27095results about How to "Reduce waste" patented technology

The invention belongs to the field of network security, and discloses a network security evaluation system and method based on a dynamic attack and defense game model, and the evaluation system comprises four functional modules of attack and defense game construction, risk prediction, dynamic defense optimization and security situation awareness. According to the method, the idea of dynamic attack and defense game is introduced, multi-level and multi-scene attack and defense simulation is carried out according to historical states, strategy data and real-time data, and the optimal attack path and attack strategy are calculated and analyzed through a game equilibrium solution; according to the method, network resources and network security resources are dynamically adjusted by relying on reinforcement learning and deep learning technologies and a defense rule base generated by pre-training, network configuration is optimized according to a real-time evaluation result, and waste of system resources is reduced. By implementing the system and the method provided by the invention, the security of the target network can be dynamically evaluated, the security defense strategy of the network can be adjusted in real time, and unknown attacks such as advanced persistent threat (APT) and zero-day attack can be effectively resisted.

The invention relates to the field of temperature control, in particular to an automatic control method and system for a constant-temperature test room. The method comprises the following steps: acquiring real-time indoor operation monitoring parameters and external environment multi-dimensional data of a constant-temperature test room; performing indoor operation state analysis on the real-time indoor operation monitoring parameters, and constructing a laboratory dynamic state space model; carrying out multi-time-point environment temperature fluctuation calculation on the external environment multi-dimensional data, and carrying out nonlinear correlation learning to generate indoor state-environment nonlinear correlation characteristics; carrying out dynamic temperature evolution simulation on the laboratory dynamic state space model based on the indoor state-environment nonlinear correlation characteristics, and carrying out dynamic temperature evolution prediction to obtain temperature prediction values of a plurality of regions; constant-temperature deviation compensation calculation is carried out on the temperature prediction values of the multiple areas, then intelligent temperature control compensation is carried out, and an intelligent temperature control strategy is constructed; according to the invention, efficient and accurate constant-temperature automatic control is realized.

The invention relates to the technical field of data processing, in particular to a decision-making method and system for multi-source information fusion. The method comprises the following steps: obtaining hyperspectral image data, visible light image data and infrared image data collected by an unmanned aerial vehicle, and collecting ground environment monitoring data collected by a ground sensor; the hyperspectral image data, the visible light image data and the infrared image data are subjected to image preprocessing, so that a standardized multispectral data set is obtained, and image preprocessing comprises geometric correction, radiation correction and atmospheric correction; and color and texture feature extraction is carried out on the visible light image in the multispectral data set, and appearance feature and growth density identification of crops is carried out so as to obtain morphological feature data. Through integrated analysis of multi-source data, abnormal growth of crops and potential pest and disease damage risks can be identified more accurately, so that engineers are assisted to make irrigation and fertilization decisions.

The invention discloses an energy storage energy management system for improving a demand protection algorithm, and the system comprises a data collection module which is used for obtaining energy storage system load data, distributed energy operation data, and energy storage system operation data; the correlation feature analysis module is used for acquiring a correlation feature set between the load and the energy supply; the control strategy generation module is used for adjusting a charging and discharging threshold value and formulating a charging and discharging control strategy; the control strategy execution module is used for formulating a charging and discharging control strategy and adjusting the working mode of the energy storage converter; the cooperative execution module is used for dynamically optimizing a power distribution strategy by dispatching distributed energy and power grid cooperative work; and the dynamic power adjustment strategy adjustment module is used for adjusting the charging and discharging protection threshold value and the power adjustment strategy through a feedback mechanism. Through an intelligent control and scheduling mechanism, the charging and discharging efficiency of the energy storage system is improved, the stability and flexibility of the power grid are enhanced, and effective guarantee is provided for safe operation of the power grid.

