711results about How to "Improve science" patented technology

The invention relates to a fault development gas storage operation upper limit pressure determination method considering an overpressure operation condition. The method comprises the following steps: 1, establishing a gas storage three-dimensional geomechanical model covering reservoir, cap strata and peripheral faults; 2, carrying out geomechanical simulation; 3, determining the shear failure resistance limit pressure bearing capacity and the tensile failure resistance limit pressure bearing capacity of the cover layer; 4, designing a gas logging permeability test experiment of the cover rock under different static confining pressures; 5, determining the gas breakthrough pressure of the cover rock under different pressure increasing coefficients; 6, determining the sealing limit pressure-bearing capacity of the capillary tube of the cover layer; 7, determining the anti-slip activation limit pressure bearing capacity and the anti-expansion fracture limit pressure bearing capacity of the fault; and 8, the operation upper limit pressure of the gas storage is determined. According to the method, the four-dimensional geomechanical model is established, the stress path of the reservoir building and gas injection process of the oil and gas reservoir is obtained through numerical simulation, and the dynamic horizontal principal stress of the reservoir building and gas injection process is calculated and determined by taking the determined reservoir top horizontal principal stress as the reference.

The invention relates to a geomechanical data visualization method, device and equipment and a medium thereof. The method comprises the following steps: acquiring original data containing rock mass structural characteristics and temperature and humidity environmental parameters, and completing grid division and load and displacement constraint boundary condition setting by adopting finite element analysis to obtain a rock mass stress distribution model; core mechanical parameter evolution of rock mass at different time nodes is simulated based on the model, and a dynamic stress distribution sequence is generated; identifying and marking stress overrun potential event association points through a threshold value, extracting a key time-space change trend, and inputting the key time-space change trend into the time-space dynamic model to obtain a risk prediction distribution diagram containing a risk level, a coverage range and an occurrence probability; and constructing a three-dimensional dynamic view by adopting visual rendering, analyzing a mechanical parameter full-cycle evolution path and geological event association mode, and outputting a three-dimensional dynamic result integrating multi-dimensional information. According to the method, the problems of static data presentation and event association deficiency in the traditional technology can be solved, and the scientificity and the high efficiency of geological engineering analysis are improved.

The invention discloses a Fluent-based numerical simulation and experimental design method for shaking of liquid in a tank of a tank truck, and belongs to the technical field of numerical simulation of fluid dynamics (CFD). The method includes the steps that the hole opening position of the wave-proof plate serves as an independent variable, multiple hole opening position schemes are designed, liquid shaking related indexes are determined to serve as dependent variables, a geometric model is established according to the actual structure of a tank truck tank body, a fluid mechanics model and boundary conditions are set, a solver is configured, output parameters and numerical simulation initial values are set, and an experiment is executed. The liquid shaking process of a certain swash plate trepanning scheme is simulated, acting force data, namely shaking index data, between liquid shaking and the inner wall of the tank body is recorded, the advantages and disadvantages of each trepanning position scheme are evaluated based on the shaking index data, and the optimal scheme is determined. According to the method, a real object experiment is replaced by CFD numerical simulation, parameter setting is carried out, the experiment cost and the experiment risk are remarkably reduced, the experiment efficiency is improved, and data support can be provided for industry design.

The invention relates to the technical field of concrete performance detection, and discloses a concrete condensation state prediction and maintenance method based on multi-sensor fusion. The method aims at solving the problems that an existing concrete condensation state judgment method is destructive and cannot reflect the real state in a structure in real time, and maintenance measures cannot dynamically adapt to actual working condition changes. The method comprises the following steps: deploying a multi-sensor combination in concrete to collect data in real time, pre-processing the data, inputting the pre-processed data into a long-short-term memory network model, and predicting a condensation state and early strength; and the maintenance equipment is automatically controlled according to the prediction result. According to the method, feedback optimization is also performed on the model by utilizing the final actual measurement intensity. Lossless and real-time monitoring and accurate prediction of the internal state of the concrete are realized, automatic and refined closed-loop regulation and control can be performed on the maintenance operation, and the scientificity and efficiency of construction management are improved.

