1376 results about "Result set" patented technology

The invention discloses a traffic engineering large model intelligent question answering system and method based on a knowledge graph, and the method comprises the steps: extracting a structured degree feature, a semantic ambiguity feature and a context association feature through receiving and analyzing a natural language query statement inputted by a user, generating a retrieval intention vector, and carrying out the retrieval of the retrieval intention vector; and dynamically selecting a retrieval path according to the intention classification model. And according to the retrieval path, constructing a structured query statement or a semantic vector, and respectively retrieving in the knowledge graph and the vector database to obtain a first retrieval result and a second retrieval result. Further performing bidirectional verification through entity consistency, semantic similarity and relation connectivity indexes, screening a candidate result set, and constructing a reasoning chain; if the inference chain is broken, a large model inference gap complementation mechanism is adopted to generate relay nodes, a complete inference chain is formed, and inference type answer output is generated based on the complete chain. According to the method, the retrieval accuracy and reasoning continuity of the question-answering system are improved.

The invention discloses a construction site safety risk intelligent early warning system and method based on BIM and big data analysis, relates to the technical field of building engineering construction safety, and solves the problem that it is difficult to transmit construction site multi-source data which is collected and preprocessed in real time in real time and carry out space mapping with a BIM model. A rule engine is difficult to carry out initial early warning; a machine learning model is difficult to analyze time series data, predict collapse risks and identify dangerous behaviors; a risk prediction model is difficult to construct and is difficult to integrate into a BIM model; and pushing and closed-loop management are difficult to carry out on the risk early warning information. According to the method, the multi-source data is collected at the construction site, the digital twinborn scene is constructed by mapping the multi-source data to the BIM model by means of space-time alignment, the multi-source data is analyzed and processed by applying technologies such as a rule engine and a machine learning algorithm, and the result is integrated to the BIM model, so that visual risk monitoring and early warning are realized.

The invention discloses a freight logistics information intelligent tracking management method and system, and belongs to the technical field of freight management, and the method comprises the steps: obtaining unique identification information of a freight unit, building the association with order content, carrier information and a preset transportation path, constructing a freight entity relation graph, and carrying out the management of the freight logistics information; performing preprocessing and modal fusion on multi-source real-time data in a transportation process, and extracting a structured cargo state feature set; based on a dynamic space-time modeling mechanism, the current freight state is intelligently judged, and a risk prediction result set is generated; predictive reconstruction is carried out on the transportation path in combination with a risk result; automatically executing path reconstruction, vehicle scheduling or user notification after the abnormal event is identified; synchronizing the evolution state and the risk data to a cloud platform for subsequent task optimization; according to the invention, full-flow intelligent sensing, real-time scheduling and abnormal closed-loop response in the logistics process are realized, and the transparency, robustness and aging control capability of a transportation system are remarkably improved.

The invention discloses an APT attack path reconstruction method based on time sequence diagram comparison clustering and a medium. A security event standardized data set is obtained; mapping each security event into a multi-modal node through a heterogeneous time sequence diagram set construction method, and generating a directed edge to construct and complete a heterogeneous time sequence diagram set; a stage embedding time sequence diagram set is obtained through the joint attack stage set; outputting a time sequence diagram similarity matrix by adopting a multi-scale diagram similarity algorithm of time alignment perception; generating an event semantic sparse matrix based on the threat intelligence knowledge graph; obtaining an image clustering result set; uncertain samples in the graph clustering result set are obtained and processed, and an APT attack path reconstruction result is obtained. The problem that an APT attack path reconstruction method mainly depends on rule matching, single-dimensional feature comparison and manual analysis of security logs or alarm streams is solved, the interpretability of APT traceability is greatly enhanced, and subjective errors of manual research and judgment are reduced.

The invention provides a network security protection method and system based on information fusion, and the method comprises the steps: firstly obtaining a multi-source heterogeneous data set, which comprises a traffic interaction data unit, an equipment log data unit and a protocol analysis data unit, of a target network, then carrying out the time sequence correlation analysis of the traffic interaction data unit, and generating a traffic behavior feature set; and executing state mode analysis on the equipment log data unit to generate an equipment operation feature set, and executing semantic recognition on the protocol analysis data unit to generate a protocol analysis feature set. Then, on the basis of a multi-dimensional feature fusion rule, cross-dimensional feature fusion processing is carried out on the feature set, and a network situation feature set is generated; and calling a threat identification model to carry out threat identification on the set, generating a threat identification result set containing threat type identifiers and influence range parameters, and finally generating a security response strategy set according to the threat identification result and issuing the security response strategy set to a security control node to execute protection operation, thereby effectively improving the network security protection capability.

