764 results about "Demand analysis" patented technology

The invention discloses an ST code generation method based on graph alignment coding large model and multi-agent collaboration, and the method comprises the steps: receiving an ST code programming demand inputted by a user through a demand analysis module, refining and analyzing the demand based on multiple rounds of interactive conversations of the user and insight agents, and generating a standardized ST code programming demand; the retrieval module receives a standardized ST code programming requirement, and retrieves and obtains related knowledge through a retrieval agent in combination with an ST code knowledge base; and the double-agent collaborative self-correction code generation module receives standardized ST code programming requirements and retrieved related knowledge, a graph alignment coding large model constructed based on a graph neural network and a cross-modal alignment technology serves as a coding agent to cooperatively work with a review agent, ST code structure information is injected into the large model, and a final ST code is generated. According to the method, high-accuracy and high-reliability ST code automatic generation can be realized, and the development efficiency of a PLC program in the industrial control field is improved.

The invention relates to the technical field of system engineering modeling, and particularly discloses an MBSE optimization method based on a large language model, which realizes MBSE whole process automation and intelligentization by constructing a'demand-knowledge-model 'dynamic closed-loop framework and fusing RAG and LLM. The method specifically comprises the following steps: constructing a domain knowledge enhancement library, and integrating LLM to construct a demand analysis engine and a dynamic modeling optimization system; a domain expert inputs a demand through a natural language interaction interface, and the demand is analyzed into structured data through LLM; generating a parameterized model conforming to the MBSE specification by combining the RAG technology with the knowledge in the library; after the model runs through a simulation tool chain, the LLM adjusts parameters according to a simulation result to generate an iteration scheme; and after the modeler passes verification, storing the model and data into a database to form a knowledge source. According to the method, domain knowledge dual-drive modeling and cross-role collaboration are achieved, the knowledge base self-evolution capacity is achieved, the problems that traditional MBSE is high in manual dependence and insufficient in semantic fault and knowledge fusion are effectively solved, and the modeling efficiency and reliability are remarkably improved.

The invention provides a supercomputing resource intelligent allocation method and system based on AI load monitoring, and the method comprises the steps: firstly obtaining a real-time load data set of a plurality of computing nodes in a supercomputing system, including a resource occupancy rate index and a task queue state parameter, then carrying out the load feature extraction processing of the real-time load data set, generating resource occupancy fluctuation characteristics and task queue evolution characteristics, performing resource demand analysis on the load characteristic set by using a pre-constructed resource demand prediction model, predicting resource demands of each computing node in a subsequent time window, determining resource allocation priorities and migration strategy parameters based on prediction results, and performing resource allocation according to the resource allocation priorities and the migration strategy parameters. The resource dynamic allocation instruction is generated and sent to the target computing node, the resource reallocation operation is triggered, intelligent and efficient allocation of super computing system resources is achieved, the resource utilization rate and the task execution efficiency are improved, and the overall energy efficiency ratio and stability of the system are optimized.

The invention discloses a CAD automatic generation system and method based on intelligent model selection and application, and aims at achieving automatic modeling under the multi-modal design requirement. The system comprises a user interaction module for receiving multi-modal input such as natural language, sketch and voice; the intelligent demand analysis module is used for combining an industrial large language model and a product knowledge graph, combining semantic analysis and generating a structured demand; the intelligent model selection calculation module is used for matching the optimal parameter combination and the component list based on a multi-objective optimization algorithm; the CAD automatic generation module calls a parametric modeling engine to generate an editable three-dimensional model; the constraint solving module is used for processing hard constraints and soft constraints in real time and dynamically adjusting model parameters; and the model output and interaction module feeds back a design state and supports user iteration. The system realizes full-process automation from the design intention to the CAD model, improves the design efficiency and accuracy, and is suitable for the fields of mechanical design, intelligent manufacturing and the like.

The invention provides an enterprise product intelligent quotation collaborative decision-making platform. A platform architecture comprises a user interaction layer, a data processing layer and a decision-making layer, and platform functions are realized by relying on an intelligent agent. When a user releases a product quotation task, an authority management agent verifies the identity and access authority of the user, and a user interaction and demand analysis agent in a user interaction layer converts the natural language demand of the user into a structured instruction; the data processing layer receives an instruction, basic data and a special cost agent in the layer pull data from an external system through a cross-system calling agent, and a price calculation agent integrates the data through multi-objective optimization to generate candidate quoted prices; and finally, a man-machine cooperation and intelligent decision-making auxiliary agent in a decision-making layer displays all candidate schemes, and an expert performs fine adjustment to select an optimal quotation. According to the invention, rapid and accurate quotation is realized for enterprise products, the quotation model is continuously optimized through continuous feedback and self-learning, and finally a highly autonomous intelligent quotation system is formed.

