Method and system for automatically assembling government and enterprise solutions based on intelligent arrangement

Abstract

The invention discloses a government and enterprise solution automatic assembling method and system based on intelligent arrangement, and the method comprises the steps: carrying out the initialization of a large model, including the initialization of a resource library, the setting of a rule and an algorithm, the development of the algorithm, data collection, the initialization of a scheme, and the generation of a large model case; analyzing government and enterprise business requirements through the initialized large model; based on the analyzed government and enterprise business requirements and the resource library, formulating an assembly rule, selecting matched resources from the resource library, and generating an assembly scheme; and performing algorithm optimization through a genetic algorithm, and optimizing the assembly scheme to obtain an optimal assembly scheme. According to the invention, various resources can be reasonably allocated, and while business requirements of government and enterprise customers are met, resource waste is avoided, the cost of a solution is reduced, and the resource utilization efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent orchestration technology, and in particular to a method and system for automatically assembling government and enterprise solutions based on intelligent orchestration. Background Art

[0002] Government and enterprise customers have a wide range of business scenarios, and each unit has different organizational structures, business processes, and service objects. The same is true for enterprises. The business priorities of manufacturing and service industries are completely different, which requires solutions to accurately match different complex needs. Traditional manual configuration or simple automatic assembly solutions are difficult to meet rapidly changing and complex business scenarios. Its poor flexibility, low degree of customization, slow deployment speed, and other problems make it difficult to meet the new needs of government and enterprise customers.

[0003] The needs of government and enterprise customers are often complex and special. Automatic assembly is based on preset modules and rules, and it is difficult to flexibly respond to diverse and non-standard business scenarios. Different government and enterprise customers may have unique requirements for business processes, functional details, etc. Automatic assembly can generally only provide a relatively general solution combination, and it is difficult to achieve requirements such as in-depth customization of software functions and fine-tuning of business logic.

[0004] Traditional solutions usually focus on the combination of local modules and often ignore the coordinated optimization of the overall business. Especially when it comes to the integration of multiple departments and multiple business systems, there are usually problems of information islands or poor connection of business processes, resulting in insufficient data sharing among systems and affecting the overall efficiency of corporate customers.

[0005] Traditional solutions are designed based on fixed business processes, with business rules and processes fixed in the code. When the business is adjusted, it is difficult to modify the system and the cycle is long. When the enterprise develops or the business volume grows, it is difficult to quickly add new functions or modules due to the inherent deficiencies of traditional systems in processing power, storage capacity and functional expansion. Traditional technologies use different system technology architectures, data formats and interface standards, resulting in a large amount of customized development during integration, which is difficult and costly. In addition, they lack intelligent algorithms and models, and cannot automatically make decisions and predictions based on data and business rules. They lack the ability to process and analyze massive and complex data, making it difficult to quickly mine data value and unable to meet real-time and in-depth analysis requirements. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a method and system for automatically assembling government and enterprise solutions based on intelligent arrangement. Through intelligent arrangement and assembly of solutions, highly adapted solutions can be accurately customized to meet their personalized needs. By rationally allocating various resources, while meeting the business needs of government and enterprise customers, resource waste can be avoided, the cost of solutions can be reduced, and resource utilization efficiency can be improved.

[0007] The present invention provides a method for automatically assembling a government and enterprise solution based on intelligent arrangement, the method comprising: Initialize the large model, including resource library initialization, setting rules and algorithms, algorithm development, data collection, solution initialization, and generating large model cases; Analyze government and enterprise business needs through the initialized large model; Based on the analyzed government and enterprise business requirements and the resource library, formulate assembly rules, select matching resources from the resource library, and generate an assembly plan; The algorithm is optimized by genetic algorithm, the assembly scheme is optimized, and the optimal assembly scheme is obtained.

[0008] Preferably, the resource library includes: a component library and a product library, the component library includes various software, technical components, business modules and data models, and the product library includes products; the initialization of the large model includes: (1) Resource library initialization: The operator imports the component library and the product library through the government and enterprise self-service portal; (2) Setting rules and algorithms: the operator encodes the components in the component library and the products in the product library through the government and enterprise self-service portal; (3) Algorithm development: The algorithm microservice develops algorithms based on the resources and product specifications in the resource library; (4) Data collection: The operator collects customer information and ordering information of government and enterprise customers through the government and enterprise self-service portal and sends it to the algorithm microservice; (5) Solution initialization: The algorithm microservice generates an initial solution based on the resources and product specifications in the resource library and user data; (6) Generate a large model case: The algorithm microservice generates a large model case based on the initial solution.

