55430results about How to "Guaranteed stability" patented technology

The invention provides an HBase client main-standby switching method and system based on fault perception, and relates to the technical field of database management, and the method comprises the steps: collecting system operation data through a distributed fault monitoring module, building an anomaly detection baseline model, carrying out fault diagnosis based on a system health assessment distribution matrix, and generating a fault characteristic spectrum; and switching is triggered according to a risk level evaluation result, and the optimal standby node is selected for sequential switching by using a deep strategy learning algorithm, so that rapid and accurate fault detection, diagnosis and intelligent switching can be realized, and the reliability and performance of the system are improved.

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 field of geological disaster monitoring and early warning, in particular to a slope catastrophe early warning method and system. The method comprises the following steps: establishing a space-time database, storing and managing preprocessed slope data from multiple monitoring sources, and sorting to obtain a slope catastrophe data set; performing feature extraction on the slope catastrophe data set to obtain a key information set reflecting slope catastrophe features; analyzing the key information set to obtain a slope catastrophe value; calculating an early warning threshold value and a real-time displacement prediction value of the side slope based on the side slope data in the space-time database, and fusing the real-time displacement prediction value and the side slope catastrophe value to obtain a fusion value; and evaluating the real-time stable state of each monitored section of the slope according to the obtained fusion value and an early warning threshold value, and judging whether to send out an early warning signal and an alarm signal or not according to an evaluation result. According to the invention, accurate identification of the slope catastrophe value and the potential risk area is realized, and the accuracy and reliability of the early warning system are improved.

The invention discloses a power distribution network power transmission line intelligent control and measurement system and method based on an AI algorithm, and the system comprises a data collection module, a data preprocessing module, a multi-modal data fusion module, an AI fault diagnosis module, a control execution module and a communication module. Comprehensive monitoring of the running state of the power transmission line is achieved, historical and current data are analyzed in real time based on an improved dynamic multi-mode adaptive optimization model DMAOM, and the self-learning capability is achieved. A flexible adaptive control strategy is introduced, and a control scheme is dynamically adjusted according to real-time data. Under the abnormal condition, the system can rapidly execute current limiting, load transferring or circuit breaking operation, and through a feedback mechanism, the system can continuously optimize monitoring and response strategies after each fault processing. According to the method, the safety, stability and operation efficiency of the power transmission line of the power distribution network are remarkably improved, and the method has important practical application value and wide market prospects.

The invention discloses a coal mine ventilation system intelligent regulation and control method based on environmental perception, and relates to the technical field of coal mine ventilation system intelligent regulation and control, and the method comprises the following steps: collecting environmental data of different areas of a mine, including gas concentration, temperature and humidity, air velocity and oxygen content, and monitoring the operation state of ventilation equipment in real time; data of all the sensors are synchronously processed through the data fusion technology, and a unified environment sensing model is generated. According to the invention, through cooperative work of the expert system and deep learning, real-time monitoring and emergency plan execution under extreme working conditions are realized, and the response speed and decision accuracy are improved. A reinforcement learning algorithm is adopted to optimize ventilation line switching, the adaptive capacity and redundancy guarantee of the system are enhanced, and it is ensured that airflow is not interrupted at the critical moment. Through abnormality comparison of the knowledge base and the rule base, false alarm and misjudgment are reduced, system efficiency is improved, operation burden is relieved, and safety and stable operation of mine personnel are guaranteed to the maximum extent.

The invention discloses a power transmission and transformation equipment fault early warning system based on online monitoring, and the system comprises the following modules: a multi-parameter sensor module which is used for collecting data in real time; the wireless sensor network module is used for transmitting the data to the data processing center for processing; the data fusion module is used for generating a comprehensive health score of the equipment by adopting a multi-modal data fusion algorithm based on a self-evolution nested network; the trend prediction module introduces an adaptive residual module to carry out trend prediction based on a nonlinear time sequence analysis technology of a generative adversarial adaptive residual network; the early warning threshold value adjusting module is used for dynamically adjusting an early warning threshold value by adopting a self-adaptive evolutionary algorithm of meta reinforcement learning; the maintenance suggestion generation module is used for generating maintenance suggestions; and the power grid dispatching linkage module is used for automatically linking a power grid dispatching system and detecting and adjusting an equipment load. According to the invention, a multi-parameter fusion and generative adversarial network method is adopted, and an adaptive residual module is introduced, so that accurate early warning of faults of power transmission and transformation equipment is realized.

