144results about How to "Reduce blindness" patented technology

The invention provides a rectal cancer neoadjuvant chemoradiotherapy curative effect prediction method and system based on magnetic resonance image analysis, and aims to improve accurate prediction of curative effects of patients with local advanced rectal cancer (LARC). According to the method, multi-modal magnetic resonance images (T1WI, T2WI, DWI and DCE-MRI) are obtained, qualitative and quantitative analysis is combined, tumor morphology, diffusion characteristics and microcirculation parameters are extracted, clinical and pathological data of a patient are integrated, and a multi-dimensional feature set is constructed. Curative effect prediction is performed through machine learning and a deep learning model, and the model can predict treatment results of complete remission, partial remission, ineffectiveness and the like in a classified manner. The system accurately predicts the reaction of a patient to neoadjuvant radiotherapy and chemotherapy through a non-invasive method, supports formulation of a personalized treatment scheme, reduces excessive treatment, optimizes the treatment effect, and has good clinical application value.

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

The invention discloses a river pollution tracing method and system based on underground water quality pollution monitoring, and relates to the technical field of river pollution tracing. According to the river pollution tracing method based on underground water quality pollution monitoring, the real-time concentration value of each pollutant in underground water is obtained in real time and analyzed to obtain the underground water pollution real-time evaluation index; according to the method, a plurality of abnormal measurement points with abnormal concentration in a set area are identified, and the traceability position coordinate of each pollutant with abnormal concentration in underground water in a river is analyzed, so that the real-time evaluation index of underground water pollution can be continuously calculated by acquiring the pollutant concentration data of each measurement point in real time; and whether the pollutant concentration exceeds the preset range or not is judged in time, so that the defects of hysteresis and untimely data updating of traditional water quality monitoring are overcome, dynamic monitoring of the pollutant concentration can be realized, and quick response when the pollutant is abnormally increased is ensured.

The invention discloses a power distribution station high-temperature alarm system and a processing device, and relates to the technical field of high-temperature alarm, and the system comprises a sensor deployment and control module, a dynamic threshold discrimination module, a hierarchical linkage disposal plan library and a disposal backtracking monitoring module. The sensor deploying and controlling module is used for constructing a three-dimensional model of the power distribution station through laser scanning and photogrammetry; according to the invention, the three-dimensional model of the power distribution station is constructed by using the laser scanning and photogrammetry technology through the sensor deployment and control module, the heating part of the equipment is accurately analyzed, and the installation feasibility and accessibility of the sensor are evaluated, so that the reasonable layout of the sensor is realized, the comprehensiveness of temperature monitoring is improved, and the accurate temperature measurement of key parts is ensured; in addition, through a dynamic threshold value judgment module, a deep learning algorithm is combined with transfer learning and generative adversarial network technologies, the limitation of a fixed threshold value is broken through, temperature abnormity is judged more accurately, tiny faults are positioned more accurately, and the situations of false alarm and missing report are effectively reduced.

The invention discloses an SNP (Single Nucleotide Polymorphism) molecular marker related to the testis size character of Hu sheep as well as a screening method and application of the SNP molecular marker, and belongs to the fields of genetic breeding and molecular biology. The marker is located in an intron region of a Hu sheep chromosome 3 LRRIQ1 gene and is remarkably related to the Hu sheep testis size character, and the accuracy and reliability of the SNP molecular marker are ensured through a scientific and rigorous screening method; according to the marker, Hu sheep individuals related to testis size characters can be rapidly and accurately screened out, the breeding efficiency and accuracy are improved, the breeding process is accelerated, the breeding cost is reduced, and the economic benefits are improved; the genetic diversity is protected, the accuracy of seminal fluid quality evaluation is improved, a powerful tool is provided for Hu sheep breeding work, and important basic data is also provided for researching a genetic mechanism of testis development and functions; the method can also be used as an auxiliary diagnosis tool to assist in identifying potential genetic defects or disease risks and promote progress and development of the breeding technology.

