A technical demand intelligent analysis and classification system

Abstract

The application relates to the technical field of technical demand analysis, and discloses a technical demand intelligent analysis and classification system. A feature extraction module of the system acquires original technical demand text, analyzes technical functions and performance index descriptions, extracts key elements, and quantifies the elements to form an element quantization dataset; an association topology module calls the dataset, identifies logical dependency relationships between the elements, constructs a multi-dimensional feature vector space, and generates a technical demand feature vector space; a dynamic response module acquires feature vector time sequence change data, monitors fluctuation intensity and correlation index offset, calculates a dynamic demand fluctuation influence factor, and forms an analysis result; a conflict detection module identifies element nodes that exceed a benchmark fluctuation threshold and are in a logical conflict area, marks the element nodes as demand conflict sensitive points, and generates a sensitive point set; and a classification output module integrates sensitive point set information, outputs a classification label in combination with feature vector space distribution states, generates a technical demand classification result, and generates an optimization analysis report.

Description

Technical Field

[0001] This invention relates to the field of technical requirements analysis technology, specifically to an intelligent technical requirements analysis and classification system. Background Technology

[0002] In current technology research and development and project management, the analysis and classification of technical requirements are crucial for ensuring accurate research direction and efficient project progress. However, existing methods for handling technical requirements still have many limitations. Traditional technical requirements analysis relies heavily on manual operations, requiring analysts to interpret the original technical requirements texts one by one to extract key information such as technical function descriptions and performance indicators. This manual approach not only consumes a significant amount of time and manpower but is also prone to incomplete or inaccurate extraction of key elements due to differences in the analysts' subjective understanding and limitations in their professional knowledge, thus affecting the reliability of subsequent requirements analysis. With the development of digital technology, some fields have begun to try to use simple software tools to assist in technical requirements analysis. However, these tools often only achieve basic text keyword extraction and cannot effectively quantify the extracted key elements, making it difficult to form a structured element dataset. In terms of handling the relationships between elements, existing tools lack the ability to identify the logical dependencies between elements in technical requirements and cannot construct a multi-dimensional space that can comprehensively reflect the characteristics of the requirements, resulting in the overall characteristics of the technical requirements not being clearly presented. Technological requirements are not static in practice; they fluctuate dynamically due to R&D progress and market changes. However, current technologies lack the ability to monitor the time-series changes in the characteristic vectors of technological requirements, failing to capture the intensity of characteristic vector fluctuations and the shifts in related indicators in a timely manner. Consequently, they cannot effectively analyze the impact of dynamic demand fluctuations. Furthermore, when demand fluctuations trigger potential conflicts, existing methods struggle to quickly identify elements whose influencing factors exceed reasonable ranges and fall within logical conflict zones. This makes it difficult to accurately pinpoint sensitive points of demand conflict, easily leading to overlooked potential conflicts and resulting in deviations in R&D direction, project delays, or even failures. In the demand classification and output stages, existing tools cannot integrate information on conflict-sensitive points with the spatial distribution of characteristic vectors. The output classification results are often simplistic, lacking targeted optimization analysis suggestions, and failing to meet the needs of refined technical requirement processing in actual R&D and project management. These problems hinder the improvement of the efficiency and quality of technical requirement analysis, restricting the overall effectiveness of technology R&D and project management. Summary of the Invention

[0003] The purpose of this invention is to provide an intelligent analysis and classification system for technical requirements, so as to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides a technical demand intelligent analysis and classification system, the system comprising: The feature extraction module obtains the original technical requirement text input, parses the technical function description and performance indicator description contained therein, extracts key elements and quantifies them to form a feature quantification dataset. The associated topology module calls all element quantification values ​​in the element quantification dataset, identifies the logical dependencies between elements, constructs a multi-dimensional feature vector space, and generates a technical requirement feature vector space. The dynamic response module acquires the time-series change data of the feature vectors in the feature vector space of the technical demand, monitors the intensity of feature vector fluctuations and the offset of related indicators, calculates the dynamic demand fluctuation impact factor, and forms the demand fluctuation impact analysis result. The conflict detection module identifies element nodes in the demand fluctuation impact analysis results whose impact factors are greater than the benchmark fluctuation threshold and are located in the logical conflict area, marks them as demand conflict sensitive points, and generates a set of technical demand conflict sensitive points; The classification output module integrates all node information in the set of sensitive points of conflicting technical requirements, combines the spatial distribution of feature vectors to output classification labels, and generates technical requirement classification results and optimization analysis reports.

[0005] Preferably, the element quantification dataset includes element quantification values, element type identifiers, and element association weight coefficients; The technical requirement feature vector space specifically includes feature vector space coordinates, logical relationship annotations between vectors, and feature difference degree between adjacent elements; The results of the demand fluctuation impact analysis include the degree of influence of characteristic fluctuation intensity on demand stability, the degree of influence of correlation index offset on demand integrity, and a comparison of demand conflict response under various dynamic conditions. The set of sensitive points for technical requirement conflicts includes spatial identifiers of sensitive points, logical conflict characteristic values ​​of sensitive points, and the correlation ratio between fluctuation intensity and offset of sensitive points. The technical requirements classification results and optimization analysis report include a list of classification nodes and joint judgment labels for multi-dimensional features of nodes.

[0006] Preferably, the feature extraction module includes: The original requirement parsing submodule obtains the semantic structure data of the original technical requirement text, identifies the technical function description unit and performance indicator description unit, divides them into independent element units and adds type identifiers to form a basic set of technical requirement elements; The element quantification processing submodule performs feature value transformation calculations based on the description units in the technical requirement element base set, stores the transformation results in association with element identifiers, and integrates element feature values ​​with associated weight coefficients to generate an element quantification dataset.

[0007] Preferably, the associated topology module includes: The vector space construction submodule calls all element quantization values ​​and spatial identification data in the element quantization dataset, establishes a multi-dimensional coordinate system based on the logical dependencies between elements, calculates the coordinate position of each element in the vector space, and generates an initial feature vector space model. The feature difference analysis submodule identifies the feature vector difference between adjacent coordinate position elements based on the initial feature vector space model, calculates the logical consistency coefficient by combining the element type identifier, updates the relationship labeling information in the feature vector space, and forms the technical requirement feature vector space.

