1096 results about "Smart data" patented technology

A driving state monitoring and feedback method and system based on multi-modal human factors intelligent data analysis, and an edge computing terminal device. The method comprises: receiving multi-modal human factors data of a tested driver that is collected in real time (S110); pre-processing the multi-modal human factors data, wherein the pre-processing comprises denoising processing and data normalization processing (S120); sending the pre-processed multi-modal human factors data into a pre-trained first state recognition model, so as to obtain a real-time recognized driver state, wherein driver states include a normal state and abnormal states, and the types of the abnormal states include a plurality of states such as a fatigue state, a distracted state and an angry state (S130); and when it is recognized that the driver state is an abnormal state, generating, for different categories of abnormal states, driving state feedback instructions to a driving intervention system, such that the driving intervention system performs state feedback adjustments on the driver on the basis of the received driving state feedback instructions (S140). The method and system can recognize different driving states of a driver in real time and then perform processing on the basis of different driving states, thereby avoiding the occurrence of traffic accidents.

A system and method for adaptive real-time multi-modal compression with dynamic resource allocation provides intelligent compression optimization based on continuously monitored device conditions. The system monitors battery level, CPU utilization, and memory availability while classifying incoming multi-modal data streams comprising image, audio, text, and sensor data to determine processing priorities. Multi-objective optimization balances compression efficiency, reconstruction quality, and energy consumption using evolutionary algorithms that generate optimal parameters for an adaptive variational autoencoder. The autoencoder features dynamically selectable processing complexity, adjustable latent space dimensionality, and modality-specific processing layers. The system automatically switches between operational modes including emergency mode triggered by resource constraints, which applies maximum compression settings and intelligent data triage. Continuous learning adapts compression parameters based on observed performance outcomes, improving future optimization decisions. The system enables homomorphic operations on compressed data and provides enhanced compression performance under varying resource constraints across diverse edge computing applications.

The invention relates to the technical field of intelligent data fusion deduction, in particular to a task planning method and system based on intelligent deduction, and the method comprises the steps: constructing a rule knowledge graph with weight annotation through integrating a real-time battlefield situation and a preset rule library, and carrying out the task planning through the real-time battlefield situation; a large-model intention understanding engine is used for analyzing a commander instruction to generate a five-dimensional intention vector, an initial scheme is generated by combining a double-engine mechanism of a rule hard constraint engine and a large-model soft constraint engine, and graph weights are dynamically adjusted through reinforcement learning to optimize a rule path. The system monitors node conflict frequency in real time, adopts a near-end strategy optimization algorithm to re-distribute weights for high-frequency conflicts, activates a new rule injection mechanism when the intention conformity is insufficient, and finally outputs a three-level structured scheme including a main scheme, an alternative scheme set and a risk early warning report, thereby realizing an intelligent closed loop from situation awareness to scheme generation. And the command decision-making efficiency and the combat collaboration are obviously improved.

The invention relates to the field of data backup, in particular to an intelligent data backup method and system based on an AI large model. The method comprises the following steps: acquiring an enterprise global data list, performing intelligent data structure deconstruction and dynamic attribute mapping modeling, and constructing a holographic data semantic perception model; performing real-time transient risk mutation detection on the holographic data semantic perception model, and constructing an intelligent backup triggering mechanism; carrying out storage resource demand prediction based on an intelligent backup trigger mechanism, carrying out multi-storage cloud environment resource dynamic scheduling, and constructing an elastic backup storage resource pool; carrying out incremental backup analysis and self-adaptive compression coding to obtain an incremental backup coding packet; and performing dynamic backup sequence adjustment and intelligent incremental backup decision on the incremental backup coding packet based on the elastic backup storage resource pool, and constructing an intelligent incremental backup execution engine. According to the method, the reliability, the accuracy and the traceability of a backup result are improved through self-adaptive intelligent incremental backup.

The invention provides an intelligent data production method and system based on a graph neural network and adaptive learning, and the method comprises the steps: collecting a text, a time sequence, an image and sensor data in real time, and converting unstructured data into a structured JSON format through a format analysis tool; extracting text entity features based on a large language model, modeling a cross-modal entity association relationship in combination with a graph neural network, and fusing multi-source heterogeneous data through a dynamic knowledge graph; the detection threshold is dynamically optimized by adopting a reinforcement learning algorithm, and the false alarm rate is reduced in combination with a double-track verification mechanism; and predicting a data trend by using the space-time diagram convolutional network, and outputting an interpretability analysis report through the generative large model. According to the invention, a multi-protocol adaptation engine and a distributed stream processing architecture are adopted, and a protocol analysis layer automatically identifies a plurality of industrial protocols, so that the manual adaptation time is reduced; and real-time synchronization of production line-level data is realized through edge node parallel acquisition and compression transmission.

