384 results about "Fuzzy reasoning" patented technology

The invention belongs to the field of data processing, and particularly relates to a dynamic cooperative control system and method for a gas turbine and a micro-grid, and the method comprises the steps: constructing a micro-grid real-time monitoring module, continuously collecting distributed energy real-time output, controllable load demands, bus voltage frequency and equipment state parameters, and carrying out the filtering and noise reduction through a preprocessing unit, thereby guaranteeing the data precision; calculating a real-time power difference value based on the preprocessed data, calling an adaptive neural fuzzy inference system, taking the power difference value, the bus voltage deviation and the frequency deviation as input, and judging whether the power difference value, the bus voltage deviation and the frequency deviation exceed a preset threshold value by means of a fuzzy rule base and a neural network model; if the threshold values are not exceeded, the current states of the gas turbine and the energy storage system are maintained; if any one exceeds the threshold value, a dynamic cooperative control instruction is triggered, precise cooperative control of the gas turbine and the micro-grid is achieved, and the operation stability, the operation efficiency and the reliability of the micro-grid are improved.

The invention provides a terahertz inversion method and system based on fuzzy reasoning and a teaching optimization algorithm, and belongs to the field of terahertz inversion. The method comprises the following steps: acquiring a time domain signal by using a terahertz time domain spectrometer, obtaining a frequency domain signal through Fourier transform, and preprocessing the frequency domain signal; constructing a transmission propagation model of terahertz waves in the to-be-detected sample, and obtaining a model terahertz frequency domain signal based on the model; establishing a composite objective function based on the actually measured terahertz frequency domain signal and the model terahertz frequency domain signal; performing parameter inversion on the composite objective function based on fuzzy reasoning and a teaching optimization algorithm to obtain an optimal individual; according to the invention, the precise matching of the key features of the terahertz signal is realized by constructing the composite objective function fusing the frequency domain overall error, the peak frequency error and the peak amplitude error. By introducing a fuzzy reasoning mechanism, the problem of premature convergence or falling into local optimum of the algorithm is effectively avoided, the convergence speed is improved, and the inversion process is more efficient and stable.

The invention discloses a power grid voltage fluctuation suppression method and system based on dynamic voltage regulation, and the method comprises the steps: synchronously collecting a three-phase voltage signal through a sensor array, generating a multi-dimensional feature vector through Park transformation, and extracting a time-frequency joint feature through a mixed multi-scale decomposition algorithm, and inputting a model fusing fuzzy reasoning and a space-time diagram neural network, generating a cooperative adjustment strategy in combination with power grid topology and equipment constraints, and optimizing model parameters through a closed-loop correction mechanism driven by a dynamic forgetting factor. The system comprises a signal acquisition module, a characteristic decomposition module, an intelligent decision module and a closed loop correction module. According to the method, high-precision suppression of power grid voltage fluctuation is realized, the multi-node cooperative adjustment capability is improved, the method can be adaptive to power grid operation state changes, technical feasibility and economic rationality are considered, and an effective scheme is provided for modern power grid voltage stable control.

The invention discloses a method and a system for adjusting the internal environment of a mobile laboratory cabin, particularly relates to the technical field of environment control, and aims to solve the problem that the existing environment adjusting method adopts a fixed control target and cannot adapt to differentiated environment requirements in a multi-task scene of a mobile laboratory. The method comprises the following steps: acquiring a current task identifier of a mobile laboratory, identifying a corresponding experiment task execution stage, and dynamically generating an inter-parameter collaborative constraint relation matrix based on a fuzzy inference rule base; a regulation and control demand vector is generated by collecting a multi-dimensional environmental parameter measurement value and combining a pre-stored environmental parameter expected value; calculating a regulation and control conflict risk entropy value based on the matrix and the vector, and performing risk judgment; when the risk tolerance is exceeded, a roundabout regulation and control strategy is generated as an optimization guide parameter; finally, an environment control instruction is generated in a coordinated mode according to the optimized guiding parameters and sent to an executing mechanism; the dynamic adaptation of the environmental parameter regulation and control strategy and the experiment task is realized, and the precision and reliability of multi-parameter cooperative control are improved.