The invention provides a diabetic patient life management method and system in combination with traditional Chinese medicine knowledge, and relates to the field of medical health data processing, the method comprises the following steps: obtaining multiple groups of sample data, the sample data at least comprising personal information of sample patients and diabetic traditional Chinese medicine syndromes, and the personal information of the sample patients at least comprising diabetes index data; based on the multiple groups of sample data, multiple key traditional Chinese medicine syndrome discrimination units are determined, and a diabetes traditional Chinese medicine syndrome diagnosis scale is generated; based on the multiple groups of sample data, determining music types matched with different diabetes mellitus traditional Chinese medicine syndromes; determining the diabetic traditional Chinese medicine syndrome of the current user based on the personal information of the current user and the filling information of the diabetic traditional Chinese medicine syndrome diagnosis scale; based on the diabetic traditional Chinese medicine syndromes of the current user and the music types matched with different diabetic traditional Chinese medicine syndromes, music, traditional Chinese medicine health maintenance skills and traditional Chinese medicine recipes are recommended, and the method has the advantage of improving the quality of life management of diabetic patients.

The invention relates to the technical field of flood control dispatching, and discloses a reservoir flood control water level dynamic control and flood recycling method and system, and the method comprises the steps: obtaining a surface rainfall forecast value of a reservoir basin based on real-time rainwater condition data and numerical weather forecast; a hydrodynamics and hydrology coupled drainage basin runoff model is driven, and an in-reservoir flood hydrograph in a predicted period is generated; judging the flood scale and grade based on the hydrograph, and when the flood is medium and small flood, calculating the dynamic flood control storage capacity of a downstream flood control object by adopting an equivalent flood control effect algorithm taking into account the pre-discharge scheduling capability; according to the dynamic flood control storage capacity, a dynamic flood control water level control value is obtained through backstepping of a water level-storage capacity relation curve of the reservoir; and finally, generating and executing a reservoir dispatching instruction based on the control value, and storing the water level of the reservoir to not exceed the dynamic value. The problems that a fixed flood control water level method is low in water resource utilization rate and inflexible in dispatching are solved, and flood resource efficient utilization on the premise of flood control safety is achieved.

This invention discloses an artificial intelligence-based method and system for the inspection and control of power distribution networks using unmanned aerial vehicles (UAVs). Relating to the field of UAV power inspection technology, the method includes: adjusting the UAV's pose in real time based on target perception feedback during the inspection process to actively align with the target and acquire images of power components; uploading valid images from the selected images to a cloud-based management platform; identifying power component entities in the selected images and constructing a spatial semantic graph based on the spatial distribution relationships between components; performing topological logic reasoning and feature consistency analysis on the spatial semantic graph to identify abnormal component states and determine the power logic attributes of faulty components; generating maintenance instructions based on the identified abnormal states and power logic attributes and pushing them to the terminal. This invention addresses the problems of high redundancy in original images, insufficient physical positioning accuracy leading to ambiguity in component logical identity identification, and difficulty in identifying long-tail atypical hidden dangers during power distribution network inspection.

The invention discloses a large-model-driven urban railway newly-opened station flow prediction method, and relates to the technical field of urban railway traffic operation management. The technical problems of low prediction precision, poor interpretability of a traditional model and insufficient reasoning flexibility in a complex urban scene caused by lack of historical passenger flow data of a newly opened station of an urban rail are solved. The method comprises the following steps: firstly, collecting pre-processed passenger flow related multi-dimensional data, and then constructing a multi-dimensional reference system to retrieve a target station similar reference or extract a macroscopic index reference; judging a scene category through a rule engine, calling a corresponding template, and filling information to generate a structured cue word; inputting the cue word into the large language model, and outputting a content prediction value and a result of a natural language reasoning chain according to the reasoning thinking chain template; and finally, checking the result, and completing optimization of the method based on feedback. The method has a good application prospect in the technical field of urban rail transit operation management.

