38results about How to "Comprehensive consideration" patented technology

The invention provides a rail transit foreign matter invasion risk identification and evaluation method and system, and belongs to the technical field of target identification of image processing. Image data of a running environment in front of a train are collected in real time; performing foreign matter detection on the acquired image data frame by frame through a foreign matter detection algorithm, and obtaining the category and position information of the detected foreign matter if the foreign matter is detected to exist in a certain frame of image; segmenting a track region in the frame of image through a track segmentation algorithm to obtain position information of the track region; based on the relative position relation between the foreign matter and the rail area, the distance between the foreign matter and the train and the category of the foreign matter, comprehensively evaluating and outputting the rail transit foreign matter invasion risk level. According to the method, the foreign matter invasion risk in the rail transit scene can be accurately identified in real time, meanwhile, a rail transit foreign matter invasion risk grade evaluation system is established, and efficient detection, identification and evaluation of the rail transit foreign matter invasion risk are achieved.

The invention relates to a roadbed compaction quality monitoring, management and maintenance method and system based on virtual and real index collaboration. The method comprises the following steps: step 1, establishing an indoor compaction test for reproducing on-site roadbed compaction conditions, and establishing a data set of the indoor test; 2, implementing a field test on roadbed pressure, and establishing a data set of the field test; 3, building a roadbed compaction quality prediction model based on virtual and real data collaborative training; and 4, carrying out construction management and control application on the roadbed compaction quality prediction model based on virtual and real index collaborative prediction. According to the method, the influence of roadbed filler parameters, road roller working parameters and external environment parameters of roadbed compaction on the compaction quality is fully considered, multiple virtual-real indexes cooperate with one another to establish an artificial neural network compaction quality prediction model of the roadbed compaction quality, and the model is optimized and improved while being migrated to engineering practical application. And real-time accurate monitoring, management and maintenance of the compaction quality are realized in the on-site roadbed compaction process.

The invention provides an urban flood disaster risk assessment method based on crowd sensing, a medium and equipment, and the method comprises the steps: collecting flood disaster text data on a social platform, carrying out the preprocessing, and constructing a flood disaster text data set; extracting flood disaster common words from the flood disaster text data set, classifying the flood disaster common words according to subject terms, and constructing a disaster descriptive lexicon based on a co-occurrence network; designing a crowdsourcing questionnaire based on the disaster descriptive word bank, issuing the crowdsourcing questionnaire to a target user group, and collecting flood disaster feedback data and geographic information fed back by the target user group; performing spatial distribution on the collected flood disaster feedback data according to geographic information; and carrying out risk assessment by combining two types of indexes which respectively represent the flood disaster situation and the flood disaster influence area. According to the invention, real-time monitoring and accurate assessment of urban flood disasters are realized, and the accuracy and real-time performance of flood disaster risk assessment are improved.

The invention provides an underground chamber stability assessment method based on deep learning and fusion uncertainty, and relates to the technical field of underground chamber stability assessment, and the method specifically comprises the steps: constructing an instability assessment model, and employing crustal stress parameters and rock mechanics parameters collected historically as a training set, the stability coefficient of the underground chamber is used as a label, and the stability of the underground chamber can be analyzed more accurately through training of a deep learning model; besides, a function relationship of the influence of the rock parameters on the stability of the underground chamber is constructed, and multiple groups of current second influence coefficients are obtained by inputting multiple groups of current rock parameters into the function, so that the comprehensiveness and the accuracy of evaluation are improved; and finally, through data processing and correction, uncertainty factors are comprehensively considered, and the stability and reliability of an evaluation result are improved. The method can overcome the technical defects that constants are difficult to determine, parameter processing is incomplete, and evaluation results are unstable in a traditional method.

The invention relates to a volcanic rock buried hill fractured oil and gas reservoir modeling method, which comprises the steps of S1, identifying fractures in imaging logging data according to the imaging logging data, analyzing occurrence characteristics of the identified fractures, grouping the fractures according to the occurrence characteristics, and explaining main control factors of fracture development in a target area; s2, based on the main control factors of the crack development of the target area, respectively constructing a crack development probability body for each main control factor, and fusing the crack development probability bodies controlled by other main control factors to form a crack development comprehensive probability body; s3, building a fracture density data body of each group of fractures by taking the bile meridian fracture linear density data of the grouped fractures as integration and taking the fracture development comprehensive probability body as a constraint condition; and S4, according to the statistical structure of the occurrence characteristics of each group of fractures, in combination with the corresponding fracture density data volume, establishing a fracture space distribution model.