[0008] Preferably, the dynamic response module includes: The time-series change monitoring submodule acquires the historical state sequence of feature vectors in the feature vector space of the technical requirements, captures the change in feature values ​​and the change in related indicators at adjacent time nodes, and arranges them according to the time dimension to form a feature fluctuation sequence and an indicator offset sequence. The fluctuation impact calculation submodule analyzes the response relationship between the change in characteristic value and the change in related indicator based on the characteristic fluctuation sequence and the indicator offset sequence, identifies the demand stability decay factor under different fluctuation conditions, and integrates them into the demand fluctuation impact analysis results.

[0009] Preferably, the collision detection module includes: The conflict area identification submodule filters element nodes whose attenuation factors are greater than the stability benchmark threshold based on the demand stability attenuation factor in the demand fluctuation impact analysis results. At the same time, it extracts the node identifiers marked as logical conflict areas in the technical demand feature vector space and generates a set of high conflict risk nodes. The sensitive point determination submodule calls the set of high-conflict-risk nodes, extracts the characteristic fluctuation intensity and index offset of the nodes in a continuous time window, calculates the fluctuation offset correlation ratio, verifies the conflict sensitivity in combination with the logical conflict feature value, and marks the nodes that reach the sensitivity threshold to form a set of technical requirement conflict sensitive points.

[0010] Preferably, the classification output module includes: The joint determination submodule acquires multi-dimensional feature data of each node in the set of sensitive points of technical requirement conflict, calculates the joint deviation between the node feature value and the classification benchmark value, and divides the classification level label according to the deviation range. The structured output submodule integrates the spatial identifiers, classification level labels, and related optimization suggestions of each node, generates structured classification results according to classification dimensions, and adds historical conflict pattern analysis to generate technical requirement classification results and optimization analysis reports.

[0011] Preferably, the system further includes: an optimization feedback module, which receives the technical requirement classification results and optimization analysis report, extracts execution deviation data and correction strategy dataset from historical classification records, generates module optimization instructions, and updates the feature extraction rule base.

[0012] The deviation analysis submodule receives the technical requirement classification results and optimization analysis report, extracts the actual execution deviation data and manual correction records from the historical classification results, and identifies the differences between the classification rules and correction strategies. Based on the differences between the classification rules and the correction strategy, the rule update submodule optimizes the feature extraction weight coefficients and the conflict sensitivity judgment threshold, generates module optimization instructions, and synchronously updates the core parameter set in the feature extraction rule base.

[0013] Preferably, the system further includes: The requirement classification and verification module acquires the execution feedback data of the technical requirement classification results and optimization analysis report in real time, compares the matching degree of new input requirement features with historical classification patterns, verifies the accuracy of classification labels, and outputs a verification difference report. The optimization feedback module generates optimization instructions based on the verification difference report adjustment module's logic.

[0014] Preferably, the system further includes: The multi-source data adaptation module is connected to the feature extraction module, receives original technical requirements inputs from different sources, converts them into a unified semantic parsing format, and then transmits them to the feature extraction module. The classification result deployment module connects to the classification output module and converts the technical requirement classification results and optimization analysis report into a set of configuration instructions that can be executed by the target deployment system.

[0015] Compared with the prior art, the beneficial effects of the present invention are: This intelligent analysis and classification system for technical requirements demonstrates significant advantages in processing technical requirements through the synergistic effect of its various modules. The feature extraction module proactively acquires the original technical requirement text input, rather than relying on manual interpretation. Through an automated parsing process, it accurately identifies the technical function descriptions and performance indicators contained within, extracts key elements, and quantifies them to form a quantitative dataset. This approach avoids the problems of incomplete and inaccurate key element extraction caused by subjective differences and limitations in professional knowledge during manual analysis. It also significantly reduces manpower and time consumption, making the extraction and quantification of key elements more objective and efficient, thus providing a reliable data foundation for subsequent analysis based on the element data. Building upon the element quantification dataset, the association topology module further utilizes all element quantification values ​​to deeply identify the logical dependencies between elements, constructing a multi-dimensional feature vector space and generating a technical requirement feature vector space. This process overcomes the limitations of traditional tools, which can only extract basic keywords and cannot present the relationships between elements. By constructing a multi-dimensional space, it transforms the originally scattered and abstract technical requirement elements into an intuitive and structured feature vector form, clearly demonstrating the overall characteristics of the technical requirements and the intrinsic connections between each element. This allows for a comprehensive presentation of the overall picture of the technical requirements, providing a clear analytical framework for subsequent dynamic monitoring and conflict detection. The dynamic response module proactively acquires time-series change data of feature vectors in the feature vector space of technical requirements, monitors the intensity of feature vector fluctuations and the offset of related indicators in real time, and calculates the dynamic demand fluctuation impact factor to generate demand fluctuation impact analysis results. This function compensates for the shortcomings of existing technologies in dealing with dynamic changes in demand, enabling timely capture of changes in technical requirements caused by various factors during the advancement process, accurately analyzing the impact of these changes, allowing relevant personnel to grasp the dynamics of demand in real time, anticipate potential problems, and avoid subsequent risks caused by undetected demand fluctuations. Based on the analysis results of demand fluctuations, the conflict detection module accurately identifies element nodes whose influencing factors exceed the baseline fluctuation threshold and are located in logical conflict areas, marking them as demand conflict sensitive points and generating a set of technical demand conflict sensitive points. This module can quickly locate potential demand conflict points, avoiding the situation where potential conflicts are difficult to detect in traditional methods, thus preventing serious problems. By identifying sensitive points in advance, it buys time for relevant personnel to take countermeasures and reduces the interference of conflicts on technology research and development and project progress. The classification output module integrates all node information from the cluster of sensitive points in the technical requirement conflict, combines the feature vector spatial distribution state to output classification labels, and generates technical requirement classification results and optimization analysis reports. This integrated output method not only provides clear and accurate classification results, but also offers targeted optimization suggestions based on a comprehensive analysis process, avoiding the problems of existing tools having single classification results and lacking effective suggestions. Based on the classification results and optimization reports, relevant personnel can more accurately grasp the core direction of technical requirements, formulate more reasonable R&D and project advancement strategies, and further improve the overall effectiveness of technical R&D and project management. Furthermore, through the organic integration of its modules, the entire system achieves intelligent processing of technical requirements from data extraction, relationship construction, dynamic monitoring, conflict identification to classification output, changing the traditional situation of low efficiency and insufficient accuracy in technical requirement analysis. It is applicable to various technical R&D and project management scenarios and can provide strong support for technical requirement processing in different fields. Attached Figure Description

[0016] Figure 1 This is a timing diagram of the intelligent analysis and classification system for technical requirements described in this invention; Figure 2 A flowchart illustrating the data structure of an intelligent analysis and classification system for technical requirements. Figure 3 A flowchart illustrating the workflow of the associated topology module; Figure 4 This is a flowchart of the dynamic response module. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Please see Figure 1 This invention provides a technical demand intelligent analysis and classification system, the system comprising: The feature extraction module acquires the original technical requirement text input and identifies the technical function description units and performance indicator description units contained within it by parsing the semantic structure of the text. These description units are segmented into independent element units and assigned type identifiers to form a basic set of technical requirement elements.

