90results about How to "Achieving Adaptiveness" patented technology

The invention discloses an unmanned aerial vehicle scheduling method and system based on multi-level collaborative decision, and belongs to the technical field of unmanned aerial vehicle intelligent scheduling. Comprising the steps of obtaining a flight task of an unmanned aerial vehicle cluster, decomposing the flight task into a plurality of sub-tasks, and determining a sub-task allocation strategy in combination with single unmanned aerial vehicle performance and sub-task requirements; according to the sub-task allocation strategy, in combination with the feature importance of the flight influence factors, an objective function is constructed, iterative optimization is performed through a particle swarm optimization algorithm, and an optimal flight task execution scheme of the single unmanned aerial vehicle is determined; and according to the optimal flight task execution scheme, multi-unmanned aerial vehicle cooperative path planning is carried out through a path planning algorithm, and an optimal task execution path is generated. The task execution efficiency of the whole unmanned aerial vehicle system can be improved, the coping capacity of the unmanned aerial vehicle system in a dynamic complex environment is enhanced, and the problems that an existing unmanned aerial vehicle is low in response speed, low in accuracy, poor in environmental adaptability and the like in task allocation and resource scheduling are solved.

The invention provides an unmanned aerial vehicle intelligent charging control method and system based on multi-scene adaptation, and relates to the technical field of unmanned aerial vehicles, and the method comprises the steps: collecting environment data through multiple types of sensors, carrying out the scene recognition and classification through a deep learning algorithm, and generating a scene feature vector and a parameter data set; a basic strategy is called from a preset charging strategy library according to the scene type, a reinforcement learning algorithm is adopted to optimize a charging strategy, a charging power adjustment curve is generated in combination with the environmental parameters, and a charging control instruction is output; and when the charging process is executed, monitoring battery state parameters in real time, performing safety threshold comparison and protection control, calculating a battery health index to evaluate a performance state, and feeding back an evaluation result to the charging optimization model to realize closed-loop control of the charging process.

The invention discloses a risk early warning method and device constructed based on dynamic rules, equipment and a medium, and relates to the technical field of risk early warning, and the method comprises the steps: extracting a user static attribute and a dynamic behavior sequence, constructing an original data set, carrying out time sequence analysis, generating a behavior time sequence response feature, carrying out cross-modal feature fusion, and constructing a space-time perception behavior vector; constructing a dynamic rule graph structure according to the risk rule and the corresponding standard space-time perception behavior vector, iteratively updating a risk rule weight based on a graph convolutional network, constructing a wavelet domain risk quantification model, and generating a multi-scale risk value; and inputting the newly generated space-time perception behavior vector into the wavelet domain risk quantification model, generating a to-be-judged multi-scale risk value, and performing risk early warning when a threshold condition is not met. Through time sequence analysis and a multi-scale risk quantification model, the accuracy of risk identification is improved, and multi-dimensional and multi-scale risk analysis is supported, so that the accuracy and real-time performance of risk management are effectively improved.

The invention belongs to the field of intelligent control systems, and particularly relates to a shield attitude real-time intelligent control method based on machine learning, according to the method, a data acquisition module can more comprehensively and accurately reflect the actual condition of shield construction, and in the control process, key parameters such as the working state of a jack and the pressure of a hydraulic system are monitored in real time, so that the control accuracy is improved. Abnormal conditions can be found in time, an emergency plan can be started, and the safety of shield construction is guaranteed. According to the real-time monitoring and feedback optimization mechanism, the safety and reliability of shield construction are effectively improved, and the construction risk is reduced.

