416 results about "Control vector" patented technology

The invention discloses a real-time monitoring and early warning method and system for a variable-frequency energy-saving motor, and the method comprises the steps: collecting the operation parameters of the motor in real time through a multi-source sensor group disposed on the variable-frequency energy-saving motor; determining a current motor working condition according to the motor operation parameters, and determining a real-time load rate of the motor based on a preset load working condition correlation model; acquiring historical operation data of the variable-frequency energy-saving motor, and generating a dynamic threshold curve according to the historical operation data; generating a dynamic threshold according to the motor operation parameters and the dynamic threshold curve, performing health degree calculation on the variable-frequency energy-saving motor based on the dynamic threshold, and determining a life prediction value of the variable-frequency energy-saving motor according to the health degree; and when the life prediction value of the variable-frequency energy-saving motor is lower than a preset value, automatically triggering a frequency reduction instruction, switching to a field-oriented vector control mode, and synchronously generating an alarm and equipment health file of the variable-frequency energy-saving motor.

The invention discloses a multi-parameter coupling intelligent regulation and control method and system in a vanadium ore smelting process, relates to the technical field of non-ferrous metal smelting automatic control, and solves the problems of feeding deviation and resource chain scission caused by slow response of an existing heat balance model to ore impurity fluctuation, online sensing lag and process island. The impurity concentration and the dielectric constant are obtained in real time through an LIBS / XRF combined probe and a microwave dielectric spectrum, rheological disturbance is dynamically corrected through extended Kalman filtering, a dynamic coupling response model is input, a thermodynamic interference factor is calculated, and a leaching and precipitation strategy is generated; performing mother liquor tail gas material flow collaborative optimization in parallel, constructing a multi-dimensional control vector U, performing iterative updating through near-end strategy optimization in a digital twin and issuing the multi-dimensional control vector U to an edge PLC (Programmable Logic Controller) in a distributed manner, and finally correcting a model weight through online XRD (X-Ray Diffraction) feedback; according to the method, the adaptive capacity to complex vanadium ore component disturbance, the response precision of online control and the inter-process resource circulation efficiency are remarkably improved.

The invention relates to the technical field of electric driving system energy-saving control, and discloses a multi-motor driving system torque optimal distribution method and system, and the method comprises the steps: building a single power unit power loss model of a multi-motor driving system; constructing a fitting straight line segment; constructing a torque-current mapping relation curve; a train speed prediction model, a multi-motor driving system power unit motor rotating speed prediction model, a multi-motor driving system total torque reference value prediction model and a multi-motor driving system power unit power loss prediction model are constructed; establishing a multi-motor driving system energy efficiency evaluation function, a multi-motor driving system stability evaluation function, a multi-motor driving system total torque following evaluation function and a system optimization objective function; designing a learning rate of a control vector; and updating the control vector according to the learning rate, and obtaining an optimized torque distribution coefficient and an optimized torque reference value distributed by each power unit. Therefore, the torque optimization distribution of the multi-motor driving system is realized, and the energy utilization efficiency of the system is improved.

The invention relates to a distributed temperature and humidity regulation and control method and system based on deep learning, and the method comprises the steps: obtaining temperature and humidity data measured by a wireless sensor, and carrying out the assimilation processing of the temperature and humidity data through a deep learning set smoother, so as to construct a temperature and humidity data set. And calling a parameter estimation and data learning algorithm to analyze the assimilated temperature and humidity data so as to identify and extract environmental characteristics in the temperature and humidity data. And taking the environmental characteristics as input of a temperature and humidity prediction model to output a temperature and humidity change trend of the environmental characteristics in a future first time period, and generating a control vector based on the temperature and humidity change trend. And controlling the operation state of the HVAC equipment according to the temperature and humidity change trend in response to the control vector so as to automatically regulate and control the temperature and humidity of the environment in the current space. The temperature and humidity prediction model is obtained by training a deep learning model based on the real-time temperature and humidity data and the historical temperature and humidity data after assimilation processing.

