456 results about "Demand analysis" patented technology

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 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 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.

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 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 an engineering construction stage user multi-dimensional demand analysis system and method, and the system comprises a multi-modal semantic model construction module which integrates a multi-source knowledge base, and extracts triple description statements from the provisions of the multi-source knowledge base to construct an engineering object ontology knowledge graph; the demand structured guidance and intelligent analysis engine module dynamically identifies an engineering stage and a user role, guides a user to perform text demand input, and performs semantic completion and standardization processing; the demand conflict detection and coordination correction module is used for detecting conflicts in user demands and generating a conflict influence analysis report and a coordination processing scheme; the intelligent drawing and dynamic optimization module is used for automatically drawing according to the triple obtained through demand analysis; and the demand analysis visualization and flow closed-loop module is used for generating an executable parameterized demand document and an association chart, and completing demand-design-verification engineering closed-loop management, so that intelligent analysis and standardization of user multi-dimensional demands are realized, and the system has the advantages of high efficiency, accuracy and intelligence.

The invention discloses a finance and taxation field agent workflow arrangement method and system based on a natural language, and the method comprises the steps: S1, widely collecting workflow arrangement cases of a finance and taxation field typical business scene, carrying out the detailed analysis of each case, and extracting a key business process and node information, S2, taking all structured workflow data as nodes, and carrying out the calculation of the workflow arrangement cases, constructing a finance and tax workflow knowledge graph by taking the semantic similarity, the function multiplexing relationship and the business process dependency relationship among all the structured workflow data as edges; s3, based on structured data and natural language description corpora constructed by the finance and tax workflow knowledge graph, performing training optimization on the base large language model by adopting a domain self-adaption and task specific fine tuning technology; s4, a user inputs a natural language demand through the interactive interface, and the natural language demand is processed by using the demand analysis agent, the retrieval agent and the arrangement agent; and S5, displaying the generated draft workflow in a visual flow chart form.

The invention relates to the technical field of building construction, in particular to a BIM (Building Information Modeling) forward design method based on full-process AI (Artificial Intelligence) driving, which comprises the following steps of: digitizing a demand, and extracting a quantitative design index through a multi-modal demand analysis module; parameter generation: generating and evaluating a first-order BIM seed model by using an AI parameter generation engine driven by a generative adversarial network; iterative optimization is carried out, and automatic form, performance and layout optimization is carried out on the model through topological optimization, energy consumption prediction and reinforcement learning; performing collaborative verification, and generating collaborative correction suggestions by adopting a conflict detection algorithm fusing a boundary box and a GJK algorithm; and result delivery: performing simulation verification through a digital twin engine and outputting a final result. According to the method, intelligence and automation of the whole process from demand to delivery are achieved, the problems that a traditional method depends on manpower, collaboration lags behind, performance analysis and design are disjointed and the like are solved, and the design efficiency, quality and scientificity are remarkably improved.

The invention provides a software system intelligent development method and system based on an AI technology, and relates to the technical field of software development. Through natural language demand analysis, intelligent component recommendation, code-free generation and continuous optimization closed loop, the problems that a traditional development mode is low in efficiency, insufficient in intelligence and rigid in system are solved, and the method has the advantages that the development threshold is lowered, the development efficiency is improved, continuous optimization of the system is achieved, and data security is guaranteed.

The invention relates to a cluster scheduling method and system for multi-unmanned aerial vehicle cooperative detection of hoisting equipment, and the method comprises the steps: obtaining detection object information from a detection request when the detection request of a user side is received; calling a preset equipment three-dimensional model based on the detection object information, and generating a detection task based on the equipment three-dimensional model; performing demand analysis on the detection task, and outputting task demand information; a cooperative detection strategy is generated based on the task demand to generate a cooperative flight route, the unmanned aerial vehicles of the corresponding model and number are called, and the multiple unmanned aerial vehicles are controlled to cooperatively execute the flight task according to the generated cooperative flight route; and based on a preset communication link, receiving detection data acquired by the unmanned aerial vehicle, mapping the detection data to a corresponding detection point location in the equipment three-dimensional model, and sending the detection data to a user side. The method and the device have the effect of improving the efficiency and the accuracy of large-scale equipment detection.