The invention discloses a multi-task collaborative robot scheduling method and system oriented to an intelligent factory, and the method comprises the steps: receiving the input data of a plurality of tasks to be executed by the intelligent factory, the input data comprising the task type, the dependency relationship between the tasks, the task priority, the resource demand of each task, and the expected completion time of each task; determining an initial task execution sequence of the tasks according to the task types, the dependency relationships and the task priorities; obtaining current resource state data of all available robots in the intelligent factory; according to the resource demand of each task, the expected completion time of each task, the initial task execution sequence and the current resource state data of all available robots, a scheduling algorithm is adopted to generate a multi-task cooperative scheduling rule; and controlling the plurality of target robots to execute the plurality of tasks according to the multi-task cooperative scheduling rule. According to the method, the resource utilization rate of the robot in the intelligent factory can be effectively improved, and the task execution efficiency is optimized.

The invention relates to the technical field of process parameter optimization, and discloses a surfactant process parameter analysis method and system based on multi-level factors, and the method comprises the steps: carrying out multi-dimensional process parameter collection and preprocessing on production equipment of a surfactant, and obtaining multi-dimensional process parameter time sequence data; thermodynamic feature extraction is carried out to obtain an equipment temperature distribution feature vector; calculating an equipment performance degradation index; performing process parameter association analysis to obtain a process parameter association probability matrix; executing a double-delay deterministic strategy gradient algorithm to obtain a parameter hierarchical control strategy; according to the technical scheme of the invention, the comprehensive improvement of the product quality, the production efficiency and the equipment reliability is realized through the integration of the equipment state monitoring, the process parameter optimization and the intelligent control strategy. And powerful support is provided for intelligent and fine management of the surfactant production process.

The invention belongs to the technical field of power grid regulation and control, and particularly relates to a park-level adjustable load-oriented resource regulation and control method and system, and the method comprises the steps: formulating an initial energy scheduling scheme according to the predicted energy demand in combination with the output characteristics of distributed energy in a park and the load condition of a power grid; and training a load regulation and control strategy model by adopting a reinforcement learning algorithm based on the initial energy scheduling scheme, and generating a personalized load regulation and control instruction for each energy consumption unit. According to the scheme, the real-time energy consumption data of the plurality of energy consumption units in the park, the output characteristics of the distributed energy and the power grid load condition can be comprehensively considered, a scientific and reasonable energy scheduling and load regulation and control strategy is formulated, and efficient utilization and green low-carbon development of park energy are realized. Meanwhile, the method also needs to have the characteristics of data sharing, accurate prediction, intelligent scheduling, rapid response and the like so as to adapt to complex and changeable energy requirements and environmental protection requirements of the park.

The invention relates to the technical field of network time delay scheduling, and discloses a deterministic network time delay scheduling method and device based on a TSN and a storage medium. The method comprises the following steps: carrying out graph theory analysis on a TSN network topology structure to obtain a network node connection relationship and link bandwidth information; extracting time delay requirements and bandwidth requirement characteristics of the data streams, and generating a data stream priority queue and a resource requirement index matrix; inputting a deep Q network, establishing a state-action mapping relation including a network load state and a time slice distribution action, and generating an initial time delay scheduling strategy model; calculating queuing time delay, transmission time delay and processing time delay of each data stream, and generating a time slice resource redistribution scheme; generating a global resource allocation matrix based on the time slice resource reallocation scheme; and parameter feedback updating is carried out on the initial time delay scheduling strategy model to obtain a target time delay scheduling strategy model, and the utilization efficiency of network resources is remarkably improved through bandwidth borrowing and time slice reallocation.

The invention relates to a heat dissipation control method, device and equipment of a storage server and a storage medium, and aims to optimize the heat dissipation performance of the server by monitoring working characteristic parameters and power consumption data of a chip in real time. The method comprises the following steps: firstly, acquiring working load and power consumption information of each chip in a server, calculating temperature distribution of each chip, and generating an initial temperature data set; then, heat generation characteristic analysis is carried out on the set, and a heat generation model under the current load is obtained; then, a heat transfer path model is established based on the model and a server structure, and heat transfer among the chips is analyzed. And according to the heat transfer path, optimizing configuration parameters of an air cooling system and a liquid cooling system in the server, and generating optimized cooling parameter settings. And finally, the optimized parameters are applied to a heat dissipation control system of the server, dynamic adjustment is performed according to real-time load and temperature feedback, and a self-adaptive heat dissipation strategy is formed, so that the heat dissipation efficiency is improved, and stable operation of the server is ensured.