The invention provides an environmental protection equipment energy consumption optimization and energy-saving regulation and control method based on multi-source data fusion, and relates to the technical field of environmental protection equipment energy-saving control, and the method comprises the steps: collecting the operation data of environmental protection equipment, carrying out the multi-scale feature extraction, recognizing the energy consumption abnormity through combining the energy efficiency curve reconstruction, and carrying out the energy-saving control of the environmental protection equipment; energy consumption sensitive parameters are extracted, energy-saving space is calculated based on load fluctuation, and an optimized parameter adjusting scheme and an optimized time sequence control sequence are generated by integrating the environment load change rate and the equipment thermal inertia coefficient. The energy consumption abnormity can be accurately identified, the energy-saving space is scientifically quantified, the intelligent optimization and dynamic regulation and control of the energy consumption of the environmental protection equipment are realized, and the operation efficiency of the equipment is obviously improved.

The invention provides a device for simulating the vertical migration rule of oil and gas field ground pollutants and a simulation method, and relates to the technical field, the device comprises a soil column leaching module, a spraying module and an underground water control module, the soil column leaching module and the underground water control module are both arranged in an underground foundation pit, and the soil column leaching module comprises a soil column experiment device; a sensor device is inserted into the soil column experimental device, the sensor device is electrically connected with the data acquisition device, the soil column experimental device is provided with a soil sampling window, the spraying module comprises a spraying pipe, one end of the spraying pipe is communicated with the reservoir, and the other end of the spraying pipe extends above the soil column experimental device. On the basis that only surface water is concerned to drive pollutant migration originally, the underground water control module and the water supply Markov bottle are additionally arranged, and fluctuation of the underground water level can be achieved; long-term and continuous in-situ monitoring of soil moisture in-situ data can be achieved in real time, non-destructive collection of samples can be achieved, and the research quality of the vertical migration rule of oil and gas field ground pollutants is improved.

The application discloses a kind of coating line adaptive spraying optimization system and method, wherein, perception module is used to collect the geometric shape information and surface characteristic information of target workpiece and environmental information;Data processing module is used to extract workpiece characteristics based on geometric shape information and surface characteristic information, and generates spraying path and spraying parameter in combination with environmental information;Control module is used to generate spraying control instruction based on optimized spraying path and spraying parameter, to control execution unit to carry out spraying operation;Feedback module is used to monitor spraying quality data obtained by spraying operation in real time, and transmit it as feedback information to data processing module.By the application, the collective characteristics of workpiece are extracted by collecting the geometric shape and surface characteristic information of workpiece, and spraying path and parameter are generated in combination with environmental information, to adapt to diversified workpieces, greatly shorten the spraying time and optimize the paint distribution, and further optimize the spraying process through feedback information.

The present application relates to a kind of occupational hazards detection report automatic generation management system, specifically to artificial intelligence technical field, by constructing the dynamic evolution of industry knowledge graph, depth mining across enterprise, across time's common high-risk mode;When generating new detection report, current task can be actively matched with historical risk mode and risk transmission evaluation, automatically generate and embed the risk early warning and control suggestion text with foresight, so as to realize the change from passive record to active early warning, significantly improve the automation level of report generation, the intelligent degree of risk identification and the scientificity and foresight of occupational hazards management.

This application relates to the field of power systems, specifically to a method and system for assessing the distributed photovoltaic (PV) absorption potential of smart grids. The method includes: calculating the coefficient of variation and its difference from the global value based on the absorption power of smart grid monitoring nodes and meteorological temperature to obtain relative fluctuation difference characteristics; obtaining temperature-power offset characteristics through the linear fitting residual of the coefficient of variation and meteorological temperature; calculating the offset under high-temperature and non-high-temperature environments and subtracting the offset to obtain correlation difference characteristics, thus constructing absorption power sensitive characteristics; constructing seasonal difference characteristics based on seasonal characteristic fluctuations and the proportion of high-temperature days; and introducing the two sensitive characteristics into the objective function of a multi-objective optimization algorithm to solve for the optimal node configuration capacity under constraints with a power flow over-limit reduction mechanism. This application aims to improve the accuracy of PV absorption potential assessment results.

