Architecture design method for software engineering construction

Abstract

The invention belongs to the field of software engineering, and discloses an architecture design method for software engineering construction. Comprising the following steps: combining a domain knowledge graph to recognize a dependency relationship between entities, calculating a node weight through a three-layer graph attention network according to dependency intensity, evaluating a service feature vector and a quality attribute demand by adopting a deep Q network, introducing a dynamic weight adjustment mechanism in mode matching, improving a cosine similarity algorithm, and adopting a self-adaptive dimension weighting strategy. A bidirectional gRPC flow protocol is adopted for communication between services, and token bucket capacity is allocated for dynamic flow limiting according to the SLA; according to the method, an ARIMA-LSTM hybrid model is adopted to predict data traffic, and an Apache Kafka cluster is configured with three Broker nodes; a fully homomorphic encryption algorithm is adopted, a zero-trust architecture is constructed, a load model is constructed in a digital twinborn verification environment, and intelligent architecture design of software engineering construction is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of software engineering, and more particularly, to an architecture design method for software engineering construction. BACKGROUND

[0002] With the rapid development of the field of software engineering, the traditional architecture design method gradually exposes its shortcomings when facing complex business requirements, high concurrency scenarios and distributed systems, such as low efficiency of requirement analysis, lack of intelligent basis for architecture pattern selection, performance bottleneck of microservice scheduling, insufficient real-time performance of data flow processing, and difficulty in ensuring security.

[0003] In the prior art, requirement analysis relies on manual experience, which is prone to miss potential conflicts; architecture pattern selection is usually based on static rules, which is difficult to dynamically adapt to business changes; microservice scheduling is prone to network congestion and consistency problems in high-load scenarios; data flow modeling lacks accurate prediction of space-time correlation; the security protection mechanism responds slowly when facing new attacks; containerized deployment is difficult to achieve efficient scheduling across cloud resources; the development collaboration platform lacks multi-modal interaction support; the degree of intelligence in the architecture verification and operation phase is insufficient, making it difficult to meet high availability requirements.

[0004] Therefore, there is an urgent need for an architecture design method that integrates artificial intelligence, distributed systems and security technology to improve the efficiency, reliability and adaptability of software engineering construction. SUMMARY

[0005] To overcome the above-mentioned defects of the prior art, in order to achieve the above-mentioned purpose, the present application provides the following technical scheme: an architecture design method for software engineering construction, comprising: an intelligent requirement analysis module extracts functional entities in a requirement document through a BERT pre-training model, identifies the dependency relationship between entities in combination with a domain knowledge graph, calculates node weights through a three-layer graph attention network according to dependency strength, and triggers a multi-objective genetic algorithm to generate an optimized solution when detecting requirement conflicts;

[0006] An architecture pattern selection module establishes an intelligent library containing k_o kinds of architecture patterns, evaluates a business feature vector and quality attribute requirements using a deep Q network, introduces a dynamic weight adjustment mechanism for pattern matching, adopts an adaptive dimension weighting strategy for the improved cosine similarity algorithm, and reduces dimensions and visualizes the optimal solution through principal component analysis;

[0007] An improved Raft consensus algorithm is used in a microservice scheduling module, a log compressor mechanism is added, a bidirectional gRPC stream protocol is used for inter-service communication, and a dynamic flow limiting mechanism allocates token bucket capacity according to SLA;

[0008] The data stream modeling module uses an ARIMA-LSTM hybrid model to predict data flow. The Apache Kafka cluster is configured with three Broker nodes, and the Flink stream processing engine is set to Exactly-Once semantics.

[0009] The security protection module uses a fully homomorphic encryption algorithm to build a zero-trust architecture, and the security incident response mechanism ensures that the handling process is triggered within 200ms. The containerized deployment module uses an orchestration engine to implement cross-cloud resource scheduling, and the grayscale release adopts a canary strategy.

[0010] The collaborative development platform provides real-time collaboration capabilities for multiple people, and intelligent code completion is based on a large language model; the digital twin verification environment builds a load model, the intelligent operation and maintenance center uses a Bayesian network to build a causal reasoning engine, and the reinforcement learning tuning system sets the Q-learning discount factor.

