987 results about "Energy reduction" patented technology

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 an intelligent heat supply pipe network optimization control method and system based on the Internet of Things, and relates to the technical field of heat supply pipe network optimization control, and the system comprises environmental parameter collection modules which are distributed in different areas covered by a heat supply pipe network and are used for collecting environmental data of each area in real time; and the Internet of Things control platform is used for receiving the environmental data from the environmental parameter acquisition module. According to the intelligent heat supply pipe network optimization control method and system based on the Internet of Things, flow and temperature data are monitored in real time, the valve opening degree is accurately controlled according to a PID control algorithm, a closed-loop control system is formed, heat supply demand changes are quickly responded, energy waste is reduced, heat supply quality is guaranteed, meanwhile, energy consumption is reduced, and the energy utilization efficiency is improved; environmental parameters and user heat using habit changes are monitored in real time, a heat supply strategy is adjusted in time, heat supply demand fluctuation is quickly responded, and it is ensured that heat supply always meets the actual demand of a user.

The invention relates to the technical field of refrigeration control, in particular to a medical refrigerator compressor refrigeration temperature control system and method based on big data, and the system comprises a storage article recognition module, a heat capacity calculation module, a compressor load adjustment module, a refrigerant flow control module and a compressor performance correction module. According to the medical refrigerator, the types and characteristics of stored articles are precisely recognized, detailed parameters are extracted, the high-precision temperature sensor and the environment parameter set are utilized, and dynamic calculation is conducted on the refrigeration requirement, so that precise adjustment of the output power of the compressor is achieved, and the temperature stability and the medicine quality in the medical refrigerator are ensured; by refining the storage requirement of each medicine, optimizing the refrigerant supply amount and achieving a more accurate temperature control strategy, and by correcting the performance parameters of the compressor and improving the overall energy efficiency and reliability, energy waste can be more effectively reduced, optimization of storage conditions is ensured, and the overall efficiency and response speed of a refrigeration system are remarkably improved.

The invention discloses a temperature and humidity control method for a marine energy storage container, and relates to the technical field of container temperature and humidity control, and the method comprises the following steps: monitoring the temperature and humidity in the container in real time, and obtaining the real-time environment parameters of the environment in the container through a sensor; through real-time monitoring, an intelligent algorithm and multi-level control, the system dynamically adjusts the temperature and humidity, and the accuracy and the response speed are improved. Different from traditional static setting, the system is combined with sensor data and a prediction model to cope with environment and load changes in advance, temperature and humidity are prevented from exceeding the standard, and equipment faults are reduced. The energy waste is reduced, the energy consumption distribution is optimized, the overall energy efficiency is improved, the cost is reduced, and the safe and stable operation of the equipment is ensured by a regionalized control and global optimization strategy.

The invention discloses a control method for a cooling circulation system of a multi-core processor, and relates to the technical field of computer hardware heat dissipation, and the control method comprises the following steps: the multi-core processor is responsible for executing a calculation task; the sensor module monitors the temperature of each core; the cooling system control module is used for receiving data from the temperature sensor and the load monitoring module, calculating the cooling requirement of each core according to the data and commanding a cooling unit to adjust; the cooling unit is used for adjusting cooling equipment to reduce the temperature of the processor according to an instruction of the control unit; and the debugging interface is used for monitoring the state of the system and displaying the temperature, the load and the cooling resource distribution condition of each core. The method has the beneficial effects that the rotating speed of the fan, the liquid cooling flow and the working state of the heat pipe system can be accurately controlled by combining the accurate analysis of the load change and the temperature data, the heat dissipation efficiency of the processor is improved, and the energy waste is reduced.