The invention relates to a temperature control method, device and equipment of semiconductor equipment and a storage medium. The method comprises the following steps: performing finite element mesh division and modal coordinate transformation on a plurality of temperature control areas of semiconductor equipment to obtain a temperature control area dynamic model; collecting measurement data of a temperature sensor and performing time-frequency domain transformation to generate a temperature prediction data sequence; gaussian mixture modeling is carried out to obtain prior distribution, and temperature parameter probability distribution is obtained; performing sub-domain division on the temperature control range to obtain a temperature inter-domain feature mapping matrix; and a temperature interval thermal coupling coefficient matrix is calculated based on the temperature control area dynamic model, a temperature control objective function is constructed in combination with the temperature prediction data sequence and the temperature inter-domain feature mapping matrix, and a temperature control quantity sequence is solved by adopting a model prediction control algorithm. According to the invention, the accuracy and dynamic response speed of temperature control are improved, and the stability of temperature control and the energy utilization efficiency are ensured.

The invention provides a riverbed evolution prediction method based on multi-source data fusion, and belongs to the technical field of hydrological resource monitoring. The method comprises the following steps: acquiring and fusing multi-source data by using different devices, performing data preprocessing to form unified three-dimensional grid data, and then generating a multi-scale feature map based on a designed feature extraction network; based on the designed multi-channel convolutional neural network, learning and extracting features on multiple channels at the same time, and performing hierarchical expression and fusion on different information sources; and finally, based on the designed time sequence regression network, extracting a complex dependency relationship in the time sequence through a double-layer LSTM structure, learning residual error information through an adaptive residual error correction module to improve prediction precision, and obtaining a final prediction result. The method shows relatively high applicability and robustness in a complex river channel environment, and can cope with various river bed form changes caused by natural factors and human activities through multi-source data fusion and an innovative feature extraction and regression method.

The invention discloses a multifunctional magnetic stimulation treatment system and an application method thereof, relates to the technical field of medical equipment, and solves the defects of unstable curative effect and limited application range caused by individual difference in the existing system. The precision of the laser beam is automatically adjusted through a self-adaptive calibration algorithm, and it is ensured that a treatment area is accurately positioned; optimal stimulation parameters are calculated through a self-adaptive magnetic field depth optimization algorithm, and pertinence and effectiveness of treatment parameters are ensured; the treatment effect is predicted through a feedback mapping algorithm, and the treatment parameters are dynamically adjusted to ensure the safety and effectiveness of the treatment process; physiological data, symptom description and medical history information of a patient are comprehensively analyzed and recognized through the complex disease pattern recognition module, a detailed disease pattern recognition report is output, the application range of the system is expanded, and personalized disease prediction and risk assessment are provided; the applicability and the curative effect of an existing system are greatly improved.

The invention discloses a quantitative training method and system for a deep neural network, and the method comprises the steps: obtaining an original neural network model and a verification data set, and generating a quantitative priority through hierarchical sensitivity evaluation and topology analysis; on the basis of the layer sensitivity matrix, differentiated quantization parameters are configured for each layer, and quantization constraints are established; constructing an error compensation mechanism, and generating an error compensation strategy; executing dual-scale adaptive quantization training, and respectively adopting an independently optimized quantization strategy for forward propagation and back propagation; dynamically adjusting quantization parameters according to the training state and the resource constraint; and evaluating model performance and resource efficiency. According to the method, through multi-dimensional sensitivity evaluation, a dynamic quantification strategy and system error compensation, the precision and efficiency of quantification training are improved, and meanwhile it is guaranteed that deployment resource constraints are met.