[0011] Preferably, the intelligent demand analysis module extracts functional entities in the demand document through the BERT pre-training model, identifies dependencies between entities in combination with the domain knowledge graph, calculates the node weights of the dependency strength through a three-layer graph attention network, and triggers a multi-objective genetic algorithm to generate an optimization plan when a demand conflict is detected. The conflict judgment criteria are that the semantic similarity between entities exceeds 0.85 and the functional goals are mutually exclusive. The genetic algorithm sets the population size to 200 and the number of iterations to 500, and outputs a standardized demand specification and an architecture constraint list. The constraints are synchronized to the architecture mode selection module through the main control center.

[0012] Preferably, the architecture mode selection module establishes an intelligent library containing k_o architecture modes, and uses a deep Q network to evaluate business feature vectors and quality attribute requirements, wherein the DQN network structure includes 3 fully connected layers and a ReLU activation function, the state space is defined as an 8-dimensional feature vector including business scale, concurrency, and data sensitivity, and the reward function sets an architecture complexity weight of 0.3, a performance indicator weight of 0.5, and an implementation cost weight of 0.2;

[0013] A dynamic weight adjustment mechanism was introduced into the pattern matching process. Every 24 hours, the architectural decision data from the past seven days is collected based on a sliding window. The feature weights are updated using exponential smoothing. The business continuity weight was increased from 0.4 to 0.6, and the maintainability weight was reduced from 0.5 to 0.3.

[0014] The improved cosine similarity algorithm adopts an adaptive dimension weighting strategy and performs logarithmic transformation on high cardinality features;

[0015] The optimal architecture solution uses principal component analysis to reduce the 16-dimensional quality attributes to a three-dimensional space, with the X-axis mapping throughput, the Y-axis mapping deployment cost, and the Z-axis mapping fault recovery time.

[0016] The visualization interface supports dynamic parameter adjustment during multi-scheme comparison, allowing real-time observation of architectural scheme changes by dragging the three-dimensional coordinate axes, and setting the architectural fitness score threshold ≥ 80 points to trigger the automatic recommendation mechanism.

[0017] Preferably, the microservice scheduling module adopts an improved Raft consensus algorithm, adds a log compressor mechanism to reduce network load by 30%, sets the leader election timeout time to 150-300 ms, deploys Envoy agents for each service instance to monitor CPU / memory metrics, uses a bidirectional gRPC stream protocol for inter-service communication, triggers the circuit breaker mechanism when the error rate exceeds 25% within 10 seconds, dynamically limits the flow according to the service level agreement (SLA) token bucket capacity allocation, and the load balancing weight calculation formula is:

[0018] Wi = 1 / (0.7 x RT_i + 0.3 x CPU_i)

[0019] Where RT_i is the response time, CPU_i is the utilization, and the service topology graph is displayed through Grafana in real time to show node status and traffic heat maps.

[0020] Preferably, the data flow modeling module uses an ARIMA-LSTM hybrid model to predict data traffic, with a prediction window of 15 minutes in the future and an error threshold of ±5%, an Apache Kafka cluster configured with 3 Broker nodes, 2 replicas per partition, an Avro Schema defining 22 core data field validation rules, a Flink stream processing engine set to Exactly-Once semantics, data consistency requirements requiring end-to-end latency ≤ 500 ms, and a key data path generating a weighted dependency graph through Neo4j, with edge weights representing data flow frequency.

[0021] Preferably, the security protection module uses a fully homomorphic encryption algorithm to process sensitive data, builds a zero-trust architecture including a blockchain-based decentralized identity authentication, an attribute-based dynamic access control policy, and a long short-term memory network anomaly behavior detection model, and a security incident response mechanism ensures that the handling process is triggered within 200 ms, and the data leakage risk probability is controlled below 0.05%.

