226results about How to "High forward-looking" patented technology

The invention discloses a dam safety monitoring agent system based on a multi-dimensional large model and digital twinning, and belongs to the technical field of safety monitoring of water conservancy and hydropower engineering. According to the method, a cognitive decision kernel is used as a core innovation module, multi-source data is collected by relying on a sky-ground hydraulic engineering integrated sensor network, cross-modal data fusion and anomaly recognition are achieved through a multi-dimensional large model cluster, and multi-physics coupling simulation is completed in combination with a digital twinborn body; the cognitive decision kernel generates an optimal combination scheme through case retrieval and measure unit adaptation degree calculation, a control instruction is output after digital twin rehearsal verification, and finally closed-loop control of dam safety real-time early warning, autonomous decision making and efficient disposal is achieved through an autonomous response mechanism. The problems of data island, passive response, expert dependence and the like of a traditional monitoring system are solved, the dam safety management intelligent level and the emergency response capability are remarkably improved, and the method has wide engineering application value.

The invention relates to the field of electrical power grid operation and maintenance, in particular to a differentiated operation and maintenance method and system based on real-time weak links of equipment. According to the method, online monitoring data and power supply reliability operation data of power distribution network equipment are collected in real time to form an equipment state multi-source data set; based on the data set, calculating an equipment health degree evaluation result by adopting a state evaluation model, and obtaining an importance degree evaluation result through an analytic hierarchy process in combination with the position and load importance of the equipment in the network topology; and inputting the two results into a preset operation and maintenance strategy matrix to match a corresponding operation and maintenance strategy type, and finally generating a differentiated operation and maintenance strategy instruction containing specific operation and maintenance measures, an execution time window and a resource allocation scheme. According to the method and the device, the problems of lack of equipment-level pertinence and unreasonable resource allocation of an operation and maintenance strategy in the prior art are effectively solved, and the operation and maintenance accuracy and the resource utilization efficiency are improved.

The invention relates to the technical field of intelligent monitoring of power equipment, in particular to an intelligent circuit breaker state monitoring method and system. The method comprises the steps that unified time sequence modeling is carried out on multi-dimensional operation data of the circuit breaker, periodic behavior fragments with internal rhythm characteristics are extracted, state feature vectors based on historical behavior self-reference are constructed, and the state feature vectors are mapped to a continuous interpretable state space to achieve dynamic visual expression of the operation state; on the basis, the state deviation intensity and the change direction consistency are quantified, so that progressive identification and early warning from normal operation to an abnormal state are realized; and combined prediction is further carried out in combination with a state trajectory and an abnormal index, a risk quantification result and an active intervention strategy are generated, and model adaptive optimization is realized through closed-loop feedback. According to the invention, intelligent operation and maintenance closed loop from real-time monitoring and abnormal early warning to predictive maintenance is completed.

The invention relates to the technical field of computer simulation, and provides an ecological restoration measure carbon sink effect quantitative simulation prediction method and system based on digital modeling. The method comprises the following steps: collecting ecological element information of a target drainage basin, extracting a multi-dimensional spatial distribution feature set, and establishing an ecological environment response simulation model under engineering disturbance in combination with digital baseline data; constructing a carbon sink effect quantitative model based on construction operation situation information, and embedding the carbon sink effect quantitative model into associated nodes of the ecological environment response simulation model to realize coupling; the dynamic influence of engineering construction operation on the drainage basin carbon sink capacity is simulated based on the coupling model, the multi-target optimization decision matrix is generated, the pre-optimization decision suggestion is output, collaborative simulation of ecological environment response and carbon sink effect quantification is achieved, and the accuracy of hydropower engineering carbon sink influence prospective evaluation and the scientificity of decision suggestion are improved.

The invention relates to the technical field of intelligent manufacturing and industrial internet of things, in particular to an intelligent real-time production line regulation and control system and method based on digital twinning, and the system comprises a state sensing and collection module which is used for obtaining the real-time operation parameters of production equipment, material flow state information and environment disturbance characteristics of a production line; the digital twin modeling module is used for fusing multi-source data, constructing accurate mapping of a production line physical entity and a virtual model through a twin model driven by dynamic data, and predicting a performance degradation trend and a disturbance propagation path of production line equipment in real time; the intelligent decision-making module is used for generating a production line real-time scheduling strategy through a distributed collaborative reinforcement learning algorithm and outputting a production line regulation and control instruction set; and the real-time regulation and control feedback module executes production line regulation and control actions through a multi-target dynamic balance algorithm and automatically generates a production line production optimization report with equipment health degree compensation. Therefore, the problems of static model construction, lagging of a decision mechanism, lack of regulation and control feedback and the like in the prior art are solved.

