data, safety requirement data, and operating environment context information, including control accuracy requirements under various working conditions, network environment security status, and production task criticality.

[0177] The real-time constraint parameter set R and the safety requirement parameter set of the CNC system are shown in the following table:

[0178]

[0179] We calculate the risk utility function for different operating environment context conditions C , find the optimal balance point:

[0180] Examples of CNC system context conditions and dynamic balance points are shown in the following table:

[0181]

[0182] By calculating the risk-utility function, we obtained the optimal parameter configuration under different contextual conditions. For example, in the normal processing state C1, the system prioritizes real-time performance and adopts a lower level of safety protection; while in the safety alert state C4, the system strengthens safety protection and appropriately relaxes real-time constraints.

[0183] This dynamic balance mechanism enables the system to automatically adjust the protection intensity according to the safety status while ensuring processing accuracy, overcoming the inherent contradiction between real-time performance and safety protection in traditional methods.

[0184] In the CNC system, we analyzed potential threat types (including data theft, control command tampering, remote unauthorized access, etc.) and the criticality of each system function to form threat signature data and system criticality data.

[0185] The threat types and characteristic data of CNC system are shown in the following table:

[0186]

[0187] The criticality of CNC system functional modules is shown in the following table:

[0188]

[0189] The CNC system hierarchical response strategy is shown in the following table:

[0190]

[0191] Unlike traditional approaches, this hierarchical response mechanism dynamically selects the optimal protection measure based on the threat type and current system status, preventing excessive interference from security measures on real-time control. For example, during high-precision machining, even if network scanning activity is detected, the system only activates basic defenses, prioritizing resources for real-time control tasks. However, during idle system hours, more comprehensive protection measures can be activated.

[0192] In CNC system applications, we have collected nearly three years of security incident data, including common network attacks and abnormal access records. By analyzing this data, we have established an attack pattern library and a corresponding defense code library.

[0193] First, we convert security events into feature vectors and cluster them based on similarity to form a set of attack patterns:

[0194] The following table shows an example of CNC system attack pattern clustering:

[0195]

[0196] An example of the CNC system defense code library is shown in the following table:

[0197]

[0198] The following table shows the (partial) data of the CNC system immune memory index table:

[0199]

[0200] Based on the characteristics of CNC control systems, we analyzed and classified the functional modules and established a hierarchical isolation architecture to ensure the continuous operation of the system's core functions when attacked.

[0201] The hierarchical structure of the CNC system functional modules is shown in the following table:

[0202]

[0203] The dependencies between modules were analyzed, and strong cross-level dependencies were reduced through optimization:

[0204] The comparison data before and after the CNC system module dependency optimization is shown in the following table:

[0205]

[0206] Through optimization, the system's isolation index was reduced from 0.65 before optimization to 0.28, reducing strong cross-level dependencies and improving the system's isolation effect when attacked.

[0207] The health status monitoring table is shown in the following table:

[0208]

[0209] The self-recovery priority is shown in the following table:

[0210]

[0211] The resource isolation and allocation strategies are shown in the following table:

[0212]

[0213] In actual CNC system operation, this hierarchical isolation architecture has demonstrated remarkable resilience. For example, during an attack targeting the remote monitoring interface L4, the system quickly isolated the module, ensuring the normal operation of the core control function L1. After security confirmation, each layer of functionality was gradually restored according to the predetermined priority. Throughout the entire process, machining tasks were not affected, and accuracy remained within the design requirements.

[0214] This fault-recovery hierarchical isolation architecture provides an "immune barrier" for the CNC system, enabling the continuity and stability of core functions to be maintained even under attack, significantly improving the system's resilience and reliability.

[0215] The results of the real-time performance and security balance test are shown in the following table:

[0216]

[0217] The test results show that this solution has achieved improvements over traditional methods in all scenarios. Of particular note are:

[0218] In high-precision machining scenarios, this solution reduced control response time by 35.2% compared to traditional methods, reduced machining accuracy fluctuation by 60.0%, and only increased CPU utilization by 16.7%. This demonstrates that this solution prioritizes real-time performance in critical machining tasks while maintaining adequate safety protection.

[0219] In security alert scenarios, this solution maintains a relatively low control response time (1.48ms), while simultaneously improving the attack detection rate (97%) and reducing the false alarm rate (4%). This demonstrates that this solution can enhance security protection while maintaining acceptable real-time control performance in the face of security threats.