[0022] Preferably, the containerized deployment module implements cross-cloud resource scheduling through an orchestration engine, dynamically adjusts the number of instances based on CPU and memory utilization thresholds, preferentially deploys services to low-latency areas using affinity scheduling algorithms, uses a canary strategy to verify new versions in stages during the gray release process, and integrates multi-layer security scanning and vulnerability automatic repair during container image construction.

[0023] Preferably, the collaborative development platform provides multi-person real-time collaboration function, the operation log realizes multi-version merging through a non-conflict replication data type algorithm, intelligent code completion generates context-related suggestions based on a large language model, an architecture visualization engine renders a three-dimensional topology graph through WebGL, and API interface automated test case generation and contract verification are supported.

[0024] Preferably, when the digital twin verification environment constructs a load model, a Poisson distribution λ=1500 times / s is set, a fault model injects a 200ms network delay and a 5% node downtime every 5 minutes, a security model simulates 5000 DDoS attacks per second, and an optimization report containing throughput reduction rate, error rate fluctuation and resource bottleneck point is generated after 24 hours of stress testing, system availability calculation uses: Availability=MTBF / (MTBF+MTTR)×100%, and a 99.999% service level agreement standard is required.

[0025] Preferably, the causal reasoning engine of the intelligent operation and maintenance center uses a Bayesian network to construct an inference graph containing 50 fault nodes, the EM algorithm is used for parameter learning and iteration 1000 times, the Q-learning discount factor γ=0.9 and the exploration rate ε=0.2 are set for reinforcement learning optimization system, the capacity planning model performs 10000 times of Monte Carlo simulation, the confidence interval is set to 95%, the average repair time MTTR is reduced from 4 hours to 2.4 hours, and the average fault interval MTBF is improved from 200 hours to 300 hours.

[0026] The technical effects and advantages of the architecture design method for software engineering construction of the present application are:

[0027] 1. By integrating the BERT pre-training model and the domain knowledge graph technology, the intelligent requirement analysis module can accurately extract functional entities and identify dependency relationships, and solve requirement conflicts by combining a multi-objective genetic algorithm, significantly improving the automation and accuracy of requirement analysis, the architecture pattern selection module adopts a deep Q network and a dynamic weight adjustment mechanism, combines an improved cosine similarity algorithm and principal component analysis, and can dynamically generate an optimal architecture scheme according to business characteristics and quality attribute requirements, the micro-service scheduling module reduces network load by 30% through an improved Raft consensus algorithm and a log compressor mechanism, and significantly improves inter-service communication efficiency by combining a bidirectional gRPC stream protocol and a dynamic throttling strategy.

[0028] 2. The data flow modeling module uses an ARIMA-LSTM hybrid model to predict data traffic, combines Apache Kafka and Flink stream processing engine to realize Exactly-Once semantic guarantee, and the security protection module uses full homomorphic encryption algorithm and zero trust architecture, combined with blockchain identity authentication and long short-term memory network anomaly detection model, to reduce the data leakage risk probability, shorten the security incident response time, improve the system security, the container deployment module realizes cross-cloud resource scheduling through the orchestration engine, combined with the elastic scaling strategy and canary gray release, improves the deployment efficiency, resource utilization, container image vulnerability repair rate. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 A flowchart of an architecture design method for software engineering construction according to the present application;

[0030] Figure 2 A structural diagram of an architecture design method for software engineering construction according to the present application. DETAILED DESCRIPTION

[0031] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0032] Embodiment 1

[0033] Please refer to Figure 1 The architecture design method for software engineering construction described in the present embodiment includes:

[0034] The intelligent requirement analysis module extracts functional entities in the requirement document through the BERT pre-training model, identifies the dependency relationship between entities in combination with the domain knowledge graph, calculates the node weight through a three-layer graph attention network according to the dependency strength, and triggers a multi-objective genetic algorithm to generate an optimization scheme when detecting a requirement conflict.