The invention belongs to the field of aero-engine dynamics, and particularly relates to an integrated double-squirrel-cage elastic support pre-adjustment concentricity design method. Comprising the following steps: step 1, acquiring an integrated double-squirrel-cage elastic supporting structure; 2, calculating the fulcrum sinkage of the integrated double-squirrel-cage elastic supporting structure; step 3, calculating a static eccentricity rate under the action of a static load according to the fulcrum sinkage, judging whether to carry out pre-adjustment concentric design or not according to the static eccentricity rate, and if so, entering step 4; step 4, calculating a preset concentric amount according to the fulcrum sinkage; and 5, carrying out pre-adjustment concentric design on the integrated double-squirrel-cage elastic supporting structure according to the pre-adjustment concentric quantity. The method has the advantages of being comprehensive in consideration factor, accurate, efficient, standard in process and the like, and the blank of the pre-adjustment concentric design technology of the integrated double-squirrel-cage elastic supporting structure is filled.

The invention discloses a farmland heavy metal pollution control zoning method, system, equipment and medium, and relates to the technical field of soil treatment and environmental protection. Comprising the following steps: determining soil heavy metal content standard exceeding conditions of cultivated land to be detected, crop heavy metal content standard exceeding conditions, potential ecological risk conditions, human health risk conditions and cultivated land grading results; comprehensively evaluating the soil heavy metal content standard exceeding condition, the crop heavy metal content standard exceeding condition, the potential ecological risk condition, the human health risk condition and the cultivated land grading result, and determining the heavy metal pollution control partition type of the cultivated land to be detected according to the evaluation result. The cultivated land is partitioned through multi-aspect evaluation and constraint of the cultivated land, factors are comprehensively considered, and the accuracy of heavy metal pollution management and control partitioning is improved.

This invention provides a method for predicting surface vibration waveforms during tunnel blasting, comprising: first, deriving the vibration waveform function of a short-charge explosive charge based on Heelan theory; then, constructing a vibration calculation form suitable for tunnel cut-out blasting based on this short-charge explosive charge vibration waveform function, and calculating the surface vibration waveform function of the cut-out holes using the superposition principle; next, simplifying and correcting the formula for the waveform function of multiple-hole blasting to obtain the surface vibration waveform function of multiple-hole blasting in the tunnel; finally, determining the specific parameters in the formula by comparing with field measured data, and obtaining a surface vibration waveform function curve suitable for field use. This method can comprehensively and accurately predict the waveform time history curve of the entire process of surface vibration during tunnel blasting, rather than just predicting a single blasting peak value. It can effectively promote the field application of surface vibration waveform prediction for tunnel blasting and is of great significance for optimizing blasting parameters and ensuring the safety of various facilities and personnel around the blasting area.

The present invention relates to the technical field of automobile structural design optimization methods, and specifically refers to a trunk lid bent tube hinge axis arrangement calculation optimization method. According to the rear modeling CAS surface, as well as known design parameters and set constraints, the trunk lid initial opening θ0 and the hinge axis initial coordinate f0 are set; a hinge optimization area is constructed, and within the hinge optimization area, the trunk lid initial opening θ0 and the hinge axis initial coordinate f0 are calculated based on the hinge span design requirements, human-machine goals, the safety gap of the hinge relative to the sealing strip movement, the safety gap requirements of the trunk lid and the rear windshield during the opening process, the assembly space distance between the hinge axis and the rear windshield, and the trunk lid closing force requirements to obtain the optimal solution for the hinge axis. The optimization method of the present invention is reasonably designed, with rich considerations, avoiding the risk of repeated modifications in the later stage, improving the efficiency of the hinge axis design and development, and has great promotion value.

The present invention provides a method for predicting the vibration response of a building structure caused by train operation, which relates to the technical field of vibration prediction of rail transit environment, and includes the following steps: obtaining train dynamics design parameters and establishing a train model; obtaining track structure design parameters and establishing a track structure model; using the train track contact relationship to establish a train-track structure model; obtaining the building structure design parameters of the train running position and establishing a building structure model; using the track structure and the building structure contact conditions to construct a train-track structure-building structure coupling model; considering the influence of track unevenness to calculate the wheel-rail contact force generated by train operation; calculating the track structure and the building structure vibration acceleration time history curve. The present invention can quickly and accurately predict the vibration response of the building structure caused by the train operation in the time domain, considering more comprehensive factors, and the established coupling model is more in line with the working conditions, which greatly improves the solution efficiency and obtains more accurate prediction results.