[0220] To more intuitively demonstrate the effectiveness of the dynamic balancing mechanism, we recorded the changes in the system's performance and safety indicators during a typical operation cycle:

[0221] The dynamic balance between real-time performance and security protection strength in different scenarios is shown in the following table:

[0222]

[0223] It can be seen that the system can automatically adjust the balance between real-time performance and security protection according to different scenarios and security conditions. For example, during high-precision processing (11:00), the system automatically reduced security protection strength to ensure extremely low control response time; and when an intrusion attempt was detected (13:45), the system quickly increased security protection strength while maintaining an acceptable control response time.

[0224] To verify the system's resilience and availability when attacked, we designed a series of simulated attack tests and compared the performance of traditional methods with this solution.

[0225] The system resilience and availability test results are shown in the following table:

[0226]

[0227] Test results show that this solution has outstanding performance in terms of system resilience and availability:

[0228] In the face of a denial-of-service attack, traditional methods only achieved a 68.5% core function availability rate. This solution, however, maintained a 99.7% core function availability rate, reducing system recovery time from 18.5 minutes to 3.2 minutes. Crucially, this solution achieved zero production downtime during the attack, compared to an average of 12.6 minutes for traditional methods.

[0229] When attacked by control command injection, this solution can maintain 96.5% machining accuracy stability, significantly higher than the 45.2% achieved by traditional methods. Furthermore, the safety shutdown trigger rate is reduced from 62.5% to 12.3%, indicating that most attacks are isolated and handled by the system without interrupting the production process.

[0230] Under the comprehensive attack scenario, this solution can maintain a 94.8% production task completion rate and a 98.2% product qualification rate, which are 65.4% and 43.6% higher than traditional methods respectively.

[0231] To further illustrate the working process of the system resilience mechanism, we recorded the state changes of each functional layer of the system during a typical attack event:

[0232] The status changes of each functional layer of the system under the attack scenario are shown in the following table:

[0233]

[0234] As can be seen, when the attack occurred, the system was able to quickly isolate the attacked module (L4 layer), preventing the attack from spreading, while maintaining 100% availability of core functions (L1 layer). The system then gradually restored functions at each layer according to the predetermined recovery priority order until full recovery. Throughout this process, core control functions remained operational, allowing production tasks to continue.

[0235] Based on the verification results of the above two key technologies, this implementation method successfully achieved a dynamic balance between real-time performance and security in CNC system applications, as well as improved system resilience and availability, providing a new security protection paradigm for industrial control systems.

[0236] In at least one embodiment of the present invention, addressing the hierarchical collaborative scheduling requirements for asynchronous decision making includes the following steps:

[0237] Step 500: construct a real-time security risk balance model;

[0238] Step 501: Perform a hierarchical risk assessment on the system real-time constraint data and security requirement data to obtain a hierarchical real-time security dynamic balance table.

[0239] The formula for calculating the stratified risk utility function is as follows:

[0240] ;

[0241] in, represents the comprehensive risk utility value; Represents real-time constraints; Express security needs; Represents the system operating environment context information; Indicates the decision-making level; Representation level Relevant real-time weight; Representation level the relevant security weights; Representation level The associated conflict penalty coefficient; In the context Real-time constraints Utility value of In the context Security requirements Utility value of Representing real-time constraints and security requirements In context The degree of conflict.

[0242] Step 502 : Calculate an inter-system security coordination parameter set based on the security policy data of each heterogeneous system and the interaction relationship between the systems.

[0243] Input data type: The security policy parameter sets for each heterogeneous system are as follows:

[0244] ;

[0245] in, Represents the system security policy parameter set; 、 、 Respectively represent the 1st, 2nd, System security policy parameters; Indicates the total number of system security policy parameters.

[0246] The parameters of the inter-system security coordination parameter set are as follows:

[0247] ;

[0248] in, Indicates that the slave system To the system The security policy coordination coefficient; They represent different system numbers.

[0249] The inter-system security coordination parameters are calculated using the security policy coordination equation as follows:

[0250] ;

[0251] in, represents the security policy coordination coefficient; Indicates the System security policy intensity level; Indicates the System security policy intensity level. Functions are based on the strength of interactions between systems Calculate the adjustment factor, Representation System and system The intensity of interaction between and They represent different system numbers.

[0252] Step 600, forming a multi-level defense response strategy;

[0253] Step 601: Based on the threat signature data and system criticality data, a multi-level defense response strategy table is formed for each level.

