2072 results about "Information fusion" patented technology

The invention discloses a power transmission line foreign matter detection method and system based on multi-modal image fusion, and relates to the technical field of intelligent operation and maintenance and state monitoring of a power system, a lightweight Ev-Mama architecture is introduced into a backbone network part of YOLOv13, the model keeps relatively low calculation complexity, and meanwhile, the power transmission line foreign matter detection efficiency is improved. And the modeling capability of the method on the long-range dependency relationship and the global semantic information is obviously enhanced. Besides, by using the CDIDF module, the EVCS module and the MHSAA module, on the basis of increasing a small amount of calculation, the scale sensing ability, the space structure modeling ability and the context understanding ability of the model are effectively improved, and the performance bottleneck of a traditional YOLO series network in the aspects of processing small targets, shielding targets and cross-scale information fusion is effectively relieved.

The invention discloses a control method for a multi-modal information fusion intelligent robot with a body. The control method comprises the following steps: initializing a system, and collecting surrounding physical environment and object state information and a natural language instruction of a user; performing scene understanding and task analysis, processing data through a multi-modal information fusion mechanism and a cross-modal attention module, and generating unified multi-modal data; task planning and priority ranking are carried out, complex tasks are decomposed into subtask sequences, and a priority ranking layer dynamically adjusts the execution sequence; performing action execution and feedback adjustment, and generating a control instruction through a self-adaptive operation control algorithm; continuous learning and strategy verification are carried out, integrated execution is realized by using a hybrid AI system, and the robustness of an operation strategy is verified through a simulation environment; and closed-loop iteration is carried out to realize real-time response of the intelligent robot with the body. According to the method, the perception understanding precision and the task execution efficiency of the intelligent robot with the body are improved, the operation precision adaptability and the system robustness flexibility are guaranteed, and the method is suitable for multiple scenes.

The invention discloses a ship navigation risk dynamic early warning method and system based on multi-source information fusion, and relates to the technical field of ship navigation control, and the method comprises the steps: carrying out the time-space registration and fusion processing of multi-source data obtained in real time, and building an environment model comprising a ship, surrounding ships, static obstacles and environment elements; fusing the risk distribution of all the risk influence factors based on an environment model, and generating a dynamic risk field reflecting the risk situation of the whole water area; performing collision avoidance decision analysis based on the dynamic risk field; the environment complexity value is fed back to the generation process of the dynamic risk field, and the risk distribution form of the dynamic risk field is changed by adjusting calculation parameters of risk fusion; and risk monitoring is carried out based on the changed dynamic risk field, and when it is detected that the risk level of the area where the ship is located exceeds an early warning level threshold value, early warning information including collision avoidance suggestions is generated in combination with a recommendation strategy.

The invention discloses a flood disaster risk intelligent early warning method fusing multi-source information, and relates to the technical field of flood disaster prediction.The method comprises the steps that multi-source data of meteorology, hydrology, terrain, remote sensing, ground monitoring terminals and the like are obtained, and a unified data set is constructed through time synchronization, space resampling, abnormal value elimination and missing data filling; constructing multi-dimensional spatio-temporal features based on factors such as rainfall process, topographic features, surface coverage, drainage capacity, river network structure and soil humidity; inputting the spatial-temporal characteristics into a multi-source information fusion model, and predicting a flood occurrence probability in a future time period; flood risk indexes are generated according to the prediction probability, real-time monitoring and historical samples, and regional risks are divided into different grades; early warning information is automatically generated and issued according to the risk level; and the early warning result is fed back and corrected through field monitoring and patrol data, so that self-adaptive updating of early warning parameters and a model structure is realized.

