261 results about "Control factor" patented technology

The invention relates to a reservoir porosity and permeability prediction method based on a dynamic committee integration model, and the method comprises the following steps: obtaining an original data set which comprises shale content, porosity, permeability, GR, AC, CNL, DEN, RT, RXO, SP, CALC, CALI, depth and lithologic labels; performing data enhancement on the original data set; determining main control factors influencing the porosity and the permeability; constructing a dynamic committee integration model; inputting the main control factors into a dynamic committee integration model; and utilizing the trained dynamic committee integration model to respectively predict the porosity and the permeability. According to the method, the logging data is processed by adopting a machine learning method, reservoir parameters can be efficiently and accurately predicted, and favorable support can be provided for oil-gas exploration and development; the dynamic committee integrated model constructed by the invention can dynamically adjust the weight of each model according to different geological conditions and data features, can more flexibly adapt to the geological condition of a research area compared with a single model, and improves the prediction precision and generalization of the model.

The invention discloses a dynamic efficiency optimization management and control method for a wind-light hydrogen storage system, which comprises the following steps of: acquiring operating parameters of an electrolytic bath based on a water electrolysis hydrogen production simulation framework of an alkali liquor medium, and analyzing joint probability distribution and correlation coefficients of historical wind-light data through a wind-light photovoltaic synergistic effect model; constructing a system load accommodating setting model; when the frequency or voltage fluctuation of the power grid is detected, starting a quasi-synchronous electric element regulation and control framework PSEERA, and adjusting the output power through a droop control equation; the comprehensive performance indexes are optimized by adopting a Ling-tuned chaos infinitesimal search (SACMESA) algorithm, and main control factors are extracted through a multi-element optimization evolution (BKEAMOEA) algorithm analyzed according to key elements to carry out multi-objective optimization; implementing a scattered point type cooperative control strategy including bottom-layer equipment control, middle-layer energy management and upper-layer system scheduling, and realizing cross-level dynamic optimization; the peak regulation and frequency modulation capability, the safety and the stability of the power grid are remarkably improved.

The invention discloses an intelligent hydraulic engineering flood discharge anti-seepage dam safety assessment method and system. The method comprises the steps of data collection, data preprocessing, dam safety assessment model construction, dam safety assessment model parameter search and dam safety real-time assessment. The invention belongs to the technical field of safety evaluation, and the scheme comprehensively considers the mean value, the standard deviation and the similarity of weight distribution, designs a screening threshold and combines dam monitoring subsets, trains a weak evaluator, adjusts the weak evaluator based on an error rate and calculates a weight, introduces category weighting and historical performance to update data weight, and improves the safety of dam monitoring. Combining the weak evaluators based on the weights to obtain a dam safety evaluation model; a first control factor is designed based on the dynamic position correlation coefficient, a second control factor is designed based on the search stage, the individual position is updated according to the random number and the control factors, the optimal parameter is determined, effective integration and analysis of dam monitoring data are achieved, and the accuracy and applicability of dam safety assessment are improved.

The invention relates to the technical field of grid-connected inverter control, and discloses a grid-connected inverter model predictive control method based on a neutral-point potential fluctuation control factor, in the process of controlling a grid-connected inverter by using model predictive control, firstly, an SVPWM method and the model predictive control method are combined, the design of an objective function is simplified, and the model predictive control factor is optimized; the optimal switch sequence can be selected only by calculating the midpoint potential deviation; secondly, neutral-point potential balance control is realized in spatial modulation, a large sector is further divided into a plurality of small sectors, the position of the small sector where a reference voltage vector is located is determined, a switch sequence is selected from the determined small sectors for traversal, the traversal frequency is reduced, and the calculation efficiency is improved; and finally, designing a midpoint potential control factor, and greatly reducing the content of high-amplitude common-mode voltage on the premise of ensuring the balance of the midpoint potential. Moreover, the overall control structure is clear and easy to implement, and the dynamic performance and the steady-state tracking performance of the controller are both improved.

The invention discloses a shale gas well dessert prediction method and device based on integrated learning. The method comprises the steps of obtaining main control factors and feature data of a shale gas well to be predicted, and inputting the main control factors and the feature data into a trained dessert prediction model to obtain dessert prediction data; the model training process comprises the following steps: determining main control factors influencing the dessert according to the correlation between each parameter in the prediction data of the sampled shale gas well in the target area and the dessert; the prediction data comprises first data and second data, the first data comprises geological data, perforation data and oil and gas production data, and the second data comprises logging data and fracturing construction data; performing feature extraction on each parameter in the second data to obtain feature data; constructing a training data set according to the main control factors and the characteristic data of the shale gas well; and training the dessert prediction model based on the training data set. The accuracy of a prediction result can be improved, and fracturing design is effectively guided.

