41 results about "Synchronous Data Flow" patented technology

The invention provides a GIS insulation defect evaluation method and device and electronic equipment, and relates to the field of data processing. According to the method, a cross-modal synchronous data stream formed by an ultrahigh frequency sensor and a mechanical vibration sensor on a GIS (Geographic Information System) is obtained, event slicing is firstly carried out, multi-dimensional features are extracted, and candidate events are screened in combination with a sparse labeling library; and generating a propagation time delay window and a path priority table by using a three-dimensional geometric model, completing cross-modal pairing and time registration, outputting a registration result, performing array manifold sparse reconstruction separation, and eliminating multipath interference to obtain purification components. And constructing a cross-modal event chain graph based on the purification components, executing spatial positioning and physical quantity inversion under the constraint of the chain graph, and finally generating a defect evaluation report in combination with a historical labeling result, thereby realizing high-precision detection and evaluation of GIS insulation defects. By implementing the technical scheme provided by the invention, the accuracy of insulation defect evaluation can be improved.

The invention discloses a full-localization ultra-high-speed multi-channel ADC (Analog to Digital Converter) acquisition and processing platform, and relates to the technical field of signal processing. The platform comprises a high-stability clock distribution calibration module, a synchronous acquisition and pulse injection module, a pulse detection and integer deviation module, a coarse-grained translation alignment module, a sub-sampling phase extraction module, a fractional delay interpolation alignment module, a hierarchical storage and flow control module and a high-speed interface synchronous output module. Multi-channel sampling clock synchronization is realized through FPGA dynamic phase adjustment; performing coarse-grained translation by injecting calibration pulses to detect integer deviation, extracting a sub-sampling phase difference, and adopting fractional delay filtering to realize sub-sampling precision alignment; hierarchical storage and intelligent flow control are carried out according to priorities, and synchronous data flow is output through a domestic high-speed interface; through a full-flow domestic chip and a synchronous correction technology, sub-sampling precision alignment and hierarchical flow control output of a multi-channel ADC at an ultrahigh sampling rate are realized.

The invention provides an NFC information interaction and management method for an intelligent signboard of power equipment, and the method comprises the steps: extracting field problem feedback data from an obtained synchronous data flow, carrying out the mode recognition and priority sorting of the feedback data through a support vector machine algorithm, and determining a high-priority problem list; according to the determined high-priority problem list, a deep neural network algorithm is adopted to carry out prediction modeling on list data, the future equipment fault trend is judged, and a predictive maintenance suggestion data set is obtained; the obtained predictive maintenance suggestion data set is fused with the real-time interaction content, a closed-loop feedback instruction is generated in a background system, and guidance information update for field operation is obtained; and extracting verification parameters from the obtained guidance information update, performing integrity verification on the parameters through a hash function, judging the reliability of the updated data, and obtaining the finally confirmed closed-loop management information.

The invention relates to an electric equipment fault prediction method based on adaptive deep reinforcement learning, and the method comprises the steps: collecting multi-source operation data of electric equipment, carrying out the preprocessing of the multi-source operation data, and obtaining a synchronous data flow; inputting the synchronous data stream into an adaptive feature extraction network based on an attention mechanism, and outputting high-dimensional fault sensitive features; the high-dimensional fault sensitive features are input into a trained fault prediction model, action probability distribution output by an intelligent agent is obtained and converted into an electric equipment fault risk index, the fault prediction model is a deep reinforcement learning model, and the electric equipment fault risk index is an electric equipment fault risk index. The deep reinforcement learning model is obtained by establishing an equipment state space, an action space and an instant reward function and training by adopting an adaptive learning rate algorithm; and according to the electric equipment fault risk index, displaying an equipment health state curve and a risk thermodynamic diagram in real time through a visual interface. According to the invention, high-precision fault prediction can be realized.

The invention discloses a production workshop intelligent monitoring method and system based on industrial big data AI analysis, and relates to the related field of data processing.The method comprises the steps that multi-source heterogeneous data streams of a production workshop are collected in real time, timestamp alignment and format standardization processing are conducted on the multi-source heterogeneous data streams through edge computing nodes, and synchronous data streams are obtained; performing dynamic quality evaluation on the synchronous data stream, and generating a data exception label when detecting that any quality index of any data source is exceptional; according to the data exception label, a differential fault-tolerant strategy is triggered to carry out data fault-tolerant repair, and a repaired synchronous data stream is obtained; and performing real-time equipment anomaly detection on the repaired synchronous data stream to obtain an equipment health state. The technical problems of inaccurate equipment state monitoring and low production workshop intelligent monitoring level in existing production workshop intelligent monitoring are solved, and the technical effects of improving the equipment state monitoring accuracy and improving the production workshop intelligent monitoring level are achieved.