The invention discloses an intelligent data analysis method and system based on an industry large model, and relates to the technical field of data analysis, and the method comprises the steps: collecting business event data, carrying out the feature vector extraction according to the data type, calculating a zoom dot product attention score matrix through employing a self-attention mechanism, and carrying out the weighted output of a multi-modal fusion vector, constructing an industry knowledge database to determine an embedded vector; and calculating a relevancy value, screening a combination of the multi-modal fusion vector and the knowledge fragment, and forming a gating input vector to carry out fusion vector calculation. According to the method, unified expression and correlation mining of heterogeneous information such as structured data, text information and images can be realized through a multi-modal feature fusion and knowledge fragment retrieval scheme, the attention degree of each modal feature can be adaptively adjusted according to a service scene through a self-attention mechanism and a model, information complementation is realized, and the accuracy of information retrieval is improved. And automatically highlighting the multi-modal dimension most related to the current task.

The invention discloses a cloud edge cooperation method for AI intelligent Internet of Things equipment data processing, and relates to the technical field of data processing, and the method comprises the steps: S1, intelligent data collection, S2, edge side AI preprocessing, S3, edge-cloud end cooperation reasoning, S4, intelligent data transmission, S5, cloud end AI big data analysis, S6, real-time feedback and self-optimization, S7, adaptive resource scheduling, and S8, full-link visualization. Through AI-driven dynamic sampling and multi-modal data fusion, the efficiency and precision of data acquisition are optimized, redundancy or omission caused by fixed sampling is avoided, meanwhile, the transmission load is reduced, layered task dynamic unloading and intelligent transmission protocol optimization are achieved, the flexibility and stability of cloud edge collaboration are improved, and the cloud edge collaboration efficiency is improved. Manual intervention is reduced through a real-time feedback and self-optimization mechanism, the autonomy of the system is enhanced, and the interpretability and fault diagnosis capability of the system are remarkably improved through a visual panel and a causal reasoning model.

The invention discloses an intelligent data query method and device based on a large model and a medium, and relates to the technical field of large model application. The method comprises the steps of receiving a natural language query text input by a user, and determining a target interface group from pre-associated interface groups through a preset business line classification rule and a keyword matching algorithm; inputting the natural language input text into a pre-trained large model to analyze a parameter set required by interface calling; wherein the parameter set comprises at least two of the following items: a time range field, a business main body field and a statistical dimension field; calling an API interface in the target interface group according to the parameter set, and obtaining original business data; performing context supplementation on the original business data based on the business logic rule to generate a standardized data result containing business background description; and reconstructing the standardized data result according to a display format preset by a user, and outputting a combined visual result containing charts, tables and text description.

The invention relates to the technical field of digital regulation and control, in particular to a hardware machining digital regulation and control system based on intelligent data. The method comprises the following steps that original machining parameters and hardware work order data are obtained through a data acquisition module, task hierarchy division and priority ranking are carried out, a priority work order task is generated, a global path planning module formulates a global process path for the priority work order task according to resource demand data, and the global process path planning module plans the priority work order task according to resource demand data. The difference comparison module identifies component differences and generates difference data through processing simulation and difference comparison, the correction path planning module traces process bottleneck nodes, carries out correction path planning and generates bottleneck local process paths, and the path fusion correction module corrects global process paths. And intelligent and digital execution of processing regulation and control is ensured. According to the invention, a more intelligent and finer hardware processing digital regulation and control system is realized.

The invention relates to the technical field of water pump energy-saving control, and discloses a water pump energy-saving control system based on an intelligent data model, which comprises a data sensing module, a modal analysis module and the like. The data sensing module collects flow, lift and power data and is provided with a dynamic monitoring window; the modal analysis module divides operation modes and generates an energy efficiency characteristic matrix; the rule construction module extracts a core energy efficiency factor and constructs an energy-saving regulation and control strategy; the priority scheduling module analyzes the control priority matrix and evaluates an energy efficiency deviation index; a mapping calibration module fits the optimal energy efficiency reference curve to generate an energy efficiency deviation gradient map; the control execution module generates a regulation and control instruction set according to the map. The system realizes fine management and energy efficiency optimization of water pump operation through multi-mode energy efficiency analysis and dynamic regulation, solves the problems of poor adaptability of traditional control working conditions, low energy-saving efficiency and the like, is suitable for water pump energy-saving control in the fields of industry, municipal administration and the like, and is accurate and efficient.