The invention provides a shale oil fracturing effect evaluation method and device, a terminal and a storage medium, and belongs to the technical field of petroleum engineering. According to the method provided by the embodiment of the invention, the fuzzy comprehensive evaluation method is utilized to perform fuzzy processing on the basic parameters such as the target reservoir parameters and the target engineering parameters, so that the values of the basic parameters are more representative and are not certain values, and therefore, an evaluation result close to the reality can be obtained, and the accuracy of a calculation result is improved; and optimizing the shale oil fracturing effect evaluation method. Moreover, by fitting the function model of the main control factors and the yield and utilizing the function model to evaluate the fracturing effect, the model construction process is simplified, and the evaluation efficiency is higher.

The invention discloses a warehouse capacity control method and device, equipment, a storage medium and a computer program product, and relates to the technical field of warehouse management, and the method comprises the steps: monitoring the warehouse capacity use condition; when the warehouse capacity use condition reaches the capacity expansion and contraction control condition, automatic capacity expansion and contraction of the warehouse are carried out according to a pre-designed warehouse automatic capacity expansion and contraction algorithm, and the warehouse automatic capacity expansion and contraction algorithm is determined based on warehouse capacity control factors. The capacity of the warehouse is expanded and shrunk through the automatic capacity expansion and shrinkage algorithm of the warehouse, the capacity management step of the warehouse can be simplified, and the management efficiency is improved.

The invention discloses a ship design optimization method and system based on equipment collaborative operation data analysis, and aims to optimize breeding auxiliary ship design. The method comprises the following steps: firstly, acquiring historical operation data and swing coefficient data of a breeding auxiliary ship under different main control factors, and performing performance evaluation; analyzing the main control factor and a performance evaluation result, and determining the influence of the main control factor on the ship performance; then, according to the breeding scale data of the target breeding place, fish technology and fish art equipment matching is carried out, and equipment information is obtained; determining required ship performance demand data according to the equipment information; and finally, in combination with the wind wave change characteristic data, the main control factor-performance influence data and the ship performance demand data, optimizing and determining the design parameters of the ship in the target farm. According to the method, through equipment collaborative analysis, the accuracy and adaptability of ship design are improved, the requirements of different breeding environments are met, and the working efficiency and stability are improved.

The invention relates to the technical field of welding, and particularly discloses an anti-fatigue welding process for a long-distance pipeline supporting steel structure. The process comprises the steps that a font groove structure is determined, and pre-welding polishing pretreatment and layered welding are conducted; fatigue samples of weld toe areas with different arc radiuses are collected, crack feature coordinates and stress concentration coefficients are extracted, failure modes are divided based on clustering, the arc radiuses are verified as main control factors of fatigue failure through multiple linear regression, and a core control range is determined by combining process capability indexes; carrying out fatigue verification on the basis of core parameter extension radius gradient, carrying out process compatibility matching in combination with a controllable interval and a thermal deformation interval of equipment, and outputting a radius candidate value through a response surface method; on-site construction constraints are collected, laboratory path space compatibility is analyzed, a formed track is optimized in a segmented mode, and constraint compensation factors are embedded. The anti-fatigue performance of the weld toe area of the supporting steel structure can be improved, and quality guarantee is provided for welding of the long-distance pipeline supporting steel structure.

The invention provides a regulation and control method and device suitable for a distributed electrochemical energy storage power station, and can be applied to the technical field of energy storage and power supply regulation and control. The regulation and control method comprises the following steps: determining the distance between each of a plurality of energy storage power stations in the distributed electrochemical energy storage power station system and a target power utilization area; based on the distance between the energy storage power stations and the target power utilization area, determining respective position regulation and control factors of the plurality of energy storage power stations, the position regulation and control factors representing the electric energy loss degree of the transmission distance; based on the respective charge state parameters of the plurality of energy storage power stations, determining respective state regulation and control factors of the plurality of energy storage power stations, the state regulation and control factors representing the influence degree of the residual capacity of the battery on power supply; and on the basis of the power consumption demand data of the target power consumption area, the position regulation and control factors of the multiple energy storage power stations and the state regulation and control factors of the multiple energy storage power stations, power supply of the multiple energy storage power stations is regulated and controlled, and power supply distribution data of the multiple energy storage power stations are obtained.