The invention discloses a homeland space planning data intelligent analysis method based on big data, and relates to the technical field of homeland space planning, and the method comprises the steps: collecting multi-source data, carrying out the quality screening, and forming a standardized data set. And setting a space element extraction rule based on a specific planning target, and mining key space elements from the standardized data. Constructing a big data analysis model, inputting key elements to calculate and obtain a spatial development tendency index, and generating a preliminary planning scheme according to the spatial development tendency index; and performing compliance verification on the scheme, generating evaluation data, and adjusting the scheme according to the evaluation data. And finally, performing space efficiency deduction calculation on the adjusted scheme, optimizing the scheme according to deduction result data, and outputting a final planning scheme. According to the method, the accuracy of data mining is improved through target-driven element extraction, and the perspectiveness and scientificity of a planning scheme are enhanced through dynamic efficiency simulation.

The invention provides a hotspot technology mining method and system based on scientific and technological literature data, and relates to the technical field of scientific and technological information mining, and the method comprises the steps: firstly obtaining a scientific and technological literature data set containing papers and patent literatures, and constructing a current technical element network through technical phrase extraction and semantic normalization processing; dividing the technical element network segment into a time-serialized technical element network segment set according to time slices, calculating nodes and edge time sequence importance indexes to judge an emerging technical cluster, carrying out dynamic graph representation learning and future associated edge prediction on the set, generating a potential technical combination prediction set, and evaluating the potential technical combination prediction set; and finally, generating an emerging technology theme list and a technology fusion direction list in combination with an emerging technology cluster node growth trend and a prediction association edge evaluation result, thereby improving the accuracy, comprehensiveness and foresight of hotspot technology mining.

The invention discloses a power distribution network power demand prediction method and device based on big data analysis and a medium, and belongs to the technical field of power demand prediction, and the method comprises the steps: dividing a power distribution network into a plurality of power nodes, collecting the power data of the power nodes, and carrying out the screening to obtain power abnormal nodes; demand information of the power abnormal node is collected, and a power consumption demand event is extracted according to the demand information; acquiring event information of the power consumption demand event, and evaluating according to the event information to obtain a propagation effect condition of the power consumption demand event; acquiring a real-time prediction demand value of a power abnormal node, and determining power change values of other power nodes in combination with a propagation effect condition; and collecting real-time environment data of the power node, predicting according to the real-time environment data to obtain a basic power demand value, and superposing the power change value to obtain a real-time power demand value. The accuracy of power distribution network power demand prediction based on big data analysis is improved.

The invention provides an intelligent detection method and system for absolute stress of a steering axle of a port machine, and relates to the technical field of stress measurement and detection. Carrying out synchronous correction and feature modeling on the collected data, and constructing an absolute stress calculation and intelligent analysis model; screening a structure benchmark operation interval based on the operation data, constructing a benchmark stress reference state, and evaluating the stability of the structure benchmark; on the basis, multi-axis decomposition is carried out on the absolute stress, a multi-axis stress decoupling coefficient is calculated, and multi-axis stress safety judgment and coordinated regulation are achieved; and further combining the stress time sequence evolution characteristics and the fatigue accumulation effect, evaluating the long-term stress evolution risk of the structure, and implementing steering, loading and maintenance strategies when the risk exceeds the limit. According to the method, online sensing, risk early warning and closed-loop optimization of the absolute stress of the steering axle are realized, and the operation safety and the structural reliability of the port machinery are improved.

The invention provides a Bayesian-based aviation equipment residual life uncertainty prediction method and system. The method comprises the following steps: S1, constructing an observation function equation and a state parameter description equation of residual life of key or important systems, modules and parts of aviation equipment; s2, constructing an agent model; s3, solving joint posterior probability distribution and performing residual life prediction; and S4, according to the prediction probability distribution of the remaining life of the aviation equipment, generating an optimization control instruction or a maintenance instruction for the aviation equipment, and acting the optimization control instruction or the maintenance instruction on the aviation equipment. The method provided by the invention not only provides a theoretical basis for solving the problem of prediction, analysis and prediction of the service life of the aviation equipment, but also clearly defines how information and uncertainty thereof flow and convert between a physical world and a digital world, and provides a brand new thought for prediction of the uncertainty of the residual service life of the aviation equipment.