[0254] The formula for calculating the hierarchical response level equation is as follows:

[0255] ;

[0256] in, Indicates that at the decision-making level Down; Indicates threat characteristics; Indicates the criticality of system components; Indicates the decision-making level; Representation level the associated threat weight; Representation level The associated system criticality weights; Representation level Related performance overhead penalty coefficients; Expressing a threat severity of the disease; Represents system components degree of criticality; Indicates performance overhead, that is, the loss of system performance caused by taking defensive measures.

[0257] Step 602: Generate an asynchronous decision coordination time table based on the system resource status and communication delay.

[0258] Input data type: hierarchical multi-level defense response strategy table;

[0259] This table contains response strategies and execution rules for different threat types at each level, which are used to guide the system to take corresponding defense measures at different levels for different threats.

[0260] The system computing resource data parameter set is as follows:

[0261] ;

[0262] in, Represents a set of system computing resource data parameters; 、 、 Respectively represent 、 、 System computing resource data parameters; Indicates the total number of system computing resource data parameters.

[0263] The communication delay data parameter set is as follows:

[0264] ;

[0265] in, Represents a communication delay data parameter set; 、 、 Respectively represent 、 、 Communication delay data parameters; Indicates the total number of communication delay data parameters.

[0266] The calculation formula of the decision frequency determination equation is as follows:

[0267] ;

[0268] in, Indicates at the level Down system frequency of decision making; Representation level Frequency of basic decision making; Representation System Resource adjustment factors; Representation level In the system The priority factor in .

[0269] Step 603: construct a system boundary security interaction matrix based on the interaction data and data flow sensitivity between systems.

[0270] The parameter set for data interaction between systems is as follows:

[0271] ;

[0272] in, Represents the set of data parameters for interaction between systems; 、 、 Respectively represent 、 、 Inter-system interaction data parameters; Indicates the total number of interaction data parameters between systems.

[0273] The data flow sensitivity data parameter set is as follows:

[0274] ;

[0275] in, Indicates data flow sensitivity data parameter; 、 、 Respectively represent 、 、 Data flow sensitivity data parameters; Indicates the total number of data flow sensitivity data parameters.

[0276] The formula for calculating the security interaction strength is as follows:

[0277] ;

[0278] in, Representation System and system The strength of security interactions between is the data type weight function; is the traffic frequency weight function; is the data sensitivity weight function; Representation System and system The type of data exchanged between them; Representation System and system Frequency of data flow between them; Representation System and system The sensitivity of the data exchanged between them.

[0279] Step 700: extract the security defense code library;

[0280] Step 701: Input the hierarchical historical security event data into a distributed attack pattern learning and memory algorithm, and output a hierarchical intelligent defense code library;

[0281] The parameter set for layered historical security event data is as follows:

[0282] ;

[0283] in, Indicates historical security events; Represents a collection of historical security events at the strategic level; Represents a collection of historical security events at the tactical level; Represents a collection of historical security events at the execution layer.

[0284] The attack pattern clustering score calculation formula in the distributed attack pattern learning and memory algorithm is as follows:

[0285] ;

[0286] in, Indicates that at the decision-making level Next, event and Attack pattern clustering score; Represents a calculation event and similarity; Representing an event and Decision-making level degree of relevance.

[0287] Step 702: Perform compatibility analysis on the hierarchical intelligent defense code library and output a system-adapted defense code mapping table;

[0288] Hierarchical intelligent defense code base. The technical characteristics and data parameter sets of heterogeneous systems are as follows:

[0289] ;

[0290] in, Represents the system technical characteristic data parameter set; 、 、 Respectively represent 、 、 System technical characteristic data parameters; Indicates system technical characteristic data parameters.

[0291] Cross-system defense code compatibility analysis uses a multi-dimensional compatibility score calculation formula as follows:

[0292] ;

[0293] in, Representation code In the system Multidimensional compatibility score on ; Represents the evaluation code With the system Technical characteristics compatibility; Represents the evaluation code In the system Performance impact on Represents the evaluation code In the system safety and effectiveness.

[0294] Step 800: Build a fault self-recovery layered isolation architecture;

[0295] Step 801: System function module data and inter-module dependency data are processed by a hierarchical isolation architecture generation algorithm to generate a hierarchical isolation elastic software architecture diagram;

[0296] The system function module data parameter set is as follows:

[0297] ;

[0298] in, Indicates system function module data parameters; 、 、 Respectively represent 、 、 System function module data parameters; Represents system function module data parameters.

[0299] The module isolation level calculation formula in the hierarchical isolation architecture generation algorithm is as follows:

[0300] ;

[0301] in, Representation Module At the level Isolation level; Represents the evaluation module At the level degree of criticality; Represents the evaluation module Dependency complexity; Represents a definition module At the level The recovery priority.

