An artificial intelligence technology development assistance system

Abstract

The application discloses an artificial intelligence technology development auxiliary system and relates to the field of artificial intelligence technology.The system comprises the following components: a multi-modal data acquisition and receiving module, a cross-modal constraint analysis module, an architecture data comparison module, a conflict identification module and an adaptive scheme generation module.Through multi-modal collaborative design, the application can comprehensively and accurately capture architecture requirements, is compatible with various data channels and formats, ensures that there is no omission of requirements, provides high-quality data for architecture comparison, relies on quantitative comparison and hierarchical adaptation mechanism, accurately identifies and resolves architecture conflicts, clearly defines conflict levels and types, generates customized adaptive schemes, adapts to mainstream development tools and realizes efficient optimization of architecture design.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, specifically to an artificial intelligence technology development assistance system. Background Technology

[0002] As artificial intelligence (AI) technology permeates multiple scenarios, architecture design, as a core aspect of technology development, faces challenges from diversified requirements and heterogeneous data formats. In current AI system development, users often express architectural constraints through multimodal methods such as hand-drawn sketches, voice descriptions, and documentation. However, architectural data is scattered across different nodes, encompassing module information, performance parameters, and relationships. Accurately integrating multimodal requirement data, parsing core constraints, and efficiently comparing them with existing architectural data are crucial for improving development efficiency and reducing architectural conflicts. Traditional architecture design relies on manual data processing, which struggles to address the semantic gap in multimodal data and the complexity of architectural data. Therefore, intelligent auxiliary tools are urgently needed to automate constraint parsing, conflict identification, and the generation of adaptation solutions.

[0003] Existing technologies in the field of architecture development assistance have the following limitations: First, data collection channels are limited, mostly supporting only text or single-format file input, unable to accommodate multimodal data such as images and voice, resulting in restricted expression of user needs and easy omission of core constraints; Second, cross-modal data processing lacks specificity, existing matching models do not incorporate semantic features of the architecture domain, relying solely on general cross-modal matching algorithms, resulting in low confidence in constraint resolution and difficulty in generating structured, high-precision constraint sets; Third, architecture data comparison lacks quantitative standards, relying mostly on subjective human judgment of bias, without establishing a conflict deviation calculation system, and the classification of conflict types and levels is vague; Fourth, adaptation solution generation lacks differentiated rules, failing to formulate specific strategies based on conflict types and levels, and the output format is singular, unable to adapt to mainstream architecture design tools, resulting in insufficient practicality. These problems lead to long architecture design cycles, incomplete conflict resolution, and seriously affect development progress and system stability.

[0004] In summary, existing technologies cannot meet the demands for precision and efficiency in architecture design under multimodal constraints. Problems such as difficulty in multimodal data integration, low accuracy in constraint parsing, fuzzy conflict identification, and insufficient targeting of adaptation solutions are prominent, leading to mismatches between requirements and existing architectures, high adjustment costs, and poor system compatibility during architecture development. Therefore, developing an AI-assisted development system capable of comprehensively collecting multimodal data, accurately parsing architectural constraints, quantitatively identifying conflicts, and generating customized adaptation solutions has become an urgent need for the current industry development. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and provide an artificial intelligence technology development assistance system. It can accurately analyze and transform architectural requirements into a set of structured constraints through pre-trained CLIP models and multimodal fusion algorithms; construct architectural data vectors using distributed storage, quantify and compare conflicts, and classify them into four conflict levels; and generate customized adaptation solutions including location, parameter, and resource adjustments based on conflict type and level. The output is diverse, ensuring practicality and operability, and helping to optimize architectural design efficiently.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: an artificial intelligence technology development assistance system, which includes the following components: a multimodal data acquisition and reception module, a cross-modal constraint parsing module, an architecture data comparison module, a conflict identification module, and an adaptation scheme generation module;

[0007] The multimodal data acquisition and reception module: acquires and receives architecture-related multimodal data input by the user, and preprocesses the data;

[0008] The cross-modal constraint parsing module receives preprocessed data, uses a built-in pre-trained CLIP-type cross-modal matching model and a multimodal architecture constraint fusion extraction algorithm to perform feature extraction and semantic matching processing on the input multimodal data, and generates a structured architecture constraint set containing constraint type, constraint parameters and constraint priority.

[0009] The architecture data comparison module receives a set of structured architecture constraints, retrieves architecture data through associated distributed storage units and constructs an architecture data vector, uses an architecture constraint comparison conflict quantification algorithm to compare the set of structured architecture constraints with the architecture data vector, and generates a structured comparison result report containing comparison dimensions, deviation status and calculation results.

