983 results about "Complex problems" patented technology

The invention discloses a knowledge exploration method and system based on a generative thinking chain and a feedback mechanism, and the method comprises the steps: receiving a multi-modal query inputted by a user, and carrying out the intention analysis to recognize a core demand; preprocessing the multi-modal query, extracting key information, and decomposing a complex problem into a plurality of sub-problems or sub-tasks; dynamically constructing a hierarchical reasoning path based on the generative thinking chain, and marking a logic basis for each step of reasoning path; reasoning path priority is optimized through dynamic weight distribution, a reasoning strategy is dynamically adjusted on line in combination with a meta-reinforcement learning framework, and a reasoning path is optimized; verifying the reasoning path by using a causal reasoning analysis tool, and generating an anti-fact path to correct error nodes; and outputting a structured text and a visual reasoning process, and optimizing model parameters in real time based on multi-user feedback aggregation of the game theory. According to the method, the problems of intelligent query and reasoning in a complex scene can be effectively solved, and the accuracy, real-time performance and credibility of knowledge service are realized.

The invention relates to the technical field of digital factory production management, and discloses a digital factory-oriented multi-target production plan optimization method and system, and the method comprises the steps: decomposing an enterprise order into a plurality of subtasks through a production task decomposition module, and extracting related information; the resource dynamic evaluation module collects data in real time to generate a resource state matrix; the multi-objective optimization algorithm module constructs a model and adopts an improved non-dominated sorting genetic algorithm to solve; the dynamic priority distribution module adjusts the priorities of the sub-tasks according to the real-time data; and the conflict resolution unit is used for solving resource allocation conflicts. The method can accurately decompose tasks, evaluate resources in real time, collaboratively optimize multiple targets, flexibly deal with abnormities, efficiently resolve conflicts, comprehensively improve the production efficiency and benefits of the digital factory, and effectively solve the complex problems in the production plan making and optimizing process of the digital factory.

The invention discloses a privacy enhanced intelligent search method and system based on multi-round iteration. The method comprises the following steps: performing hierarchical semantic analysis on a query input by a user; splitting the complex query into sub-queries based on a task dependency graph algorithm; according to the sub-query, retrieving an evidence fragment from the multi-source data, constructing a semantic element coverage matrix to detect a knowledge gap, and if an uncovered element exists, generating a supplementary sub-query for iterative completion until a preset termination condition is met; integrating cross-modal data through a federated learning technology, and generating a structured knowledge graph fragment in combination with semantic vector alignment and an evidence fusion algorithm; performing dynamic desensitization processing on the retrieval result; and a closed-loop iterative updating mechanism is formed based on a user explicit and implicit feedback optimization retrieval strategy. The problems of traditional intelligent search in the aspects of semantic understanding depth, complex problem reasoning, search result accuracy and integrity, user privacy security and the like are effectively solved.

The invention relates to a task planning and scheduling method which is quicker, lighter and finer in task splitting based on a large model, and aims to improve the capability of processing complex tasks by calling a tool through the large model. According to the method, on the basis of a FastAPI framework, a Text-to-SQL technology, a local knowledge base, Function Calling and other core technologies are integrated, user intention understanding is completed through a large model, dynamic calling and fusion of an API interface, the knowledge base and a database are achieved on the basis of intention understanding, and the task planning, tool calling and decision support capacity of the large model in a complex scene is remarkably enhanced. By introducing a thinking chain technology of multi-level deep task decomposition, the system can decompose a complex task into a plurality of sub-tasks, an execution path is optimized, and the accuracy and execution efficiency of a multi-step reasoning task are improved. A user query tool calling module is researched and developed, user requirements can be accurately recognized and decomposed, proper tools are automatically selected and called for task processing, and efficient solving of complex problems and optimal configuration of resources are achieved.

The invention discloses a medical information multi-agent expert thinking chain collaborative reasoning method and system, and the method comprises the steps: constructing a sub-expert agent and a summarizer agent based on a large language model, processing the medical information, and outputting a processing conclusion of the medical information; and secondly, constructing an evaluator agent, performing medical information conflict evaluation on the output of the sub-expert agent and the summarizer agent, activating a multi-agent debate mechanism when the conclusion output of the summarizer agent exceeds a medical conflict threshold, and outputting whether a processing conclusion is credible or not. Then constructing a coordinator agent, selecting called sub-expert agents and summarizer agents, and generating a plurality of forward reasoning thinking chains; and finally, the evaluator agent evaluates whether the output of the forward reasoning thinking chain exceeds a medical conflict threshold, and iteratively updates an output processing conclusion. According to the method, complex problems in medical information processing problems can be analyzed, and reasoning accuracy and interpretability are remarkably improved.

