A method to reduce technical complexity in domain-driven design

Abstract

This invention discloses a method for reducing the technical complexity of Domain-Driven Design (DDD), relating to the field of software engineering technology. It includes: ① Domain modeling and unified language construction: Utilizing Domain Event Sourcing (DOM) technology to construct a domain model; using formal methods to define a Domain-Specific Language (DSL) to ensure consistency between business and technical semantics; introducing type theory and algebraic data types (ADTs) to capture business constraints and behaviors through a precise type system. Through precise domain modeling and reasonable subdomain division, this invention allows developers to understand business requirements more quickly, reducing communication costs and accelerating the development process; employing algorithms such as cluster analysis and information entropy, it automatically identifies and classifies objects and behaviors within the domain, significantly reducing the internal complexity of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of software engineering, and in particular to a method for reducing the technical complexity of domain driven design. Background Art

[0002] With the rapid development of information technology, especially in the era of cloud computing and big data, software development faces unprecedented challenges. Especially in industries with high reliability and high safety requirements such as nuclear power, software systems often have complex business logic and technical implementation; domain-driven design (DDD), as a software development methodology, provides a set of systematic tools and practices to solve complex business needs. However, the traditional DDD approach has limitations when dealing with large-scale, distributed cloud-native applications, and needs to be further optimized to meet the needs of modern software engineers.

[0003] After searching, the Chinese patent application number CN201910192561.9 discloses a business entity implementation method and system based on domain-driven design, with the following steps: S1. Establish a business entity model through a business entity modeling tool; S2. Establish a business entity operation framework; S3. Establish a cache framework in the business entity operation framework; S4. Establish an incremental mechanism in the business entity operation framework; S5. Split the business logic into an entity logic layer and a service logic layer; S6. Establish a business entity designer; S7. Establish a JIT generator. The business entity implementation method in the above patent has the following shortcomings: it cannot cope with complex businesses and solve complex technical applications well, and needs to be improved. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a method for reducing the technical complexity of domain driven design.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A method for reducing technical complexity of domain-driven design, comprising:

[0007] ①Domain modeling and unified language construction:

[0008] Use Domain Event Sourcing technology to build domain models;

[0009] Use formal methods to define domain-specific languages ​​(DSLs) to ensure consistency between business and technical semantics;

[0010] Introducing type theory and algebraic data types (ADTs) to capture business constraints and behaviors through a precise type system;

[0011] ②Sub-domain division and bounded context management:

[0012] Apply modularity theory to break down large fields into manageable subfields;

[0013] Use relevant concepts from category theory and graph theory to establish connections and mappings between sub-fields;

[0014] Use the microservice architecture model to achieve physical boundaries and service autonomy in sub-domains;

[0015] ③Algorithm design with reduced technical complexity:

[0016] Automatically identify and classify similar domain objects based on cluster analysis algorithms;

[0017] Use complex network analysis to evaluate and optimize the internal structure and interactions of domain models;

[0018] Based on information entropy, quantify the complexity of the domain model and provide optimization directions;

[0019] ④Full-cycle management platform construction:

[0020] A cloud-native application development platform based on domain-driven design, providing a tool set and plug-in system to support custom extensions and rapid iteration;

[0021] Integrate continuous integration / continuous deployment (CI / CD) processes to automate the build and deployment process.

[0022] Preferably, the method further comprises:

[0023] Intelligent evolution of domain models:

[0024] Use machine learning algorithms to analyze historical data and predict future changes in domain models;

[0025] Use model-driven engineering (MDE) technology to automatically adjust and optimize domain models based on prediction results;

[0026] Combined with AI-assisted design tools, it provides design suggestions and code corrections.

[0027] Preferably, the method further comprises:

[0028] Code generation and automated testing:

[0029] Based on the code generator, the domain model is automatically converted into high-quality code;

[0030] Integrate test-driven development (TDD) practices to automatically generate test cases and ensure code quality;

[0031] Use assertions and design by contract to ensure code quality and consistency with business rules.

[0032] Preferably, the method further comprises:

[0033] Performance optimization and resource scheduling:

[0034] Design domain-aware performance analysis tools to identify performance bottlenecks and provide optimization suggestions;

[0035] Use containerization and virtualization technologies to optimize resource allocation and scheduling;

[0036] Combined with the elastic scaling capabilities of cloud computing, computing resources can be dynamically adjusted to meet different load requirements.