[0019] The element quantization processing submodule performs feature value transformation calculations on the description units in the basic set, and stores the transformed element quantization values ​​along with the element type identifier and the element association weight coefficient, integrating them to generate an element quantization dataset containing multi-dimensional data.

[0020] The association topology module calls upon the quantified values ​​of all elements in the dataset, constructs a multi-dimensional feature vector space coordinate system based on the logical dependencies between elements, calculates the coordinate positions of each element in this space, and generates an initial feature vector space model. The feature difference analysis submodule further analyzes the feature vector differences between adjacent elements in the model, calculates the logical consistency coefficient in combination with the element type, updates the spatial relationship labels, and forms a complete technical requirement feature vector space.

[0021] The dynamic response module monitors the time-series changes of feature vectors in the vector space, captures the fluctuations of feature values ​​and the offsets of related indicators, calculates the dynamic demand fluctuation impact factor by analyzing the response relationship between the two, and finally integrates them to form the demand fluctuation impact analysis results.

[0022] Based on the analysis results, the conflict detection module identifies element nodes whose influencing factors exceed the preset benchmark fluctuation threshold and are located within the logical conflict area, marks them as demand conflict sensitive points, and collects all sensitive points to generate a set of technical demand conflict sensitive points.

[0023] The classification output module integrates all node information in the sensitive point set, combines its distribution in the feature vector space, calculates the joint deviation between the node feature value and the classification benchmark value to divide the classification level label, and outputs the technical requirement classification results and optimization analysis report in a structured manner.

[0024] Example 1: See Figure 2 In the operation of the intelligent analysis and classification system for technical requirements, the data structure and output format defined in this embodiment constitute the core framework for information representation and final delivery within the system. The system processes complex raw technical requirement texts, and its output is structured classification and optimization guidance. When a technical requirement document for a "high-speed data acquisition system" is input into the system, the feature extraction module begins its work. The document contains descriptive statements such as "achieving a sampling rate of millions per second," "supporting multi-channel synchronous acquisition," "power consumption less than 50 watts," and "data throughput latency less than 1 millisecond." The raw requirement parsing submodule identifies and segments these descriptive units. For example, "achieving a sampling rate of millions per second" is identified as a performance indicator descriptive unit and is appended with a "PERF" type identifier and a unique serial number, such as "PERF-001." Similarly, "supporting multi-channel synchronous acquisition" is identified as a technical function descriptive unit and receives the identifier "FUNC-005." All such identified units constitute the basic set of technical requirement elements.

[0025] The element quantization submodule performs numerical transformation on each unit within the base set. For "PERF-001: Millions of samples per second," the described metric is a specific numerical value "1,000,000," with the unit "Samples / s." After unit normalization, its element quantization value can be directly calculated as a single numerical value. For qualitative function descriptions like "FUNC-005: Supports multi-channel synchronous acquisition," the submodule invokes a semantic vectorization model. This model maps the descriptive text into a high-dimensional numerical vector, and then uses dimensionality reduction techniques to extract a representative scalar value as its element quantization value. Simultaneously, the submodule assigns a correlation weight coefficient to each element. The initial value of this coefficient may come from a domain knowledge base, indicating that "sampling rate" is a core performance indicator with a high weight, while "multi-channel" is an important functional feature with a medium weight. Ultimately, each element, with its element type identifier at its core, stores the calculated element quantization value and the assigned element correlation weight coefficient. These three elements together form a structured element quantization dataset, providing the data foundation for all subsequent analyses.

[0026] The topology module reads this dataset, and its vector space construction submodule begins its work. It constructs a multidimensional space using the logical dependencies between features. For example, "high sampling rate (PERF-001)" might have a resource conflict dependency with "low power consumption (PERF-002)" and a positive correlation dependency with "low latency (PERF-003)". Based on the strength and nature of these dependencies, the submodule assigns a coordinate position to each feature in a virtual multidimensional coordinate system, thus generating an initial feature vector space model. The feature difference analysis submodule then analyzes this model. It calculates the quantization differences between neighboring feature points in the space, such as calculating the feature difference degree between the "high sampling rate" point and the "low power consumption" point. Combining their type identifiers (same attribute performance indicators), the submodule calculates a logical consistency coefficient, which may reveal inherent numerical contradictions between the two objectives, thus labeling these two points as having a "logical conflict" relationship in the spatial model. After such analysis of all adjacent point pairs, the resulting technical requirement feature vector space is no longer just a set of points, but a rich network model that includes precise coordinates, relationship labels between points (such as dependencies and conflicts), and quantified differences.

[0027] The time-series change monitoring submodule of the dynamic response module continuously monitors this vector space, potentially recording the historical changes of the "sampling rate" metric during each demand revision, forming its characteristic fluctuation sequence. Simultaneously, it monitors changes in the "power consumption" metric associated with the "sampling rate," forming a metric offset sequence. The fluctuation impact calculation submodule analyzes these sequences to investigate whether an increase in the "sampling rate" is always accompanied by a significant increase in "power consumption." By analyzing this dynamic response pattern, the submodule calculates the demand stability decay factor for the "sampling rate" element and assesses the impact of its fluctuations on overall demand stability, as well as the impact of "power consumption" offsets on demand integrity. All these analyses, combined with conflict response simulations under other changing assumptions, are integrated into a comprehensive demand fluctuation impact analysis report.