[0010] The conflict identification module: receives the structured comparison result report, classifies the conflict type and conflict level according to the comparison data in the report and the judgment criteria in the architecture rule base, and generates a conflict information report;

[0011] The adaptation scheme generation module receives conflict information reports, adopts specific adaptation rules corresponding to conflict types and conflict levels from the architecture rule base, combines the structured architecture constraint set and architecture data vector, generates a targeted architecture adaptation scheme, and outputs it to the user terminal.

[0012] Furthermore, the multimodal data acquisition and reception module acquires and receives architecture-related multimodal data through three input channels: local file upload, real-time voice input, and image scanning. The local file upload channel reads image and voice files stored on the user terminal through a system preset interface; the real-time voice input channel collects the user's verbal input of changes in requirements by calling the terminal's microphone device; and the image scanning channel captures hand-drawn architectural sketches through the terminal's camera. The image data in the acquired and received data is in PNG and JPG formats, and the voice data is in WAV and MP3 formats.

[0013] Furthermore, in the cross-modal constraint parsing module, the training dataset for the pre-trained CLIP-class cross-modal matching model includes: architecture sketch constraint text samples and speech requirement constraint text samples. The architecture sketch constraint text samples cover five typical architectures: microservices, distributed systems, layered systems, pipelined systems, and event-driven systems, with 20,000 sets of labeled data for each architecture. The speech requirement constraint text samples cover three types of voiceprint samples: male, female, and child, with speech rates covering the range of 100 to 200 words per minute. When fine-tuning the pre-trained CLIP-class cross-modal matching model, the learning rate is set to 5e-5, and the number of iterations is 30 rounds. The generated structured architecture constraint set is in vector form, containing six fields: constraint ID, modality source, constraint type, parameter value, accuracy level, and priority. Among them, the accuracy level is divided according to the resolution confidence: a confidence level of not less than 95% is high accuracy, 80% to 95% is medium accuracy, and less than 80% is low accuracy.

[0014] Furthermore, the multimodal architecture constraint fusion extraction algorithm is used to perform feature extraction and semantic matching processing on the input multimodal data, with the formula: C=∑(m∈{I,V})ω m ·F m (S m )·Y m (λ), where C is the set of structured architecture constraints; m is the multimodal input type identifier; I is the image input; V is the speech input; ω m F represents the modal weighting coefficient. m S is the modal feature mapping function; m This is the raw input data for a single mode; Y m (λ) represents the architecture domain adaptation factor; λ represents the domain semantic relevance in the architecture rule base; firstly, feature information corresponding to each input modality data is extracted, where visual morphological features are extracted from architecture sketch data and semantic features are extracted after converting voice requirement data into text; then, the feature information of each modality is compared and matched with the preset architecture constraint semantic base to identify the constraint meaning corresponding to each modality data; finally, the constraint information obtained from matching different modalities is integrated, deduplicated, supplemented and improved to form a unified structured architecture constraint set.

[0015] Furthermore, the specific process of constructing the architecture data vector in the architecture data comparison module is as follows:

[0016] The distributed storage unit is divided into a basic information storage partition, a performance parameter storage partition, and an architecture relationship storage partition according to data type. The module establishes a connection with the distributed storage unit through a preset distributed storage access protocol. It first sends an authentication request containing the unique identifier of the project to the storage unit. After successful authentication, it obtains data access permissions.

[0017] The process of retrieving architecture data is as follows: The module sends a retrieval instruction carrying the unique identifier of the project to the distributed storage unit. After receiving the instruction, the master node of the storage unit retrieves the ID, function identifier, and deployment location information of each module in the current project from the module basic information storage partition, retrieves the response time, concurrent processing volume, and resource utilization information of each module from the performance parameter storage partition, and retrieves the call chain and dependency hierarchy information between each module from the architecture relationship storage partition. The above three types of information are integrated into the complete architecture data of the current project and fed back to the architecture data comparison module.

[0018] This module first performs standardization processing on the integrated complete architecture data, converting the discrete function identifiers in the module basic information into preset numerical codes, normalizing the numerical data in the performance parameters to the range of 0 to 1, and converting the architecture relationship into an adjacency matrix between modules. Then, in the order of module basic information features, performance parameter features, and architecture relationship features, the standardized data of various types are sequentially concatenated according to preset dimensions to form an architecture data vector with fixed dimensions. Each dimension of the vector corresponds to a feature value of a type of architecture data.