[0035] The intelligent requirement analysis module extracts functional entities in the requirement document through the BERT pre-training model, identifies the dependency relationship between entities in combination with the domain knowledge graph, calculates the node weight through a three-layer graph attention network according to the dependency strength, and triggers a multi-objective genetic algorithm to generate an optimization scheme when detecting a requirement conflict. The conflict determination criterion is that the semantic similarity between entities is greater than 0.85 and the functional targets are mutually exclusive. The genetic algorithm is set to a population size of 200 and an iteration number of 500. The output is a standardized requirement specification and an architecture constraint list. The constraint conditions are synchronized to the architecture mode selection module through the master control center.

[0036] A company needs to develop a high-concurrency e-commerce system, and the requirement document contains user management, order processing, payment gateway, and other functional modules. The intelligent requirement analysis module first extracts functional entities (such as "user registration" and "order query") through the BERT pre-training model, and identifies the dependency relationship between entities (such as "order query" depends on "user registration") combined with the domain knowledge graph. Through a three-layer graph attention network to calculate node weights, it is found that there is a conflict between "payment gateway" and "order processing" (semantic similarity 0.9, mutually exclusive functional goals). The system triggers a multi-objective genetic algorithm, sets the population size to 200 and the number of iterations to 500, generates an optimized solution, outputs a standardized requirement specification and architecture constraint list, and synchronizes the constraint conditions to the architecture pattern selection module.

[0037] The architecture pattern selection module establishes an intelligent library containing 12 architecture patterns, evaluates the business feature vector and quality attribute requirements using a deep Q network, introduces a dynamic weight adjustment mechanism in the pattern matching process, improves the cosine similarity algorithm using an adaptive dimension weighting strategy, and visualizes the optimal solution through principal component analysis dimension reduction.

[0038] The architecture pattern selection module establishes an intelligent library containing 12 architecture patterns, evaluates the business feature vector and quality attribute requirements using a deep Q network, where the DQN network structure contains 3 fully connected layers and ReLU activation functions, the state space is defined as an 8-dimensional feature vector including business size, concurrency, data sensitivity, etc., and the reward function sets the architecture complexity weight to 0.3, the performance indicator weight to 0.5, and the implementation cost weight to 0.2.

[0039] A dynamic weight adjustment mechanism is introduced in the pattern matching process, which calculates the architecture decision data of the past 7 days based on a sliding window every 24 hours, updates the feature weights using an exponential smoothing method, increases the business continuity weight from 0.4 to 0.6, and reduces the maintainability weight from 0.5 to 0.3.

[0040] The improved cosine similarity algorithm uses an adaptive dimension weighting strategy, and the high-base features are processed using logarithmic transformation.

[0041] The optimal architecture solution is reduced to a three-dimensional space through principal component analysis of the 16-dimensional quality attributes, with X-axis mapping throughput, Y-axis mapping deployment cost, and Z-axis mapping fault recovery time.

[0042] The visualization interface supports dynamic parameter adjustment when comparing multiple solutions, allowing real-time observation of architecture solution changes by dragging the three-dimensional coordinate axes, and setting the architecture fitness score threshold to ≥80 points to trigger the automatic recommendation mechanism.

[0043] The architecture mode selection module assesses the quality attribute requirements based on the business feature vector (100 million users per day, 5000 times per second, high data sensitivity), and matches the scheme from the 12 architecture mode intelligent library using a deep Q network (DQN network structure containing 3 fully connected layers and ReLU activation function). The reward function sets the architecture complexity weight to 0.3, the performance index weight to 0.5, and the implementation cost weight to 0.2. The dynamic weight adjustment mechanism uses an exponential smoothing method to update the feature weight based on the past 7 days of data in a sliding window every 24 hours (the business continuity weight is increased from 0.4 to 0.6, and the maintainability weight is reduced from 0.5 to 0.3). The improved cosine similarity algorithm performs logarithmic transformation processing on high cardinality features, and finally reduces the 16-dimensional quality attributes to a three-dimensional space through principal component analysis (X-axis maps throughput, Y-axis maps deployment cost, and Z-axis maps fault recovery time). The optimal architecture scheme is generated and displayed through a visual interface, with an architecture fitness score of 85 points, triggering the automatic recommendation mechanism.