[0037] Preferably, the specific steps of domain modeling and unified language construction are:

[0038] S11: Determine the scope and boundaries of the domain model;

[0039] S12: Identify and define key concepts and terms in the field;

[0040] S13: Use formal methods to build domain models and ensure consistency of syntax and semantics;

[0041] S14: Introducing type theory and algebraic data types (ADTs) to accurately describe business rules and logic;

[0042] S15: Provide unified communication and design tools through domain-specific languages ​​(DSLs) to reduce communication ambiguity.

[0043] Preferably: the sub-domain division and bounded context management specifically include:

[0044] S21: Analyze business processes and identify different functions and business logic;

[0045] S22: Apply modularization theory to decompose large areas into manageable sub-areas according to business responsibilities;

[0046] S23: Use functors and natural transformations in category theory to determine the connections and dependencies between subdomains;

[0047] S24: Establish a bounded context mapping table to clarify the interaction patterns and conversion rules between different sub-domains;

[0048] S25: Adopt microservice architecture to achieve service autonomy and flexible deployment in sub-domains.

[0049] Preferably: the platform construction of the full-cycle management specifically includes:

[0050] S31: Design and implement a cloud-native application development platform that supports DDD;

[0051] S32: Integrate CI / CD processes to automate code building, testing, and deployment;

[0052] S33: Provides a rich development tool set and plug-in system to support rapid iteration and custom extensions;

[0053] S34: Build a version control system for the domain model to track the change history and evolution path of the model;

[0054] S35: Establish a monitoring and alarm mechanism to monitor the performance indicators and resource usage of the application in real time.

[0055] Preferably: the performance optimization and resource scheduling are specifically:

[0056] S41: Design domain-aware performance analysis tools to identify performance bottlenecks and propose optimization measures;

[0057] S42: Use Docker containerization technology to achieve rapid deployment and isolated operation of applications;

[0058] S43: Use VMware virtualization technology to optimize resource allocation and scheduling strategies;

[0059] S44: Combine the elastic scaling function of cloud services to dynamically adjust computing resources to adapt to different load requirements.

[0060] Preferably, in the algorithm design with reduced technical complexity, objects and behaviors in the field are automatically identified and classified based on a cluster analysis algorithm, specifically:

[0061] Use K-Means algorithm to divide the samples in the data set into k clusters, so that the samples in each cluster are as similar as possible, and the samples between different clusters are as different as possible;

[0062] The specific steps are:

[0063] Initialization: Randomly select k samples from the data set as the initial cluster centers;

[0064] Assignment: Assign each sample to the nearest cluster center to form k clusters;

[0065] Update: Recalculate the center of each cluster;

[0066] Iteration: Repeat the assignment and update steps until the cluster center no longer changes or the preset number of iterations is reached;

[0067] The update formula of cluster center is:

[0068]

[0069] Among them, C i is the center of cluster i, m is the number of samples in cluster i, x j is a sample in cluster i.

[0070] Preferably: in the intelligent evolution of the domain model, a machine learning algorithm is used to analyze historical data to predict the future change trend of the domain model, time series analysis is used to predict the future change trend of the domain model, and an ARIMA model is used to analyze the dynamic changes of time series data and make predictions;

[0071] The specific steps include:

[0072] Model identification: Determine model parameters through autocorrelation function (ACF) and partial autocorrelation function (PACF) graphs;

[0073] Parameter estimation: Based on the determined model parameters, estimate the parameter values ​​using maximum likelihood estimation or other statistical methods;

[0074] Model testing: Perform residual analysis on the model to test the adaptability and accuracy of the model;

[0075] Prediction: Use the constructed model to predict future values;

[0076] Among them, the ARIMA (p, d, q) model is expressed as:

[0077]

[0078] Where L is the lag operator, and θ i are the parameters of the AR and MA parts, d is the number of differences, and X t is the original time series, Z t It's noise.

[0079] The beneficial effects of the present invention are:

[0080] 1. Through precise domain modeling and reasonable division of sub-domains, the present invention enables developers to understand business requirements more quickly, reduce communication costs, and accelerate the development process; it adopts cluster analysis, information entropy and other algorithm designs to automatically identify and classify objects and behaviors in the domain, significantly reducing the complexity within the system.