The invention relates to an ultra-deep well perforation test combined operation tubular column safety evaluation method. The method comprises the following steps that S1, a packer lower portion perforation detonation annulus pressure evolution finite element model and a dynamic negative pressure evolution numerical model after perforation detonation are established; s2, establishing a perforation instant tubular column coupling impact vibration evolution model, and determining main control factors; s3, establishing a dynamic evolution numerical model of the shock absorber at the moment of perforation, and obtaining a dynamic evolution model of the lower oil pipe of the packer, the shock absorber and the perforating gun at the moment of perforation; and S4, evaluating the safety of the pipe column at the lower part of the packer by taking the Mises yield criterion as an evaluation standard. The influence of perforation detonation transient wellbore pressure and dynamic negative pressure on a lower tubular column system of the packer is fully considered, the dynamic response rule of the tubular column is disclosed through numerical simulation and theoretical analysis, risk control measures are put forward, and the safety of the packer is improved. Theoretical basis and technical support are provided for optimal design of an ultra-deep well perforation test combined operation scheme and safety of an oil testing process.

The embodiment of the present invention discloses a method and system for predicting mud loss before drilling based on the analogy of main control factors, which analyzes the data of the loss area, determines the main control factor set affecting the loss and the mud loss amount at the loss position; obtains the correlation between each factor in the main control factor set and the mud loss amount through correlation analysis; obtains the value of each main control factor in the area to be predicted, and obtains the mud loss amount prediction result of the area to be predicted based on the obtained correlation between each factor and the mud loss amount. The mud loss prediction before drilling in complex well conditions is realized, and the formation is predicted for well loss before drilling, which provides guidance for drilling, avoids the drilling loss layer section, and improves the drilling success rate.

The invention, which relates to the technical field of temperature control, discloses a temperature regulation and control system comprising a target making module, a comprehensive monitoring module, a central decision module, an optimization control module and a temperature control execution module. The target formulating module generates a real-time target temperature table by considering the environmental parameters based on the environmental correction strategy; the comprehensive monitoring module obtains and feeds back a real-time monitoring parameter table; the central decision-making module considers the environmental parameter change and the real-time temperature change of each key component based on a progressive change perception strategy to decide whether to update a target thermometer or carry out temperature regulation and control; the optimization control module adopts a grey wolf optimization fuzzy PID algorithm to optimize a control factor, and obtains a real-time optimal temperature control power adjustment scale; and the temperature control execution module starts temperature control equipment through a conventional temperature control power meter, receives an adjustment execution instruction and updates real-time temperature control power, so that multi-component cooperative temperature regulation and control which dynamically cope with the environment and adjust the total power to be minimum is realized.

The invention discloses an anti-icing testing method, device and equipment for a super-hydrophobic surface structure of energy equipment and a storage medium, and relates to the technical field of super-hydrophobic surface testing. Designing a super-hydrophobic surface structure based on the environmental main control factor to obtain a structure parameter set; simulating the super-hydrophobic surface structure by using an environment load condition determined based on the environment working condition matrix and a structure parameter set to obtain an equipment critical vibration frequency, and determining a current surface structure morphology based on the structure parameter set and the equipment critical vibration frequency; and determining a morphology simulation error based on the current surface structure morphology and the actual surface structure morphology of the super-hydrophobic surface structure, and monitoring the super-hydrophobic surface structure based on the morphology simulation error, the structure parameter set, the equipment critical vibration frequency and the preset control mechanical excitation frequency to obtain the ice growth rate and the ice adhesion work. The efficiency of anti-icing testing of the super-hydrophobic surface structure is improved.

The invention provides a three gorges reservoir area landslide stage division and main control factor identification method and system. The method comprises the following steps: establishing an initial factor set according to geological features and monitoring data of a landslide; optimizing the initial factor set twice to screen out a core factor set; the influence of each core factor on landslide deformation is researched, a multivariate stepwise regression model considering aging, rainfall and reservoir water level is established, the landslide deformation process is decomposed according to each component, calculation formulas of each component are listed in sequence, and a final multivariate stepwise regression model is obtained; analyzing landslide deformation curves in different time periods, identifying stage turning points in combination with evaluation indexes, and accurately dividing landslide deformation stages; and analyzing primary and secondary influences of internal and external influence factors on the landslide in different stages by comparing the fitting variable amplitude value with the actually measured variable amplitude value, and determining the main control factors of each stage and the current main control factor according to the change of the proportion of each component in each stage. According to the invention, landslide main control factors in different stages can be identified.