The invention discloses a trend prediction-based ladder support stability test method and system. The method comprises the following steps of: acquiring a plurality of sensing data of a plurality of support legs of a target ladder in a process of applying tension to the target ladder; according to the sensing data, calculating an overturning possible parameter corresponding to each supporting leg at each historical time point; predicting an overturning trend corresponding to the target ladder according to the overturning possible parameter corresponding to each historical time point; and determining the supporting stability corresponding to the target ladder according to the overturning trend corresponding to the target ladder and historical test data. Therefore, accurate ladder overturning risk quantification and stability evaluation based on full-time-sequence trend prediction and collaborative analysis can be realized, scientificity and foresight of safety detection of a ladder structure are improved, and the risk of personnel falling caused by single-point stress unbalance or non-discovered dynamic overturning is reduced.

This invention discloses a method and apparatus for predicting power supply equipment faults based on deep learning, relating to the field of power system equipment fault prediction and deep learning application technology. The method includes: acquiring power grid topology, equipment operating status, historical fault records, real-time equipment load, and environmental condition data; constructing a spatiotemporal correlation graph based on the power grid topology and equipment operating status, and calculating the correlation strength using a graph neural network; combining the correlation strength and historical fault records, calculating the fault time delay using a long short-term memory network and determining the set of propagation paths; fusing multiple data points to calculate the cross-regional fault propagation probability and generating a list of predicted fault paths; updating the fault prediction output input to the long short-term memory network, and verifying and optimizing it using real-time power grid operating data to achieve accurate prediction of cross-regional cascading faults, ensuring the safe and stable operation of the power grid.

The present application relates to a kind of occupational hazards detection report automatic generation management system, specifically to artificial intelligence technical field, by constructing the dynamic evolution of industry knowledge graph, depth mining across enterprise, across time's common high-risk mode;When generating new detection report, current task can be actively matched with historical risk mode and risk transmission evaluation, automatically generate and embed the risk early warning and control suggestion text with foresight, so as to realize the change from passive record to active early warning, significantly improve the automation level of report generation, the intelligent degree of risk identification and the scientificity and foresight of occupational hazards management.

This invention discloses an electromechanical braking system and its control method based on heterogeneous motor redundancy, relating to the field of brake-by-wire technology for new energy vehicles. The system includes the following steps: During vehicle braking, a permanent magnet synchronous motor (PMSM) acts as the main drive source, outputting braking torque, while a switched reluctance motor (SRM) operates in standby or low-power auxiliary mode, forming a heterogeneous motor redundant drive structure. This invention achieves complementary failure modes and reduces the risk of common-mode failures by employing a heterogeneous redundancy structure of a PMSM and a SRM; simultaneously, it optimizes system economy by utilizing the low cost and low standby energy consumption characteristics of the SRM. A deep learning health prediction model is introduced to achieve early fault prediction and proactive switching, and a rapid, seamless switching mechanism within 20ms ensures braking continuity and system safety.

The utility model provides a monitoring and early warning device for a debris flow gully in a canyon fall section, and relates to the technical field of natural disaster early warning. The device comprises a supporting column, a connecting frame, a sliding rail, a debris flow monitoring module and a rainfall monitoring module. Wherein the supporting columns are embedded and fixed on the two sides of a debris flow gully; the connecting frame is fixedly connected to the upper end of the supporting column. The sliding rail is fixed to the lower end of the middle of the connecting frame and extends in the same direction as the debris flow gully. The debris flow monitoring module is slidably connected to the sliding rail and can follow the debris flow state in the debris flow gully when the debris flow disaster occurs. And the rainfall monitoring module is fixed at the upstream position of the debris flow gully. According to the scheme, the debris flow monitoring module can move along the channel for monitoring through the sliding track, dynamic data collection is achieved in combination with the rainfall monitoring module, the problem that traditional fixed-point monitoring cannot adapt to complex canyon terrains is solved, and the accuracy and timeliness of early warning are improved.