The invention discloses an operation and maintenance question-answering system and method based on a large language model workflow, and mainly relates to the technical field of information security. Comprising a question semantic classification module used for performing semantic analysis and classification on questions through a text classification large language model when the questions are input, and identifying categories of the questions; the process selection and execution module is used for selecting and executing a corresponding process according to the semantic category of the question to generate a preliminary answer; and the answer generation and formatting module is used for processing the preliminary answers through a text generation large language model and formatting the preliminary answers into final answers which can be understood by the user. The method has the beneficial effects that the accuracy of operation and maintenance knowledge retrieval can be improved, problems can be converted into operation and maintenance instructions to execute related operations, meanwhile, the method has the capability of analyzing and deciding operation and maintenance problems, and the response quality and the capability of processing complex problems are remarkably improved.

The invention specifically relates to the technical field of natural language processing, and discloses an intelligent interactive customer service system based on an AI large model, which comprises a multi-modal input analysis module, an intention preliminary detection module, a large model semantic understanding module, a dynamic knowledge base association module, a dialogue strategy generation module, a natural language generation module and a service feedback work order module, through deep cooperation of a multi-modal input analysis module and a large model semantic understanding module, entity and attribute information is accurately extracted, context semantic association information is mined, and through the cooperative effect of an intention preliminary detection module, a dynamic knowledge base association module and a dialogue strategy generation module, based on a semantic analysis result output by a large model, a dialogue strategy is generated. According to the method and the system, matching calculation is carried out with the constructed dynamic knowledge base, rich knowledge support is provided for solving complex problems, personalized knowledge content is recommended for different users through the natural language generation module and the service feedback work order module, and the reply matching degree and the complex problem processing capacity are greatly improved.

The invention provides a cue word hierarchical optimization method for complex problem decomposition, which belongs to the field of artificial intelligence and natural language processing, and comprises task analysis, hierarchical cue word generation, feedback optimization mechanism, example reinforcement learning and multi-round interactive optimization. The execution effect and adaptability of complex tasks are improved, and the reasoning quality and the stability of generated results are improved.

The invention provides an illusion detection method and device based on semantic embedding and structure entropy detection. The method comprises the following steps: aiming at an input question, generating a plurality of possible answer sets by utilizing a trained language model; converting each answer into a vector by using a pre-trained sentence embedding model, and calculating cosine similarity between the answers so as to construct a similarity graph; representing the similarity graph through a coding tree, and calculating the structure entropy of each node to identify nodes with relatively high semantic uncertainty; dynamically adjusting a threshold value by adopting a moving average method to ensure that high-entropy output is marked as illusion in time; and the answers marked as illusion are eliminated, so that the accuracy of the output answers and the information consistency are ensured. According to the method, the performance of the model on complex problems is effectively improved, and the generation of misleading information is reduced. Preliminary experiments show that according to the method, the illusion output proportion is remarkably reduced on multiple data sets, and the semantic consistency and accuracy of generated answers are enhanced.

The invention provides a large model and mapping knowledge domain fusion method, application method and system, and the method comprises the steps: carrying out the mapping knowledge domain embedding of an obtained mapping knowledge domain to generate mapping knowledge domain embedded information, and generating a natural language question based on the mapping knowledge domain embedded information; the knowledge graph embedded information and the natural language question sentences are input into an entity relation learning model, entity expressions and relation expressions corresponding to the natural language question sentences are output, then sub-graphs corresponding to the natural language question sentences are extracted based on the natural language question sentences, the entity expressions and the relation expressions, and the sub-graphs are converted into natural languages one by one. And combining the entity relationship learning model and the knowledge graph embedded information into the large model to generate a target reasoning model so as to realize fusion of the knowledge graph and the large model. Therefore, the model can extract key information and perform deep understanding and reasoning, the reasoning ability of the model in processing complex problems is enhanced, and the definition and interpretability of subsequent output contents of the model are further improved.

