529 results about "Complex problems" patented technology

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 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 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 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 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 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 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 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 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.

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.

The invention discloses a use method of a physical information neural network for an optimal control problem, and belongs to the field of optimal control problems. The method comprises the following steps: adding physical constraints into a loss function of a physical information neural network, and dynamically adjusting weights according to loss changes in a training process through a self-adaptive weight mechanism, so that the network pays more attention to an area with relatively large loss, thereby improving the solving precision of a model for complex problems. According to the method, under the condition of not needing any data, by fusing constraints of a physical equation and using a self-adaptive weight mechanism, the optimal control law is more accurately approached, a traditional method for refining a grid is replaced, and the problem that in the face of two-dimensional, three-dimensional or high-dimensional problems, the optimal control law is more accurately approached by the traditional method is solved. The calculation amount is large due to the fact that the grids need to be refined continuously.

The invention discloses a large model-knowledge graph driven data blood relationship traceability reasoning method and system. According to the method, through a closed-loop framework of KG-driven dynamic decomposition, small-model lightweight reasoning and visual credibility verification, the defects are systematically solved, and an interpretable, low-cost and easy-to-expand data consanguinity depth traceability scheme is provided for scenes with high credibility requirements such as data governance, compliance auditing and root cause analysis. According to the system, aiming at complex problem requirements, after a complex problem is input, the problems of no interpretation, low reliability and the like in data blood relationship traceability are solved through two modules, namely problem reasoning and verification presentation. The modular design not only conforms to the technical decoupling principle, but also facilitates independent optimization, and is suitable for medical treatment, scientific research and other scenes with high requirements for interpretability.

The invention relates to a digital textbook knowledge explanation method based on a time sequence knowledge graph, which comprises the following steps: receiving an explanation request from a user side, and associating the explanation request with a specific teaching content unit in a pre-constructed time sequence knowledge graph by performing intention analysis on the explanation request; based on the current learning progress of the user and a target teaching content unit corresponding to the explanation request, extracting a knowledge explanation constraint sub-graph conforming to the sequential logic from the sequential knowledge graph; and based on the knowledge explanation constraint subgraph, generating explanation content conforming to the sequential logic, and outputting the explanation content to a user side. The explanation content is strictly constrained to the current learning progress of the student, concept outlines are avoided, and the explanation accuracy and acceptability are improved; a complex problem solving method is systematically decomposed and is clearly explained according to a teaching sequence, so that the understanding quality is improved; the confusion of students caused by outline exceeding or complex steps is reduced, and the learning efficiency and effect are improved.

The embodiment of the invention discloses a 4K splicing processor and a 4K splicing display system. The 4K splicing processor comprises a driving circuit used for processing a 4K resolution signal, the driving circuit comprises an FPGA chip U5, TVS array chips U10, U11, U12 and U13 and DP signal line connectors CN9 and CN10; the model of the U5 is Spartan-6, the models of the U10, the U11, the U12 and the U13 are all RClamp0524P, the CN9 is connected with the U5 through the U10 and the U11, and the CN10 is connected with the U5 through the U12 and the U13; the U5 receives the input 4K resolution signal through the CN9 to process the 4K resolution signal and outputs the processed 4K resolution signal through the CN10 to achieve 4K resolution signal looping. According to the 4K splicing processor and the 4K splicing display system, the problem that 4K splicing display system equipment is complex is solved.

The invention discloses a multi-agent-based context knowledge internalization, sharing and multiplexing system and method. The system comprises a multi-source knowledge capture module, a situation information extraction and structuring module, a knowledge internalization and incremental learning module, a situation awareness knowledge reuse and recommendation module and a knowledge quality evaluation and maintenance module, wherein each module is composed of a special intelligent agent or a functional unit and works cooperatively; according to the method, interaction, cooperation and process data and expert input of a task execution full cycle of a multi-agent system are captured through a multi-source module, preprocessing is carried out through a situation extraction module, a situation is associated to generate structured knowledge, and then the structured knowledge is fused into a dynamic knowledge system through an internalization module to achieve incremental learning. In a new task, matching knowledge is retrieved and recommended through the context awareness module, and finally the knowledge quality is periodically maintained by the evaluation module to ensure accuracy and timeliness; according to the method, the autonomous learning ability, the knowledge reuse efficiency and the intelligent level of solving complex problems of the multi-agent system are remarkably improved.