[0044] The microservice scheduling module uses an improved Raft consensus algorithm, adds a log compressor mechanism, and uses a bidirectional gRPC stream protocol for inter-service communication. Dynamic throttling allocates token bucket capacity based on SLA.

[0045] The microservice scheduling module uses an improved Raft consensus algorithm, adds a log compressor mechanism to reduce network load by 30%, sets the leader election timeout time to 150-300 ms, deploys Envoy agents to monitor CPU / memory metrics for each service instance, uses a bidirectional gRPC stream protocol for inter-service communication, triggers the circuit breaker mechanism when the error rate exceeds 25% within 10 seconds, dynamically throttles token bucket capacity based on the service level agreement (SLA), and calculates the load balancing weight formula as follows:

[0046] Wi = 1 / (0.7 x RT_i + 0.3 x CPU_i)

[0047] Where RT_i is the response time and CPU_i is the utilization. The service topology graph is displayed in real-time through Grafana, showing node status and traffic heat maps.

[0048] The data flow modeling module uses an ARIMA-LSTM hybrid model to predict data traffic, Apache Kafka cluster is configured with 3 Broker nodes, and Flink stream processing engine is set to Exactly-Once semantics.

[0049] The data flow modeling module uses an ARIMA-LSTM hybrid model to predict data traffic, with a prediction window of 15 minutes in the future and an error threshold of ±5%. The Apache Kafka cluster is configured with 3 Broker nodes, each partition with 2 replicas, and the Avro Schema defines 22 core data field verification rules. The Flink stream processing engine sets the Exactly-Once semantic guarantee, and the data consistency index requires an end-to-end delay of ≤500ms. The key data path generates a weighted dependency graph through Neo4j, with edge weights representing data flow frequency.

[0050] The data flow modeling module uses an ARIMA-LSTM hybrid model to predict order data traffic, with a prediction window of 15 minutes in the future and an error threshold of ±5%. The Apache Kafka cluster is configured with 3 Broker nodes, each partition with 2 replicas, and the Avro Schema defines 22 core data field verification rules. The Flink stream processing engine sets the Exactly-Once semantic guarantee, and the data consistency index controls the end-to-end delay within 450ms. The key data path generates a weighted dependency graph through Neo4j, with edge weights representing data flow frequency, optimizing data flow efficiency by 35%.

[0051] The security protection module uses a full homomorphic encryption algorithm to build a zero-trust architecture, and the security event response mechanism ensures that the handling process is triggered within 200ms. The containerized deployment module realizes cross-cloud resource scheduling through the orchestration engine, and the canary strategy is used for gray release.

[0052] The security protection module uses a full homomorphic encryption algorithm to process sensitive data and builds a zero-trust architecture, including decentralized identity authentication based on blockchain, attribute-based dynamic access control strategy, and long-short term memory network anomaly behavior detection model. The security event response mechanism ensures that the handling process is triggered within 200ms, and the data leakage risk probability is controlled below 0.05%.

[0053] The containerized deployment module realizes cross-cloud resource scheduling through the orchestration engine, and the elasticity scaling strategy dynamically adjusts the number of instances based on CPU and memory utilization thresholds. The affinity scheduling algorithm preferentially deploys services to low-latency areas, and the canary strategy is used for gray release process to verify new versions in stages. The container image construction process integrates multi-layer security scanning and vulnerability automatic repair.

[0054] The security protection module uses a full homomorphic encryption algorithm to process payment data and builds a zero-trust architecture, including decentralized identity authentication based on blockchain, attribute-based dynamic access control strategy, and long-short term memory network anomaly behavior detection model. After detecting abnormal login behavior, the security event response mechanism triggers the handling process within 180ms, and the data leakage risk probability is controlled below 0.03%.

[0055] The containerization deployment module enables cross-cloud resource scheduling through the orchestration engine, the elastic scaling strategy dynamically adjusts the number of instances according to CPU and memory utilization thresholds, and the affinity scheduling algorithm prioritizes service deployment to low-latency areas. The canary strategy is used for phased verification of new versions in the gray release, multi-layer security scanning is integrated into the container image building process, the vulnerability repair rate reaches 98%, and the deployment efficiency is improved by 70%.