[0019] Furthermore, the specific formula for the architecture constraint comparison conflict quantification algorithm is: Δ=α×(|CA| / C)×β, where Δ is the architecture constraint conflict deviation degree; α is the constraint priority weight; C is the total number of features in the structured architecture constraint set; A is the total number of features in the current project architecture data vector; |CA| is the absolute difference between the total number of features in the structured architecture constraint set and the total number of features in the current project architecture data vector; (|CA| / C) is the relative deviation of the total number of features; β is the architecture rule adaptation coefficient. First, the comparison dimensions of the structured architecture constraint set and the architecture data vector are matched accordingly; then, for each comparison dimension, the actual data in the architecture data vector is compared with the requirements in the constraint set to determine whether there is a deviation; finally, the degree of deviation of each dimension is quantified according to the set standard, and all comparison dimensions, corresponding deviation situations and quantification results are integrated to form a structured comparison result report.

[0020] Furthermore, the architecture rule base of the conflict identification module includes module layout compatibility rules, performance threshold rules, and conflict determination rules:

[0021] The module layout compatibility rules are as follows: Deployment constraints for different functional modules are clearly defined. The minimum spacing requirement is based on the physical distance between deployment nodes: the minimum spacing between core functional modules is ≥2 deployment units, and the minimum spacing between non-core modules and core modules is ≥1 deployment unit. Prohibited deployment areas include dedicated node areas occupied by core services, high-load node areas with resource load rates ≥80%, and edge node areas with network latency ≥300ms. Connection method restrictions are clearly defined: direct cross-network segment connections and unencrypted protocol connections are prohibited between modules. Core modules must use a dedicated gateway for forwarding connections, while non-core modules use conventional LAN connections.

[0022] The performance threshold rules are set according to three application scenarios: high-concurrency processing, big data analysis, and real-time response. The core performance indicators include response time, concurrent processing volume, data throughput, and resource utilization. In high-concurrency scenarios, the minimum response time requirement is ≤500ms, the maximum requirement is ≤2s, and the reasonable fluctuation range is ±10%. The minimum concurrent processing volume requirement is ≥1000QPS, the maximum requirement is ≤5000QPS, and the reasonable fluctuation range is ±15%.

[0023] The conflict determination rule is based on the architectural constraint conflict deviation Δ as the core criterion, and divides the conflict into four levels: Δ < 0.3 is no conflict, 0.3 ≤ Δ < 1.0 is a minor conflict, 1.0 ≤ Δ < 2.0 is a general conflict, and Δ ≥ 2.0 is a serious conflict.

[0024] Furthermore, the specific adaptation rules are a set of structured rules pre-defined in the architecture rule base, each uniquely corresponding to a conflict type and conflict level. Specifically, they include module layout conflict adaptation rules and performance requirement conflict adaptation rules.

[0025] The module layout conflict adaptation rules are as follows: Severe conflict adaptation rules include rules for replanning module deployment nodes, rules for reconstructing cross-level call chains, and rules for prioritizing the position of core modules; General conflict adaptation rules include rules for adapting module interface compatibility and rules for optimizing local connection relationships; Minor conflict adaptation rules include rules for fine-tuning module deployment positions and rules for adjusting the startup order of non-core dependent modules.

[0026] The performance requirement conflict adaptation rules are as follows: Severe conflict adaptation rules include hardware resource expansion configuration rules, core business algorithm replacement adaptation rules, and multi-indicator collaborative optimization rules; General conflict adaptation rules include performance threshold dynamic adjustment rules and resource allocation ratio optimization rules; Minor conflict adaptation rules include interface parameter sensitivity adjustment rules and non-core function resource usage optimization rules.

[0027] Furthermore, the targeted architecture adaptation solution includes module location adjustment schemes, parameter optimization schemes, resource allocation adjustment schemes, and detailed optimization suggestions. For severe module layout conflicts, the solution specifies the order of module adjustments: first, adjust non-core dependent modules, then adjust core modules, and the module location coordinates use both absolute and relative coordinates for labeling. For general performance requirement conflicts, the solution includes specific parameter optimization values ​​and algorithm optimization directions. For minor conflicts, the solution covers suggestions for fine-tuning module interface parameters. The solution output formats include structured documents, SVG vector format visual architecture diagrams, and executable scripts supporting API interfaces of mainstream architecture design tools.