The invention relates to the technical field of artificial intelligence, in particular to a method, a system and equipment for constructing a primary and middle school education tutoring large model and a medium. The method comprises the following steps: firstly, taking Qwen2.5-3B-Instruct as a basic model, and constructing a training data set according to an obtained teaching data set; then, according to the training data set, constructing a reward function to obtain a reward value, and calling a reinforcement learning method to adjust model parameters to obtain a reinforced basic model; and finally, according to the constructed teacher style data set and the set personalized cue words, the strengthened basic model is finely adjusted in a supervision instruction fine adjustment mode, a middle and primary school education tutoring large model is obtained, through two-stage fine adjustment, the reasoning ability of the model is improved, and the calculation efficiency is improved while the low calculation resource requirement is kept. The processing capability of the old people model for complex problems is improved; and a clear teacher style is endowed to the old people model.

The invention discloses an electric power field credible question-answering system based on a knowledge graph and multiple agents, and belongs to the technical field of intelligent power grids. The electric power field credible question-answering system comprises the steps that an existing knowledge base is stored through a vector knowledge base, the knowledge graph and a relational database, and then a multi-agent system is constructed based on a large language model; a complex problem is decomposed into a plurality of sub-problems by adopting a logic form language in real-time dialogue, solving is carried out based on operators such as planning, reasoning and retrieval, symbolization and structuring of a problem solving process are realized, knowledge is aligned and integrated by using a semantic reasoning technology, a semantic relationship between the knowledge is identified and established, and a real-time dialogue result is obtained. And realizing credible questions and answers in a manner of recalling related knowledge. According to the method, the deep fusion of the unstructured text, the structured data and the semantic relationship is realized by constructing the multi-modal knowledge storage system, the knowledge coverage rate and the semantic consistency are remarkably improved, and the understanding and response capabilities of the system to compound problems are improved.

The invention relates to a double-side rubber coating mold for double-color injection molding production, and belongs to the technical field of molds, the double-side rubber coating mold comprises a fixed mold and a movable mold which are matched with each other, and a first cavity used for forming a framework and a second cavity used for coating rubber on the formed framework are formed between the fixed mold and the movable mold; the second cavity comprises a positioning cavity for positioning a framework and an end rubber coating cavity which is communicated with the positioning cavity and is used for forming a framework end sealing element; an extension cavity for forming rubber coating connecting layers on the front and back surfaces of the framework is formed at the joint of the end rubber coating cavity and the positioning cavity, and the extension cavity comprises a front surface coating cavity and a back surface coating cavity which are respectively positioned on the sides where the fixed mold and the movable mold are positioned. By adopting the scheme that the first cavity and the second cavity share the same mold, the problems of complex working procedures caused by double molds / double machines of a double-side rubber-coated structure product and resource occupation of an injection molding machine are solved, the development and production cost of the mold can be reduced, and the production efficiency of the product is improved.

The invention discloses a knowledge graph enhanced reasoning method and a knowledge graph enhanced reasoning system for high-risk field medical decision, which are used for strictly limiting the reasoning of a large language model within a high-quality knowledge graph range, so that the reliability is improved, and factual errors and'model illusion 'are fundamentally eliminated. Through an innovative constraint generation and traceability mechanism, each conclusion can be traced to a direct evidence in the knowledge graph, the interpretability is enhanced, and the complete transparency of the reasoning process is realized. The mixed query driven sub-graph retrieval method can deeply understand the intention of a user, accurately position and extract structured knowledge related to complex problems, improves the retrieval precision, and has a retrieval effect far better than that of traditional keyword or vector retrieval.