The invention relates to the technical field of logistics transportation scheduling, in particular to a logistics management system based on intelligent scheduling, which comprises a data acquisition module, a demand analysis module, an intelligent scheduling module, a path planning module, a monitoring and feedback module, an optimization module and the like. The data acquisition module is used for collecting information such as goods, vehicles, storage, customer demands and traffic road conditions; the demand analysis module predicts logistics demands by using a time sequence analysis or machine learning algorithm; the intelligent scheduling module adopts a greedy and dynamic programming mixed algorithm to allocate resources; the path planning module calculates an optimal path according to road conditions and starting and ending points; the monitoring and feedback module monitors logistics parameters in real time and feeds back abnormity; and the optimization module optimizes the scheduling scheme according to the fitness function based on the genetic algorithm. According to the invention, data and resources of all links of logistics are effectively integrated, accurate demand prediction, efficient resource configuration, transportation path optimization, real-time monitoring feedback and overall performance improvement of the system are realized, the logistics cost is significantly reduced, and the transportation efficiency and the service quality are improved.

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 provides a production scheduling calculation method of a production plan scheduling platform. The method comprises the steps of data collection, demand prediction intelligent analysis, productivity planning and bottleneck analysis, production scheduling plan making, real-time monitoring and dynamic adjustment, plan execution and feedback analysis and report generation and decision support. Through distributed scheduling and real-time dynamic adjustment, the production plan can respond to emergencies such as equipment failure and order change in production in time, the continuity and stability of the production process can be ensured, production stagnation or delay caused by the emergencies is avoided, meanwhile, the production scheduling plan can be rapidly adjusted through real-time data feedback, and the production efficiency is improved. Resource utilization is optimized, waste is reduced, production efficiency is improved, intelligent productivity analysis and bottleneck identification are utilized, an enterprise can comprehensively know the use condition of each production resource, and possible bottleneck problems are found and solved in advance.

The invention discloses a video monitoring safety control system and method based on the Internet of Things, particularly relates to the technical field of video monitoring safety control, and is used for solving the problem of low video monitoring control efficiency. Multi-source data are collected through an Internet of Things sensor network, the time sequence consistency is ensured by adopting a clock synchronization protocol, the data are preprocessed, the accuracy is ensured, then correlation analysis is carried out by utilizing a Bayesian network and principal component analysis, historical information and scene modes are extracted, risk levels of all areas are determined, and the risk levels of all the areas are determined. And determining a resource scheduling priority according to the risk level. And by simulating scene change, the risk early warning capability of the system is improved. According to the system, camera resources are dynamically scheduled through a collaborative optimization algorithm, monitoring resources of a high-risk area are preferentially allocated, redundant monitoring is reduced, power consumption and bandwidth use are optimized, and therefore the response speed and precision of video monitoring in a complex environment are improved, and the safety of video monitoring is improved.

The invention discloses an AI spraying system control method capable of achieving autonomous 3D scanning, and the method comprises the steps: obtaining three-dimensional surface data of a to-be-coated workpiece through high-precision 3D scanning equipment, constructing a surface model of the workpiece according to the data, and extracting surface feature parameters. Coating parameters set by a user are received, the coating parameters comprise the viscosity, the chemical property and the film thickness requirement of paint, the surface characteristic parameters and the coating parameters are input into the spraying optimization model, and an initial spraying track and technological parameters are generated through calculation in combination with historical data. Environment parameters are collected and associated with the initial process parameters, and the adjusted spraying track and the adjusted process parameters are generated through dynamic environment compensation. And through a robot control system, coating operation is executed according to the adjusted parameters, the spraying effect is optimized in real time, and it is ensured that the surface of the workpiece is evenly coated. Efficient automation of the spraying process is achieved, the coating quality and consistency are remarkably improved, and the automatic spraying device is particularly suitable for coating of workpieces with complex curved surfaces or changeable shapes.