The invention provides a non-woven fabric processing technology control method and system, and relates to the technical field of technology control, and the method comprises the steps: obtaining the application scene information of a non-woven fabric, carrying out the multi-dimensional demand analysis, and obtaining the multi-dimensional processing demand information of the non-woven fabric; determining a non-woven fabric processing control scheme; preparing raw materials to obtain a raw material preparation result; performing fibrosis treatment to obtain a fibrosis treatment result; arranging, laying and forming a net to obtain a net layer processing result; carrying out consolidation treatment to obtain a non-woven fabric product; performing quality detection to obtain a non-woven fabric product quality detection result; and performing scheme optimization, and performing non-woven fabric processing technology control according to a scheme optimization result. The technical problems that a non-woven fabric processing technology in the prior art is often based on a fixed scheme, changes of market demands are difficult to respond quickly, accurate demand analysis means are lacked, and the intelligent level of the non-woven fabric processing technology is insufficient are solved.

The invention discloses a control method and system based on self-adaption MPC large structure dynamic sliding shipment, the system comprises a sensor module, a computer system and a central controller, a pressure sensor is arranged between the upper end of a hydraulic sliding shoe and a base plate of an offshore oil and gas platform module and used for collecting the pressure value of a structure supporting point; the total station is arranged around a ship deck and used for monitoring the height change of each sliding shoe relative to a wharf; the liquid level sensors are arranged at four corners of the hull and used for collecting draft data of the four corners of the hull. The inclinometers are mounted on a bow and two sides of the barge and are used for collecting six-degree-of-freedom inclination angles of the barge; the computer system carries out calculation through a self-adaptive MPC control algorithm to obtain an optimal control strategy; and the central controller dynamically adjusts the ballast water distribution and the hydraulic sliding shoe height of the ship based on the optimal control strategy. Therefore, the ship loading safety, continuity and efficiency can be remarkably improved, the ship loading time is shortened, and the damage risk of the structure is reduced.

The invention discloses a curved surface self-adaptive gluing device and a use method, and belongs to the technical field of gluing. In the device, a glue barrel penetrates through a fixed base plate in an up-down moving mode, the lower end of the glue barrel is provided with a glue opening enabling high-viscosity glue liquid to flow out smoothly, and a piston capable of moving up and down is arranged in the glue barrel; the guide wheels are arranged at the lower end of the glue barrel, distributed on the left and right sides of the glue opening and used for being in contact with a coated surface; the floating mechanism is fixed with the rubber sleeve and can be connected with the fixed substrate in an up-and-down floating manner; the glue scraping mechanism is fixed to the fixed base plate, located right behind the glue opening and used for scraping glue in constant-force fit contact with the coated surface. The gluing device is good in gluing quality, high in efficiency, free of damage to the coated surface of a coated component, convenient to use and particularly suitable for curved surface coating and plane coating.

The invention relates to a decision-making method combining a big language model and a rule engine, and the method comprises the following steps: S1, extracting key information and a user intention from a text in a business scene based on the big language model, and converting the extracted information into structured data; s2, performing business logic processing on the structured data by using a rule engine module to generate a preliminary decision result; s3, converting the preliminary decision result into a natural language text, and feeding back the natural language text to the user; and S4, storing a natural language text corresponding to the preliminary decision result, obtaining feedback data of the user, analyzing the feedback data to obtain a rule adjustment value, adjusting the weight and priority of each rule in the rule set of the rule engine module based on the rule adjustment value to obtain an optimized rule engine module, and outputting the optimized rule engine module. And the optimized rule engine module is used for outputting a next decision result. Compared with the prior art, the method has the advantage of improving the efficiency of the existing decision-making scene.

The invention discloses an optical diffraction element defect detection method and system based on deep learning and a medium. The method comprises the following steps: acquiring a first to-be-detected image, preprocessing the first to-be-detected image to obtain a first ROI sub-image of the first to-be-detected image, and establishing a geometric rectangular coordinate system; a first deep learning model deployed in advance is loaded to perform semantic segmentation on the first ROI subgraph, the first deep learning model is used for identifying the first dispensing area and outputting a first feature graph, the first deep learning model is based on a Unit + + network structure, and a multi-scale large-kernel convolution module is adopted by both an encoder and a decoder of the first deep learning model; detecting a first defect in the first ROI sub-graph by adopting a first 2D traditional algorithm; the first feature map detects a second defect and a third defect by using a second 2D traditional algorithm. According to the method, a deep learning algorithm is combined with a 2D traditional algorithm, so that the detection speed and accuracy of the optical diffraction element can be improved.