[0028] Compared with existing technologies, this artificial intelligence technology development assistance system has the following beneficial effects:

[0029] I. This invention achieves comprehensive capture and accurate transformation of architectural requirements through the collaborative design of multimodal data acquisition and cross-modal constraint parsing. The multimodal data acquisition module supports three channels: local file upload, real-time voice input, and image scanning. It is compatible with PNG and JPG image formats and WAV and MP3 voice formats, covering common user requirement expression forms and ensuring that no requirement data is omitted. The cross-modal constraint parsing module adopts a CLIP class matching model trained on architecture domain labeled data. Combined with a multimodal architecture constraint fusion extraction algorithm, it extracts the visual morphological features of image data and the semantic features of speech-to-text data by adjusting the modality weight coefficient and architecture domain adaptation factor. Then, it compares and matches with the architecture constraint semantic library. After integration and deduplication, a structured constraint set containing fields such as constraint ID, modality source, and priority is formed. The accuracy level is divided according to confidence level to ensure the relevance and accuracy of constraint parsing and provide a high-quality data foundation for architecture comparison.

[0030] Second, this invention relies on a quantitative comparison and hierarchical adaptation mechanism to achieve accurate identification and efficient resolution of architectural conflicts. The architecture data comparison module retrieves multi-partition architecture data through a distributed storage unit, constructs a fixed-dimensional architecture data vector after standardization, and uses an architecture constraint conflict quantification algorithm, combined with constraint priority weights and architecture rule adaptation coefficients, to accurately calculate the conflict deviation. The conflict identification module divides the conflict into four levels based on the quantification results, clarifying the conflict type and severity. The adaptation scheme generation module calls a special adaptation rule that uniquely corresponds to the conflict type and level. For different conflict scenarios such as module layout and performance requirements, it generates customized solutions including position adjustment, parameter optimization, and resource allocation. The output formats include structured documents, SVG visualization charts, and executable scripts, and are compatible with mainstream development tools, ensuring the practicality and operability of the solution and achieving efficient optimization of architecture design.

[0031] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0033] Figure 1 A block diagram illustrating the modular components of an auxiliary system for developing an artificial intelligence technology.

[0034] Figure 2 Flowchart for cross-modal architecture constraint analysis of developing an auxiliary system for an artificial intelligence technology;

[0035] Figure 3 A schematic diagram of the architecture data comparison and transmission for an auxiliary system developed for an artificial intelligence technology. Detailed Implementation

[0036] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0037] Example 1

[0038] Adaptation Scenarios for Microservice Architecture Changes in Internet Companies

[0039] In developing an existing microservice architecture, an internet company needs to add a user behavior analysis module and adjust the performance metrics of the core transaction module. Technical personnel submit data through three input channels: local file upload, real-time voice input, and image scanning. The local file upload channel uploads a PNG format architectural modification sketch, indicating the deployment location of the new module and its relationship with the existing payment and user management modules. The real-time voice input channel utilizes the terminal microphone, requiring the new module to support 1200 data queries per second, increase the core transaction module's concurrent processing capacity to 3000 QPS, and control the response time within 800ms. The voice data format is WAV. The image scanning channel captures a JPG format hand-drawn flowchart of the module's interaction process using a camera. After receiving the data, the system performs format validation and preprocessing to ensure comprehensive and accurate recording of all required information, providing a complete and standardized data source for subsequent constraint parsing.

[0040] The cross-modal constraint parsing module receives preprocessed data and calls the built-in pre-trained CLIP-class cross-modal matching model. This model's training dataset contains sketch constraint text samples corresponding to microservice architectures, accurately adapting to the constraint recognition needs of microservice scenarios. The module employs a multimodal architecture constraint fusion extraction algorithm, with the formula: C=∑(m∈{I,V}})ω m ·F m (S m )·Y m (λ), where C is the set of structured architecture constraints; m is the multimodal input type identifier; I is the image input; V is the speech input; ω m F represents the modal weighting coefficient. m S is the modal feature mapping function; m This is the raw input data for a single mode; Y m (λ) represents the architecture domain adaptation factor; λ represents the domain semantic relevance in the architecture rule base. First, visual morphological features of the image data are extracted, including deployment location identifiers, functional identifiers, and relational features. After converting the voice data to text, performance constraint semantic features are extracted. Then, these two types of features are compared and matched with the architecture constraint semantic base to identify module layout constraints and performance constraints. Finally, these are integrated and deduplicated to generate a structured set of architecture constraints in vector form, containing six fields: constraint ID, modality source, constraint type, parameter value, precision level, and priority. This structure and standardizes the constraint information, providing a clear reference benchmark for subsequent architecture data comparison. Among these, the newly added module deployment location constraint and the core transaction module concurrent processing volume constraint have a resolution confidence level of 96% (high precision), the response time constraint has a resolution confidence level of 88% (medium precision), and all constraints have a high priority. Figure 2 As shown.