The invention relates to the technical field of railway earthquake safety, in particular to a rail-bridge system earthquake resistance evaluation method, a medium and equipment. The method comprises the following steps: establishing a finite element model of a track-bridge system; carrying out nonlinear time-history analysis on the track-bridge system under different working conditions; analyzing control factors of earthquake response of the track-bridge system; establishing a mapping relation between the seismic response of the track structure and the seismic response of the main beam; calculating the earthquake response of the main beam, and evaluating the earthquake displacement response of the track structure; and the anti-seismic evaluation of the track-bridge system is realized. Through the method, the method can be used for all high-speed railway bridge types and rails at present, the anti-seismic evaluation of the rail-bridge system based on the driving performance can be indirectly and quickly realized based on the mapping relation, a large amount of time-consuming and labor-consuming train-rail-bridge system finite element simulation calculation does not need to be carried out, and the calculation efficiency is improved. The efficiency is effectively improved, the cost is reduced, the calculation is simple and convenient, and the calculation result is accurate.

The invention discloses a coal mine high-energy mine earthquake prediction and main control factor judgment method based on machine learning. The method comprises the following steps: acquiring micro-earthquake data, geological data and mining data during mining of a working face, and determining indexes corresponding to various types of data; constructing a plurality of machine learning models and carrying out model training; performing comprehensive evaluation on each model through a set prediction index, and obtaining an optimal model; and explaining a machine learning model prediction result by adopting an SHAP method, and sorting various prediction indexes by taking the size of an SHAP value as a main control factor judgment standard to obtain a main control factor. The method can comprehensively analyze the factors causing the mine earthquake, improves the accuracy of mine earthquake prediction, and guarantees the safe mining of a coal mine and the precise prevention and control of the mine earthquake.

The invention provides a borehole trajectory anti-collision scanning method and device, electronic equipment and a storage medium, and belongs to the technical field of borehole detection.The method comprises the steps that first inclinometry data and first borehole trajectory calculation data of a reference well and second inclinometry data and second borehole trajectory calculation data of a comparison well are obtained; inputting the first inclinometry data, the second inclinometry data, the first borehole trajectory calculation data and the second borehole trajectory calculation data into an improved cuckoo algorithm by taking the nearest spatial distance between the trajectory points of the reference well and the comparison well as an objective function to obtain the trajectory point with the smallest spatial distance between the reference well and the comparison well; wherein the improved cuckoo algorithm comprises the following steps: on the basis of an original cuckoo algorithm, adjusting an inertia weight and a step size control factor based on the number of iterations, and dynamically adjusting a parasitic nest elimination probability based on a fitness variance of a population. The technical problems that time and labor are wasted and convergence is poor in the prior art are effectively solved.

The invention relates to a low-permeability reservoir crack initiation expansion main control factor prediction method comprising the following steps: S1, collecting geological, well drilling, well logging and seismic data of a target low-permeability reservoir, carrying out data analysis, correction and synthesis, analyzing the change rule of lithology and physical property parameters, and determining the change rule of the lithology and physical property parameters; establishing a three-dimensional geologic model of the target low-permeability reservoir based on the analysis result; s2, determining rock mechanical parameters of the target low-permeability reservoir based on a sample experiment method; s3, performing stress field analysis based on the three-dimensional geological model and the rock mechanical parameters, simulating regional stress field distribution by applying a finite element numerical simulation method, and analyzing a simulation result; s4, on the basis of stress analysis, crack generation and expansion simulation is carried out by adopting an expansion finite element method; and S5, executing global sensitivity analysis, and determining the influence of the input parameters of the three-dimensional geologic model on the crack generation and expansion simulation result, so as to identify dominant factors for controlling crack formation.

The invention relates to the technical field of aluminum template optimization, and discloses an aluminum template parameter optimization method based on variable control. The method comprises the following steps: acquiring an initial parameter set and an optimization target parameter of an aluminum template assembly; executing a multi-stage control optimization process, the process including a plurality of control stages, each control stage processing only a portion of the parameters of the initial set of parameters while generating control factors for passing to the next control stage and feedback factors for optimizing the current control stage; and adjusting the initial parameter set based on data interaction of the control factor and the feedback factor so as to enable the initial parameter set to meet the optimized target parameter. According to the method, the parameters are processed stage by stage, mutual interference of multiple parameters is avoided, dynamic optimization is achieved by means of interaction of a control factor and a feedback factor, pertinence, controllability and efficiency of parameter optimization are improved, material waste and cost increase are reduced, the performance of the aluminum formwork is guaranteed, and the requirements of modern building engineering for the aluminum formwork are met.

