206results about How to "Rapid assessment" patented technology

The invention discloses a power distribution network distributed photovoltaic bearing capacity real-time evaluation method, which comprises the steps of determining a mathematical model for power distribution network photovoltaic bearing capacity evaluation, and constructing a Markov decision model for power distribution network photovoltaic bearing capacity dynamic evaluation based on the mathematical model; a soft actor-commentator SAC algorithm is used as a basic framework, graph sampling and aggregation GraphSAGE are used for improving the SAC algorithm, a graph deep reinforcement learning algorithm is constructed, the graph deep reinforcement learning algorithm is used for solving a Markov decision model, and a trained graph strategy network is obtained; carrying out the subsequent real-time evaluation of the distributed photovoltaic bearing capacity of the power distribution network through employing the trained graph strategy network; according to the method, the limitation that precise modeling is needed in a traditional method is effectively overcome, and a more accurate real-time evaluation result is provided.

The invention discloses a 3D printing process intelligent control method and system based on real-time feedback, and relates to the technical field of 3D printing, a data acquisition module collects a printing layer surface image and printing data in real time, constructs an image data set and a printing data set, and performs cleaning and standardization processing on the data in a preprocessing module; the image analysis module analyzes surface defects of a printing layer by using a convolutional neural network, generates a defect score D, compares the defect score D with a preset defect score threshold D1, evaluates the state of the printing layer and triggers early warning, and the comprehensive evaluation module calculates a crack generation probability Z by combining printing data and the defect score; and the intelligent control module compares the crack generation probability Z with a preset crack generation probability threshold value Zl, evaluates the printing state and feeds back and controls printing parameters according to a result, so that crack and structure defects in the printing process are effectively prevented and reduced, and the printing precision and the finished product quality are improved.

The invention provides a rice seed vigor rapid detection method based on near infrared spectrum, and belongs to the technical field of spectral analysis. The method comprises the following steps: performing different degrees of aging treatment on rice seeds in a high-temperature and high-humidity environment, taking unaged rice seeds as a control group, collecting spectral data of the rice seeds subjected to different degrees of aging treatment, and preprocessing the data; taking out the seeds of each treatment group, and measuring physiological indexes including germination rate, germination potential and the like; based on the preprocessed spectral data, a data enhancement method is adopted to fully embody internal characteristics and category differences of sample data; a Conv1D-GRU framework is applied, the model can capture spatial features and time sequence information in spectral data at the same time, a full-connection layer predicts extracted comprehensive features by means of vitality, a classification result is output through a softmax layer, a hybrid optimization population search algorithm is used for further optimizing the performance of the model, and the performance of the model is improved. Therefore, the activity of the rice seeds can be rapidly detected according to different aging degrees of the rice seeds.

The invention discloses an artificial precipitation data acquisition system and evaluation method based on a remote sensing image, and relates to the technical field of ecological environment restoration and meteorological science. Comprising the following steps: acquiring remote sensing image data and rainfall data of meteorological stations in a region, performing geometric correction and radiation correction on the remote sensing image data based on ENVI software to obtain corrected remote sensing data, and calculating the rainfall data of the meteorological stations on the basis of the corrected remote sensing data and regional historical rainfall data. The rainfall change data and the vegetation change data in different time periods before and after artificial rainfall enhancement are respectively obtained through a regression analysis method and a pixel dichotomy method, the rainfall change data and the vegetation change data in different time periods before and after artificial rainfall enhancement are compared, the influence of rainfall enhancement on vegetation recovery is quantitatively analyzed, and the rainfall enhancement effect is analyzed by utilizing spatial-temporal dynamic change. According to the method, the influence of rainfall enhancement on the ecological environment can be evaluated more comprehensively and accurately, and a new scientific basis is provided for ecological restoration of arid regions.

