180results about How to "Realize dynamic optimization" patented technology

The invention relates to the technical field of numerical control machine tools, and discloses a five-axis high-precision numerical control machine tool control method, device and equipment and a storage medium, and the method comprises the steps: collecting temperature data and displacement data of a five-axis high-precision numerical control machine tool, and carrying out state space modeling to obtain a machine tool motion state model; stress data and thermal error influence factors of the feed shaft are calculated; feeding shaft stress data and the thermal error influence factors are input into the compensation model, real-time compensation parameters are calculated, compensation control is conducted on the machine tool feeding shaft, and a compensation control result is obtained; according to the compensation control result, an initial machining track optimization model is constructed, and optimized machining parameters are generated; performing segmented machining control based on the optimized machining parameters to obtain real-time machining data; the initial machining track optimization model is optimized based on the real-time machining data, a target machining track optimization model is obtained, and the dynamic response performance and the control precision of the five-axis high-precision numerical control machine tool are improved through the method.

The invention relates to the technical field of intelligent spectrum regulation and control, and discloses an intelligent spectrum regulation and control system based on multi-modal data fusion and dynamic optimization, which comprises a multi-modal data acquisition module, a real-time data processing module, a dynamic weight distribution algorithm, a deep reinforcement learning regulation and control algorithm and a distributed collaborative optimization module. According to the system, real-time fusion and dynamic priority adjustment of multi-dimensional data are realized through acquisition of multi-modal data (environment spectrum, temperature and humidity, carbon dioxide concentration and user behavior) and dynamic weight distribution based on LSTM (Long Short Term Memory); dynamic optimization of a spectrum regulation and control strategy is realized by combining a deep reinforcement learning model and utilizing a weighted reward function of a spectrum output error and energy consumption, and efficient light supplement and low energy consumption requirements are balanced; a distributed optimization algorithm with edge computing and cloud collaboration is adopted, computing resources are dynamically allocated, task processing efficiency is greatly improved, and system power consumption is reduced.

The invention relates to the technical field of supply chain management, in particular to a cloud baking raw material supply chain optimization management system which comprises a demand dynamic adjustment module, a multi-source supplier collaborative management module, a logistics delivery tracking module, an emergency supply remediation module and a supply chain optimization module. Wherein the demand dynamic adjustment module is used for generating raw material demand prediction; the multi-source supplier collaborative management module is used for determining order allocation and delivery priorities through an optimization algorithm; the logistics delivery tracking module is used for monitoring the delivery state of the supplier in real time; the emergency supply remedy module is used for automatically selecting a proper alternative supplier under the condition of logistics abnormity; and the supply chain optimization module is used for optimizing supplier selection and order allocation. According to the invention, through dynamic optimization of supplier selection, order distribution and logistics abnormity response mechanisms, comprehensive optimization of supply chain management is realized in combination with user feedback information, and high efficiency, stability and flexibility of the supply chain are ensured.

The invention relates to a multi-mode AIGC cold-chain logistics path optimization decision-making method and a multi-mode AIGC cold-chain logistics path optimization decision-making system. The method comprises the following steps: inputting position data, temperature data, road condition data and weather data in a cold-chain logistics system into an AIGC model of a stack auto-encoder for feature extraction and reconstruction error calculation to obtain a feature index set; performing space-time interval division and similarity matching on the cold chain distribution path to obtain an interval reference path library; inputting a hierarchical control model for stage processing to obtain an initial control parameter; an objective function including temperature constraint, time window constraint and vehicle dynamics constraint is constructed for solving, and a path optimization scheme is obtained; performing road condition random disturbance and temperature fluctuation influence analysis to obtain a path risk scoring index; and performing feedback correction and iterative optimization on the initial control parameter to obtain a target control parameter. According to the invention, the temperature control precision is ensured, and the distribution efficiency and the system reliability are effectively improved.