The present invention provides an intelligent matching method, device, equipment and storage medium for multi-scenario emergency measures, and belongs to the field of emergency data processing technology. The method includes: obtaining the scene information of the current scene; wherein the scene information includes the scene purpose, scene scale, risk category, risk level and risk stage; matching the current scene with the historical scene in the emergency measures database based on the scene information of the current scene, and obtaining a matching scene similar to the current scene; wherein the emergency measures database stores scene information and emergency measures of multiple historical scenes; based on the difference in scene information between the current scene and the matching scene, adjusting the emergency measures corresponding to the matching scene, and obtaining the emergency measures corresponding to the current scene. The present invention can improve the implementation effect of the generated emergency measures, and has higher efficiency and more comprehensive and objective considerations than manually formulated emergency measures.

The present invention discloses a method, system and storage medium for negotiating and dispatching emergency resources in a distributed environment, including the following steps: gridding and numbering the layout area of ​​emergency materials; obtaining the state matrix of emergency materials; obtaining the value of emergency materials in each grid area; dispatching the emergency materials corresponding to the designated support area to the corresponding area to be replenished. The present invention defines and represents the state of the layout area and the state of emergency materials, so that the state of emergency materials can be expressed more vividly and is conducive to subsequent calculations; obtains the value of resources in each grid area, thereby providing a basis for subsequent resource dispatch; supplies resources to high-value areas through low-value areas to maximize the value and maximize the overall benefits.

This application discloses an anomaly localization method and a cloud management platform based on a cloud management platform, which can improve the efficiency of anomaly localization to a certain extent. The method includes: the cloud management platform can create a large-scale computing node cluster for tenants, which can contain multiple computing nodes. If these multiple computing nodes fail to execute a job task, the cloud management platform can first obtain the logs generated by these multiple computing nodes during the execution of the job task, and generate log space characteristics and log time characteristics of these multiple computing nodes based on the logs. Then, the cloud management platform can evaluate these multiple computing nodes based on these log space characteristics and these log time characteristics to obtain evaluation values ​​for these multiple computing nodes. Subsequently, the cloud management platform can identify computing nodes whose evaluation values ​​are greater than or equal to a preset threshold as computing nodes that have experienced an anomaly.

The invention discloses a method and a system for measuring and calculating the highest gathered number of passengers in a station based on pull-in card swiping data, and the method comprises the steps: carrying out the fitting based on the pull-in card swiping data of passengers of different trains, obtaining a logarithmic normal distribution function of the arrival time of the passengers of the different trains, and obtaining key parameters mu and sigma; training a decision tree model; outputting key parameters mu and sigma of each train of the station to be measured and calculated through the decision tree model, obtaining a logarithmic normal distribution function of arrival time of passengers of each train, and calculating an arrival distribution curve of the passengers of each train; and superposing the passenger arrival distribution curves of the trains to obtain a passenger arrival distribution curve of the whole station, wherein the highest point of the ordinate is the highest number of gathered passengers of the station. According to the method, various factors such as station environment attributes, train attributes and different train passenger arrival laws are comprehensively considered, the problems that an existing maximum gathered passenger number calculation method is simple in consideration factor and large in calculation result deviation are solved, the building scale of a passenger station building can be reasonably determined, and railway construction investment is saved.

This application belongs to the field of battery pack technology, and particularly relates to a method for determining the aging value of a battery pack, an electronic device, and a readable storage medium. The method calculates the cycle aging value and the static aging value based on the battery pack's cycle aging parameters, a preset first parameter table, and static aging parameters, and then determines the battery pack's aging value based on these two values. This application considers both the aging effects of cyclic charging and discharging and the aging effects of static storage when determining the aging value, providing a comprehensive assessment of the factors affecting battery pack aging. This results in higher accuracy and reliability of the battery pack's aging value, thereby reducing the risk of battery pack use.