The invention provides an intelligent data query method based on a natural language, and relates to the technical field of intelligent data processing and natural language interaction.The intelligent data query method comprises the steps that firstly, enterprise original data is subjected to standard treatment, and a standardized theme database and a data directory and index definition document are constructed; key semantics are extracted based on unstructured knowledge, and a domain knowledge vector library is fused and constructed by combining document text fragments and vector representation of a mapping relation between historical questions of a user and an SQL (Structured Query Language). And after receiving a natural language question of a user, calling a large language model to identify a task type, and distinguishing knowledge questions and answers, data query and complex analysis. Executing corresponding operations according to different types: directly retrieving a vector library by knowledge questions and answers to generate answers; extracting keywords in data query and generating a query request in combination with context; and in the complex analysis, predefined workflow is judged and executed or intelligent agent processing is called, and query or analysis requirements are output.

The invention relates to the technical field of coal mine water disaster prevention and control, and particularly discloses a hidden structure detection system for mine water disaster intelligent monitoring, early warning and prevention, which is used for solving the problems of low mine hidden structure detection precision, mine water disaster early warning lag and large seismic wave propagation path simulation error. Comprising a multi-source data comprehensive detection module, a real-time imaging module, a micro-seismic multi-scale intelligent analysis module, a seismic wave signal identification module, a regional difference analysis module and an intelligent data processing module. According to the invention, through fusion of multi-source data, real-time imaging, micro-seismic analysis, seismic wave identification and intelligent data processing technologies, precise detection of a mine hidden structure, high-precision prediction of mine earthquakes and water disasters and real-time risk early warning are realized, and timeliness and accuracy of mine water disaster prevention and control are improved.

The invention discloses an artificial intelligence data analysis method and system based on machine learning, and the method comprises the following steps: 1, collecting and standardizing original data, and carrying out the embedding processing, and obtaining a data feature vector; 2, mapping the data feature vector into a Riemannian manifold space; 3, calculating the distance between every two sample points by using Gromov-Hausdorff measurement, and establishing an initial causal latent map; 4, constructing a causal potential energy tensor field; 5, obtaining an evolved causal latent map by adopting a Ricci curvature disturbance mechanism; 6, calculating a geodesic distance in the evolved causal latent map as an information propagation path; 7, constructing a causal relationship model; and 8, executing anti-fact reasoning based on the causal relationship model, simulating the causal influence of input variable disturbance on an output result, and performing visual output. According to the method, Riemannian manifold embedding and curvature evolution methods are fused, and intelligent data analysis of causal structure modeling is realized.

The invention discloses a GIS disconnecting switch multi-state intelligent sensing system, and relates to the field of GIS disconnecting switch state monitoring. A self-calibration multi-mode sensor is deployed for multi-source data acquisition, and novel sensors including terahertz imaging and the like are included; in data preprocessing, deep learning noise reduction is applied, fuzzy entropy is used for dynamic weighted fusion, and abnormal values are processed by an improved algorithm; feature extraction is combined with a plurality of frontier algorithms to process vibration signals, and CRNN is used to analyze acoustic signals; the QPSO evidence theory is adopted for state fusion perception, the node relation is learned by means of GNN, and the weight is adjusted according to the information gain rate; state assessment and early warning are based on transfer learning, GAN and LSTM-attention mechanisms, and early warning priorities are ranked by FAHP. According to the invention, multi-mode accurate acquisition, intelligent data processing, deep feature mining, innovative fusion perception, accurate evaluation and early warning and efficient fault diagnosis and positioning are realized, the state of the GIS isolation switch can be comprehensively and accurately perceived, the system is self-learned and optimized, the equipment safety is guaranteed, the risk of a power system is reduced, and the power operation and maintenance benefits are improved.

The invention relates to the technical field of data monitoring, and discloses an intelligent data monitoring method and a monitoring platform, and the intelligent data monitoring method comprises the steps: constructing a dynamic heterogeneous hypergraph model, and representing the complex relation and time sequence evolution characteristics among multi-source data nodes; based on a dynamic heterogeneous hypergraph model, realizing high-order relationship representation and calculation, and obtaining embedded representation of nodes and relationships; performing heterogeneous graph information transmission and aggregation by utilizing embedded representation of nodes and relationships, and capturing complex interaction characteristics among multiple nodes; according to the complex interaction characteristics, an abnormal propagation rate model is established, and accurate prediction of an abnormal diffusion path is realized; based on the abnormal propagation rate model and the abnormal diffusion path, multi-level collaborative anomaly detection is implemented, and collaborative faults across subsystems are identified; according to the invention, the abnormal propagation path can be predicted in advance, the abnormal prediction accuracy is improved, and the system fault response time is advanced.