The application discloses an unmanned aerial vehicle intelligent sensing and intention reasoning system and device, which comprises image acquisition, target detection, knowledge graph, multi-modal large model, airborne computing power and edge-cloud cooperation module. The system is guided by target detection, enhances scene knowledge through the knowledge graph, improves the analysis ability of the multi-modal large model, and realizes the intelligent monitoring of the unmanned aerial vehicle based on the RK3588 platform. Real-time monitoring is realized by using the maneuverability of the unmanned aerial vehicle, which can not only analyze the scene, but also infer and predict the behavior intention and situation, thereby realizing early warning. The local and cloud systems are equipped with a historical database, a visual interface and a large parameter model, and the returned information is deeply analyzed. The system can be applied to the scenes of border patrol, city security, animal protection and the like.

The application discloses a kind of inventory optimization methods of charging facility spare parts replacement cycle and failure rate analysis, and the core process of this method includes building data layer, multidimensional data is cleaned and optimized, to realize standardized storage;Based on the LSTM algorithm, a multidimensional linkage prediction model is built, the loss effect of design defects and environmental factors is quantified to optimize the model, and the accurate spare parts replacement cycle threshold and equipment failure rate are output;According to the flow properties of spare parts, a differentiated inventory management mode is designed, and the optimal management mode of slow-moving parts is selected;A full-cost quantification model is built to calculate the total cost and service level under each mode;Finally, with the goal of minimizing cost, an intelligent inventory control model is built, which dynamically outputs inventory warning thresholds, replenishment quantities and times;Through the algorithmization and parameter quantification of the whole process, the application realizes fine and intelligent management of inventory, significantly reduces operating costs and stockout risk, and improves the stability of charging facilities.

This invention discloses a method and system for intelligent assessment and injury risk warning of fitness movements based on motion phase perception, belonging to the field of sports biomechanics. It includes: collecting user motion images, basic health physiological data, and musculoskeletal geometric parameters to construct an assessment dataset; identifying skeletal points, dividing the movement execution phase, and extracting the temporal sequence of key joint angles; constructing a collaborative pattern dictionary grouped by movement type and execution phase; dynamically invoking time-varying collaborative constraints using a soft gating mechanism; constructing a combined loss function by fusing inverse dynamic physical consistency terms; and achieving real-time torque prediction of multiple joints based on a spatiotemporal graph neural network. It also involves calculating joint contact forces and soft tissue stress distribution by simplifying the musculoskeletal model, constructing a cumulative injury factor and a dynamic safety threshold, and combining a contrastive learning motion quality embedding network to generate a motion correctness score and an individualized injury risk assessment value. This invention solves the problems existing in the prior art.

This application relates to a method, system, equipment, and medium for intelligent control of gas concentration in the tunnel boring machine (TBM) environment. The method includes: acquiring a three-dimensional reconstructed reduced-order model of the gas concentration field; acquiring multi-source monitoring data of the tunneling environment and standardizing it to obtain a standardized input dataset; correcting the gas concentration field state vector based on the standardized input dataset and the model to obtain a corrected gas concentration field state vector; performing multi-step prediction of the gas concentration change trend based on the corrected gas concentration field state vector to obtain a gas concentration risk evolution sequence for future periods; and optimizing the control parameters of the ventilation and exhaust equipment based on the future gas concentration risk evolution sequence and the operating constraints of the ventilation and exhaust equipment to obtain a control command sequence. This method enables three-dimensional global perception and proactive forward control of gas concentration, solving the problems of control lag and blind spots.

The present application relates to the technical field of data processing and business intelligence, and discloses a multi-dimensional data driven self-brand growth value prediction method, which extracts a main feature vector of a previous time step, calculates a dynamic weight for a same-product white label control group to generate a dynamic benchmark, and accordingly peels off a pure brand-driven incremental index, and combines a repeat purchase decay rate and a search index to construct a three-dimensional state vector; through eigenvalue decomposition on a state covariance matrix, a current main feature vector and a maximum eigenvalue are obtained, and the main feature vector is fed back to a weight calculation link to form a closed loop; finally, the decomposition result and a historical state sequence are respectively input into a pre-trained machine learning model to output a brand life cycle stage and a future brand asset valuation. The present application accurately anchors a reference system through a dynamic feedback mechanism, and realizes comprehensive and accurate diagnosis and prediction of brand value.