A distributed spatio-temporal joint trajectory planning method for an intelligent agent cluster relates to the technical field of robot autonomous control, and comprises the following steps: planning the moving trajectory of each intelligent agent; collision conflict information between any intelligent agent and other intelligent agents in the cluster is detected in sequence, and the collision conflict information is sent to the other intelligent agents through network broadcasting; any agent generates a conflict graph after receiving the collision conflict information; based on the conflict graph, an approximate minimum vertex coverage algorithm is adopted to solve the minimum subset of the agents needing to re-plan the trajectory; re-planning the moving trajectory of the agents in the minimum agent subset at the same time; repeating the steps of collision conflict information detection, conflict graph generation, minimum agent subset construction and moving trajectory re-planning until the number of detected conflict information is zero, and determining the final moving trajectory of each agent; the method is used for solving the problems that traditional space-time coupling processing is poor, expandability is poor, dependence on a front-end path is high, and multi-constraint processing is complex.

The invention discloses a method for optimizing the AGC load regulation rate of a power station, and the method comprises the steps: building a digital twinborn body of a unit, building a boiler thermal positive and negative balance model, a fuel heat value prediction model and a self-adaptive control parameter library based on the historical operation data of a target unit, and receiving and processing the operation data of the unit in real time; dynamically predicting a boiler theoretical calorific value demand corresponding to the AGC instruction by using a long-short-term memory recurrent neural network, and calculating a deviation between an actual calorific value and a theoretical calorific value; when the deviation exceeds a preset threshold value, multi-mode cooperative compensation control of feed-forward compensation, parameter self-tuning and energy closed-loop calibration is implemented in parallel; the AGC instruction response time is controlled within 50-55 seconds, and model self-updating is achieved through online tracking. According to the method, the defects that traditional thermal power station fuel calorific value calibration is slow in response, a single control model is difficult to deal with complex problems, AGC response time is long and the like are overcome, the power station AGC load regulation rate and thermal power generating unit frequency modulation performance are remarkably improved, and power grid frequency stability is guaranteed.

The invention belongs to the technical field of unmanned system reliability, and provides an unmanned cluster fault mode analysis and transverse and longitudinal propagation analysis method and system based on a task chain, electronic equipment and a storage medium. The method comprises the steps of model construction, fault mode recognition, severity setting, fault transmission recognition, adjacency matrix construction, network model construction and fault transmission influence analysis. According to the method, the unmanned cluster is subjected to hierarchical division, so that the problem of complex calculation process of a physical model is avoided, and the real-time performance of fault mode analysis is improved; through fault mode identification, severity setting and fault transmission identification, analysis of complex cooperation relation and interaction among individuals is realized, and adaptability of a dynamic change environment is improved. Through adjacency matrix construction, network model construction and fault transmission influence analysis, the analysis of the mutual dependency relationship between different fault modes is improved, and the accuracy of the propagation path is improved.

The invention relates to the technical field of product service maintenance, in particular to a product service maintenance method and system based on a problem classification model, and the method comprises the steps: collecting the problem description data of a steward in real time, carrying out semantic analysis and classified storage, generating a problem classification model, judging the emergency degree and complexity of a problem, and selecting a response strategy. The system integrates a natural language processing module, a voice recognition module and a problem classification module, accurate problem positioning and automatic solution generation are achieved through multi-modal data fusion, and meanwhile a manual intervention mechanism is triggered to ensure that complex problems are processed in time. According to the method, the problem feedback efficiency and the solving speed can be remarkably improved, the average response time is shortened, the labor cost is reduced, the initiative and safety of operation and maintenance of the rail transit equipment are improved, and remarkable economic benefits are brought to the industry.

The invention discloses an operation and maintenance method and system based on an AI large model, and relates to the technical field of artificial intelligence operation and maintenance. The method comprises the following steps: acquiring monitoring data, analyzing the monitoring data, and decomposing the analyzed monitoring data into a plurality of field feature sets; determining a knowledge vector corresponding to each domain feature set; obtaining historical normal operation data of each knowledge vector, calculating a deviation value according to the historical normal operation data, and determining an abnormal symptom vector according to each deviation value; determining potential root cause vectors in the knowledge vectors according to the knowledge vectors and the abnormal symptom vectors; searching all paths, calculating a comprehensive weight value of each path, and taking the path with the highest comprehensive weight value as a root cause reasoning chain of the composite problem; and extracting a processing strategy corresponding to each knowledge vector, and performing optimization according to the logic sequence of the root cause inference chain and the processing strategy to generate a solution. By implementing the technical scheme provided by the invention, the system operation and maintenance efficiency can be improved.