[0009] Preferably, the resource library initialization further includes: Integrate various software related to government business, including office software, business management systems and data analysis tools, label the functions and record the performance parameters of each software to quickly match the needs during assembly.

[0010] Preferably, the analyzing of government and enterprise business requirements by the initialized large model includes: The natural language processing capability of the big model is used to parse the government business needs in text form, extract key information, identify the intentions behind the government business needs, break down the government business needs into specific and actionable tasks, and determine the logical relationships and priorities between tasks. Based on historical data and industry standards, the business processes are optimized and coordinated through the logical reasoning capabilities of the big model.

[0011] Preferably, the formulation of assembly rules includes: formulating classification standards for resources in the resource library, formulating a method for calculating the degree of match between the functions of the resources and the functions of the government and enterprise business requirements, establishing comparison rules between the performance parameters of the resources and the performance indicators of the government and enterprise business requirements, and formulating rules for the order of resource assembly.

[0012] Preferably, the matching degree calculation method includes a similarity algorithm.

[0013] Preferably, the algorithm optimization is performed by genetic algorithm to optimize the assembly scheme to obtain the optimal assembly scheme, which includes: Encode each component or parameter in the assembly scheme, use binary code to indicate whether a component is selected or not, use integer code to indicate the value of a parameter, randomly generate a certain number of initial assembly schemes as a population according to the coding rules, determine the evaluation index of the assembly scheme according to the government business needs, convert the evaluation index into a fitness function, calculate the fitness, select a better individual from the current population as the parent generation according to the fitness, repeat the genetic operation, continuously generate a new population, so that the individuals in the population gradually approach the optimal solution, stop the iteration when the termination condition is met, and output the optimal assembly scheme.

[0014] Preferably, the method further comprises: Before returning the optimal assembly solution to government and enterprise customers, formulate test methods and acceptance criteria to verify whether the optimal assembly solution meets the government and enterprise business requirements, establish rules and tools for evaluating solution performance indicators, determine rules and methods for identifying and evaluating potential risks of solutions, evaluate the optimal assembly solution from multiple angles to check whether it meets the government and enterprise business requirements and actual application scenarios. If not, adjust the parameters of the genetic algorithm and re-optimize the algorithm. The parameters of the genetic algorithm include parameters of population size, crossover probability, and mutation probability.

[0015] Preferably, the method further comprises: Monitor the implemented solutions in real time, collect performance data on transaction success rate, data accuracy, business process execution correctness, and user questions and suggestions during use, optimize the orchestration strategy and the resource library, regularly review the monitoring data and the effects of optimization measures, and continuously improve the implemented solutions.

[0016] Based on the same inventive concept, the present invention also provides a system for automatically assembling government and enterprise solutions based on intelligent arrangement, which is used to implement the method described, and the system includes: The initialization module is used to initialize the large model, including resource library initialization, setting rules and algorithms, algorithm development, data collection, solution initialization, and generating large model cases; A parsing module, used to parse government and enterprise business needs through the natural language processing capability of the initialized large model; An assembly module, used to formulate assembly rules based on the parsed government and enterprise business requirements and the resource library, select matching resources from the resource library, and generate an assembly plan; The optimization module is used to optimize the algorithm through a genetic algorithm to optimize the assembly plan and obtain the optimal assembly plan.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention uses artificial intelligence and natural language processing technology to understand the complex and diverse needs of government and enterprise customers. In the demand analysis stage, natural language processing technology can be used to parse text information such as documents, demand descriptions, and communication records provided by customers. For example, the business scenarios and functional expectations described by customers are extracted and converted into structured data that can be understood by computers, providing accurate demand input for subsequent solution assembly.

[0018] 2. The present invention uses the rule engine in intelligent orchestration. The rule engine is one of the core parts of intelligent orchestration. It screens and combines resources according to predefined rules. These rules are formulated based on business logic, industry best practices, technical specifications, etc.

[0019] 3. The present invention uses optimization algorithms to optimize assembly plans. Optimization algorithms optimize assembly plans with the goal of minimizing costs, maximizing performance, and minimizing delivery time. They can find the optimal solution in a huge resource combination space and balance the use of various resources and performance indicators. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A flowchart of a method for automatically assembling a government and enterprise solution based on intelligent arrangement provided by the present invention; Figure 2A schematic diagram of the structure of a system for automatically assembling government and enterprise solutions based on intelligent arrangement provided by the present invention; Figure 3 A flowchart of a method for automatically assembling a government-enterprise solution based on intelligent orchestration provided by the present invention. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] The present invention is further described in detail below in conjunction with the accompanying drawings.