The invention discloses a charging pile power regulation and control method and system, and particularly relates to the technical field of charging pile management, data is collected from a multi-dimensional data source and summarized to form a preliminary information set, and a standard information set is obtained through format standardization processing; feature data of the charging pile are extracted based on the standard information set, and charging task analysis and resource occupation analysis are carried out respectively; inputting the analysis result into a pre-trained machine learning model to generate a charging priority coefficient; constructing a priority matrix, and dividing the charging piles into high priorities and low priorities according to the charging priority coefficients; and finally, dynamically adjusting a power distribution strategy according to the priority type, optimizing low-priority task power distribution while meeting high-priority task requirements, and improving the operation efficiency and the resource utilization rate of the charging station. According to the method, the real-time operation state and the charging task characteristics of the charging piles are combined, the power distribution strategy is dynamically adjusted, the priority division and power distribution proportion of the charging piles are adjusted in real time, and the self-adaptive capacity of operation of the charging station is improved.

An energy scheduling system of a high-power liquid cooling charging pile belongs to the technical field of liquid cooling charging piles, and comprises a power scheduling module, a liquid cooling management module, a data acquisition and monitoring module, an energy storage and energy recovery module, a communication and interaction module, an intelligent scheduling module and a remote management and analysis module. Through the intelligent scheduling and energy optimization technology, power distribution can be adjusted in real time according to the vehicle demand and the power grid state, overload is avoided, and the charging efficiency is improved; and secondly, an advanced liquid cooling management technology is adopted, and temperature monitoring and cooling liquid flow rate adjustment are combined, so that heat balance and stable operation of the equipment under a high-load condition are ensured, the service life of the equipment is prolonged, and energy waste is reduced. In addition, the system can store redundant electric energy and release the electric energy in the peak load period of the power grid through the energy storage and energy recovery module, so that the power grid pressure is effectively reduced, and the energy utilization rate is also improved.

The invention relates to an optimization method for dynamic monitoring of an underground water level. Comprising the following steps: uniformly arranging a plurality of sensors in an underground water monitoring area, and monitoring multi-dimensional parameters including water pressure, temperature and humidity; the sensors form a gridding monitoring system through a wireless network; inputting multi-dimensional data acquired by the sensor array into a data fusion module, performing cooperative processing on the data by using a data fusion algorithm, and filtering noise and abnormal values; a unified water level change model is constructed, so that data of different parameters complement each other; dynamically adjusting the weight in the fusion algorithm regularly according to the historical data and the new measurement value; in the monitoring area, based on the dimensions of time and space, a space-time analysis model of underground water level changes is constructed, and the periodicity and regularity of water level fluctuation are excavated; and performing trend analysis on the historical water level data by adopting a time sequence analysis algorithm including ARIMA or LSTM to generate short-term and long-term water level prediction.

The invention discloses a distributed heterogeneous computing power reasoning task dynamic scheduling method and system. The system comprises a computing power architecture adaptation module, a task scheduling module, a model updating module, a capability service module, a dynamic scheduling module, a resource evaluation module and a state sensing module. The computing power architecture adaptation module is used for identifying and matching a local heterogeneous computing power architecture and a remote heterogeneous computing power architecture; the task scheduling module is used for selecting a proper computing power terminal from a distributed heterogeneous computing power environment; the model updating module is used for updating the AI model in real time; the capability service module is used for providing a unified AI model service calling interface; the dynamic scheduling module is used for dynamically allocating computing power resources; the resource evaluation module is used for monitoring and evaluating the state of the computing power resource; the state sensing module is used for monitoring the health state of the computing power node in real time and responding to a fault in time; the scheduling efficiency of computing power resource scheduling is improved, and stable operation of the reasoning task is guaranteed.