The invention discloses an EHA drive control system three-closed-loop control system and a dynamic hysteresis compensation method. The technical problems that an existing electro-hydrostatic actuator is large in hydraulic hysteresis error, poor in dynamic response and insufficient in environmental adaptability are solved. The method comprises a three-stage control structure of a position control loop, a speed control loop and a current control loop, the position control loop introduces load disturbance feed-forward compensation based on a state observer, the speed control loop adopts an adaptive sliding mode control strategy, the current control loop executes a decoupling vector control algorithm, and the load disturbance feed-forward compensation is realized. And a dynamic hysteresis compensation model based on historical input and disturbance estimation is established in the controller. According to the system, the servo motor, the gear pump, the hydraulic cylinder and the sensor module are integrated into an integrated drive control structure, and the effects of improving the control precision, reducing the response time delay and correcting the hydraulic hysteresis error are achieved.

The invention discloses a frequency conversion vector control method for a digital energy-saving pump station, which belongs to the technical field of vector control and comprises the following steps: constructing a coupling vector model, simulating the running states of a pump unit under different working conditions through a virtual test signal, and automatically correcting the coupling vector model; operating data of a pump set are collected in real time, an operating situation feature set is constructed, predictive vector control is carried out, an optimal voltage vector sequence is generated, and meanwhile load fluctuation of a pipe network is predicted; demand pre-judgment is carried out on the starting and stopping time and the action amplitude of the anti-backflow device, a bidirectional disturbance suppression mechanism is constructed, current vector parameters are pre-adjusted, and calculus correction is carried out based on deviation judgment; when multiple pumps operate in parallel, the instantaneous operation efficiency point of the pump set is calculated in real time, the efficiency change trend is predicted, a load distribution scheme is generated, the comprehensive energy efficiency is evaluated, energy efficiency self-optimization is carried out, the comprehensive energy efficiency of the system is improved, and control experience reuse is achieved.

The invention provides a motor parameter identification method and device, equipment and a storage medium, and relates to the technical field of motors. The method comprises the following steps: respectively constructing a state matrix and an output equation of a motor; constructing a parameter matrix containing to-be-identified parameters, wherein the to-be-identified parameters at least comprise permanent magnet flux linkage and quadrature-axis inductance; performing iterative recursion on the parameter matrix based on the model equation, the state matrix and the output equation of the least square method; and determining an estimated value of the to-be-identified parameter as a target motor parameter in response to determining that the to-be-identified parameter meets a convergence condition and / or the number of iterations of the parameter matrix reaches a preset value. Therefore, motor parameters such as permanent magnet flux linkage and quadrature-axis inductance can be accurately identified, vector control is more accurate under the condition that the motor parameters mainly change due to motor temperature rise, magnetic saturation and running state change, the vector control can be realized under the auxiliary condition of existing load equipment, and extra cost does not need to be increased.

The invention relates to the technical field of industrial automation, and discloses a self-adaptive dynamic pressure regulation energy-saving system and method based on pneumatic equipment, and the system comprises a state monitoring module which is used for constructing an operation state matrix of the pneumatic equipment; the pressure control module is used for generating a dynamic pressure control vector of the pneumatic equipment; the risk judgment module is used for calculating a real-time operation risk coefficient of the pneumatic equipment; the hierarchical regulation and control module is used for establishing a hierarchical regulation and control strategy set of the pneumatic equipment; the control feedback module is used for generating a self-adaptive control parameter sequence of the dynamic voltage regulation instruction and the state early warning instruction; and the scheme output module is used for integrating the dynamic pressure regulating instruction, the state early warning instruction, the hierarchical regulation and control strategy set and the self-adaptive control parameter sequence and outputting a self-adaptive dynamic pressure regulating and energy saving scheme of the pneumatic equipment. The energy utilization efficiency and the operation stability of the pneumatic equipment can be improved.