[0023] like Figure 1 As shown, the present invention provides a method for automatically assembling a government and enterprise solution based on intelligent arrangement, comprising: S1. Initialize the big model, including resource library initialization, setting rules and algorithms, algorithm development, data collection, solution initialization, and generating big model cases; In the embodiment of the present invention, the resource library includes: a component library and a product library. The component library includes various software, technical components, business modules and data models, and the product library includes products. Initializing the large model includes: (1) Resource library initialization: Operation personnel import component libraries and product libraries through the government and enterprise self-service portal; Resource library initialization also includes: Integrate various software related to government business, including office software, business management systems and data analysis tools, label the functions and record the performance parameters of each software to quickly match the needs during assembly.

[0024] Specifically, a component library containing various technical components and business modules is established. The big model selects matching components and products from the component library and product library according to the solution requirements, evaluates functional characteristics, performance indicators, compatibility, etc., selects the most suitable components and products, ensures that they can be seamlessly integrated, designs standard and unified interfaces, ensures that data and information can be smoothly transmitted and interacted between parts, generates interface documents, and guides developers to implement interface docking.

[0025] (2) Setting rules and algorithms: Operation personnel encode components in the component library and products in the product library through the government and enterprise self-service portal; (3) Algorithm development: The algorithm microservice develops algorithms based on the resources and product specifications in the resource library; Product specifications specifically refer to some product attributes that affect product pricing, such as the CUP, memory, storage disk and other product attributes of the cloud host, which are collectively referred to as product specifications.

[0026] (4) Data collection: Operation personnel collect customer information and ordering information of government and enterprise customers through the government and enterprise self-service portal and send it to the algorithm microservice; (5) Solution initialization: The algorithm microservice generates the first-generation solution based on the resources and product specifications in the resource library and user data; (6) Generate large model cases: The algorithm microservice generates large model cases based on the initial solution.

[0027] S2. Analyze government and enterprise business needs through the initialized big model; In the embodiment of the present invention, parsing the government and enterprise business requirements through the initialized big model includes: Through the natural language processing capabilities of the big model, the government business needs in text form are analyzed, key information is extracted, the intentions behind the government business needs are identified, the government business needs are broken down into specific and actionable tasks, and the logical relationships and priorities between tasks are determined. Based on historical data and industry standards, the business processes are optimized and coordinated through the logical reasoning capabilities of the big model.

[0028] Specifically, we use big model technology to conduct in-depth analysis of the business needs of government and enterprise customers. We input the detailed needs of government and enterprise business into the big model in text form, and use its powerful natural language processing capabilities to parse the needs and extract key information. We identify the intentions behind the business needs, break down the vague needs into specific and actionable tasks, clarify the logical relationships and priorities between the tasks, and at the same time, combine historical data and industry standards to provide accurate demand input for subsequent solution assembly. We use the logical reasoning ability of the big model to ensure the consistency and correctness of the process, and optimize and coordinate complex processes.

[0029] S3. Based on the analyzed government and enterprise business needs and resource library, formulate assembly rules, select matching resources from the resource library, and generate an assembly plan; In the embodiment of the present invention, formulating assembly rules includes: formulating classification standards for resources in the resource library, formulating a matching degree calculation method between the functions of resources and the functions of government and enterprise business requirements, establishing a comparison rule between the performance parameters of resources and the performance indicators of government and enterprise business requirements, and formulating a sequence rule for resource assembly. The matching degree calculation method can use a similarity algorithm.

[0030] Based on the needs of government and enterprise business, we set rules for assembling functional requirements, performance requirements, security requirements and other categories, and formulate classification standards for technical components, business modules, data models and other resources in the resource library. According to the functional requirements of the needs, we formulate a calculation method for the matching degree between resource functions and demand functions, and use a similarity algorithm for evaluation. According to the performance indicators of the needs, we establish comparison rules between resource performance parameters and demand performance indicators. According to the business process logic and data flow, we formulate rules for the order of resource assembly to ensure the process consistency of the solution.

[0031] S4. Optimize the assembly plan by using genetic algorithm to obtain the optimal assembly plan.