[0041] After receiving the structured architecture constraint set, the architecture data comparison module establishes a connection with the distributed storage unit through a preset distributed storage access protocol. It sends a unique project identifier to complete authentication and obtain access permissions, ensuring the security and relevance of architecture data retrieval. It sends retrieval commands to the storage unit, retrieving the module IDs, function identifiers, and deployment locations from the module basic information storage partition; retrieving the current concurrent processing volume (2000 QPS) and response time (1.2s) of the core transaction module from the performance parameter storage partition; and retrieving the call chains and dependencies between the core transaction module and other modules from the architecture relation storage partition, integrating them into complete architecture data. The complete data undergoes standardization processing: function identifiers are converted into numerical codes, performance parameters are normalized to the 0-1 range, and relations are converted into adjacency matrices. These are then concatenated in a preset order to form a fixed-dimensional architecture data vector, providing a unified comparison dimension for different types of architecture data. An architecture constraint comparison conflict quantification algorithm is adopted, with the formula: Δ=α×(|CA| / C)×β, where Δ is the architecture constraint conflict deviation degree; α is the constraint priority weight; C is the total number of features in the structured architecture constraint set; A is the total number of features in the current project architecture data vector; |CA| is the absolute difference between the total number of features in the structured architecture constraint set and the total number of features in the current project architecture data vector; (|CA| / C) is the relative deviation of the total number of features; and β is the architecture rule adaptation coefficient. This algorithm matches the constraint set with the corresponding dimensions of the data vector, clearly presenting the deviation between the performance parameters of the core transaction module and the deployment location of the newly added module and the constraints. It generates a structured comparison result report containing the comparison dimensions, deviation status, and calculation results, providing a quantitative basis for conflict identification.

[0042] The conflict identification module receives a structured comparison result report and analyzes it based on the module layout compatibility rules, performance threshold rules, and conflict determination rules in the architecture rule base. The module layout compatibility rules clearly define nodes with a load rate ≥80% as prohibited deployment areas. The node at the expected location of the newly added module has a load rate of 85%, accurately identifying it as a module layout conflict. The performance threshold rules state that the reasonable fluctuation range for concurrent processing volume in high-concurrency scenarios is ±15%. The current concurrent processing volume deviates from the constraint range, and the response time exceeds the constraint requirements, accurately identifying it as a performance requirement conflict. Combining the architecture constraint conflict deviation calculation result Δ value of 1.5, and according to the conflict determination rule 1.0 ≤ Δ < 2.0 as a general conflict, a conflict information report is generated, including the conflict type and conflict level, clearly defining the core conflict issue and providing direction for the generation of adaptation solutions.

[0043] The adaptation solution generation module receives conflict information reports, calls the specific adaptation rules corresponding to general conflicts in the architecture rule base, uses local connection relationship optimization rules for module layout conflicts, and uses dynamic adjustment rules for performance thresholds and resource allocation ratio optimization rules for performance conflicts to ensure the adaptability and relevance of the solution. Combining constraint sets and data vectors, the module generates adaptation solutions, deploying new modules to node areas with a 60% load rate, meeting the spacing requirements with core modules; the concurrent processing capacity of the core transaction module is gradually adjusted to 3000 QPS, the response time is optimized to 750ms, and CPU resources are allocated to it by 15%; detailed optimization suggestions, including interface compatibility configuration instructions, comprehensively cover conflict resolution needs. The solution is output to the user terminal in the form of structured documents, SVG vector format visual architecture diagrams, and executable scripts supporting API interfaces of mainstream architecture design tools, facilitating direct implementation of architecture adjustments by technical personnel and efficiently completing requirement change adaptation.

[0044] In this embodiment, the AI ​​technology development assistance system, targeting the microservice architecture requirement changes scenario of internet companies, comprehensively collects information such as architecture modification sketches and voice requirements through multimodal data acquisition channels, and ensures data standardization through preprocessing. The cross-modal constraint parsing module uses a pre-trained CLIP-type model and a multimodal architecture constraint fusion extraction algorithm to generate a structured constraint set, providing a clear benchmark for comparison. The architecture data comparison module retrieves complete architecture data and constructs vectors, accurately presenting deviations through an architecture constraint comparison conflict quantification algorithm. The conflict identification module defines general conflict types based on a rule base, and the adaptation scheme generation module calls corresponding specific rules to output multiple forms of adaptation schemes, such as... Figure 1 As shown, all modules throughout the process work together to efficiently resolve conflicts arising from changes in architectural requirements, facilitating rapid architectural adjustments.