The invention discloses an intelligent factory data intelligent analysis and management system, and relates to the technical field of data analysis. Multi-source data are uniformly accessed through an industrial gateway, and a knowledge graph is constructed after cleaning and standardization; an edge node deploys a lightweight AI model to realize real-time analysis such as equipment anomaly detection; intelligent distribution of cloud side tasks is realized based on a decision model; optimizing production scheduling and quality control by using an algorithm; a zero-trust architecture is adopted to guarantee safety, and efficient energy management is realized in combination with reinforcement learning; all the modules work cooperatively, and the intelligent level of a factory is improved. The operation efficiency and quality of the intelligent factory are effectively improved. Efficient data fusion processing is realized, and equipment anomaly detection is more accurate; the order delivery period is shortened, and the product reject ratio is reduced; network security protection is enhanced, and energy waste is reduced; the decision response speed is accelerated, the decision accuracy is improved, cost reduction and efficiency improvement of enterprises are comprehensively assisted, and the competitiveness is enhanced.

The invention discloses an AI computing power equipment intelligent energy consumption management system and method based on big data, and relates to the technical field of data center energy consumption management, the method comprises the following steps: obtaining real-time operation data of AI computing power equipment, the real-time operation data comprising load characteristic parameters, energy consumption characteristic parameters, environment characteristic parameters and equipment state parameters; obtaining a load discrimination value according to the load characteristic parameter; obtaining an energy consumption discriminant value according to the energy consumption characteristic parameters; acquiring an environment discrimination value according to the environment characteristic parameters; acquiring a state discrimination value according to the equipment state parameter; fusing the load discriminant value, the energy consumption discriminant value, the environment discriminant value and the state discriminant value to generate a comprehensive energy efficiency index; judging whether the comprehensive energy efficiency index exceeds a preset dual-threshold decision interval or not; if the upper limit threshold value is exceeded, triggering graded consumption reduction response, including dynamic frequency modulation, task migration or equipment dormancy; and if so, determining that the current operation mode is in a high-energy-efficiency state and maintaining the current operation mode.

The invention relates to the technical field of mine environment control, and discloses an intelligent environment monitoring and control method in a mine laneway. The method comprises the steps that a real-time environment parameter set of a mine laneway is collected, the set contains gas concentration data, dust concentration data and temperature and humidity data, and real-time capture of laneway environment multi-dimensional parameters is achieved; then, based on the collected real-time environment parameter set, calculating an environment safety evaluation index so as to scientifically and objectively reflect the roadway environment safety condition; a dynamic control strategy is generated according to the environmental safety evaluation indexes, and the strategy comprises ventilation equipment regulation and control parameters and early warning level parameters and can be flexibly adjusted according to environmental changes; and finally, the ventilation equipment regulation and control parameters are sent to a mine ventilation system, and roadway environment regulation and control are executed. According to the method, environmental parameters can be comprehensively monitored, safety risks can be accurately evaluated, ventilation equipment can be flexibly regulated and controlled, the early warning function can be perfected, energy waste can be reduced, labor intensity can be reduced, and underground operation safety and mining efficiency can be guaranteed.

The invention relates to the technical field of intelligent power grids, in particular to a virtual power plant multi-level collaborative optimization control method based on digital twinning. According to the invention, data of the power generation end, the power storage end, the power transmission end and the load end are collected and processed in real time, a digital twin model is established, and accurate modeling and real-time monitoring can be carried out on each device of the virtual power plant. Through real-time optimization scheduling and cooperative control, energy loss can be minimized, supply and demand can be balanced, the energy use efficiency is improved to the maximum extent, and the cost is reduced; according to the method, the genetic algorithm and the particle swarm optimization algorithm are combined, scheduling optimization is carried out based on a multi-level collaborative optimization model, a virtual power plant can rapidly respond according to changes of real-time environment and load requirements, fossil energy reduction and clean energy increase are preferentially considered in the power generation equipment scheduling process, and greenhouse gas emission reduction is facilitated.