[0028] The conflict region identification submodule of the conflict detection module reports its actions accordingly. It filters out elements whose stability decay factors exceed a threshold, such as "sampling rate" and "power consumption." Simultaneously, it locates nodes already marked as "logical conflict" regions in the feature vector space. Taking the intersection of these two, it finds that both "sampling rate" and "power consumption" nodes are located within a high-risk intersection, thus including them in the high-conflict-risk node set. The sensitive point determination submodule then refines the nodes in the set. It extracts the fluctuation intensity data of the "sampling rate" node within the most recent time windows and the offset data of "power consumption," calculating a fluctuation offset correlation ratio. Simultaneously, it obtains the logical conflict feature values ​​of the node in the vector space. Combining these two aspects of data, if the combined sensitivity exceeds a preset threshold, then "sampling rate" and "power consumption" are finally marked as formal demand conflict sensitive points. All such marked points and their detailed information (spatial location, conflict value, correlation ratio) constitute the technical demand conflict sensitive point set.

[0029] The joint decision submodule of the classification output module classifies each node in the sensitive point set. It calculates the joint deviation of the multidimensional feature data of the "sampling rate-power consumption" node pair from the ideal benchmark, and classifies it as "severe conflict" based on the deviation range. The structured output submodule then integrates information from all such nodes: their spatial identifiers indicate the specific requirements where the problem lies, the classification level labels indicate the severity, and optimization suggestions such as "the balance between sampling rate and power consumption needs to be re-evaluated" are automatically generated. All this information is organized according to specific dimensions and accompanied by comparative analysis with historical conflict cases, ultimately generating a detailed technical requirement classification result and optimization analysis report, providing decision-makers with a clear problem location and solution direction.

[0030] Example 2: See Figure 3This embodiment describes the specific operational flow of the feature extraction module and the association topology module in the intelligent analysis and classification system for technical requirements. These two modules process the original technical requirement text sequentially, transforming it from unstructured natural language descriptions into structured, relation-rich spatial models, laying the data foundation for subsequent analysis. A technical requirement document for a "high-reliability distributed storage cluster" can serve as a specific processing example. This document contains descriptive statements such as "automatic data replication synchronization," "automatic detection and isolation of faulty nodes," "read / write throughput of no less than 10GB / s," "data recovery time objective (RTO) of less than 30 seconds," and "single node power consumption controlled within 200 watts." The original requirement parsing submodule in the feature extraction module first receives this text input. This submodule integrates natural language processing functions to perform word segmentation, syntactic analysis, and semantic role labeling on the text. Through a predefined library of technical functions and performance indicators, it can identify key descriptive units in the text. For example, "automatic data replication synchronization" is identified as a unit describing a system function, with the core verb "synchronize," the object "data replication," and the modifier "automatic." This unit is segmented from the original text and assigned a type identifier, such as "FUNC-101," indicating that it belongs to the functional category. Similarly, "read / write throughput not less than 10GB / s" is identified as a unit describing a performance indicator, with a quantified value of "10GB / s" and a comparison relationship of "not less than," and is assigned the identifier "PERF-201." All the identified, segmented, and labeled independent units are aggregated to form the basic set of technical requirement elements corresponding to this technical requirements document. This set is an intermediate product; it is still a collection of text fragments and identifiers.

[0031] The element quantification processing submodule begins operating on the basic set of technical requirement elements. The core task of this submodule is to convert textual descriptions into numerical values, i.e., to perform feature value conversion calculations. The conversion calculation strategies differ for different types of elements. For performance indicators containing explicit numerical values ​​and units, such as "PERF-201: Read / write throughput not less than 10GB / s," the conversion process is relatively straightforward. The submodule parses the numerical value "10" and the unit "GB / s," and then, based on an internal unit conversion table, converts them into a scalar value in a standard unit, which serves as the element quantification value for that element. Simultaneously, the "not less than" comparison relationship is converted into a constraint condition and stored separately. For functional descriptions like "FUNC-101: Automatic data copy synchronization," the conversion process is more complex. The submodule invokes a semantic vectorization model, typically trained on a large amount of technical literature, capable of mapping text to a vector in a high-dimensional semantic space. This high-dimensional vector is then compressed into one or a few representative scalar values ​​through dimensionality reduction techniques such as principal component analysis, serving as the element quantification value for that functional description. While this value does not represent a physical quantity, it can characterize the semantic features of the function in the numerical space. Simultaneously with calculating the element quantification value, the submodule also assigns an association weight coefficient to each element. This coefficient may be determined based on the element's frequency and position in the original text (such as in titles or chapter highlights), or according to a predefined domain knowledge base (e.g., the assumption that "data reliability" related functions generally have higher weights than "power consumption" related indicators). Ultimately, each element is associated with three core pieces of data: an element type identifier (e.g., "FUNC-101"), the calculated element quantification value (one numerical value), and the assigned association weight coefficient (another numerical value). All this data is integrated and stored to generate a structured element quantification dataset, completing the crucial transformation from text to numerical values.

[0032] The vector space construction submodule of the associated topology module then calls the element quantization dataset. This submodule reads all element quantization values ​​in the dataset and constructs a multidimensional feature vector space based on predefined or relational mining algorithms-discovered logical dependencies between elements. These logical dependencies may include: a negative dependency between "automatic data replication synchronization (FUNC-101)" and "read / write throughput (PERF-201)" (synchronization overhead may reduce throughput); a strong positive dependency between "automatic fault node detection and isolation (FUNC-102)" and "data recovery time target (PERF-202)" (fast detection and isolation are prerequisites for fast recovery); and a positive dependency between "read / write throughput (PERF-201)" and "single node power consumption (PERF-203)" (high performance is usually accompanied by high power consumption). Based on the type and strength of these dependencies, the submodule assigns a coordinate position to each element in a multidimensional coordinate system. For example, elements with strong positive dependencies are placed closer together in space, while elements with negative dependencies or conflicts are placed further away or in a specific relative position. This process generates an initial feature vector space model, which maps all the required elements to a set of spatial points.