[0056] The collaborative development platform provides real-time collaboration for multiple developers, and intelligent code completion is based on large language models; the digital twin verification environment builds load models, the intelligent operation and maintenance center uses Bayesian networks to build causal reasoning engines, and the reinforcement learning optimization system sets the Q-learning discount factor.

[0057] The collaborative development platform provides real-time collaboration for multiple developers, and operation logs are merged through a conflict-free replication data type algorithm, intelligent code completion is based on large language models to generate context-related suggestions, and the architecture visualization engine renders three-dimensional topology graphs through WebGL and supports automatic test case generation and contract verification for API interfaces.

[0058] When building load models in the digital twin verification environment, set the Poisson distribution λ = 1500 times per second, the fault model injects a 200ms network delay and a 5% node downtime every 5 minutes, the security model simulates 5000 DDoS attacks per second, and the stress test lasts for 24 hours to generate an optimization report containing throughput reduction rate, error rate fluctuation, and resource bottleneck points. System availability calculation uses: Availability = MTBF / (MTBF+MTTR) x 100%, and requires a 99.999% service level agreement standard.

[0059] The causal reasoning engine of the intelligent operation and maintenance center uses Bayesian networks to build an inference graph containing 50 fault nodes, parameter learning uses the EM algorithm for 1000 iterations, the reinforcement learning optimization system sets the Q-learning discount factor γ = 0.9, the exploration rate ε = 0.2, the capacity planning model performs 10000 Monte Carlo simulations, the confidence interval is set to 95%, the key indicators require the mean time to repair MTTR to be reduced from 4 hours to 2.4 hours, and the mean time between failures MTBF to be improved from 200 hours to 300 hours.

[0060] The collaborative development platform supports real-time collaboration for multiple developers in a development team, operation logs are merged through a conflict-free replication data type algorithm, intelligent code completion is based on large language models to generate context-related suggestions, API interface automated testing coverage reaches 95%, and collaboration latency is controlled within 80ms.

[0061] The digital twin verification environment constructs a load model, sets a Poisson distribution λ = 1500 times per second, the fault model injects a 200 ms network delay and a 5% node downtime every 5 minutes, and the security model simulates 5000 DDoS attacks per second. After 24 hours of stress testing, an optimization report is generated, and the system availability reaches 99.999%, with the throughput reduction rate controlled within 4%.

[0062] The intelligent operation and maintenance center uses a Bayesian network to build a causal inference graph containing 50 fault nodes, the parameter learning uses an EM algorithm for 1000 iterations, and the reinforcement learning optimization system sets the Q-learning discount factor γ = 0.9 and the exploration rate ε = 0.2. The capacity planning model performs 10000 Monte Carlo simulations with a 95% confidence interval, the mean time to repair (MTTR) is reduced from 4 hours to 2.4 hours, the mean time between failures (MTBF) is increased from 200 hours to 300 hours, and the operation and maintenance efficiency is improved by 60%.

[0063] Embodiment 2

[0064] The embodiment discloses an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the running mode of the above-mentioned architecture design method for software engineering building when executing the computer program.

[0065] Since the electronic device introduced in the embodiment is the electronic device used to implement the architecture design method for software engineering building in the embodiment, the specific implementation of the electronic device and its various forms can be understood by those skilled in the art based on the architecture design method for software engineering building in the embodiment, so the implementation of the electronic device in the method in the embodiment will not be described in detail. As long as the electronic device used to implement the architecture design method for software engineering building in the embodiment is implemented by those skilled in the art, it belongs to the scope of protection of the present application.

[0066] The above formulas are dimensionless values calculated, the formulas are obtained by collecting a large amount of data to simulate the latest real situation, and the preset parameters and threshold values in the formulas are set by those skilled in the art according to the actual situation.