The invention provides a coal mine fluidized mining dynamic disaster prevention and control method and system, and relates to the technical field of disaster prevention and control. The method comprises the following steps: establishing a risk map based on mining area multi-field information, setting a seismic source intensity upper limit, a path attenuation target and an energy absorption displacement target, implementing in-situ transformation under the constraint of a control index to form a distributed micro-fracture network, and calculating a residual dynamic load spectrum to weaken a seismic source; according to the residual dynamic load spectrum, constructing an artificial barrier and an energy absorption band with wave impedance abrupt change so as to realize propagation energy reduction; a flexible energy absorption protection system is configured according to the residual dynamic load spectrum and the propagation attenuation coefficient, micro-seismic, stress and displacement data are monitored in real time, and parameter self-adaptive correction is achieved through linkage treatment triggered by a threshold value. According to the method, a source-diameter-field integrated active prevention and control system is constructed, dynamic regulation and control and closed-loop management in the fluidized mining process are achieved, and the intrinsic safety and the intelligent level of deep mining are remarkably improved.

The invention discloses an intelligent prediction and scheduling management system based on hazardous waste incineration waste heat recovery power generation, relates to the technical field of hazardous waste incineration waste heat recovery power generation, and aims to monitor key parameters such as temperature, pressure, flow and flue gas components in the hazardous waste incineration process in real time, and determine the hazardous waste incineration waste heat recovery power generation through data processing and feature extraction. And the hazardous waste incineration heat value, the waste heat boiler steam production amount and the power generation amount are accurately predicted. Meanwhile, the system combines power grid load change and price fluctuation, realizes intelligent regulation and control of equipment operation and maximization of waste heat recovery efficiency and generating capacity through an optimization scheduling algorithm, can improve energy recovery efficiency to the maximum extent, reduces energy waste, improves economic benefits and environmental sustainability of the system, and has wide application prospects. The power generation benefit can be improved in the field of hazardous waste incineration, and reference can be provided for other industrial waste heat recovery power generation systems.

The invention discloses an energy scheduling method, device and equipment based on a virtual power plant, and a storage medium. The method comprises the following steps: obtaining corrected predicted power of distributed renewable energy sources of the virtual power plant; a distribution optimization model of the virtual power plant is constructed, the distribution optimization model comprises a target function and constraint conditions, the target function takes response cost, energy storage cost, regulation and control cost and carbon transaction cost of the virtual power plant as targets, and the constraint conditions are charge and discharge characteristics of a distributed power supply; in the actual operation process, the corrected and predicted power is input into the distribution optimization model, solving optimization is carried out through a path optimization algorithm in an uncertain environment, and a scheduling strategy is obtained; and performing energy scheduling on the power distribution network accessed to the virtual power plant according to the scheduling strategy, thereby realizing cooperative and clustered operation of various power resources in the virtual power plant, improving the operation efficiency of the power distribution network, reducing energy waste, and improving the reliability of overall energy supply.

The invention provides a distributed photovoltaic energy storage method and system based on a deep learning model, and the system comprises a data collection and preprocessing module, a deep learning model construction module, a model training and optimization module, an energy storage control strategy module, a cooperative work and optimization module, and a system monitoring and feedback module. A deep learning model can accurately predict photovoltaic power generation capacity and load requirements, realizes optimized charge and discharge control of energy storage equipment, maximally utilizes photovoltaic energy and reduces energy waste, and meanwhile, the deep learning model can quickly learn and adapt to a new operation scene and a data mode, so that the energy storage efficiency is improved. The system can cope with various complex and changeable environments and load conditions, and through accurate prediction and an intelligent control strategy, the fluctuation of photovoltaic power generation is effectively stabilized, the stable power supply of the micro-grid under different working conditions is ensured, and the fluctuation of voltage and frequency is reduced.

The invention discloses sorting equipment for computer hardware, which belongs to the technical direction of computer hardware processing, and comprises a computer hardware conveying table, three groups of cameras are arranged on the table top of the computer hardware conveying table in an array mode, and the three groups of cameras are respectively a first camera, a second camera and a third camera. A computer hardware shell is further placed on the computer hardware conveying table, a mounting hole is formed in the side face of the computer hardware conveying table, and a first camera, two sets of hardware clamping blocks and two sets of pressure sensors are used in cooperation; pressure detection of different forces can be carried out on corners of computer hardware shells of different sizes, defective products are automatically sorted out, manual collection by workers is not needed, the burden of the workers is relieved, defect detection operation can be independently carried out on the four corners of the computer hardware shells, and the working efficiency is improved. And after cracking, subsequent pressure detection operation does not need to be carried out, the products are directly sorted into defective products, energy consumption is reduced, and the effects of energy conservation and emission reduction are achieved.