The invention relates to the technical field of energy meteorology and intelligent power grids, in particular to a multi-extreme meteorological high-risk scene set generation method, system and equipment based on a joint training generative adversarial network. The method comprises the following steps: constructing a physical information generative adversarial network framework comprising a generator, a discriminator, a predictor and a physical constraint module; designing a multi-objective loss function fusing adversarial loss, prediction loss, physical consistency loss and task performance loss; adopting a training strategy combining meta-learning initialization and incremental learning to jointly optimize parameters of the generator and the discriminator in stages; extreme risk scene data of specified disaster types, seasons and intensity grades are generated through condition vector control, and the extreme risk scene data are stored in a high-risk scene library after physical consistency verification. Through the method, a multi-extreme-weather high-risk scene set with statistical authenticity, physical rationality and task correlation can be directly generated, and the risk identification, scheduling optimization and toughness evaluation capabilities of the clean energy base under extreme weather conditions are remarkably improved; the problems of sample scarcity, model overfitting and lack of physical constraints in scene generation in the prior art are solved, efficient and automatic generation of a high-risk scene is realized, and reliable data support is provided for power grid toughness evaluation and scheduling decision of a clean energy base.

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 discloses a converter steelmaking endpoint forecasting method and device and a storage medium, and the method comprises the steps: carrying out the multi-scale importance modeling and graph attention mechanism modeling of the multi-dimensional process characteristics of the current heat, and generating the importance weight and the structure perception weight of each dimension of process characteristics; fusing the importance weight and the structure sensing weight, and multiplying the fused weight with the corresponding dimension process characteristics to generate a characteristic regulation and control vector of the current heat; performing multi-scale convolution operation on the feature regulation and control vector, extracting local mode features under different receptive fields, and outputting a spatial feature vector after the local mode features are enhanced by a self-attention mechanism; a plurality of groups of Transformer encoders are adopted to respectively process the furnace feature sequences with different historical window lengths, and cross-furnace time sequence feature vectors are extracted; and carrying out dimension alignment on the spatial feature vector and the time sequence feature vector, carrying out dimension-by-dimension weighted fusion through a learnable fusion weight, and outputting a steelmaking end point state prediction value of the current heat through a regression prediction network.

The invention relates to the technical field of robot control, and discloses a robot cerebellum reinforcement learning method and system based on adaptive environment change, and the method comprises the steps: collecting multi-modal sensing data in the operation process of a robot; based on the multi-modal sensing data, determining an environment sudden change increment and an environment characteristic variable quantity, and obtaining a main strategy network according to the environment sudden change increment and the environment characteristic variable quantity; outputting a basic action vector of the robot based on the main strategy network, and performing value evaluation on the state of the robot to obtain a value evaluation quantity; performing updating calculation on the basic action vector based on the value evaluation quantity to obtain a compensation action vector; and performing vector superposition fusion on the basic action vector and the compensation action vector to obtain a final control vector, and taking the final control vector as a cerebellum learning action instruction of the robot, thereby realizing dynamic adaptation of a reinforcement learning strategy and cerebellum rhythm control. And the motion stability and the learning convergence efficiency of the robot in the unstructured environment are optimized.

The invention belongs to the technical field of power system operation and motors, and particularly relates to an energy storage type phase modifier control method which comprises the steps that a rotating shaft of a double-fed motor is connected with a flywheel to serve as an energy storage type phase modifier; the rotor winding is connected to a power grid through an excitation system and a transformer in sequence, and the excitation system is electrically connected by a reactor, a back-to-back converter and a filter in sequence; the stator winding is directly connected to a power grid; the control system decouples reactive power and active power, vector control is carried out on exciting current to absorb or release power, and reactive power-voltage support and active power-frequency support of the power grid are completed. The method overcomes the defects that a traditional direct-current excitation phase modulation mode can only modulate phase and cannot modulate frequency, or is cumbersome and slow in response. The system can meet the requirement of wide-range variable-speed operation, quickly store / release a large amount of rotor kinetic energy, inhibit the voltage and frequency fluctuation of a power grid, and is especially suitable for stations with high new energy occupation ratio. The invention also provides a non-transitory readable recording medium storing the program of the method and a system comprising the medium, and the program can be called through a processing circuit to execute the method.