[0081] 2. The cloud-native application development platform that supports DDD in the present invention integrates the CI / CD process, automates construction and deployment, and greatly improves the speed and quality of software delivery; it uses machine learning algorithms to predict future change trends of domain models, and automatically adjusts and optimizes them, so that the domain models can continuously adapt to business changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 A framework diagram of a method for reducing technical complexity of domain-driven design proposed in the present invention. DETAILED DESCRIPTION

[0083] The technical solution of the present invention is further described in detail below in conjunction with specific implementation methods.

[0084] Embodiment 1:

[0085] A method for reducing technical complexity of domain-driven design, comprising:

[0086] ①Domain modeling and unified language construction:

[0087] Use Domain Event Sourcing technology to build domain models;

[0088] Use formal methods to define domain-specific languages ​​(DSLs) to ensure consistency between business and technical semantics;

[0089] Introducing type theory and algebraic data types (ADTs) to capture business constraints and behaviors through a precise type system;

[0090] ②Sub-domain division and bounded context management:

[0091] Apply modularity theory to break down large fields into manageable subfields;

[0092] Use relevant concepts from category theory and graph theory to establish connections and mappings between sub-fields;

[0093] Use the microservice architecture model to achieve physical boundaries and service autonomy in sub-domains;

[0094] ③Algorithm design with reduced technical complexity:

[0095] Automatically identify and classify similar domain objects based on cluster analysis algorithms;

[0096] Use complex network analysis to evaluate and optimize the internal structure and interactions of domain models;

[0097] Based on information entropy, quantify the complexity of the domain model and provide optimization directions;

[0098] ④Full-cycle management platform construction:

[0099] A cloud-native application development platform based on domain-driven design, providing a tool set and plug-in system to support custom extensions and rapid iteration;

[0100] Integrate continuous integration / continuous deployment (CI / CD) processes to automate the build and deployment process.

[0101] Wherein, the method further comprises:

[0102] Intelligent evolution of domain models:

[0103] Use machine learning algorithms to analyze historical data and predict future changes in domain models;

[0104] Use model-driven engineering (MDE) technology to automatically adjust and optimize domain models based on prediction results;

[0105] Combined with AI-assisted design tools, it provides design suggestions and code corrections.

[0106] Wherein, the method further comprises:

[0107] Code generation and automated testing:

[0108] Based on the code generator, the domain model is automatically converted into high-quality code;

[0109] Integrate test-driven development (TDD) practices to automatically generate test cases and ensure code quality;

[0110] Use assertions and design by contract to ensure code quality and consistency with business rules.

[0111] Wherein, the method further comprises:

[0112] Performance optimization and resource scheduling:

[0113] Design domain-aware performance analysis tools to identify performance bottlenecks and provide optimization suggestions;

[0114] Use containerization and virtualization technologies to optimize resource allocation and scheduling;

[0115] Combined with the elastic scaling capabilities of cloud computing, computing resources can be dynamically adjusted to meet different load requirements.

[0116] The specific steps of domain modeling and unified language construction are as follows:

[0117] S11: Determine the scope and boundaries of the domain model;

[0118] S12: Identify and define key concepts and terms in the field;

[0119] S13: Use formal methods to build domain models and ensure consistency of syntax and semantics;

[0120] S14: Introducing type theory and algebraic data types (ADTs) to accurately describe business rules and logic;

[0121] S15: Provide unified communication and design tools through domain-specific languages ​​(DSLs) to reduce communication ambiguity.

[0122] The sub-domain division and bounded context management specifically include:

[0123] S21: Analyze business processes and identify different functions and business logic;

[0124] S22: Apply modularization theory to decompose large areas into manageable sub-areas according to business responsibilities;

[0125] S23: Use functors and natural transformations in category theory to determine the connections and dependencies between subdomains;

[0126] S24: Establish a bounded context mapping table to clarify the interaction patterns and conversion rules between different sub-domains;

[0127] S25: Adopt microservice architecture to achieve service autonomy and flexible deployment in sub-domains.