The invention relates to the technical field of park management, in particular to a smart park energy efficiency management system and method based on an IBMS. The method comprises the following steps: acquiring real-time park data; based on a preset energy efficiency prediction model, analyzing the real-time park data, and predicting energy efficiency change; analyzing the energy efficiency change based on the real-time park data, and determining the credibility of each energy efficiency change; and determining an energy efficiency management scheme according to the credibility. Energy distribution is optimized, and energy waste is reduced. The credibility evaluation model is helpful for identifying the reliability of the prediction result, and ensures that the prediction result is not trust blindly. By analyzing the equipment health degree index and the fault reason, the credibility of each energy efficiency change is more accurately judged, so that wrong scheduling caused by inaccurate prediction is avoided. And according to the credibility of the prediction result, a corresponding energy efficiency management scheme is generated, so that the accuracy and effectiveness of energy scheduling are ensured, and energy waste and equipment failure are avoided.

The invention discloses an air conditioner control method, device and equipment based on millimeter wave radar and a medium. The method comprises the following steps: acquiring position data and motion data of personnel in a space area in real time through a millimeter wave radar, and dynamically dividing the space area into a plurality of temperature control areas through a clustering algorithm according to the position data; according to the area of the temperature control area, the personnel density and the motion data, the thermal load capacity corresponding to the temperature control area is calculated through a preset thermal load prediction model; and air conditioner operation parameters of the temperature control area corresponding to the thermal load capacity are adjusted according to the thermal load capacity. The thermal load capacity of different temperature control areas is calculated through the thermal load model, the different temperature control areas can adjust the operation parameters of the air conditioner according to the corresponding thermal load capacity, in this way, each temperature control area can accurately meet the temperature requirement of the corresponding temperature control area, regional accurate dynamic regulation and control of the air conditioner are achieved, energy waste is reduced, and the energy efficiency is improved. And the comfort is improved.

The invention provides a variable-frequency control method and system for noise of an exhaust system of a transformer substation room based on a genetic algorithm, and the method comprises the steps: collecting the temperature and noise data of the transformer substation room in real time through a distributed sensor network, and building a multi-target optimization model through the genetic algorithm in combination with a variable-frequency speed regulation technology; and the noise and the temperature are used as constraint conditions to be jointly solved with an optimization target. The genetic algorithm dynamically adjusts the operation frequency of the exhaust fan through the operations of population initialization, fitness evaluation, selection, crossover, variation and the like, so that the requirements of temperature control and noise minimization are balanced. The rotating speed of the exhaust fan can be adjusted in a self-adaptive mode. Compared with a traditional single-machine fixed wind speed or simple linkage control method, the technology can remarkably improve the comprehensive energy efficiency of the system, reduce energy waste and improve the operation environment of the transformer substation room. The technology is suitable for noise and temperature control requirements of various types of indoor transformer substations and similar scenes, and has high applicability and popularization value.

The invention discloses a multi-energy coupling-containing optimal scheduling method, which comprises the following steps: S1, system modeling and basic data preparation: establishing models of renewable energy sources, energy coupling equipment and energy storage equipment, collecting and processing basic data, modeling a multi-energy coupling system, and obtaining a multi-energy coupling system model; comprising the links of production, transmission, conversion, consumption and the like of various energy sources, and the model can accurately reflect the dynamic characteristics and constraint conditions of the system; and S2, load prediction and demand analysis. According to the optimal scheduling method containing multi-energy coupling, complementarity and collaboration among different energy sources can be fully considered, and collaborative utilization of various energy sources is realized through an optimal scheduling strategy, so that energy waste can be reduced, and the overall utilization efficiency of the energy sources can be improved, for example, in an electric power, natural gas and thermal power integrated energy system, the optimal scheduling method has a wide application prospect. By coordinating the operation of the subsystems, the energy utilization efficiency can be maximized on the premise of meeting the requirements.

