150 results about "Topological mapping" patented technology

The invention relates to the technical field of building engineering, in particular to a building data processing method and system based on multiple building specifications, and the method comprises the steps: building model data standardization processing: extracting geometric attributes, material attributes and spatial topological relations of components through a BIM software interface, and generating model data in a standard format; constructing a multi-source specification rule tree, performing semantic analysis on national standard, local standard and industrial standard provisions, extracting triple constraint conditions, and fusing to generate a unified rule tree containing hierarchical relationships and conflict marks; on the basis of a spatial topology mapping relationship between the model and the rule tree, identifying a specification conflict and generating an optimization rule set according to a specification effectiveness level and a spatial attribute priority; and finally, real-time compliance verification is executed, and a three-dimensional compliance report including conflict positioning, article basis and correction suggestions is output. According to the method, multi-source specifications can be automatically adapted, model violation components are accurately recognized, a correction scheme is given, and the specification compliance and the verification efficiency in the design stage are improved.

The invention relates to the technical field of image recognition, in particular to a license plate recognition system and method based on an image technology and a medium. The method comprises the following steps: acquiring area sensing data and a camera image set, and performing deformation effect compensation to obtain an environment compensation image set; performing image diffusion reverse enhancement on the environment compensation image set to obtain a license plate area enhanced image set; performing character region high-dimensional topological mapping based on the license plate region enhanced image set to obtain a character segmentation matrix; extracting character morphological characteristics according to the character segmentation matrix, and performing character recognition on the character morphological characteristics to obtain a character recognition result; and carrying out cross-character semantic compensation on the character recognition result to obtain a semantic compensation license plate character vector, and carrying out multi-target cross verification on the semantic compensation license plate character vector to obtain a license plate recognition result. According to the invention, the accuracy and robustness of license plate recognition can be improved.

The invention relates to the technical field of power network resource management, and discloses a power network resource intelligent management method and system based on 6G and deep learning. The method comprises the following steps: acquiring node data through a 6G technology to obtain a topological mapping table, preprocessing the data to obtain a feature tensor, training a model cluster by adopting a space-time diagram convolution and long-short-term memory network hybrid architecture, analyzing a network state based on a model, generating a scheduling scheme, and issuing a control instruction through 6G network fragmentation to realize intelligent management of a power network. According to the invention, through the combination of the 6G communication positioning integration technology and the space-time diagram convolutional network and long-short term memory network mixed architecture, the real-time data acquisition precision and intelligent analysis capability of the power network are improved. Based on a collaborative optimization mechanism of multi-source data fusion and a deep learning model cluster, the comprehensiveness of power grid state evaluation and the accuracy of resource scheduling are enhanced.

The invention provides a wind-light-water multi-energy system short-term scheduling decision-making method considering source load uncertainty. The method comprises the following steps: acquiring a source load scene set with relevance by applying a hierarchical normalized flow vine Copula generator based on an hour-by-hour source load time sequence; carrying out scene reduction on the generated source load scene set by adopting a density sensitive synchronous reduction algorithm, and extracting a typical source load scene subset; inputting the typical source load scene subset into a rigid-elastic polymorphic constraint transfer double-layer scheduling model, and solving the model by adopting a double-channel elite iterative algorithm to obtain a multi-target solution; and carrying out risk feature decoupling and risk feature tensor construction on the multi-target solution, thereby obtaining an optimal scheduling scheme by adopting an entropy-risk topological mapping decision model. According to the method, the economical efficiency, the safety and the decision-making credibility of short-term scheduling of the system are improved.