The invention relates to an intelligent conference interaction method and device based on a large language model and a medium, and the method comprises the steps: converting a conference voice into a text through a first large language model, and carrying out the slicing of the text; vectorizing conference text slices based on a natural language processing technology; storing the vectorized conference text slice into a conference domain of the corresponding conference; using the second large language model to receive a user question, and combining with a question and answer historical record to generate a composite question; searching related conference texts in the conference domain through similarity calculation and returning answers; through similarity calculation and networking query, relevant content is retrieved and returned for recommendation; and adjusting a similarity calculation function according to the feedback of the user for the answer and the recommendation. Compared with the prior art, the conference voice is recognized in real time and the content is retrieved in real time by using the large language model, so that a user can crossly understand and learn similar conference content, and the conference efficiency and the intelligent degree are improved.

The invention relates to the technical field of image processing application, and particularly discloses an artificial intelligence image processing method based on intelligent traffic, which comprises the steps of image acquisition and preprocessing, feature extraction, artificial intelligence model construction and training, intelligent analysis and decision support, intelligent image processing and feedback, result output and application and the like. According to the artificial intelligence image processing method based on the intelligent traffic, information extracted from features is used for establishing an artificial intelligence analysis model, the intelligent analysis capability of an intelligent traffic management system is remarkably enhanced, and in the third step, the extracted features and historical data are subjected to mapping calculation, so that the intelligent analysis capability of the intelligent traffic management system is improved. The constructed artificial intelligence analysis model can learn and identify the mode in the traffic image so as to predict the optimal processing method, and the establishment of the model enables the intelligent traffic management system not only to process the current image data, but also to perform learning and prediction based on historical data, thereby improving the intelligent level of the system.

The invention provides an unmanned cluster cooperative control method based on similar organism and data driving fusion and related equipment. The method comprises the following steps: selecting a biological-like control model, and changing numerical values of decision parameters in the biological-like control model from traditional fixed values into self-adaptive time-varying decision parameters which change along with environmental perception through subsequent steps; aiming at the task requirement of the unmanned cluster, constructing a heterogeneous cross drilling environment; establishing a deep reinforcement learning framework based on a heterogeneous cross drilling environment; training a deep reinforcement learning framework by using historical state information and action data of an unmanned cluster, so that the deep reinforcement learning framework can generate adaptive time-varying decision parameters adaptive to a heterogeneous cross drilling environment; and inputting real-time state information sensed by the unmanned cluster at the current moment into the trained deep reinforcement learning framework to obtain an adaptive time-varying decision parameter, and taking the adaptive time-varying decision parameter as a decision parameter in the biological-like control model to perform cooperative control on the unmanned cluster.

The invention provides a self-adaptive scheduling method for an agricultural water supply system, and the method comprises the steps: constructing a reservoir scheduling model for a flood season and a non-flood season, and carrying out the self-adaptive scheduling of a reservoir decision according to different emphasis points of the flood season and the non-flood season; according to flood season reservoir scheduling, an optimal scheduling model is established by taking the minimum benefit difference rate and the minimum flood control risk rate of a decision-making stage and a reservation stage as optimization objectives, and a weighted summation method is adopted to convert a Xingli flood season objective function and a flood season flood control objective function into single objective functions to construct the optimal scheduling model. Solving the flood season reservoir scheduling model to obtain a flood season reservoir scheduling optimal solution; according to the non-flood season reservoir dispatching, a reservoir dispatching model is constructed with the maximum economic benefits of a decision-making stage and a reservation stage as the target, a corresponding non-flood season target function is constructed based on the maximum economic benefits of the decision-making stage and the reservation stage, and a theoretical optimal value is corrected by solving the theoretical optimal value and according to constraint conditions, so that the reservoir dispatching efficiency is improved. And obtaining a non-flood season reservoir scheduling optimal solution.