The embodiment of the invention provides an energy storage system fault processing method and device, equipment, a medium and a product, and relates to the field of photovoltaic power generation, the method solves the problem of low fault processing efficiency in the prior art by constructing a cloud-side-end cooperative fault processing architecture, changes the traditional static threshold judgment logic, and improves the fault processing efficiency. A decision-making mode combining multi-source information fusion and dynamic prediction is adopted, firstly, preliminary diagnosis of real-time data is achieved through edge calculation, the instantaneity of response is ensured, and then historical trend analysis, equipment health prediction and multi-dimensional real-time state evaluation are fused through a cloud platform. According to the method, the dynamic fault evaluation capable of accurately reflecting the actual severity and development trend of the fault is generated, so that the system can be adaptive to equipment aging and environment change, and a grading response strategy accurately matched with the fault grade is triggered, thereby realizing the crossing from passive alarm to active and accurate fault management and control, and improving the fault processing efficiency.

The invention relates to the technical field of fault diagnosis, in particular to a pole-mounted circuit breaker fault diagnosis method based on multi-information fusion, and the method comprises the steps: firstly obtaining electric quantity modal data such as three-phase current and coil current and mechanical quantity modal data such as mechanism vibration and voiceprint, and constructing a time-frequency domain alignment feature tensor; then, through a graph space-time attention fusion model, deeply mining physical structure association and time sequence evolution laws among heterogeneous data, and generating a dynamic space-time feature matrix; then, respectively constructing a mechanical evidence body for representing the state of the transmission chain of the operating mechanism and an electrical evidence body for representing the working condition of the vacuum arc-extinguishing chamber by adopting a feature level-decision level mixed framework; an improved D-S evidence theory is applied for fusion, when high-conflict evidences are detected, a self-adaptive arbitration mechanism is automatically triggered, conflict weights are dynamically attenuated or distributed to uncertain items, misjudgment is effectively avoided, and high-robustness collaborative diagnosis of the electromechanical state is achieved.

The invention relates to the technical field of intelligent operation and maintenance, in particular to an artificial intelligence operation and maintenance decision support method and system for multi-source information fusion. The method comprises the following steps: acquiring multi-source operation and maintenance data to perform multi-dimensional feature extraction to obtain multi-dimensional operation and maintenance feature data; performing continuous spatial cross-modal embedding according to the multi-dimensional operation and maintenance feature data to obtain cross-modal embedded data; performing heterogeneous feature coupling graph generation on the cross-modal embedded data to obtain coupling graph data; performing heterogeneous space fusion coding according to the coupling graph data to obtain fusion coding data; performing expert knowledge driving graph embedding on the fusion coding data to obtain operation and maintenance fusion graph data; performing root cause positioning reasoning according to the operation and maintenance fusion graph data to obtain root cause positioning data; and performing operation and maintenance decision generation according to the root cause positioning data to obtain operation and maintenance decision data. Through multi-source information fusion and intelligent reasoning, the root cause positioning accuracy and intelligent operation and maintenance decision efficiency of the system can be effectively improved.

The invention provides an unmanned aerial vehicle full-time perception image reconstruction method based on multi-mode cooperative reinforcement learning and degeneration decoupling. A frequency perception feature modulation model, a dual-mode dual-domain transformation module and a dynamic bidirectional guide mechanism are included. According to the system, firstly, feature information of different frequency bands is adaptively separated and modulated through a frequency sensing feature modulation model, and decoupling and compensation of composite unknown degradation are achieved; realizing cross-domain interaction and information fusion of visible light and infrared characteristics in a spatial domain and a channel domain by using a bimodal dual-domain transformation module; and finally, realizing collaborative enhancement of cross-modal degradation perception through a bidirectional dynamic guide mechanism, and generating an unmanned aerial vehicle visible light reconstruction image and an infrared super-resolution image with higher structural consistency and texture fidelity. According to the method, deep fusion and degeneration decoupling of multi-modal information can be realized in a complex degeneration environment, and the imaging quality and the environmental adaptability of an unmanned aerial vehicle full-time sensing system are remarkably improved.