The invention discloses an unmanned aerial vehicle cooperative air combat decision-making method based on GRU-MAPPO deep reinforcement learning, and relates to an air combat game maneuvering strategy. The method comprises the following steps: starting from a complex air combat environment, analyzing a motion model, a maneuvering action, an air combat situation and a reward function of an unmanned aerial vehicle, and establishing a multi-unmanned aerial vehicle air combat environment model which comprises an unmanned aerial vehicle particle motion model, an unmanned aerial vehicle maneuvering action model, an air combat situation evaluation model and an unmanned aerial vehicle reward function model; for a completely cooperative multi-agent environment, a partially observable Markov decision chain framework is established, and a working mode of centralized training and distributed execution is adopted; for unmanned aerial vehicle collaborative decision making, a near-end strategy optimization PPO algorithm is expanded to the multi-agent field, and a GRU is used to replace a full-connection neural network in an original algorithm, so that a GRU-MAPPO algorithm suitable for multi-agent deep reinforcement learning is obtained; a simulation experiment shows that the method can improve the collaborative decision-making capability and the air combat winning rate of the unmanned aerial vehicle.

The invention discloses a game theory improved model-based cloud computing node storage calculation training resource competition scheduling method. The method comprises the following steps of S1, constructing a global resource scheduling system; s2, carrying out initial division on the cloud computing nodes in each logic partition; s3, executing a local resource competition game model; s4, the global resource scheduling system collects a local resource competition result of each logic partition and residual available resources in each logic partition; s5, executing the global collaborative game model; s6, dynamically updating the resource state information of the cloud computing node; s7, the global resource scheduling system performs adaptive adjustment on resource demand strategies in the local game model and the global cooperative game model; and S8, the global resource scheduling system stores and analyzes past resource competition results and task execution conditions, and each cloud computing node updates a resource demand strategy according to the data. According to the invention, the problems of resource bottleneck and overload in the prior art are solved, and the resource utilization efficiency and the scheduling flexibility are greatly improved.

The invention relates to the technical field of intelligent robots, in particular to a service robot intelligent emotion interaction method, which comprises the following steps: acquiring emotion related data of a user through a multi-modal sensor on a service robot, analyzing the acquired data based on a deep learning model, and extracting emotion features of the user; future emotion changes are predicted through a time sequence analysis algorithm, a service robot generates a personalized interaction strategy through an emotion decision-making system according to the current and predicted emotion states, and the system adopts a self-adaptive algorithm based on reinforcement learning, so that the interaction strategy can be continuously optimized along with the emotion changes of a user; the robot performs modeling and optimization on long-term interaction data by utilizing a self-learning mechanism in combination with emotional feedback of the user, so that the precision and interaction effect of emotional response are gradually improved; according to the method, the problems of inaccurate emotion recognition, inflexible interaction strategy and lack of adaptive optimization in the prior art are effectively solved, and the interaction experience of the service robot and the user is remarkably improved.

The invention relates to the technical field of motor control, and discloses a control method and system of a permanent magnet direct current brushless slotless motor. The method comprises the following steps: performing temperature compensation and signal filtering on voltage data of a Hall sensor to obtain rotor position information and temperature characteristic data; performing magnetic field intensity dynamic compensation calculation according to the rotor position and the temperature data to obtain magnetic field intensity parameters; adjusting the amplitude and phase of the three-phase current according to the magnetic field intensity parameter, and generating a current control quantity; extracting and compensating a harmonic component to obtain a torque smooth control instruction; all losses are calculated and optimized, and efficiency optimization parameters are generated; and performing feature extraction and anomaly judgment on the operation parameters, and outputting a protection control strategy. Accurate detection of motor position signals, stable control of torque, optimal control of loss and real-time monitoring and protection of abnormal states are achieved under the high-temperature working condition, and therefore stable operation and the service life of the motor under the high-temperature environment are guaranteed.

The invention provides a real-time data monitoring system in a knitted fabric production process, and relates to the technical field of data monitoring, and the system comprises a real-time monitoring module which is used for directly collecting actual production data from equipment and the environment of a production workshop through various sensors; the data analysis module is used for comparing the initial prediction value of the prediction model with actual production data to output final prediction data and generating optimization parameters according to the final prediction data; the prediction production module is used for establishing a virtual production model according to the actual production data, and the prediction model predicts an initial prediction value of the output variable based on the input data; and the dynamic adjustment module is used for generating a dynamic deviation threshold value according to the deviation trend data and generating a corresponding adjustment strategy according to whether the deviation value falls into the deviation threshold value or not. The precision and stability of the production process are improved, and the influence of human factors is reduced.