The invention discloses an unmanned aerial vehicle adaptive path planning system and method based on information fusion and collaboration, and the system comprises an environment sensing module which is used for collecting the landform, obstacle and threat information of a mining area in real time, and constructing a three-dimensional digital elevation model; the global path planning module is used for generating an initial global path by adopting an improved Theta * algorithm, and optimizing a path turning point through a father node relink; the dynamic obstacle avoidance module is used for generating a local obstacle avoidance path by combining real-time obstacle information with a speed sampling range constrained by an unmanned aerial vehicle kinetic model based on an improved DWA algorithm; the autonomous decision module is used for dynamically adjusting the man-machine authority level based on fuzzy reasoning and switching a planning mode; the execution module is used for realizing trajectory tracking through motor rotating speed control and outputting a rotating speed change curve of each motor and unmanned aerial vehicle state parameters; and the data fusion module is used for synchronizing the remote sensing data and the path planning result to the cloud and supporting multi-machine collaboration and historical data backtracking.

The invention discloses a low-altitude economic unmanned aerial vehicle data processing method and system based on a large model, and relates to the technical field of low-altitude economic data processing, and the method comprises the steps: extracting low-altitude multi-target decision features of a low-altitude semantic decision map, carrying out the fuzzy reasoning of the low-altitude multi-target decision features, and generating an anti-interference control instruction set; based on the anti-interference control instruction set, fault logs and task execution data during operation of the unmanned aerial vehicle are collected, and an evolution strategy library is generated through association rule mining; and performing causal analysis according to the evolution strategy library, generating a cluster coordination rule upgrade package, performing dynamic verification on the cluster coordination rule upgrade package, and outputting a self-healing strategy. According to the method, the reliability and the safety of task execution of the unmanned aerial vehicle are improved by constructing the semantic association large model and generating the anti-interference control instruction set.

The invention relates to the technical field of robot health physical examination, and discloses a robot noninvasive health physical examination and monitoring platform. A health data acquisition module of the system integrates physiological parameters acquired by a multi-modal sensor to generate a standardized health data set; the physical sign load analysis module calculates parameter fluctuation difference to generate a physical sign load distribution state value; the physical examination parameter optimization module screens optimal physical examination parameters to generate an operation parameter set; the environment adaptability adjusting module generates an environment response physical examination scheme in combination with the environment and body position data; a health threshold value dynamic setting module adjusts an abnormal judgment threshold value to generate a grading early warning threshold value; the health trend prediction module generates a health risk prediction value through fuzzy reasoning; and the physical examination feedback control module adjusts operation parameters and scanning frequency to generate a self-adaptive regulation and control strategy. According to the platform, the accuracy and the intelligent level of health physical examination are improved, and the non-invasive health monitoring requirement of the user can be better met.

The invention belongs to the technical field of mechanical lifting platforms, and particularly relates to a pushing chain type synchronous lifting platform and a control method.The synchronous lifting platform is formed by combining two lifting platform bodies, and each lifting platform body comprises a pushing chain driving system and a guiding mechanism; the multi-axis virtual axis electronic gear is combined with a fuzzy reasoning library to realize synchronous accurate control of the double servo motors, and the synchronous control system has the advantages of high speed, high bearing capacity, multi-point control, high precision, low initial height, large stroke, small occupied space, no maintenance, high efficiency, energy conservation and the like.

The invention discloses a mechanical arm control method and system based on fuzzy self-adaptive PID control, relates to the technical field of mechanical arm control, and solves the problem that mechanical arm control is insufficient in flexibility when facing different working conditions. According to the embodiment of the invention, the universe expansion and contraction factor is adjusted through the error, then the close linkage of the quantization factor and universe expansion and contraction is realized through the self-adaptive quantization factor calculation, the fuzzy reasoning technology is combined, the intelligent mapping from the error information to the PID parameter increment is realized, the real-time updating of the PID parameter and the control output calculation are realized, and the real-time updating of the PID parameter is realized. The flexibility and adaptability of the control system are improved; and then output is controlled through incremental PID, so that a complete control link from parameter increment to actuator driving is realized, a complete closed loop is formed by a cascade control architecture, functional modules are tightly coupled, parameters are coordinated, and the whole-process intelligence from error sensing to execution control is realized.