The invention discloses a mineral resource potential assessment method based on remote sensing and deep learning. The method comprises the following steps: drawing a mineral distribution map layer, a lithologic structure map layer and an alteration map layer; determining the weight of each layer by using a geologic metallogenic theory, adjusting the weight of each layer by using a random forest, and superposing the layers to obtain a comprehensive graph; and carrying out image classification by adopting a self-developed deep learning model based on the comprehensive graph to obtain a prospecting target area. According to the mineral resource potential evaluation method based on remote sensing and deep learning disclosed by the invention, a plurality of regions with potential mineral resources can be successfully identified and are consistent with a field exploration result; the blindness of exploration can be effectively reduced, the positioning of the prospecting target area is optimized, and the success rate of exploration work is greatly improved.

The invention belongs to the technical field of digital teaching, and particularly relates to a digital teaching service method based on a large model. Through cyclic feedback and continuous optimization, the teaching content can be adjusted according to the actual learning effect of the student, each knowledge point can be effectively taught, the overall teaching quality is improved, personalized teaching content and suggestions can be provided according to the learning behavior and the learning effect of each student by using a large model and data analysis, and the teaching efficiency is improved. The learning requirements of different students are met, the teaching duration is optimized according to the mastering condition of each knowledge point and the teaching duration score of the students, so that the students can master knowledge in the most suitable time, the learning efficiency is improved, the blindness and uncertainty in the teaching are reduced by collecting and analyzing a large amount of teaching data, and the learning efficiency is improved. The setting of the feedback period ensures the continuous improvement of the teaching content, enables the teaching process to be continuously optimized, and adapts to the continuously changing learning demands and environments of students.

The invention relates to the technical field of sewage treatment, in particular to a distributed rural sewage treatment station intelligent optimization method and system. According to the method, firstly, the water quality parameters and the operation parameter data are collected and cleaned, the preprocessed feature data are input into the prediction model to obtain the prediction result, the prediction result is compared with the real-time operation data to obtain the deviation data, and the model parameters are dynamically optimized and issued according to the deviation data, so that the blindness of parameter setting in a traditional method is reduced; the control strategy can be adjusted in real time according to the actual operation condition, the operation cost is remarkably reduced while the effluent reaches the standard, and the treatment efficiency is improved.

The invention discloses an AI-based postoperative rehabilitation nursing system and method for old people, relates to the technical field of medical treatment, and aims to calculate a rehabilitation coefficient of a current nursing period based on a feature set, accurately evaluate rehabilitation states of a patient in different nursing periods and intelligently judge whether current nursing intervention needs to be adjusted or not. By dynamically optimizing the intervention strategy, the problem of excessive or insufficient intervention in traditional nursing is avoided. And the intervention optimization instruction is received and executed, and the intensity adjustment coefficient of the nursing intervention in the next time period is automatically generated, so that the intelligent adjustment of the nursing intensity is realized, the nursing scheme is more personalized, and the intervention blindness is reduced. In the optimized nursing intervention process, physiological and metabolic parameters of the patient are continuously monitored, an optimized feature set is generated, the nursing intensity is further adjusted, a closed-loop nursing process is formed, and the closed-loop dynamic optimization mechanism improves the nursing response speed and adjustment precision and greatly shortens the rehabilitation cycle of the patient.

The invention provides a high-voltage circuit breaker partial discharge monitoring and early warning method and system, and relates to the technical field of partial discharge monitoring, and the method comprises the steps: obtaining the feature information of a target circuit; a circuit analysis model is called for analysis, and a target analysis result is obtained and comprises a plurality of target point locations; dynamically monitoring the first target point location through an ultrahigh frequency sensor to obtain first partial discharge information; calling a signal analysis model for processing and analysis to obtain a first signal feature set; activating a fault diagnosis model to analyze the first signal feature set to obtain a first diagnosis tag; and when the partial discharge fault of the target high-voltage circuit breaker at the first target point location is displayed, a first early warning signal is sent out. The technical problems that in the prior art, partial discharge monitoring mostly depends on artificial experience or fixed rules, intelligent analysis means are lacked, misjudgment or missed judgment is prone to occurring, accurate positioning is difficult, and the safety of the high-voltage circuit breaker is reduced are solved.