[0128] The construction of the full-cycle management platform specifically includes:

[0129] S31: Design and implement a cloud-native application development platform that supports DDD;

[0130] S32: Integrate CI / CD processes to automate code building, testing, and deployment;

[0131] S33: Provides a rich development tool set and plug-in system to support rapid iteration and custom extensions;

[0132] S34: Build a version control system for the domain model to track the change history and evolution path of the model;

[0133] S35: Establish a monitoring and alarm mechanism to monitor the performance indicators and resource usage of the application in real time.

[0134] The performance optimization and resource scheduling are specifically as follows:

[0135] S41: Design domain-aware performance analysis tools to identify performance bottlenecks and propose optimization measures;

[0136] S42: Use Docker containerization technology to achieve rapid deployment and isolated operation of applications;

[0137] S43: Use VMware virtualization technology to optimize resource allocation and scheduling strategies;

[0138] S44: Combine the elastic scaling function of cloud services to dynamically adjust computing resources to adapt to different load requirements.

[0139] Embodiment 2:

[0140] A method for reducing the technical complexity of domain-driven design. This embodiment is based on Embodiment 1:

[0141] In the algorithm design with reduced technical complexity, objects and behaviors in the field are automatically identified and classified based on cluster analysis algorithms, specifically:

[0142] Use K-Means algorithm to divide the samples in the data set into k clusters, so that the samples in each cluster are as similar as possible, and the samples between different clusters are as different as possible;

[0143] The specific steps are:

[0144] Initialization: Randomly select k samples from the data set as the initial cluster centers;

[0145] Assignment: Assign each sample to the nearest cluster center to form k clusters;

[0146] Update: Recalculate the center of each cluster (usually the mean of the samples within the cluster);

[0147] Iteration: Repeat the assignment and update steps until the cluster center no longer changes or the preset number of iterations is reached;

[0148] The update formula of cluster center is:

[0149]

[0150] Among them, C i is the center of cluster i, m is the number of samples in cluster i, x j is a sample in cluster i.

[0151] In the intelligent evolution of the domain model, a machine learning algorithm is used to analyze historical data to predict the future change trend of the domain model, and time series analysis is used to predict the future change trend of the domain model. The ARIMA model is used to analyze the dynamic changes of time series data and make predictions;

[0152] The specific steps include:

[0153] Model identification: Determine model parameters through autocorrelation function (ACF) and partial autocorrelation function (PACF) graphs;

[0154] Parameter estimation: Based on the determined model parameters, estimate the parameter values ​​using maximum likelihood estimation or other statistical methods;

[0155] Model testing: Perform residual analysis on the model to test the adaptability and accuracy of the model;

[0156] Prediction: Use the constructed model to predict future values;

[0157] Among them, the ARIMA (p, d, q) model is expressed as:

[0158]

[0159] Where L is the lag operator, and θ i are the parameters of the AR and MA parts, d is the number of differences, and X t is the original time series, Z t It's noise.

[0160] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for reducing the technical complexity of domain-driven design, characterized in that: include: ①Domain modeling and unified language construction: Use domain event tracing technology to build domain models; Use formal methods to define domain-specific languages ​​to ensure consistency between business and technical semantics; Introducing type theory and algebraic data types to capture business constraints and behaviors through precise type systems; ②Sub-domain division and bounded context management: Apply modularity theory to break down large fields into manageable subfields; Use relevant concepts from category theory and graph theory to establish connections and mappings between sub-fields; Use the microservice architecture model to achieve physical boundaries and service autonomy in sub-domains; ③Algorithm design with reduced technical complexity: Automatically identify and classify similar domain objects based on cluster analysis algorithms; Use complex network analysis to evaluate and optimize the internal structure and interactions of domain models; Based on information entropy, quantify the complexity of the domain model and provide optimization directions; ④Full-cycle management platform construction: A cloud-native application development platform based on domain-driven design, providing a tool set and plug-in system to support custom extensions and rapid iteration; Integrate continuous integration / continuous deployment processes to automate the build and deployment process; The method further comprises: Performance optimization and resource scheduling: Design domain-aware performance analysis tools to identify performance bottlenecks and provide optimization suggestions; Use containerization and virtualization technologies to optimize resource allocation and scheduling; Combined with the elastic scaling capabilities of cloud computing, computing resources can be dynamically adjusted to meet different load requirements; The performance optimization and resource scheduling are specifically as follows: S41: Design domain-aware performance analysis tools to identify performance bottlenecks and propose optimization measures; S42: Use Docker containerization technology to achieve rapid deployment and isolated operation of applications; S43: Use VMware virtualization technology to optimize resource allocation and scheduling strategies; S44: Combine the elastic scaling function of cloud services to dynamically adjust computing resources to adapt to different load requirements.