The invention relates to the technical field of electric energy quality management of an electric power system, and discloses an industrial power grid voltage fluctuation suppression method and system, and the method comprises the steps: firstly building a system-level voltage and current forward transmission model composed of a power unit, a filter circuit and power grid impedance which are cascaded; reversely constructing a feedforward controller based on a voltage and current forward transfer model, and generating a main compensation voltage instruction according to the power grid disturbance quantity detected in real time; state parameters of each power unit are obtained, a differential equation is constructed, and a comprehensive real-time health degree index is obtained through fuzzy reasoning; dynamically correcting an equivalent impedance parameter in the forward transfer model by using the index, and calculating a voltage distribution weight of each unit; and decomposing the total compensation voltage instruction into each unit modulation signal according to the weight. Through systematic modeling, intelligent state evaluation and dynamic fault-tolerant distribution, high-speed, accurate and high-reliability suppression of the voltage fluctuation of the industrial power grid is realized.

The invention provides a patient rehabilitation tracking system, a rehabilitation training regulation and control method based on active perception and environment self-adaption, equipment and a medium, and the method comprises the steps: determining a cooperative motion state of a nerve-muscle-bone system according to a multi-modal physiological data flow of rehabilitation training tracking of a target patient; performing intention-muscle force cooperative identification based on the cooperative motion state to obtain motion intention and muscle force contribution distribution of the target patient; determining a dynamic risk map of the target patient during rehabilitation training; variable universe fuzzy reasoning is carried out on the motion intention, the muscle force contribution distribution and the dynamic risk map, and then a rehabilitation adjustment instruction matched with the rehabilitation state of the target patient is obtained; the rehabilitation progress of the target patient is continuously tracked, and the rehabilitation training robot is controlled to execute self-adaptive power-assisted compensation according to the rehabilitation adjustment instruction. By means of the scheme, motion intention decoding and muscle force contribution analysis can be conducted on rehabilitation training of the patient, and rehabilitation training regulation and control are conducted in combination with the dynamic environment risk.

The invention relates to the technical field of power equipment, in particular to an intelligent circuit breaker electric operation mechanism integrating multi-parameter detection and an intelligent monitoring system.The intelligent circuit breaker electric operation mechanism comprises a basic mechanism unit, a detection unit, an intelligent control unit and a communication unit, and the basic mechanism unit comprises a double-layer sliding block compensation system and a transmission mechanism; the double-layer sliding block compensation system is composed of a first sliding block, a second sliding block and a spring set, and a thin film pressure sensor is integrated on the bottom face of the second sliding block. According to the intelligent circuit breaker electric operation mechanism integrated with multi-parameter detection and the intelligent monitoring system, the health state and fault assessment system achieves fault assessment through data receiving, feature fusion, health modeling and fuzzy reasoning, and the intelligent monitoring system comprises a remote monitoring platform. And data display, fault early warning, remote control and reference model optimization are supported, intelligent control and multi-dimensional state monitoring of the circuit breaker are realized, and the fault diagnosis accuracy and the reliability of a power system are improved.

The invention discloses a supply chain financial risk assessment system based on a block chain technology and an assessment method thereof, and relates to the technical field of block chains, and the method comprises the steps: deploying system nodes on a server of each participant of supply chain finance, and constructing a block chain network; selecting nodes with high priorities according to the node weights to participate in block verification and confirmation to obtain transaction confirmation time; obtaining a compatibility index of a new version logic contract and an old version after the smart contract upgrade proposal, calling a parameter contract interface to adjust a financing interest rate, and determining a smart contract upgrade effect; and evaluating a supply chain financial risk condition based on fuzzy reasoning according to the transaction confirmation time in combination with an intelligent contract upgrading effect, and adjusting a consensus process of the block chain network and an intelligent contract construction process according to an evaluation result. The problems that the transaction confirmation time of the block chain is relatively long and the smart contract is difficult to flexibly adjust according to the change of the actual business condition are solved; and the processing efficiency and the system adaptability of the supply chain financial business are improved.