The invention discloses a multi-source information fusion-based dynamic cooperative regulation and control method for a transmitting-end power grid energy storage station, and relates to the field of power grid energy storage regulation and control. The method comprises the following steps: compressing global heterogeneous data, and extracting a low-dimensional critical feature flow; constructing a dynamic coupling potential energy field, and quantifying gradient intensity to capture recessive instability; generating a layered fractal regulation strategy, resolving conflicts and outputting an instruction and a residual error; constructing a topological phase change trigger network, and triggering regulation and control through spectrum gap jump; causal tracing and model evolution are carried out, and parameters are optimized to form a closed loop. According to the invention, through fractal cooperation, topological response and evolution closed loop, regulation and control collaboration, instability response speed and system adaptability are improved, and safe and stable operation of a power grid is guaranteed.

The invention discloses a force sense feedback control method for an intelligent mechanical arm, which comprises the following steps of: 1, acquiring data through a multi-modal sensor and fusing the data to obtain a multi-dimensional perception vector; 3, calculating a force sense tracking error and a change rate and triggering an event-driven control decision mechanism; 4, designing a nonlinear compensation control rule and outputting a control torque instruction, wherein a control system comprises a multi-mode sensing module, a dynamic prediction module, an event-driven control module and a cooperative calculation module; according to the method, multi-mode sensing information is fused with the lifting force sense representation capacity, advanced adjustment is achieved in combination with a dynamic force sense prediction mechanism, event-driven control is used for reducing calculation redundancy, robustness to complex interference is enhanced through a nonlinear compensation strategy, and finally high-precision and low-delay force sense control of the mechanical arm in a dynamic interaction scene is achieved.

The invention discloses a ship anti-collision early warning method based on multi-source heterogeneous information fusion, and the method comprises the steps: S1, obtaining the multi-source target information of a ship navigation radar, an AIS, and an infrared camera, and unifying the targets of all sensors to a same coordinate system; s2, preprocessing the radar and the AIS target; s3, performing information fusion on the preprocessed radar and AIS target; s4, performing information fusion on the target after radar and AIS fusion and the infrared image recognition target; and S5, based on the dynamic information of the final fusion target and the state of the ship, calculating the relative distance and orientation with the ship, and when the target is located in a preset fan-shaped area right in front of the ship and the relative distance is smaller than a dynamic danger threshold value, triggering a multi-stage acousto-optic character alarm. According to the invention, various sensor information can be integrated to detect and track the water surface target, and the robustness and the detection rate are improved, so that a more accurate early warning effect can be achieved on the water surface obstacle when the ship sails.

The invention belongs to the field of intelligent vehicle-mounted technology, and particularly relates to a multifunctional vehicle-mounted sensing detection system based on multi-source information fusion, which comprises multi-source information acquisition, high-precision space-time synchronization and heterogeneous data preprocessing. A heterogeneous sensing system with feature level deep fusion, vehicle state dynamic performance evaluation and adaptive decision and risk evaluation capabilities is constructed, and a driving decision is dynamically adjusted through fusion perception and vehicle performance. The vehicle state and dynamic performance evaluation module is constructed by deeply integrating vehicle internal state data acquired by a vehicle-mounted diagnosis system into a perception fusion and decision planning process, and the core contradiction of disjunction of a perception result and a vehicle dynamic performance strategy is effectively solved. The system can evaluate key performance parameters such as power, braking, steering and the like of the vehicle and potential faults of the key performance parameters in real time, and the driving strategy and the safety margin are dynamically adjusted in the self-adaptive decision and risk evaluation module according to the actual physical limitation of the vehicle.

The invention relates to the field of multi-source information fusion, intelligent traffic and computer vision, and particularly discloses a highway event false alarm suppression method and device based on roadside perception and video verification. The method comprises the following steps: carrying out preliminary event detection and feature extraction by fusing multi-source road side data such as radar and thunder vision; carrying out space-time clustering on the detection result to screen high-suspected alarms; for highly-suspected alarms, scheduling video equipment to carry out directional analysis and deep verification, and carrying out real-time consistency verification on video analysis results and preliminary perception features in the process; and finally, based on a verification result and a comprehensive evaluation strategy, deciding whether effective event information is published or not. According to the method, a closed-loop processing flow of'preliminary perception-clustering screening-video directional verification-multi-source consistency verification 'is constructed, false alarms caused by environmental interference, single sensor errors and the like are effectively identified and inhibited, and the accuracy and reliability of automatic detection of highway events are greatly improved.