The application provides a sensor system of a logistics robot and a data processing method thereof, and belongs to the technical field of intelligent logistics, robot perception and artificial intelligence signal processing. The method comprises the following steps: collecting multi-source data of the logistics robot in the moving process in real time through a multi-source sensor, and generating a multi-source sensor original data set; performing data fusion preprocessing on the multi-source sensor original data set, and generating a fused synchronous data stream; based on the synchronous data stream, the dynamic barycenter position data of the goods are obtained through a barycenter inversion algorithm combined with a three-dimensional model of the goods and pressure distribution characteristics, and corresponding timestamp information is recorded synchronously to generate a dynamic barycenter position-time sequence data of the goods; and through the fusion of the multi-source sensor data, the dynamic barycenter position of the goods can be inverted in real time and accurately, and the perception ability of the robot to the barycenter change is greatly improved.

This invention relates to a reliable translation method for synchronous dataflow languages ​​based on the 8114 criterion, belonging to the field of code generation. The invention obtains the abstract syntax tree of the Ctemp intermediate language through lexical and syntactic analysis and translation of Lustre source code; it traverses the Ctemp abstract syntax tree and designs translation methods for control flow statements and assignment statements within the tree structure; it traverses expressions within statements and designs translation methods that add type casting to ensure that statements are semantically correct while avoiding implicit type conversions; it extends the theorem on type relations in Cltype for verifying type operations; and it extends the theorem on type casting and binary operations in Ctemp semantics to achieve semantic formal verification. This invention significantly improves the compliance of C code generated by code generation tools with the GJB 8114 specification.

The invention relates to an intelligent manufacturing production line real-time monitoring method and device, equipment and a medium. The method comprises the following steps: acquiring observation data and simulation output data in real time, performing time synchronization alignment processing to obtain a physical-virtual sensor synchronization data stream, and aggregating all comprehensive mismatch degrees according to the comprehensive mismatch degrees of each physical sensor-virtual sensor pair to obtain a global mismatch index; when the global mismatch index exceeds a preset threshold value, identifying a mismatch salient region based on each comprehensive mismatch degree, and performing sensitivity analysis on key physical parameters of the mismatch salient region to obtain a suspicious parameter subset; constructing a loss function according to the observation data and the simulation output data, and minimizing the loss function to solve the suspicious parameter subset to obtain an optimal parameter correction value; and updating the model parameters based on the optimal parameter correction value to obtain a corrected digital twinborn model. By adopting the method, the digital twin model can keep high-fidelity virtual mapping capability in the full life cycle of the production line.

This invention discloses a fault diagnosis method for scraper conveyors, relating to the field of fault diagnosis technology for coal mine transportation equipment. The invention includes synchronously acquiring the drive motor current signal, drive sprocket vibration signal, and driven sprocket acoustic emission signal to construct a synchronous data stream; extracting the heavy-load starting current fluctuation range and extracting corresponding vibration and acoustic emission signal segments; performing phase-synchronized integer-cycle extraction and envelope demodulation analysis on the vibration signal based on the real-time rotation phase of the sprocket to generate a phase-synchronized vibration envelope demodulation spectrum; calculating the cumulative energy change rate of the acoustic emission signal and dynamically generating weighting coefficients to weight and enhance the vibration envelope demodulation spectrum to obtain an enhanced fault feature spectrum; matching this feature spectrum with a preset fault feature spectrum library to output the fault type and fault confidence level. This invention can accurately remove signal aliasing interference under heavy-load starting conditions, improve the purity and sensitivity of fault feature extraction, and significantly improve the accuracy of fault diagnosis.