The invention relates to the technical field of underground pipe gallery asset management, and discloses a BIM-driven underground pipe gallery asset dynamic management and emergency decision-making platform, and the platform comprises a dynamic topology mapping module which achieves the dynamic grid density adjustment of a BIM model through monitoring the change rate of environment parameters, and generates a logic topology network synchronized with a physical pipe gallery structure in real time; the asset state reverse derivation unit is used for reversely deducing asset degradation based on environmental data by using a physical rule library dynamically loaded in a time-sharing domain; the logic rule self-triggering engine constructs a multi-level response rule chain containing space-time prediction and optimizes an emergency plan; and the edge-cloud collaborative architecture synchronously realizes real-time decision-making of low bandwidth dependence through intelligent cache and asynchronous increment. When dynamic topological mapping is driven by the change rate of environmental parameters, the real-time state of the underground pipe gallery can be represented more finely, and a high-reliability data basis is provided for subsequent accurate analysis and decision making.

The invention relates to the technical field of electrical digital data processing, and discloses a statistical entropy and multi-factor fused text similarity data processing method, which comprises the following steps that: a processor extracts substring sets which do not contain maximum common values of a first data sequence and a second data sequence, and calculates the quadratic sum of the lengths of substrings to generate local statistical entropy; traversing the maximum common substring set to obtain storage address indexes of the maximum common substring set in the first data sequence memory space and the second data sequence memory space, and constructing a topological mapping vector of a mapping structure displacement relationship; calculating the total number of inverted pairs of the topology mapping vector by using a merge sorting algorithm, and generating a normalized topology dissipation index; and by taking the local statistical entropy as an information carrier and taking the topological dissipation index as a structural damping factor, executing nonlinear damping modulation operation to obtain a final similarity score, and solving the technical problem that the block-level displacement cannot be identified by linear scanning logic by quantizing topological entropy increase of data distributed in a storage space.

The invention relates to the technical field of electrical digital data processing, and discloses a system conflict point intelligent review system based on a knowledge graph and a large language model, which comprises the following steps: constructing a dual-mode storage space containing an unstructured index and a structured logic graph, analyzing target text extraction features and triggering graph-based generation logic; converting the topological structure of the associated sub-atlas into a natural language instruction sequence to construct a forced logic constraint template, filling the template with a text, and inputting a pre-training language model to generate a verification result; according to the method, the discrete atlas topology is mapped into the linear logic constraint, random divergence of the generative model is restrained on the calculation principle, and precise decoupling and dynamic evolution of unstructured semantics and structured logic are achieved.

The invention relates to the technical field of environmental monitoring, in particular to an ecological environment and environmental pollution monitoring system and method, and a multi-source heterogeneous data acquisition module comprehensively acquires water quality, weather and space monitoring data. The system comprises a topological mapping data preprocessing module which is used for abnormal data detection and data vacancy filling; the state evolution analysis module is used for constructing an environment state transition network and analyzing an environment state evolution rule; the environment comprehensive evaluation module is used for calculating a water safety level index and a water pollution condition index and generating an environment quality evaluation report; the pollution diffusion prediction module is used for predicting a pollutant diffusion path and concentration distribution and identifying a high-risk area; the intelligent early warning and visualization module is used for generating graded early warning information and visually displaying the environment state and the pollution diffusion condition through multi-dimensional data, so that the coverage and the data integrity of environment monitoring are remarkably improved, the environmental pollution problem can be timely found and dealt with, and the ecological environment safety is guaranteed.

The invention discloses a cooperative scheduling method for a zero-carbon park complementary energy storage system, and the method comprises the steps: enabling an electric energy quality index to be explicitly incorporated into an optimization target through multi-source resource dynamic modeling and scene prediction, and building a strong coupling relation between a physical constraint and a scheduling decision; the hierarchical execution mechanism gives consideration to global optimization and local quick response, realizes undisturbed switching under abnormal working conditions, forms a prediction-optimization-execution-feedback closed-loop control system, and can accurately describe physical connection and electrical characteristics of a park power grid by establishing a power distribution network equivalent model and acquiring topological parameters, thereby realizing the optimal control of the park power grid. And basic network data is provided for subsequent optimization. By determining the controllable resource set and completing topological mapping, the position and the regulation and control range of each device in the power grid can be determined, and mistaken sending or conflict of instructions can be avoided. An apparent power upper limit constraint and SOC dynamic model is established, overload operation of equipment can be avoided, the energy storage charging and discharging capacity can be accurately represented, and the performability of a scheduling scheme is ensured.