The invention relates to the technical field of traffic planning, in particular to a transfer connection optimization method in multi-mode traffic integrated planning, which comprises the steps of data acquisition and demand analysis, multi-mode transfer network topology design, multi-mode timetable collaborative optimization, transfer service system development and facility transport capacity dynamic matching. According to the transfer connection optimization method in the multi-mode traffic integrated planning, a dynamic and static combined multi-source data acquisition framework is constructed based on a multi-dimensional data fusion system, the data precision can be improved, the network can be perceived in real time, association modeling of weather events and demand fluctuation is realized based on construction of a multi-agent simulation system, and the optimization of the transfer connection in the multi-mode traffic integrated planning is realized. A dynamic weight mechanism is introduced based on a hypergraph model, the robustness of the algorithm is improved, a layered optimization framework processes global optimization through upper-layer integer programming, dynamic adjustment is achieved through lower-layer deep reinforcement learning, a buffer time intelligent insertion strategy can improve the late absorption capacity, and a facility utility function model is established to improve the transfer efficiency.

The invention relates to the technical field of AGV control, in particular to a multi-AGV scheduling method. The method comprises the following steps: obtaining a real-time logistics instruction of a working area, carrying out logistics scheduling demand analysis and global task planning, and constructing a global execution task atlas; aGV real-time monitoring parameters are obtained, multi-state comprehensive evaluation is carried out, dynamic task demand allocation processing is carried out according to the global execution task atlas, and an AGV dynamic task set is constructed; carrying out AGV operation behavior intention identification one by one according to the AGV dynamic task set, carrying out multi-task cooperative carrying evolution, and constructing a multi-level space-time path network; performing potential AGV collision early warning according to the multi-level space-time path network, and marking local AGV space-time collision nodes; and performing dynamic task priority decision according to the global execution task atlas, performing intelligent conflict planning on local AGV space-time conflict nodes, and constructing a local conflict scheduling strategy. According to the invention, through cooperative control of multiple AGVs, the operation efficiency, the scheduling precision and the safety level of the operation area are improved.

The invention provides an intelligent service recommendation method based on multi-dimensional scene perception and dynamic portrait modeling. The intelligent service recommendation method comprises the steps that 1, distributed edge computing nodes are deployed, space-time tetrad data operated by a user are collected in real time, user behaviors and labels are stored, and a high-dimensional recommendation database is built; 2, constructing a dynamic portrait engine, and constructing a user long-term behavior pattern library; 3, deploying a real-time streaming and offline double-engine architecture, fusing real-time scene matching and offline portrait prediction, and performing personalized function and user deep demand analysis; step 4, for new users, synchronously calling geofences to obtain regional hot services, and realizing cold start optimization based on meta reinforcement learning in parallel; and 5, establishing an intelligent recommendation closed loop associated with weather characteristics. According to the method, multi-dimensional features such as space-time tetrad data, user tag information and dynamic interest weights are fully utilized, and the real-time performance, interpretability and generalization ability of a recommendation system can be effectively improved.

The invention relates to the field of data processing, and particularly provides a big data mining method and system based on digital enterprise management, and the method comprises the steps: obtaining multi-modal session data generated in the process of carrying out digital service interaction between a target enterprise and a user, extracting a user demand feature set from the multi-modal session data, and based on a preset multi-task learning framework, carrying out joint training on the user demand feature set, generating a demand analysis model, calling the demand analysis model to analyze the user demand feature set, generating an optimization strategy set associated with the target enterprise service process, and sending the optimization strategy set to the server. And updating the digital service execution logic of the target enterprise according to the optimization strategy set, and performing incremental training on the demand analysis model based on user feedback data. According to the invention, the response efficiency and the function adaptability of the digital service process can be improved.

The invention relates to an intelligent interview scoring system capable of explaining decisions based on a large language model, and the system comprises a multi-mode resume analysis and feature coding unit, a resume feature adaptive matching unit, an interactive scoring and knowledge enhancement unit, and an answer quality evaluation unit. Text, image and audio features are extracted through a cross-modal attention mechanism of a multi-modal large language model, resume features are encoded into dynamic word vectors, and entity-level feature vectors are extracted; the post description text is encoded into a demand feature vector by a resume feature adaptive matching unit; calculating semantic similarity between the resume entity feature vector and the demand feature vector; the interactive scoring and knowledge enhancement unit dynamically retrieves knowledge fragments to generate a preliminary evaluation report containing a scoring basis; and the answer quality evaluation unit fuses the information density, the fluency and the integrating degree to generate a final score. And the whole-process intelligence from demand analysis to final decision making is realized.