The application discloses a kind of urban distribution network form evaluation method, device and power system.Therein, the method comprises: from the storage unit of distribution network power system, obtain the preset form evaluation index system corresponding to urban distribution network;Control the communication unit of distribution network power system receives the urban distribution network data uploaded by terminal equipment communicated with distribution network power system, and the urban distribution network data is obtained by terminal equipment according to the data requirement of each three-level index in preset form evaluation index system Collection;Control the processor in distribution network power system according to urban distribution network data, each three-level index is quantitatively calculated, and the original value of each three-level index is obtained;Control the weight corresponding to each three-level index original value respectively according to multiple indexes, determine the form evaluation result of urban distribution network.The application solves the technical problem that new type distribution network multidimensional form characteristics is difficult to fully depict in related art, caused by incomplete evaluation.

The application provides a die cutting and hot stamping equipment intelligent state monitoring and fault diagnosis system and method, and relates to the field of intelligent monitoring.The method comprises the following steps: collecting working parameters of multiple key parts of the die cutting and hot stamping equipment, constructing a time sequence collection window, slidingly collecting the working parameters, and obtaining characteristic information reflecting the equipment state; based on the characteristic information, constructing an anomaly detection model, performing anomaly detection on the equipment state, obtaining an anomaly score, and judging whether the equipment state is abnormal according to the anomaly score; for the characteristic information judged as abnormal, constructing a fault diagnosis model based on the fault type to which the characteristic information belongs, performing fault diagnosis on the equipment state, and generating a diagnosis result; and generating a comprehensive diagnosis and operation and maintenance decision report according to the diagnosis result.The application realizes comprehensive perception of the internal state of a closed host through multi-sensor collaborative monitoring and dynamic time sequence collection, breaks through the limitations of traditional monitoring, and provides accurate data basis for early fault warning and predictive maintenance.

The present application belongs to the technical field of data analysis, and particularly relates to a high-throughput biosensing system for early warning of algal blooms and pathogens, which comprises multiple sampling units, a microfluidic chip with a multi-channel structure, and a high-throughput surface-enhanced Raman detection unit with an integrated metal nanostructure surface, and obtains the original surface-enhanced Raman spectrum of the water sample through a Raman spectrum acquisition mechanism. The system uses compressed spectrum sampling and sparse reconstruction methods to reduce the data volume and maintain the integrity of the spectrum information, constructs a pathogen Raman fingerprint dictionary through a dictionary learning method, and realizes the identification of different algal metabolites, algal toxins and pathogen-related molecular components and the concentration interval judgment thereof in combination with a sparse representation classification mechanism. Based on the type and concentration interval of the pathogens, the system further generates algal bloom risk warning and pathogen risk warning, and realizes early discovery of abnormal changes in the water body. The present application has significant advantages in stability, sensitivity, throughput and real-time performance.

The invention discloses a multi-source collaborative dynamic time sequence TCP prediction method and system, a medium and a program product, and the method comprises the steps: collecting multi-source heterogeneous data, including DVH data, CT data, CPP data and longitudinal follow-up data of a plurality of time nodes, of a patient in a whole radiotherapy period; performing hierarchical preprocessing and data format unification on the multi-source heterogeneous data; constructing a dynamic time sequence feature fusion matrix containing a time dependency relationship; constructing a deep learning model integrating a time sequence processing unit and a cross-modal fusion unit, wherein the model supports an incremental learning iteration updating mechanism; using the model to output TCP prediction results of a new patient at different time nodes; and if the new patient is a special case, calling the clinical rule adaptation model, and correcting the TCP prediction result by adopting a mixed correction strategy combining rule matching and doctor experience weight. According to the method, multi-source heterogeneous data can be deeply fused, time dynamic association is captured, modal feature contribution degree is quantified, continuous iterative updating is supported, and individual differences are adapted.