The invention belongs to the technical field of computers, and particularly relates to a zero-code agent construction platform. The system comprises a module database, a semantic analysis and intention decomposition unit, a task flow tree generation unit and an agent construction unit, the semantic analysis and intention decomposition unit is used for performing image language analysis on a natural language instruction input by a user, decomposing the natural language instruction into a plurality of intention units with a sequence, and constructing an intention vector; the task flow tree generation unit is used for generating a plurality of corresponding task flow trees according to the intention vector; and the agent construction unit is used for analyzing all the task flow trees, finding out an optimal task flow tree, calling all target function modules in the task flow tree and completing the construction of the agent. According to the method, the precision of semantic analysis, the flexibility of task logic generation and the automation degree of agent construction are remarkably improved.

The invention discloses an industrial firewall isolation method of a DCS (Distributed Control System). The objective of the invention is to solve the problem that existing industrial network security protection has defects for a DCS system. According to the method, firstly, DCS architecture analysis and risk assessment are carried out, and protection key points and data flow directions are accurately determined; then, a firewall strategy fitting the characteristics of each area (such as a core control unit, an operation station and an external network interface) is customized, and mechanisms such as multiple data encryption are covered; and then firewall deployment and configuration optimization are carried out to ensure protection continuity and rule timeliness. And abnormal conditions are timely handled through a real-time monitoring and dynamic adjustment mechanism, and system recovery under a security event is guaranteed through an emergency response and backup recovery mechanism. And a regular safety drill and evaluation mechanism is arranged to continuously strengthen the protection capability. According to the method, the network security level of the DCS is comprehensively improved, stable and safe operation of industrial production is guaranteed, and the method has remarkable practical value.

The invention provides a large model interaction method and system based on multi-model collaborative dialogue, and relates to the technical field of artificial intelligence, and the method comprises the steps: dividing multi-modal data into a pixel block matrix and extracting feature vectors, constructing a hexagonal honeycomb-shaped coding matrix to store data information, and generating an attention score matrix by adopting a multi-head attention mechanism; constructing a model communication network based on the transmission data unit, and calculating a professional degree score through graph convolution; according to the method, performance data is collected to generate an evaluation graph, and an optimal node cooperation strategy is output in combination with dominant learning, so that the efficiency and accuracy of multi-model cooperation dialogue are improved, and the robustness and expandability of the system are enhanced.

The invention relates to the technical field of fault monitoring, and discloses a pump unit fault monitoring method based on multi-dimensional perception fusion identification, and the method comprises the steps: obtaining the operation stage information of a key monitoring region, and determining a fault monitoring threshold value of a to-be-monitored pump unit based on the operation stage information; analyzing the historical operation record, judging whether the fault monitoring threshold needs to be optimized or not based on an analysis result, if so, setting an optimization coefficient corresponding to the fault monitoring threshold, and obtaining an optimized fault monitoring threshold; analyzing the abnormal working condition data, judging whether the optimized fault monitoring threshold needs to be compensated or not based on an analysis result, if so, calculating an abnormal working condition influence index of the abnormal working condition data, setting a compensation coefficient of the optimized fault monitoring threshold based on the abnormal working condition influence index, and obtaining a compensation fault monitoring threshold; and determining the fault early warning level of the pump unit to be monitored according to the compensation fault monitoring threshold value. According to the invention, the monitoring accuracy and effectiveness are ensured.

The invention relates to a time sequence optimization method and system for a time-sharing light source controller. The method comprises the following steps: acquiring a high-speed optocoupler trigger signal, an eight-channel output voltage signal and an inter-channel delay signal of a time-sharing line scanning light source controller, and generating a multi-channel time sequence characteristic data matrix; performing dynamic subspace decomposition and bidirectional residual operation to generate an anti-interference enhanced feature vector; performing long-range dependency relationship analysis between channels, and generating channel cooperative control characteristics; constructing a hierarchical predictive control model, and generating an optimal trigger time sequence; issuing the optimal trigger time sequence to each output channel, collecting channel output trigger delay data in real time, and calculating a compensation trigger time sequence; and generating an optimization control parameter group and updating the optimization control parameter group to the hierarchical predictive control model. According to the invention, the time sequence jitter problem of multi-channel output is effectively solved, and the synchronism and stability of light source triggering are ensured.