The invention relates to the technical field of electric digital data processing, and discloses a multi-dimensional data logic processing method based on an artificial intelligence algorithm, which comprises the following steps: receiving a target discrete data packet, extracting metadata, and calculating to generate a dimension entropy feature vector representing data logic complexity; inputting the vector into a preset topological mapping model, and outputting an initial adjacent matrix; calling a feedback suppression mask matrix generated based on a historical operator utility state, and executing bitwise logic AND operation with the initial adjacent matrix to generate a corrected effective topological matrix; the matrix is analyzed, a logic operator function pointer is dynamically indexed in an instruction cache, and a directed acyclic execution linked list is constructed; according to the method, redundant logic nodes in AI prediction are definitely eliminated through a bit operation mask mechanism based on historical feedback, and deterministic convergence of processing delay and optimal matching of computing power resources are achieved.

The invention relates to the technical field of bridge engineering structure health monitoring, in particular to a service life prediction method and system for a bridge connection part, and the method comprises the steps: collecting the strain of a grouting sleeve, a reinforcement-grouting interface ultrasonic reflection signal and environment temperature and humidity data through a distributed sensor array; a three-dimensional dynamic void rate model is generated through fusion of a spatial topological mapping algorithm, a correlation function of load circulation and void expansion is established, the dynamic void rate is input into a bond strength coupling degradation model, after strength attenuation is quantized in combination with steel bar corrosion data and bond failure early warning is triggered, stress redistribution is calculated through multi-physics coupling simulation, and the dynamic void rate is obtained. Early warning is taken as a starting node, the actual load spectrum and temperature and humidity data are combined, Monte Carlo sampling is adopted to simulate rigidity degradation and output the residual life probability, the system comprises a multi-source collaborative sensing unit, a micro-void evolution analysis unit and a life prediction decision unit, and the safety control precision of bridge connection parts is improved.

The invention discloses a dynamic topology mapping and GPU heterogeneous flood coupling model real-time early warning method, and solves the problems of inflexible coupling of a one-dimensional model and a two-dimensional model, low calculation efficiency, poor dynamic response capability and the like in traditional flood simulation. A dynamic topology mapping mechanism is introduced to respond to emergencies such as dike burst and overflow in real time, the calculation efficiency is remarkably improved by utilizing a GPU heterogeneous parallel architecture, rapid and accurate simulation of the whole process of flood formation, evolution and submerging is achieved, reliable technical support is provided for flood control dispatching and emergency decision making, and the method is suitable for large-scale popularization and application. The method comprises the following steps: constructing a two-dimensional hydrodynamic model based on an unstructured grid, establishing a dynamic topology mapping mechanism and realizing GPU parallel numerical calculation; a two-dimensional model lateral connection coupling mechanism is constructed, dam overtopping flow is calculated in real time, and breach flow dynamic calculation of an improved DAMBRK method is carried out; multi-target parameter calibration, multi-dimensional model verification and one-two-dimensional model GPU collaborative parallel computing are realized.

The invention discloses an underground pipeline intelligent detection and mapping method based on image recognition, and the method comprises the following steps: 1, obtaining continuous video images, associating feature matching pairs of adjacent key frames, and forming a pose parameter set; 2, inputting the key frame into an improved YOLO-World detection network, and outputting a detection result set; 3, obtaining a geometric consistency matching set according to the detection result set; 4, performing multi-view triangularization on the geometric consistent matching set to form a weight factor; 5, introducing a weight factor, and executing incremental beam adjustment optimization on the pose parameter set and the three-dimensional sparse point set to obtain a sparse semantic point cloud; and step 6, outputting an underground pipe network topology map. According to the invention, high-precision and high-robustness intelligent identification and topological mapping in a complex underground pipeline environment are realized.