The invention relates to the related technical field of data governance analysis, in particular to an intelligent data governance system based on behavior path analysis, and the system comprises a path generation module which generates a data flow path through a graph neural network according to a data behavior log, and draws up a behavior path graph; the first risk propagation path set determination module and the second risk propagation path set determination module respectively use graph diffusion and characteristic parameter marks to determine risk paths so as to operate a blocking mechanism to carry out data access management. The technical problems that data access depends on single rule matching and single-point anomaly detection, global correlation analysis on data operation behaviors is lacked, and complex attacks cannot be effectively detected are solved, the behavior path map is generated through the graph neural network, the influence range of abnormal operation is quantified by using the graph diffusion model, and the data access efficiency is improved. The technical effects of predicting the potential path of sensitive data leakage in advance, dynamically adjusting the confidence, improving the risk node identification accuracy, carrying out real-time blocking treatment and guaranteeing the data access security are achieved.

The invention provides an intelligent data labeling method and system based on multi-modal fusion and large model verification, belongs to the field of artificial intelligence and data processing, and innovatively fuses multi-modal information such as an OCR recognition result, a layout structure, original image visual features and deep semantic analysis of a large language model (LLM). And a precise automatic labeling result credibility evaluation mechanism is constructed. According to the method, various errors in automatic labeling can be accurately recognized and adaptively corrected, and the errors comprise conventional error correction based on hard coding rules and complex semantic error correction driven by LLM. Meanwhile, the system can continuously optimize the data labeling capability of the system through an efficient man-machine cooperation and closed-loop feedback learning mechanism, and automatically precipitate domain knowledge assets. The invention aims to solve the problems of recognition accuracy bottleneck, heavy manual proofreading burden, lack of intelligent judgment and error correction, knowledge accumulation lag and the like in traditional document data labeling, so that the efficiency, accuracy and automation level of document data labeling are remarkably improved.

The disclosed embodiments provide systems and methods for performing queries via an intelligent query engine. Various embodiments include receiving an input query and parsing the input query into a query representation object; generating an evaluation plan based on the query representation object, wherein the evaluation plan comprises a graph of computation nodes, each computation node specifying a granularity for grouping, associated filters, an aggregation schema, and join dependencies required for node computation, and wherein the evaluation plan is ordered to account for hierarchical dependencies among the computation nodes; translating the evaluation plan into an executable query optimized for a specific target data store, wherein the translation adapts query syntax and join structures to capabilities of a target data store; and executing the translated query on the target data store, resolving dependencies and aggregations according to the evaluation plan to generate query results satisfying the input query.

The invention discloses a digital intelligent service system for enterprise services, which belongs to the field of enterprise digital services and comprises a user demand analysis module, an intelligent data adaptation module, a service resource matching module, a service efficiency evaluation module and a user-friendly interface. The user demand analysis module is used for semantic analysis and classification decision making; the intelligent data adaptation module identifies and analyzes the protocol type, and establishes an intelligent data transmission channel; the service resource matching module carries out resource pre-screening and accurate matching; the service efficiency evaluation module tracks an identifier and captures a four-dimensional index system in real time, and the dynamic evaluation unit realizes service state grading early warning through a sliding window algorithm; according to the invention, accurate demand identification is realized, the response speed and the data transmission efficiency are improved, the user satisfaction is improved, the service matching accuracy is enhanced, the resource utilization efficiency is optimized, the service capability and the customer experience are improved, and maximization of economic benefits and social values is realized.

The invention discloses an intelligent storage system supporting multi-source heterogeneous data fusion management, which relates to the technical field of multi-source heterogeneous data fusion, and comprises a data source adaptation module, an intelligent data source adapter is configured in a DataWorks data integration module, multi-source heterogeneous data is accessed through the intelligent data source adapter, and the data source adaptation module is connected with the DataWorks data integration module; extracting a semantic feature vector and a technical feature set; the neural symbol hybrid inference module inputs the semantic feature vector and the technical feature set into a neural symbol hybrid inference engine, calculates a similarity matrix between multi-source heterogeneous data fields through a BERT-based neural network, and imports the technical feature set into a field knowledge graph constructed by Neo4j for symbol logic verification to generate a unified metadata model; according to the method, a semantic similarity matrix calculated by a BERT-based neural network is combined with symbol logic verification of a Neo4j knowledge graph through a neural symbol hybrid inference engine, so that automatic semantic alignment and logic consistency verification of multi-source heterogeneous data are realized.