The invention provides a code generation method and system based on a waterfall model and multi-agent collaboration, and the method comprises the steps: constructing a multi-agent collaboration framework which comprises a problem analysis agent, a solution agent, a pseudo-code agent, a coding agent and a restoration agent based on a software development waterfall model thought; and the interaction process of each agent is coordinated through a dynamic cooperation algorithm. Wherein the problem analysis intelligent body checks similar problems and solutions; the solution intelligent agent generates and evaluates candidate schemes; performing scheme conversion by the pseudo-code intelligent agent; the coding agent generates an executable code; and the repair agent performs fine-grained repair on the code in grammar, runtime and semantic levels through a two-dimensional repair mechanism. According to the method, the limitation of a single agent in a complex programming task is broken through, automatic code generation covering the whole process of demand analysis, scheme design, code implementation and test repair is realized, and the quality of generated codes and the reliability of operation are remarkably improved.

The invention relates to the technical field of electric power energy storage, in particular to an energy storage demand analysis method of a high-proportion new energy power grid, which comprises the following steps of: obtaining a power predicted value and a measured value through a prediction platform to calculate deviation, performing sliding window segmentation, dynamically adjusting length based on volatility to extract variance characteristics, and calculating the energy storage demand of the high-proportion new energy power grid; and the center number is optimized through K-means clustering, a classification result is output, a capacity adjustment value is calculated according to a matching grade adjustment coefficient, time points are extracted and sorted according to a load and output correlation coefficient, and an energy storage capacity space-time distribution table is generated. According to the invention, through prediction of deviation sequence sliding window segmentation and variance feature extraction, dynamic identification of new energy output fluctuation intensity, construction of a deviation fluctuation and energy storage capacity mapping model, quantification of load and output coupling intensity, and construction of a multi-dimensional energy storage correction system, energy storage and source load dynamic matching is realized, and response sensitivity is improved. The risk of resource mismatching is reduced, the robustness of the system is enhanced, and the cooperative efficiency of charging and discharging strategies is optimized.

The invention provides a multi-dimensional driver capability evaluation and intelligent matching scheduling system, and the system comprises a data collection module which is used for obtaining driver driving behavior data, vehicle state data and environment data in real time; the preprocessing module is used for carrying out noise filtering, missing value filling and standardization on the acquired data; the multi-dimensional capability evaluation module is used for calculating a driving safety score, an efficiency score and an emergency response score of the driver through a dynamic weight distribution algorithm based on the preprocessed data; the demand analysis module is used for analyzing the route complexity, the time sensitivity and the special service demand of the passenger order; the matching scheduling module is used for generating a matching result according to the driver ability score and the passenger demand and outputting a scheduling instruction; the dynamic optimization module monitors the driver state and the road condition change in real time and adjusts the matching weight; and the interaction module is used for pushing real-time scheduling information and abnormal event early warning to the driver and the passenger. The scheduling efficiency and safety can be improved, and the passenger travel experience and the operation management level are improved.

The invention discloses a human resource information management system and method based on big data, and relates to the technical field of human resources, and the system comprises a project demand analysis module which is used for extracting a skill list required by new project post personnel, determining task complexity, and generating skill parameter requirements; the employee screening module is used for obtaining the basic information of all the employees in service and dividing the employees in service into two types of crowds, one type is the crowd meeting the new project employment, and the other type is the crowd not meeting the new project employment, through big data analysis and multi-dimensional evaluation, accurate matching of the project demand and the employee ability is achieved, and the work efficiency is improved. Project execution quality and efficiency are improved, enterprise internal human resource potential is fully excavated, personnel can be reasonably allocated, and waste and idleness of human resources can be avoided.