The invention provides a task scheduling method and system based on multi-agent collaboration, and the method comprises the steps: firstly receiving an externally input target task demand text, carrying out the structural analysis processing, obtaining a task element set, calling a task planning module of a main agent to carry out the planning disassembly of the task element set, generating a sub-task sequence set, and carrying out the calculation of the sub-task sequence set; the sub-task units are in one-to-one correspondence with preset professional agent types, the sub-task units are allocated to matched professional agents for execution according to professional attribute identifiers of the sub-task units, a sub-task execution instruction is generated, and each professional agent executes sub-task operation based on the instruction, generates a sub-task execution result set and feeds back the sub-task execution result set to the main agent; and finally, the main agent performs multi-source data integration processing on the sub-task execution result set to generate a final task result file meeting task output requirements, so that task scheduling efficiency, quality and flexibility can be improved, and complex task requirements are met.

The invention discloses a search method and system based on computer natural language processing, and the method comprises the steps: extracting a synonym set and a context association relationship of keywords in a query text through a preset semantic knowledge graph, and generating a semantic vector representing a semantic dimension in combination with a deep learning model; extracting a historical behavior feature sequence from the query log based on the semantic vector, and analyzing a user search intention by adopting an attention mechanism model; performing similarity matching on the pre-constructed database by utilizing a semantic matching algorithm, and screening an information matching set meeting a threshold value; performing distributed processing on the matching set through a context-aware dynamic fragmentation algorithm, and constructing an index fragmentation cluster; semantic aggregation is realized by adopting a cross-fragment graph attention network, and an optimized search result set is generated through dynamic semantic projection. According to the method, through multi-modal fusion of the semantic knowledge graph and deep learning and in combination with a dynamic distributed processing architecture, the accuracy of search intention recognition and the efficiency of large-scale semantic matching are improved.

The invention provides a multi-modal heterogeneous model retrieval enhancement method and system, and the method comprises the steps: building a knowledge and application example double-corpus based on user multi-modal query, and designing a joint retrieval mechanism to obtain a result set; mapping and scheduling to obtain feature representation through special processing channels for texts, images and audios and a Spiking neural network with a segmented trapezoidal topological structure; constructing a three-stage cascade architecture of a basic model, an advanced model and human experts, and obtaining a decision path and answer candidate set in combination with a recursive and discarding decision mechanism; a Hamiltonian graph network is used for representing a multi-modal relation, and a gradient-free descent method is used for rapidly training and optimizing model parameters; an enhanced retrieval result is obtained through cross-modal semantic alignment and dynamic retrieval window adjustment; and high-quality response is obtained through context-aware sorting and retrieval enhanced reasoning. According to the method, the multi-modal information retrieval processing efficiency and the heterogeneous model reasoning response quality are improved.

The invention discloses a railway station facility full life cycle information query method based on digital twinning. The method comprises the steps that a unified facility object with a unique logic identifier is generated based on multi-source heterogeneous data aggregation; constructing a facility tense version model containing time sequence nodes by using an event-driven rule; in combination with an engineering constraint knowledge base and statistical characteristics, evaluating physical feasibility and statistical association strength between events, introducing life cycle stage risk factors, and constructing a facility full life cycle causal chain model; and based on the model, responding to the explanatory query, executing a constrained path search in the facility full life cycle causal chain model, and generating a life cycle query result set containing a causal path. According to the method, the pseudo-correlation problem generated by pure data mining can be effectively solved, the cumulative influence of early-stage risks in the design and construction stage on later-stage operation is quantified, and accurate tracing and explanation of cross-stage fault causes are realized.

Methods and systems for optimally weighting search results in a hybrid search framework are described. Responsive to a search query, query embeddings are obtained using corresponding embedding generators. The query embeddings are provided to search operators corresponding to the embedding generators to obtain corresponding search result sets having search results and associated scores. Optimal weights for each of the corresponding embedding generators are determined using a machine learning model, based on the search query. The search result sets are combined, based on the determined weights and the associated scores, yielding a combined search result set. The disclosed methods and systems dynamically optimize weights applied to search result sets that are retrieved using more than one vector-based search operator (e.g., where each search operator performs a vector-based search using embeddings generated by a corresponding embedding generator), for generating more relevant search results within the hybrid search framework.