The invention discloses a customer service robot system integrating knowledge base question and answer retrieval and work order processing, which comprises a natural language understanding and generating module, a knowledge retrieval module, a business system integration module, a dialogue management and decision module and a cue word management module, and relates to the technical field of artificial intelligence. Through cooperation of a large language model and a dialogue management module, the system realizes multiple rounds of deep understanding of a natural language, and can actively guide a user to clarify a demand, such as gradually inquiring an equipment model and fault details during repair, memorizing a dialogue context and avoiding information omission. Compared with traditional regular customer service, the method has the advantages that the accuracy of understanding complex problems is remarkably improved, open dialogue scenes such as technical consultation and fault diagnosis are supported, user interaction experience is closer to manual service seats, and the problems that multi-round dialogue understanding is limited, progress data cannot be obtained in real time, multiple knowledge bases are fused, and the automation degree of the business process is low are solved.

The invention discloses a big language model logical reasoning method, device and equipment, a storage medium and a product, and relates to the technical field of big language models.The method comprises the steps that a logical reasoning tree is constructed according to a preset problem, an intermediate step generated by a logic big language model and a reasoning path; alternately training the logic big language model and the logic tree scoring model to obtain a trained logic big language model and a trained logic tree scoring model; and according to the trained logic tree scoring model, evaluating a plurality of next-step reasoning paths generated by the trained logic big language model according to the logic reasoning tree to obtain an optimal reasoning path. According to the method, the training logic tree scoring model is used for replacing an existing prompt large model scoring strategy, a prompt or a high-level large language large model does not need to be preset in the scoring process, and the logic tree scoring model and the logic large model are alternately trained, so that the accuracy of model reasoning is improved in the face of complex problems.

The invention discloses a park integrated energy scheduling method and system based on a master-slave game model, and relates to the technical field of energy scheduling, and the method comprises the steps: collecting basic operation parameter data in a park integrated energy system; constructing an upper-layer game model taking an operator as a dominant, setting constraints, and making an electric heating and cooling price strategy and a unit output plan by taking operator profit maximization as a target; constructing a lower-layer game model, and making a load energy consumption strategy by taking user energy consumption cost minimization as a target; and converting the upper-layer game model and the lower-layer game model into a mixed integer linear programming problem, and obtaining an electric heating cold price scheduling scheme and an output scheduling scheme of each unit by adopting an optimization solution algorithm. The building thermal inertia and demand response mechanism is introduced, the KKT condition, the linear relaxation technology and the strong dual theory are used for converting the complex problem into the MILP model easy to solve, optimization of operator and user benefits is achieved, and the calculation speed and the solving efficiency are remarkably improved.

The invention discloses a knowledge graph enhanced large model multi-hop reasoning method and system, according to a question needing to be answered, path knowledge taking an entity as a center is extracted from an external knowledge graph, and is integrated and injected into the understanding and reasoning process of a large model, so that the reasoning ability of the large model in solving a complex problem is enhanced, and the reasoning efficiency of the large model is improved. And the illusion phenomenon is reduced. Firstly, entities in a question are extracted based on a given question; then, searching multi-hop path knowledge taking the entity as a starting point and limited-length path knowledge taking the entity pair as a starting point and an ending point from an external knowledge graph; then, an encoder is used for calculating embedded vectors of a given problem and path knowledge and calculating the similarity between the embedded vectors, and the path knowledge with the highest similarity is reserved; aggregating the screened path knowledge by using a large model to form global knowledge; and finally, integrating the screened path knowledge and the aggregated global knowledge into cue words by using a predefined instruction template, and exciting a large model to generate answers based on provided knowledge.

The invention belongs to the technical field of knowledge graph and big language model combination, discloses a big language model inference enhancement method based on structure perception knowledge graph representation, and provides a structure perception knowledge injection framework which utilizes a graph neural network to combine with multi-source structure information coding to enhance entity representation. And through a contrast learning mechanism, realizing alignment of semantics and a structure space, and meanwhile, designing an adaptive robustness fusion pruning training strategy, dynamically screening high-quality evidence sub-graphs, and guiding a language model to carry out structured reasoning. According to the method, deep integration and optimal utilization of the structure information and the semantic information are realized, the accuracy, robustness and resource efficiency of the model under a complex query task are remarkably improved, and a stronger embedding basis is provided for generation quality, so that the generation accuracy, stability and efficiency under a complex problem are improved.