This invention relates to an intelligent decision support system for rectal cancer prognosis integrating digital pathological images, belonging to the field of medical image processing technology. The system includes a data acquisition module for collecting multiple sets of sample data; a feature selection module for identifying multiple key medical imaging features of rectal cancer based on the multiple sets of sample data; a model building module for acquiring a pre-trained deep learning model and constructing a rectal cancer prognosis prediction model based on transfer learning, the pre-trained deep learning model, the multiple sets of sample data, and the multiple key medical imaging features of rectal cancer; an image acquisition module for acquiring pre- and post-operative medical images of the rectal cancer patient to be evaluated; and a recurrence prediction module for predicting the recurrence probability of the rectal cancer patient to be evaluated based on the pre- and post-operative medical images of the patient using the rectal cancer prognosis prediction model. This system has the advantage of improving the accuracy of non-invasive prediction of postoperative recurrence of rectal cancer.

The invention belongs to the technical field of coal mine safety monitoring, and discloses a coal mine tunnel roof disaster real-time early warning system and method. Comprising the following steps: performing time sequence reasoning on monitoring data acquired in real time, respectively judging whether the key physical quantity of the roof triggers primary early warning or secondary early warning by acquiring an expected value prediction curve and an uncertainty boundary curve of the key physical quantity of the roof in a future time step, and acquiring corresponding early warning information when the early warning is triggered; constructing a dynamic causal graph, inputting the early warning information and the currently acquired key physical quantity into the dynamic causal graph for reasoning, calling candidate intervention measures based on the acquired current state node to perform anti-fact reasoning in the dynamic causal graph, and acquiring a risk evolution trajectory and a risk suppression efficiency index of the candidate intervention measures; and the risk suppression efficiency indexes are sorted and preferentially selected, and a causal decision early warning report containing the optimal intervention scheme is output, so that the scientificity and effectiveness of risk suppression are improved.

The invention provides a parameterization calculation method for a top cover modal and a pitch diameter of a water turbine, and belongs to the technical field of hydraulic mechanical structure dynamics and simulation automation. The method comprises the steps of parameter input, parametric modeling, automatic simulation control, pitch diameter identification and analysis and result output: after a parameter set is input, constructing a parametric modeling module based on Python and ANSYS Spaceclaim, and automatically generating a three-dimensional geometric model containing a basic panel, a rib plate and a connecting hole; an automatic simulation control module is constructed based on Python and ANSYS Workbench, and full-process automatic simulation is completed to obtain a modal analysis result; pitch diameter characteristic parameters are automatically recognized through a least square discrete Fourier transform algorithm of non-equiangular sampling and confidence degree judgment; and outputting results to form a standardized database. According to the method, automation and quantification of the whole process are achieved, modeling and simulation efficiency is improved, manual misjudgment is avoided, the method is suitable for different types of water turbine top covers, and reliable data support is provided for structural optimization and resonance risk assessment.

This invention provides a perception fusion arbitration method and system based on vehicle-to-everything (V2X) communication. The method includes: acquiring a ground truth model of the perception system for the road environment and meteorological information; acquiring perception information and data quality parameters from each sensor in the vehicle-mounted perception system; retrieving the influence coefficients of each sensor corresponding to the meteorological information and calculating the perception quality score of each sensor based on the data quality parameters to determine the optimal sensor; selecting candidate arbitration ratios from a pre-set perception fusion arbitration ratio case library; performing simulated fusion calculations on the perception information; comparing the matching degree of each fusion result with the ground truth model of the perception system to determine the optimal arbitration ratio; and sending the result to the vehicle controller. This invention achieves adaptive output of the optimal vehicle-mounted sensor fusion weight ratio according to the specific meteorological environment, significantly reducing the perception misidentification rate in adverse environments such as rain, snow, fog, and sandstorms, ensuring the perception prediction accuracy of the autonomous driving system in all weather conditions.