The invention provides an adaptive cruise system adjusting method, a vehicle and a storage medium, the method is applied to the field of automatic driving, and the method comprises the following steps: determining a target control factor of an adaptive cruise system based on an attention parameter of a driver of the vehicle and an initial control factor of the adaptive cruise system of the vehicle, the target control factor is used for adjusting a control parameter of the adaptive cruise system, and the attention parameter is used for evaluating the concentration degree of the driver driving the vehicle; determining a target control parameter of the adaptive cruise system based on the target control factor and an initial control parameter of the adaptive cruise system; and driving control is conducted on the vehicle through the self-adaptive cruise control system according to the target control parameters. According to the method, the control factor of the self-adaptive cruise system can be dynamically adjusted according to the real-time state of the driver, and the vehicle is controlled in real time according to the control factor, so that the vehicle is controlled to run safely under the condition that the driver is in a distraction state, and the probability of accidents of the vehicle is reduced.

The invention relates to a site selection method for a low-altitude manned aircraft. The method comprises the following steps: obtaining a primarily selected low-altitude manned aircraft site based on potential demand points and control factors; obtaining candidate low-altitude manned aircraft stations through a clustering algorithm; a hierarchical service range model is introduced, the user demand quantity of each candidate low-altitude manned aircraft station is obtained in combination with the effective load of the low-altitude manned aircraft, and a low-altitude manned aircraft station site selection model is constructed; and solving the site selection model by adopting a Newton-Raphson optimization algorithm, wherein the target function of the site selection model is that the user demand quantity is maximum and the cost is minimum. Compared with the prior art, the site selection method has the advantages of realizing scientific, reasonable and accurate site selection of the low-altitude manned aircraft and the like.

The invention discloses a natural gas hydrate productivity main control factor analysis method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining sample data of various variable factors of natural gas hydrate productivity, and carrying out the preprocessing of the sample data; constructing a preliminary causal structure between variable factors based on a physical mechanism, and performing direct causal relationship evaluation based on the preliminary causal structure and the preprocessed sample data to obtain a target causal graph; and performing causal inference analysis based on the target causal diagram to obtain main control factors of the natural gas hydrate productivity. According to the natural gas hydrate productivity main control factor analysis method based on causal inference, the causal relationship of productivity influence is accurately identified, the scientificity and reliability of main control factor screening are improved based on the causal relationship, and more powerful technical support is provided for natural gas hydrate development. The method can be widely applied to the technical field of data processing.

The invention relates to the technical field of digital control, in particular to an automatic control system and method for producing lithium carbonate. The method comprises the following steps: acquiring sensing data of a lithium carbonate production line, and binding the sensing data to obtain a process section field state factor set; performing fine-tuning embedding time state transition according to the process section field state factor set to obtain a multi-dimensional control characterization flow; performing control instruction coding based on the multi-dimensional control representation flow to obtain a multi-channel control factor set; carrying out field load parameter re-calculation and micro-cycle feedback modulation by combining the obtained real-time lithium carbonate production line sensing data to obtain a control modulation response matrix; based on the control modulation response matrix, reverse adjustment is carried out on the process diagram structure, and a process anomaly feedback body is obtained; the real-time response capability of the lithium carbonate production line to raw liquid fluctuation, equipment state change and working condition abnormity is enhanced, and the product purity, the yield and the production line operation stability are effectively improved.

The invention belongs to the technical field of data processing, particularly relates to a fault processing method and system for an anti-icing simulation system, and aims to solve the problems that the early warning accuracy is not high and processing instructions are easy to mismatch in the prior art. The method provided by the invention comprises the following steps: acquiring multi-source simulation data of an anti-icing simulation system; determining a temperature drop rate and a humidity change rate according to the environment simulation data, and determining a dynamic regulation and control factor in combination with the system characteristic data; valve time sequence data of the anti-icing simulation system are obtained, the valve time sequence data and the dynamic regulation and control factors are input into a pre-trained time sequence prediction model for fault prediction, and a fault prediction probability is obtained; taking the fault prediction probability as a query vector, retrieving a plan node in a pre-constructed knowledge graph, and generating a predictive instruction; and when the fault prediction probability reaches a preset threshold value, executing the predictive instruction. Based on the method, the early warning accuracy and the instruction adaptability in the fault processing stage are effectively improved.