The invention relates to the technical field of digital twinborn management, and discloses a full-life-cycle digital twinborn management system and a modeling method. A model driving unit; a decision optimization unit; and a credible evidence storage unit. The modeling method comprises the following steps: S1, analyzing a product design bill of materials; s2, establishing a topological mapping relation between the sensor nodes and the virtual model area; s3, running Monte Carlo simulation to predict the defect probability; and S4, binding and uploading the new virtual model version and the physical product serial number. Aiming at the problem of data dispersion in the prior art, the system automatically converges three types of heterogeneous data of geometric dimension design, working condition manufacturing and maintenance monitoring into a shared cache pool through a main data table of a unified metadata structure, so that cross-stage semantic alignment and time sequence synchronization are realized, data format difference is eliminated, and data consistency is remarkably improved.

The invention provides a cross-medium printing process parameter adaptive optimization method based on AI migration knowledge, and relates to the technical field of intelligent printing. Comprising the steps of initialization and data connection establishment, multi-source data acquisition and knowledge migration fusion, topological mapping and initial parameter generation, ecological simulation and parameter dynamic evolution, cross-scale collaborative optimization and parameter fine adjustment, narrative derivation and optimized path planning, inter-equipment parameter collaboration and global optimization, and reverse process reconstruction and parameter self-repairing. According to the cross-medium printing process parameter adaptive optimization method based on the AI migration knowledge, multi-source data collection and knowledge fusion are achieved through the cross-medium printing process parameter adaptive optimization method based on the AI migration knowledge, and the cross-medium printing process parameter adaptive optimization method based on the AI migration knowledge has the advantages that the multi-source data collection and the knowledge fusion are achieved; and the printing requirements of various media such as paper, plastic films and ceramics can be effectively met, and the efficiency and accuracy of parameter optimization are improved.

The invention relates to the technical field of computer data processing and network security, in particular to an illegal character automatic cleaning system, which comprises a rule compiling module for monitoring rule change, performing semantic fusion and topological mapping on a rule set, constructing a deterministic finite automaton and mapping the deterministic finite automaton into a state transition table; the state switching module is used for constructing a double-buffer context and operating lock-free switching through an atomic pointer to realize hot updating; the speculation execution module is used for carrying out vectorization pre-scanning based on a state transition table by utilizing single-instruction multi-data stream parallel loading, identifying a walk path, falling into a safe state for releasing and falling into a trap state for triggering external verification; the self-adaptive feedback module is used for counting trap state triggering frequency, generating a rule allergy report and dynamically adjusting the size of a read fragment; according to the method, the contradiction between rule flexibility and execution efficiency is solved, and high-performance cleaning based on speculative execution is realized.

The invention discloses a rapid modeling method based on topological mapping, and the method comprises the following steps: setting a plurality of measurement sites at an equipment site, and collecting the geometric position information and point cloud data of equipment through a three-dimensional laser scanner; preprocessing the collected point cloud data, including noise point removal, point cloud segmentation, equipment point cloud clustering and data simplification; based on the point cloud data, the topological relation of the devices is automatically identified, and a topological relation graph between the devices is generated; mapping the topological relation graph into a three-dimensional digital model of the equipment, and automatically generating a three-dimensional model of the equipment by using template matching and component matching technologies; and according to the identified topological relation graph, integrating the equipment model and the connection relation thereof to form a complete digital model of the transformer substation or the equipment system. Through automatic processing of point cloud data, intelligent identification of a topological relation and a component matching technology, limitation of traditional modeling is broken through, and rapid, accurate and automatic equipment modeling is achieved.

The invention relates to the technical field of power system dispatching, in particular to a virtual power plant low-carbon economic dispatching method and system.The method comprises the steps that firstly, historical output data of all virtual power plants are obtained, and output prediction data considering uncertainty are obtained through prediction according to a time sequence; then constructing a carbon emission reduction responsibility distribution space, determining a carbon emission reduction responsibility weight of each virtual power plant through topological homeomorphic mapping, and solving a target function with the lowest carbon emission cost by combining a carbon emission constraint index to obtain an output optimal planning sequence; finally, a discrete-continuous hybrid dynamic model is constructed, cooperative scheduling of multiple energy storage devices is achieved, the situation that time differences of different scheduling periods are not integer multiples is processed, a carbon emission reduction responsibility dynamic allocation mechanism is constructed through the topological mapping theory, output prediction uncertainty is quantified through the probability measurement theory, and the probability prediction uncertainty is improved. And performing optimal scheduling on each virtual power plant.