The application discloses a compressor service life prediction method and an electronic starting controller. The method comprises the following steps: obtaining working condition parameters of a compressor, performing time sequence unification and preprocessing on the working condition parameters, and generating quantitative indexes, wherein the quantitative indexes comprise long-time sequence sensitive indexes and short-time sequence fluctuation indexes; extracting physical characteristics and model automatic characteristics of the long-time sequence sensitive indexes and the short-time sequence fluctuation indexes, and obtaining a time sequence sample set; training and testing a pre-constructed fusion model by using the time sequence sample set, obtaining an evaluation result of the fusion model, and deploying based on the evaluation result. The long-period aging characteristics and high-frequency fluctuation characteristics in the compressor are processed in parallel by using the fusion model. Compared with the existing service life prediction mode, the overall precision is higher, the comprehensive consideration factors are more comprehensive, and the double paths can also adjust the hierarchical parameters to adjust the overall precision to retain stable characteristics and adapt to the dynamic working conditions of the compressor.

The invention relates to the field of Internet technology and finance, and particularly discloses a product recommendation method and device, a storage medium and computer equipment, and the method comprises the steps: obtaining sample data; based on the sample data, training the initial recommendation model to obtain a target recommendation model; when any client accesses the dialogue system, obtaining second demand information and second user information of a target user corresponding to any client, and real-time product information and real-time risk information of each current product; and inputting the second demand information, the second user information, the real-time product information and the real-time risk information into a target recommendation model, obtaining a second product recommendation list corresponding to the target user based on model output of the target recommendation model, and returning the second product recommendation list to any client. According to the method, the user demand and the time-varying characteristics of financial products and the like are integrated in the product recommendation process, so that the accuracy and success rate of product recommendation can be greatly improved.

The invention discloses a calibration method and system for a pantograph-catenary contact pressure sensor, and the method comprises the steps: sequentially applying a force with a specified strength at each intersection point of a sliding plate and a support of a pantograph to be measured, respectively measuring the voltage of each intersection point when the force is applied each time, and obtaining a sensitivity coefficient matrix of all intersection point pressure sensors based on the voltages; during application, the actual contact pressure of the pressure sensor is obtained by utilizing the sensitivity coefficient matrix according to the real-time voltage of the to-be-measured pantograph at each intersection point.

The invention provides an emotion recognition method and device, and relates to the technical field of data processing. The method comprises the following steps: performing feature extraction according to acquired multi-modal data for emotion recognition to obtain various feature vectors; performing feature fusion on the multiple feature vectors to obtain fused features, and performing sentiment classification on the fused features by adopting a first preset classifier to obtain a sentiment recognition result; or adopting a second preset classifier to carry out sentiment classification on the multiple feature vectors to obtain a classification result corresponding to each feature vector, and fusing the classification results corresponding to all the feature vectors to obtain a sentiment recognition result. According to the method, emotion recognition is performed based on the multi-modal data, comprehensive utilization of various types of data is realized by performing feature extraction, fusion and classification on the multi-modal data, the emotion of the corresponding individual can be considered more comprehensively, the limitation of single-modal data is made up, and the accuracy and comprehensiveness of emotion recognition of the corresponding individual are improved.

The invention discloses a method for predicting a gas deviation factor of a sulfur-containing gas reservoir, which belongs to the technical field of exploration and development, and is characterized by comprising the following steps: S1, collecting parameter data; s2, carrying out dimensionless operation; s3, calculating an absolute value, a minimum value and a maximum value of a difference value; s4, calculating each correlation coefficient; s5, calculating the correlation degree; s6, sorting is carried out; s7, defining related variables of the BP neural network; s8, calculating input and output of each layer of neurons; s9, calculating a partial derivative of the error function to each neuron of the output layer; s10, calculating a partial derivative of the error function to each neuron of the hidden layer; s11, correcting a connection weight; s12, correcting the connection right; s13, carrying out error calculation; and S14, obtaining a prediction result. Modeling is carried out on the basis of a large amount of experimental data, the gas deviation factor of the sulfur-containing gas reservoir is simulated and calculated, and the method has the advantages of being comprehensive in consideration factor, objective in calculation method, reliable in result and easy and convenient to operate.