The invention relates to the technical field of solid waste incineration automatic control, and discloses a solid waste incineration equipment automatic control method and system.The method comprises the steps that real-time data of solid waste components in a hearth are collected, and an initial multi-dimensional parameter set is obtained through preprocessing; calculating a balance point of the heat value and the water content through fuzzy reasoning; when the balance point exceeds a threshold value, parameter interaction influence is predicted, and a multi-dimensional control vector is generated; performing data fusion and historical data matching in combination with real-time incineration process indexes to obtain an enhanced load distribution adjustment factor; carrying out weight comparison based on the adjustment factor and the balance point, judging a load balance influence index, and carrying out matching correction if the index exceeds a compatible threshold value; updating a load distribution scheme according to a correction result, and generating a final operation strategy; and a control instruction is sent through a feedback cycle mechanism and is continuously optimized, so that closed-loop control is realized. The device can adaptively treat dynamic changes of solid waste components, and the incineration efficiency and stability are improved.

The invention relates to the technical field of 5G communication, in particular to an optimization method of a high-performance 5G communication module. Comprising the following steps: acquiring key operation parameters in a multi-carrier concurrent state, and constructing a characteristic graph model reflecting a parameter evolution relationship; mining a state clustering mode through a graph polarization spectral clustering algorithm, and identifying key paths and core parameter nodes which influence communication performance in combination with a multi-hop path optimization algorithm of a graph attention mechanism; taking the key path strategy graph as input, combining target communication scene features, and adopting a graph structure alignment meta-learning algorithm to generate an optimal parameter configuration strategy; the strategy is applied to an actual module, a self-adaptive control model fusing fuzzy reasoning and reinforcement learning is constructed, and dynamic evaluation of the strategy execution effect and fine parameter tuning are achieved. According to the invention, the performance scheduling efficiency of the 5G communication module under the complex network condition is improved, and the intelligence, generalization ability and scene adaptability of the system are obviously enhanced.

The invention discloses an automobile armrest dynamic damping adjustment system and method based on multi-sensor fusion, particularly relates to the technical field of automobile armrest adjustment, and is used for solving the problem of poor armrest damping self-adaptive adjustment precision. Multi-source data such as vehicle postures, power behaviors, contact behaviors and hand torque are collected, resampling, denoising and phase compensation are carried out, a fusion state vector is constructed, the future state of the vehicle is predicted based on a short-time historical state sequence, state increment is extracted, and a dynamic risk index is formed; a current state and a risk trend are combined to construct an optimization model integrating supporting, smoothing and restraining, controller parameters are dynamically adjusted through a fuzzy reasoning mechanism, self-adaptive adjustment of armrest damping is achieved, and in the damping execution process, cooperative features and dynamic correction strategies of a control instruction and feedback output are analyzed, so that the damping performance is improved. Therefore, the response precision and stability of the dynamic damping of the automobile armrest under the complex working condition are improved.

The invention provides an argon flow and micro-positive pressure linkage control method for an electroslag furnace. The method comprises the steps that the oxygen content and micro-positive pressure signals in the furnace are collected; the future furnace condition is predicted through the deep belief network model; optimizing initial parameters of the fuzzy PID controller in an off-line manner by using a particle swarm algorithm; on the basis of the real-time signal and the prediction result, adaptively adjusting controller parameters on line; and finally, an argon flow and dust removal valve opening control instruction is output in a linkage mode. According to the method, future furnace conditions are predicted through the deep belief network model, the control advancement is realized, and the problem of large lag of the system is solved; offline parameter optimization is carried out on the fuzzy PID controller through a particle swarm algorithm, and excellent initial performance adapting to different working conditions is provided for the fuzzy PID controller; the parameters of the controller are adaptively adjusted through online fuzzy reasoning, so that the system has robustness for coping with a dynamic process; finally, accurate and stable linkage control of the oxygen content in the furnace and the micro-positive pressure is achieved through linkage calculation of the argon flow and a dust removal valve opening instruction.