[0302] Step 802 : The cross-system module dependency data and system criticality data are converted into an asynchronous collaborative recovery strategy table through an asynchronous collaborative recovery strategy algorithm;

[0303] The cross-system module dependency data parameter set is as follows:

[0304] ;

[0305] in, Indicates cross-system module dependency data parameters; Respectively represent 、 、 Cross-system module dependent data parameters; Indicates cross-system module dependency data parameters.

[0306] The system criticality data parameter set is as follows:

[0307] ;

[0308] in, Indicates system criticality data parameters; 、 、 Respectively represent 、 、 System criticality data parameters; Indicates system criticality data parameters.

[0309] The parameter set for restoring resource data is as follows:

[0310] ;

[0311] in, Indicates the recovery resource data parameters; Respectively represent 、 、 Recovery resource data parameters; Indicates the recovery resource data parameters.

[0312] The asynchronous collaborative recovery strategy algorithm is calculated through the recovery sequence formula as follows:

[0313] ;

[0314] in, Representation Module In the system The recovery sequence in ; Represents the decision-making level set Perform sum operation on all levels of ; It's a level The weight of Represents the evaluation module At the level degree of criticality; Representation Module A collection of other modules; Representation Module For modules The dependency weight of Indicates dependency on modules The dependency weights of all modules are summed up.

[0315] Step 900, integrating the autoimmune industrial software generation framework;

[0316] Step 901, integrating the key components generated in the previous steps, and generating a hierarchical asynchronous collaborative autoimmune industrial software generation framework through a hierarchical framework integration algorithm.

[0317] Input data categories: hierarchical real-time security dynamic balance table (output of step 501); inter-system security collaboration parameter set (output of step 502); hierarchical multi-level defense response strategy table (output of step 601); asynchronous decision collaboration timing table (output of step 602); system boundary security interaction matrix (output of step 603); hierarchical intelligent defense code library (output of step 701); system-adapted defense code mapping table (output of step 702); hierarchical isolated elastic software architecture diagram (output of step 801); asynchronous collaborative recovery strategy table (output of step 802).

[0318] Specific output results: A hierarchical asynchronous collaborative autoimmune industrial software generation framework, a software framework that defines the integration rules and collaboration mechanisms between components.

[0319] The component integration score of the hierarchical framework integration algorithm is calculated as follows:

[0320] ;

[0321] in, Presentation Component and At the level and The integrated score between and Represents two components to be integrated; and Components and The level to which it belongs;

[0322] Represents the evaluation component and compatibility between them;

[0323] Indicates the evaluation level and The efficiency of interaction between

[0324] Represents the evaluation component and The degree of support for asynchronous collaboration.

[0325] Step 902: Incorporating the technical characteristics of heterogeneous systems, transform the layered asynchronous collaborative autoimmune industrial software generation framework into a code template library with multi-system heterogeneous adaptability. Input data type: layered asynchronous collaborative autoimmune industrial software generation framework;

[0326] The selection optimization of code templates adopts a multi-factor scoring mechanism. The calculation formula is as follows:

[0327] ;

[0328] in, Represents a code template On the target system Comprehensive rating on Represents a code template; represents the target system; Represents an evaluation code template In the system Functional coverage on Represents an evaluation code template System adaptability; Represents an evaluation code template In the system on efficiency.

[0329] This solution significantly improves system performance by combining a real-time security risk-balanced autoimmune industrial software generation system with an asynchronous decision-making hierarchical collaborative scheduling mechanism: response time is reduced by 78%, and task efficiency is increased by 56%; security policy conflicts are reduced by 95%, and the collaborative defense success rate is increased by 87%; the heterogeneous system adaptation success rate is 94.7%, and the development cycle is shortened by 68%; fault recovery time is shortened by 86%, and availability reaches 99.2%; resource utilization is increased by 62%, and collaborative efficiency is improved by 91%, effectively solving the computing bottlenecks, communication delays and security collaboration problems of large-scale heterogeneous industrial control systems.

[0330] The above describes an embodiment of the present invention, but this embodiment is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Ordinary technicians in this field can also make more forms of equivalent embodiments based on the inspiration of this embodiment, all of which are protected by this embodiment.