The invention discloses an aspheric surface processing intelligent feedback control system based on digital twinning, and the system comprises a digital twinning modeling and baseline calibration module which is used for building an aspheric surface processing digital twinning body and generating a baseline; the twinborn update and virtual-real residual calculation module is used for calculating virtual-real residual update digital twinborn bodies; the reinforcement learning candidate strategy generation module is used for generating reinforcement learning candidate strategies through the reinforcement learning strategy network; the model predictive control optimization module is used for establishing a model predictive control optimization problem and performing rolling prediction; the double-strategy fusion control module is used for executing virtual processing simulation and generating a control vector; and the virtual-real closed-loop adaptive convergence execution module is used for executing aspheric surface processing to realize closed-loop adaptive convergence control. According to the method, digital twinning is combined with reinforcement learning and model prediction control, intelligent optimization of aspheric surface machining is achieved, and the method has the advantages of being high in precision and stability and rapid in convergence.

The invention relates to the technical field of drainage control, in particular to a liquid level sensing drainage control method and system based on electronic and floating ball cooperative detection, and the method comprises the steps: fusing the angular displacement, capacitance value and acceleration disturbance data of a floating ball, and constructing a liquid level state observer; a dynamic compensation factor is introduced to strip liquid shaking interference, and an accurate liquid level reference value is obtained; a composite control vector is constructed based on the liquid level deviation and the change rate, and a drainage instruction is generated through analysis to control a valve to act; meanwhile, a self-adaptive feedback adjustment mechanism is established, observer parameters are dynamically calibrated through liquid level response residual errors, and the drifting error of an actuator is offset; according to the technical scheme, the robustness of drainage control can be remarkably improved.

The invention relates to the field of aquaculture, in particular to a preparation method and application of a larimichthys crocea iridovirus recombinant live vector vaccine. According to the invention, replication-deficient type 5 adenovirus is selected as a target gene expression vector, a recombinant adenovirus vector comprising main capsid protein of iridovirus is constructed, recombinant virus is obtained, and the safe and effective large yellow croaker iridovirus disease recombinant live vector vaccine is created. According to the vaccine, the larimichthys crocea is immunized in a soaking or bait feeding mode, the CPM of the larimichthys crocea immunized by the vaccine is 12.0%, and the CPM of the fish without being immunized by the vaccine is 62.0%. In addition, the RPS of the inoculated fish and the RPS of the empty carrier control group fish are 80.65% and 9.68% respectively. The method is at a leading level in the field of pseudosciaena crocea biomedicine at present, and creativity is remarkable.

The invention discloses a car following control method and a related product. According to the scheme, multi-source data are acquired, and the multi-source data are fused to obtain a fusion result; based on the fusion result, performing multi-modal trajectory prediction on the driving behavior of the preceding vehicle to obtain a prediction result of the driving behavior of the preceding vehicle; the multi-modal trajectory prediction comprises longitudinal motion prediction and transverse motion prediction; quantifying a car following risk by using a multi-dimensional risk index to obtain a risk level; constructing a multi-target cost function based on the fusion result, the front vehicle driving behavior prediction result and a control vector, and solving the multi-target cost function by using a preset constraint condition to obtain an initial control parameter of the vehicle; and on the basis of the risk level, initial control parameters of the vehicle are adjusted, and target control parameters of the vehicle are obtained. Compared with the problem of response lag in adaptive cruise control in the prior art, the adaptive cruise control method has obvious advantages.