The invention provides an open earphone audio optimization method based on a self-adaptive compensation algorithm, and aims to improve the audio auditory experience of a user. The method comprises the following steps: firstly, acquiring an environmental noise signal through an earphone microphone, receiving a target audio signal, analyzing auditory perception of a user in a current environment by combining an ISO226 loudness curve and the like, and determining auditory sensitivity of the user to different frequency bands (including high frequency, intermediate frequency and low frequency); and then, enhancing the high-frequency part, deepening the low-frequency part and performing smooth transition compensation on the intermediate-frequency part by using a self-adaptive compensation algorithm. And then, dynamically adjusting the low-frequency part by using a hyperbolic filter, generating an adjusted audio signal, and generating a harmonic signal based on the amplitude and frequency characteristics to further optimize the tone quality. Finally, the optimized audio signal and the generated harmonic signal are provided for a user together, and the effects of automatically optimizing audio output in different environments and enhancing tone quality performance are achieved.

The invention relates to an air conditioner dynamic optimization method and system based on deep learning, and belongs to the technical field of air conditioner intelligent control. The method comprises the steps of obtaining air conditioner operation data, constructing an air conditioner operation data set through air conditioner data preprocessing according to the air conditioner operation data, and obtaining an air conditioner user preference model through multi-layer perceptron training and testing according to the air conditioner operation data set, according to the air conditioner user preference model, an air conditioner user preference DQN model is constructed through an initialized DQN; environment sensor data are collected in real time, a user air conditioner optimal control strategy is output through the air conditioner user preference DQN model according to the environment sensor data, and air conditioner equipment is regulated and controlled in real time through an air conditioner control system according to the user air conditioner optimal control strategy. Therefore, the air conditioner parameters are dynamically optimized based on the user comfort level and the energy-saving target.

The invention relates to the technical field of digital printing machines, in particular to an intelligent ink droplet control method and system of a digital printing machine and the digital printing machine. The method comprises the steps of real-time data acquisition, multi-dimensional parameter modeling, real-time ink droplet behavior prediction, self-adaptive ink jet parameter adjustment, closed-loop feedback control, medium characteristic self-adaptive matching, intelligent multi-region optimization and environmental factor compensation. Ink droplet behaviors and environmental data are collected in real time through a high-speed camera and various sensors, a nonlinear model is constructed by using a deep learning technology, and a dynamic parameter optimization curve is generated. According to the method, the printing quality and efficiency are remarkably improved, and the method is suitable for various media and complex scenes.

The invention relates to the technical field of automatic inspection, in particular to an automatic road surface inspection robot control method and equipment, and the method comprises the steps: collecting environment data and robot motion state data through integrating a plurality of sensors, carrying out the preprocessing, carrying out the data fusion through employing a Kalman filtering algorithm, and generating a comprehensive road surface state map; a disease area is accurately identified and positioned through a convolutional neural network, dynamic path planning and polling strategy optimization are performed through a reinforcement learning algorithm, adaptive control of a complex road surface environment is realized through a fuzzy control algorithm, and meanwhile, a targeted road surface maintenance suggestion and an emergency processing scheme are generated according to real-time polling data, so that the real-time polling of a road surface is realized. According to the method, the scientificity and timeliness of road surface maintenance and traffic management are effectively improved, the road surface diseases can be accurately recognized and the inspection path can be optimized in a complex environment, and the road surface inspection efficiency and the intelligent level of road maintenance are remarkably improved.