The invention relates to a configurable degenerate readout circuit and a detection method, in particular to a configurable degenerate readout circuit and a detection method for detecting various biological signals. The problems that an existing sensor reading circuit is large in power consumption and poor in universality are solved. The circuit comprises a data selector, a configurable degenerate reading circuit and an analog-to-digital converter, the data selector is provided with an input interface, adapts to various sensors and processes four sensing signals from the corresponding sensors, so that the whole circuit measures the outputs of the four sensors at the same time, and the input end and the output end of the data selector are connected with the output end of the front-end sensor; the configurable degenerate reading circuit is provided with an output end and an input end, and is reconstructed into four measuring circuits through the control of a switch; and the analog-to-digital converter converts the analog voltage output by the front-end circuit into digital codes and outputs the digital codes to finish detection. The invention belongs to the technical field of biological signal reading circuits.

The application relates to the technical field of equipment health management, and discloses a battery replacement cabinet fault prediction and health management method and system. The method comprises the following steps: acquiring an original temperature sequence, an original humidity sequence, a battery module number and a battery replacement cabinet position number, performing abnormal value correction to obtain a corrected temperature sequence and a corrected humidity sequence; performing abnormal fluctuation point screening according to the corrected temperature sequence and the corrected humidity sequence to generate an abnormality judgment record; removing interference according to the abnormality judgment record to obtain a refined feature set; performing abnormal risk analysis according to the refined feature set to obtain an overheating risk report; generating an intervention instruction according to the overheating risk report to obtain an initial execution scheme; driving operation is performed according to the initial execution scheme to obtain an equipment operation log; and performing reliability evaluation according to the equipment operation log to obtain a reliability evaluation conclusion. The method can realize early warning and health management of battery replacement cabinet faults.

The invention discloses an intelligent control system and method for intelligent equipment and a storage medium, and relates to the technical field of intelligent control. The intelligent control system comprises an analysis object determination module, a regulation and control sequence generation module, a target regulation and control sequence determination module, an equipment updating analysis module and a real-time monitoring response module; the analysis object determination module is used for taking an intelligent home decoration system database constructed by the intelligent Internet of Things corresponding to each local area network as an analysis object, and the regulation and control sequence generation module is used for generating a regulation and control sequence of corresponding intelligent equipment constructed by instruction trigger factors; the target regulation and control sequence determination module is used for screening a regulation and control sequence with a unique instruction trigger factor associated with a plurality of intelligent devices as a target regulation and control sequence; the equipment updating analysis module is used for judging whether the direct association event needs to update the target regulation and control sequence; and the real-time monitoring response module is used for responding to the updated target regulation and control sequence when the intelligent regulation and control event formed by the instruction trigger factor is monitored in real time.

This invention belongs to the technical field of power equipment operation and maintenance, power distribution line construction, and high-altitude operation machinery. A pole-mounted transformer replacement device based on a truck-mounted crane is characterized by comprising a main frame and support system, a translational load-bearing system, a chain drive system, and a vertical lifting and centering system. The main frame and support system includes a main load-bearing frame, stabilizing legs, and lifting lugs. The translational load-bearing system includes a translational load-bearing platform and a linear guide rail slider assembly. The chain drive system mainly consists of a geared motor, a drive gear, a driven gear, a short transmission chain, a drive sprocket shaft, a bearing support seat, a long transmission chain, and a reducer mounting support. The vertical lifting and centering system includes a centering mechanism, a vertical lifting mechanism, and a vertical lifting support plate. The centering mechanism includes a horizontal drive motor, a horizontal push rod mechanism, and a guide bracket. This device can be transported by truck, deployed quickly, and is suitable for operation in confined and electrified environments.