The invention provides a magnet power supply control system, and relates to the technical field of data processing, and the system is used for obtaining a parameter setting instruction input by a control terminal, determining control input parameters of each time point according to a set current value and an actual voltage value, constructing a second data matrix according to a historical operation sequence index value, the first data matrix and the second data matrix are superposed to obtain a fusion data tensor, gradient features are extracted according to the fusion data tensor, response fitting degrees between the gradient features and all control paths are calculated, a mode fitting index set is obtained, the maximum response fitting degree is extracted, the state conversion direction is determined, and a state conversion index is obtained; and calling a control parameter mapping path, mapping the logic condition of the physical circuit change into a digital control sequence to obtain a control factor matrix, generating an execution instruction, and sending the execution instruction to a power output execution layer. Hardware control can be replaced by data driving, and stable operation of the magnet power supply is ensured.

The invention discloses an intelligent bonding silver wire equipment control method and system based on the Internet of Things, and relates to the technical field of equipment control. An intelligent bonding silver wire equipment control system based on the Internet of Things comprises an equipment data acquisition module, an equipment abnormity judgment module and an equipment linkage control module. According to the method, the bonding external environment data, the bonding production process data and the bonding equipment state data are collected in real time, and the silver wire surface data are combined, so that the precise control on the bonding silver wire equipment can be realized, the production parameters can be optimized, and the bonding quality can be improved; in combination with dynamic equipment control analysis and dynamic silver wire state analysis, an environment control factor, a production process control factor, an equipment state control factor and a silver wire bonding process influence factor can be adjusted in real time, so that equipment can adapt to different production conditions, and the production flexibility is improved.

The invention discloses an intelligent ship power control system which comprises a data collection module, a power control strategy adjustment trigger module, a ship navigation state recognition model construction module, a ship power strategy generation module and a power intelligent control module. The invention relates to the technical field of ship power control, in particular to an intelligent ship power control system, which innovatively introduces a power control strategy adjustment triggering method of a dual judgment logic mechanism, and effectively realizes intelligent updating of a ship power control strategy. The stability of an identification result is improved through identification model construction of a dynamic hyperbox expansion mechanism of overlapping risk avoidance and a membership function construction method of boundary dynamic regulation and control; a multi-stage strategy generation method is designed, an improved optimization algorithm which introduces a population search direction control factor and a self-adaptive reverse guidance mechanism is adopted, the global optimization ability of the control strategy is improved, and fine control over the ship power strategy in the complex navigation environment is achieved.

A gas reservoir water invasion intelligent regulation and control method, equipment and a storage medium relate to the technical field of oil and gas development, and mainly comprise the steps of randomly generating a production simulation model to construct a multi-target water invasion dynamic data set, training a machine learning model by using a normalized data set, and establishing a multi-target water invasion dynamic data set; calculating a main control factor influencing the dynamic state of water invasion and a weight corresponding to the main control factor by utilizing the trained machine learning model, establishing a comprehensive evaluation model of the drainage well, inputting corresponding parameters of a production well with water output in a well pattern of a production area into the comprehensive evaluation model of the drainage well, determining a drainage well pattern consisting of the production well and the drainage well, and evaluating the drainage well. Establishing a drainage and mining system multi-objective optimization mathematical model, and solving to obtain Pareto solution sets and well control parameters corresponding to different drainage and mining system schemes by using a multi-objective optimization algorithm; according to the method, the Pareto solution sets of different schemes and the corresponding well control parameters are accurately obtained through the multi-objective optimization algorithm, the water invasion damage of the gas reservoir is intelligently regulated and controlled, and a support effect is provided for stable production of the gas reservoir.

The invention provides a main control factor analysis method and system influencing oil and gas reservoir single well yield and a medium, and relates to the technical field of oil yield prediction, and the method specifically comprises the steps: collecting the oil reservoir single well yield and influence factors; processing the yield and each influence factor, calculating a Pearson's correlation coefficient, and determining a main control factor; according to the yield of the ith single well and the initial value of the yield of the ith single well, the sensitivity coefficient of the bth main control factor of the m single wells is obtained; and combining the Pearson's correlation coefficient with the sensitivity coefficient to generate a comprehensive evaluation coefficient, and evaluating the influence of each main control factor on the single well yield. According to the method, the influence degree of each factor on the yield can be quantified, the main control factor of the oil and gas reservoir single well yield can be determined, the comprehensive influence of each main control factor on the single well yield can be comprehensively reflected through the comprehensive evaluation coefficient, related factors can be identified, and the factor is prevented from generating significant influence on the yield under specific conditions.