The invention discloses a low-altitude atmosphere three-dimensional wind field inversion method based on wind profile radar vertical observation, relates to the technical field of three-dimensional wind field inversion, and aims to solve the problem of poor inversion effect caused by inaccurate model parameters in the wind field inversion process. The difference between simulation data and actual observation data is evaluated from multiple angles, a comprehensive optimization basis is provided, parameter calculation optimization is performed by adopting a particle swarm optimization method, the method has the characteristics of high convergence speed and high global search capability, is suitable for optimization of complex problems, and is used for confirming model requirements according to a wind field inversion rule. The model is selected by combining the evaluation result of the model library, it is ensured that the selected model has high pertinence and applicability, the prediction precision and reliability of the model are improved, model initialization is conducted through wind field fusion data, data information of multiple sources is fully fused, and the comprehensiveness and accuracy of the data are improved.

The invention relates to a multi-agent-based material performance prediction and synthesis system, and the system comprises a multi-agent data enhancement module which is configured to be used for firstly disassembling a complex problem into a plurality of subtasks, and then constructing a fine tuning data set comprising Sub-CoQ question and answer pairs by starting multi-source parallel retrieval; the multi-expert debate module is configured to be used for simulating decision conflicts of different roles in material engineering and generating a direct preference optimization DPO data set through debate; the training and verification module is configured to be used for training and verifying a large model MatMind in the field of materials by utilizing supervised fine tuning SFT and reinforcement learning RLHF based on the fine tuning data set and the DPO data set; and the material performance prediction and synthesis module is configured to be used for realizing intelligent recommendation of a material performance prediction and synthesis process by importing input parameters into the large model MatMind.

The invention discloses a joint precoding design method in a communication and sensing integrated scene. Aiming at a non-cellular cooperative communication and sensing integrated system, the invention provides a joint precoding design scheme based on unsupervised deep learning, and the scheme comprises the steps: building a signal and channel model in a communication and sensing integrated scene; constructing a deeply expanded joint communication sensing precoding design network; the collected training data is utilized to train the deep-expanded joint communication sensing precoding design network; in the deployment stage, the joint communication sensing pre-coding design network is used for outputting optimization algorithm process parameters, and then communication pre-coding vectors and sensing pre-coding vectors under different time slots of the communication and sensing integrated scene are obtained by using the constructed optimization algorithm process. According to the method, deep learning driving is fully utilized, various interferences which are mutually coupled and are difficult to process in the cellular-free cooperative sensing integrated system are inhibited, the problem that the algorithm is complicated in the prior art is solved, and the calculation overhead is greatly reduced.

The embodiment of the invention discloses a method and system for constructing intelligent customer service interaction based on a large language model RAG framework, and the method comprises the steps: carrying out the logic division of a knowledge document through a plurality of division methods, obtaining fragments, inputting the processed fragments into an embedded model, carrying out the vectorization, obtaining fragment vectors, and storing the fragment vectors into a vector database; the standard question and answer data vectors are stored in a vector database; inputting the processing result data into an embedded model for vectorization to obtain a processing result data vector, and storing the processing result data vector into a vector database; a user consultation question is converted into a semantic vector through an embedded model, a plurality of index results are re-ordered once through a Rianker algorithm, and a final index result is obtained; and inputting the final index result as a context and a user consultation question into a large language model to generate a question result. The problems that in the prior art, knowledge base construction seriously depends on manpower, manual maintenance is needed, and complex problems cannot be handled are solved.

The invention discloses a knowledge-based visual question-answering method and device based on big and small model collaboration and a medium, belongs to the technical field of visual question-answering, and solves the problem of how to improve the accuracy of complex questions faced by a visual question-answering technology. In the image description extraction step, an image description and key object label set which is highly associated with a natural language problem is generated, and in the entity enhancement processing step, key entities are extracted and a clarity problem and an entity example which are used for resolving semantic ambiguity are generated. In the candidate answer generation step, a plurality of semantically complementary answers are generated under different reasoning dimensions on the basis of a multi-round generation strategy, optimized candidate answers are screened on the basis of a model built-in scoring mechanism, in the context example retrieval step, examples most related to current input are retrieved, and finally, the candidate answers are obtained. And the large language model receives a unified input prompt formed by splicing output results in the steps, and inference is performed in an autoregression mode to generate answers, so that the accuracy of the visual question-answering technology facing complex questions is effectively improved.