The invention provides an enterprise space appeal matching method based on an AI model, and the method comprises the following steps: collecting the multi-source sensing data of an enterprise space, and building a structured time-space information matrix through an access control, energy consumption, position and personnel label sensing terminal; constructing a causal relationship graph by using a structural causal modeling algorithm, and identifying key causal factors directly related to the spatial appeal; causal invariant characterization is extracted based on a multi-environment invariant risk minimization method, and an intervention effect model is established in combination with controllable intervention variables such as partition adjustment, capacity limitation and use strategies; generating an anti-fact sample of the candidate space unit by using an anti-fact reasoning mechanism, and calculating a matching adaptation score; establishing a matching optimization model according to constraint conditions such as topology, capacity, safety and moving lines; according to the method, high-precision and interpretable matching distribution under the conditions of complex space resources and multi-dimensional appeals is realized, and the method has the effects of flexible deployment, high matching stability and sustainable optimization.

The invention discloses a multi-terminal parallel authentication authorization and equipment perception method for a key task communication system, which belongs to the technical field of key task communication, and specifically comprises the following steps: S1, configuration preparation: a server pre-configures a terminal initial configuration file for a terminal; the initial configuration file of the terminal comprises lt; m < cpt >-UE-idgt; element, lt; m < cpt >-UE-idgt; the elements comprise the next-level lt; the method comprises the following steps: carrying out Instance-ID-URNgt; an element; lt; the method comprises the following steps: carrying out Instance-ID-URNgt; the elements are instantiated to be unique identifiers by the configuration terminal; s2, configuration issuing and effectiveness: the server issues the initial configuration file of the terminal to the terminal, and the initial configuration file of the terminal is restarted to take effect through the terminal; s3, authentication and information reporting: the terminal initiates an authentication and service authorization request, wherein the request carries the result; the method comprises the following steps: carrying out Instance-ID-URNgt; an element; s4, service authorization and configuration issuing: the server receives the request, analyzes the terminal instantiation unique identifier, records analysis information, and determines whether to issue matched user configuration file data to the terminal; according to the invention, the terminal registration authentication process is deeply coupled with the terminal type and capability evaluation, and intelligent decision-making can be carried out based on terminal difference.

The present invention discloses a three-terminal flexible adaptive tactile sensor and a manufacturing method, comprising a first end (1), a second end (2) and a third end (3) from top to bottom; the first end (1) is a surface electrode, used to contact with the outside world to generate electrons; the second end (2) is an intermediate electrode, used to conduct electrons; the third end (3) is a bottom electrode, one end of the first end (1) is connected to the positive electrode of a power supply through a wire, and the other end of the first end (1) is connected to a host computer through a wire; one end of the second end (2) is connected to the host computer; one end of the third end (3) is connected to the negative electrode of the power supply through a wire, and the other end of the third end (3) is connected to the host computer through a wire. The present invention can self-adjust the corresponding modulus according to different pressure conditions, that is, the response is completed in the first stage under different moduli, thereby achieving an adaptive effect.

The application relates to the field of battery life prediction, in particular to a multi-feature fuel cell life prediction method based on a long short-term memory network; the method comprises the following steps: collecting voltage attenuation data and collecting running state data to construct an original input data set; analyzing the running state data, calculating a correlation coefficient according to the voltage attenuation data and each running state data, analyzing the correlation coefficient, and obtaining an input feature set; processing the input feature set and the voltage attenuation data to construct a neural network training data structure; dividing the training data structure into a training set and a test set; constructing a network architecture solution space to be searched, analyzing the network architecture solution space, constructing a fitness function, and determining an optimal network architecture parameter combination based on the fitness function; and inputting the training set into the optimal network architecture parameter combination for training to generate a target detection prediction result. The application can improve the prediction accuracy of the voltage attenuation trend.