The invention relates to the field of artificial intelligence medical treatment, in particular to an AI-driven clinical decision tree dynamic evolution training system, which comprises a multi-source medical data acquisition module which is responsible for acquiring medical images, medical examinations, biomarkers, pathological images and clinical information data, a clinical decision path data pool receives and structurally processes the data, and an AI-driven clinical decision tree dynamic evolution training module is established; a training data set is formed, a topological mapping space construction module constructs a high-dimensional topological space based on the data set to represent a clinical decision path, a multi-order progressive evaluation module performs multi-dimensional performance evaluation on the decision path mapped to the high-dimensional space, and a decision path training and analysis module optimizes the decision path according to an evaluation result. The optimized model is stored in a decision path library and forms a training feedback cycle with a clinical decision path data pool, and high-dimensional expression of a complex medical relationship is realized through a topological mapping space construction module, so that the diagnosis capability of the model on multi-cause and multi-symptom diseases is remarkably improved.

The invention discloses a topological mapping monitoring and correcting method for abnormal scapular brachial rhythm after breast surgery, and belongs to the field of medical rehabilitation and computer vision. The method comprises the following steps: acquiring a trunk dynamic three-dimensional point cloud and infrared thermal imaging data; constructing a multi-layer collaborative dynamic topology network fusing a skeleton structure, soft tissue deformation and physiological activeness; extracting an intrinsic motion field and comparing the intrinsic motion field with the ideal model to generate deviation measurement; and calculating a dynamic instability index, comparing the dynamic instability index with a threshold value, and triggering optimization energy correction path calculation and instruction output. According to the method, the multi-dimensional topological mapping and dynamics prediction technology is adopted, accurate recognition and feed-forward early warning of motion abnormity under the influence of deep tissue adhesion after the breast surgery can be achieved, rehabilitation of a patient is guided through an energy consumption minimization path, and the accuracy of shoulder joint function remodeling and the individualized treatment effect are improved.

The invention discloses an image processing method and device for an aerial remote sensing image and a storage medium, and relates to the field of image analysis, and the method comprises the steps: obtaining an aerial image shot by an unmanned plane in real time, and carrying out the image preprocessing of the aerial image, and obtaining a preprocessed aerial image; performing rigid framework extraction on the preprocessed aerial image to obtain an original incomplete framework graph after gravel interference is removed; carrying out fracture framework repair and completion on the original incomplete framework diagram to obtain an unmanned aerial vehicle visual angle topological structure diagram; performing elastic topological mapping matching on the unmanned aerial vehicle visual angle topological structure diagram and a pre-stored satellite reference diagram to obtain a matching result; and carrying out pose calculation according to a matching result, and outputting accurate coordinate information of the unmanned aerial vehicle in the world coordinate system. According to the method and the device, high-precision positioning can be realized in an extreme environment in which the GPS is rejected and the surface texture is subjected to unstructured deformation.

The invention relates to the technical field of liquid crystal display, and discloses a liquid crystal module character display method, which comprises the following steps of: performing stroke decomposition and topological relation establishment on Chinese characters in a target character set to generate a character stroke data set; performing two-dimensional character topological mapping and compressed encoding on the to-be-displayed characters to form compressed character description data; executing hierarchical rendering and stroke synthesis in a character reconstruction engine to generate a target character dot matrix; pixel array electric field distribution optimization is carried out, and sub-pixel-level precision character edge display data are obtained by controlling the driving voltage gradient; performing electric field compensation based on the character edge display data with sub-pixel-level precision to form character display data with enhanced edge sharpness; stroke self-adaptive anti-aliasing processing is carried out on the character display data with enhanced edge sharpness, clear Chinese character display data is output, the visual smoothness of character edges in a small-size display environment is optimized, and the overall recognition degree of characters is improved.