[0032] In the embodiment of the present invention, the assembly scheme is optimized by using a genetic algorithm to obtain the optimal assembly scheme, which includes: Encode each component or parameter in the assembly plan, use binary code to indicate whether a component is selected or not, and use integer code to indicate the value of the parameter. According to the coding rules, randomly generate a certain number of initial assembly plans as the population, determine the evaluation index of the assembly plan according to government and enterprise business needs, convert the evaluation index into a fitness function, calculate the fitness, and select the better individuals from the current population as the parent generation based on the fitness. Repeat the genetic operation and continuously generate new populations so that the individuals in the population gradually approach the optimal solution. Stop the iteration when the termination condition is met and output the optimal assembly plan.

[0033] Specifically, the components or parameters of the assembly scheme are encoded, and binary coding is used to indicate whether the components are selected or not, and integer coding is used to indicate the values ​​of the parameters. A complete assembly scheme is represented as a chromosome, which is composed of multiple genes, and each gene corresponds to a component or parameter. According to the coding rules, a certain number of initial assembly schemes are randomly generated as the population to ensure that the population has a certain diversity. Combined with previous experience or known better schemes, some initial individuals are generated to improve the initial quality of the population. According to the needs of government and enterprise business, the indicators for evaluating the quality of the assembly scheme are determined, and the evaluation indicators are converted into fitness functions. The fitness is calculated comprehensively by weighted summation and other methods. According to the fitness value, roulette selection, tournament selection and other methods are used to select better individuals from the current population as parents, so that individuals with high fitness have a greater chance of being inherited to the next generation. At a lower mutation probability, some genes of the individual are mutated, and the value of the gene is changed to increase the diversity of the population and avoid the algorithm from falling into the local optimum. Repeat the genetic operation, continuously generate new populations, and make the individuals in the population gradually approach the optimal solution. When the termination conditions are met, such as reaching the preset number of iterations or the fitness value no longer significantly improving, the iteration is stopped and the optimal assembly plan is output.

[0034] In an embodiment of the present invention, the method further includes: Before returning the optimal assembly solution to government and enterprise customers, develop test methods and acceptance criteria to verify whether the optimal assembly solution meets government and enterprise business needs, establish rules and tools for evaluating solution performance indicators, determine rules and methods for identifying and evaluating potential risks of the solution, and evaluate the optimal assembly solution from multiple angles to check whether it meets government and enterprise business needs and actual application scenarios. If not, adjust the parameters of the genetic algorithm and re-optimize the algorithm. The parameters of the genetic algorithm include population size, crossover probability, and mutation probability.

[0035] In an embodiment of the present invention, the method further includes: Monitor implemented solutions in real time, collect performance data on transaction success rate, data accuracy, business process execution accuracy, and user questions and suggestions during use, optimize orchestration strategies and resource libraries, regularly review monitoring data and the effects of optimization measures, and continuously improve implemented solutions.

[0036] Specifically, we monitor the implemented solutions in real time, collect performance data such as transaction success rate, data accuracy, and correctness of business process execution, provide user feedback channels, and collect user questions and suggestions during use through online questionnaires and customer service feedback. We use statistical analysis, correlation analysis and other methods to explore potential problems and patterns in the data, such as predicting the implementation effect of the solution through trend analysis. We promptly repair and optimize the problems found through monitoring and feedback, continuously optimize the orchestration strategy and resource library, regularly review the monitoring data and the effects of optimization measures, continuously improve the solution, and continuously improve the assembly quality.

[0037] The method of the embodiment of the present invention accurately sets user permissions through configuration in the system of a provincial government and enterprise customer mall. The intelligent orchestration solution is more stable and efficient, with more complete functions, and can meet the high requirements of users for business systems. It provides a better user experience for government and enterprise customers. In terms of server resource allocation, intelligent orchestration dynamically adjusts the configuration and number of servers according to the prediction of business load, improves resource utilization, and realizes the reasonable allocation and utilization of resources.

[0038] The present invention can accurately customize highly adapted solutions to meet the personalized needs of government and enterprise customers through intelligent arrangement and assembly solutions. Intelligent arrangement can automate and optimize the assembly process, quickly integrate software, hardware, network and other resources, and help government and enterprise customers quickly achieve business transformation or upgrade. With the help of the intelligent arrangement system, various resources can be reasonably allocated. While meeting the business needs of government and enterprise customers, it avoids waste of resources, reduces the cost of solutions, and improves resource utilization efficiency.