The invention discloses a bird flight path intelligent tracking and monitoring method and system based on big data analysis. The method comprises the following steps: S1, generating an original bird observation data set; s2, outputting a standardized bird observation data set; s3, calling a multi-target tracking algorithm based on the standardized bird observation data set to obtain a bird trajectory fragment data set; s4, constructing a lion group optimization search model, and inputting the bird trajectory fragment data set into the lion group optimization search model to form a candidate trajectory data set; s5, inputting the candidate trajectory data set into a space-time Transform model configured with rotation position embedding, and generating a continuous flight path data set; and S6, outputting a cross-region bird migration monitoring result set. According to the method, bird migration continuous tracking requirements under different time periods, multiple geographical zones and heterogeneous equipment acquisition conditions can be covered, application scenes of ecological protection, migration research and airspace management and control are effectively supported, and the method has remarkable engineering popularization and ecological protection values.

The invention relates to the technical field of artificial intelligence, and discloses an education data report content interaction method and system generated based on retrieval enhancement, and the method comprises the steps: judging whether a natural language problem is an education field problem or not through a large language model, and if yes, carrying out semantic analysis to generate a structured query instruction; when the problem relates to cross-document association analysis, retrieving the structured semantic index database to generate a retrieval result set; if policy association analysis is involved, matching a policy knowledge graph by combining semantic similarity calculation and an entity linking technology, and then performing cross-modal fusion processing to obtain a retrieval result set; and inputting the retrieval result set into the retrieval enhancement generation model, and calling an education field language model to generate an analysis report. According to the method, the industrial pain points of inaccurate intention recognition, low cross-document analysis efficiency, incapability of dynamically combining with latest policies and the like in a traditional interaction mode can be solved, the efficiency and quality of data report interaction in the education field are remarkably improved, and the user interaction experience is optimized.

Implementations of the present disclosure provide receiving, by a conversational commerce optimizer system, a user input from a user, the user input being provided to a chatbot of an e-commerce platform, providing a refined query based on the user input, selecting, by the conversational commerce optimizer system, a foundation model that is to be queried using the refined query, determining a set of search results from a database that stores data representative of products available through the e-commerce platform, prompting the foundation model using a prompt that is generated based on the refined query and the set of search results, the foundation model providing an output responsive to the prompt, and providing a response to the user through the user interface of the chatbot, the response being generated based on the output.

The invention relates to the technical field of parameter optimization, in particular to a plastic mold forming parameter optimization method and system based on big data, and the method comprises the following steps: collecting pressure data, calculating an acceleration sequence, recognizing a peak window, matching a pressure-temperature ratio, and building a combined cluster to obtain an optimization result set. According to the method, a speed increasing sequence is constructed based on the die cavity pressure change rate, a high-dynamic section is screened through a peak value ratio, automatic identification of an effective forming section is achieved in combination with continuous ratio stability judgment, forming pressure, die temperature and a cooling time sequence are collected to construct a parameter combination sequence, trend consistency is compared, and available combinations are screened; the parameter acquisition granularity, the process identification precision and the parameter group suitability are obviously improved, the problems of subjective interference and error accumulation in the parameter configuration process are effectively avoided, the objective quantitative analysis capability of the forming process state is enhanced, and the accuracy of the forming process state is improved. And the plastic mold forming process stability and the parameter configuration efficiency are improved.

The invention discloses a power transmission line state simulation and prediction method based on digital twinning, and the method comprises the following steps: accessing meteorological data, electrical data, space and asset data and historical state data, carrying out the preprocessing, and generating a multi-source spatial-temporal feature sequence set; constructing a double-domain cross-coupling time convolution twinborn model, and obtaining corresponding feature representation through a quick response path and a slow response path; physical consistency state representation is generated through a physical constraint cross gating layer; outputting a prediction result set through a simulation engine; online observation data are obtained, and the model is corrected based on virtual and real residual errors; and generating a current prediction result set by using the corrected model, and converting the current prediction result set into a scheduling and operation and maintenance strategy set. According to the method, the double-domain cross-coupling time convolution twin model and a virtual-real closed-loop correction mechanism are adopted, multi-scale state simulation prediction of the power transmission line is achieved, and the method has the advantages of being high in precision, robustness and performability.