2. The method for reducing technical complexity of domain-driven design according to claim 1, characterized in that: The method further comprises: Intelligent evolution of domain models: Use machine learning algorithms to analyze historical data and predict future changes in domain models; Use model-driven engineering techniques to automatically adjust and optimize domain models based on prediction results; Combined with AI-assisted design tools, it provides design suggestions and code corrections.

3. The method for reducing technical complexity of domain-driven design according to claim 2, characterized in that: The method further comprises: Code generation and automated testing: Based on the code generator, the domain model is automatically converted into high-quality code; Integrate test-driven development practices to automatically generate test cases and ensure code quality; Use assertions and design by contract to ensure code quality and consistency with business rules.

4. The method for reducing technical complexity of domain-driven design according to claim 1, characterized in that: The specific steps of domain modeling and unified language construction are: S11: Determine the scope and boundaries of the domain model; S12: Identify and define key concepts and terms in the field; S13: Use formal methods to build domain models and ensure consistency of syntax and semantics; S14: Introduce type theory and algebraic data types to accurately describe business rules and logic; S15: Provide unified communication and design tools through domain-specific languages ​​to reduce communication ambiguity.

5. The method for reducing technical complexity of domain-driven design according to claim 4, characterized in that: The sub-domain division and bounded context management specifically include: S21: Analyze business processes and identify different functions and business logic; S22: Apply modularization theory to decompose large areas into manageable sub-areas according to business responsibilities; S23: Use functors and natural transformations in category theory to determine the connections and dependencies between subdomains; S24: Establish a bounded context mapping table to clarify the interaction patterns and conversion rules between different sub-domains; S25: Adopt microservice architecture to achieve service autonomy and flexible deployment in sub-domains.

6. The method for reducing technical complexity of domain-driven design according to claim 1, characterized in that: The platform construction of the full-cycle management specifically includes: S31: Design and implement a cloud-native application development platform that supports DDD; S32: Integrate CI / CD processes to automate code building, testing, and deployment; S33: Provides a rich development tool set and plug-in system to support rapid iteration and custom extensions; S34: Build a version control system for the domain model to track the change history and evolution path of the model; S35: Establish a monitoring and alarm mechanism to monitor the performance indicators and resource usage of the application in real time.

7. The method for reducing technical complexity of domain-driven design according to claim 1, characterized in that: In the algorithm design with reduced technical complexity, objects and behaviors in the field are automatically identified and classified based on cluster analysis algorithms, specifically: Use the K-Means algorithm to divide the samples in the data set into k clusters, so that the samples in each cluster are as similar as possible, and the samples between different clusters are as different as possible; The specific steps are: Initialization: Randomly select k samples from the data set as the initial cluster centers; Assignment: Assign each sample to the nearest cluster center to form k clusters; Update: Recalculate the center of each cluster; Iteration: Repeat the assignment and update steps until the cluster center no longer changes or the preset number of iterations is reached; The update formula of cluster center is: Among them, C i is the center of cluster i, m is the number of samples in cluster i, x j is a sample in cluster i.

8. The method for reducing technical complexity of domain-driven design according to claim 2, characterized in that: In the intelligent evolution of the domain model, a machine learning algorithm is used to analyze historical data to predict the future change trend of the domain model, and time series analysis is used to predict the future change trend of the domain model. The ARIMA model is used to analyze the dynamic changes of time series data and make predictions; The specific steps include: Model identification: Determine model parameters through autocorrelation function and partial autocorrelation function graphs; Parameter estimation: Based on the determined model parameters, estimate the parameter values ​​using maximum likelihood estimation or other statistical methods; Model testing: Perform residual analysis on the model to test the adaptability and accuracy of the model; Prediction: Use the constructed model to predict future values; Among them, the ARIMA (p, d, q) model is expressed as: Where L is the lag operator, and θ i are the parameters of the AR and MA parts, d is the number of differences, and X t is the original time series, Z t It's noise.

Images