[0039] like Figure 2As shown, based on the same inventive concept, an embodiment of the present invention further provides a system for automatically assembling government and enterprise solutions based on intelligent arrangement, the system comprising: Initialization module 100 is used to initialize the large model, including resource library initialization, setting rules and algorithms, algorithm development, data collection, solution initialization, and generating large model cases; The parsing module 200 is used to parse the government and enterprise business needs through the natural language processing capability of the initialized large model; The assembly module 300 is used to formulate assembly rules based on the parsed government and enterprise business requirements and resource library, select matching resources from the resource library, and generate an assembly plan; The optimization module 400 is used to optimize the assembly plan by using a genetic algorithm to obtain the best assembly plan. Embodiment 1

[0040] like Figure 3 As shown, the first embodiment of the present invention provides a method for automatically assembling government and enterprise solutions based on intelligent coding. The implementation subjects of the method include operators and government and enterprise customers. The implementation means or tools include the government and enterprise self-service portal, the Jiusi big model (a big model released by Beijing Sitechi Information Technology Co., Ltd.) and algorithm microservices. Through intelligent coding, the behavior of multiple intelligent bodies can be coordinated to achieve more complex functions and solve larger-scale problems. Specifically, the method includes: 1.1. Operation personnel initialize the resource library through the government and enterprise self-service portal and import the component library and product library; 1.2. Operation personnel set rules and algorithms through the government and enterprise self-service portal to encode components in the component library and products in the product library; 1.3. Algorithm microservices develop algorithms based on resources and product specifications; 1.4. Operation personnel collect customer information and ordering information of government and enterprise customers through the government and enterprise self-service portal and send it to the algorithm microservice; 1.5. Algorithm microservices generate the first generation of solutions based on resources and product specifications and user data; 1.6. The algorithm microservice generates a large model case based on the initial solution and inputs it into the Jiusi large model; 2.1. Government and enterprise customers input their government and enterprise business needs through the government and enterprise self-service portal; 2.2. The government and enterprise self-service portal inputs the government and enterprise business needs into the Jiusi Big Model in text form. The Jiusi Big Model analyzes the government and enterprise business needs through natural language processing technology, extracts information, and identifies intent; 2.3. Jiusi Big Model looks for cases similar to government and enterprise business needs based on knowledge reserves; 2.4. Jiusi big model generates customer portraits and matches model cases; User profiling is the process of labeling and classifying users’ basic information, interests, hobbies, consumption habits, behavioral characteristics, etc. It can divide users into different segments, thereby achieving personalized identification and services for users.

[0041] 2.5. The algorithm microservice selects matching components from the component library based on the matching model cases and evaluates their characteristics; 2.6. The Jiusi big model assembles matching components according to the process and generates solutions; 2.7. The Jiusi Big Model tests the solution based on the solution-simulated business scenarios; 2.8. Jiusi Big Model returns the tested solutions to government and enterprise customers; 2.9. Government and enterprise customers input the implementation effect of the solution through the government and enterprise self-service portal; 2.10. The government and enterprise self-service portal sends the implementation effect of the solution to the algorithm microservice; 2.11. Algorithm microservices optimize component libraries and their own algorithms based on the implementation effect of the solution; 2.12. Algorithm microservices optimize the algorithms and knowledge reserves of the Jiusi large model.

[0042] Compared with the prior art, the present invention has the following advantages: Through intelligent arrangement, we can quickly analyze needs and automatically assemble appropriate solutions. Compared with traditional methods, the implementation cycle can be shortened by 30% - 50%. Based on big data analysis and artificial intelligence technology, we can understand customer needs more accurately, and the assembled solutions are more in line with actual needs. According to statistics, the accuracy of the solutions can be increased by more than 70%.

[0043] When the business needs of government and enterprise customers change, intelligent orchestration can quickly adjust the solution and add or modify the corresponding functional modules. This flexibility enables the solution to better support the business development of customers and reduce the re-planning and implementation costs caused by business changes. By optimizing the combination of hardware, software and service resources, resource waste is avoided and costs are reduced. It is estimated that 20% - 30% of resource investment can be saved.

[0044] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for automatically assembling government and enterprise solutions based on intelligent arrangement, characterized in that: The method comprises: Initialize the large model, including resource library initialization, setting rules and algorithms, algorithm development, data collection, solution initialization, and generating large model cases; Analyze government and enterprise business needs through the initialized large model; Based on the analyzed government and enterprise business requirements and the resource library, formulate assembly rules, select matching resources from the resource library, and generate an assembly plan; The algorithm is optimized by genetic algorithm, the assembly scheme is optimized, and the optimal assembly scheme is obtained.