The invention discloses a dynamic retrieval enhanced generation and deep reasoning method and framework based on an intelligent agent, and belongs to the technical field of natural language processing, and the method comprises the following steps: S1, inputting a problem into a big language model which analyzes the problem and divides the problem into three difficulty levels, the problems comprise a simple difficulty problem, a medium difficulty problem and a complex problem; s2, respectively processing the problems according to different difficulties of the problems, and retrieving related knowledge from a corpus; and S3, processing the knowledge obtained in the S2 by the large language model, and outputting a final result. According to the dynamic retrieval enhanced generation and deep reasoning method and framework based on the intelligent agent, the performance of a large language model in various complex tasks is enhanced, especially in the aspect of multi-hop reasoning, the overall efficiency and accuracy of a system are improved through the synergistic effect between modules, and the system is suitable for large-scale popularization and application. And the method is suitable for various knowledge-intensive application scenes.

The invention relates to the technical field of ammonia-nitrogen wastewater degradation, in particular to a deamination reactor optimization method based on CFD and BP neural networks. The method comprises the following steps: performing three-dimensional modeling according to geometric parameters of a deamination reactor, and obtaining a sample set; training a neural network through the sample set; and predicting the cavitation volume fraction and length in the deamination reactor by using a neural network. According to the deamination reactor optimization method based on the CFD and the BP neural network, the cavitation intensity of the deamination reactor is predicted based on a CFD numerical simulation result in combination with the neural network. CFD numerical simulation has the defects of low simulation speed, tedious operation steps and large workload, the cavitation intensity in the device with different parameters is calculated by constructing a neural network model, the amount of information to be processed is large, the calculation speed is high, complex problems can be rapidly processed, and the calculation efficiency is improved.

The invention discloses an AI (artificial intelligence) agent construction system based on hybrid retrieval and father-child segmentation, which comprises the following steps of: dividing a subclass knowledge base according to domain knowledge, performing father-child segmentation processing, and constructing a hierarchical semantic network; vectorization embedding and deep semantic reconstruction are carried out on the user question text; retrieving the reconstructed problem by adopting a mixed retrieval algorithm combining sparse retrieval and dense retrieval, and forming a high-score sub-segment set according to a comprehensive score obtained by dynamic weight distribution; mapping the sub-segments to the parent segment through a hierarchical backtracking algorithm, aggregating brother nodes to form an extended candidate set, and generating an associated sub-segment set after duplicate removal and re-retrieval; and finally inputting a large language model to generate a complete answer. According to the method, the problems of context segmentation, low retrieval accuracy and complicated knowledge base maintenance of traditional document segments are solved, the answer coverage and accuracy of an intelligent question-answering system are remarkably improved, and the method is suitable for knowledge question-answering scenes in the complicated technical fields such as intelligent network connection automobiles and the like.

The invention relates to the technical field of reinforcement learning, in particular to a language model reinforcement learning training extension method and device based on reflection, and the method comprises the steps: inputting a target natural language reasoning problem into a pre-constructed large language model, so as to generate a target reflection thinking chain of the large language model; based on the target reflection thinking chain, performing reinforcement learning training expansion on the large language model to obtain a target inference model; and evaluating the relationship between the target performance and the generation length of the target inference model to obtain an evaluation result, and determining the target effectiveness of the inference expansion of the target inference model by using the evaluation result. Therefore, the problems that the practical understanding of large-scale reinforcement learning training is limited and the ability of a large language model to solve complex problems is reduced due to less extension potential and attribute attention of reinforcement learning from human feedback in related technologies are solved.

The invention discloses a network management racing item question answering system based on a knowledge graph, and belongs to the technical field of knowledge graph teaching. In the system, the association relationship among massive network equipment, protocol configuration and fault cases is stored in a knowledge graph structuralization mode, and natural language processing and graph database retrieval technologies are combined; core knowledge points of complex problems can be quickly positioned, and strong scene adaptability and expansion potential are shown through modular design and a dynamic cooperation mechanism. The knowledge graph management module supports incremental updating and cleaning fusion of multi-source heterogeneous data, can integrate newest competition cases and technical specifications in time, and guarantees the frontier and authority of a knowledge base; and the interaction optimization module automatically identifies a knowledge blind area according to the score of the user on the answer and triggers graph completion. The self-feedback and self-iteration mechanism not only reduces the manual maintenance cost, but also provides a technical basis for cross-domain knowledge migration.