The invention belongs to the field of artificial intelligence and deep learning, and provides a small sample continuous learning method and system based on continuous knowledge protection decomposition in order to solve the problems of imbalance and poor adaptability of new and old knowledge. The small sample continuous learning method based on continuous knowledge protection decomposition comprises the following steps: based on playback sample data of each category and a continuous learning model backbone network, obtaining knowledge sensitive components and redundant capacity components through singular value decomposition; in the incremental adaptation training process, pre-training linear weight matrixes corresponding to the knowledge sensitive components are frozen; constructing a learnable adapter by using the redundant capacity component, and updating the pre-training linear weight matrix; and obtaining small sample playback data again, and carrying out singular value decomposition and incremental adaptation training operation again based on the updated pre-training linear weight matrix. Through dynamic updating of the covariance matrix, balance and efficient adaptation of new and old knowledge are realized, and the accuracy of a classification result is improved.

The invention discloses a multi-energy system optimization scheduling method based on a carbon flow theory and deep reinforcement learning. The method comprises the following steps: firstly, carrying out mathematical description on a multi-energy system dynamic optimization problem based on a carbon emission flow theory, and establishing an objective function and constraint conditions of multi-energy system optimization scheduling; then, the scheduling decision problem is expressed as a Markov decision process framework, a reinforcement learning environment is created, and a state space, an action space and a reward function of the multi-energy system are defined; meanwhile, in the depth deterministic strategy gradient algorithm, the training efficiency is improved by introducing an experience replay mechanism based on proportion priority, the overfitting risk in the network training process is reduced by adopting an L2 regularization measure, and the generalization ability of the network in the optimization scheduling decision of the multi-energy system is improved. The method can respond to random fluctuation of a source side and a load side in real time, outputs a coordinated scheduling strategy, ensures low-carbon operation of a multi-energy system, and overcomes the defect that a traditional energy scheduling system cannot quickly adapt to changes.

The invention relates to the technical field of fermentation, in particular to a dough fermentation temperature control device which comprises a fermentation box, a heating device, a cooling device, a rotating shaft, a dough bearing disc and a dough elasticity detection device. Judging whether the initial heating power of the heating device needs to be adjusted according to the fermentation temperature rise slope comparison result to obtain second heating power; according to the fermentation temperature comparison result, whether a cooling device is started or the current fermentation dough temperature is detected or the fermentation dough continues to be heated with second heating power is judged; judging whether the volume of the fermented dough needs to be detected or not according to the fermentation temperature comparison result; and adjusting the second heating power according to the volume growth curve comparison result to obtain second constant heating power. By measuring the temperature, the volume and the elasticity of the dough, the fermentation temperature in the fermentation process is adjusted, so that the dough fermentation accuracy is ensured.

The invention discloses a transcutaneous electrical stimulation device based on a large language model, and relates to the technical field of wearable devices. The transcutaneous electrical stimulation equipment based on the large language model comprises a data acquisition and preprocessing module, a feature extraction and fusion module, a model training and prediction module and a real-time information processing and response module, real-time data processing and intervention capabilities are integrated, human body data can be analyzed in real time, electrical stimulation parameters can be automatically adjusted, an instant treatment or adjustment scheme is provided for the problems of arrhythmia, chronic pain and the like, and the timeliness and effectiveness of treatment are greatly improved. And secondly, by fusing the physiological signal features and the symptom text features, the equipment can more comprehensively understand the health condition of the user, so that a more accurate treatment scheme is provided. Besides, the equipment also has the functions of anomaly detection and trend analysis, can predict potential health risks, and triggers an alarm in time when a serious anomaly is detected, thereby effectively guaranteeing the life safety of the user.