The invention relates to the technical field of garbage disposal, and discloses a household garbage AI intelligent unmanned transport vehicle and a garbage disposal transport method.The transport vehicle comprises a garbage transport vehicle body, a data acquisition module, a path optimization module, a dynamic path adjustment module, a multi-vehicle collaborative scheduling module and an execution module; the method comprises the steps that a garbage transportation task is initialized, and garbage stations and the state of a transportation vehicle are collected; calculating an optimal path, and performing real-time adjustment based on dynamic data; by adopting multi-vehicle cooperative scheduling, path conflicts are avoided, and the transportation efficiency is optimized; garbage collection, transportation and delivery are completed, and transportation data are recorded. According to the method, efficient automation of garbage transportation is achieved through intelligent data collection, path optimization, dynamic adjustment and multi-vehicle cooperative scheduling, the optimal path is calculated through the variational method and the Euler-Lagrange equation, the driving route is dynamically adjusted in combination with the Hamilton-Jacobi-Bellman equation, multi-vehicle scheduling is optimized based on the differential game and the Nash equilibrium, and the efficiency of garbage transportation is improved. And high-efficiency transportation and no path conflict are ensured.

The invention relates to the technical field of automatic data analysis, in particular to an intelligent data report generation method and system based on an MCP protocol. The method comprises the following steps: constructing a semantic intention vector through a context semantic structure according to an obtained natural language instruction of a user; constructing a semantic task path graph based on the semantic intention vector by utilizing a knowledge graph; according to the constructed semantic task path diagram, performing intention path and tool capability mapping based on an MCP protocol; constructing a task flow based on an MCP protocol and performing task scheduling optimization; carrying out data source unified modeling based on the constructed task flow, and generating a stable and controllable calculation task flow; and visual expression generation from data to graphs is realized through an intelligent recommendation and structural modeling mechanism. According to the method, a semantic-driven MCP plug-in tool chain and a chart capability component index are constructed, so that a full-automatic closed loop from a natural language intention to data analysis and chart generation is realized, and meanwhile, the use threshold of non-technical users is reduced.

The invention provides a one-way isolation network intelligent data analysis method and device based on a large model and a medium, and belongs to the technical field of data processing and network security. Matching an accessible node area list according to a preset organization structure level; the system receives and analyzes the instruction to determine a query intention and an associated data range; the system converts a natural language query instruction into a cross-node SQL statement through a large model module, and verifies the legality of the SQL statement based on user permission; and the system unidirectionally transmits the encrypted SQL instruction to a target node area through a hardware optical shutter, triggers the node area to execute local statistical operation and returns a structured result. Dynamic data pulling in a one-way network environment is achieved through a hardware optical shutter and data pump technology, natural language query is converted into SQL in real time in combination with distributed query optimized by a large model, and the problems that data statistics is lagged and user operation intervention is needed in a traditional isolation network are solved.

The invention discloses an intelligent data management system and method based on multi-source data collection, and relates to the technical field of data processing. Multi-source data from multiple data sources are obtained, an original data set with credibility marks is generated, data records in the original data set are classified according to data types, and for time series data, data records in the original data set are classified according to credibility marks. Extracting trend features by applying a segmented multi-model time sequence feature extraction framework; for the data of the unstructured data, extraction of unstructured features is carried out through a feature extraction model based on a multi-layer perceptron; for structured data, a knowledge graph technology is adopted to establish an adaptive association relationship, association relationship data is generated, trend features, unstructured features and the association relationship data are used as input of a data value evaluation model, and data asset performance evaluation of dynamic weight is generated; and intelligent management and value evaluation of the multi-source heterogeneous data are realized.