This invention discloses a method for monitoring the condition of natural gas valves based on digital twins, belonging to the field of industrial equipment condition monitoring and intelligent control. It includes: acquiring synchronous data streams and topological data of the physical valve to construct a self-evolving twin model; performing dual-rate dynamic monitoring based on valve operating stage attributes to generate a state evolution sequence; extracting physical damage parameters based on a set of constraint equations; constructing and fusing independent damage evidence chains to generate a comprehensive health assessment result; and initiating closed-loop verification to output decisions and update the model. This invention employs a dual-rate monitoring strategy and multi-source evidence chain fusion decision-making, enabling accurate extraction of latent damage features and achieving virtual-real closed-loop self-evolution, thus improving the monitoring accuracy and early warning timeliness of natural gas valves under complex operating conditions.

The invention belongs to the technical field of energy intelligence, and provides a regional integrated energy system multi-level agent construction method, which comprises the following steps: carrying out space-time alignment processing on real-time operation data to obtain a synchronous data stream; respectively mapping parameters in the synchronous data stream into specific visual attribute primitives to obtain a multi-level state semantic image; performing visual semantic segmentation on the multi-level state semantic image to obtain visual semantic units, and constructing a spatial correlation map between the visual semantic units; performing visual difference comparison on the spatial correlation map and a historical same-period spatial correlation map to obtain an abnormal visual semantic unit; performing multi-frame tracking verification on the abnormal visual semantic unit, and integrating an abnormal unit identifier and an abnormal type into an abnormal event report; and carrying out hierarchical decision making on the abnormal event report and the spatial correlation graph to obtain a collaborative regulation and control instruction. According to the invention, the efficiency of cooperative management of the regional integrated energy system can be improved.

The invention discloses a multi-modal-based immersive self-adaptive treadmill rehabilitation training method and system, and the method comprises the steps: constructing a multi-modal synchronous data stream through synchronously collecting real-time posture data, surface electromyogram signals and treadmill state data of a user; further analyzing the gait quality, muscle activation and interaction state of the user in real time, and generating a multi-dimensional state vector; based on a preset self-adaptive strategy table, the state vector is compared with an expected threshold value of the current training stage, and a cooperative adjustment instruction aiming at the speed / gradient of the treadmill, the dynamic characteristics of the virtual scene and audio feedback is generated through intelligent decision; finally, closed-loop self-adaptive control is formed through multi-channel real-time feedback execution, and the training stage is automatically transited when conditions are met. According to the method, deep interaction and self-adaptive linkage of the physical training environment and the virtual immersion scene are realized, safer and more personalized rehabilitation training experience rich in sense of participation can be provided, and the accuracy and effect of rehabilitation training are effectively improved.

The application provides a kind of cabin cover full life cycle management method and system based on digital twinning, method includes: constructing cabin cover structure-thermal-fluid multi-physical field coupling digital twinning model, obtains key parameter and environment data real-time synchronization data stream. Simulate the influence of different materials and process parameters on the performance of cabin cover using virtual trial platform, generate the optimal solution of material distribution. Use smart contract to verify the qualification of suppliers, generate tamper-proof records, and use residual analysis model and XGBoost algorithm for fault prediction. Based on the fault prediction result, generate adaptive control instruction and optimal maintenance work order. Based on the optimal maintenance work order, generate an environmentally friendly disassembly path and a pollution emission control scheme. The application realizes precise management of cabin cover full life cycle by constructing multi-physical field coupling digital twinning model, solves the problem of large deviation between traditional single physical field simulation and actual working condition, and improves the performance prediction accuracy.

The invention provides an intelligent riding monitoring method and system based on multi-modal data fusion, and relates to the technical field of riding safety monitoring, and the method comprises the following steps: obtaining a synchronous data stream in real time, including first motion data from a vehicle inertial measurement unit and second motion data from a user helmet inertial measurement unit, and physiological signal data from the physiological sensor; calculating a motion instability index in real time based on the first motion data and the second motion data; if the motion instability index is larger than a first preset threshold value, the target input data set is configured to not include the physiological signal data; if the motion instability index is smaller than or equal to a first preset threshold value, the target input data set is configured to include physiological signal data; and the target input data set is input into the riding risk level calculation model, and the riding risk level is calculated, so that false alarms can be avoided, and the accuracy and reliability of the riding monitoring system are remarkably improved.