The invention relates to the technical field of music recommendation, in particular to an intelligent audio recommendation control method and system. The method comprises the following steps: detecting a real-time face image of a user in an initial music playing process, carrying out multi-period emotion evolution modeling, and constructing a user emotion state evolution curve; identifying the initially played music, and performing emotion resonance matching based on the emotional state evolution curve of the user to obtain emotion resonance response data; constructing a global music feature map of the user music library; performing deep rhythm demand analysis on the emotion resonance response data according to the global music feature map, and extracting an emotion demand adaptive music set; adaptive music recommendation decision making is carried out based on the emotion demand adaptive music set, and recommendation control execution is carried out. The music emotion demand of the user is predicted through accurate user emotion perception, adaptive music is efficiently and accurately recommended, the satisfaction degree of the user is improved, and good auditory experience is provided.

The invention discloses a new line training demand analysis method based on data driving, and relates to the technical field of computers. The method aims at solving the problems that training demand analysis depends on subjective experience, data islands are serious, response lags and quantitative prediction models are lacked in the prior art. The method comprises the steps of collecting and integrating multi-source heterogeneous data such as physical assets, human capital and process knowledge; constructing a time sequence dynamic association map representing the preparation state; predicting a future preparation state by using the space-time diagram neural network model; quantizing a personnel capability gap based on the prediction result and the operation baseline, and generating a capability gap matrix; performing priority ranking and clustering on the training requirements in combination with the key weight of the system to form a structured training theme; and dynamically generating a training plan, and updating the atlas in a closed-loop manner through training feedback data. Accurate prediction, quantitative evaluation and adaptive optimization of training requirements are realized, and scientificity and foresight of new line preparation are improved.

The invention relates to the technical field of agricultural irrigation, and particularly discloses an intelligent agricultural water and fertilizer integrated irrigation system, which comprises an environment and crop state sensing network used for collecting soil, weather and crop phenotype data; the multi-source data fusion and demand analysis engine is used for associating data and analyzing water and fertilizer demands of individual crops; the dynamic decision-making unit based on the crop growth model is used for generating a partition variable irrigation and fertilization instruction through rolling optimization; and the partition variable execution mechanism is used for accurately executing the differentiated instructions. By means of the scheme, personalized precise water and fertilizer management of different crops in the field is achieved, the water and fertilizer utilization efficiency is remarkably improved, and balanced growth of the crops is promoted.

The invention discloses a large model edge collaborative decision-making method and system oriented to a dynamic scene, and relates to the technical field of data processing and analysis, and the method comprises the steps: calling original monitoring nodes to establish an original monitoring topological structure after an emergency event occurs; verifying the activation identifier through communication, analyzing function loss, and establishing first demand data; an emergency scene and second demand data are configured, a temporary edge agent is configured after a demand is analyzed, and networking is carried out; after networking succeeds, the edge agent reads the monitoring data and independently makes a decision to form a first decision result; and finally executing communication interaction and role evolution conflict game analysis to generate a second decision result. The technical problems that in the dynamic scene, a traditional data processing mode is difficult to adapt to environment changes, response is delayed, and multi-agent decision conflicts cannot be coordinated are solved, and the technical effects that data processing is timely and efficient, decision making is accurate and reliable, and the collaborative decision making requirement in the dynamic scene is met are achieved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a demand-driven data processing method, device and equipment and a medium, and the method comprises the steps: obtaining demand information, and generating a structured demand analysis report through a demand analysis agent; generating an executable code by combining a strategy conversion agent with a zero code operation platform; generating a test case through the automatic test agent and executing an automatic test; when the test result is wrong, adjustment is carried out through the result feedback agent; when the test result is passed, executing the executable code to generate a processing result, and verifying the intelligent experience certificate by the accuracy; and storing the verified processing result, and performing multi-level data analysis through the data analysis agent to obtain an analysis result. By establishing a closed loop of demand analysis, rule conversion, test verification and data analysis, rapid conversion and accurate verification from the demand to the result are realized, the processing efficiency is improved, and the error risk is reduced.