The invention discloses a smart energy consumption analysis method, and relates to the technical field of energy consumption analysis, and the method comprises the steps: obtaining the real-time energy consumption data of a plurality of energy devices; based on the spatial and temporal distribution characteristics of the energy consumption data, predicting the energy consumption of the equipment by using a time sequence prediction algorithm; an energy consumption prediction result is adjusted in combination with external variables, and a personalized energy efficiency analysis report is provided; based on an energy efficiency analysis result, proposing an optimization suggestion; real-time energy consumption monitoring is carried out, and abnormal energy consumption conditions are fed back and alarmed. According to the method provided by the invention, a comprehensive, intelligent and efficient energy efficiency analysis and optimization framework is provided for the smart energy system by integrating a plurality of innovative technologies, and the energy utilization efficiency can be effectively improved, the energy consumption can be reduced, the energy waste can be reduced, and the energy efficiency of the smart energy system can be improved by real-time data acquisition and intelligent analysis of a plurality of energy devices. Meanwhile, personalized energy efficiency reports and optimization suggestions are provided for users, and more intelligent energy management is realized.

The invention discloses a charging station flow intelligent regulation and control sharing system based on a big data model. The system comprises a data acquisition module which acquires charging station flow data, user behavior data, power grid load data and environment data in real time; the big data analysis engine is used for predicting future charging demand distribution based on a spatial-temporal feature extraction algorithm and a deep neural network model; the dynamic regulation and control module is used for generating a charging pile distribution strategy according to a prediction result and optimizing charging power distribution; the intelligent division module is used for dynamically calculating the division proportion based on the real-time load, the service priority and the partner weight; and the visual interaction platform is used for providing real-time monitoring and scheduling suggestions for an operator and a user. Through fusion of multi-source data prediction, a multi-target optimization algorithm and a dynamic division strategy, efficient distribution of charging resources and fair distribution of income are realized. The system significantly reduces the waiting time of the user, improves the utilization rate of the charging pile, reduces the energy waste, and is suitable for urban, highway and multi-region cooperation scenes.

The invention relates to the technical field of power loss, and discloses a power line loss analysis system and method for power transmission and transformation engineering. The specific method comprises: step one: acquiring collected data in real time, calculating and obtaining a real-time power line loss value according to the collected data, processing and analyzing the real-time power line loss value, so as to determine whether the real-time power line loss value is abnormal; step two: acquiring line temperature in real time, deducing a relationship formula between line temperature and real-time power line loss value according to historical data of line temperature and real-time power line loss value, drawing a temperature influence curve diagram based on the relationship formula, and obtaining a corresponding measure plan according to the temperature influence curve diagram; the power line loss situation can be reflected more accurately, and the accuracy and efficiency of power line loss analysis can be improved; it is helpful to reduce power line loss, reduce energy waste and environmental pollution, promote energy conservation and emission reduction and sustainable development, improve the power factor of the power system, and make the power system operate more efficiently and stably.

The invention relates to the technical field of boilers, in particular to a boiler container waste heat recovery device. The boiler container waste heat recovery device comprises a boiler assembly, a material raising and collecting assembly made of high-temperature-resistant materials, a waste heat recovery assembly and a plurality of cleaning and knocking assemblies, the boiler assembly comprises three L-shaped supporting rods, a boiler part, a smoke exhaust pipe and a collecting pipe, and the material raising and collecting assembly comprises a blocking part, a scraping part and a valve part. The waste heat recovery assembly comprises a conveying pipe and a water storage piece. The boiler part is used for heating water in the boiler part, the smoke exhaust pipe and the collecting pipe are used for exhausting smoke and impurities generated by fuel, the blocking part is used for filtering the smoke and the impurities generated by the fuel, the scraping part is used for scraping the impurities at the bottom of the blocking part and fixing fuel particles, and the waste heat recovery assembly can fully utilize heat exhausted by the boiler part, so that energy waste is reduced; and the multiple cleaning and knocking assemblies can sweep and knock the conveying pipe, so that the heat transmission efficiency is improved, and energy waste is reduced.