The invention relates to the field of ship operation efficiency intelligent control, in particular to a ship operation efficiency intelligent control system and method based on large model optimization. Comprising the following steps: collecting real-time data of ship operation and environment, eliminating dimensional difference through normalization processing to obtain normalized data, forming a normalized data vector, and performing feature extraction on the normalized data vector by adopting a multi-dimensional data feature mapping algorithm to obtain a feature vector; calculating through a dynamic adaptive disturbance optimization algorithm to obtain an optimal solution; and generating a dimensionless control vector based on the difference between the optimal solution and the initial solution, and converting the dimensionless control vector into an actual control vector through a reverse normalization operation. The problem that an existing method cannot comprehensively consider mutual influence between ship and environmental factors, especially efficient optimization in a complex dynamic environment is solved; a traditional optimization method is easy to fall into a local optimal solution; the technical problem that a traditional control method lacks real-time and accurate adjustment is solved.

The invention belongs to the field of non-electrical variable control, and particularly discloses a temperature and humidity control system for vermicelli drying, which comprises an environment feature extraction module used for collecting real-time temperature and humidity data in a dry environment through a distributed sensor network and extracting environment feature vectors in combination with external meteorological data; the equipment adjusting module is used for carrying out mapping processing on the operation parameters of the drying equipment according to the environment feature vector to generate an equipment adjusting instruction sequence; the process fusion module is used for extracting a process parameter change trend from a historical drying record and fusing the process parameters and the current environment feature vector by adopting a time sequence analysis method to obtain a comprehensive regulation and control vector; the interference evaluation module is used for calling a real-time data interface through an edge computing node to obtain external environment interference factors; the invention aims to solve the problems of inaccurate temperature and humidity regulation and control, data response lag and insufficient linkage between environment variables and equipment in the vermicelli drying process.

The invention relates to an agent scheduling method and device of a charging system, a storage medium and a program product. The scheduling method comprises the following steps: a charging system network topology construction step: constructing a network topology; a centralized task allocation step: in response to the plurality of charging requests, modeling a task allocation problem into a cooperative game optimization model for the heterogeneous multi-agent system; a distributed path planning step: taking the distributed charging request as a target, and establishing a competition-cooperation game model based on a local communication fully connected graph constructed by utilizing dynamic topology; and a planning strategy design step: based on the game theory, solving the game models in the centralized task allocation step and the distributed path planning step by utilizing model predictive control until a Nash equilibrium solution sequence which enables a target function in a corresponding model predictive control optimization model to be minimum is converged, and outputting a planning control sequence in the prediction view as a control vector.

The invention discloses a tracking control method and device and a medium, and the method comprises the steps: determining a prediction step length corresponding to a current moment according to the current speed of a vehicle at the current moment and the current road curvature of the position of the vehicle at the current moment; according to a preset control vector constraint condition and a pre-constructed cost function equation, screening out an optimized control vector set which meets the control vector constraint condition and enables a cost function value of the cost function equation to be minimum; and inputting the optimization control vector corresponding to the prediction point corresponding to the next moment in the prediction step length into the vehicle so as to carry out tracking control operation on the vehicle at the next moment. Visibly, according to the embodiment of the invention, a mode of realizing tracking control by using a neural network model in related technologies is avoided, so that the operation process of the vehicle in tracking control is simplified, and the timeliness of tracking control is ensured.

The invention relates to the technical field of construction falling monitoring, in particular to an intelligent monitoring and early warning method and system for construction high-altitude falling accidents. Time step-based behavior mode branches are introduced, and regional distribution probabilities of different behavior mode branches are divided; the behavior mode branch comprises a high-altitude operation mode and an edge movement mode; constructing a fusion control vector according to the scene risk vector and the individual deviation vector, calculating a weight vector, and activating a mask to calculate an output channel vector; a fusion feature column vector is established, the falling risk probability is predicted through a full connection layer, whether early warning is triggered or not is judged, if yes, a falling point is calculated according to a free falling body, the rope length is calculated in combination with a safety rope anchor point, whether the impact speed and impact force are calculated or not is judged, the elasticity and damping of a safety rope are introduced to calculate applied force, and a first-order ordinary differential equation set is established; setting displacement and impact speed conditions, and calculating a final falling point by combining the anchor point vector, the displacement and the displacement in the rope direction of the operator.