The invention discloses a user tagging management and demand analysis system based on big data e-commerce, and the system comprises the following steps: a collection module which is used for collecting multi-modal behavior data, and generating a context-aware behavior representation vector; the label extraction module is used for constructing a content feature channel, a behavior feature channel and an emotion feature channel and extracting a multi-granularity label candidate set; the atlas construction module is used for constructing a label atlas according to the multi-granularity label candidate set; the intention reasoning module is used for inputting the label atlas and the current behavior representation of the user into an intention reasoning network and outputting a potential demand representation vector of the user; the clustering module is used for inputting the user potential demand representation vector into an improved grey wolf demand clustering algorithm for clustering analysis to generate a user demand group tag; and the structure output module is used for generating a user-label-demand ternary structure. According to the method, multi-channel modeling and the improved grey wolf algorithm are fused, and accurate identification of e-commerce user tags and demands is realized.

The invention relates to a computing power scheduling method and device based on resource affinity, equipment and a medium, and the method comprises the following steps: collecting the load state data of each computing node through a computing power node monitoring module; then demand analysis is carried out on a task submitted by a user, a task resource demand vector is generated, affinity matching is carried out on the task resource demand vector and node load data, and a resource affinity matching value is obtained; then analyzing the dependency relationship between the nodes, calculating a resource dependency coefficient, and weighting the resource dependency coefficient with the affinity matching value to obtain a comprehensive affinity value; sorting the comprehensive affinity values in a descending order to form a node priority sequence; and finally, computing power mapping scheduling is carried out based on the sequence, and a final scheduling scheme is generated. According to the method, the resource matching degree and the node cooperation relation are comprehensively considered, the scheduling efficiency and the resource utilization rate are improved, and the technical problems that the resource utilization rate is low, task response delay is high and loads among the nodes are uneven due to a traditional scheduling method are solved.

The invention discloses a real-time reservation and scheduling system for shared parking, and relates to the technical field of intelligent traffic and smart city management, and the system comprises a demand analysis module which is used for extracting parking lot scene types, time requirements and priority information from user parking request data, generating standardized demand description through dynamic weight distribution, and sending the standardized demand description to a user; the process decomposition module is used for decomposing a parking scheduling process into demand receiving, resource matching and path planning business units according to parking lot scene types and time requirements in the user demand analysis result, and determining a business unit set; according to the real-time reservation and scheduling system for shared parking, through dynamic weight distribution and real-time response optimization, efficient resource distribution and path planning are ensured, and the parking scheduling efficiency and the user experience are remarkably improved.

The invention discloses an agent development arrangement scheduling method and system based on a natural language, and belongs to the technical field of agent development creation, and the agent development arrangement scheduling method based on the natural language comprises the following steps: S100, obtaining a user demand and a demand constraint condition according to a natural language instruction input by a user; according to the user demand and the demand constraint condition, decomposing the user demand to form a plurality of sub-targets; according to each sub-target, generating a sub-target cue word template corresponding to each sub-target, and further obtaining a plurality of sub-target cue word templates; and S200, obtaining a plurality of tools corresponding to the plurality of sub-targets, analyzing the tool capability, evaluating the matching degree of the tools and the plurality of sub-targets, and obtaining an optimal tool list. According to the method, user demand analysis, task decomposition and tool scheduling are completed through a natural language processing technology, manual intervention is greatly reduced, and the efficiency of agent development is improved.

The invention discloses an electric power resource scheduling method and device suitable for extreme weather, terminal equipment and a storage medium, and belongs to the technical field of electric power scheduling, and the method adopts nonparametric kernel density estimation to construct a wind and light output joint probability distribution function representing output characteristics of wind power output and photovoltaic output in extreme weather. When flexibility demand analysis is carried out, the advantages of nonlinear correlation between variables and tail risks can be analyzed by means of a joint distribution probability function, wind power prediction errors and photovoltaic prediction errors are accurately analyzed, and then adjustment demand risks of net load flexibility demands are quantified in combination with convolution operation. The problems that the joint probability distribution characteristics of wind and light output in extreme weather cannot be accurately described and the regulation demand risk cannot be ignored in the current flexibility demand evaluation method depending on the normal distribution independent assumption are solved. And the power supply reliability and the operation stability of the power system in extreme weather can be ensured by the formulated power dispatching plan.

According to the automatic software development method and device and the computer program, intelligent demand analysis is achieved through a natural language processing technology, dynamic task arrangement is conducted through a finite state automaton, collaborative development is completed by means of a multi-specialized role agent, intelligent verification is implemented based on a template matching mechanism, and the development efficiency is improved. And a complete automatic closed loop from demand to delivery is constructed. According to the scheme, full-process automation is realized, manual intervention is greatly reduced, and the development efficiency is remarkably improved; through division and cooperation of multiple agents, the professionality and decision-making quality of each link are ensured, and the capability limitation of a single agent is overcome; flexible and reliable process control is provided based on state machine management, and development state changes are dynamically adapted; and the standard consistency of the delivery result is ensured by combining template verification. According to the method, the problems of long development period, large quality fluctuation and the like caused by chain splitting, low intelligent level and excessive dependence on manpower of an existing development tool are effectively solved, and a feasible path is provided for comprehensive intelligence of software development.

