34results about How to "Accurate decision basis" patented technology

The invention provides a field agricultural production scene perceptual computing method and device. The method comprises the steps that binocular image data and growth environment data of crops are collected; point cloud data processing is carried out according to the collected binocular image data, and the plant height, the plant spacing and the row spacing of the crops are computed; digital image processing is carried out according to the collected binocular image data, and the canopy density and growth vigor information of the crops are computed; the plant height, the plant spacing, the row spacing, the canopy density and the growth vigor obtained by computation serve as the form structure indexes of the crops, and a crop growth model is optimized in real time; according to the growth environment data, the optimized crop growth model is combined, and the growth state of the organs of the crops is computed. The method reasonably integrates the computer graphic image, vision and automation technology, the form structure indexes of the crops are detected and serve as auxiliary analysis parameters, the yield of field agricultural production is forecasted, the completeness degree of production and management auxiliary decision is increased, and therefore the management efficiency of field agricultural production is improved.

The invention relates to an intelligent dynamic network structure modeling and processing method applied to an urban integrated pipeline system. The method comprises the following steps of: establishing urban integrated pipeline service models, relationships among the service models, service model data templates, and index, code and symbolization dynamic relationships of each service model; and establishing the utilization of service model service interface modules and service model intelligent space data models. The method has the advantages of fully utilizing the characteristics of an urban integrated pipeline system network topological structure, putting forward a dynamic symbolization definition, improving the conventional processing method, achieving the management unity of the urban integrated pipeline system, establishing space and non-space data models quickly, truly realizing space-time range-based multi-dimensional management of space data and meeting the effective management requirement of urban information management on each information resource, along with great popularization significance.

The invention discloses a well wall instability risk quantitative evaluation method based on a reliability theory. The well wall instability risk quantitative evaluation method comprises the followingsteps: S1, according to basic data of a drilled stratum andS2, according to the input parameter uncertainty statistical table established in the step S1; S3, establishing a well wall collapse and well wall fracture instability limit state model; S4, according to the well wall instability limit state model in the step S3, respectively solving a well wall collapse pressure value and a well wall rupture pressure value under the limitation of a basic random variable; S5, giving a wellbore pressure mean value and a variable coefficient, and adopting Monte Carlo simulation to generate a wellbore pressure value under the given mean value and the variable coefficient; and S6, counting calculation results to obtain a quantitative evaluation result of the risk of borehole wall collapse and fractureinstability. The method has the beneficial effects that the well wall instability risk under the influence of parameter uncertainty can be quantitatively evaluated, and more accurate and effective decision basis can be provided for drilling technicians and constructors.

The embodiment of the invention provides a drilling leakage crack width prediction method based on neural network data mining. The method comprises the steps that historical drilling data, a leakage stoppage case, an imaging logging real crack width and other data information of a target block are collected; data preprocessing is carried out on the collected data information, wherein the preprocessing content comprises data cleaning, integration and conversion; the preprocessed historical drilling data is used as input, the crack width is used as output, the imaging logging real crack width isused as a standard value, and a crack width prediction neural network model is obtained through supervision training and optimization; and the drilling instant data related to the target positive drilling well is imported into the neural network model, and the model automatically judges the corresponding well depth crack width at the moment. By utilizing the technical scheme provided by the embodiment of the invention, the crack width of the positive drilling well can be conveniently and accurately predicted in real time, so that decision support is provided for leakage stoppage constructionpersonnel, and the one-time leakage stoppage success rate of crack leakage is improved.

The invention discloses a reliability degree assessment method for multilevel state monitoring data fusion. The reliability degree assessment method specifically comprises the following steps of: determining states in a system and unit degradation process according to system and unit degradation laws, determining a unit state combination corresponding to the various states of a system, and collecting the state monitoring data or information of the system and units during a service process; updating the current state probability of the units in the system according to multilevel state monitoring data or information; dynamically estimating the reliability of the multi-state system in the remainder service period. According to the method disclosed by the invention, the state monitoring data or information of a system level and a unit level in the multi-state system is fused, the logical combination relationship between the system and unit degradation laws is combined, and the current state probability of each unit in the multi-state system is determined by constructing a Bayes recursive model, thus predicating the future state and reliability of the system; meanwhile, the errors of the state monitoring data or information are considered, thus the method disclosed by the invention is higher in universality.

The invention relates to an automatic generating system and method of power grid operation mode data. The system comprises a data communication module, a real-time operation mode data analysis module, a data verifying module, a parameter identification module, a data conversion module, a BPA flow file analysis module, a BPA stable file analysis module, an off-line parameter matching module, a BPA flow file output module and a BPA stable file output module; the data communication module is used for downloading a QS file of a regulation and control center locally; the real-time operation mode data analysis module is used for analyzing the QS file into metadata; the data verifying module is used for performing topology verification and calculation island partition on the metadata corresponding to the QS file; the parameter identification module is used for performing parameter identification on the metadata; the data conversion module is used for converting the metadata into metadata corresponding to a BPA flow file; the BPA flow file analysis module is used for analyzing the BPA flow file into metadata; the BPA stable file analysis module is used for analyzing a BPA stable file into metadata; the off-line parameter matching module is used for matching stable metadata corresponding to BPA flow data; the BPA flow file output module is used for generating the flow file; the BPA stable file analysis module is used for generating the stable file. The automatic generating system and method improve the automation degrees and work efficiency.