The invention relates to the technical field of peanut oil production control, in particular to an intelligent optimization system for a peanut oil production control process, which comprises a process parameter modeling module for collecting peanut oil production parameters, performing linear combination on the peanut oil production parameters, analyzing the correlation of the peanut oil production parameters to quality indexes, and calculating the quality indexes of the peanut oil production parameters; and obtaining a process parameter mapping matrix. According to the method, through deep correlation analysis of multiple parameters in the production process, the mapping relation between the process parameters and the oil quality indexes is established, and the precision and reliability of quality control are improved. In the process control, the specific influence of each production link on the quality index is accurately reflected through standardized and linear combinatorial analysis on the parameters such as squeezing temperature, pressure, time, stirring speed and the like. By predicting the quality index under the current production condition, the response speed to the quality change is improved, so that the quality index deviation can be quickly analyzed and evaluated in real-time data monitoring.

The invention discloses a design scheme generation method based on an information retrieval technology, and belongs to the field of information retrieval and image-text generation. Collecting the text description and an image matched with the content of the text description, preprocessing the text description and the image respectively, and performing feature extraction processing to obtain extracted visual feature monk and semantic features: extracting the visual features in the image data; for character description data matched with the image, processing the character image by using a semantic segmentation network so as to identify and segment a character region, extracting uniformly distributed semantic feature points through a quadtree distribution algorithm, and integrating the semantic feature points of all the regions to form a complete semantic feature point set corresponding to the image data including characters; a multi-scale feature map is constructed by using a feature pyramid network, the extracted visual features and semantic features are mapped to a unified feature space in the multi-scale feature map, and it is ensured that key information of the respective features is reserved in the mapping process.

The invention relates to the technical field of gene engineering, and discloses a KASP marker related to thousand seed weight of wheat and application of the KASP marker. The SNP locus is located at the 33397938bp position of the wheat 1A chromosome, and the polymorphism is C / T. Through phenotypic difference and exon capture analysis, the SNP site closely related to the thousand seed weight is determined, and a specific KASP primer combination is designed, so that rapid and accurate identification of the wheat thousand seed weight is realized. The application of the technology has significant significance in accelerating high-yield breeding of wheat. According to the method, the selection efficiency is improved, the breeding period is shortened, and wheat varieties with ideal thousand seed weight can be quickly identified and cultivated. By using the KASP primer combination, an effective tool is provided for molecular marker-assisted selection of wheat yield-related traits, and the overall yield and quality of crops can be improved.

The invention discloses a wind power plant grid-connected stability rapid evaluation method and system based on an impedance model, and relates to the technical field of power grid systems, and the method comprises the steps: S1, building a physical model of a wind power plant grid-connected system; s2, establishing an impedance model of a wind power plant and a power grid; s3, selecting a proper evaluation index; s4, performing impedance matching analysis; s5, evaluating dynamic response characteristics; s6, performing steady-state analysis; s7, evaluating the influence of grid connection on the power grid; and S8, designing a control strategy, and building the system based on the method. According to the wind power plant grid-connected stability rapid evaluation method and system based on the impedance model, comprehensive and rapid evaluation of the wind power plant grid-connected stability is realized by constructing the accurate wind power plant and power grid impedance model and combining various evaluation indexes and control strategies, and potential stability problems can be effectively identified and solved; and powerful support is provided for safe and efficient grid connection of the wind power plant.

The invention relates to the technical field of breach image processing, in particular to a breach rapid situation awareness method and system based on multi-source data. The method comprises the following steps: acquiring dam monitoring station data; performing hydrological sensor network setting on the dam monitoring station data to obtain dam hydrological sensor network data; performing ultrasonic water level change trend analysis on the dam hydrological sensor network data to generate an ultrasonic water level change trend chart; performing breach node sensing on the dam hydrological sensor network data according to the ultrasonic water level change trend chart to generate breach node data and breach prediction node data; and according to the breach node data, macroscopic disaster risk analysis is carried out on the breach area, and a breach area macroscopic disaster risk schematic diagram is generated. According to the invention, through real-time data integration, comprehensive risk assessment, unmanned aerial vehicle monitoring, dynamic prediction model and multi-party cooperative response, the accuracy and response speed of breach rapid situation awareness are improved.