The invention discloses an industrial silicon intelligent power distribution and control system and method. The system comprises a data acquisition and processing module, a shared feature extraction module, an intelligent control module and a power distribution module which are connected in sequence. Multi-mode operation data is collected in real time, a shared feature extraction module is adopted to perform feature extraction on the operation data, data input is provided for subsystems of the intelligent control module, and information isolated island is avoided; through cooperative work of all the modules, global optimization control over the industrial silicon production process is achieved. The intelligent control module comprises an intelligent industrial silicon submerged arc furnace baking system, an automatic electrode lifting system, an automatic submerged arc furnace transformer gear adjusting system and an automatic electric energy quality adjusting system, and each system performs task prediction based on corresponding characteristics by adopting a pre-trained model and outputs optimized parameters; and the power distribution module generates a power distribution instruction based on the optimization parameters, so that intelligent distribution of power resources is realized, and dynamic adjustment and optimal distribution of energy consumption are realized.

The invention relates to the technical field of new energy storage, and discloses a liquid leakage detection and collection method for a galvanic pile of a flow battery energy storage system, which comprises the following steps of: arranging a humidity sensor, a gas sensor, a temperature sensor, a visual sensor and a pressure sensor in a key area of the galvanic pile to form a high-density detection network of multi-source data; sensor data are collected and transmitted to a control center in real time, multi-source data are fused through Kalman filtering, Bayesian estimation and a deep learning model, and feature information of liquid leakage is obtained; and based on feature information of multi-source data fusion, a multi-point interpolation method and three-dimensional space modeling are used to realize positioning of a liquid leakage area. The intelligent grading response and control technical scheme is adopted, and factors such as leakage diffusion and environmental conditions are dynamically analyzed, so that grading self-adaptive response of operations such as isolation, cooling, load shedding and shutdown is realized, and compared with the scheme that only single isolation or shutdown is adopted to cope with a leakage event in the prior art.

The invention discloses an unmanned aerial vehicle control system and method based on the Internet of Things, and relates to the technical field of data encryption, and the method comprises the steps: enabling an unmanned aerial vehicle to be connected with a central control platform through the Internet of Things, and enabling the unmanned aerial vehicle to receive a task demand; a preliminary flight path is generated according to a task weight, task start time, task end time and route length in a task demand, the unmanned aerial vehicle performs routing inspection according to the preliminary flight path, meanwhile, environment data of a target site are collected through a sensor and preprocessed, and the task demand is determined based on the preprocessed environment data and the task demand. And calculating a comprehensive priority score, dynamically adjusting a flight path, executing a task according to the dynamically adjusted flight path, monitoring a flight state in real time, detecting abnormality and triggering an emergency strategy.

The invention discloses an intelligent water pump group scheduling system and method based on irrigation demand response, and aims to solve the problems of complex scheduling, heavy communication burden and uneven upstream and downstream water supply of a small watershed distributed pump station group. The method comprises the following steps: acquiring crop water demand, weather and soil data, and calculating irrigation demand; establishing a water pump operation and photovoltaic power generation output model; then, on the premise that the photovoltaic power generation utilization rate is maximum, water pump irrigation power is preliminarily distributed, and surplus or vacant electric power is treated; and finally, monitoring water volume, water level and photovoltaic power generation data in real time, and dynamically adjusting the pumping power of the water pump. Through intelligent decision making and dynamic adjustment, the irrigation requirement is met, the problem that downstream water supply is insufficient due to upstream excessive water pumping is effectively avoided, meanwhile, power use is optimized, the external power introduction cost is reduced, and dual optimization of irrigation and power use is achieved.

The invention relates to the technical field of agricultural engineering, in particular to a reclamation land soilless culture and green manure intercropping culture system which comprises a reclamation land matrix preparation module, a soilless culture module, a green manure planting module and a microorganism bionic regulation and control module. The reclamation land matrix preparation module is used for preparing a matrix for soilless culture of a reclamation land; the soilless culture module comprises a culture bed, a nutrient solution supply unit and an environment control unit; the green manure planting module is provided with a special area for planting green manure crops, and the improvement of a soil structure and the optimization of nutrient circulation are realized by alternately or simultaneously planting the green manure and the crops; and the microbial bionic regulation and control module monitors and regulates and controls a microbial community structure in the matrix of the reclamation land in real time. According to the invention, not only are the ecological balance and stability of microbial communities improved, but also the nutrient absorption and disease resistance of crops are enhanced, and the growth conditions and yield of the crops are optimized.