The invention relates to the technical field of circuit fault processing, and discloses an automatic identification and remote isolation method for a fault section of a low-voltage distribution line, and the method comprises the steps: building a reference fingerprint database based on channel state information through a central coordination node; monitoring real-time channel frequency response and executing threshold comparison to judge suspected fault points; triggering an active impedance control unit of the terminal agent node to execute controlled perturbation; a transmission line network model is used for calculating a theoretical transmission function variable quantity, fitting analysis is carried out on the theoretical transmission function variable quantity and a measured value, and a physical fault is judged and load interference is stripped through a normalized residual coefficient; calculating a residual drift distance between adjacent nodes based on the topological mapping table to execute differential positioning so as to lock a fault physical interval; and issuing a tripping message, and driving the circuit breaker to execute physical isolation at the power frequency zero crossing point. According to the scheme, on-line fault identification and isolation are realized through active impedance perturbation and distributed residual analysis, and the robustness of fault judgment in a complex load environment is improved.

The invention relates to the technical field of network security, in particular to a system network security evaluation system and method based on artificial intelligence, and the system comprises a data collection unit, a data analysis unit, a security monitoring unit, a security evaluation unit and a data processing unit. Accurate analysis and anomaly identification of user behaviors are realized through multi-dimensional spatial-temporal feature topological mapping, probability state transition analysis and entropy weight dynamic optimization technologies, and a data analysis unit constructs a spatial-temporal feature topological structure, analyzes an operation state transition probability and calculates a feature weight based on information entropy; the safety monitoring unit carries out deep detection on abnormal operation and determines the type of potential safety hazards; the safety evaluation unit generates a risk rectification form and evaluates the rectification condition; and the data processing unit dynamically updates a sensitive operation threshold according to rectification feedback to form closed-loop optimization. According to the method, the false alarm rate can be effectively reduced, the detection rate of hidden attacks can be improved, the self-adaptive capability is relatively high, and potential threats can be found in advance.

The invention relates to a gradient-based tactile topology mapping model construction method. The method comprises the steps of obtaining functional imaging data and structural imaging data of magnetic resonance scanning of a testee; constructing a voxel-level functional connection matrix based on a voxel space in the functional imaging data; constructing a vertex-level structure connection matrix based on a cortex space in the structure imaging data; based on a gradient dimension reduction analysis processing method, matrix similarity calculation and dimension reduction analysis are carried out on the function connection matrix and the structure connection matrix, and a plurality of gradient components and variance interpretation quantities are obtained; and on the basis of the variance interpretation quantity, determining a functional main gradient and a second structural gradient, and constructing a tactile topology mapping model in combination with a tactile topology distribution rule. According to the method, the problem that the current tactile perception topological mapping is not accurate is solved, and more accurate mapping of tactile perception and brain activation is realized.

The invention relates to the technical field of vehicle-mounted equipment, and discloses a high-vibration environment-oriented vehicle-mounted graphic server active anti-seismic system, which comprises the following steps of: firstly, acquiring a real-time vibration data flow of a vehicle-mounted graphic server, constructing an energy spectrum, and analyzing a resonance modal cluster to generate a damping regulation and control path; analyzing a dominant resonance mode, and identifying a critical oscillation node to calculate an inertia compensation coefficient; nonlinear filtering is carried out on the vibration spectrum based on the coefficient to obtain an anti-interference frequency band set, a servo driving instruction set is generated through topological mapping, a dynamic stiffness parameter is inquired, and a parameter distribution vector is marked; then, the vector is loaded to an active memory controller, an energy dissipation state is monitored, a gain threshold value combination is adaptively adjusted, and an anti-seismic robustness index is calculated; and finally, generating vibration characteristic lines, extracting steady-state, transient and chaotic components, and constructing a multi-cascade active anti-seismic mechanism. According to the method, the anti-seismic stability of vehicle-mounted graphic calculation can be guaranteed.