The invention relates to the technical field of operation training evaluation, in particular to a training quality evaluation method and system based on pilot attention distribution. The method comprises the following steps: collecting data required by pilot attention distribution training quality evaluation according to a specific flight mission scene; based on the collected data, calculating a standard attention distribution model of the pilot in a specific flight mission scene, and generating attention distribution and actual attention distribution of the standard attention distribution model; and comparing the attention distribution of the standard attention distribution model with the actual attention distribution, evaluating the training quality of the attention distribution of the pilot in the specific flight mission scene, and forming and displaying an evaluation result. According to the method, the attention distribution mode of the pilot is quantified, the eye movement tracking technology and task-related subjective and objective data are combined, the performance of the pilot in simulator training can be objectively and accurately evaluated, and the scientificity, fairness and safety of flight training are improved.

The invention discloses a circuit breaker contact wear degree intelligent evaluation system, and belongs to the technical field of circuit breaker contact wear evaluation, the circuit breaker contact wear degree intelligent evaluation system comprises a camera, a data collector, a server and a display, the server comprises a circuit breaker type analysis module, a signal processing module, a wear evaluation module, a wear prediction module and a health monitoring module, the circuit breaker type analysis module, the signal processing module, the wear evaluation module, the wear prediction module and the health monitoring module are in communication connection in sequence; the display comprises a data display module; according to the invention, the wear degree of the contact head of the circuit breaker can be intelligently evaluated, and meanwhile, the system also has the functions of wear degree prediction, residual use frequency calculation and wear frequency early warning, so that the maintenance and management efficiency of the circuit breaker can be greatly improved, and the equipment fault risk is reduced.

The invention discloses a method for quantitatively analyzing a left auricle occlusion effect based on a CT image. The method comprises the following steps: step 1, acquiring a heart CTA image; 2, performing three-dimensional reconstruction to obtain a heart three-dimensional image, and obtaining a left atrium three-dimensional image by using the left atrium segmentation model; 3, obtaining a left auricle occluder area by using the left auricle occluder segmentation model; 4, constructing a three-dimensional model of the left auricle occluder on the basis of the segmented left auricle occluder area so as to obtain a three-dimensional image of the left auricle occluder; 5, obtaining a three-dimensional plane of the left auricle plugging device, a front view of the plugging device and a left auricle opening plane; 6, left auricle occlusion postoperative evaluation parameters are extracted, wherein the parameters at least comprise the size, position and classification of residual shunt, the position of an occluder, the shoulder exposing position and height, the endothelialization degree of the occluder and the position of instrument-related thrombus; and 7, evaluating the left auricle occlusion effect according to the obtained evaluation parameters after the left auricle occlusion operation.

The invention provides a software code optimization method and system based on big data, equipment and a medium, and relates to the technical field of computers. The method comprises the steps that a target code library and corresponding running log data are acquired, and a first data set is obtained; cleaning, converting and aggregating the first data set from the programming language dimension, the framework dimension and the project specification dimension to obtain a second data set; training a machine learning model by using the second data set, so that the machine learning model can identify code quality, performance bottlenecks and potential defects; analyzing a code module written by a user by using the trained machine learning model to obtain a code analysis result; and according to a code analysis result, generating a corresponding code segment from the target code library, and displaying the code segment by using a visual interface. According to the technical scheme, accurate and comprehensive code optimization suggestions can be given.