The invention discloses an integrated adjustable ejector for a vehicle-mounted hydrogen supply circulating system, which relates to the technical field of ejectors for vehicle-mounted hydrogen supply circulating systems and comprises a hydrogenation pipeline, a working pipeline and a circulating return pipeline. The circulating return pipeline comprises an integrated adjustable ejector, a hydrogen compressor, a steam-water separator, a circulating return pipeline one-way valve and a pressure reducing valve; the integrated adjustable ejector comprises a shell, a hydrogen inlet and outlet, a storage cavity, a valve, a control device, an execution controller and a monitoring and temperature control module. The integrated adjustable ejector is installed on the circulating backflow pipeline, the monitoring and temperature control module is integrated on the ejector, the hydrogen flow, pressure and temperature are monitored in real time, and the hydrogen temperature is adjusted so that stable operation can be achieved; the fuel cell reactor hydrogen flow sensor converts flow required by a fuel cell reactor into an electric signal, transmits the electric signal to the control device, and controls the execution controller to adjust the valve, so that dynamic optimization is realized, and various working condition requirements of a hydrogen fuel cell automobile are met.

The invention relates to the technical field of photovoltaic power generation, and discloses a phase-locked loop-free photovoltaic power generation system multi-power quality cooperative improvement method and system, and the method comprises the steps: processing a collected current signal of a three-phase grid-connected point of a photovoltaic power generation system to obtain a current fundamental component and a current harmonic component of the grid-connected point, and determining a fundamental reactive power; determining the capacity required by harmonic suppression and the capacity required by reactive compensation according to the current harmonic component of the grid-connected point and the fundamental reactive power; obtaining the residual capacity of the photovoltaic power generation system, and carrying out an optimization algorithm to determine the optimal compensation coefficient of harmonic suppression and reactive compensation; and transmitting the optimal compensation coefficient of harmonic suppression and reactive compensation to a photovoltaic inverter control algorithm to complete multi-power quality collaborative improvement of the photovoltaic power generation system. The technical problems of large delay, slow control response speed, need of a phase-locked loop and poor power quality management target collaboration in the prior art are solved.

The invention relates to the technical field of carton production detection, and discloses a carton production line intelligent detection system comprising an industrial camera module used for collecting carton appearance images; the dynamic speed sensor is used for monitoring the conveyor belt speed; the mechanical vibration sensor is used for detecting vibration interference of the conveyor belt; the image processing module is used for correcting and analyzing the acquired image; the control module is used for feeding back and controlling the detection result in real time; and the storage module is used for data storage and model training. Through dynamic multi-physical coupling modeling, the influence of optical distortion, speed fluctuation and vibration interference on size detection is comprehensively considered. Through a distributed optimization algorithm and a real-time feedback control mechanism, various errors in a dynamic environment are effectively compensated, the size detection precision reaches the micron level, a high-speed sensor and an industrial camera module are adopted for data acquisition, a detection algorithm is optimized through a distributed parallel computing technology, and the size detection precision is improved. And the response time of the system is controlled within milliseconds.

The invention discloses a cloud storage acceleration method and system based on edge computing, and relates to the field of electrical digital data process.The method comprises the steps that a file access track ring is constructed; extracting historical access files as a first associated file set; calculating the access time difference of each historical access file; determining a plurality of association clusters and two association file sets according to the access time difference; performing cyclic displacement in the second associated file set to obtain a displacement associated cluster; extracting a third associated file set; determining a prefetching file; setting an annular cache queue, and determining a prefetching position according to the position information; when the space of the annular cache queue is smaller than a preset value, the prefetched file corresponding to the prefetched position in the preset interval of the annular cache queue is reserved, and a final prefetched file is obtained; and when it is detected that the user accesses the target file, loading the final prefetched file from the cloud storage server to the edge server. The method and device are used for solving the problem that the cache hit rate of the associated files is reduced, and the cloud storage acceleration speed is increased.