The invention discloses a live broadcast e-commerce compliance auditing method and system based on penetration type supervision, and relates to the technical field of e-commerce compliance supervision, and the method comprises the steps: carrying out the Hash abstract calculation of a judgment result data set and a multi-source data set, carrying out the partitioning storage of the data set in a block chain, verifying the storage process through a timestamp and a multi-node consensus protocol, and carrying out the verification of the verification result; forming an evidence storage data chain; automatically matching the evidence storage data chain with a compliance rule, identifying potential illegal behaviors and risk levels, and forming a compliance result set; performing fusion calculation on the compliance result set, historical violation records and user feedback data to generate a comprehensive risk score, and automatically triggering real-time compliance intervention measures according to the comprehensive risk score to form an intervention result set; and re-storing the intervention result set, the comprehensive risk score and the violation behavior in the block chain to form a closed-loop evidence storage data chain. According to the invention, supervision transparency is enhanced, data integrity is ensured, and reliable support is provided for real-time intervention and closed-loop auditing.

The invention relates to the technical field of distributed agent collaboration and graph database query optimization, in particular to a graph query processing method and system based on multi-agent collaboration, and the method comprises the following steps: reasoning a coordination agent to analyze the semantics and intention of user query by using a large language model; a structured multi-agent execution plan is output, and an entity search agent or a relation analysis agent needing to be called is explicitly called; the agent coordinator receives the multi-agent execution plan and selects agents according to task types and task allocation; obtaining a distributed intermediate result set; starting a result aggregation mechanism, and performing typed processing, priority ranking and deduplication merging on the distributed intermediate result set to obtain an arrangement result; and outputting an answer for answering the initial question of the user. The system comprises an agent execution plan output module, a multi-task execution module and an arrangement result reasoning module. According to the method, efficient, extensible and fault-tolerant graph query processing is realized.

The invention discloses a large-model knowledge retrieval method based on knowledge graph enhancement, which comprises the following steps: S1, collecting multi-source data, constructing an original knowledge triple set, and generating a knowledge graph; s2, inputting natural language query data, executing semantic analysis and intention recognition, and forming a query semantic vector and a structured query template; s3, performing combined convolution operation on the target entity node and the relation vector through an improved combined graph convolution network CompGCN to generate a candidate knowledge list and distribute confidence; s4, calculating the similarity between the query vector and the knowledge vector by utilizing a semantic embedding mechanism and DistilBERT acceleration to obtain a first retrieval result set and a credibility score; s5, fusing the two types of retrieval results to obtain a knowledge result set; and S6, generating an answer text according to the fused knowledge result set, and outputting a reasoning path, entity reference and a relation link. According to the method, the accuracy, the response speed and the knowledge reasoning transparency of complex query processing are improved.

The embodiment of the invention relates to the technical field of data analysis, in particular to a national secret log auditing system which is characterized in that firstly, an operation behavior record sequence generated in the running process of a national secret application system is collected by the national secret log auditing system, and the sequence is composed of log entries containing identity verification information, resource access path information and state transition description information; performing context semantic association analysis on the operation behavior record sequence to obtain a semantic element extraction result and a semantic dependency relationship; then performing multi-level compliance verification based on a national secret security audit rule system, and generating a compliance judgment conclusion of a log entry level and a cross-entry-level abnormal behavior pattern recognition report; and finally, according to the judgment conclusion and the identification report, constructing a national secret log security auditing result set which comprises a risk level evaluation result, a violation evidence chain association graph and a security reinforcement strategy suggestion list, thereby effectively improving the accuracy and comprehensiveness of national secret log auditing.

The invention relates to the technical field of behavior recognition, and discloses a lightweight abnormal behavior recognition system and method based on edge computing, and the method comprises the steps: obtaining multi-dimensional heterogeneous behavior observation data covering a target region through an edge side behavior collection node system, and constructing a behavior dynamic feature vector set which can be iteratively updated; performing structure self-adaptive decoupling processing on the behavior dynamic feature vector set, and recording the fluctuation convergence rate of an abnormal category in real time in a model iteration process; judging the behavior recognition stability in the model reasoning stage, and constructing a behavior transfer trajectory map in combination with the environmental transaction interference factors; extracting density disturbance parameters of the abnormal behaviors, and generating a behavior intervention evaluation result set; and performing hierarchical risk judgment on the current identification behavior result, and automatically generating an edge execution regulation and control instruction set. The method has the advantage of improving the efficiency.