The invention relates to the technical field of water treatment, discloses an intelligent flocculant adding method and system based on water quality dynamic data, and aims to solve the technical problems that an existing adding mode is extensive, lagged in control and difficult to adapt to dynamic water quality fluctuation. The method comprises the following steps: collecting turbidity and flow data of a water inlet end; carrying out feedforward prediction by utilizing a long-short-term memory network model, calculating a theoretical dosage and driving a metering pump to execute; and the effluent turbidity is monitored in real time, and feedback correction is performed by using a PID algorithm. The system comprises an online monitoring unit, an intelligent control unit, a precise adding execution unit and an effect feedback unit. Through composite control of feedforward prediction and feedback correction, advanced and accurate addition of the flocculant is realized, the drug consumption cost is reduced, the impact load is effectively dealt with, and the stability of the effluent quality and the intelligentization of system operation and maintenance are ensured.

The application discloses a sending-end water and electricity and receiving-end new energy collaborative operation method and system, medium and equipment, and the method comprises the following steps: constructing a sending-receiving end integrated annual operation simulation model, the sending end contains cascade hydropower stations, and the receiving end contains thermal power, new energy, energy storage, AC and DC power grids. Taking the minimization of the annual total operation cost as an objective function, considering the real-time power balance, the water quantity and storage capacity of the cascade hydropower stations, the operation of the thermal power unit, the transmission capacity of the cross-regional DC power transmission, the configuration of the rotating reserve capacity and other constraints. Through power flow analysis and reserve evaluation, the redundant constraints are removed and the model is reduced. A parallel computing strategy based on time sequence decomposition is adopted, the problem is decomposed into sub-problems according to weeks or months, and the annual optimal collaborative operation strategy is obtained by parallel solving. The annual operation of the sending-receiving end can be simulated in detail, the optimization can be solved quickly, the collaborative optimization strategy for adaptively coping with extreme scenarios can be provided, and the safety, economy and clean energy consumption level of the cross-regional power grid under high proportion of new energy access can be improved.

The present application relates to the technical field of smart park management and intelligent traffic, in particular to a smart park parking decision optimization method and system, comprising the following steps: S1: collecting multi-dimensional original data related to park parking through multi-source sensing equipment, and preprocessing the collected original data. Through the weighted feature fusion and coarse-fine granularity adaptive prediction model system, the present application dynamically optimizes the prediction accuracy combined with the online learning mechanism, accurately outputs the multi-time and space granularity parking demand and traffic situation results, provides reliable data support for decision making, and according to the cloud-edge-end collaborative architecture and high-fidelity digital twin simulation environment, through the hierarchical dynamic multi-objective optimization algorithm and iterative verification mechanism, generates the optimal parking scheduling and traffic guidance strategy that meets the constraints of complex scenes and takes into account multiple demands, greatly improves the scientificity and feasibility of decision making.

The present application relates to the technical field of data processing and business intelligence, and discloses a multi-dimensional data driven self-brand growth value prediction method, which extracts a main feature vector of a previous time step, calculates a dynamic weight for a same-product white label control group to generate a dynamic benchmark, and accordingly peels off a pure brand-driven incremental index, and combines a repeat purchase decay rate and a search index to construct a three-dimensional state vector; through eigenvalue decomposition on a state covariance matrix, a current main feature vector and a maximum eigenvalue are obtained, and the main feature vector is fed back to a weight calculation link to form a closed loop; finally, the decomposition result and a historical state sequence are respectively input into a pre-trained machine learning model to output a brand life cycle stage and a future brand asset valuation. The present application accurately anchors a reference system through a dynamic feedback mechanism, and realizes comprehensive and accurate diagnosis and prediction of brand value.

The invention discloses a riverbed type plant dam section anti-sliding stability safety coefficient prediction method, which comprises the following steps of: acquiring actually-measured deformation data of a plurality of monitoring points of a riverbed type plant dam section in a time interval, and eliminating an aging component in the actually-measured deformation data as a monitoring value; substituting the time-varying parameter inversion target function to obtain the time-varying parameter of the dam section rock mass in the time interval through inversion; in the time interval, finite elements are adopted to calculate the anti-sliding stability safety coefficients of the dam section under multiple groups of different upstream water levels, downstream water levels and uplift pressure reduction coefficients; repeating all the previous steps in a plurality of continuous time intervals, and then training the SVR model by adopting the upstream water level, the downstream water level, the uplift pressure reduction coefficient, the time-varying parameter and the anti-sliding stability safety coefficient of all groups to obtain a prediction model; and obtaining the upstream water level, the downstream water level, the uplift pressure reduction coefficient and the time-varying parameter of the river bed type plant dam section at the monitoring moment, and inputting the prediction model to obtain the anti-sliding stability safety coefficient at the monitoring moment.