According to the multi-source information fusion robot positioning method and system for the unstructured environment provided by the invention, fusion processing is carried out on multi-source information data such as laser point cloud, environment image information and acceleration which are acquired in real time, so that the multi-source information fusion robot positioning method and system for the unstructured environment can be realized in a severe weather state and a dynamic unstructured environment. Carrying out high-robustness real-time positioning under the condition of violent illumination change; in a geometrically degraded roadway area, registration positioning of laser point cloud emission fails due to lack of enough external characteristics, and at the moment, the visual positioning module can still work normally and complete robot repositioning by detecting matched image information; in an environment lacking enough illumination, the laser point cloud and acceleration information can still provide enough positioning fusion data input for the computing unit.

The invention discloses a remote sensing image semantic change detection method based on semantic enhancement Transform and three-dimensional convolution, and aims to solve the problems of insufficient semantic and change information fusion and difficult narrow and long ground feature feature extraction in the existing method. According to the method, an SET-3DC network is constructed, 'feature extraction-feature interaction-semantic enhancement-change information fusion 'is taken as a core link, multi-scale dual-time-phase features are extracted through a dual-branch feature extractor (DFE), cross-branch feature interaction is realized through a channel feature exchange module (CFE), and multi-scale dual-time-phase features are extracted through a multi-scale dual-time-phase feature extraction module. A semantic branch decoder (SEAA-SD) based on semantic enhancement and axial attention strengthens local details, global semantics and narrow and long ground feature features, a change information extraction module (CIE) based on three-dimensional convolution fuses multi-source change information and aligns time-space correlation, and a network is optimized in combination with a joint loss function. Through multi-module cooperation and innovative structural design, the precision and internal consistency of semantic segmentation and change detection are improved, and the method is suitable for a high-precision remote sensing image semantic change detection scene.

The invention relates to the technical field of subway passenger flow prediction, and discloses a space-time perception subway passenger flow prediction method based on large model and multi-source information fusion, which comprises the following steps: constructing a data agent based on a large language model and a model context protocol; generating text description of each subway station and a corresponding spatial context embedding vector based on the peripheral interest point data of each subway station by utilizing a data agent; constructing an enhanced graph neural network according to the spatial context embedded vector, and performing spatio-temporal joint prediction based on the historical passenger flow data and the spatial context embedded vector by using the enhanced graph neural network to obtain a preliminary passenger flow prediction result; and constructing a cue word template, and performing domain knowledge calibration based on the cue word template by using the pre-trained large language model to obtain a final passenger flow prediction result. According to the method, external multi-source information is deeply fused, so that the urban rail transit passenger flow prediction task performance, robustness and interpretability are remarkably improved.

The invention provides a multi-modal fusion virtual simulation experiment teaching system, and relates to the technical field of virtual simulation, and the system comprises an information collection module which is used for obtaining the interaction information of a learner; the information fusion module is used for constructing a multi-modal input data set; the instruction analysis module is used for analyzing and generating an operation instruction sequence; the teaching intervention strategy generation module is used for generating a self-adaptive teaching intervention strategy; the teaching intervention strategy execution module is used for executing a self-adaptive teaching intervention strategy; and the learning effect evaluation module is used for quantifying the learning effect evaluation score. The technical problems that in traditional virtual simulation experiment teaching, multi-modal operation behaviors of learners are difficult to accurately capture in real time, personalized teaching intervention is lacked and learning effect evaluation is incomplete can be solved, and virtual experiment simulation and intelligent teaching intervention driven by multi-modal information fusion are achieved. And the experimental interaction experience and the learning effect evaluation precision are improved.