The invention provides a sandstone type uranium ore prospecting method based on a tectonic unit boundary, and the method comprises the steps: obtaining the geological data of a sedimentary basin, determining the spatial distribution and the tectonic and stratum characteristics of tectonic units in the sedimentary basin, and determining the types of beneficial metallogenic tectonic units according to the spatial distribution and the tectonic and stratum characteristics of the tectonic units; and determining a first-level exploration target region of the sandstone type uranium mine by combining the gravity data and / or seismic data and / or oil and gas bearing conditions of the favorable metallogenic structure units for different types of favorable metallogenic structure units. According to the method, on the basis of deep analysis of uranium-controlled metallogenic characteristics of the tectonic unit boundary, the upheaval and fracture of the tectonic unit boundary are taken as important marks for prospecting, and geological and geophysical prospecting methods are combined, so that a novel efficient and low-cost prospecting method is formed, an optimal target of a sandstone type uranium ore prospecting area can be accurately locked, and the prospecting efficiency of the sandstone type uranium ore prospecting area is improved. The blindness in the prospecting process can be obviously reduced, and the exploration efficiency is improved.

The invention discloses an adrenal vein blood sampling auxiliary system based on three-dimensional reconstruction. The system comprises an image acquisition module used for acquiring medical image data of an adrenal gland and a surrounding human organ tissue structure; the image preprocessing module carries out preprocessing operation on the acquired image data; the contour recognition module recognizes contours of adrenal gland, blood vessels and peripheral human organ tissue structures from the preprocessed image; the three-dimensional reconstruction module constructs a three-dimensional model of the adrenal vein and the peripheral human organ tissue structure according to the contour information; the model correction module is used for correcting and perfecting the three-dimensional model; the rendering module carries out rendering processing on the corrected three-dimensional model so as to enhance the visualization effect; and the positioning auxiliary module provides positioning auxiliary information for adrenal vein blood sampling operation. The system can effectively cope with the complex condition of a blood vessel loop, improves the three-dimensional reconstruction precision, provides accurate guidance for adrenal vein blood sampling, and assists in clinical decision making.

The invention relates to a bridge construction quality detection method, system and platform supported by machine vision, and relates to the field of quality detection, and the method comprises the steps: carrying out the first-order crack recognition, and obtaining a target region; collecting a target region image, performing image salient region identification to obtain an image saliency and an image salient region, and dividing a salient region image of the image salient region; configuring an identification node number according to the image saliency, and performing second-order crack identification on the salient region image to obtain crack feature information; and configuring a bridge deformation detection range according to the crack feature information and the image saliency for deformation detection to obtain a deformation detection result, and combining the crack feature information as a bridge quality detection result. The technical problems that a traditional bridge construction quality detection method cannot efficiently recognize bridge crack defects and deformation defects, the missed detection probability is large, the detection precision is low, and the detection efficiency is low can be solved; and the efficiency, the accuracy and the reliability of bridge quality detection can be remarkably improved.