Claims

1. A method for intelligently generating manufacturing software development solutions based on a large model, characterized in that: include: Receive real-time constraint data and security requirement data from industrial control systems, and use real-time security risk balancing algorithms to generate a real-time security dynamic balance sheet; Import threat signature data and system criticality data, and use the biological immune hierarchical response algorithm to form a multi-level defense response strategy table; Extract security defense code libraries and immune memory index tables from historical security event datasets using attack pattern learning and memory algorithms; Input system function module data and inter-module dependency data, and use a multi-layer isolation and self-recovery algorithm to build a hierarchical isolation architecture diagram and a set of self-recovery control components; output an autoimmune industrial software generation framework and a code template library. The multi-layer isolation and self-recovery algorithm includes: dividing system function modules into a core control layer, a basic function layer, an auxiliary function layer, and a non-critical function layer according to their criticality; calculating the dependency strength between modules, and reducing strong cross-level dependencies by minimizing the isolation index. The calculation formula is as follows: ； in, is the isolation index; A graph structure representing the functional modules of the system; Representation diagram The set of edges in ; is the number of edges; Indicates the connection module and modules edge; Represents an edge The weight of and Represents modules respectively and modules The level where Representation Module and modules The absolute value of the level difference; The recovery priority of the module in the recovery priority table is calculated as follows: ； in, Representation Module Recovery priority; Represents a specific module; Representation Module Level Representation Module The number of dependencies; Representation Module Criticality score; 、 Represent the first, second and third weight coefficients respectively.

2. The method for intelligently generating a manufacturing software development solution based on a large model according to claim 1, characterized in that: The biological immune graded response algorithm includes the calculation formula of the defense response level function as follows: ； in, is the defense response level function; for threats; For threats the degree of danger; Information related to system functions; The criticality of the system function; To address threats When the system function status is hour; 、 、 are the first, second and third adjustment parameters respectively.

3. The method for intelligently generating a manufacturing software development solution based on a large model according to claim 1, characterized in that: The attack pattern learning and memory algorithm includes: converting each security event into a feature vector. The feature vector similarity calculation formula is as follows: ； in, is the feature vector similarity; and Represent two different security events respectively; Indicates that security incidents The transformed feature vector; Represents the feature vector and The dot product of and Represents the eigenvectors and Model.

4. The method for intelligently generating a manufacturing software development solution based on a large model according to claim 1, characterized in that: The process of integrating, processing and outputting the self-immune industrial software generation framework includes: collecting output components; establishing data exchange channels between components to form a unified information flow network; generating a layered code template library, including basic function layer templates, security protection layer templates, self-learning layer templates, self-recovery layer templates and coordination control layer templates; creating a code optimization rule set; and building a self-assessment and continuous optimization engine.

5. The method for intelligently generating a manufacturing software development solution based on a large model according to claim 1, characterized in that: The contextual conditions in the real-time security dynamic balance table include: network environment security level; production task criticality; historical attack frequency; resource load status; and production continuity requirements.

6. The method for intelligently generating a manufacturing software development solution based on a large model according to claim 1, characterized in that: In the process of generating a security defense code library, when generating corresponding efficient defense code for each attack mode, it also includes: analyzing the key features of the attack mode, extracting the core attack behavior and intrusion path; optimizing the computing resources of the generated defense code; and verifying the functional effectiveness and operational security of the defense code.

7. The method for intelligently generating a manufacturing software development solution based on a large model according to claim 1, characterized in that: It also includes implementing an incremental learning mechanism to update the attack pattern set and defense code base based on newly collected security event data. The calculation formula is as follows: ； ； in, Indicates time The updated attack pattern set; Indicates time The attack pattern set when Is a function used to collect security events based on the newly collected About time Attack pattern set Update to get time Attack pattern set ; Represents a newly collected security event set; Indicates time Updated defensive code base; Indicates time defensive code base when Is a function used to update the attack pattern set About time Defensive code base Update to get time Defensive code base .

8. A large-scale model-based intelligent generation system for manufacturing software development solutions, characterized by: A method for intelligently generating a manufacturing software development solution based on a large model according to any one of claims 1 to 7, comprising: A real-time security risk balancing module is used to receive real-time constraint data and security requirement data from the industrial control system and generate a real-time security dynamic balance table using a real-time security risk balancing algorithm; The immune system hierarchical response module is used to import threat signature data and system criticality data, and use the biological immune hierarchical response algorithm to form a multi-level defense response strategy table; The security self-learning and memory module is used to extract the security defense code library and immune memory index table from the historical security event data set through the attack pattern learning and memory algorithm; The fault self-recovery module is used to input system function module data and inter-module dependency data, and uses a multi-layer isolation self-recovery algorithm to build a hierarchical isolation architecture diagram and a self-recovery control component set; The system integration module is used for component integration processing and outputs the autoimmune industrial software generation framework and code template library.

Images