The invention relates to the technical field of relay protection, in particular to a new energy grid-connected line phase-to-phase distance protection adaptability quantitative evaluation method and a new energy grid-connected line phase-to-phase distance protection adaptability quantitative evaluation system. Calculating conventional power supply and new energy short-circuit current data of the bus at the side when the three-phase fault of the bus at the receiving end occurs, and preprocessing the short-circuit current data; calculating a short-circuit ratio of a conventional power supply to new energy based on the preprocessed short-circuit current data; and judging the distance protection adaptability of the output grid-connected line according to the short-circuit comparison. According to the method, the ratio of the conventional power supply to the new energy short-circuit current is calculated, and the short-circuit ratio threshold value 1.73 is introduced as a criterion, so that quantitative evaluation of the new energy grid-connected line distance protection adaptability is realized. According to the method, the defect that a traditional evaluation method depends on complex transient simulation is overcome, the evaluation efficiency is remarkably improved, and meanwhile the accuracy of an evaluation result is guaranteed. The invention provides a quantitative criterion of new energy grid-connected line distance protection adaptability for the first time.

The invention discloses a slope instability whole process calculation method, system and equipment based on a particle method. The method is based on a smoothed particle hydrodynamics (SPH) particle calculation method, combines a Riemannian large deformation theory, and realizes landslide simulation under a regional scale through a C + + / CUDA writing program. According to the method, the whole process of generation, connection, starting and accumulation of the sliding surface can be truly represented, the influence mechanism of rock and earth mass properties such as an internal friction angle, cohesion, particle size, elasticity modulus and Poisson's ratio on the landslide process can be analyzed, meanwhile, through a parallel heterogeneous CPU / GPU programming architecture, the calculation efficiency is improved by nearly a hundreds of times compared with the traditional CPU programming efficiency, and the calculation cost is greatly reduced. The watershed scale landslide flow velocity and accumulation range prediction is realized, the adaptability is very high, the defect that discrete element and finite element methods are limited by simulation scale and calculation efficiency is overcome, and a new thought is provided for large-scale landslide disaster prediction.

The invention discloses a model library-based spaceflight radio frequency signal quality multi-domain evaluation and compensation method, and belongs to the technical field of wireless communication. The implementation method comprises the steps of determining signal system parameters based on various CCSDS standards and a spaceflight protocol system, extracting related parameters of spaceflight standards in a time-frequency domain, generating standard time-frequency domain radio frequency signals, converting the standard time-frequency domain radio frequency signals into a related domain, a modulation domain, an energy domain and a polarization domain, and establishing spaceflight radio frequency signal sub-model libraries of the domains; obtaining a time-frequency domain measured signal through a receiver, integrating time domain information and frequency domain information, and constructing a time-frequency domain sub-evaluation model; constructing a multi-domain comprehensive evaluation model by adopting a parallel data transmission mode and a weight data processing method; calculating the weight of the evaluation result of each domain through an entropy weight method, and obtaining a comprehensive quality evaluation result of the spacecraft radio frequency signal; and based on the distortion coefficient, a robust signal equalization algorithm is adopted to process the signal, the influence of the distortion effect on the signal quality is compensated, and the robustness and the anti-interference capability of signal transmission are improved.

The invention relates to the technical field of battery performance test and analysis, and discloses a lithium battery test analysis method based on deep learning and feature analysis, which comprises the following steps: S1, establishing a dynamic environment model, and obtaining dynamic distribution of temperature and humidity in a battery use environment, the dynamic distribution of the temperature and the humidity is based on the combined influence of diffusion, convection and external disturbance; s2, dynamic environment features are extracted, and main amplitudes and frequencies of temperature and humidity serve as environment feature vectors; and S3, based on the lithium ion diffusion model, a coupling relationship between lithium ion concentration distribution and environmental characteristics is established, and the lithium ion concentration distribution is influenced by temperature and humidity changes in a dynamic environment. By adopting the technical scheme based on dynamic environment modeling and feature extraction, dynamic distribution of temperature and humidity is described through a partial differential equation, and environment feature vectors are extracted, so that the technical effect of accurately simulating and quantifying the influence of complex environment change on battery performance is achieved.