The invention relates to the technical field of agricultural equipment control, in particular to a rain-sheltering cultivation greenhouse control method and system based on intelligent perception. Importing the real-time growth state model diagram into a dictionary search tree for search matching, and obtaining growth moisture requirement range data of the pepper seedlings in the current growth stage in the target greenhouse; collecting real-time moisture data of soil in the target greenhouse at a plurality of preset time nodes, and analyzing according to the collected real-time moisture data and the growth moisture requirement range data in combination with a Bezier curve algorithm to obtain a soil moisture suitability deviation coefficient of the pepper seedlings in the target greenhouse; the moisture condition of soil in the target greenhouse is analyzed according to the soil moisture suitability deviation coefficient, the target greenhouse is regulated and controlled according to the analysis result, the greenhouse can be intelligently regulated and controlled according to the soil moisture condition, it is ensured that pepper seedlings grow under the appropriate soil moisture condition, and the yield of the pepper seedlings is improved. Therefore, the growth quality and yield of pepper seedlings are improved.

The invention discloses a japonica rice amphiphilic hybridization target gene plant screening method based on high-throughput sequencing, and belongs to the technical field of agricultural biology. A high-throughput sequencing technology is utilized, the genetic difference of parent genomes is accurately analyzed, specific molecular markers covering target gene areas are developed, plants containing target genes can be rapidly recognized, the screening efficiency and accuracy are greatly improved, the breeding period is shortened, resources are saved, and the method is suitable for large-scale popularization and application. Through combination of molecular marker detection and phenotypic expression of target characters, in the construction process of hybridization and backcross populations, target genes are fixed generation by generation, stable inheritance of the target characters, joint analysis of genetic types and phenotypes and multi-environment testing are ensured, correlation between target genotypes and the target characters and environmental adaptability of the target genotypes and the target characters are verified, and the target genotypes and the target characters are identified. The stability and reliability of the breeding material are improved, in candidate plant screening and restorer line creation, an excellent hybrid combination is constructed, genotype optimization is realized, and the quality and yield potential of the breeding material are improved.

The invention provides an engineering safety management method and system based on a BIM model, and relates to the technical field, and the method comprises the steps: obtaining engineering information, constructing an engineering BIM model, carrying out the monitoring region division of the engineering BIM model, obtaining an engineering model monitoring region, and obtaining the region group association information; calculating an area scheme influence coefficient to obtain a risk area, and calculating a type influence coefficient of each engineering monitoring type to obtain a risk type; calculating an engineering cumulative risk influence coefficient, comparing the engineering cumulative risk influence coefficient with a preset cumulative influence threshold value, obtaining cumulative risk judgment of the engineering BIM model, and further obtaining risk influence early warning information; according to the risk early warning information, the abnormal risk monitoring type and the risk area information, reversely obtaining the wind direction source information, and adjusting the risk source information until the risk early warning information does not exist, the method can comprehensively and accurately identify and evaluate the potential risk in the project, and realizes real-time monitoring and effective early warning.

The invention relates to the field of information matching, in particular to a building material intelligent purchase matching and recommendation method, which comprises the following steps: S1, building material product feature acquisition; s2, analyzing user demand characteristics; s3, performing similarity analysis; s4, user evaluation analysis; s5, carrying out regional applicability analysis; s6, building material product recommendation analysis; according to the method, the similarity between the characteristics of the various building material products and the user demand characteristics is obtained through analysis according to the characteristic parameters of the various building material products and the user demand characteristic evaluation parameters, and the user evaluation coefficients of the suppliers of the various building material products are obtained through obtaining the user evaluation analysis of the suppliers of the various building material products. According to the method, regional climate applicability and regional environment applicability of various building material products are obtained, regional applicability evaluation coefficients of the various building material products are obtained through analysis, recommendation indexes of the various building material products are obtained through comprehensive analysis, and all preferred building material products are screened out in combination with prices; and the building material product meeting the actual demand of the user can be more accurately recommended to the user.