The invention discloses a potential knowledge unit group discovery method integrating a large language model and hyperlink prediction, and belongs to the technical field of crossing of artificial intelligence and knowledge discovery. Comprising the following steps: extracting a knowledge unit, and constructing a co-occurrence knowledge super network; generating semantic description by using a large language model, encoding the semantic description into content features, and extracting network topology features; and after fusing the two types of features, calculating the probability that any knowledge unit group forms a hyperedge through a hypergraph neural network prediction model trained by a mixed negative sample, and screening and outputting a potential knowledge unit group. According to the method, the deep semantic understanding of the large language model and the high-order structure information of the super network are fused, so that the accuracy, foresight and robustness of knowledge recombination discovery are improved, and an effective technical tool is provided for interdisciplinary innovation trend prediction and emerging frontier detection.

The invention discloses a multi-virtual power plant low-carbon retail pricing method based on a non-cooperative game, and belongs to the technical field of power system optimization and energy market transaction, and the method comprises the following steps: building a control model of a single virtual power plant; 2, correcting a dynamic carbon emission factor; 3, deducing the optimal retail electricity price of the plurality of virtual power plants; according to the invention, a multi-virtual power plant non-cooperative game pricing model based on dynamic carbon emission factor-economic benefit double chains is established, a value chain of low-carbon excitation and economic benefit is broken through, an accurate low-carbon benefit acquisition mode is created for the virtual power plant, the virtual power plant is promoted to preferentially dispatch renewable energy sources, and the economic benefit of the virtual power plant is improved. And the low-carbon operation level and the market competitiveness of the regional power system are effectively improved.

This invention discloses an intelligent UAV patrol decision-making method based on image recognition and semantic analysis after a typhoon disaster. The method includes: acquiring multi-source post-typhoon data and preprocessing it to form multi-modal post-disaster basic data; performing semantic analysis on disaster reporting and repair text data to extract typhoon characteristic information, affected equipment information, and prone-to-fault information to generate prior disaster information; fusing and analyzing the prior disaster information with equipment operation data to determine the suspected damage status of each piece of equipment or section to be inspected and key inspection targets; generating UAV patrol routes by combining equipment ledger data, meteorological data, and UAV operation constraints; controlling the UAV to collect target images and performing image enhancement and image recognition to obtain equipment damage identification results; generating post-disaster patrol decision results based on the identification results and transmitting them back to the repair command platform. This invention can improve the accuracy of post-disaster patrol target selection, the robustness of image recognition, and the efficiency of repair resource allocation.

This invention relates to the field of radio frequency device design optimization technology, and provides an AI-based duplexer design optimization method and system. The method includes: predicting the event frequency characteristics of the future application environment; determining a correction factor based on the magnitude of the exceedance when the event frequency exceeds a threshold; correcting the key structural parameters of the duplexer to be designed based on the correction factor; and completing the duplexer design based on the corrected key structural parameters. Compared to existing methods that rely solely on experience or a single indicator for design, this invention achieves closed-loop optimization from identifying additional environmental factors to adaptive parameter adjustment, exhibiting higher accuracy and foresight, while avoiding the cost increase caused by over-design, and significantly improving the duplexer's adaptability and long-term reliability in complex environments.

This invention discloses a method and system for full life-cycle management of roads and bridges, relating to the field of road and bridge technology. The method includes acquiring the BIM model of the bridge during the design phase and structural state data and environmental-load data for each stage of the bridge's life-cycle, and constructing a unified model for structural performance degradation. A performance function is generated through multi-factor temporal coupling processing. The performance function is then spatiotemporally aligned and fused with the BIM model, structural state data, and environmental-load data for each stage of the bridge's life-cycle to form a digital structural ontology. Structural state data is extracted from the digital structural ontology, and a dynamic knowledge graph with semantic reasoning capabilities is constructed to output the structural health status. Based on the performance function and structural health status, the failure probability trend is determined and high-risk components are identified, taking into account the bridge's road network level, design capacity, and preset weights for traffic interruption losses, public safety risks, and ecological environment disturbances.

This disclosure proposes a method for synthesizing multimodal data for decision-making attribution explanation in the mining industry, comprising the following steps: First, extracting causal logic from industry texts to construct a structured rule base; second, collecting on-site multimodal data and filtering out data pairs strongly correlated with rules through scenario-based weight modeling and cross-modal association mechanisms; third, using a multimodal generative adversarial network with rule constraint mechanisms and combined with rule mutation technology to synthesize multimodal data with complete logical chains and covering various complex working conditions; finally, ensuring data quality and achieving self-evolution of the knowledge base through a three-level verification and rule feedback mechanism, and storing qualified data in a hybrid storage database. This invention solves the problems of complex causal relationships and scarce high-quality training samples in the mining field, and can automatically and in batches generate logically rigorous and scenario-diverse multimodal data, improving the training efficiency and generalization ability of attribution decision models.