[0045] Example 2

[0046] Optimization scenarios for distributed big data analytics architecture in financial institutions

[0047] Financial institutions need to optimize their existing distributed big data analytics architecture, improving data processing throughput and adjusting module dependencies. Technical personnel submit data through two channels: system image scanning and local file upload. The image scanning channel uses a camera to capture hand-drawn JPG sketches of the architecture optimization, annotating the direction of adjustments to data storage and analysis / computation modules, as well as changes in dependencies. The local file upload channel uploads MP3 format audio files, recording the optimization requirements: increasing data throughput to 500MB / s, controlling the latency of analysis / computation and data storage module calls to within 200ms, and removing direct dependencies from the risk control module. After receiving the data, the system performs preprocessing to ensure data format compliance and complete, comprehensive information, laying a solid data foundation for subsequent cross-modal constraint analysis.

[0048] The cross-modal constraint parsing module calls a pre-trained CLIP-type cross-modal matching model. This model's training dataset includes distributed architecture sketch constraint text samples and multi-class voiceprint speech requirement constraint text samples, with a speech rate ranging from 100 to 200 words per minute, accurately adapting to the multi-modal requirement parsing of distributed architecture scenarios. A multi-modal architecture constraint fusion extraction algorithm is employed. First, visual morphological features of the hand-drawn sketch are extracted, including module adjustment identifiers and dependency change features. After converting MP3 format audio files to text, performance requirements and dependency change semantic features are extracted. Then, these two types of features are compared and matched with an architecture constraint semantic library to comprehensively identify performance constraints and module relationship constraints. Finally, the data is integrated and deduplicated to generate a structured architecture constraint set, transforming scattered multi-modal requirements into unified and standardized constraint information, facilitating subsequent architecture data comparison. The data throughput and call latency constraints achieved a 98% confidence level (high accuracy), while the module dependency change constraints achieved a 79% confidence level (low accuracy), with all constraints having a medium priority.

[0049] The architecture data comparison module establishes a connection with the distributed storage unit and, through authentication, sends retrieval commands to retrieve data storage, analysis and calculation, and risk control module IDs, function identifiers, and deployment locations from the module basic information storage partition. It also retrieves the current data throughput (350MB / s) and call latency (280ms) from the performance parameter storage partition, and the direct call links between the analysis and calculation and risk control modules, as well as other module dependencies, from the architecture relation storage partition. This data is integrated into complete architecture data, ensuring comprehensive and complete comparison data. Standardization processing is performed on the complete data: function identifiers are converted to numerical codes, performance parameters are normalized, and relational relationships are converted to adjacency matrices. These are then concatenated to form an architecture data vector, achieving a unified presentation of different types of architecture data. An architecture constraint comparison conflict quantification algorithm is employed, matching the comparison dimensions to accurately capture issues such as unmet constraints in data throughput and call latency, and unremoved direct dependencies between modules. After quantifying the degree of deviation, a structured comparison result report is generated, providing clear and quantitative evidence for conflict identification. Figure 3 As shown.

[0050] The conflict identification module analyzes the structured comparison results report based on the architecture rule base. In the performance threshold rules, a deviation exists between the data throughput and constraint requirements in a big data analysis scenario, resulting in call latency exceeding the constraints, accurately identifying this as a performance requirement conflict. The module association constraint requires the removal of direct dependencies, but the call chain still exists, clearly identifying this as a module layout association conflict. Combining the architecture constraint conflict deviation calculation result Δ value of 0.8, and according to the conflict judgment rule 0.3 ≤ Δ < 1.0 as a minor conflict, a conflict information report is generated, including the conflict type and level, accurately defining the severity and core type of the conflict, providing clear guidance for the formulation of adaptation solutions.

[0051] The adaptation solution generation module invokes specific adaptation rules for minor conflicts in the architecture rule base. Module layout conflicts are addressed using rules adjusting the startup order of non-core dependent modules, while performance conflicts are addressed using rules adjusting interface parameter sensitivity and optimizing non-core functional resource usage. This ensures precise matching between the solution and the conflict type and level. The adaptation solution is generated by combining constraint sets and data vectors, eliminating direct call links between analysis / calculation and risk control modules and achieving indirect association through an intermediate data forwarding module. Data transmission interface parameter sensitivity is adjusted, and non-core functional resource usage is optimized, increasing data throughput to 510MB / s and reducing call latency to 190ms. Detailed suggestions are included, such as fine-tuning module interface parameters and resource usage optimization steps, comprehensively covering key conflict resolution points. The solution is output in the form of structured documents, SVG vector format visual architecture diagrams, and executable scripts. Financial institution technical personnel can import the scripts directly using mainstream architecture design tools to efficiently complete architecture optimization.