The invention discloses a power grid optimization scheduling method and system based on high-proportion photovoltaic. The method relates to the technical field of optimized dispatching of a high-proportion photovoltaic power grid, and comprises the following steps: carrying out group classification on users based on modularization according to historical power consumption data, and obtaining historical load response data of each module; constructing a user load behavior model according to the historical load response data of each module, and extracting power utilization behavior characteristics; analyzing the difference data of each module and the overall module, and comparing the difference data with a preset standard threshold value to obtain a target module with the difference data greater than the standard threshold value; inputting the historical load response data of the target module into a preset personalized user electricity consumption prediction model to obtain load prediction data of a personalized group; and taking the load prediction data of the personalized group as an ideal output target of the photovoltaic system equipment, and combining the system benefit and the ideal output target of the photovoltaic system equipment to obtain the operation distribution condition of the photovoltaic system equipment.

The invention discloses a high-voltage switch mechanical characteristic parameter optimization platform, and relates to the technical field of high-voltage switch mechanical characteristic parameter optimization, and the platform comprises a parameter collection module which is used for collecting mechanical characteristic parameters of a high-voltage switch in real time, and a data analysis module which is used for analyzing the mechanical characteristic parameters of the high-voltage switch based on the collected mechanical characteristic parameters. And analyzing the relevance between the high-voltage switch performance and the mechanical characteristic parameters through an adaptive optimization algorithm, and generating optimization reference data. According to the mechanical characteristic parameter optimization platform for the high-voltage switch, the influence degree and the mutual relation of each parameter on the performance can be accurately determined; based on the generated optimization reference data and adjustment reference data, parameters such as the on-off time, the switching-on time and the switching-off time of the high-voltage switch can reach better values, so that the comprehensive performance index is improved, parameter combinations can be comprehensively evaluated under various working conditions, potential performance problems can be found and adjusted in time, and the service life of the high-voltage switch is prolonged. And the high-voltage switch always keeps good performance in a complex actual operation environment.

The invention discloses a photovoltaic module inspection method and inspection system based on an unmanned aerial vehicle. The inspection method for the photovoltaic module based on the unmanned aerial vehicle comprises the following steps: performing first inspection on the photovoltaic module to obtain a first coordinate of a fault module; taking the first coordinate as a first inspection point, and planning a first inspection path based on the first inspection point; according to the first inspection path, utilizing the unmanned aerial vehicle to obtain an electroluminescent image set corresponding to the fault component; fault identification is carried out according to the electroluminescent image set, a second fault type of the target fault component is obtained, and the flight height of the unmanned aerial vehicle during first inspection is larger than the flight height of the unmanned aerial vehicle during flying of the first inspection path. According to the photovoltaic module inspection method based on the unmanned aerial vehicle provided by the embodiment of the invention, the blindness of inspection can be reduced, targeted fault type acquisition and fault cause acquisition are performed on the fault module, and the fault identification accuracy is ensured while high-efficiency inspection is ensured.

The invention discloses an air-ground cooperative unmanned vehicle path planning method in a complex environment, and the method comprises the steps: firstly obtaining distribution point position information, distribution point material demand information, environment information, distribution center position information and unmanned vehicle information; secondly, preliminarily performing global path planning according to the information; then, the unmanned vehicle runs according to a path obtained by global planning, and when the unmanned vehicle senses that the surrounding environment cannot pass through, a built-in unmanned aerial vehicle of the unmanned vehicle lifts off to recollect surrounding environment information to obtain updated environment information; and finally, according to the updated environment data, local path planning is carried out on each path through traversal constraint, re-planning path constraint and energy consumption constraint, and a distribution path of the unmanned vehicle in a complex environment is obtained. Aiming at the requirements of maneuverability, rapidity and continuity of the unmanned vehicle in a complex environment, the unmanned vehicle is not restrained by an existing road, so that the emergency material distribution time is shortened, and the rescue efficiency is improved.

The invention belongs to the related technical field of intelligent robot environment exploration, and discloses an autonomous environment exploration method and system for a mobile robot in an unknown environment, and the autonomous environment exploration method generates a new exploration trajectory branch through a reverse backtracking trajectory until the whole environment is completely explored. A concept of an obstacle shielding map is introduced in a local environment exploration process, and an area which is shielded by an obstacle and cannot be accurately sensed in a sensing range of a robot sensor is set as an obstacle shielding area, so that various logic misjudgments caused by unknown factors due to shielding of the obstacle to the robot sensor are reduced; and the accuracy of an environment exploration process and the adaptability to a complex unknown environment are enhanced.