The invention discloses a central air conditioner energy-saving optimization system based on load prediction, and relates to the technical field of air conditioner energy saving.According to the central air conditioner energy-saving optimization system based on load prediction, a VMDTransformerGRU hybrid deep learning model is introduced, through multi-source data fusion, the precision of cold load prediction is remarkably improved, and the prediction model can recognize the load change trend in advance. According to the improvement, the dynamic response capacity of the system is effectively enhanced, pre-regulation and control can be carried out before load changes, energy waste is reduced, a water chilling unit, a cooling tower and a pump are modeled to be independent intelligent agents, multi-agent deep reinforcement learning (MADRL) is adopted, and all the intelligent agents can autonomously learn and optimize control strategies of the intelligent agents. The intelligent agents cooperate with one another, efficient operation of the whole system is achieved through autonomous decision, and operation conflicts between devices are avoided. In this way, dynamic coordination and joint optimization among all the devices are achieved, invalid energy consumption is reduced, and the overall energy-saving effect and operation stability of the system are remarkably improved.

The invention discloses a road traffic signal control system and method, and the method comprises the steps: collecting real-time data through a sensor, a camera and a geomagnetic device, combining historical data with emergency analysis, and predicting a flow mode and a change trend; green light time distribution is dynamically optimized, a fixed timing, inductive and adaptive control strategy is adopted, and traffic light duration and priority adjustment is achieved; through regional cooperation and linkage control, green wave bands and branch intersection red light optimization are set, and peak period traffic signals are intelligently dispatched. A machine learning model is used to predict a flow peak value, environment and accident detection data are combined, a signal lamp period is dynamically adjusted, vehicle waiting time, traffic efficiency, carbon emission and traffic satisfaction are evaluated, and a traffic control strategy is comprehensively optimized. According to the road traffic signal control system and method, the traffic flow in different time periods is predicted by adopting the flow prediction model based on big data analysis and machine learning, and the signal period is adjusted in advance.

The invention relates to the technical field of crop production, in particular to a big data-based crop yield prediction and synergistic management system, which comprises a data acquisition module, a data processing module, a multi-scale crop growth prediction module and a plant stomata dynamic adjustment and transpiration monitoring module, the data acquisition module acquires multi-dimensional agricultural data; the data processing module receives and preprocesses the collected multi-dimensional agricultural data; the multi-scale crop growth prediction module predicts growth trends of crops in different time dimensions; and the plant stomata dynamic adjustment and transpiration monitoring module analyzes and predicts the transpiration rate of the crops, and adjusts the irrigation amount in real time in combination with the prediction result of the growth trend of the crops. According to the invention, the sustainability of water resource utilization is improved, the waste of agricultural resources is reduced, and finally the benefits of agricultural production and the sustainability of the environment are improved.

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 discloses a cloud computing storage, calculation and training resource allocation method based on reinforcement learning and dynamic resource scheduling. The method comprises the following steps: S1, establishing a unified resource scheduling framework; s2, constructing a state space and an action space; s3, generating a state representation for predicting future resource demands; s4, initializing a reward function of key performance index weighted comprehensive evaluation; s5, generating an initial storage resource, computing resource and training resource allocation strategy; s6, continuously adjusting a reward function of the reinforcement learning model based on the self-supervised learning feedback signal; s7, dynamically adjusting a distribution scheme of the storage resources, the computing resources and the training resources through quick response of the resource scheduling framework; s8, optimizing resource allocation among a plurality of cloud computing nodes or platforms; s9, automatically identifying different stages of the current task through a self-supervised learning model; and S10, finally generating an optimal allocation scheme. According to the staged resource scheduling method, the condition of overload or insufficiency of resources is effectively avoided, and the task execution time is greatly shortened.

The invention provides an electromagnetic field composite powder feeding type plasma arc additive manufacturing formability regulation and control method and system, and relates to the technical field of plasma arc additive manufacturing. The flowing direction and speed of liquid metal in a molten pool in the deposition process are regulated and controlled through the target composite electromagnetic field, and the morphology and the internal organization structure of a deposition layer are controlled; in the process of regulating and controlling the liquid metal in the molten pool, process parameters and molten pool dynamic image data in the deposition process are collected in real time, and a pre-trained state recognition model is used for conducting self-adaptive adjustment on the process parameters of a subsequent deposition layer; and after deposition of each layer is completed, a laser scanning system is adopted for measuring the actual geometric size and surface appearance of the deposition layer, deposition manufacturing of the next layer is executed based on the optimized composite electromagnetic field control parameters, the corrected layered slicing data and the process path, and the execution is executed circularly till manufacturing of the whole workpiece is completed.