The invention provides an intelligent parking lot management system and method based on multi-dimensional data, relates to the field of intelligent management, and aims to understand the air quality change trend of an underground parking lot by performing time sequence analysis on CO concentration values, PM2.5 concentration values and PM10 concentration values of the underground parking lot at multiple time points, and decide whether to start a ventilation system or not based on the air quality change trend. Therefore, the air quality change in the parking lot can be mastered more accurately, ventilation measures can be taken in time, it is ensured that the air quality is maintained at the safety level, unnecessary operation of a ventilation system can be reduced, and energy waste can be reduced.

The utility model discloses a high-temperature-resistant flash valve exhaust nozzle structure which comprises a pipe body and an energy reduction structure. The energy reduction structure comprises at least two stages of ceramic energy reduction units which are connected in series, and the ceramic energy reduction units are connected in the pipe body; the ceramic energy reduction unit comprises a first-stage energy reduction assembly and a second-stage energy reduction assembly; the first-stage energy reduction assembly comprises a first ceramic spray pipe and a first liner pipe; the first lining pipe sleeves the outer side of the first ceramic spray pipe; the first liner tube is arranged in the tube body; the second-stage energy reduction assembly comprises a second ceramic spray pipe and a second liner pipe; the second lining pipe sleeves the outer side of the second ceramic spray pipe; the second liner tube is arranged in the tube body; the end of the first ceramic spray pipe abuts against the end of the second ceramic spray pipe. And a medium flows out of the pipe body after sequentially passing through the first-stage energy reduction assembly and the second-stage energy reduction assembly from the inlet end of the pipe body. According to the multi-stage energy reduction device, the multi-stage energy reduction assemblies connected in series are adopted, medium pressure can be decomposed into stage-by-stage release, and therefore the fluid speed is effectively reduced, and dependence on a high-temperature adhesive is also avoided.

The utility model provides a low-energy-consumption fused salt micro-channel heat exchanger performance testing platform supporting long-time operation. The platform comprises a fused salt loop, a steam loop and a heat conduction oil heat storage loop. Wherein the steam loop can perform primary steam energy recovery, is led out from a steam side outlet of a tested fused salt micro-channel heat exchanger, is sequentially connected with a steam pipeline of the fused salt preheater, a steam pipeline of the steam-water-oil heat exchanger and the steam-water electric heater, and then is connected into a steam side inlet of the tested fused salt micro-channel heat exchanger; wherein the fused salt preheater is connected to a pipeline between a low-temperature fused salt tank and a high-temperature fused salt tank in the fused salt loop, and recycled steam energy is used for heating low-temperature fused salt; the heat conduction oil heat storage loop can conduct secondary energy recovery, and residual steam energy in the steam-water-oil heat exchanger is further recovered into heat conduction oil. According to the test platform, high-temperature steam energy discharged by the test platform can be fully recycled and utilized, and long-time operation of the test platform is realized while energy waste is reduced.

The intelligent energy-saving fan operation control method comprises the steps that a plurality of sampling points are preset in a workshop, a reference light source is arranged on the opposite side of the sampling points, and the light source emits polarized light with preset intensity to penetrate through a dust area; acquiring environment image data of sampling points preset in a workshop; analyzing dust concentration data of the sampling points according to the environment image data; according to the position relation between each sampling point and the corresponding fan and the dust concentration index, the initial power of the corresponding fan is calculated through a distance weighting algorithm; and monitoring the dust concentration change rate of each region in real time, and dynamically adjusting the power of each fan by adopting a PID (Proportion Integration Differentiation) algorithm when detecting that the dust concentration change rate of the target region reaches a critical value. The dust concentration in the workshop is analyzed based on the environment image data, the fan power is dynamically adjusted, accurate response to dust concentration changes in different areas is achieved, and therefore the fan operation efficiency is improved, and energy waste is reduced.