The invention discloses a side information enhancement sequence recommendation method and system based on causal intervention depolarization, and relates to the technical field of recommendation. According to the method, the recommendation accuracy and robustness are improved by capturing the evolution laws of the user interests on different time scales. By introducing a trainable frequency domain filtering weight, noise interference is effectively suppressed, and the stability and generalization ability of user interest representation are enhanced. The influence of false correlation is eliminated by introducing a learnable causal mask matrix and a differential fusion mechanism, depolarization of systematic interference factors is realized, and the fairness and causal rationality of a recommendation result are improved. And specialized modeling of different user behavior modes is realized through the global-local double-gating hybrid expert network module, so that the expandability and the response speed of the system are improved. And deep interaction of cross-modal features is realized through a dual-path feature weighted fusion module, so that the comprehensiveness of user interest description and the personalized level of recommendation are remarkably improved in a multi-source information fusion scene.

The invention provides an underwater positioning system based on effective sound velocity compensation and multipath equalization, which relates to the technical field of marine equipment and comprises an environment sensing and modeling module, a signal propagation characteristic analysis module, an intelligent signal processing and distance measuring module and a fusion positioning and quality evaluation module. The environment sensing and modeling module is used for constructing a dynamic three-dimensional sound velocity field and evaluating the reliability of the environment, the signal propagation characteristic analysis module is used for establishing a multi-path propagation model and extracting path characteristic parameters, and the intelligent signal processing and distance measuring module is used for suppressing multi-path interference and realizing high-precision distance measuring calculation. The fusion positioning and quality evaluation module is used for multi-source information fusion positioning and forming a quality feedback closed loop; the system can effectively cope with sound velocity temporal and spatial change and multipath interference in a complex marine environment, remarkably improves the precision, stability and robustness of underwater positioning of the underwater vehicle, and is suitable for the fields of deep sea detection, ocean engineering and the like.

The invention provides a regional wind field multi-source heterogeneous work order data fast structuring system fused with a large language model, which comprises a plurality of modules, and is characterized in that a data input module receives various work order images; the OCR layout recognition module comprehensively recognizes the data, extracts key information, determines a spatial position and outputs text fragments with coordinates and layout anchor point information, and the rule field extraction module recognizes format category fields from the text fragments and calculates rule matching confidence; the semantic field recognition module recognizes descriptive fields from unstructured content and outputs related confidence information, the fusion decision module calculates final confidence of field candidates according to a predetermined function and judges output or complementation according to a threshold value, and the data consistency verification module is connected with an asset database to verify key information. The data complementation module generates complementation candidates and confidence coefficients, the man-machine collaborative closed-loop module pushes suggestions and receives feedback, and the result output module outputs standardized data. According to the method, the multi-source heterogeneous work order data can be effectively processed.

The invention provides a multi-agent-based code conversion system and method. The method comprises the following steps: preprocessing an input source code; performing block processing on the source code to obtain source code blocks, obtaining variable information and array size information of the blocks corresponding to the source code, and integrating the variable information and the array size information into complete variable information; fusing the code description file, the code blocks of the source codes and the variable information of the blocks corresponding to the source codes to generate target code blocks; debugging and checking the target code blocks in combination with the source code blocks; the code description file is used for guiding the intelligent agent to establish a jumping type relation in the code conversion process, and it is ensured that the generated target code better meets the actual function requirement of hardware; a block identifier is inserted into a long code to divide the long code into a plurality of blocks, so that the limitation on long code conversion is relieved; codes of different styles can be output by simply adjusting the cue word of the intelligent agent; through multiple times of debugging verification, the conversion quality of the target code is ensured.