The invention belongs to the technical field of fault detection, and relates to a high-voltage transmission line fault detection and identification method. The method comprises the following steps of: dividing candidate fault sections according to a topological structure by synchronously acquiring steady-state and transient-state traveling wave signals of key nodes of a power grid; identifying a fault by using a multi-feature fusion criterion and extracting traveling wave features; executing mode recognition-based steady-state section judgment and multi-terminal ranging and polarity verification-based transient positioning in parallel to generate two types of positioning result sets; and finally, carrying out intelligent fusion judgment on the two types of results under topological constraints. According to the method, the problems that a traditional single-point signal analysis method cannot determine a fault section, lacks space positioning capability and is insufficient in adaptability are solved, section-level accurate positioning of the fault is realized, and the positioning accuracy, robustness and fault processing efficiency are remarkably improved.

The invention provides a transformer substation operation and maintenance-based lock control terminal work log query method and system, and the method comprises the steps: collecting an original operation log stream generated by a transformer substation lock control terminal in a preset time period, serializing the original operation log stream to obtain a log data sequence, and then carrying out the context correlation analysis and recognition of an entity interaction relationship and behavior attribute description in a log text; a structured log metadata set is obtained after standardized integration; constructing a spatial topological index based on the physical layout and the electrical connection relationship of the substation equipment, mapping the equipment associated information in the spatial topological index to a corresponding node position of the spatial topological index, and generating a spatial enhanced log data set; receiving a natural language query request, generating a structured query expression, performing multi-dimensional retrieval matching on the spatial enhancement log data set, and outputting a preliminary query result set; and carrying out time sequence correlation analysis and operation influence evaluation on the log records to generate a query report. According to the invention, efficient and accurate query of the work log of the transformer substation lock control terminal can be realized.

The invention relates to the technical field of data quality evaluation, in particular to a quality evaluation system and method based on river channel data. The method comprises the following steps: obtaining multi-source river channel data, and carrying out parallel data fusion to obtain river channel fusion data to be evaluated; performing error fluid propagation simulation based on the to-be-evaluated river fusion data to obtain an error statistical result set; performing spatial deviation multi-scale analysis according to the error statistical result set to obtain an elevation error distribution diagram; randomly selecting an underwater point cloud sample based on the to-be-evaluated river fusion data, and performing spatial interpolation point cloud precision calculation on the underwater point cloud sample to obtain a point cloud precision error matrix; performing quality detection and correction on the to-be-evaluated river fusion data by combining the point cloud precision error matrix and the elevation error distribution diagram to obtain quality optimization data; and based on the quality optimization data, carrying out river channel map profile visualization to generate a river channel optimization map profile. The method is helpful for improving the precision and consistency of the river surveying and mapping data.

The invention discloses an automatic logistics conveying line scheduling optimization method and device and a related medium, and the method comprises the steps: obtaining multi-source operation data, and carrying out the data preprocessing, and obtaining a standardized operation state sequence; performing parameter mapping on the standardized operation state sequence to construct state features to obtain an environment state vector sequence; performing strategy agent solving on the environment state vector sequence to generate initial scheduling data; inputting the initial scheduling data into a verification model for verification processing to obtain plan execution data; and respectively performing control instruction analysis, equipment mapping and message coding according to the plan execution data, generating a to-be-issued scheduling control message, performing transmission adaptation processing, and outputting a scheduling control result set. The scheduling control message to be issued is output after the plan execution data obtained through calculation is correspondingly processed, and the scheduling control result set for controlling the logistics conveying line is obtained through transmission adaptation processing, so that the collaboration of the logistics system is greatly improved.

The invention provides an information resource matching recommendation method and system based on context awareness, and the method comprises the steps: firstly obtaining a context data set which is generated by real-time interaction of a target user and comprises user operation behavior data and scene awareness parameter data, and then carrying out the demand feature extraction of the context data set, the method comprises the following steps: acquiring a dynamic demand feature and a context association feature, matching the dynamic demand feature and the context association feature with a pre-stored information resource library to generate a real-time information resource matching result set, and optimizing a matching strategy of a target user matching model in real time according to a dynamic feedback parameter of the real-time information resource matching result set. According to the method, a resource matching strategy is optimized to obtain an optimized resource matching strategy, and finally, a target information resource set related to the dynamic demand characteristics is pushed to a target user terminal device based on the optimized resource matching strategy, so that more accurate and personalized information resource recommendation can be realized, and the recommendation quality and the efficiency of obtaining effective information by a user are improved.