The invention provides a three-dimensional ventilation digital twinning system, which comprises a ventilation information investigation module, a three-dimensional visual modeling module, a ventilation system analysis module and a ventilation system optimization module, and is characterized in that the ventilation information investigation module collects underground ventilation system engineering, production operation, ventilation structures and ventilation equipment materials, combines mining area ventilation lines, and optimizes the ventilation system engineering, the production operation, the ventilation structures and the ventilation equipment materials; temperature, humidity, air volume and wind direction information parameter detection is carried out on main nodes of an underground ventilation system; the three-dimensional visual modeling module is used for establishing a three-dimensional visual model of the whole mine ventilation system according to 1: 1 in combination with ventilation information basic data and related materials; the ventilation system analysis module diagnoses and analyzes air volume distribution, air speed standard reaching, ventilation resistance distribution and a diesel oil equipment tail gas diffusion path in a multi-dimensional mode based on a ventilation numerical value analysis model; the change condition of the mine ventilation system can be scientifically and reasonably predicted, and the mine ventilation cost and working conditions can be quantitatively analyzed.

The invention provides a component design method for a marine structural steel welding material. The component design method comprises the steps that S1, a uniform mechanical property data sample of the marine structural steel welding material is obtained, and a model is built; s2, multiple sets of input data related to alloy element components of the welding material are obtained through complete set crossover operation, mechanical property predicted values corresponding to the input data are obtained on the basis of the constructed model after standardization processing, and a database containing the mechanical property predicted values corresponding to the input data is established; and S3, target mechanical property parameters are set, and alloy element composition design data meeting preset property parameter conditions are retrieved and output in the database. According to the method, modeling is conducted through uniform mechanical property data samples, the interaction among alloy elements in the welding material can be accurately reflected, and the mechanical property of the welding material is accurately predicted within the temperature range of-60 DEG C to 20 DEG C under the pressure of 300 MPa to 1000 MPa; and blindness in component design of the marine high-strength steel welding material is reduced, and the research and development period is greatly shortened.

The invention relates to the technical field of multi-component copper alloy design, in particular to high-hardness and high-conductivity copper alloy components, a process design method, a storage medium and equipment. The method comprises the following steps: establishing a quaternary thermodynamic database; the Pandat software is adopted to calculate and obtain microstructure data changing along with the Co content at different aging temperatures; an open source library pymoo is adopted to find out the Pareto front of the precipitation phase fraction and the Cu atom solid solubility; and determining the target aging time by adopting an aging experiment. Computer program instructions are stored on the storage medium, and the method is implemented when the computer program instructions are executed by a processor. The apparatus includes at least one processor, at least one memory, and computer program instructions stored in the memory, the method being implemented when the computer program instructions are executed by the processor. The high-hardness and high-conductivity copper alloy composition and process design can be efficiently achieved, and the constraint relation that the hardness and the conductivity of the copper alloy material restrict each other can be broken through.

The invention belongs to the technical field of geopolymer industrial materials, and particularly relates to a method for predicting geopolymer proportion and geopolymer-aggregate interface bonding performance based on molecular simulation. According to the method, geopolymer models with different calcium contents are constructed through molecular dynamics simulation, the mechanical properties of the geopolymer models are simulated, then simulation results are verified through experiments, the method can effectively predict the influence of the raw material ratio on the mechanical properties of the geopolymer, the time and cost required by the experiments are remarkably reduced, and the method is suitable for popularization and application. And an important reference is provided for subsequent design and optimization of geopolymer materials. Besides, interface models of the geopolymer, the coal gangue aggregate and the granite aggregate are constructed, the interface interaction between different types of aggregates and the geopolymer is explored on the molecular level, the combination degree of the geopolymer and the different aggregates is evaluated by calculating the interface interaction energy between the geopolymer and the different aggregates, and a splitting experiment is carried out for verification. And valuable guidance is provided for selection and application of aggregates in actual engineering.