The invention provides a system device for intelligently sorting coal gangue. The system device comprises a feeding unit, a machine vision recognition unit, a main control unit and a sorting execution unit which are connected in sequence, the feeding unit comprises a vibrating feeder and a refining and shaping mechanism; the machine vision identification unit comprises a high-speed industrial camera and a near infrared spectrum camera; a coal gangue recognition model based on deep learning is arranged in the main control unit; the sorting execution unit comprises a plurality of high-pressure electromagnetic air valve nozzles which are evenly distributed in the transverse direction of the conveying belt. The vibration feeding mechanism and the material uniformizing and shaping mechanism are combined, so that single-layer uniform distribution of materials is ensured, and overlapping and shielding of the materials are effectively avoided; machine vision and near infrared spectrum multi-modal information fusion is adopted, and a deep learning algorithm is combined, so that the accuracy and adaptability of coal gangue recognition are remarkably improved; and through the time delay compensation and accurate positioning technology, it is ensured that the sorting execution mechanism accurately hits the target, and the sorting efficiency is improved.

The invention relates to the technical field of intelligent diagnosis of power equipment, and discloses a switch cabinet partial discharge diagnosis method based on multi-information fusion and dynamic intelligent regulation, which comprises the following steps: firstly, collecting multi-source partial discharge signals and constructing a defect basic database, then carrying out multi-source feature generation, principal component dimension reduction and BPNN key feature screening on the signals, and carrying out multi-source feature extraction on the signals; then, a dynamic BPA value is constructed based on the key features; and then, the defect type is identified by fusing multi-source information through an improved D-S evidence theory, and the defect is positioned in combination with a TDOA and an optimization algorithm. According to the method, the accuracy and reliability of defect type judgment after multi-source information fusion are improved.

The invention requests to protect an emotion analysis method based on a text-guided comparative learning and gating fusion mechanism, which is used for solving the problems of insufficient modal feature representation discrimination and insufficient modal information fusion in multi-modal emotion analysis. Firstly, features of three modes are extracted in a feature extraction module, then a text mode is selected as a dominant mode, a text-guided weighted comparison learning strategy is designed, the strategy screens positive and negative sample pairs through the text mode, and weights are dynamically distributed for hard pairs and easy pairs, so that the model keeps universal features while focusing on the hard pairs, and the model is optimized. Therefore, the discrimination capability of modal feature representation is improved. Secondly, in a feature fusion module, a text-guided gating fusion mechanism is provided, weights of text modals and cross-modal fusion features are adaptively adjusted through a gating unit, and a self-attention mechanism is used to suppress fusion noise, so that the modals are fused more fully. Experiments on public data sets CMU-MOSI and CMU-MOSEI show that compared with a reference method, the method provided by the invention has advanced performance on multiple indexes such as F1, ACC-2 and the like.

The invention discloses a leg health state evaluation and fault tracing system and method based on a time sequence large model, and the method comprises the steps: an input layer is responsible for carrying out the synchronous collection, normalization and time alignment of various sensor signals, and constructing time sequence input data in a unified format; the feature fusion layer adopts a sliding window mechanism to carry out Patch segmentation on an original signal, and local representation is enhanced in combination with feature engineering; a channel attention mechanism is further introduced, the weight of each channel is adaptively adjusted according to the dynamic relevance between the sensors, and information fusion and feature screening are achieved; the model layer constructs a long-term dependence modeling framework based on a time sequence large model and is integrated with an LoRA low-rank adaptation module, and the prediction layer performs uncertainty quantification on a health state prediction result through dynamic confidence interval estimation; and the application layer completes fault tracing and key component positioning according to time sequence attention distribution and channel weight change, and synchronously generates a health trend curve, confidence interval distribution and a visual early warning interface.

The invention discloses a vehicle control method and device based on multi-source information fusion and actuator health state monitoring. The vehicle control method comprises the steps that driver state data, vehicle dynamics data and environment information data are obtained; sensing and fusing the multi-source data to generate comprehensive sensing data; performing scene classification according to the comprehensive perception data, identifying and judging a current driving scene type, and determining a control demand of current driving; health state monitoring is conducted on vehicle actuators, and the working performance and reliability of all the actuators are evaluated in real time; according to a scene judgment result and an actuator health state monitoring result, dynamically adjusting weight distribution of each control target in a decision making process; and based on the adjusted weight distribution, executing multi-target collaborative optimization, generating an optimal control strategy, converting the optimal control strategy into a control instruction, and issuing the control instruction to a vehicle execution mechanism. And the technical bottlenecks of the existing system in complex scene adaptability, actuator collaborative optimization, fault handling capacity and the like can be solved.