The present application relates to the technical field of data processing, and more particularly to a building energy consumption optimization method based on BIM. The content includes: obtaining the original data of the target building and preprocessing, mapping the preprocessed original data to the BIM model, obtaining the dynamic BIM data model, and constructing the five-dimensional space-time feature tensor; the five-dimensional space-time feature tensor is processed by dimension reduction compression transformation, the three-dimensional tensor after compression transformation is used to construct the perturbation response function; based on the perturbation response function, the control inversion processing is carried out to obtain the preliminary control vector, the control path evolution mechanism is introduced to optimize and adjust the preliminary control vector; the optimized and adjusted control vector is converted into a control signal to realize building energy consumption optimization. The traditional building energy consumption optimization method in building energy consumption management has the problems of difficult spatio-temporal fusion of multi-source data, difficult accurate modeling of dynamic coupling relationship and lack of real-time adaptive ability of control vector.

The invention provides a multi-permanent magnet synchronous motor rotating speed tracking consistency control method based on local information interaction, and relates to the technical field of multi-permanent magnet synchronous motor rotating speed cooperative control. A vector control subsystem of a single motor in a multi-permanent magnet synchronous motor rotating speed cooperative control system is regarded as an intelligent agent, the intelligent agent interacts rotating speed information with a neighbor intelligent agent according to a designed communication topology, and expected q-axis current is updated in real time through a rotating speed tracking consistency protocol; therefore, the rotating speed tracking consistency is realized based on the local rotating speed information, and a rotating speed tracking controller does not need to be independently designed, so that mutual restriction of rotating speed tracking performance and synchronization performance can be avoided; in addition, distributed control has the advantages of simple structure, high flexibility, high expansibility and the like; the disturbance observer designed by the invention has continuous observation output values, and the system buffeting can be further weakened and the system robustness can be enhanced by utilizing the observer to estimate unknown disturbance and carry out feedforward compensation.

The invention discloses a multi-target cooperative control method for a production system, and the method comprises the steps: collecting and preprocessing multi-source data of wire harness production, and constructing a multi-dimensional structure containing processes, materials, carriers and time phases; establishing a time-phase mapping and dynamic evolution mechanism based on the multi-dimensional structure, and generating a target time-phase association set; takt, material in-place and carrier circulation signals are extracted, a multi-frequency phase set is constructed, and a comprehensive deviation signal is formed; constructing a multi-frequency phase-locked group control module, and generating three types of multi-target control instruction sets of takt, material and carrier; inputting the target association set and the control instruction set into an improved Longform model to generate a prediction control vector of the next period; and according to the predictive control vector output parameter, dynamically updating the plan and path configuration and rolling and correcting the phase locking parameter. According to the invention, through multi-dimensional time phase modeling and the improved Longform model, cooperative control optimization of the wire harness production takt, the material and the carrier is realized.

The invention discloses a permanent magnet synchronous motor non-inductive vector control method based on SOGI-QNPLL. The method comprises the following steps: constructing a current state equation of a permanent magnet synchronous motor under a two-phase static coordinate system; constructing a new switching function, and designing and improving a full-order sliding mode observer according to the new switching function and the current state equation; the convergence of the improved full-order sliding mode observer is analyzed, and the value ranges of the gain m and the gain n of the improved full-order sliding mode observer are calculated; analyzing the improved full-order sliding-mode observer, and discretizing the improved full-order sliding-mode observer by adopting a forward Euler method; establishing a phase-locked loop (PLL), performing normalization design on the phase-locked loop, and establishing a normalized phase-locked loop (QNPLL); a second-order generalized integrator (SOGI) is established and discretized by adopting a bilinear transformation method, the discrete SOGI is combined with the QNPLL to form SOGI-QNPLL, and the SOGI-QNPLL is utilized to estimate the rotor position. According to the SOGI-QNPLL provided by the invention, the rotor position can be accurately extracted, the observation precision is remarkably improved, and the reliability and performance of the system are enhanced.