The invention relates to a multi-component alloy fatigue strength evaluation method based on slip localization, which comprises the following steps: acquiring material parameters of a multi-component alloy for a fatigue test, and establishing a micro-scale model on a computer based on the material parameters; carrying out simulated fatigue loading on the micro-scale model by adopting a molecular dynamics simulation means; after simulation fatigue loading enters a steady-state stage, dividing the micro-scale model into a plurality of subunits with the same size, calculating the slip irreversibility of each subunit based on the shear strain of each atom in each subunit, and further calculating the slip irreversibility difference between the subunits; calculating a non-uniform parameter based on the slip irreversibility of each subunit and the slip irreversibility difference; performing normalization and grid removal processing on the non-uniform parameter to obtain a slip localization index; and constructing a fatigue strength evaluation model based on the material parameters and the slip localization indexes.

The invention belongs to the technical field of energy management, and particularly relates to a regional energy management optimization method based on edge computing and multi-objective optimization, which comprises the following steps of: determining edge equipment with qualified processor performance based on processor performance parameters of the edge equipment, establishing an edge equipment site diagram based on geographical sites of the qualified edge equipment, applying the initial multi-objective optimization scheme to the regional energy simulation model, obtaining an energy efficiency evaluation value and an energy efficiency comprehensive difference value, comparing the source efficiency comprehensive difference value with an energy efficiency comprehensive difference standard value stored in a database, matching a pre-selected management optimization scheme, and applying the pre-selected management optimization scheme to the regional energy simulation model, and based on the optimal management optimization scheme, adjusting an optimization scheme decision according to a real-time evaluation result. According to the invention, the flexibility of overall regional energy management can be improved, and sustainable development of long-term regional energy management can be maintained.

The invention discloses a pavement pit slot crack three-dimensional model establishment method based on point cloud data, and belongs to the technical field of engineering measurement and pavement detection, and the method comprises the following steps: S1, data acquisition and processing; s2, preprocessing the original point cloud data; s3, identifying the point cloud of the crack of the pit slot; and S4, establishing a three-dimensional model of the pit slot crack. According to the method, three-dimensional modeling is carried out by using the point cloud data, pits and cracks on the road surface can be accurately identified and positioned, and the problem that tiny damage is easily ignored in a traditional manual detection method is avoided; through the automatic point cloud data processing and clustering thought and the triangulation algorithm, manual intervention is reduced, the data processing efficiency is improved, and an accurate three-dimensional model can be quickly generated.

The invention relates to the technical field of medical data processing, and discloses a gastrointestinal function outcome evaluation system for a digestive system disease patient, which comprises the following steps: firstly, obtaining gastrointestinal function index data of a to-be-tested person by using gastrointestinal ultrasonic image data of the to-be-tested person, and then evaluating the gastrointestinal function outcome of the to-be-tested person according to the gastrointestinal function index data of the to-be-tested person. Intestinal function ultrasonic monitoring index data (intestinal canal compression degree) and gastric function ultrasonic monitoring index data (gastric residual volume) of the person to be tested are obtained, and then a gastrointestinal function outcome evaluation result of the person to be tested is obtained. According to the gastrointestinal function outcome evaluation method, an accurate gastrointestinal function outcome evaluation result can be quickly obtained only by inputting the gastrointestinal ultrasonic image of the to-be-tested person into the gastrointestinal function outcome evaluation system of the patient with the digestive system disease, invasive examination does not need to be carried out on the to-be-tested person, the safety and comfort of the to-be-tested person in the gastrointestinal function outcome evaluation process can be greatly improved, and the accuracy of gastrointestinal function outcome evaluation is improved. And effective and reliable data are provided for scientific treatment decision making.