[0052] This embodiment focuses on the optimization needs of distributed big data analytics architecture in financial institutions. The system collects multimodal data, including hand-drawn sketches and voice requests, from multiple channels, and provides a complete data source for analysis after preprocessing. The cross-modal constraint parsing module uses pre-trained models and related algorithms adapted to the distributed architecture to transform scattered requirements into a standardized set of constraints. The architecture data comparison module retrieves comprehensive architecture data and standardizes it, accurately capturing deviations through a conflict quantification algorithm. The conflict identification module determines the type of minor conflict, and the adaptation solution generation module matches the corresponding specific rules, outputting an optimization solution that can be directly implemented. The system relies on preset rules and algorithms throughout the entire process to provide full-process assistance from requirement collection to solution implementation, accurately resolving performance and correlation conflicts in architecture optimization, and improving the efficiency and accuracy of architecture optimization.

[0053] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. An artificial intelligence technology development assistance system, characterized in that, The system comprises the following components: a multimodal data acquisition and reception module, a cross-modal constraint parsing module, an architecture data comparison module, a conflict identification module, and an adaptation scheme generation module; The multimodal data acquisition and reception module: acquires and receives architecture-related multimodal data input by the user, and preprocesses the data; The cross-modal constraint parsing module receives preprocessed data, uses a built-in pre-trained CLIP-type cross-modal matching model and a multimodal architecture constraint fusion extraction algorithm to perform feature extraction and semantic matching processing on the input multimodal data, and generates a structured architecture constraint set containing constraint type, constraint parameters and constraint priority. The architecture data comparison module receives a set of structured architecture constraints, retrieves architecture data through associated distributed storage units and constructs an architecture data vector, uses an architecture constraint comparison conflict quantification algorithm to compare the set of structured architecture constraints with the architecture data vector, and generates a structured comparison result report containing comparison dimensions, deviation status and calculation results. The conflict identification module: receives the structured comparison result report, classifies the conflict type and conflict level according to the comparison data in the report and the judgment criteria in the architecture rule base, and generates a conflict information report; The adaptation scheme generation module receives conflict information reports, adopts specific adaptation rules corresponding to conflict types and conflict levels from the architecture rule base, combines the structured architecture constraint set and architecture data vector, generates a targeted architecture adaptation scheme, and outputs it to the user terminal.

2. The artificial intelligence technology development assistance system according to claim 1, characterized in that, The multimodal data acquisition and reception module acquires and receives architecture-related multimodal data through three input channels: local file upload, real-time voice input, and image scanning. The local file upload channel reads image and voice files stored on the user terminal through a system preset interface; the real-time voice input channel collects the user's verbal input of changes in requirements by calling the terminal's microphone device; and the image scanning channel captures hand-drawn architecture sketches through the terminal's camera. The acquired and received data includes image data in PNG and JPG formats, and voice data in WAV and MP3 formats.

3. The artificial intelligence technology development assistance system according to claim 1, characterized in that, In the cross-modal constraint parsing module, the training dataset for the pre-trained CLIP-type cross-modal matching model includes: architecture sketch constraint text samples and speech requirement constraint text samples. The architecture sketch constraint text samples cover five typical architectures: microservices, distributed systems, layered systems, pipelined systems, and event-driven systems, with 20,000 sets of labeled data for each architecture. The speech requirement constraint text samples cover three types of voiceprint samples: male, female, and child, with speech rates ranging from 100 to 200 words per minute. When fine-tuning the pre-trained CLIP-type cross-modal matching model, the learning rate is set to 5e-5, and the number of iterations is 30 rounds. The generated structured architecture constraint set is in vector form, containing six fields: constraint ID, modality source, constraint type, parameter value, accuracy level, and priority. The accuracy level is divided according to the resolution confidence: a confidence level of not less than 95% is high accuracy, 80% to 95% is medium accuracy, and less than 80% is low accuracy.

4. The artificial intelligence technology development assistance system according to claim 1, characterized in that, The multimodal architecture constraint fusion extraction algorithm is used to perform feature extraction and semantic matching processing on the input multimodal data. The formula is: C=∑(m∈{I,V})ω m ·F m (S m )·Y m (λ), where C is the set of structured architecture constraints; m is the multimodal input type identifier; I is the image input; V is the speech input; ω m F represents the modal weighting coefficient. m S is the modal feature mapping function; m This is the raw input data for a single mode; Y m (λ) is the architecture domain adaptation factor; λ is the domain semantic relevance in the architecture rule base.