The invention provides a crop accurate management method and system based on a crop growth model. The method comprises the following steps: collecting environment data of a crop growth period by integrating a remote sensing technology, a field intelligent sensor and an unmanned aerial vehicle, and carrying out space-time alignment to form a comprehensive environment data set; and dynamically adjusting parameters of the crop growth model by using a Bayesian rule, evaluating the influence of environmental uncertainty on crops in combination with Monte Carlo simulation, and providing yield prediction with a confidence interval. Based on the prediction, a particle swarm optimization algorithm is applied to formulate management schemes such as irrigation, fertilization, pest control and harvesting opportunities, and meanwhile, a multi-objective optimization strategy is adopted to balance economic benefits and environmental protection. After the scheme is implemented, the crop growth response is monitored in real time so as to reinforce learning and automatically adjust management measures, and precise agricultural management is realized. According to the technical scheme provided by the embodiment of the invention, the crop management precision and efficiency are improved.

The invention provides a first-aid platform work order management method based on digital twinning and artificial intelligence, and belongs to the technical field of information technology application innovation, and the method comprises the steps: collecting historical first-aid data and real-time data of a first-aid scene, and constructing a task scheduling system based on a knowledge graph; decomposing the task into a plurality of sub-tasks according to the work order data, and allocating an optimal first-aid resource combination for the sub-tasks; an optimization strategy is set to carry out collaborative allocation control on each resource, and task allocation is optimized; the optimized task allocation is monitored in real time, and meanwhile, in combination with real-time data flow monitored in real time, it is ensured that the change of the dynamic environment is responded at any time in the task execution process; and re-inputting the optimized task allocation and resource scheduling strategy into the knowledge graph, and carrying out closed-loop optimization. According to the invention, the problems of inaccurate task scheduling, weak real-time response capability, low resource utilization rate and the like in the existing work order management system of the emergency platform are effectively overcome, and the overall efficiency and the automation level of the emergency platform are greatly improved.

The invention relates to the technical field of raw material management systems, in particular to a raw material management system for building engineering construction. The management system comprises a raw material information management module, a purchase management module, an inventory management module, a use management module, an optimization allocation module, a raw material quality evaluation module, an environmental impact evaluation and material selection module, a supply chain transparency module, a life cycle management module and a raw material demand prediction and inventory optimization module. An automatic purchase and supplier collaborative optimization module; the system uniformly manages data information of links of raw material purchasing, inventory, use and the like through a centralized data platform, forms a complete material management chain, avoids information dispersion and lag, realizes real-time sharing of data of each link, improves transparency of management and accuracy of decision making, and meanwhile, greatly improves management efficiency. The complexity and error rate of manual operation can be remarkably reduced through automatic purchase order generation, intelligent inventory management and supplier collaboration.

The invention discloses a coal mine ventilation optimization and air quality regulation and control method based on adaptive learning, and relates to the technical field of coal mine ventilation optimization, and the method comprises the following steps: arranging a plurality of sensors in a mine, carrying out the real-time collection and monitoring of key environment parameters, and uploading the collected data to a central control system; and comparing the collected real-time data with historical data, and dividing the collected real-time data into conventional working condition data and abnormal working condition data according to environment states. Through real-time monitoring of various sensors and a deep learning algorithm, accurate ventilation control is achieved, a problem area is rapidly positioned in an emergency, airflow is dynamically adjusted, and safety of miners is guaranteed. According to the system, an LSTM model is adopted to predict an event trend in advance, preventive regulation and control measures are taken in time, real-time adjustment is supported, and the strain capacity is improved. Through a multi-stage ventilation mode, the system optimizes energy consumption and equipment operation efficiency, reduces unnecessary energy waste and maintenance cost, and realizes balance between safety and economy.