The invention relates to the technical field of electric power system automation, and particularly discloses an electric power system wind power consumption capability limit evaluation method considering transient stability, and the method comprises the steps: carrying out the sampling in a feasible space of an electric power system, and generating a sample data set; using the sample data set to offline train a deep neural network as a transient stability evaluator, and predicting a transient stability index of the power system; based on the wind power output short-term prediction data and the load prediction data, considering the transient stability constraint of the power system, and determining a day-ahead scheduling plan of each generator in the power system; and constructing an evaluation model of the wind power consumption capability limit under the current scheduling plan, and accelerating the solving process by means of constructing an initial solution in an auxiliary manner through constraint preprocessing and a heuristic algorithm. The method has the advantages that the calculation speed of the evaluation model is effectively improved, the transient stability of the power system is considered, the wind power consumption capability limit is quickly evaluated, and auxiliary opinions and decision support are provided for real-time dispatching of dispatchers.

The invention discloses a road ponding point prediction method fusing historical waterlogging points and spatial clustering, and the method comprises the following steps: S1, carrying out the rapid numerical simulation of surface ponding through the rapid estimation of rainfall runoff and displacement, and obtaining a depth estimation value of the surface ponding; s2, on the basis of the historical waterlogging point position, the accumulated water high-risk area is determined by integrating the estimated value of the surface accumulated water depth; s3, adopting an improved DBSCAN clustering algorithm, and taking the ponding high-risk area as input to predict the ponding point location; and S4, outputting a predicted ponding point with latitude and longitude coordinates, a geographic address and maximum ponding depth information based on geocoding. The position address information is obtained on the basis of the surface ponding depth prediction matrix, so that the ponding address information based on points is formed, and emergency disposal personnel can conveniently and quickly arrive at the site.

The invention provides a method for rapidly evaluating the residual life of a high-temperature material for active service equipment, and belongs to the field of life evaluation in the field of materials. The method comprises the following steps: firstly, obtaining high-temperature creep relaxation rates and high-temperature creep endurance life information of two or more samples at different temperatures, and determining main curve equations and corresponding parameters ZSRT of different time sequence tissue states; thirdly, main curve equations of different time sequence organization states and corresponding parameters Zc are obtained, and the incidence relation between the ZSRT and the Zc is established; 5, for the to-be-evaluated alloy, obtaining a high-temperature creep relaxation rate main curve equation and a corresponding parameter ZSRT of the to-be-evaluated alloy; and 6, calculating the parameter Zc of the to-be-evaluated alloy by using the incidence relation between the ZSRT and the Zc, and establishing a high-temperature creep endurance life main curve equation through the parameter Zc to realize life evaluation. The method has the advantages of short test time, less material loss, high data utilization rate and the like, and is particularly suitable for evaluating the endurance life caused by the change of the structure state.

The invention provides a cranial nerve fatigue assessment method and device, equipment and a medium, and relates to the technical field of smart medical treatment, and the specific implementation mode comprises the steps: collecting multi-dimensional eyeball movement features of an assessment object in the process of performing visual guidance on the assessment object to execute a plurality of eye movement tests, the multi-dimensional eyeball activity features are controlled and realized by different functional structures of the brain; the multi-dimensional eyeball movement features are input into a fatigue assessment neural network model, the neural network model outputs a cranial nerve fatigue assessment value, and the fatigue assessment neural network model is a neural network model constructed based on a mesh mapping relation of the eyeball movement feature-brain structure-fatigue performance. The fatigue manifestation is a change condition of eye movement activity characteristics during cranial nerve fatigue. According to the method, the problems of low efficiency, single evaluation dimension, one-sided judgment basis and the like of an existing fatigue evaluation method are solved, the evaluation accuracy and reliability are improved, and comprehensive, systematic and efficient fatigue monitoring is realized.

The utility model relates to the technical field of SSD (Solid State Disk) testing, in particular to an auxiliary testing device for an SSD board card, which comprises a fixed plate connected with a base through a support column, and a cable fixing component arranged on the upper surface of the fixed plate; the nut holes are correspondingly connected with the positioning holes of the SSD board card to be tested through screws; the cable fixing part is used for fixing a test cable; the fixing plate comprises a hollow area, a cooling fin is embedded in the hollow area, and the cooling fin is in corresponding contact with the chip position of the SSD board card to be tested; the base comprises a plurality of fans. By using the device, each chip on the SSD bare board can be effectively cooled through the fan module, and the SSD board card and the test cable can be fixed.