The present application relates to a kind of unmanned boundary scene risk assessment method, system and electronic equipment, belong to unmanned technical field, including steps: obtaining boundary scene information and vehicle speed, and determine scene type risk index, traffic density risk index and road type risk index, vehicle speed risk index, then comprehensive above each risk index obtains the risk index of unmanned boundary scene.The present application considers the risk index of quantitative characterization environment risk degree by considering boundary scene type, traffic density, road type and vehicle speed in the risk assessment method of unmanned boundary scene, comprehensively assesses potential driving risk in driving environment, provides theoretical basis for improving the safety of unmanned technology, compared with prior art, more comprehensive consideration factor, more reasonable evaluation method, provides safer, more accurate data support for further driving decision.

The invention provides a method for predicting a hot crack area of an aluminum alloy part, which comprises the following steps: S1, in the solidification and cooling process of an aluminum alloy casting, calculating the stress distribution when the temperature of the aluminum alloy casting reaches the solidus temperature; s2, calculating the strain when the temperature of the aluminum alloy casting reaches the position near the solidus temperature, and obtaining an area with the strain larger than 0; s3, measuring the yield strength near the solidus temperature of the attached casting sample of the aluminum alloy casting by using an experimental means; s4, aiming at each position of the aluminum alloy casting, comparing the stress in the S1 with the yield strength in the S3 to obtain an area of which the stress is greater than the yield strength; and S5, comparing the area obtained in the S2 with the area obtained in the S4 to obtain an overlapped area of the two areas, namely the hot crack area of the aluminum alloy casting. The method has the beneficial effects that the stress, the strain and the yield strength of the aluminum alloy casting are comprehensively considered according to the definition of the hot crack, the considered factors are more comprehensive, the prediction of the hot crack area is more accurate, the reference value provided for process optimization is greater, and the cost is saved.

This invention discloses a method for predicting the time required for personnel to return to the working face after blasting mining in underground non-coal mines. It relates to the field of mine safety prediction technology. First, a negative feedback neural network model is established to correlate the personnel return time with four relevant indicators: roadway, ventilation duct, explosives, and gas. An initial model of the negative feedback neural network is configured. Sample data is collected from multiple channels. The weights and biases of the negative feedback neural network are globally optimized to obtain a prediction model for the personnel return time after blasting mining within the allowable error range. This prediction model is then trained and tested. Decision-makers use the prediction model to predict the return time and guide relevant personnel to return to the working face to continue operations. This invention achieves intelligent and accurate prediction of the personnel return time after blasting mining, effectively improving the accuracy and speed of the prediction results, making the predictions more scientific and effective, and enhancing their reliability.

The invention relates to the technical field of coal seam mining, and discloses a coal seam ascending mining site actual measurement analysis method and device. The method comprises the steps that the bottom layer covering type of a current coal mine coal seam is judged, and a coal seam capable of being mined is screened; judging whether the height of the adjacent coal seam fissure zone in the coal seam capable of being mined is communicated with the coal seam fissure zone; judging whether the coal seam can be upwards mined according to the communication condition; the upward mining condition of the upward mining coal seam is judged; and mining the coal seam capable of being mined according to the upward mining condition of the coal seam capable of being mined upward. According to the method, main influence factors which influence coal seam ascending safe mining are the interlayer spacing of the upper coal seam and the lower coal seam, the mining height of the lower coal seam, the lithology, the interlayer structure, the coal seam inclination angle, the mining time interval and the like are analyzed, and the feasibility of ascending mining is demonstrated through a three-zone judgment method, a ratio method, a surrounding rock balance method and a roof damage range calculation result; and a reliable theoretical basis is provided for coal mining and safety management.

The present invention discloses a mechanical estimation method for the long-cycle fatigue life of an ocean floating structure. First, a three-dimensional model of the ocean floating structure is built, and the model is substituted into a fluid dynamics numerical model to obtain four failure evaluation indicators under ocean working conditions. Then, corresponding splines are prepared from the ocean floating structure to be evaluated, and the splines are subjected to a material performance cycle test on a fatigue testing machine, and the attenuation values ​​of the corresponding materials under different loading working conditions are obtained through testing. Linear regression calculation is performed based on the obtained data to obtain attenuation curves of the four indicators, and four thresholds are substituted into the attenuation curves to obtain the maximum life of the destructive failure of the corresponding four indicators, respectively. The minimum value of the four lifespans is the estimated life of a specific structure built of a specific material under specific working conditions. The expected lifespan evaluated by the estimation method of the present invention is consistent with the actual lifespan, and the service life of the ocean floating structure is effectively and accurately evaluated.