The application provides a kind of intelligent riding monitoring method and system based on multi-modal data fusion, it is related to cycling safety monitoring technical field, method includes the following steps: real-time acquisition synchronous data stream, including first motion data from vehicle inertial measurement unit, second motion data from user helmet inertial measurement unit and physiological signal data from physiological sensor;Based on first motion data and second motion data, real-time calculation motion instability index;If motion instability index is greater than first preset threshold, target input data set is configured to not include physiological signal data;If motion instability index is less than or equal to first preset threshold, target input data set is configured to include physiological signal data;Target input data set is input to cycling risk level calculation model, and cycling risk level is calculated, false alarm can be avoided, and the accuracy and reliability of cycling monitoring system are significantly improved.

The invention relates to the technical field of intelligent wearable equipment, and particularly discloses a multi-mode biosensor monitoring sock system. The system comprises a flexible sock body, a multi-mode sensor array integrated on the flexible sock body, a master control synchronization module and a data processing module. The master control synchronization module generates global hardware synchronization pulses based on a single high-precision crystal oscillator, directly triggers analog-to-digital converters of all pressure, inertia and myoelectricity sensor channels, and realizes synchronous sampling of a hardware level. And the data processing module gives a uniform timestamp to the synchronous data stream and then sends the synchronous data stream. According to the method, high-precision time alignment of multi-modal signals is ensured, a reliable basis is provided for subsequent data fusion and gait analysis, and the power consumption and complexity of the system are reduced.

The invention relates to the technical field of unmanned aerial vehicles, and provides an unmanned aerial vehicle task interactive deduction method and system based on historical flight data, and the method comprises the steps: a data playback module loads and plays back historical flight data, and outputs a synchronous data stream; the data injection module injects new data into the synchronous data stream and outputs an updated data stream; the calculation engine module calculates a speculative flight state through increment according to the updated data flow and outputs a speculative result flow; and the branch comparison module generates result branches according to the speculation result flow and performs comparison display on the result branches. Through close cooperation of all the modules, continuity and consistency of data streams from loading to final comparison are ensured, the data playback module provides basic data streams, the data injection module intervenes in modification and updates the data streams, the calculation engine module receives and processes the data streams to generate speculation results, and the branch comparison module summarizes multiple outputs for analysis. A complete interactive deduction chain is formed, and high interactivity and prediction accuracy are achieved.

The invention relates to the technical field, in particular to a high-temperature furnace intelligent control system and method based on machine learning, and the method comprises the steps: a data collection module obtains and processes sensor data and outputs synchronous data streams; the twinborn modeling module is used for driving digital twinborn simulation by utilizing a synchronous data stream to obtain physical field data in the furnace; the causal analysis module identifies a key causal chain from the physical field data to generate a causal graph; the long-range prediction module adopts a neural network architecture integrated with a gating memory unit, and a causal diagram is combined to guide an attention mechanism to output a long-period prediction value; the strategy optimization module brings the predicted value into a reward function, and generates a control instruction through reinforcement learning and advanced simulation verification; the robust control module executes the instruction and monitors and predicts uncertainty to realize safe switching; the meta-learning module dynamically optimizes system parameters based on cross-batch performance data. According to the method, the gradient disappearance problem of the recurrent neural network in long-term dependence modeling is solved, and the control precision of the slow dynamic process of the high-temperature furnace is improved.

The embodiment of the invention relates to equipment and a method for communication. When it is determined that a first packet in the first data stream and a second packet in the second data stream are to be synchronized, the terminal device performs synchronization of the first packet and the second packet based on at least one of a mapping between a data stream and a QoS stream, a mapping between a packet and a QoS stream, a mapping between a packet and a PDU set, a mapping between a packet and an LCH, a mapping between a packet and an importance value, or a mapping between a packet and a sub-LCH. In this way, coordination or synchronization of multi-modal data may be achieved.

The invention relates to a fault diagnosis method, system and equipment based on rolling mill vibration signals and a medium, and the fault diagnosis method solves the problem of isolation of process parameter and vibration signal monitoring through multi-source signal synchronous acquisition and time base alignment to generate a synchronous data stream with a unified timestamp; a dynamic trigger acquisition and hierarchical storage strategy is adopted, an abstract feature packet and an original waveform packet are generated according to the working condition state of equipment, and the full-time original data storage pressure is remarkably reduced; a joint judgment mechanism based on a frequency spectrum peak value and a process parameter change rate is used for accurately identifying a fault event and effectively inhibiting false alarms under working conditions such as roller replacement acceleration; and finally, realizing predictive maintenance decision through typical fault data migration and a residual life prediction model. According to the method, the storage resource optimization and the operation and maintenance efficiency improvement are realized while the fault identification accuracy is guaranteed.