[0033] The feature difference analysis submodule further refines this model. It scans the entire spatial model, identifying geometrically adjacent feature point pairs. For each pair, it calculates the degree of difference between their feature quantification values. For example, it calculates the feature difference between the "read / write throughput (PERF-201)" point and the "single-node power consumption (PERF-203)" point. More importantly, it combines the feature type identifiers (same attribute performance indicators) of these two points with their known logical dependencies (positive dependencies) to calculate a logical consistency coefficient. This coefficient assesses whether the distance and numerical differences in the current space truly reflect their logically expected relationship. Based on this coefficient, the submodule updates or enriches the annotations of the logical relationships between vectors in the spatial model; for example, confirming a "performance coupling" relationship or marking outliers inconsistent with expected logic. After traversing and calculating all adjacent point pairs and updating relationship annotations, a complete technical requirement feature vector space is finally formed. This space not only contains the coordinates of the elements, but also the calculated differences between points and rich logical relationship annotations, transforming a textual requirements document into a numerical space model that can be used for further mathematical analysis and reasoning.

[0034] Example 3: See Figure 4The time-series change monitoring submodule of the dynamic response module continuously monitors the state of its feature vector space. This submodule maintains a time-series database, recording state snapshots of each feature vector in the space at specific time intervals or version numbers. For example, in version V1.0, a performance metric regarding "system response latency" (assuming its element quantization value is...) The timeframe was initially defined as less than 100 milliseconds. In subsequent version V1.1, due to new business requirements, this metric was tightened to less than 50 milliseconds (its element quantization value became...). The time-series change monitoring submodule will capture this change and calculate the amount of change in the feature value between adjacent versions. Meanwhile, this submodule monitors other metrics that are logically related to "system response latency," and these relationships are defined in the relational annotations of the feature vector space. For example, the "computing resource utilization" metric may have a strong correlation with it. The submodule found that when response latency requirements tightened, the value of the "computing resource utilization" metric also increased from 60% to 85%, and the change was denoted as [missing information]. All these changes, arranged along the time dimension, form the characteristic fluctuation sequence (record) of this element. Historical data) and the index offset sequence of related indicators (records) (History).

[0035] The fluctuation impact calculation submodule performs in-depth analysis of these sequences. The built-in analysis algorithms in this submodule are dedicated to research... and The module examines the response relationship between these metrics. It attempts to answer the questions: At what cost will an improvement in core performance indicators lead to increased consumption of related resources? How does this cost evolve? To quantify this dynamic impact, the submodule introduces a computational model to assess demand stability. This model calculates a dynamic demand fluctuation impact factor, the core idea of ​​which is to evaluate the degree of deviation in related indicators caused by a change in a unit characteristic value, considering its historical trend. A possible quantification method is embodied in the following formula:

[0036] In this formula: This represents the calculated demand stability decay factor. This indicates the total number of data points within the analyzed time window. Indicates the first The change in the characteristic value of the monitored element within a time interval. Indicates the first The change in a certain indicator that is related to the above elements within a time interval. It is a very small positive number used to prevent the denominator from being zero, thus ensuring the mathematical stability of the formula. It is a weighting coefficient used to assign greater importance to recent data, reflecting the time-sensitive nature of fluctuations; its value decays over time. This factor This study comprehensively reflects the cumulative impact of a series of historical changes on demand stability. Ultimately, the influence of characteristic fluctuation intensity on demand stability, the impact of related indicator shifts on demand integrity, and comparative data from demand conflict response simulations under various dynamic assumptions (such as further resource constraints and increased performance requirements) are integrated to form a comprehensive analysis of the impact of demand fluctuations. This result not only identifies current instability points but also predicts potential evolutionary risks.

[0037] The conflict zone identification submodule of the conflict detection module operates based on the analysis results. This submodule receives the analysis results of the impact of demand fluctuations and extracts the demand stability attenuation factor of all elements from them. It will be each element's The value is compared with a preset stability benchmark threshold. Compare. All The key nodes are filtered out, forming a set of high-volatility-risk nodes. These nodes are considered to have had a negative impact on system stability that exceeds acceptable limits due to their historical fluctuations. Simultaneously, this submodule accesses the technical requirement feature vector space and extracts the identifiers of all nodes pre-labeled as logically conflicting regions. For example, in the vector space, "system response latency" and "computing resource utilization" may have been labeled as regions with "resource competition" conflicts by the feature difference analysis submodule based on their inherent relationship. Taking the intersection of the high-volatility-risk node set and the node identifiers of logically conflicting regions generates a high-conflict-risk node set. The nodes in this set exhibit high instability dynamically and are at the center of contradictions statically.

[0038] The sensitive point determination submodule performs final verification and refinement of the set of high-conflict-risk nodes. This submodule extracts detailed data for each node within a continuous time window, including its characteristic fluctuation intensity. Statistics such as mean or variance and bias of related indicators ( The specific value of the two statistics. It calculates the ratio of these two statistics to obtain a fluctuation offset correlation ratio. This is used to quantify the coupling strength between dynamic fluctuations and static conflicts. Furthermore, the submodule calls the pre-calculated logical conflict feature values ​​of this node in the feature vector space. This value, generated in a previous module, characterizes the severity of its static logic conflict. The sensitivity point determination submodule correlates the fluctuation offset ratio. Conflicting eigenvalues Combined, input a sensitivity verification function Perform the calculation. This function defines the judgment rule for the combined effect of dynamic and static risk factors. If the calculation result... Exceeding the preset sensitivity threshold If so, the node is ultimately marked as a confirmed demand conflict sensitive point. All marked sensitive points and their detailed information (including their spatial identifiers and logical conflict characteristic values) are listed. Correlation ratio of fluctuation offset These data are collected to form a set of sensitive points of conflict in current technological requirements. This set accurately identifies the most vulnerable and critical points of conflict in current technological requirements, providing clear input for subsequent classification and optimization.

[0039] Example 4: This describes the specific operational flow of the classification output module and optimization feedback module in a technical requirement intelligent analysis and classification system. These two modules are crucial for the final delivery and self-evolution of the system. The classification output module comprehensively judges and outputs structured results for identified conflict-sensitive points, while the optimization feedback module optimizes the system's core parameters by analyzing historical execution deviations, forming a closed-loop learning and improvement mechanism. Assume the system is processing the technical requirement of a "high-performance image processing platform," and its conflict detection module has output a set of technical requirement conflict-sensitive points containing multiple sensitive points. This set may include element nodes such as "image processing resolution," "real-time frame rate," "hardware computing unit power consumption," and "data transmission bandwidth." Each node is accompanied by detailed data such as its spatial identifier, logical conflict characteristic value, and fluctuation offset correlation ratio.