The invention relates to the field of visual element recommendation, in particular to a personalized visual communication element recommendation method and system based on Internet big data. The method comprises the following steps: acquiring user multi-dimensional internet behavior data, and mining platform user interaction behaviors one by one to obtain a user multi-platform interaction content data pool; performing user interaction intention speculation on the user multi-platform interaction content data pool, and performing personalized interaction interest preference mining to obtain user interaction interest preferences; deep interaction demand mining is carried out according to user interaction interest preference, adaptive portrait evolution is carried out, and a personalized user demand portrait is constructed; and performing visual element analysis and multi-modal visual mining according to the user multi-platform interactive content data pool to obtain multi-modal visual element information of each homepage. According to the method and the device, the accuracy and the quality of visual element recommendation are improved through accurate user aesthetic demand analysis.

The invention relates to the technical field of fast moving consumer goods data analysis, and discloses a marketing data analysis-based big data intelligent delivery system which comprises a demand analysis unit, a scene analysis unit, a fusion unit, a division unit and a delivery unit. The method comprises the steps of generating user demand potential energy intensity, dividing the user demand potential energy intensity into three types of circle layers, accurately identifying user groups with potential consumption demands but weak current feedback, adopting differentiated delivery decisions according to types of price sensitivity, information missing degree, decision complexity and the like for main retardation factors of different circle layers, and correcting a strategy in combination with a consumption willingness value. Decision resistance of users in each circle layer can be effectively eliminated, potential consumption potential is converted, resource mismatching is avoided, and the return rate of long-term big data delivery of home fast moving consumer goods is increased.

The invention provides an intelligent test platform, which comprises a test demand analysis module used for carrying out demand analysis on input test demand information based on a demand analysis agent to obtain a plurality of test demand items; the test case generation module is used for calling a case generation agent to generate a plurality of test cases corresponding to a plurality of selected target test demand items in response to a selection operation on the plurality of test demand items; the test script generation module is used for calling a script generation agent to generate at least one test script corresponding to at least one checked target test case in response to check operation on the test cases; the test data demand analysis module is used for calling a data demand agent to perform demand analysis on the test case to obtain a plurality of test data demands; and the test data generation module is used for calling a data generation agent to generate test data corresponding to the test data demand. According to the invention, the test efficiency and the test reliability are improved.

The invention discloses a customer demand analysis method based on AI artificial intelligence CRM system data, and the method comprises the following steps: S1, collecting multi-source heterogeneous customer data, executing the preprocessing, and carrying out the fusion; s2, customer behavior features are extracted and fused to form feature vector representation, feature importance evaluation is carried out, and high-correlation feature subsets are screened; s3, inputting the high-correlation feature subset into a swarm intelligence optimization module, and outputting initial network configuration parameters; s4, constructing and initializing a customer demand prediction model according to the initial network configuration parameters; s5, training the customer demand prediction model, and dynamically adjusting the structure of the customer demand prediction model; s6, continuing to iteratively train the customer demand prediction model; and S7, generating a customer demand prediction result. According to the method, intelligent swarm optimization and deep learning methods are fused, accurate prediction of customer demands is realized, and the method has the advantages of high adaptability, accurate prediction and fast response.

The invention provides a big data-based material demand analysis and prediction method and device and a storage medium, and the method comprises the steps: obtaining a multi-source historical material data set of a supply chain link where a target enterprise is located, carrying out the cross-link integration processing of the multi-source historical material data set, and obtaining a cross-link integrated data set, performing demand feature extraction processing on the cross-link integrated data set to obtain a material demand dynamic feature and a material trend evolution feature of each material circulation sequence, and based on a preset dynamic analysis model, performing joint trend matching processing on the material demand dynamic feature and the material trend evolution feature to obtain a material demand dynamic feature and a material trend evolution feature; and generating a predicted demand distribution result of the material circulation sequence, generating a material supply optimization strategy according to the predicted demand distribution result, and feeding back the material supply optimization strategy to the supply chain management system to trigger material allocation operation. According to the method, the hysteresis of manual decision making can be avoided, and meanwhile, the accuracy and the anti-interference capability of strategy execution are enhanced.