The invention discloses a method and system for analyzing the maximum throughput of a synchronous data flow diagram, and the method specifically comprises the following steps: S1, solving the execution times of all roles of an SDFG in one iteration according to a balance equation, and representing a result with a vector q; s2, according to the vector q obtained in the step S1, traversing the SDFG according to a key dependency formula by taking any role instance as a source point, and finding a role instance which is inevitably in a connected component with a key loop as a source point role instance; s3, traversing the SDFG according to a key dependency formula by taking the source point role instance obtained in the step S2 as a source point, and creating and initializing a parameter shortest path forest; and S4, dynamically selecting the optimal edge by using the minimum heap, iteratively updating the attribute of the corresponding sub-tree until a termination condition is detected, and returning the weight of the current edge, namely the maximum throughput. According to the method, the conversion process from the SDFG to the HSDFG can be omitted, meanwhile, traversal paths on the SDFG are reduced, and then the throughput analysis efficiency is greatly improved.

The application discloses a conversion method and system from RSML ‑e model to Luster synchronous data flow language. ‑e The method converts RSML ‑e model into Luster, which involves type conversion, Boolean expression conversion, and-or table conversion, variable conversion, macro conversion and function conversion. Type conversion handles undefined values by creating enumerations and structs. Boolean expression conversion includes truth judgment, logical negation, previous state reference and equality judgment. and-or table conversion constructs sub-expressions by columns and connects them with logical or. Variable conversion is converted into Luster nodes, and ordinary variables and input variables are processed with initial values and conditions respectively. Macro and function conversion are converted into nodes with input and return values, and the contained logic is converted into Boolean expressions. The overall process realizes the mapping of RSML ‑e logic to Luster data flow, so that the safety properties of the system can be verified using model checking tools, improving system security.

A formal automatic translation method of a state machine model comprises: adding a state model analysis structure corresponding to state transition data of a state machine model layer on the basis of a synchronous data flow layer to form an intermediate translation layer. The state transition data comprises current input values, a current state, a next state, current output values, state transition relationships and clock data. The state transition data and state machine structure data of the state machine model layer semantics are translated into state machine model data of the synchronous data flow layer semantics. The state machine model data of the synchronous data flow layer semantics is translated into state machine model data of C language semantics. The state machine translation method adopted by the present application keeps the semantics of each layer, simplifies the translation process from the state machine to the synchronous data flow language, makes the state machine be translated into a conditional block first, and then be translated into the synchronous data flow language, and further reduces the verification time of the state machine and improves the verification efficiency and accuracy.

This application provides a hydrodynamic analysis method and apparatus for offshore photovoltaic platforms. The method acquires synchronous data streams from a multi-source sensor network deployed on the platform. These synchronous data streams contain multi-dimensional monitoring data reflecting the platform's operational status, including performance data reflecting the platform's energy capture performance, environmental data reflecting the platform's environmental conditions, and hydrodynamic data reflecting the platform's structure and the mechanical response of the mooring system. A data fusion model is used to perform cross-domain correlation analysis on the multi-dimensional monitoring data, establishing dynamic correlations between performance data, environmental data, and hydrodynamic data. Based on these dynamic correlations, assessment information for evaluating the platform's overall operational status is generated. This method significantly improves the accuracy of hydrodynamic performance prediction for offshore photovoltaic platforms through nonlinear time-domain analysis involving multi-load coupling and dynamic wetted surface adaptation.

This invention discloses a method and system for publishing multimodal data in industrial production for artificial intelligence, belonging to the field of industrial intelligence and data processing technology. The method includes: collecting multimodal data and performing hardware-level time synchronization and preprocessing to obtain a synchronized data stream; extracting multimodal features from the synchronized data stream in parallel and using an attention mechanism to fuse networks and generate a unified feature vector; constructing a five-dimensional quality assessment system and intelligently enhancing it; performing structured annotation and version management; adapting and publishing datasets according to AI tasks; implementing multi-level data anonymization and fine-grained access control, embedding invisible digital watermarks, and generating audit logs; and enabling multi-terminal access through a publishing platform. This invention solves the problems of difficult data alignment, poor quality, and low reusability, achieving secure data sharing and providing a high-quality, standardized data infrastructure for industrial AI applications.