The invention relates to a vehicle charging control method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a nominal charging current limiting value of a power battery at a current temperature; and determining a charging limit index of the power battery according to the nominal charging current-limiting value and the charging limit coefficient. And performing charging control on the power battery of the target vehicle according to the charging limit index and the vehicle working condition of the target vehicle. When the charging control is carried out on the power battery of the target vehicle, the nominal charging current limiting value of the power battery of the target vehicle at the current temperature is considered, and the charging limiting index of the power battery is determined based on the nominal charging current limiting value and the charging limiting coefficient; and finally, based on the charging limit index and the vehicle working condition of the target vehicle, charging control is performed on the power battery of the target vehicle, consideration factors are more comprehensive, the influence of adverse factors on the power battery is avoided, and the service life of the power battery can be effectively prolonged.

The present invention discloses a method and device for predicting the fracture pressure of anisotropic reservoirs, including: collecting basic parameters required for calculation; solving by adopting high-order mixed partial differential characteristic equation method, stress decomposition and superposition method, establishing a horizontal wellbore stress model, and calculating stress components at any position; using a three-dimensional displacement discontinuity method to solve the strain dislocation of surrounding rocks caused by perforation holes to generate additional induced stress, and then superimposing the total stress distribution parameters of the horizontal wellbore, and obtaining the induced stress of the perforation holes through the stress decomposition and superposition principle; calculating the stress distribution around the perforation holes according to the corresponding reservoir parameters of the perforation holes; calculating the anisotropic tensile strength using the tensile strength of vertical bedding and parallel bedding, and further calculating the fracture pressure of the anisotropic reservoir based on the anisotropic failure criterion. The anisotropic reservoir fracture pressure prediction model provided by the present application has more complete considerations than the prior art, and the calculation results are more accurate.

The present invention discloses a substation air conditioning load aggregation dispatching system, which is composed of a first-level dispatching platform, a second-level dispatching platform and a station-side auxiliary controller to form a three-layer structure; the intelligent air conditioning controller collects and uploads the data information of the air conditioning load, and after the second-level dispatching platform receives the air conditioning load aggregation dispatching instruction, it disassembles the air conditioning load aggregation dispatching instruction, constructs the air conditioning removal instruction according to the air conditioning removal strategy and sends it to the station-side auxiliary controller for execution, so as to achieve the air conditioning load aggregation dispatching target. The present invention aggregates and dispatches the air conditioning load as part of the power load in the new intelligent power grid dispatching automation system, enriches the consideration factors of the power load, can further improve the intelligence level of the power grid, optimize the operation and energy distribution of the power grid, and improve the safety and reliability of the power system.

The invention relates to a vehicle driving control method and device, computer equipment and a storage medium. The method comprises the following steps: determining area attribute information of a driving area associated with a driving path where a target vehicle is located; according to the area attribute information, a driving road section associated with the driving area is partitioned, and path blocks of the driving road section are obtained. And controlling the driving speed of the target vehicle according to the path block, the area attribute information and the path environment information of the driving road section. According to the method, when the target vehicle runs in the automatic driving mode, the path environment information is considered, the path blocks and the area attribute information are also considered, more factors are considered, and the safety of the target vehicle in the automatic driving mode can be effectively improved.

The invention discloses an ultra-low permeability reservoir horizontal well refracturing well selection method. The method comprises the following steps that S1, a fine three-dimensional geological model of a target block is established; s2, establishing a three-dimensional geomechanical model of the target block; s3, carrying out reservoir numerical value and geomechanical simulation, and taking a simulation result as a pre-selected data set; s4, determining characteristic indexes influencing the refracturing effect; s5, establishing a refracturing well selection mathematical model, solving the weight of each characteristic index, and calculating the Euclidean distance between the candidate well characteristic indexes and the positive and negative ideal solutions; and S6, calculating the relative attachment degree between each characteristic index of the re-fracturing candidate horizontal well and the ideal solution, and sorting and optimizing the re-fracturing candidate horizontal well. According to the method, the geology and oil reservoir dynamic change process of candidate well initial pressure fracturing can be truly reflected, the accuracy and scientificity of characteristic index value taking are improved, the influence of rock mechanics, reservoir physical properties, fracturing construction and production dynamic parameters on the refracturing yield increasing effect is comprehensively considered, the considered factors are comprehensive, and the accuracy is high.