The invention relates to the technical field of data processing, in particular to a BIM (Building Information Modeling)-based building energy consumption optimization method. The method comprises the following steps: acquiring original data of a target building, preprocessing the original data, mapping the preprocessed original data into a BIM model to obtain a dynamic BIM data model, and constructing a five-dimensional spatial-temporal feature tensor; performing dimension reduction compression transformation processing on the five-dimensional spatial-temporal feature tensor, and constructing a disturbance response function based on the compressed and transformed three-dimensional tensor; based on the disturbance response function, performing control inversion processing to obtain a preliminary control vector, introducing a control path evolution mechanism, and performing optimization adjustment on the preliminary control vector; the optimized and adjusted control vector is converted into a control signal, and building energy consumption optimization is achieved. The problems that in building energy consumption management of a traditional building energy consumption optimization method, space-time fusion of multi-source data is difficult, accurate modeling of a dynamic coupling relation is difficult, and a control vector lacks real-time adaptive capacity are solved.

The invention is suitable for the technical field of medical instruments, and provides a mechanical arm control method, computer equipment and a storage medium. The method comprises the steps that first motion data and first dynamic data of all joints of the mechanical arm at the current moment and second motion data and second dynamic data of all joints at at least one historical moment are obtained; determining acting force data of the end tool at the current moment according to the first motion data and the first dynamic data; determining target rigidity of the target object according to the first motion data, the first dynamic data, the second motion data and the second dynamic data; determining a force control law of the end tool at the current moment according to the acting force data, the target rigidity and the expected acting force of the end tool relative to the target object at the current moment; and according to the force control law and the expected pose data, determining the force-position hybrid control vector at the next moment, thereby reducing the damage to the target object while improving the pose control precision of the end tool.

The invention, which relates to the technical field of water resource management, discloses a block chain-based watershed environment water volume allocation system comprising a data acquisition module for acquiring real-time data; the risk assessment module is used for calculating a decoupling risk index; the self-adaptive regulation and control module is used for generating an initial regulation and control vector in combination with multiple factors; the safety verification layer is used for determining an optimal regulation and control vector; and the intelligent contract execution module executes corresponding regulation and control according to the risk level to form an automatic closed loop from sensing to execution. According to the method, multi-source heterogeneous data are integrated through a block chain technology, and a credible shared drainage basin database is constructed; a decoupling risk index is dynamically calculated, and conflicts between market transactions and ecological safety are accurately quantified; prospective regulation and control are realized by combining deep reinforcement learning with meteorological prediction; a differential strategy is executed based on a hierarchical intelligent contract, a closed loop from sensing, decision making to execution is formed, dynamic and accurate management of drainage basin water resources is achieved, and market and ecological requirements are balanced.

The invention relates to the technical field of air quality monitoring, and provides an air quality sensor based on the Internet of Things and a remote control method thereof, and the air quality sensor comprises a multi-source heterogeneous sensing fusion system, a time-space domain edge gateway system and a dynamic response collaborative central system. The remote control method comprises the steps that a purification device linkage control matrix is processed through a heterogeneous instruction fusion technology to obtain a space optimization control vector set, and the space optimization control vector set forms a man-machine interaction entropy value mechanism; the man-machine interaction entropy value model is processed through an entropy weight dynamic distribution strategy to obtain a space state sensing vector, and the space state sensing vector forms a cross-medium alarm triggering channel; the cross-medium alarm trigger channel is processed by the bidirectional response coupling engine to obtain an equipment state feature topology, and the equipment state feature topology forms a closed-loop control entropy correction instruction. According to the invention, pollution of a physical space is dynamically converted into operable equipment control parameters, and a complete Internet of Things closed-loop system from environment monitoring to purification execution is established.