The invention discloses a self-adaptive multi-source fusion target detection and tracking method, and belongs to the technical field of photoelectricity. The method comprises the following steps: firstly, acquiring visible light data, infrared data and radar data which are synchronously acquired to generate a synchronous data stream; performing time-space association and registration processing on data in the synchronous data stream to generate an associated data set; extracting visible light features, infrared features and radar features of the same target based on the associated data set; inputting the visible light features, the infrared features and the radar features into a multi-feature recognition algorithm for fusion processing, and obtaining a target recognition and tracking result; and the synchronous data stream and the target identification and tracking result are stored, and the multi-feature identification algorithm is labeled and the model parameters are adjusted based on the synchronous data stream and the target identification and tracking result, so that the target detection accuracy and the system adaptive capability in a complex environment are remarkably improved.

This invention relates to the field of fault code data processing technology, and particularly to a method and system for processing fault code data of centrifugal oil mist lubricators. The method includes parallel acquisition of operating environment parameters and original fault codes, appending high-precision timestamps to generate a synchronous data stream, calling a fault knowledge base and associating contextual data to form a dataset, extracting time-domain and frequency-domain features to generate multi-dimensional fault feature vectors, encapsulating them into standardized fault case units, storing them for updating the diagnostic prediction model, and optimizing the knowledge base based on maintenance feedback. The system comprises five collaborative modules: synchronous data acquisition, context-aware associative feature adaptive extraction, standardized encapsulation, case storage, and model iteration. This invention achieves standardized and precise data processing, improves fault diagnosis efficiency and accuracy, supports system adaptive evolution, reduces operation and maintenance costs, and is suitable for efficient fault handling of centrifugal oil mist lubricators.

The invention relates to the technical field of video processing, in particular to a screen recording splicing method based on multi-region frame selection, which comprises the following steps: initializing computer hardware resources, constructing a screen recording acquisition link and setting a multi-thread framework; setting a to-be-recorded screen area, collecting a data stream of the to-be-recorded screen area, and identifying boundary coordinates of the screen recording area; creating an extraction class for each area to be subjected to screen recording, starting multiple threads, and presetting screen recording acquisition parameters; acquiring a data stream of screen recording by utilizing the extraction class function; and performing time stream synchronization, data stream decoding and merging of a plurality of screen recording areas. The problem that an existing screen recording merging method is poor in compatibility and timeliness is solved.

The invention discloses a method for improving the computing power of an embedded processing platform, and belongs to the technical field of photoelectricity, the method is executed on the embedded processing platform, and the processing platform comprises an FPGA, an RK3588 chip and an HX5 acceleration chip. The method comprises the following steps of: receiving visible light data, infrared data and radar data which are synchronously acquired by using an FPGA (Field Programmable Gate Array) to generate a synchronous data stream; performing time-space correlation and registration processing on the synchronous data stream by using the FPGA and the HX5 acceleration chip cooperatively to generate a correlation data set, and extracting visible light features, infrared features and radar features of the same target based on the correlation data set by using the HX5 acceleration chip; a visible light feature, an infrared feature and a radar feature are input into a multi-feature recognition algorithm for fusion processing by using an HX5 acceleration chip to obtain a target recognition and tracking result, and the target recognition and tracking result is compressed and coded by using an RK3588 chip and is transmitted to external equipment, so that the real-time processing capability of front-end equipment is remarkably improved.

The invention discloses an electric power meteorological high-influence weather trend prediction method based on meteorological numerical forecasting, and particularly relates to the field of electric power meteorological data prediction under an end-cloud collaborative architecture, and the method comprises the steps: obtaining meteorological data through a meteorological observation device at an end side, and obtaining electric power equipment data through an electric power equipment monitoring device; performing timestamp calibration and interpolation processing on the meteorological data and the power equipment data according to a unified acquisition interval to form a time sequence data stream, transmitting the time sequence data stream to a cloud, correcting a time synchronization error through a cloud time reference signal, and outputting a synchronous data stream; a bidirectional coupling model of meteorological observation data and power equipment operation data is constructed under an end-cloud collaborative architecture, and dynamic fusion and adaptive prediction of meteorological change and power equipment response are realized in combination with causal relationship reasoning and game equilibrium calculation. The problem that trend prediction and real-time feedback adjustment are difficult to achieve under complex meteorological conditions is solved.