The invention discloses an oil reservoir balanced injection and production parameter determination method based on multi-algorithm analysis. The method comprises the steps that S1, a balanced injection and production influence parameter simulation model is established through oil reservoir numerical simulation software; s2, obtaining a prediction model result through numerical simulation operation, screening out balanced injection and production influence parameters, and summarizing various factor rules of different reservoir conditions; s3, correlation analysis is conducted on the oil reservoir data; and S4, determining balanced injection and production main control factors and weights thereof under different reservoir conditions. The invention further discloses a system. The method comprises the following steps: performing correlation analysis on oil reservoir data, and determining main control factors and weights of balanced injection and production under different reservoir conditions, thereby comprehensively analyzing numerous influence factors, considering the influence factors under different reservoir conditions, accurately screening out core parameters influencing balanced injection and production, and then determining the weights of the core parameters. A key basis is provided for formulating a scientific exploitation scheme, the oil reservoir exploitation process can be effectively optimized, the recovery efficiency is improved, energy waste is reduced, and therefore the economic benefits of oil reservoir exploitation are improved.

The invention relates to the technical field of virtual power plants, and discloses a virtual power plant load prediction and optimization control method, which comprises the following steps: obtaining historical operation load data of a virtual power plant for characteristic decomposition to obtain a trend component, a seasonal component and a residual component, inputting the trend component, the seasonal component and the residual component into a load prediction model based on LSTM (Long Short Term Memory), and obtaining a load prediction value in a future set time period; calculating theoretical output of renewable energy sources to determine supply and demand conditions of energy supply and load prediction values; controlling energy storage charging or discharging and controlling the fossil fuel generator set to be started or stopped by combining the peak and valley conditions of the load prediction value; actual operation data of the virtual power plant are collected in real time, deviation meeting the predicted value is compared, and the energy storage charging and discharging strategy and the fossil fuel generator set output strategy are adjusted. According to the method, by accurately capturing load characteristics and cooperatively optimizing a multi-energy scheduling and dynamic adjustment mechanism, the operation stability and economy of the virtual power plant are improved, the energy waste is reduced, and the adaptability to a power system is enhanced.

The invention relates to the field of equipment control, in particular to a water tank penetrating air source self-adaptive adjusting method and system. The method comprises the following steps: acquiring a finishing mill start-stop signal and a wire rod head position signal in real time; dynamically calculating the residual distance from the head of the wire rod to the penetrating water tank, and generating a dynamic delay opening threshold value; the electromagnetic valve is controlled to open the pneumatic ball valve in a sectional manner; monitoring the covering length of the wire rod and converting the covering length into an equivalent gas resistance value; a dynamic pressure compensation amount is generated through a pressure closed-loop controller; the opening degree of the pneumatic ball valve is adjusted accordingly; when the finishing mill stops, the pneumatic ball valve is triggered to be closed, and a gradient pressure relief turn-off program is executed if necessary. By means of the distributed sensor array and the multi-mode recognition neural network, real-time accurate monitoring and analysis of the operation state of the pump set are achieved, dependence of post personnel is reduced, and the automation degree and stability are improved. And by dynamically optimizing the control strategy, the pump set system operates efficiently, energy waste is reduced, the energy utilization efficiency is improved, and the production cost is reduced.

The invention relates to the technical field of heat supply management, in particular to an intelligent heat supply management platform which comprises a demand prediction module, a strategy making module, a real-time adjusting module, a heat supply abnormity monitoring module, a heat supply efficiency evaluation module and a heat supply maintenance management module. According to the method, the heat consumption data and the external temperature data of the user are automatically analyzed, the platform is allowed to recognize the heat consumption mode, future demand changes are predicted, and real-time optimization of the heat supply adjustment direction is ensured, so that compared with a traditional method, the energy utilization efficiency is effectively improved, dynamic optimization of the heat supply amount and the heat supply time is achieved, and the energy utilization rate is increased. The reliability of the system is improved through real-time monitoring and automatic abnormality diagnosis, potential faults are responded quickly, energy waste is reduced, the operation cost is reduced, and the system can plan maintenance activities in advance, prevent sudden faults, prolong the service life of equipment and improve the quality of heat supply service by predicting future maintenance requirements.