The invention discloses an intelligent data stream processing system based on Flink and an implementation method thereof, and belongs to the technical field of distributed streaming data processing, and the implementation method comprises the following steps: a data source access layer uniformly accesses multi-source data; the entity monitoring layer captures service entity change in a non-intrusive manner based on JPA, generates a standardized message body and asynchronously delivers the standardized message body to double channels; the unified event model layer maps the data into a standardized event and expands the standardized event; the Flink stream processing engine layer is used for complex event processing, state management and window calculation, and supports declarative pipeline definition and dynamic construction; the anomaly detection layer performs multi-dimensional anomaly detection; the distributed task scheduling layer schedules tasks according to a DAG model, and realizes load balancing through hotspot splitting and state transition; and the result storage and visualization layer adopts a cold and hot separation strategy to store data, and provides a dynamic visualization interface. The real-time performance, the consistency and the intelligent level of data processing are remarkably improved, and the development and maintenance cost is reduced.

The invention provides an intelligent data security exposed surface risk assessment method, system and device and a medium, and relates to the technical field of information security, and the method comprises the steps: constructing an exposed surface asset map through an asset fingerprint recognition engine, and calculating an asset exposure index; constructing a vulnerability knowledge base, and identifying potential vulnerabilities; based on the standard vulnerability score, the vulnerability utilization tool activeness and the attack event frequency in the set time period, calculating the triggering probability of the potential vulnerability as a dynamic vulnerability score; malicious IP access frequency and leakage record matching degree are obtained through firewall logs and dark web monitoring data, and threat intelligence factors are obtained through calculation; calculating an exposed surface risk value in combination with the asset exposure index, the dynamic vulnerability score and the threat intelligence factor; and based on a time decay model, according to the exposed surface risk value and the business influence factor, calculating a residual risk value, and obtaining an exposed surface risk assessment result. The accuracy of risk assessment is further improved through multi-dimensional data fusion.

The invention provides an intelligent data processing method and device, and relates to the intelligent field, and the intelligent data processing method comprises the steps: obtaining a task reference video of an intelligent agent; performing multi-modal feature extraction on the task reference video to obtain visual semantic features of the task reference video and body posture features of the task execution object; fusing the visual semantic features and the body posture features to obtain fused features; performing task identification on the task reference video and performing behavior intention identification on the task execution object according to the fusion feature to obtain an identification result; according to the recognition result, effective video clips of the task reference video are determined, and the effective video clips are used for verification and / or reference of operation in the task execution process of the agent with the body. According to the method, automatic processing of the intelligent data is realized through a multi-mode technology, the data processing efficiency and accuracy are improved, and the labor cost is saved.

Techniques include generating, by an artificial intelligence agent, a request for artificial intelligence data. The techniques further include presenting the request via a user interface. The techniques further include receiving the artificial intelligence data. The techniques further include providing the artificial intelligence data to the artificial intelligence agent for at least one of training or inference.

The invention relates to the technical field of intelligent control of automobile air conditioners, and discloses an intelligent regulation and control method of an automobile air conditioner controller based on a self-adaptive neural network, which realizes comprehensive perception of environment information inside and outside an automobile through distributed multi-source data intelligent fusion processing. Establishing a data quality evaluation model and an intelligent data interpolation algorithm to ensure data reliability; constructing a lightweight dual-path adaptive neural network model by adopting a knowledge distillation technology; establishing a hierarchical multi-objective optimization control architecture which comprises a three-layer optimization structure of a strategic layer, a tactical layer and an operation layer, and realizing organic combination of long-term planning, medium-term prediction and real-time control; the method further comprises key technologies such as multi-level data missing fault-tolerant processing, actuator self-adaptive compensation control, system-level fault self-healing and degradation control and the like, and stable and reliable intelligent regulation and control can be achieved under the extreme environment condition. According to the invention, the control precision, the energy consumption optimization and the user experience in extreme environments such as polar regions, plateaus and deserts are improved.

The invention provides a marketing strategy generation method and device, equipment, a storage medium and a program product, and relates to the technical field of data processing, and the method comprises the steps: carrying out the vector space alignment of a user behavior feature and a knowledge base vector, so as to determine the semantic association degree of the user behavior feature and the knowledge base vector; obtaining entity vectors related to the user behavior characteristics from a knowledge base on the basis of the semantic association degree; predicting the consumption intention of the user based on the spliced data of the user behavior characteristics and the entity vectors; generating a marketing strategy based on the user consumption intention; wherein the user behavior features are obtained by fusing semantic features and structured features in the user multi-source heterogeneous data, and the semantic features are obtained by processing the user multi-source heterogeneous data based on a large model. According to the method, the generalization ability of a large model and the semantic association ability of knowledge base vector optimization are fused, and a real-time dynamic intelligent data mining framework is constructed, so that a high-precision personalized marketing strategy is generated.