The application discloses a network congestion control method and device based on fuzzy rules, and the method comprises the following steps: setting corresponding fuzzy rules according to network types, wherein the network types include a data center network and a wide area network; obtaining network parameters and performing fuzzy processing, fuzzy reasoning and defuzzification on the network parameters to obtain an adjustment factor ΔF, wherein the network parameters include a bandwidth utilization rate, an ECN marking rate and a queue length; obtaining the size of a current congestion window and adjusting the size of the congestion window in real time according to the adjustment factor ΔF; the steps of fuzzy processing and fuzzy reasoning further comprise detecting the current network type, and selecting corresponding fuzzy rules and dynamically adjusting the weight coefficients corresponding to each network parameter in the process of fuzzy reasoning according to the detected network type. The network congestion control method based on fuzzy rules can adjust the network congestion window in real time, realizes the intelligentization and automation of network congestion control, and improves the network management efficiency.

The invention discloses an adaptive modulation coding method and system based on fuzzy logic, and belongs to the field of wireless communication, and the system comprises a dynamic window module which dynamically adjusts the window size of a fuzzy set according to the received signal strength indication and the change trend of the packet reception rate; the input fuzzification module takes the actual received signal strength indication and the packet reception rate as input and converts the actual received signal strength indication and the packet reception rate into fuzzy variables through fuzzification processing; the fuzzy reasoning module is used for defining different membership functions, mapping continuous received signal strength indications and packet reception rate measurement values into a fuzzy set, considering membership value combinations of all RSSI and PRR fuzzy categories, calculating a weighted total score and outputting a fuzzy decision value; and the defuzzification module is used for converting into an actual modulation coding selection scheme through a defuzzification process. By adopting the method and the system, the adaptability and the response speed of the system to complex and changeable channel conditions are improved, and the accuracy and the reliability of adaptive modulation coding selection are improved.

The embodiment of the invention provides an intelligent bone drilling self-adaptive control method for a mechanical arm, which comprises the following steps: acquiring displacement deviation of the tail end of a drill bit of an orthopedic surgical robot, obtaining an error change rate through differential calculation, and combining a bone layer type as an input variable of a fuzzy controller and a feeding speed as an output variable; input variables are fuzzified through a membership function, and a fuzzy language variable set is obtained; according to the set and a preset rule base, constructing a multi-rule group structure comprising three types of scenes of free space, cancellous bone and cortical bone, and setting corresponding fuzzy rule subsets for different bone layers; designing a ''minimum-maximum'' rule activation and combination strategy for reasoning by adopting a Mamdani type fuzzy reasoning method according to fuzzification input and a multi-rule group structure, and generating a fuzzification output variable set; and defuzzification is performed through a gravity center method to obtain a continuous speed instruction, and dynamic regulation and control of the drill bit feeding speed are achieved. When approaching a penetration point, the device automatically decelerates and safely stops, so that the safety and accuracy of an operation are effectively guaranteed, and over-drilling injury is prevented.

The invention discloses an infant state recognition method and system based on traditional Chinese medicine five-tone monitoring analysis, and the method comprises the steps: synchronously collecting the crying sound, physiological signals and behavior videos of an infant, extracting the Mel-frequency cepstral coefficient of the audio, the heart rate variability, galvanic skin response and respiratory rate of the physiological signals, and the facial expression and limb movement features, and carrying out the recognition of the state of the infant through the Mel-frequency cepstral coefficient of the audio, the heart rate variability, galvanic skin response and respiratory rate. Performing structured integration by using a multi-modal feature fusion model; in combination with the five-tone theory of traditional Chinese medicine, a corresponding relation between audio features and five-organ states is established, and the robustness of five-tone and five-organ mapping is improved through fuzzy reasoning and a Bayesian mechanism; the system dynamically adjusts the weight coefficient of each mode, adapts to the individual and emotion historical trend, achieves more accurate emotion recognition and physiological evaluation, achieves cross-mode and multi-layer information fusion, improves the accuracy and interpretation ability of infant emotion and five-internal-organ state recognition, and provides a scientific basis for clinical evaluation and health management.