[0040] The joint decision submodule of the classification output module first acquires the sensitive point set. This submodule internally maintains a set of classification benchmark values, derived from domain best practices, historical successful project data, or expert experience. For each node in the sensitive point set, the submodule extracts its multi-dimensional feature data, including logical conflict feature values ​​(representing static conflict strength), fluctuation offset correlation ratios (representing the coupling degree between dynamic fluctuations and conflicts), and its position information in the feature vector space. The core task of the submodule is to calculate the joint deviation between the feature data of each node and its corresponding classification benchmark value. This is a multi-attribute decision-making process that requires comprehensive consideration of the deviations of each feature dimension and their relative importance. For example, for the "image processing resolution" node, its logical conflict feature value may be much higher than the benchmark, indicating a serious static logical contradiction; at the same time, its fluctuation offset correlation ratio may also be high, indicating that the requirement has changed frequently and significantly in the past. The joint decision submodule uses an algorithmic model (such as weighted distance calculation or a multi-dimensional scoring card) to synthesize these discrete deviations into a total joint deviation value. Based on the preset range of this value, each node is assigned to a category level and labeled with the corresponding category level. Possible labels include "critical conflict," "major conflict," "general conflict," or "needs observation," thus forming a list of category nodes.

[0041] The structured output submodule begins its work. It receives a list of classification nodes from the joint decision submodule, where each node is associated with its spatial identifier and classification level label. This submodule's responsibility is to combine this information with specific optimization recommendations and organize it into a complete report. It generates associated optimization recommendations for each node, which may originate from a built-in knowledge base storing typical solutions for various common conflicts. For example, for nodes labeled "high resolution," "high frame rate," and "low power consumption" as "critical conflicts," the optimization recommendation might be "suggest evaluating the use of a dedicated image processing chip (ASIC) to optimize performance-to-power ratio" or "consider introducing a dynamic resolution scaling mechanism to balance real-time performance and image quality." All this information—node identifiers, classification labels, and optimization recommendations—is organized dimensionally, such as by conflict type or by functional module. Furthermore, the submodule includes a historical conflict pattern analysis, comparing currently sensitive conflict patterns with historical cases in the system log, highlighting similarities and specificities to provide deeper insights. Finally, all of this content is compiled to generate a technical requirement classification result and optimization analysis report. This report not only identifies the problems and their severity level, but also provides preliminary directions for solutions. See Table 1 for a fragment of the classification results of potential technical requirement conflict sensitivities generated by the structured output submodule.

[0042] Table 1: Examples of Sensitive Points in Technical Requirement Conflicts of Image Processing Platforms Coord_Img_Res 0.87 1.45 0.92 Key Conflict The evaluation uses a dedicated image processing chip (ASIC). Coord_Frame_Rate 0.82 1.20 0.88 Key Conflict Introducing a dynamic resolution scaling mechanism Coord_Power 0.78 0.95 0.75 Main conflict Optimize the underlying algorithm logic and reduce computational complexity. Coord_Bandwidth 0.65 0.88 0.60 General Conflict Investigate higher bandwidth interconnect protocols or compression technologies The optimization feedback module is activated after the report is generated, aiming to enable the system to learn from real-world applications. The deviation analysis submodule receives the current technical requirement classification results and optimization analysis report, while simultaneously extracting historical classification records related to that requirement from the system logs. These historical records contain deviation data from the analysis results of similar requirements in the past, generated during actual project execution, as well as records of manual corrections made by project personnel based on actual circumstances. The deviation analysis submodule identifies specific discrepancies between the recommendations generated by the automatic classification rules and the manual correction strategies. For example, the system might repeatedly mark "data transmission bandwidth" related issues as "general conflicts," but historical records show that the development team needs to invest far more resources than expected to resolve such issues each time, indicating a significant actual execution deviation. This discrepancy reveals potential deficiencies in the system's current classification rules or parameter settings, underestimating the actual impact of such conflicts.

[0043] The rule update submodule conducts in-depth analysis based on the discrepancies identified by the deviation analysis submodule, attempting to pinpoint the key parameters causing these differences. For example, the analysis might indicate that the association weight coefficient for "bandwidth" category features was set too low, leading to an underestimation of its importance in subsequent analyses; or that the sensitivity threshold for such conflicts was set too leniently during the conflict sensitivity determination phase. The rule update submodule then generates targeted module optimization instructions, calculating new, optimized weight coefficients and determination threshold parameters. These adjusted core parameters are synchronously updated in the feature extraction rule base and the conflict detection module configuration. When dealing with new technical requirements, especially when encountering similar "bandwidth" elements, the system will use the updated parameters for calculation and analysis, potentially producing more accurate classification results that better reflect actual engineering challenges. In this way, the system achieves continuous self-optimization based on historical feedback.

[0044] Example 5: The requirement classification verification module begins its workflow after the technical requirement classification results and optimization analysis report are generated. This module does not immediately terminate processing but continues to run, actively acquiring execution feedback data of the classification results in the actual application environment. This feedback data originates from records generated after the analysis report is imported into a project management system, requirement tracking tool, or adopted and applied in the actual development process. The module extracts data from these external systems through predefined interfaces, such as task completion status, requirement implementation progress, and the actual resources invested and final results achieved in resolving conflicts identified in the report. After acquiring this feedback data, the verification module compares it with the system's internal historical classification database. This historical database stores a large number of practically validated classification cases and their final effect evaluations. The core operation of the module is to perform similarity matching calculations between newly generated requirement features (i.e., the feature vectors, conflict patterns, classification labels, etc. of the newly classified requirements) and known classification pattern features with clear conclusions in the historical database. This process involves distance calculation of high-dimensional vectors, consistency verification of classification labels, and topological comparison of conflict patterns. Through this in-depth matching analysis, the module aims to verify whether the currently automatically assigned classification labels align with historical experience or practical application feedback. The matching calculation results generate a verification discrepancy report. This report does not simply provide a right or wrong answer, but rather details which entries in the classification results have high matching degrees and strong credibility; and which entries deviate significantly from historical patterns, requiring further review or potentially posing a risk of misjudgment. This report becomes a crucial basis for evaluating the quality of the system's analysis. The optimization feedback module receives this verification discrepancy report and uses its content as one of its key inputs. The logic for generating optimization instructions within the optimization feedback module is adjusted based on this report. If the verification discrepancy report consistently shows that a certain type of performance conflict is underestimated by the system (for example, the system often classifies it as a "general conflict," but historical execution feedback indicates that its actual impact is "severe"), then the generation of optimization instructions will more specifically adjust the feature extraction weights or conflict judgment thresholds related to performance metrics, thereby making the system's self-optimization direction more accurate and more aligned with engineering realities.