The invention provides a potential information identification method and system in combination with a heterogeneous network environment, and the method comprises the steps: firstly obtaining a heterogeneous network environment data set of a target network environment, which comprises original information resources of various data formats, and carrying out the feature extraction of the heterogeneous network environment data set to obtain an associated feature set; performing hierarchical pattern matching processing on the associated feature set based on a pre-trained information pattern analysis model to generate a potential information identification result set containing significant value information resource attribute identifiers and distribution paths, and generating a dynamic resource integration strategy according to the potential information identification result set; and the resource management system is used for adjusting a resource scheduling rule and a storage path mapping relation, finally feeding back a dynamic resource integration strategy to the data management system, triggering a resource directory updating operation, and improving heterogeneous network resource utilization and management efficiency.

The invention discloses a graphical user interface test case generation method based on a multi-agent framework, and relates to the field of graphical user interface tests.The graphical user interface test case generation method based on the multi-agent framework comprises the following steps that S1, a user demand document is obtained, and file text content and file format information are analyzed and extracted; s2, performing paragraph segmentation on the file text content to form logic semantic units; s3, carrying out correlation analysis on the segmented paragraphs, and obtaining a ternary file group; s4, performing paragraph annotation addition on the ternary file group, and constructing a semantic annotation result set; s5, extracting the agent framework to generate a graphical user interface test case, performing test recording, and storing a test result; s6, the result is verified, and a test case list and a software test report are generated. By conducting multi-dimensional analysis on the text content and the format structure in the document, the reduction efficiency from natural language description to structural semantic construction is improved.

The invention relates to the technical field of air conditioning system control, and discloses an air conditioning system intelligent flexible regulation and control method based on an AI algorithm, and the method comprises the steps: obtaining an original environment parameter flow containing temperature, humidity and energy consumption signals, inputting the original environment parameter flow into a pre-trained intelligent flexible regulation and control network to generate a regulation and control strategy matching result set, and determining each parameter regulation and control instruction set according to the regulation and control result set. The network is trained by historical operation data, and the data comprises historical regulation and control features (fusing time sequences and periodic features), operation mode labels and weight coefficients extracted from continuous air conditioner operation parameters. Periodic characteristics are extracted through periodic decomposition, an operation mode label is determined based on a decision algorithm, and network parameters are dynamically updated according to a weight coefficient. The system is provided with a multi-modal fusion module, a time sequence prediction module, a multi-target optimization module and a hierarchical regulation mechanism, influence factors can be dynamically distributed, multi-target self-adaptive regulation and control are achieved, and the regulation and control precision, the energy efficiency and the environmental adaptability of the air conditioning system are improved.

The invention relates to the technical field of computer information processing, and discloses a high-fidelity terrain surface interpolation method and system for large-scale point cloud data, and the method comprises the steps: carrying out the multi-level cleaning of original point cloud data, and generating a cleaned point cloud data set; performing adaptive tile grid division based on the cleaned data to generate a tile set covering the target area; in each tile, taking the to-be-interpolated grid point as a query point, constructing and optimizing a local Gaussian process model, and obtaining a real elevation predicted value of the query point; processing all query points in the tiles in parallel to obtain a tile elevation interpolation result set; performing weighted average on each tile result to generate a global seamless terrain surface; according to the method, the problems of poor expandability and surface discontinuity caused by local interpolation in large-scale point cloud data processing are effectively solved, and high-precision and high-fidelity terrain surface construction is realized.

The invention provides a multi-sensor fusion and environment feature recognition method and system for digital twin equipment, and the method comprises the steps: constructing a digital twin model of the equipment in a visual platform, and setting a self-updating mechanism; deploying a multi-sensor array at a key installation part of the equipment physical entity, collecting multi-source sensing data and uploading the multi-source sensing data to a twin platform; carrying out noise reduction processing on the multi-source sensing data and completing data fusion to obtain fused data; constructing a diffusion model corresponding to a noise scheduling strategy, generating new sensing data and a corresponding label, and forming analysis data; a multi-channel deep learning model is constructed to extract feature parameters from analysis data, regression prediction is carried out, and an identification result is obtained; the system is integrated to a twin platform and is combined with a control decision function. According to the invention, real-time regression prediction of environment characteristic parameters based on sensing data is realized, deep fusion of environment perception and control decision of equipment is promoted, and perception capability and adaptive adjustment capability of a control decision system of the equipment are improved.