The invention relates to the technical field of ultrasonic image analysis, and discloses an image detection method and system for prolonging the second birth process of double-fetal vaginal delivery and SPPH risk based on ultrasonic Speckle-tracing, and the method comprises the steps: obtaining an ultrasonic two-dimensional gray-scale image sequence; marking a conjoint analysis area between the cervical wall and the front and rear walls of the vagina; constructing a joint area displacement field; constructing a joint region strain trajectory; extracting parturition process characteristic indexes; and executing risk assessment through a fuzzy rule. The technical problem that in the prior art, risk judgment depends on clinical experience, and especially under the condition that continuous stretching occurs on the cervix uteri and vagina combined wall in the second birth process of double-fetal vaginal delivery, early recognition of the evolution process of the tissue from reversible stretching to irreversible instability is difficult to achieve is solved. Due to the fact that joint area displacement field modeling and logarithmic nonlinear strain analysis are introduced, quantitative characterization and continuous risk description of the mechanical behavior of the tissue in the labor process are achieved.

The invention discloses a crop irrigation short-term prediction method and system based on fuzzy control, and relates to the technical field of intelligent agriculture and irrigation control, and the method comprises the steps: collecting weather, multi-depth soil moisture content and field time sequence data of a crop growth stage, and obtaining irrigation feedback; processing the data, and inputting a time sequence prediction model to output water demand prediction values of one to three days and confidence obtained by mapping historical verification errors; fuzzifying the water demand predicted value, the real-time root zone moisture content and the growth stage index, performing weighted deduction and fuzzification based on a fuzzy rule base with a rule dynamic weight, and outputting planned irrigation duration or irrigation amount; and calculating the actual water consumption and updating the dynamic weight of the rule in the post-irrigation evaluation time window according to the water balance and the root zone soil moisture reserve variation. Through confidence risk suppression and post-irrigation closed-loop self-tuning, the stability of short-term prediction driving irrigation decision is improved, the over-irrigation and over-seepage risks are reduced, and the utilization efficiency of irrigation water is improved.

The invention discloses a laboratory environment regulation and control auxiliary system based on artificial intelligence. The system comprises a data acquisition module, a laboratory scene recognition module, a laboratory environment suitability evaluation module, a laboratory environment regulation and control strategy generation module and an environment intelligent regulation and control auxiliary module. The invention relates to the technical field of data processing, in particular to a laboratory environment regulation and control auxiliary system based on artificial intelligence, which innovatively introduces a laboratory scene recognition module to recognize a laboratory scene in real time and effectively improve the environment regulation and control precision; a clustering center based on mahalanobis distance is introduced to initialize, a laboratory scene recognition objective function fused with prior reinforcement constraints is constructed, a weak correlation clustering center recognition strategy and membership redistribution optimization are used for carrying out clustering algorithm improvement, and the accuracy of laboratory scene recognition is improved; and a historical optimal position memory mechanism is introduced, and a dimension learning neighborhood search strategy is adopted to improve an optimization algorithm, so that the precision of a laboratory environment regulation and control strategy is improved.

The invention relates to the technical field of vehicle control, in particular to an electric vehicle steering torque collaborative distribution optimization method. Comprising the following steps: collecting running state data in the running process of the electric vehicle, and carrying out state estimation on the running state data to generate a vehicle running state vector; further, a road condition self-adaptive adjusting mechanism is constructed, and an expected yaw velocity is obtained through calculation; generating a total steering torque based on the expected yaw velocity; based on the total steering torque and the actuator state data set, designing a self-driven adjustment mechanism based on the resource contribution rate, and calculating the resource contribution rate of each actuator; an actuator state prediction mechanism is introduced, and the resource contribution rate of each actuator is corrected; and based on the corrected resource contribution rate and the total steering torque of each actuator, steering torque is distributed to each actuator, and steering is executed based on the steering torque distributed by each actuator. The problems that control of all execution systems is dispersed, steering response lags behind, energy consumption is increased, and steering precision is poor are solved.