The invention relates to a method for evaluating the risk of road damage caused by seismic landslide under different seismic motions, and the method comprises the steps: building a probability multi-factor influence model of seismic landslide occurrence through employing a Bayesian probability method and a machine learning model, and building a sample set according to the historical co-seismic landslide data in a region, taking the sample set as the input of a seismic landslide occurrence probability multi-factor influence model, expanding and applying the seismic landslide occurrence probability multi-factor influence model to a seismic landslide region, taking different seismic oscillation peak accelerations as seismic landslide trigger factors, and calculating the seismic landslide occurrence true probabilities under the different seismic oscillation peak accelerations; and obtaining seismic landslide area road data, and generating a risk level distribution diagram of road damage caused by landslide through spatial analysis by combining the seismic landslide area road data with the seismic landslide occurrence true probabilities under different seismic oscillation peak accelerations. According to the invention, rapid and accurate post-earthquake landslide road damage risk assessment is realized.

The invention discloses an NFC testing method based on machine learning, and relates to the technical field of wireless communication testing, and the testing method comprises the specific steps: S100, data collection and preprocessing: carrying out the basic testing of an NFC signal under different environment conditions, collecting the multi-dimensional data of signal intensity, transmission speed and error rate, and carrying out the data collection and preprocessing; by introducing deep fusion of the electromagnetic field theory and the machine learning technology, dimensions and methods of NFC testing are greatly enriched, the electromagnetic field theory serves as an important branch of physics, an accurate mathematical model and an experimental verification method of the electromagnetic field theory provide a solid theoretical basis for propagation characteristics of NFC signals in a complex environment, and the electromagnetic field theory and the machine learning technology are combined in advance. In combination with the strong data processing and prediction capability of a machine learning algorithm, the attenuation, reflection and diffraction phenomena of NFC signals in different media can be predicted theoretically, the model can be continuously corrected and perfected through actual test data, and the accuracy and reliability of a prediction result are ensured.

The present invention provides a method for evaluating the effective transformation volume of multi-scale artificial fractures, comprising: step 1, calculating the fracture conductivity according to parameters such as proppant type, sand concentration, proppant density and crushing rate; step 2, establishing a multi-medium seepage mathematical model, performing numerical solution, and obtaining a flow field for tight reservoir fracturing development considering the starting pressure gradient; step 3, calculating the normal inflow velocity of the fluid in the fracture; step 4, establishing a method for identifying the effective length of a single fracture; step 5, calculating the effective transformation volume coefficient of multi-stage fracturing; step 6, obtaining the effective transformation volume of the fracture network on the basis of obtaining the effective transformation volume coefficient. The method for evaluating the effective transformation volume of multi-scale artificial fractures can obtain the relationship between different proppant particle sizes, sand ratios, fracture complexity and other parameters and the effective transformation volume, and then optimize these parameters to guide efficient development on site.

The invention relates to the related technical field of ship safety and pollution prevention, in particular to an oil spill area measuring and calculating method based on improved edge detection, and the method comprises the steps: obtaining a thermal infrared remote sensing image of an oil film region through the cruise monitoring of an unmanned aerial vehicle, and processing the collected thermal infrared image; the thermal infrared image is measured and calculated by adopting an improved Canny algorithm to obtain the final area of an oil spilling region, the measured and calculated actual area of the oil spilling region is visually displayed, and the improved Canny algorithm effectively eliminates impurity information under the condition of ensuring the completeness of edge information features, so that the accuracy of the oil spilling region is improved, and the accuracy of the oil spilling region is improved. And in the aspect of oil film area edge main information response, the accuracy of edge identification is improved while the identification efficiency is ensured.