The invention relates to the technical field of beverage preparation, in particular to a processing method of a functional beverage containing embryo elements, which comprises the following steps: separating corn germs from corn kernels, drying the corn germs, and performing microwave treatment at preset microwave power to obtain inactivated corn germs; when it is judged that preparation of the inactivated corn germs does not meet a preset standard according to the oxidation risk characterization value, preset microwave power is increased or the inactivated corn germs are abandoned; the method comprises the following steps: crushing and breaking walls of inactivated corn germs meeting preset standards to obtain germ powder, and performing enzymolysis treatment on the germ powder to obtain a corn embryo base solution; when it is judged that the preparation of the embryo base solution does not conform to a preset standard according to the curve goodness of fit, the reason that the preparation of the embryo base solution does not conform to the preset standard is determined according to the absorbance; and diluting, homogenizing and sterilizing the corn embryo base solution to prepare the functional beverage containing the embryo. The processing efficiency of the functional beverage is improved.

The invention provides a clearing scheduling method, system, equipment and medium for a pumped storage unit participating in a spot market in consideration of water head influence, and the method comprises the steps: analyzing the actual operation data of a pumped storage power station, and respectively constructing a relation model of the maximum pumping and generated output of the unit and a water head; determining a water head value of the unit in the time period t, and calculating the maximum water pumping output and the maximum power generation output of the unit in the time period t through the relation model; obtaining declaration information that the unit participates in the spot goods electric energy market, making a plurality of constraint conditions by using the calculated maximum output, carrying out spot goods electric energy market clearing operation based on the plurality of constraint conditions and the declaration information, and obtaining an output plan of a t + 1 time period; and performing loop operation to obtain an output plan curve of the unit on the operation day. The method ensures that the output result accords with the actual operation condition of the pumping and storage unit, improves the accuracy of the output calculation result and the clearing optimization result of the unit, and guarantees the execution reliability of the pumping and storage output plan.

The application discloses a hydropower station time-sharing power generation benefit dynamic control method fusing power grid load demand, obtains multi-dimensional operation data of a hydropower station and power grid time-sharing load and electricity price data, constructs a data correlation model based on an attention mechanism, generates a fusion feature data set through dynamic weight distribution, takes time-sharing power generation benefit maximization as an objective function, combines reservoir water balance, unit output limitation and downstream ecological flow constraints, introduces a deep reinforcement learning algorithm to construct a dynamic control model, trains the model and optimizes parameters by using historical and simulation data, then inputs real-time data to generate an optimal time-sharing power generation control scheme, issues an instruction and monitors output deviation, and realizes dynamic control; the method can accurately match power grid load and time-sharing electricity price, improves power generation benefit while meeting multiple constraints, and is suitable for various hydropower station dispatching scenarios which need to consider power grid response and benefit optimization.

This application relates to a supplementary lighting system and method for climate chambers, belonging to the field of artificial climate chamber technology, to solve the problems of low accuracy in judging lighting needs and poor effectiveness in utilizing natural light in existing climate chamber supplementary lighting. The method includes: collecting signals of natural light intensity, incident direction, and photosynthetically effective spectral distribution; constructing a comprehensive lighting judgment value to determine the lighting needs; comparing the incident direction with the optimal light-incident direction of the light guide plate and initiating the rotation adjustment of the tracking light beam; and combining the transmission effect feedback calibration of the fiber optic light guide system. This application achieves accurate judgment of lighting needs and efficient utilization of natural light through the synergy of multi-dimensional signal acquisition, comprehensive judgment, directional light tracking, and transmission calibration, solving the problems of poor adaptability and high energy consumption caused by single-parameter detection and fixed light guides in traditional supplementary lighting.