The invention discloses an estuary region ship lock hydrodynamic simulation method and system, and relates to the field of numerical simulation, and the method comprises the steps: obtaining estuary region ship lock data; hydrological data are collected; according to estuary area ship lock data and hydrological data, a hydrodynamic model based on an Euler-Lagrange circulation model is constructed, and the hydrodynamic model comprises a bottom friction resistance coefficient # imgabs0 # describing the influence of riverbed roughness on water flow movement; utilizing the hydrodynamic model to obtain the simulated flow velocity of the estuary region ship lock through numerical solution; using an ensemble Kalman filtering algorithm, comparing the simulated flow velocity with the actually measured flow velocity, and performing optimization training on the bottom friction resistance coefficient # imgabs1 # to obtain an optimal bottom friction resistance coefficient # imgabs2 #; the optimal bottom friction resistance coefficient # imgabs3 # is used as input, the hydrodynamic model is used for simulating the hydrodynamic process of the estuary region ship lock, and the water flow field change process of the estuary region ship lock is obtained. Aiming at the problem that in the prior art, the bottom friction resistance coefficient cannot dynamically adapt to the complex estuary environment, accurate simulation of the complex hydrodynamic environment of the ship lock in the estuary area is achieved.

The invention discloses an electric power data acquisition method based on an electric power system, relates to the technical field of data acquisition, and solves the technical problem of dynamically adjusting the acquisition frequency and the acquisition period according to the actual condition of electric power data. And whether the current acquisition period and frequency setting are reasonable can be found in time. If the data format is not reasonable, the collection period corresponding to the historical data which is similar to the current time period and has the minimum collection period comparison value is screened out based on the historical data to serve as the adjustment standard, dynamic optimization of the collection period is achieved, and the transmission speed of the normal data format is obtained and sorted; and the relationship between the conversion response time of the conversion data format and the data capacity is analyzed, and the most suitable normal data format is selected for transmission according to the actually acquired power data capacity, so that the data acquisition and transmission efficiency can be improved.

The invention provides a photovoltaic power generation hydrogen production control method and system, a program product and a storage medium, and relates to the technical field of photovoltaic power generation. The system determines whether adjustment is needed or not by judging the relation between the current illumination parameter and the preset threshold value. In the adjusting state, the system calculates the total energy receiving power, and dynamic adjustment of the target power point is achieved. When the target power point is larger than the total energy receiving power, the system analyzes according to the temperature of the electrolytic cell and calculates needed energy and excess energy, so that whether the temperature of the electrolytic cell is increased or the working voltage point is adjusted is determined. According to the dynamic adjustment mechanism based on multiple parameters, the system can flexibly distribute energy according to real-time working conditions, the situation that electric energy is forced to be connected to a grid is avoided, meanwhile, the performance of the system is improved through temperature adjustment when the hydrogen production power is not high, and finally the energy utilization efficiency is improved.

The invention relates to the technical field of invisible orthodontic appliance wearing monitoring and adjusting systems, in particular to an invisible orthodontic appliance wearing monitoring and adjusting system and method based on computer vision, and the system comprises a cloud storage module which is used for storing initial oral data and re-consultation oral data of a patient; the cloud processing module is used for analyzing the oral cavity picture uploaded by the patient and executing analysis software; the client module is used for managing an electronic medical record of a patient, providing treatment information, obtaining an analysis result and generating personalized feedback; according to the invention, a computer vision algorithm is combined with oral medical knowledge, so that not only can the position change of teeth be accurately identified and tracked, but also the judgment ability of the device can be continuously optimized through a deep learning algorithm. Meanwhile, the cloud computing architecture adopted by the invention enables the system to process mass data, and powerful data support is provided for formulating a personalized treatment scheme.