The invention relates to the technical field of aircraft path planning, and particularly provides an autonomous path planning method based on multi-rotor multi-aircraft cooperative hoisting, which comprises the following steps: constructing a dynamic node network through an asymmetric topological mapping algorithm based on real-time deformation feedback data of a hoisting target object, generating a self-adaptive bearing frame, and carrying out multi-rotor multi-aircraft cooperative hoisting on the basis of the self-adaptive bearing frame; the internal stress gradient distribution is converted into an initial potential energy field for path planning; based on the characteristics of the initial potential energy field of the bearing frame, a vortex interference mechanism between rotor wing groups is introduced, and dynamic track seed points are embedded into the initial potential energy field; generating a trajectory probability cloud with a non-uniform density feature; regulating and controlling phase parameters of a topological structure elastic modal unit of the derivative field; and implanting a bipolar feedback channel in the trajectory probability cloud, triggering trajectory solidification through an energy difference threshold, and generating a collaborative path group meeting dynamic balance. According to the invention, full-autonomous dynamic path planning and real-time adaptive adjustment of the multi-rotor cluster in hoisting operation are realized.

The invention discloses a hydraulic control optimization system and method based on differential pressure direction change pattern recognition, and the method comprises the steps: carrying out the phase-space topology reconstruction and singular spectrum analysis according to the pressure signals of an oil inlet cavity and an oil return cavity of a hydraulic cylinder, and determining the topology mapping characteristics of a differential pressure direction; based on the topological mapping features, spatial features are extracted through a multi-scale convolutional network, and mode reference features are determined in combination with an attention mechanism; carrying out real-time differential pressure load mode identification by utilizing a manifold mapping space; constructing a mode comparison learning mechanism based on a historical load mode, predicting a mode trend and determining hydraulic control target parameters; and analyzing errors between displacement feedback of the piston rod of the hydraulic cylinder and control target parameters through the disturbance observer, and dynamically updating mode reference characteristics. Accurate real-time recognition and dynamic optimization control of the differential pressure fluctuation mode of the hydraulic system are achieved, and the operation stability and control precision of the system are remarkably improved.

The invention belongs to the technical field of biological information calculation, and discloses a pet feed additive formula calculation model optimization system, which comprises a data interface for acquiring raw material parameters and data containing environmental stress factors; the memory stores a heterogeneous neural network model, and comprises a first time sequence convolution subnet, a second cascade processing subnet and a global gating distribution unit which are arranged in parallel and are coupled through a lateral suppression channel; the processor operates the model, the global gating distribution unit is used for dynamically adjusting the computing power weight between the subnets according to the system energy state tensor, and back propagation iteration is carried out based on a nonlinear constraint penalty term. The problems of convergence difficulty and negative migration when a general model processes high-dimensional strong-coupling biological data are solved, and the prediction precision and safety of formula optimization are improved.

The invention discloses a virtual interaction training method and device for a visual reconstruction brain-computer interface, and the method comprises the steps: generating a virtual reality scene, and extracting an importance representation related to a current task in the virtual reality scene; key visual information is extracted, and the key visual information is mapped to the spatial position of the cortex electrode array through a preset visual field-cortex topological mapping model; converting the mapped key visual information into space-time stimulation parameters of the cortical electrode based on a pre-acquired individualized perception function constraint; collecting behavior data of task execution of the user under the spatio-temporal stimulation parameters to calculate a behavior error comprehensive score; and dynamically adjusting difficulty parameters of the task according to the behavior error comprehensive score so as to complete closed-loop adaptive training. The problems of single training scene, subjective parameter adjustment, system closed-loop deficiency and insufficient evaluation standards are solved, and personalized and quantitative evaluation, safety and high efficiency of visual reconstruction brain-computer interface postoperative rehabilitation training are realized.