2. The method according to claim 1, characterized in that The resource library includes: a component library and a product library. The component library includes various software, technical components, business modules and data models, and the product library includes products. Initializing the large model includes: (1) Resource library initialization: The operator imports the component library and the product library through the government and enterprise self-service portal; (2) Setting rules and algorithms: the operator encodes the components in the component library and the products in the product library through the government and enterprise self-service portal; (3) Algorithm development: The algorithm microservice develops algorithms based on the resources and product specifications in the resource library; (4) Data collection: The operator collects customer information and ordering information of government and enterprise customers through the government and enterprise self-service portal and sends it to the algorithm microservice; (5) Solution initialization: The algorithm microservice generates an initial solution based on the resources and product specifications in the resource library and user data; (6) Generate a large model case: The algorithm microservice generates a large model case based on the initial solution.

3. The method according to claim 2, characterized in that The resource library initialization also includes: Integrate various software related to government business, including office software, business management systems and data analysis tools, label the functions and record the performance parameters of each software to quickly match the needs during assembly.

4. The method according to claim 1, characterized in that The analysis of government and enterprise business requirements by the initialized large model includes: The natural language processing capability of the big model is used to parse the government business needs in text form, extract key information, identify the intentions behind the government business needs, break down the government business needs into specific and actionable tasks, and determine the logical relationships and priorities between tasks. Based on historical data and industry standards, the business processes are optimized and coordinated through the logical reasoning capabilities of the big model.

5. The method according to claim 2, characterized in that: The formulation of assembly rules includes: formulating classification standards for resources in the resource library, formulating a method for calculating the degree of match between the functions of the resources and the functions of the government and enterprise business requirements, establishing comparison rules between the performance parameters of the resources and the performance indicators of the government and enterprise business requirements, and formulating rules for the order of resource assembly.

6. The method according to claim 5, characterized in that The matching degree calculation method includes a similarity algorithm.

7. The method according to claim 2, characterized in that: The algorithm optimization is performed by genetic algorithm to optimize the assembly scheme, and the optimal assembly scheme is obtained, which includes: Encode each component or parameter in the assembly scheme, use binary code to indicate whether a component is selected or not, use integer code to indicate the value of a parameter, randomly generate a certain number of initial assembly schemes as a population according to the coding rules, determine the evaluation index of the assembly scheme according to the government business needs, convert the evaluation index into a fitness function, calculate the fitness, select a better individual from the current population as the parent generation according to the fitness, repeat the genetic operation, continuously generate a new population, so that the individuals in the population gradually approach the optimal solution, stop the iteration when the termination condition is met, and output the optimal assembly scheme.

8. The method according to claim 1, characterized in that The method further comprises: Before returning the optimal assembly solution to government and enterprise customers, formulate test methods and acceptance criteria to verify whether the optimal assembly solution meets the government and enterprise business requirements, establish rules and tools for evaluating solution performance indicators, determine rules and methods for identifying and evaluating potential risks of solutions, evaluate the optimal assembly solution from multiple angles to check whether it meets the government and enterprise business requirements and actual application scenarios. If not, adjust the parameters of the genetic algorithm and re-optimize the algorithm. The parameters of the genetic algorithm include parameters of population size, crossover probability, and mutation probability.

9. The method according to claim 1, characterized in that: The method further comprises: Monitor the implemented solutions in real time, collect performance data on transaction success rate, data accuracy, business process execution correctness, and user questions and suggestions during use, optimize the orchestration strategy and the resource library, regularly review the monitoring data and the effects of optimization measures, and continuously improve the implemented solutions.

10. A system for automatically assembling government and enterprise solutions based on intelligent arrangement, used to implement the method described in any one of claims 1 to 9, characterized in that: The system comprises: The initialization module is used to initialize the large model, including resource library initialization, setting rules and algorithms, algorithm development, data collection, solution initialization, and generating large model cases; A parsing module, used to parse government and enterprise business needs through the natural language processing capability of the initialized large model; An assembly module, used to formulate assembly rules based on the parsed government and enterprise business requirements and the resource library, select matching resources from the resource library, and generate an assembly plan; The optimization module is used to optimize the algorithm through a genetic algorithm to optimize the assembly plan and obtain the optimal assembly plan.

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