[0067] The above-mentioned is only the preferred embodiment of the present application, the protection scope of the present application is not limited to the above-mentioned embodiment, any technical solution belonging to the idea of the present application is also included in the protection scope of the present application. It should be noted that, for ordinary technical users in the technical field, some improvements and decorations without departing from the principle of the present application are also considered as the protection scope of the present application.

Claims

1. A method for designing an architecture for software engineering, characterized in that: include: The intelligent requirements analysis module uses the BERT pre-trained model to extract functional entities from the requirements document, combines it with the domain knowledge graph to identify dependencies between entities, calculates the dependency strength using a three-layer graph attention network to calculate node weights, and triggers a multi-objective genetic algorithm to generate an optimization solution when requirements conflicts are detected. The architecture pattern selection module establishes an intelligent library containing k_o architecture patterns, uses a deep Q network to evaluate business feature vectors and quality attribute requirements, introduces a dynamic weight adjustment mechanism for pattern matching, improves the cosine similarity algorithm and adopts an adaptive dimension weighting strategy, and uses principal component analysis to reduce the dimension of the optimal solution and visualize it; The microservice scheduling module uses an improved Raft consensus algorithm, a new log compression mechanism, and bidirectional gRPC streaming protocol for inter-service communication. Dynamic current limiting allocates token bucket capacity based on SLAs. The data stream modeling module uses an ARIMA-LSTM hybrid model to predict data flow. The Apache Kafka cluster is configured with three Broker nodes, and the Flink stream processing engine is set to Exactly-Once semantics. The security protection module uses a fully homomorphic encryption algorithm to build a zero-trust architecture, and the security incident response mechanism ensures that the disposal process is triggered within 200ms; The containerized deployment module uses an orchestration engine to implement cross-cloud resource scheduling, and the canary strategy is used for grayscale releases. The collaborative development platform provides real-time collaboration capabilities for multiple people, and intelligent code completion is based on a large language model; the digital twin verification environment builds a load model, the intelligent operation and maintenance center uses a Bayesian network to build a causal reasoning engine, and the reinforcement learning tuning system sets the Q-learning discount factor.

2. The architecture design method for software engineering construction according to claim 1, characterized in that: The intelligent demand analysis module extracts functional entities from the demand documents through the BERT pre-training model, identifies dependencies between entities in combination with the domain knowledge graph, and calculates node weights through a three-layer graph attention network. When a demand conflict is detected, a multi-objective genetic algorithm is triggered to generate an optimization plan. The conflict judgment criteria are that the semantic similarity between entities exceeds 0.85 and the functional objectives are mutually exclusive. The genetic algorithm sets the population size to 200 and the number of iterations to 500, and outputs a standardized demand specification and an architectural constraint list. The constraints are synchronized to the architectural mode selection module through the main control center.

3. The architecture design method for software engineering construction according to claim 1, characterized in that: The architecture model selection module establishes an intelligent library containing k_o architecture models and uses a deep Q network to evaluate business feature vectors and quality attribute requirements. The DQN network structure contains 3 fully connected layers and a ReLU activation function. The state space is defined as an 8-dimensional feature vector including business scale, concurrency, and data sensitivity. The reward function sets the architecture complexity weight to 0.3, the performance index weight to 0.5, and the implementation cost weight to 0.

2. A dynamic weight adjustment mechanism was introduced into the pattern matching process. Every 24 hours, the architectural decision data from the past seven days is collected based on a sliding window. The feature weights are updated using exponential smoothing. The business continuity weight was increased from 0.4 to 0.6, and the maintainability weight was reduced from 0.5 to 0.

3. The improved cosine similarity algorithm adopts an adaptive dimension weighting strategy and performs logarithmic transformation on high cardinality features; The optimal architecture solution uses principal component analysis to reduce the 16-dimensional quality attributes to a three-dimensional space, with the X-axis mapping throughput, the Y-axis mapping deployment cost, and the Z-axis mapping fault recovery time. The visualization interface supports dynamic parameter adjustment when comparing multiple solutions, allowing real-time observation of architectural solution changes by dragging the three-dimensional coordinate axis, and triggering the automatic recommendation mechanism when the architectural fitness score threshold is set to ≥80 points.