The invention discloses a material-based laser scanning system and method, and relates to the technical field of industrial automation, and the system comprises the following components: a laser scanning unit, an environment sensing sensor group, a dynamic compensation and calibration module, a feedback control unit and a visual monitoring interface. According to the invention, the environment change of a scanning site is monitored in real time through the integrated environment sensing sensor group, and the laser scanning data is processed in real time by using an advanced dynamic compensation and calibration algorithm, so that the measurement deviation caused by environmental factors such as vibration, temperature change and dust interference can be effectively reduced; the measurement precision of laser scanning in complex environments such as a coal yard is remarkably improved, meanwhile, the stability of long-term measurement is ensured by continuously optimizing and calibrating model parameters and automatically adjusting laser scanning parameters, and reliable data support is provided for material management.

The invention discloses a cloth defect detection method and system based on machine vision, and relates to the technical field of detection and evaluation, and the method comprises the following steps: optimizing a cloth detection environment, deploying an industrial camera and a sensor to capture an image of a cloth surface and environmental factor data, and preprocessing the collected data; according to the preprocessed data, defect features and environment interference features are extracted, and the extracted defect features and environment interference features are marked. According to the cloth flaw detection method and system based on machine vision, continuous and uninterrupted detection of cloth surface flaws is realized through automatic image acquisition and processing, and compared with a traditional manual detection mode, the detection efficiency is greatly improved, the labor cost is reduced, and the detection efficiency is improved. Automatic detection can ensure that products on a production line quickly pass through a detection area, and defect positions are found and marked in time, so that subsequent treatment is quicker and more accurate.

The invention relates to the technical field of heat exchange modules, and discloses a multi-cavity cooperative control method and device for a micro-channel heat exchange module and electronic equipment, and the method comprises the steps: carrying out the numerical simulation calculation of a plurality of heat exchange cavities of the micro-channel heat exchange module, and obtaining a training data matrix; coefficient calculation and heat flow coupling relation analysis between cavities are carried out, and a heat flow distribution mathematical model is generated; substituting the heat flow distribution data, the temperature distribution data and the flow distribution data into a non-homogeneous heat conduction equation, and constructing a multi-objective optimization function; executing chaotic mapping processing and multi-target collaborative differential evolution to obtain an optimal control scheme set; carrying out Q-learning state value function solving, and generating a cooperative control instruction; and inputting the cooperative control instruction into an execution unit, and performing real-time adjustment and online updating on inlet temperature parameters and flow parameters of each heat exchange cavity to obtain a target heat flow distribution model. Therefore, information interaction and optimized cooperation among the heat exchange cavities are achieved, and the heat exchange efficiency is remarkably improved.

The invention relates to the technical field of unmanned aerial vehicle clusters, in particular to an unmanned aerial vehicle cluster formation method. Comprising the following steps: system initialization and configuration, cluster communication and data exchange, collaborative algorithm calculation and control input, positioning and navigation assistance, global and dynamic task planning setting, energy management and optimization strategy implementation, and biological enlightenment formation control application. Compared with a traditional formation operation method, the unmanned aerial vehicle cluster formation method is higher in autonomy, and the unmanned aerial vehicle cluster formation is more accurate through an advanced autonomous flight control system and a sensor technology. Information sharing and cooperative actions among the unmanned aerial vehicles in the cluster formation are realized through an efficient communication technology, and even if part of the unmanned aerial vehicles fail, other unmanned aerial vehicles can still continue to execute tasks.

The invention provides an OracIeRAC-based CDC synchronization method and system, and relates to the technical field of OracleRAC, and the method comprises the steps: deploying an incremental log analyzer, a session state collector and a resource monitor, and respectively collecting atomic operation, concurrent transaction information and node performance indexes; and identifying data stream features by using an intelligent classification model, constructing a priority matrix, and performing fine-grained acquisition on high-priority data based on an adaptive sampling strategy. According to the method, multi-dimensional evaluation indexes are further utilized, a time sequence prediction model is constructed to predict node loads, data synchronization tasks are distributed to low-load nodes based on a load balancing matrix, and a two-stage submission protocol is adopted to ensure data consistency. By monitoring the synchronization state in real time and dynamically adjusting the allocation strategy, the CDC synchronization efficiency and stability in the Oracle RAC environment are effectively improved, and the synchronization delay is reduced.