[0045] The multi-source data adaptation module, serving as the system's input front-end, is designed to address the challenges posed by the diverse sources of technical requirement information. In practical applications, original technical requirements may exist in various forms. It could be a Word document or PDF file written in natural language, records and fields in a requirements management tool's database (such as Jira or Doorz), a file based on a structured format (such as XML or JSON), or even text fragments from meeting minutes or email communications. The multi-source data adaptation module is designed to receive inputs from these different sources and in different formats. The module integrates multiple format parsers and converters to identify the original format of the input data. For Word or PDF documents, the module uses a text extraction engine to remove all formatting tags and extract the plain text content. For database records, the module reads the content of specified fields through the corresponding database connector. For XML or JSON files, the module accurately locates and extracts relevant field values ​​containing the technical requirement description based on a predefined schema or tag path. Despite the wide variety of input formats, the core function of this module is to convert all extracted content into a unified semantic parsing format defined within the system. This unified format defines the structure in which technical requirement description text should be presented to downstream modules, ensuring that the input received by the feature extraction module is consistent in form and processing logic regardless of the source. Through this transformation and standardization, data from any source is converted into the same "language," thus guaranteeing the stability and consistency of subsequent analysis processes.

[0046] The classification results deployment module is located at the end of the entire processing flow, acting as a bridge connecting the system output with the external world. Its input is the technical requirement classification results and optimization analysis report generated by the classification output module. Internally, this report is a structured data object containing rich information such as a list of conflict sensitivities, classification levels, and optimization suggestions. However, its value is greatly diminished if this information cannot be translated into practical action. The responsibility of the classification results deployment module is to complete this "last mile" of transformation. The module has multiple pre-built or configurable output adapters, each targeting a specific deployment system, such as mainstream project management systems, agile development Kanban tools, operation and maintenance monitoring platforms, or internal enterprise work order systems. After receiving the internal structured report, the module selects the appropriate adapter based on the predetermined deployment goals. This adapter is familiar with the target system's application programming interface specifications, data models, or configuration syntax. It maps each classification result and optimization suggestion in the report into instructions that the target system can recognize and execute. For example, an optimization suggestion marked as a "critical conflict," such as "evaluate the use of a dedicated image processing chip," might be transformed into "creating a high-priority research task" in the project management system and automatically assigned to the hardware engineering team; a "general conflict" warning about "insufficient bandwidth" might be transformed into "adding a metric to be observed" in the monitoring dashboard of the operations and maintenance platform. Through this transformation, the analytical conclusions generated by the system are no longer merely reports on paper, but become a set of executable configuration instructions that drive actual project development, resource allocation, and decision-making processes, truly achieving a closed loop from analysis results to engineering practice.

[0047] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0048] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A technology requirement intelligent analysis and classification system, characterized in that, The system includes: The feature extraction module obtains the original technical requirement text input, parses the technical function description and performance indicator description contained therein, extracts key elements and quantifies them to form a feature quantification dataset. The associated topology module calls all element quantification values ​​in the element quantification dataset, identifies the logical dependencies between elements, constructs a multi-dimensional feature vector space, and generates a technical requirement feature vector space. The associated topology module includes: The vector space construction submodule calls all element quantization values ​​and spatial identification data in the element quantization dataset, establishes a multi-dimensional coordinate system based on the logical dependencies between elements, calculates the coordinate position of each element in the vector space, and constructs a multi-dimensional feature vector space based on the logical dependencies between elements. In a multi-dimensional coordinate system, a coordinate position is determined for each element, generating an initial feature vector space model. The feature difference analysis submodule identifies the feature vector difference between adjacent coordinate position elements based on the initial feature vector space model, and calculates the logical consistency coefficient in combination with the element type identifier. The logical consistency coefficient is used to evaluate whether the distance and numerical differences in the current space truly reflect their logical relationship, and updates the relationship labeling information in the feature vector space to form the technical requirement feature vector space. The dynamic response module acquires the time-series change data of the feature vectors in the feature vector space of the technical demand, monitors the intensity of feature vector fluctuations and the offset of related indicators, calculates the dynamic demand fluctuation impact factor, and forms the demand fluctuation impact analysis results. The demand fluctuation impact analysis results include the degree of influence of feature fluctuation intensity on demand stability, the degree of influence of related indicator offset on demand integrity, and the comparison of demand conflict response under various dynamic conditions. The dynamic conditions are hypothetical dynamic conditions, including further resource constraints and further increases in performance requirements. The conflict detection module identifies element nodes in the demand fluctuation impact analysis results whose impact factors are greater than the benchmark fluctuation threshold and are located in the logical conflict area, marks them as demand conflict sensitive points, and generates a set of technical demand conflict sensitive points. The logical conflict area is a region in the technical demand feature vector space that is pre-marked according to the logical dependency relationship between elements and represents the contradictory relationship between elements. The element node is the coordinate point in the technical demand feature vector space that represents the quantified element. The classification output module integrates all node information in the set of sensitive points for conflicting technical requirements, outputs classification labels based on the distribution status of the feature vector space, and generates classification results and optimization analysis reports for technical requirements. The output of classification labels based on the distribution status of the feature vector space refers to determining and outputting the coordinate position information of each node in the set of sensitive points for conflicting technical requirements in the feature vector space of the technical requirements.

2. The intelligent analysis and classification system for technical requirements according to claim 1, characterized in that, The element quantification dataset includes element quantification values, element type identifiers, and element association weight coefficients. The element association weight coefficients are determined based on the frequency and position of the element in the original text or according to a predefined domain knowledge base. The technical requirement feature vector space specifically includes feature vector space coordinates, logical relationship annotations between vectors, and feature difference degree between adjacent elements; The results of the demand fluctuation impact analysis include the degree of influence of characteristic fluctuation intensity on demand stability, the degree of influence of correlation index offset on demand integrity, and a comparison of demand conflict response under various dynamic conditions. The set of sensitive points for technical demand conflicts includes spatial identifiers of sensitive points, logical conflict feature values ​​of sensitive points, and correlation ratios between fluctuation intensity and offset of sensitive points. The logical conflict feature values ​​are used to characterize the severity of static logical conflicts, and the offset correlation ratio is the ratio of the feature fluctuation intensity to the statistical value of the offset of the correlation index, which is used to quantify the coupling strength between dynamic fluctuations and static conflicts. The technical requirement classification results and optimization analysis report include a list of classification nodes and a joint judgment label of multi-dimensional features of the nodes. The multi-dimensional features include logical conflict feature values, fluctuation offset correlation ratios, and position information in the feature vector space. The optimization analysis report not only indicates the problem and its severity level, but also provides related optimization suggestions.