The invention discloses an interactive terminal system for artificial intelligence recruitment, and relates to the technical field of artificial intelligence and human resources. The system comprises an intelligent interaction module, a demand analysis module, a data storage module, an intelligent matching engine, a post issuing module, an intelligent evaluation module, a process management module and a user feedback and model optimization module. The system receives an unstructured recruitment demand through a multi-modal interface, analyzes and generates structured post data based on a natural language processing technology, realizes accurate man-post matching through a multi-dimensional matching algorithm, and automatically completes post release, skill evaluation, video interview analysis and process management. According to the method, the problems of inaccurate demand understanding, low matching efficiency and tedious process in traditional recruitment are effectively solved, intelligent and automatic management of the whole recruitment process is realized, and the recruitment efficiency and talent matching accuracy are remarkably improved.

The invention discloses a data center intelligent energy-saving control system and method based on artificial intelligence, and the system comprises an SLA-energy-saving collaborative decision center, a business support module and a feedback iteration module, and the SLA-energy-saving collaborative decision center is in communication connection with the business support module and the feedback iteration module. An intelligent energy-saving coherent process of demand analysis, strategy generation, execution feedback and iterative optimization is formed, and action linkage of all links is achieved through real-time data interaction; a unified data model unit, an SLA constraint disassembly unit and a global strategy generation unit are arranged in the SLA-energy-saving collaborative decision center, reliability requirements of different tenants are converted into specific control standards through the SLA constraint disassembly unit, and the reliability requirements of the different tenants are optimized by combining a priority mechanism of task scheduling and differential configuration of all equipment parameters. The SLA standard reaching rate of 99.999% or above of the core tenants can be guaranteed, and more energy-saving space can be mined for the common tenants and the basic tenants.

The invention discloses a cardiovascular intervention catheter storehouse scheduling system based on demand analysis, and relates to the technical field of medical informatization, and the system comprises a data collection module which is used for obtaining an operation plan, a doctor identifier, a step sequence and an angiography image; the feature extraction module is used for generating a situation feature vector according to the contrast image; the standard deduction module is used for deducing subsequent steps and corresponding catheter sets; the selection probability calculation module is used for querying a doctor preference knowledge base in combination with the doctor identifier, the step code and the feature vector to obtain catheter selection probability distribution; the first judgment module is used for outputting a predicted catheter model if the catheter selection probability exceeds a first threshold value, otherwise, counting the catheter use frequency of the historical data of the doctor; the second judgment module is used for outputting the corresponding catheter model if the use frequency exceeds a second threshold value, otherwise, determining a predicted catheter model from the standard catheter set according to a rule; the accuracy and clinical suitability of catheter prediction can be improved.

The invention discloses a demand development method, device and equipment. The method comprises the following steps: calling a demand analysis agent to analyze a natural language demand into a structured function demand unit comprising an input specification, an output specification and an execution constraint; the calling code generation agent generates source codes corresponding to the development requirements according to data type definitions and function calling specifications in the structured function requirement unit and the local project code library; calling a code test agent to generate and execute a test case based on the source code and the structured function demand unit; and after determining that a software deployment condition is met based on a test result, calling a code deployment agent to generate a deployment script and configuration information, and submitting the deployment script and the configuration information to a deployment execution engine to complete automatic deployment. According to the application, end-to-end automatic development from natural language requirements to application deployment can be realized.

The invention relates to the technical field of data acquisition and analysis, in particular to a natural disaster engineering rescue data demand analysis and acquisition method, which comprises the following steps: based on different natural disaster site information, identifying a current engineering rescue task type, constructing a mapping model between the task type and a data demand, and outputting a task feature vector; outputting a data demand priority matrix in combination with the disaster emergency level, the task timeliness requirement and the core component distribution; inputting the data demand priority matrix into an acquisition path planning module, and generating an acquisition task sequence in combination with field reachability analysis and sensing resource states; and executing a structured data acquisition operation according to the guidance of an acquisition task sequence, outputting a feedback correction factor to update a task feature vector, and realizing self-adaptive iteration of a data acquisition scheme. The method has the advantages of high adaptability, high precision, high resource utilization rate and the like, and is suitable for natural disaster engineering rescue scenes with variable task types, complex environments and limited decision windows.