The invention discloses a shear thickening fluid with a magnetorheological effect and a preparation method thereof, and belongs to the technical field of intelligent materials. The magnetorheological shear thickening fluid consists of the following components in percentage by mass: 15-30% of polyethylene glycol, 15-30% of spherical silicon dioxide and 50-65% of magnetic particles. The preparation method of the magnetorheological shear thickening fluid comprises the following steps: mixing the polyethylene glycol and the spherical silicon dioxide to prepare the shear thickening fluid, adding the magnetic particles, and mixing again to obtain the magnetorheological shear thickening fluid. The raw materials of the magnetorheological shear thickening fluid are easy to obtain, safe and reliable; the preparation method is short in technological process, simple and safe to operate and wide in applicability. The magneto-rheological shear thickening fluid can unify the rate sensitive characteristic of a shear thickening material and the magneto-sensitive characteristic of a magneto-rheological material into a whole, and provides material support for research and development of a damping execution device with self-adaption, self-enhancement and self-adjustment functions and application of the damping execution device to an important engineering structure.

The application relates to a power distribution network voltage control method based on a reactive virtual power plant, which comprises the following steps: constructing a double-layer collaborative regulation model based on discrete and continuous reactive regulation equipment in the power distribution network under the management of the reactive virtual power plant, and establishing power distribution network physical constraints; under the power distribution network physical constraint condition, day-ahead scheduling converts the optimization control of the discrete reactive regulation equipment into a first multi-agent reinforcement learning of a discrete action space strategy for solving, and generates day-ahead scheduling planning to be issued to an intra-day correction layer; under the conditions that the power distribution network physical constraints and the day-ahead scheduling plan are taken as boundary constraints, intra-day correction converts the control of the continuous reactive regulation equipment into a second multi-agent reinforcement learning of a continuous action space strategy for solving, and generates intra-day correction instructions; the continuous reactive regulation equipment is adjusted based on the intra-day correction instructions to obtain node voltage in the power distribution network; the node voltage in the power distribution network is fed back to the day-ahead scheduling layer to realize closed-loop collaboration of voltage optimization regulation.

The application discloses a kind of vehicle-mounted Ethernet dynamic flow scheduling method and system, it is related to vehicle communication field, comprising: T1, the data stream feature registration and global view management of the equipment of access vehicle-mounted network are carried out;T2, time window is taken as scheduling unit, multiple time slots are divided in time window, and hierarchical time slot scheduling model is constructed;T3, based on flow quota, network data is monitored and flow shaping in real time, and burst flow is inhibited;T4, according to the trigger of high priority emergency data stream, time slot preemption and time window dynamic reconstruction are executed;T5, according to network transmission quality feedback, the bandwidth quota of each data stream is adaptively adjusted.Through the above scheme, time window and three-level time slot physically isolate burst flow, ensure that safety data bandwidth such as braking, steering is monopolized, significantly reduce the packet loss rate, realize key business strong guarantee.

The invention discloses an anti-DDoS (Distributed Denial of Service) function abstraction method applicable to a cloud native system, which belongs to the technical field of network security and is characterized by comprising the following steps of: a, abstracting an anti-DDoS function into two types of capabilities; b, constructing a first capability pool and a second capability pool according to the capability classification; and c, the cloud native system automatically generates rules through user requirements, and dynamically adjusts capability configuration according to network traffic characteristics, attack modes and system security requirements. According to the method, the anti-DDoS function is abstracted into the capacity in the overlay network, unified deployment is carried out in the overlay network, deployment is flexible, and flexible combination and linkage of the anti-DDoS system function can be achieved.

The invention discloses a device cooperative control method and system based on a distributed system, a terminal and a storage medium, and the method comprises the steps: obtaining the operation state data and communication link information of a plurality of physical devices, mapping each physical device into a node in a dynamic topological graph, and carrying out the cooperative control of the plurality of physical devices based on the operation state data and the communication link information; constructing feature representation of the distributed system; inputting the feature representation into a collaborative decision-making model for topological adaptive processing and inter-device long-range dependency relationship modeling, and outputting collaborative decision-making instructions corresponding to the physical devices; distributing the collaborative decision instruction to corresponding physical equipment for execution, and collecting system-level efficiency feedback generated when the collaborative decision instruction is executed; and dynamically optimizing the collaborative decision model according to the efficiency feedback and a preset system-level resource constraint so as to continuously adapt to the topology and state change of the distributed system. According to the method, the self-adaption to the dynamic change network topology is realized, and accurate collaborative decision and efficient resource control can be carried out.