The invention relates to the technical field of ship energy efficiency management, and discloses an energy efficiency improvement method and system based on course-speed-trim collaborative optimization, which constructs a course feature code based on navigation environment forecast information and electronic chart information, and combines real-time state information and decision variables of a ship to improve the energy efficiency of the ship. Generating voyage number accumulated energy consumption expectation by using a ship energy consumption prediction model fused by physical information; taking expectation minimization as a target, coupling navigation time constraints, host working condition constraints and ship stability safety boundaries, constructing a three-variable collaborative optimization model of the route path, the planned navigational speed of each route segment and the planned trim value, solving the model, and outputting a collaborative optimization scheme; and on-line parameter adaptive updating is performed on the model based on real-time data during navigation, and dynamic re-planning is triggered. The global voyage number energy consumption optimization is realized through a three-variable collaborative optimization framework and physical information fusion prediction, and the adaptability and robustness of an optimization scheme in a dynamic environment are improved by means of an online learning and rolling optimization mechanism.

The invention discloses an intelligent equipment life cycle information fusion operation and maintenance optimization method, and relates to the technical field of intelligent equipment fusion operation and maintenance, and the method comprises the following steps: based on a judged nested task execution state, extracting state jump features from state data of load, current, vibration and the like collected in an equipment operation process, calculating state change amplitude, duration and frequency by using a time sequence window comparison algorithm, and obtaining a state hopping feature set associated with task switching; and based on the task level mapping table and the state jump feature set, constructing a graph convolutional neural network model, taking a nesting relationship between tasks as a topological structure, inputting each task node and the corresponding state jump feature into the model, and identifying each stage of the life cycle of the intelligent equipment. According to the method, the problem that life cycle division is affected by nested tasks is solved, and accurate state recognition of the intelligent equipment and dynamic optimization of operation and maintenance parameters are realized.

The invention relates to the technical field of rare earth metal hydrometallurgy, in particular to a rare earth molten salt electrolysis truss type multifunctional metal output system and a control method.The system comprises a main body supporting and moving frame, an integrated function execution module set, a bottom cleaning function module, an electrolytic bath station unit and an intelligent control system; wherein the integrated function execution module group, the bottom cleaning function module, the electrolytic tank station unit and the intelligent control system are respectively arranged in the main body support and motion frame, and the main body support and motion frame is arranged in a gantry truss structure; the four functions of pot lifting, casting, cooling and bottom cleaning in rare earth electrolysis are integrated on the integrated bearing platform of the same gantry truss type rack, and through multi-sensing information fusion and intelligent control, the system can automatically complete the whole process operation of crucible extraction, molten metal casting, cooling forming and electrolytic bath bottom cleaning; and the exposure of workers in high-temperature and high-risk environments is obviously reduced, and the production safety and efficiency are improved.

The invention discloses a big language model dialogue recommendation method based on multi-modal geographic information fusion and context modulation, which comprises the following steps of: firstly, coding and fusing a text address, geographic coordinates and numerical attributes to obtain a geographic context matrix; then intention modeling is carried out to generate user intention representation, then context feature modulation is utilized, dynamic modulation is carried out on the user intention representation by utilizing an asymmetric fusion mechanism, a context guiding signal containing geographical constraints is generated, and finally, the context guiding signal is spliced before the user intention representation. The big language model input to the parameter freezing obtains user preference vectors and calculates recommendation scores with the candidate positions, the candidate positions are sorted according to the recommendation scores, and the candidate positions with high scores are recommended to the user. According to the method, on the premise that internal parameters of the large language model are not changed, the geospatial reasoning ability is effectively given to the large language model, and the accuracy and interpretability of dialogue recommendation are remarkably improved.