The invention discloses an electric power skill training and scoring evaluation method based on a large model algorithm, and belongs to the technical field of electric power skill training and scoring evaluation, and the method comprises the steps: collecting and processing student information data and historical operation behavior data, and integrating the processed data into a unified data warehouse for storage; analyzing the information data and the power skill post demand of each student by using a natural language processing technology to generate a demand analysis result; generating a training content scheme according to the demand analysis result based on a pre-selected large model; by analyzing learning behavior data, constructing a student learning style model, and further optimizing a training content scheme to obtain an optimal training scheme; a three-dimensional electric power working scene model is constructed by using computer graphics and VR / AR technologies; through establishing an operation evaluation model, real-time scoring evaluation is carried out on electric power skill operation of students, and an evaluation result of each student is obtained.

The invention provides a multi-dimensional data intelligent analysis and evaluation system for a building, and belongs to the technical field of data analysis, and the system comprises an information extraction module which is used for obtaining the living demand of a target customer, and carrying out the word segmentation processing and word segmentation mapping of the living demand, and obtaining a plurality of pieces of demand information of the target customer; the building determination module is used for determining a target residence of the target customer according to the plurality of pieces of demand information, and determining a plurality of target buildings based on the target residence; the demand analysis module is used for analyzing each piece of demand information based on a pre-trained demand analysis model and determining multi-dimensional demand data; and the building recommendation module is used for obtaining the building information of each target building, calculating the demand score of each target building according to the multi-dimensional demand data corresponding to each piece of demand information, and carrying out building recommendation for the target client. Intelligent and accurate matching of the demand and the building is realized, and the house selection efficiency and satisfaction degree are improved.

The invention provides an automatic software development system based on multi-agent cooperation, and the system comprises a demand analysis agent which is used for receiving a natural language demand input by a shared working area, generating a demand specification document containing an acceptance standard based on the natural language demand, and sending the demand specification document to the shared working area; the detailed design agent is used for receiving a demand specification document input by the shared working area, performing system design based on the demand specification document, generating a design specification document and sending the design specification document to the shared working area; the project development agent is used for receiving a design specification document input by the shared working area, performing code development based on the design specification document, generating an executable code and sending the executable code to the shared working area; and the shared working area is used for storing and forwarding the natural language demand input by the user and the output of the demand analysis intelligent agent, the detailed design intelligent agent and the project development intelligent agent. The development efficiency can be improved, the labor cost is reduced, and the software quality is improved.

According to the embodiment of the invention, the instrument type selection method comprises the following steps: demand collection: obtaining a spoken type selection demand input by a user; the demand analysis is used for converting the spoken type selection demand input by the user into a demand of applying a standard instrument term; type selection searching: searching in an instrument type selection knowledge graph based on the analyzed demand to obtain an instrument entity meeting the demand; and performing model selection evaluation, calculating weighted scores of the instrument entities meeting the requirements based on weights of different model selection elements, and obtaining the instrument entities of final model selection, based on the method, customer requirements can be more fully and accurately understood, meanwhile, the entity association ability and retrieval ability of the knowledge graph are further enhanced, and the user experience is improved. Therefore, the instrument type which better meets the requirements of the user is recommended for the user. The invention further provides an instrument type selection system and a storage medium.

The invention discloses a multi-Agent cooperation AADL model AGREE verification statement automatic generation and verification method. The method comprises the following steps: S1, constructing a domain knowledge base; s2, the Agent is analyzed by the AADL model to extract key information; s3, the demand analysis Agent splits atomic propositions from the natural language demand; s4, the AGREE generator Agent generates an AGREE attachment code; s5, the model fusion Agent automatically embeds the AGREE specification into the target AADL component; s6, the AADL verification Agent performs iterative verification and automatic repair, and a multi-dimensional verification system is constructed; and S7, executing AGREE-based AADL architecture formalized verification, and performing logic attribute verification on the grammar compliance model. According to the invention, innovative fusion of a formalized verification method and an intelligent technology is realized, and the core problem that formalized verification of the AADL architecture is difficult to implement is effectively solved.