The invention discloses a control method for ultra-low-altitude flight of a large unmanned aerial vehicle, and the method comprises the following steps: building a kinetic model of the unmanned aerial vehicle, the kinetic model comprising dynamic equations of position, speed, attitude control and control input; performing error tracking control on the attitude and the speed of the unmanned aerial vehicle by adopting a Backstepping nonlinear control method based on the dynamic model; estimating external disturbance and dynamic parameters of the unmanned aerial vehicle in real time by combining an L1 adaptive control method, and adaptively adjusting control input to compensate wind speed change; predicting a future flight state by using a model prediction control algorithm, optimizing a flight path and compensating wind speed disturbance; a safe trajectory is planned in real time through a dynamic window method, and obstacles are avoided in combination with dynamic constraints. According to the invention, through organic combination of Backstepping control, L1 adaptive control, MPC and DWA algorithms, the complex problems of wind speed disturbance, obstacle avoidance, high-precision operation and the like in ultra-low-altitude flight can be solved.

According to the PCIe Switch system and the PCIe switch, a PCIe hardware IP is used as a bottom layer, and a basic PCIe interface function for physical layer and link layer management is provided; and identifying and configuring all connected devices to construct a device address routing mapping table through a Soft Switch IP mounted on the PCIe hardware IP, and determining a specific target of data transmission based on the constructed mapping table so as to route the data to a specific position of the corresponding device. According to the invention, flexible equipment enumeration can be carried out by selecting a pure hardware mode or a software and hardware combined mode according to actual requirements, so that the data can be correctly and efficiently transmitted to the target endpoint from the source equipment, and the complex problems of address resolution and routing management are solved. Compared with the prior art, the method can replace the existing PCIe Switch IP of a manufacturer, provides higher flexibility and expansibility, obtains better balance in the aspect of cost control, and is suitable for various applications needing large-scale PCIe equipment connection, virtualization and high-speed data exchange.

The invention relates to an oil and gas geology intelligent question and answer generation method and system, processing equipment and a storage medium, and the method comprises the steps: inputting a natural language question, carrying out the analysis, and extracting a core entity, related attributes and a query intention of the natural language question; matching the extracted core entity and related attributes with tags in a pre-constructed hierarchical tag system, positioning to a corresponding knowledge sub-graph, and determining a query knowledge range; on the basis of the inquired knowledge range, according to the extracted core entity and the inquiry intention, a triple association method is adopted, multi-hop neighbor nodes most relevant to the core entity are extracted from the knowledge graph, and a dynamic sub-graph relevant to the input natural language problem is constructed; and inputting the constructed dynamic sub-graph and the label information into a pre-constructed generative model to generate a corresponding answer, so that accurate analysis and multi-dimensional data integration of complex problems in the field of oil and gas geology can be realized, and the method can be widely applied to the technical field of oil and gas geology intellectualization.

The invention discloses a signal enhancement method and device in an industrial plant area, and relates to the field of wireless communication, and the method comprises the steps: firstly, obtaining complete channel state information, and guaranteeing that all decisions are based on real channel characteristics instead of theoretical assumptions; and secondly, if the complete channel state information does not meet the target QoS demand quantization parameter, maximizing a receiving signal to interference plus noise ratio and meeting the target QoS demand are taken as optimization targets, an optimization result is obtained based on channel field intensity distribution information of a preset digital twin model, and finally, the optimization result is controlled and executed to form closed-loop feedback. According to the method, optimization is carried out based on channel field intensity distribution information of a unit space in a digital twin model, and a complex problem needing to be solved by a high-dimensional iterative solution algorithm in related technologies is converted into table lookup and interpolation operation based on space coordinates, so that the calculation complexity is remarkably reduced, the time for obtaining an optimization result is greatly shortened, and the calculation efficiency is improved. Therefore, the time when the terminal is in a poor-quality signal state is greatly reduced.