The invention provides a fuel cell energy management method and device based on fuzzy control, and the method comprises the steps: carrying out the fuzzy processing of a current data value corresponding to an impact factor, and determining a target fuzzy set discourse domain corresponding to the current data value of the impact factor, and the membership degree of the target fuzzy set discourse domain in the target fuzzy set discourse domain; performing fuzzy reasoning in combination with a target fuzzy set discourse domain corresponding to the influence factor and a preset fuzzy rule table, and determining a fuzzy set discourse domain to which the output power of the fuel cell belongs; and according to the fuzzy set discourse domain to which the output power of the fuel cell belongs and the membership degree of the influence factor on the target fuzzy set discourse domain to which the influence factor belongs, performing sharpening processing on the output power of the fuel cell by using a preset defuzzification method, and determining a predicted value of the output power of the fuel cell. Fuel cell power output management is achieved through the fuzzy control theory, the energy utilization efficiency of the fuel cell hybrid power system is improved, and the driving experience of a driver is improved.

The invention relates to the technical field of charging switching control, in particular to a dynamic switching system for cooperative work of wireless charging and wired charging, which performs node modeling and edge weight propagation on multiple parameters such as magnetic field intensity, current fluctuation ratio, contact resistance and temperature threshold through a graph neural network. Structured expression and dynamic updating of a nonlinear coupling relation among parameters are achieved, abnormal feature aggregation and channel state characterization are more refined, fuzzy reasoning and weight adjustment are conducted on multiple variables such as power output, the heat growth rate, the voltage drop rate and the energy efficiency ratio through fuzzy logic control, the stability of a channel priority sequence is enhanced, and the stability of the channel priority sequence is improved. Through dynamic detection of power curve intersection points and transition section time sequence calculation, continuous power distribution is converted into a smooth function interval, instantaneous energy abrupt change during switching is reduced, closed-loop correction of multi-period signals is achieved, and the system keeps stable operation and energy efficiency output consistency under high-dimensional parameter disturbance.

The invention discloses a network congestion control method and device based on a fuzzy rule, and the method comprises the following steps: setting a corresponding fuzzy rule according to network types which comprise a data center network and a wide area network; network parameters are obtained, fuzzification processing, fuzzy reasoning and defuzzification are carried out on the network parameters, an adjustment factor delta F is obtained, and the network parameters comprise the bandwidth utilization rate, the ECN marking rate and the queue length; obtaining the size of the current congestion window, and adjusting the size of the congestion window in real time according to the delta F; a current network type is detected between the fuzzification processing step and the fuzzy reasoning step, a corresponding fuzzy rule is selected according to the detected network type in the fuzzy reasoning process, and a weight coefficient corresponding to each network parameter is dynamically adjusted. According to the network congestion control method based on the fuzzy rule, the network congestion window can be adjusted in real time, intelligence and automation of network congestion control are achieved, and the network management efficiency is improved.

The invention relates to the technical field of energy-saving control, and discloses a self-adaptive energy-saving control method and system for building heating and ventilation equipment based on digital twinning. The method comprises the steps of collecting temperature and humidity in a building, heating and ventilation equipment operation, external environment and personnel flow data, extracting energy consumption fluctuation characteristics and performing coupling analysis on the energy consumption fluctuation characteristics and equipment operation data. A distributed lag regression model is constructed in combination with external environment data, the environment influence weight and trend are calculated, and a high-dynamic region is identified through fuzzy reasoning. Energy consumption is predicted based on high-dynamic area data, a waste value is calculated, equipment operation parameters are optimized, and a target power sequence is generated through population iteration. An adjusting instruction is generated through analysis and deployed to a control system, thermal sensation feedback and instantaneous power data are collected, comfort evaluation is conducted, and the adjusting instruction is updated for optimization control. According to the method, dynamic balance of energy conservation and comfort can be realized.

The invention discloses a temperature control system and method based on an electronic cigarette, and belongs to the technical field of electronic cigarette temperature control, and the method comprises the steps: building a temperature difference mapping matrix through obtaining a heating body infrared radiation signal and a cigarette oil liquid level heat conductivity change parameter; establishing a liquid level-heating surface coupling dynamic model by taking the matrix as input, and extracting nonlinear temperature control characteristic factors; constructing a multi-dimensional state recognition vector based on the factors, and performing fuzzy reasoning in combination with a real-time flow signal of a user suction sensor to generate a dynamic target temperature control curve; a heating body voltage is driven through pulse width modulation, a thermal coupling state identification value is updated according to infrared change and an evaporation rate in a sucking process, a temperature control curve is dynamically corrected, and closed-loop self-adaptive temperature control is realized; according to the method, the temperature control precision and responsiveness can be improved, the smoking experience is improved, and the intelligent control level of the electronic cigarette is enhanced.