The invention relates to a double-layer double-objective optimization scheduling method based on a large language model, and the method comprises the steps: building a double-layer double-objective optimization model in a server, and building an upper-layer model and a lower-layer model for an AISTS problem based on a plurality of different variables, different constraint conditions and different objective functions; and prompting a dual-objective optimization model of adjustment and algorithm evolution to form a two-stage method based on LLM, the method can overcome the defects that a traditional algorithm needs to design the algorithm manually and adjust parameters manually, the calculation cost is high, the efficiency is low, complex problems are difficult to effectively decompose and coordinate double-layer optimization, a dynamic collaborative optimization framework is lacked, and the flexibility of algorithm combination is low.

The invention discloses a method for sharing a host based on multiple input devices, and belongs to the technical field of computers, and the method comprises the following steps: after a management user logs in a multi-seat configuration tool, creating second seats with the same number as new input devices according to a Udev rule, and enabling the management user to be a user operating the input device corresponding to a first seat of the host; according to a binding instruction sent by the management user, the second seats and the new input devices are bound one by one, and operation users of the input devices corresponding to the second seats are common users; according to a permission configuration instruction sent by the management user, configuring an application window permission for each common user; and sending the binding relationship between the second seat and the new input device and the application window authority to a window manager, so that the window manager manages the application window according to the binding relationship and the application window authority. According to the invention, the problems of disordered management and complex configuration when multiple users share one host window are solved, and more flexible and efficient equipment management experience is provided for the users.

A multi-objective optimization method for a distribution network containing distributed power sources taking into account carbon emissions, comprising the following steps: 1) constructing a multi-objective optimization model for the distribution network; 2) using a hierarchical analysis method to determine the weights of each objective function in the above-mentioned multi-objective optimization model; 3) using an improved beetle whisker particle swarm algorithm to improve the global search capability and local search accuracy of the algorithm for solving the above-mentioned multi-objective optimization model. By integrating a multi-objective optimization model and an improved hierarchical analysis method, the present invention not only optimizes economic indicators, but also takes carbon emission costs into consideration for the first time, thereby achieving the dual economic and environmental benefits of distribution network planning. The improved beetle whisker particle swarm algorithm used further improves the search efficiency and solution accuracy, ensuring the robustness and rapid convergence of the algorithm when dealing with high-dimensional complex problems. The method effectively reduces network losses and carbon emissions, improves voltage distribution, and provides technical support for the green transformation and sustainable development of power systems.

The invention discloses a large model training method, a question and answer method, related equipment and a computer program product. The method comprises the steps of obtaining question and answer training data; the problem samples are sent to a to-be-trained large model for reasoning, and prediction output of the large model is obtained; calculating an exploration reward based on hidden layer state characteristics generated in a big model reasoning process, wherein the exploration reward is used for encouraging the big model to process the problem sample by adopting an unknown reasoning path; calculating a result reward based on the predicted output and the answer tag; and updating parameters of the large model by adopting a reinforcement learning mode according to the exploration rewards and the result rewards. Exploration rewards are additionally added in the reinforcement learning process, a large model can be encouraged to explore an unknown reasoning path, local optimum is avoided, the probability that the large model discovers a better reasoning path when facing a complex problem is improved, the performance of the large model on a complex reasoning task is improved, and the reasoning efficiency of the large model is improved. And the accuracy of the answering result of the complex reasoning question is improved.

A reference point is arranged on the ground to calibrate a ground world coordinate system, a robot base coordinate system is established based on robot base features, and a rigid transformation matrix between the two is calculated to achieve spatial alignment. A multi-sensor network is constructed, a visual sensor and a laser radar are associated to a double-reference coordinate system, and data are synchronously collected and unified to the same coordinate system. And according to the spatial consistency error of the data, dynamically evaluating the credibility weight of the sensor, constructing a joint error equation, and based on least square method optimization, weighting fusion data and outputting calibration parameters. According to the invention, the problems of dependence on single sensor precision, lack of a system-level calibration scheme and complex calibration process are solved, and the calibration precision and efficiency of the robot system are improved.