5. The artificial intelligence technology development assistance system according to claim 1, characterized in that, The specific process of constructing the architecture data vector in the architecture data comparison module is as follows: The module first performs standardization processing on the integrated complete architecture data, converts the discrete function identifiers in the module basic information into preset numerical codes, normalizes the numerical data in the performance parameters to the 0 to 1 range, and converts the architecture association relationship into an adjacency matrix between modules; then, in the order of module basic information features, performance parameter features, and architecture association relationship features, the standardized data of various types are sequentially concatenated according to preset dimensions to form an architecture data vector with fixed dimensions, where each dimension of the vector corresponds to a feature value of a type of architecture data.

6. The artificial intelligence technology development assistance system according to claim 1, characterized in that, The specific formula for the architecture constraint comparison conflict quantification algorithm is: Δ=α×(|CA| / C)×β, where Δ is the architecture constraint conflict deviation degree; α is the constraint priority weight; C is the total number of features in the structured architecture constraint set; A is the total number of features in the current project architecture data vector; |CA| is the absolute difference between the total number of features in the structured architecture constraint set and the total number of features in the current project architecture data vector; (|CA| / C) is the relative deviation of the total number of features; and β is the architecture rule adaptation coefficient.

7. The artificial intelligence technology development assistance system according to claim 1, characterized in that, The architecture rule base of the conflict identification module includes module layout compatibility rules, performance threshold rules, and conflict determination rules: The module layout compatibility rules are as follows: Deployment constraints for different functional modules are clearly defined. The minimum spacing requirement is based on the physical distance between deployment nodes: the minimum spacing between core functional modules is ≥2 deployment units, and the minimum spacing between non-core modules and core modules is ≥1 deployment unit. Prohibited deployment areas include dedicated node areas occupied by core services, high-load node areas with resource load rates ≥80%, and edge node areas with network latency ≥300ms. Connection method restrictions are clearly defined: direct cross-network segment connections and unencrypted protocol connections are prohibited between modules. Core modules must use a dedicated gateway for forwarding connections, while non-core modules use conventional LAN connections. The performance threshold rules are set according to three application scenarios: high-concurrency processing, big data analysis, and real-time response. The core performance indicators include response time, concurrent processing volume, data throughput, and resource utilization. In high-concurrency scenarios, the minimum response time requirement is ≤500ms, the maximum requirement is ≤2s, and the reasonable fluctuation range is ±10%. The minimum concurrent processing volume requirement is ≥1000QPS, the maximum requirement is ≤5000QPS, and the reasonable fluctuation range is ±15%. The conflict determination rule is based on the architectural constraint conflict deviation Δ as the core criterion, and divides the conflict into four levels: Δ < 0.3 is no conflict, 0.3 ≤ Δ < 1.0 is a minor conflict, 1.0 ≤ Δ < 2.0 is a general conflict, and Δ ≥ 2.0 is a serious conflict.

8. The artificial intelligence technology development assistance system according to claim 1, characterized in that, The specific adaptation rules are a set of structured rules pre-set in the architecture rule base, each uniquely corresponding to a conflict type and conflict level. Specifically, they include module layout conflict adaptation rules and performance requirement conflict adaptation rules. The module layout conflict adaptation rules are as follows: Severe conflict adaptation rules include rules for replanning module deployment nodes, rules for reconstructing cross-level call chains, and rules for prioritizing the position of core modules; General conflict adaptation rules include rules for adapting module interface compatibility and rules for optimizing local connection relationships; Minor conflict adaptation rules include rules for fine-tuning module deployment positions and rules for adjusting the startup order of non-core dependent modules. The performance requirement conflict adaptation rules are as follows: Severe conflict adaptation rules include hardware resource expansion configuration rules, core business algorithm replacement adaptation rules, and multi-indicator collaborative optimization rules; General conflict adaptation rules include performance threshold dynamic adjustment rules and resource allocation ratio optimization rules; Minor conflict adaptation rules include interface parameter sensitivity adjustment rules and non-core function resource usage optimization rules.

9. The artificial intelligence technology development assistance system according to claim 1, characterized in that, The targeted architecture adaptation solution includes module location adjustment schemes, parameter optimization schemes, resource allocation adjustment schemes, and detailed optimization suggestions. For severe module layout conflicts, the solution specifies that the order of module adjustment is to prioritize non-core dependent modules, followed by core modules, and the module location coordinates are labeled with both absolute and relative coordinates. For general performance requirement conflicts, the solution includes specific parameter optimization values ​​and algorithm optimization directions. For minor conflicts, the solution covers suggestions for fine-tuning module interface parameters. The solution outputs include structured documents, SVG vector format visual architecture diagrams, and executable scripts that support API interfaces of mainstream architecture design tools.

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