The invention provides a game resource scheduling management method and system based on cloud computing, and relates to the technical field of game resources, the method comprises the following steps: decomposing a server of a cloud game application, and establishing a multi-attribute micro-service; constructing a heterogeneous resource pool containing computing nodes of different architecture types; performing request type demand analysis according to the received game session request, and determining a demand micro-service combination; based on the hardware preference features and the performance portraits, configuring computing nodes of the micro-services, and deploying corresponding micro-service instances on the computing nodes; and based on the demand micro-service combination, establishing communication connection between corresponding attribute micro-service instances through a service grid, and generating a collaborative scheduling execution instruction. By means of the method and device, the technical problem that in the prior art, due to the fact that resource scheduling is coarse in granularity and hardware resources are not fully utilized, computing resources are wasted, hardware loads are unbalanced, and the performance of the game server is affected is solved, and the resource utilization rate is increased through fine-granularity task-level scheduling.

The invention provides a multi-agent collaborative task process arrangement method and system, and the method comprises the steps: carrying out the processing of a task demand through a large language model by employing a demand analysis agent, and obtaining a structured task description; determining a workflow topological graph based on the structured task description by using a process modeling agent; scheduling and operating a task execution agent corresponding to each subtask by utilizing an execution engine according to the workflow topological graph; determining an actual execution path of each sub-task based on the task execution log of each sub-task by using a deviation identification agent, comparing the expected execution path with the actual execution path by using the deviation identification agent, and comparing the expected index data with the task execution index data to obtain a comparison result; and optimizing the workflow topological graph based on the comparison result by using a process modeling agent. Therefore, a complete closed loop of demand analysis, flow generation, execution, monitoring, diagnosis, optimization and regeneration of flow arrangement can be realized.

The invention relates to the technical field of logistics management systems, and particularly discloses an artificial intelligence-based supply chain logistics information management system, which comprises a data acquisition module, a demand analysis module, a logistics decision module, a dynamic sensing module and a supply chain monitoring module. Through multi-source data acquisition and standardized preprocessing, the problem of traditional system data is solved, and a foundation is laid for analysis; on the basis of an improved LSTM model and a dynamic threshold value, demands are accurately predicted, risks are early warned, and inventory and stockout losses are reduced; a hybrid algorithm is adopted to collaboratively optimize paths, assess inventory and dynamically adjust environmental interference, and balance logistics efficiency and cost; a toughness index is generated by means of an Attention-LSTM model, and risk quantitative traceability and rapid disposal are realized in combination with hierarchical response and visualization; the modules are in real-time cooperative closed loop, cooperate with parameter self-optimization and threshold updating, adapt to industry and environment changes, and provide stable and intelligent support for supply chain management.

The invention relates to the technical field of intelligent power grids, in particular to a charging pile off-peak parameter adjustment control system and method, and the system comprises a charging demand analysis module, a load fluctuation evaluation module, a power demand distribution module, a parameter adjustment control module and a dynamic adjustment and optimization module. According to the invention, by analyzing the charging demand and the power grid load information, generating the correlation model of the charging task and the power grid load, optimizing the distribution of the charging task and accurately evaluating the influence of load fluctuation, a basis is provided for the distribution of the power demand, the priority execution of the charging task in the load valley period is ensured, and the impact of the power grid load is avoided; the undistributed power demand is compared with the residual bearing capacity of the power grid, the matching sequence of the charging task and the power grid nodes is dynamically adjusted, the flexibility and accuracy of power grid load adjustment are improved, the scheme abandons the practice of depending on a preset time period or a simple algorithm, and the power grid demand is accurately responded based on real-time load change. And optimal balance of the power grid load is realized.