The invention discloses an AI-based digital value-added service method for claim and debt demand analysis, and the method comprises the steps: receiving a claim and debt original file set uploaded by a client, and generating a structured clause element set which comprises clause types, core elements and expression mode features; generating a risk quantification label of each key term based on the structured term element set; inputting the structured clause element set and the risk quantification label into a pre-trained clause value-risk collaborative optimization model, and generating a group of optimization clause revision suggestion sets for maximizing a user set value target on the premise of risk controllability; revising the suggestion set for the optimization terms, and generating a feasible revision strategy sequence sorted according to the adaptation degree; and based on the feasible revision strategy sequence, determining a final term revision scheme and matched risk slow release measure suggestions, and taking the final term revision scheme and the matched risk slow release measure suggestions as output of the digital value-added service. According to the embodiment of the invention, the accuracy and decision-making efficiency of credit and debt management can be improved.

The invention relates to the technical field of architecture deployment, in particular to an intelligent architecture deployment system and method based on a large language model. The method specifically comprises the steps that an information interaction module stores a deployment strategy rule set; the demand analysis module analyzes the deployment intention primitives through a preset mapping relation to generate a structured target blueprint; the scheme evaluation module calls a deployment strategy rule set to dynamically calculate the decision weight of the conflict identifier to generate a decision priority, generates a clear inquiry according to the decision priority, corrects a structured target blueprint, and iteratively generates a comprehensive evaluation result; the deployment decision-making module generates a candidate strategy matrix in combination with the multi-target tradeoff point and a preset strategy axis; performing predictive reasoning on the candidate strategy matrix, quantifying a risk adjustment utility score and outputting an optimal execution parameter set; and the scheme generation module outputs an architecture deployment scheme based on the optimal execution parameter set. According to the method, intelligent reasoning, scheme generation and prediction decision making are carried out by utilizing a large language model, and autonomous decision making and automatic generation of architecture deployment are realized.

The invention discloses a multi-agent real-world clinical curative effect evaluation and accurate decision-making system, and relates to the technical field of medical data processing. According to the invention, a research demand analysis agent generates a research scheme after understanding the input content of a user and transmits the research scheme to a data management agent, a statistical analysis modeling agent, a result report generation agent, a data security agent and the data management agent privacy and encrypt data uploaded by the user; meanwhile, additional feature construction is carried out according to the research requirement of a user to form brand new analysis data used by a subsequent statistical analysis modeling agent, and after the statistical analysis modeling agent obtains the data, an analysis result is obtained by calling external software and is transmitted to a result report generation agent; and generating a clinical evaluation report together with the research scheme transmitted by the research demand analysis agent. According to the invention, full-process automatic, specialized and safe research support is realized.

The invention relates to the technical field of data processing, and discloses a method for automatically generating a patchwork pattern based on an intelligent algorithm, and the method comprises the following steps: S1, user demand analysis: collecting and analyzing design demands, including color preference, style requirements, pattern complexity and other parameters, input by a user; s2, preparing a patchwork element data constructing the patchwork element database, wherein the patchwork element database comprises geometrical shapes, textures, color matching schemes and historical design cases; s3, generating a patchwork pattern: adopting a GAN deep learning model to generate a candidate patchwork pattern; s4, optimizing the pattern of the patchwork: optimizing and generating the pattern in combination with reinforcement learning; s5, performing image segmentation and optimization algorithm: through the image segmentation and optimization algorithm, ensuring seamless connection when the cloth splicing blocks are spliced, and adapting to actual cloth processing requirements; s6, editing a patchwork design scheme: generating a patchwork design scheme in an editable format for a user to preview and modify; and S7, outputting the patchwork pattern: outputting the final patchwork pattern.

The invention discloses an intelligent material recommendation method and system based on image communication, and belongs to the field of image communication and intelligent recommendation, and the system comprises a demand analysis and image collection module which is used for analyzing a user demand and collecting and transmitting a reference image; the image feature deep analysis module is used for extracting multi-level features of the reference image; the material library feature index module is used for material characterization and index establishment; the intelligent material generation and collaborative screening module is used for calling a generation model to generate candidate materials and screening an optimal generation material through a multi-dimensional scoring system; the intelligent matching recommendation module is used for performing multi-dimensional matching on the materials in the material library and the optimal generated materials to generate a recommendation list; according to the method, candidate materials can be dynamically generated based on reference image features and demand keywords through fusion of generative AI, and multi-scene personalized demands such as personal life records, enterprise commercial propaganda and mobile terminal short videos are covered.