The utility model provides a nursing bracket for neurosurgery, which relates to the technical field of mechanical engineering and comprises a bracket, two supporting mechanisms are assembled at the top of the bracket, two rotation limiting mechanisms are assembled in the bracket, each supporting mechanism comprises a first supporting column, two supporting blocks and two arc-shaped plates, a first column rod is fixedly connected to the top of the first supporting column, a connecting rod is fixedly connected to the side end of each second supporting column, two column grooves are symmetrically formed in each second supporting column, and the side end of each column block is slidably connected into the corresponding column groove. According to the nursing bracket for the neurosurgery department, the traditional nursing bracket used for the neurosurgery department is improved, the adaptability of the traditional nursing bracket used for the neurosurgery department to arms of doctors with different body types is improved through an additional structure, and when the arms of the doctors are placed, the self-adaption effect on the arms of the doctors can be achieved simply through the supporting mechanism.

The invention discloses an intelligent floating type electrochemical water treatment system which comprises electrochemical water treatment equipment, a power supply cabinet, a control cabinet and a remote client, side buoys are arranged on two side plates of the electrochemical water treatment equipment, two wiring electrodes are arranged at the top of the electrochemical water treatment equipment, and a scale depositing bag is arranged at the bottom of the electrochemical water treatment equipment; a negative plate and a positive plate are arranged in a box body of the electrochemical water treatment equipment and are respectively connected with two wiring electrodes; the power supply cabinet is connected with the two wiring electrodes through a control cable; a regulator is arranged on the power supply cabinet; the control cabinet is connected with the power supply cabinet through a signal cable; and the remote client is connected with the control cabinet, receives the current data and the voltage data transmitted by the control cabinet in real time, and adjusts the output current or the output voltage of the power supply cabinet through the control cabinet. According to the invention, the phenomena of equipment submerging and electrode burning loss caused by too high water level of the pool can be avoided, and the running current or voltage of the electrochemical water treatment equipment can be remotely monitored and adjusted to meet the requirement of water treatment.

The invention relates to an automobile seat posture self-adaption system and method based on TOF sensors. The automobile seat posture self-adaption system comprises the front-row seat TOF sensors installed above front-row seats, rear-row seat TOF sensors installed above rear-row seats and a seat posture position self-adaption adjusting system integrated in an automobile controller. The front-row seat TOF sensor and the rear-row seat TOF sensor scan seat three-dimensional position data in real time through infrared light reflection, and the seat posture and position self-adaptive adjusting system is used for recognizing and distinguishing passengers and recording the posture of the passengers according to infrared light scanning and storing the posture and position of the seats of the passengers. According to the passenger seat posture position memory, the corresponding position is automatically adjusted after the corresponding passenger sits on each seat again; the system has the advantages that by introducing a self-learning and self-adaptive mechanism, the self-learning and self-adaptive capabilities of the system are realized, and the requirements of different passengers on seats can be better met.

The invention discloses a parameter-adaptive excitation PID (Proportion Integration Differentiation) control method, system and equipment and a readable storage medium, and the method comprises two control links: one control link is a main control link, namely, when the terminal voltage of a synchronous motor is changed, a variable parameter compensation link is set before conventional series / parallel PID control, and a variable parameter compensation link is set before conventional series / parallel PID control; according to the compensation link, the series / parallel PID parameters can be dynamically compensated according to the working points of the synchronous motor, parameter self-adaption and optimal control are achieved, and the reactive response speeds of the synchronous motor at all the working points can tend to be consistent under the condition that the zero pole of the system is not changed; and the second step is a parameter limiting link, that is, when the deviation is smaller than a deviation given value, PID parameters are attenuated in time, so that the terminal voltage of the synchronous motor is fast and stable, and the running stability of the synchronous motor can be ensured while the dynamic reactive response speed is effectively improved.