The invention relates to the technical field of electronic display, and discloses a liquid crystal display screen display control system based on multi-mode driving. The system comprises a content feature analysis module, a mode decision engine, a driving parameter library and a liquid crystal driving signal generation module. Multi-dimensional feature vectors are extracted through the content feature analysis module, a nonlinear decision mechanism combining fuzzy reasoning and hysteresis control is adopted, and single fixed threshold judgment in the prior art is thoroughly abandoned. The system can accurately capture dynamic characteristics of display content, effectively avoids the problem of mode oscillation in a threshold critical region, and realizes smooth and stable switching of driving modes. According to the invention, the hysteresis comparator and the continuous frame decision logic are introduced, so that the anti-interference capability and decision stability of the system are remarkably enhanced. The system does not trigger mode switching due to instantaneous noise or slight jitter of a single-frame image, so that the continuity of display experience is ensured, and extra power consumption caused by frequent switching is reduced.

The invention relates to the technical field of heavy-load robot control, in particular to a self-adaptive robust control method of a heavy-load robot fusing differential homeomorphic mapping. According to the method, position information of each joint is collected in real time and accurately compared with an expected trajectory, a ternary input vector is output, differential homeomorphic mapping parameters are updated in real time through a T-S fuzzy inference system, and a joint space state is mapped to a new coordinate space based on the optimized parameters, so that the position information of each joint is accurately compared with the expected trajectory. Neighbor joint information is obtained through distributed communication to generate a cooperative control item, a sliding mode surface is constructed in combination with a local tracking error to generate a robust control item, a compensation item is generated based on adaptive law estimation parameter uncertainty, three-item parallel control output is formed, dynamic weight distribution and fusion are performed on the three control items, a basic control moment is obtained, and the control precision is improved. And then a feed-forward compensation item based on a nominal dynamical model is superposed to generate a final comprehensive control torque, and the comprehensive control torque optimizes energy distribution while ensuring the performance.

The invention discloses a multi-domain battlefield task decision-making method and system based on a knowledge graph. The multi-domain battlefield task decision-making method based on the knowledge graph comprises the steps that the relation between battlefield elements and tasks in a database is analyzed, and situation entity entries of the battlefield elements are extracted; utilizing a predefined relation template to construct a relation between the situation entity entries; constructing a decision knowledge base based on fuzzy reasoning; based on the decision knowledge base, constructing a knowledge reasoning model by using a dynamic Bayesian network technology; and receiving a combat instruction, generating a multi-dimensional decision suggestion by using the knowledge reasoning model based on a combat rule matched with the knowledge graph in the decision knowledge base, and displaying a decision result through the user interaction module. According to the method, multi-source information can be integrated in a multi-domain combat environment, cross-domain collaborative decision is realized, and the adaptability of a complex battlefield is improved; the knowledge graph and the fuzzy reasoning technology are combined, so that the uncertainty and fuzziness of the battlefield situation are effectively processed, and the decision-making ability is remarkably enhanced.

The invention discloses a self-adaptive software test case generation optimization method, which comprises the following steps of: carrying out structured analysis on service requirements of a target system, extracting multi-dimensional test requirements of performance, resources, risks and coverage rates, and carrying out tagging and normalization processing to form a quantifiable index system; based on a meta-strategy layer decision engine, fusion reinforcement learning and fuzzy reasoning are carried out to realize multi-target weight distribution and dynamic compromise; generating and optimizing a preferred case set by means of a trade-off adaptive algorithm, performing conflict marking and constraint cooperative adjustment, collecting feedback data through automatic testing, correcting weight distribution and conflict detection rules according to actual results, and continuously iterating case generation strategies; the objective achievement degree is periodically evaluated, the strategy model is reconstructed or switched to adapt to scene changes, the accuracy of test requirement expression and the self-adaptability of the use case generation strategy are improved, and efficient collaborative optimization of multi-objective testing and dynamic adaptation of service scenes are achieved.