The invention discloses a critical patient nursing intelligent monitoring system and method, and relates to the technical field of intelligent monitoring. Collecting physiological data and activity data of a patient in real time by using a multi-sensor system, and establishing an initial health file of the patient; according to the collected physiological data and activity data, daily activities and behavior modes of the patient are analyzed, and an individualized health baseline is established; monitoring physiological data and activity data of the patient in real time, identifying an abnormal trend, and generating an early warning signal; preferentially distributing nursing measures according to the state of the patient; the nursing measure effect is evaluated, and if the expected nursing effect is not achieved, the monitoring strategy is readjusted; according to the invention, the health state of the patient is comprehensively monitored and accurately analyzed in real time, so that abnormity can be found and intervened in time when the abnormity occurs, the patient can be nursed and treated in time, waste of nursing resources and nursing errors are reduced, and the treatment effect and the nursing quality of the critical patient are improved.

The invention relates to the technical field of industrial automatic control, and discloses an electric control cabinet internal intelligent layout optimization and thermal management system, which comprises an intelligent layout optimization module, which adopts a particle swarm optimization algorithm to comprehensively analyze the multi-dimensional parameter size, the weight, the heat dissipation requirement and the like of internal equipment of an electric control cabinet, realizes layout optimization, and provides a thermal management module for the internal equipment of the electric control cabinet; the method comprises the following steps: receiving equipment parameters input by a user or automatically extracted from an equipment database, including but not limited to physical sizes, power consumption, thermal loads and the like of the equipment, and optimizing targets, including maximizing a space utilization rate, minimizing heat dissipation obstruction, optimizing a maintenance path and ensuring a safe distance between the equipment. According to the internal intelligent layout optimization and heat management system of the electric control cabinet, by comprehensively applying artificial intelligence, three-dimensional modeling, a multi-element heat dissipation technology and an Internet of Things technology, optimal layout and efficient heat management of internal equipment of the electric control cabinet are achieved, and the operation efficiency, safety and maintainability of the electric control cabinet are remarkably improved.

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 belongs to the technical field of power prediction, and discloses a new energy power prediction method and system based on multi-source meteorological data fusion, and the method comprises the steps: obtaining meteorological data from different meteorological data sources in real time or at regular time through a data obtaining module; carrying out preliminary screening and preprocessing on the obtained data by utilizing a data processing module; the data fusion module is used for integrating meteorological data from different sources by adopting an advanced data fusion algorithm to eliminate data redundancy; a mathematical model or a machine learning model suitable for new energy power prediction is constructed by using the prediction model module based on meteorological data of multi-source fusion; and the result output and evaluation module is used for outputting the prediction result to a user in a visual form, and carrying out real-time or regular evaluation on the prediction result. The method has the characteristics of diversified data sources, advanced data fusion technology, flexible prediction model construction, high real-time performance and accuracy, wide application scenarios and the like.

The invention discloses an intelligent management platform suitable for a power plant, and relates to the technical field of power plant management, and the platform employs an intelligent scheduling system and a DDPG algorithm, and dynamically adjusts a power generation strategy according to the real-time equipment health score, residual life prediction, and the change of energy price and load demand. The power plant can quickly make adjustments when the load demand changes sharply, preferentially dispatch efficient and low-emission equipment, improve the overall energy efficiency and reduce energy waste, and through integration of a sensor network and edge computing, key operation parameters of the equipment are collected in real time, and a dynamic maintenance plan is generated based on health scores and residual life prediction of the equipment. The equipment about to break down is preferentially processed, the random forest model is used for health state evaluation and fault probability prediction, the health score of the equipment can accurately reflect the running state of the equipment, the maintenance time is dynamically adjusted in combination with the load condition of the equipment, and excessive maintenance or maintenance lag is avoided.

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.