4. The architecture design method for software engineering construction according to claim 1, characterized in that: The microservice scheduling module adopts an improved Raft consensus algorithm, and a new log compression mechanism is added to reduce the network load by 30%. The leader election timeout is set to 150-300ms. Each service instance deploys an Envoy proxy to monitor CPU / memory indicators. Inter-service communication uses the bidirectional gRPC streaming protocol. The circuit breaker mechanism is triggered when the error rate exceeds 25% within 10 seconds. Dynamic current limiting allocates token bucket capacity according to the service level agreement (SLA). The load balancing weight calculation formula is: Wi = 1 / (0.7 × RT_i + 0.3 × CPU_i) RT_i is the response time, CPU_i is the utilization rate, and the service topology diagram uses Grafana to display the node status and traffic heat map in real time.

5. The architecture design method for software engineering construction according to claim 1, characterized in that: The data flow modeling module uses an ARIMA-LSTM hybrid model to predict data flow, with a prediction window of 15 minutes and an error threshold of ±5%. The Apache Kafka cluster is configured with three broker nodes, with two replicas per partition. The Avro Schema defines 22 core data field validation rules. The Flink stream processing engine uses Exactly-Once semantic guarantees, with a data consistency requirement of end-to-end latency of ≤500ms. A weighted dependency graph is generated using Neo4j for critical data paths, with edge weights representing data flow frequency.

6. The architecture design method for software engineering construction according to claim 1, characterized in that: The security protection module uses a fully homomorphic encryption algorithm to process sensitive data, and builds a zero-trust architecture that includes decentralized identity authentication based on blockchain, attribute-based dynamic access control strategy, and long-short-term memory network abnormal behavior detection model. The security incident response mechanism ensures that the disposal process is triggered within 200ms, and the data leakage risk probability is controlled below 0.05%.

7. The architecture design method for software engineering construction according to claim 1, characterized in that: The containerized deployment module implements cross-cloud resource scheduling through an orchestration engine. The elastic scaling strategy dynamically adjusts the number of instances based on CPU and memory utilization thresholds. The affinity scheduling algorithm prioritizes deploying services to low-latency areas. The grayscale release process uses a canary strategy to verify new versions in stages. The container image building process integrates multi-layer security scanning and automatic vulnerability repair.

8. The architecture design method for software engineering construction according to claim 1, characterized in that: The collaborative development platform provides real-time multi-person collaboration capabilities, operation logs achieve multi-version merging through a conflict-free replication data type algorithm, intelligent code completion generates context-related suggestions based on a large language model, and the architecture visualization engine renders three-dimensional topology maps through WebGL, and supports automated test case generation and contract verification of API interfaces.

9. The architecture design method for software engineering construction according to claim 1, characterized in that: When constructing the load model in the digital twin verification environment, the Poisson distribution λ is set to 1500 times / second. The fault model injects 200ms of network delay and 5% node downtime rate every 5 minutes. The security model simulates 5000 DDoS attacks per second. After the stress test lasts for 24 hours, an optimization report including throughput drop rate, error rate fluctuation, and resource bottleneck points is generated. The system availability is calculated using: Availability = MTBF / (MTBF+MTTR)×100%, and the service level agreement standard of 99.999% is required to be met.

10. The architecture design method for software engineering construction according to claim 1, characterized in that: The causal reasoning engine of the intelligent operation and maintenance center uses a Bayesian network to construct an inference graph containing 50 fault nodes. Parameter learning uses the EM algorithm with 1,000 iterations. The reinforcement learning tuning system sets the Q-learning discount factor γ = 0.9 and the exploration rate ε = 0.

2. The capacity planning model performs 10,000 Monte Carlo simulations with a confidence interval set to 95%. Key indicators require that the mean time to repair (MTTR) be reduced from 4 hours to 2.4 hours and the mean time between failures (MTBF) be increased from 200 hours to 300 hours.

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