This application relates to the field of IoT detection technology, specifically to an online accuracy detection method for filling machines using multi-sensor fusion. The method includes: collecting multi-source heterogeneous sensor signals from the filling machine head and adding consistent timestamps based on business events; reconstructing a two-dimensional event matrix using the cumulative mass flow corresponding to the flow signal as the progress coordinate, locating progress anchor points, and defining a transient window; within the transient window, performing adaptive cancellation on the weighing signal using the pressure signal as a reference to obtain a steady-state weighing value, performing dynamic density correction on the flow signal using temperature and pressure signals to obtain a flow integral value, and performing dynamic weight fusion based on the real-time signal-to-noise ratio to obtain a fused output value; using the fused output value at the end of the transient window for qualification judgment and source tracing to generate a traceability tag, and combining the final filling quality detection value to generate an accuracy data package for online detection. This application effectively improves the metrological robustness of the final filling value under valve-closing transients and enhances the traceability of the detection.

The invention relates to the technical field of ecological restoration, and provides an ecological restoration prediction and disturbance restoration method for an ecologically sensitive and fragile area in power grid construction. A remote sensing image of the ecological sensitive fragile area is obtained, a first area with the real-time NDVI value lower than a preset NDVI value and a second area of the disturbed slash are determined, and the second area belongs to the first area; the first area and the second area are subjected to ecological environment detection, ecological detection data are determined, and the ecological detection data comprise vegetation distribution data and soil property state data; according to the vegetation distribution data, a first ecological package is configured, and the first ecological package is used for vegetation restoration; according to the soil property state data, a second ecological package is configured, and the second ecological package is used for soil property improvement; an ecological package configuration network based on ecological restoration prediction is built, and the ecological package configuration network comprises a vegetation restoration configuration layer, a soil improvement configuration layer and a restoration prediction layer.

A global sensitivity analysis method for pressure recovery well test design based on time series belongs to the technical field of intelligent development of oil and gas fields. It is an improvement on the global sensitivity analysis method. This method considers the changes of all uncertain input parameters at the same time, adds the time-related variable t to the nonlinear relationship, and quantifies the contribution of each parameter to the total variance of model prediction at different times through sensitivity indicators. Specifically, when multiple key design parameters are uncertain parameters at the same time, the influence weight of each parameter changing with time is calculated, and the pressure recovery test is guided by the global sensitivity analysis results of multiple parameters. Quantitatively evaluate the influence of multiple uncertain parameters on pressure and pressure derivatives, and the influence of their interaction with other parameters on pressure, so as to determine the importance of parameters and provide a reference for the well shut-in time.

The invention relates to the technical field of fracturing in oil and gas field development, in particular to a method for water injection pressure recovery degree and soaking time of an old well energy storage group, which comprises the following steps of: 1, establishing a multiphase flow fluid-solid coupling seepage model, and carrying out historical fitting of production dynamics before oil well refracturing to obtain four-dimensional ground stress and formation pressure distribution of an oil reservoir; calculating the remaining oil reserve; 2, according to the remaining oil reserve threshold value interval of the area, it is determined that the group refractures the group well, if the remaining oil reserve is within the threshold value interval, the operation is continued, and if the remaining oil reserve is not within the threshold value interval, the operation is stopped; and 3, according to production characteristics before water injection and energy increasing of the target group well. According to the method, blindness of refracturing construction design of the old well energy storage group is reduced, the overall reconstruction effect of fracturing is enhanced to the maximum extent, the reservoir reconstruction volume and the resource utilization degree are improved, and efficient development of tight reservoir oil and gas resources is achieved.