The invention provides an intelligent garden environment monitoring data analysis method and system. Sensors are deployed to collect temperature, humidity and other data in real time to form a data set. Analyzing an environment parameter change mode of each region by adopting a time sequence clustering algorithm, and determining characteristics and trends; and carrying out dimension reduction on the data by using a multi-dimensional scale transformation algorithm to a three-dimensional space for visualization, and generating an environment state map. And evaluating the plant fitness in combination with a plant physiological ecology knowledge base, and generating a scoring matrix to guide the preliminary layout scheme. And dynamically adjusting the layout by applying reinforcement learning, selecting an optimal scheme, and predicting a future environmental evolution path based on the optimal scheme and historical data. An automatic control system framework is constructed, a self-adaptive control algorithm is designed, intelligent regulation and control of plant layout are realized, garden management efficiency is improved, and ecological protection and sustainable development are promoted. Therefore, full-chain intelligent management from environment monitoring to plant layout optimization is realized, and the intelligent level of garden environment monitoring and management is remarkably improved.

The invention relates to the technical field of power systems, and discloses a digital twinning-based power grid support performance evaluation system and method. Comprising a data acquisition and processing module, a digital twin model construction and integration module, a multi-scene simulation and prediction evaluation module, an intelligent scheduling and fault prediction module, a multi-source data fusion and big data analysis module, a performance evaluation and optimization decision module, and a visualization and man-machine interaction module. The data acquisition module is used for acquiring real-time states, load data and external environment data of equipment in a physical power grid. Through an intelligent scheduling algorithm, a digital twinborn model and a fault prediction technology, the system can monitor and optimize a new energy access process in real time, solves the challenge of new energy power generation fluctuation on power grid stability, and the algorithm can dynamically adjust power grid load distribution and predict potential faults in advance under the condition of centralized access or fluctuation of new energy. And the stability of power transmission and the safety of equipment are ensured.

The invention relates to the technical field of communication networks, and discloses a service channel time slot allocation method, which comprises the following steps of: 1, acquiring network data in real time from network equipment, the network data comprising bandwidth utilization rate, delay, packet loss rate and service flow type; 2, predicting future network flow fluctuation through a long-short-term memory network model based on historical flow data and real-time network data; and step 3, dynamically adjusting the allocation of time slot resources according to the network flow fluctuation prediction result, and preferentially allocating the time slot resources for high-priority services. According to the method, the dynamic adjustment of the time slot resources is realized by combining the flow prediction of the LSTM model and the Q-learning reinforcement learning algorithm, so that the system can flexibly allocate the resources according to the actual network load condition, the resource waste is reduced, the utilization efficiency of the bandwidth is improved, and the utilization rate of the overall network resources is remarkably improved especially under the condition of high network load.

The invention discloses an exhibition hall intelligent control system and method based on the Internet of Things technology, and the method comprises the following steps: collecting environment data in an exhibition hall in real time through a self-adaptive multi-mode sensor network, and dynamically adjusting the sampling frequency of a sensor through a multi-dimensional data sudden change monitoring algorithm by the system, the central processing module is combined with historical and real-time data, time sequence multi-dimensional regression analysis is adopted, an environment change prediction model is generated, then equipment control parameters are generated, the system dynamically adjusts the operation states of temperature equipment, illumination equipment and the like in the exhibition hall according to behavior data of visitors and the environment parameters, the experience of the visitors is optimized, and the experience of the visitors is improved. The intelligent anomaly detection module monitors the operation state of equipment in real time and identifies and isolates fault equipment in time, and the system preferentially guarantees equipment operation and reduces energy consumption when visitors are dense through a long-term energy consumption optimization learning algorithm, so that energy-saving management and long-term self-optimization of the exhibition hall are realized.

The invention discloses a brain tumor MRI image segmentation model and method based on an improved Swin UNETR network, and belongs to the technical field of image segmentation. The brain tumor MRI image segmentation model is obtained by improving a Swin UNETR as a baseline model and comprises an encoder, a jump connection and a decoder, the decoder comprises a plurality of convolution fusion modules and an attention mechanism module, feature information of different levels is fused in the decoder by using the plurality of convolution fusion modules, and the attention mechanism module is used for identifying the feature information of the different levels of the feature information of the different levels of the feature information of the different levels of the feature information of the different levels of the feature information of the different levels of the convolution fusion modules. And the attention mechanism module is used to focus the feature information obtained by the plurality of convolutions. By adopting the scheme of the invention, the problem that the existing medical image segmentation technology based on deep learning is insufficient in brain tumor MRI image segmentation precision and is difficult to realize accurate segmentation especially in the face of different types of tumors and variability can be effectively solved, the calculation amount is small, and the image segmentation efficiency can be effectively improved.