3. The intelligent analysis and classification system for technical requirements according to claim 2, characterized in that, The feature extraction module includes: The original requirement parsing submodule obtains the semantic structure data of the original technical requirement text, identifies the technical function description unit and performance indicator description unit, divides them into independent element units and adds type identifiers to form a basic set of technical requirement elements; The element quantification processing submodule performs feature value transformation calculations based on the description units in the technical requirement element base set, stores the transformation results in association with the element identifier, and integrates the element feature values ​​and association weight coefficients to generate an element quantification dataset. Each element has its element type identifier as the core, and stores the calculated element quantification value and the assigned element association weight coefficient in association.

4. The intelligent analysis and classification system for technical requirements according to claim 2, characterized in that, The dynamic response module includes: The time-series change monitoring submodule acquires the historical state sequence of feature vectors in the feature vector space of the technical requirements, captures the change in feature values ​​and the change in related indicators at adjacent time nodes, and arranges them according to the time dimension to form a feature fluctuation sequence and an indicator offset sequence. The fluctuation impact calculation submodule analyzes the response relationship between the change in characteristic values ​​and the change in related indicators based on the characteristic fluctuation sequence and the indicator offset sequence. It identifies the demand stability decay factor under different change conditions. The different change conditions are assumed dynamic conditions, including further resource constraints and further increases in performance requirements. The demand stability decay factor comprehensively reflects the cumulative impact of a series of historical changes on demand stability. It integrates the impact of characteristic fluctuation intensity on demand stability, the impact of related indicator offset on demand integrity, and the demand conflict response simulation comparison data under various assumed dynamic conditions to form the demand fluctuation impact analysis results.

5. The intelligent analysis and classification system for technical requirements according to claim 4, characterized in that, The collision detection module includes: The conflict area identification submodule filters element nodes whose attenuation factors are greater than the stability benchmark threshold based on the demand stability attenuation factor in the demand fluctuation impact analysis results. At the same time, it extracts the node identifiers marked as logical conflict areas in the technical demand feature vector space, and takes the intersection of the high volatility risk node set and the logical conflict area node identifiers to generate a high conflict risk node set. The sensitive point determination submodule calls the set of high-conflict-risk nodes, extracts the characteristic fluctuation intensity and index offset of the nodes in a continuous time window, calculates the ratio of characteristic fluctuation intensity to associated index offset to obtain the fluctuation offset correlation ratio. The fluctuation offset correlation ratio is used to quantify the coupling strength between dynamic fluctuation and static conflict. Combined with the logical conflict feature value, the sensitivity verification function is used to verify the conflict sensitivity. The logical conflict feature value is used to characterize the severity of its static logical conflict. Nodes that reach the sensitivity threshold are marked to form a set of technical requirement conflict sensitive points.

6. The intelligent analysis and classification system for technical requirements according to claim 5, characterized in that, The classification output module includes: The joint determination submodule acquires multi-dimensional feature data of each node in the set of sensitive points of technical demand conflict. The multi-dimensional feature data includes logical conflict feature values, fluctuation offset correlation ratio and position information in feature vector space. It calculates the joint deviation between node feature values ​​and classification benchmark values. The joint deviation is integrated into a total joint deviation value by the algorithm model. The classification level label is divided according to the deviation range. The structured output submodule integrates the spatial identifiers, classification level labels, and related optimization suggestions of each node, and generates structured classification results according to classification dimensions. The classification dimensions include grouping by conflict type or grouping by functional module. It also generates technical requirement classification results and optimization analysis reports by adding historical conflict pattern analysis. The historical conflict pattern analysis compares the current sensitive conflict patterns with historical cases in the system records and points out their similarities and particularities.

7. The intelligent analysis and classification system for technical requirements according to claim 6, characterized in that, The system also includes: an optimization feedback module, which receives the technical requirement classification results and optimization analysis report, extracts execution deviation data and correction strategy datasets from historical classification records, wherein the execution deviation data is the deviation data generated by the analysis results during the actual project execution, and the correction strategy is the record of manual corrections made by project personnel to the system suggestions based on the actual situation, generates module optimization instructions and updates the feature extraction rule base, wherein the module optimization instructions are for the configuration of the feature extraction rule base and the conflict detection module, and the feature extraction rule base is a rule base that stores the core parameters used for feature extraction; The deviation analysis submodule receives the technical requirement classification results and optimization analysis report, extracts the actual execution deviation data and manual correction records from the historical classification results, and identifies the differences between the classification rules and correction strategies by comparing the suggestions generated by the automatic classification rules and the manual correction strategies. The rule update submodule optimizes the feature extraction weight coefficient and conflict sensitivity judgment threshold based on the differences between the classification rules and the correction strategy. The feature extraction weight coefficient is the association weight coefficient assigned to each element during the feature extraction process. It generates module optimization instructions and synchronously updates the core parameter set in the feature extraction rule base.

8. The intelligent analysis and classification system for technical requirements according to claim 1, characterized in that, The system also includes: The requirement classification and verification module acquires execution feedback data from the technical requirement classification results and optimization analysis report in real time, compares the matching degree between new input requirement features and historical classification patterns, verifies the accuracy of classification labels, and outputs a verification difference report. The optimization feedback module adjusts the generation logic of the optimization instructions based on the verification difference report.

9. The intelligent analysis and classification system for technical requirements according to claim 1, characterized in that, The system also includes: The multi-source data adaptation module is connected to the feature extraction module, receives original technical requirements inputs from different sources, converts them into a unified semantic parsing format, and then transmits them to the feature extraction module. The classification result deployment module connects to the classification output module and converts the technical requirement classification results and optimization analysis report into a set of configuration instructions that can be executed by the target deployment system.

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