The invention discloses a method for supplying and transmitting voice, data, static pictures, dynamic pictures, etc. to railway emergency communication by utilizing the existing sectional cables and communication column resources of railways so as to realize multimedia service transmission in the railway emergency communication. The technical proposal comprises the steps of: establishing an emergency command center taking railway bureaus as a unit, fitting out a central side emergency communication access device and realizing point-to-point emergency communication and point-to-multipoint emergency communication; according to the site geographical conditions and the existing line conditions of the railway line at the current section, fitting out emergency communication site side and / or station side devices which can carry out point-to-point emergency communication and point-to-multipoint emergency communication with the central side emergency communication access device. The invention also discloses a device for realizing multimedia service transmission in the railway emergency communication.

The invention provides a Web configuration picture storage and display method and system based on SVG, and the method comprises: building a monitoring picture in a mode of dragging configuration, enabling the content of a picture element corresponding to the monitoring picture to generate an XML format file, generating a picture script, storing the XML format file and the picture script, and transmitting the XML format file and the picture script to a background service; receiving the XML format file and the picture script by the background service, and converting the XML format file and the picture script into an HTML page capable of running in the browser; and loading the monitoring picture in the picture running state, rendering picture elements in a browser, registering the monitoringpicture to background service, refreshing data of the monitoring picture, and refreshing the picture elements in response to a user interaction event. Industrial monitoring HMI software in a browser environment is realized, the production efficiency of enterprises is improved, the cost of the enterprises in implementation of industrial monitoring projects is reduced, and finally a visual, comprehensive and accurate decision basis is provided for an enterprise management layer.

The invention relates to a reservoir flood control scheduling calculation method based on reservoir area and downstream risk decision feedback. The method comprises the following steps: (1) collectingreservoir flood control scheduling information; (2) calculating a node time period needing risk decision making according to reservoir flood control compensation flood regulation calculation; (3) starting from the node time period, counting reservoir area return water inundation loss E according to a downstream flood prevention safety decision, and counting downstream inundation loss E 'accordingto the reservoir area flood prevention safety decision; and (4) comparing E with E ', and selecting a flood control compensation scheduling mode. According to the method, the flood loss of the reservoir area or the downstream can be quickly identified, and a decision scheme with acceptable loss is provided for a management department as an optimal scheme.

The invention provides a method for researching the adaptability of reservoir group scheduling rule types and parameters to climatic changes, which comprises the following steps of: carrying out synthesis and stability evaluation on different scheduling rules by carrying out reservoir group scheduling rule type and parameter response mechanism research under the climatic changes, and proposing a stable and reliable reservoir group scheduling rule type. The evolution mechanism of the scheduling rule to climatic change is disclosed through the adaptability of the type and parameters, theoreticalsupport can be provided for reservoir group optimization scheduling under the climatic change condition, and then an important scientific basis can be provided for relieving energy shortage and guaranteeing sustainable utilization of water resources and sustainable development of national economy. And the method has important theoretical and practical significance.

The invention relates to an energy index calculation technology, in particular to a multi-cave multi-machine water conservancy and hydropower engineering energy index calculating method. The method mainly solves the problems that currently, due to a processing mode with constant force coefficients, the influence of the difference of the energy conversion efficiency of a water turbine at differentmoments on the force and the power generation amount of a power station cannot be truly reflected, and the energy index accuracy is poor; moreover, due to an existing multi-cave multi-machine power station energy index calculation method, power generation diversion flow distribution among caves and machines is conducted generally with the assistance of manual experience, since a calculation targetand principle are not definite and the subject factors are large, during design of cave-machine schemes, it is difficult to ensure that energy indexes among the schemes have a consistent comparison basis, and the scheme reliability is poor. The multi-cave multi-machine water conservancy and hydropower engineering energy index calculating method is characterized in that with the maximum annual power generation amount of a hydropower station being a target energy index, a calculation model is built to calculate other energy indexes, when the multi-cave multi-machine water conservancy engineering power generation flow is optimally distributed, the annual power generation amount of the hydropower station is the largest, and the method is applicable to multi-cave multi-machine water conservancy and hydropower engineering.

The invention discloses a BP neural network-based pre-drilling mud leakage prediction method for complex well conditions. The method comprises the steps of S1, collecting historical seismic attribute data of a target area and engineering and geological data information corresponding to leakage cases; s2, performing standardization processing on the collected data, and performing conversion processing on all the data in order to facilitate implementation of the method; s3, taking the preprocessed historical seismic data as input, taking a leakage condition as output, taking a real leakage state as a standard value, and performing supervised training and optimization to obtain a leakage prediction neural network model; and S4, inputting instant seismic data of a target area, and automatically judging the leakage condition corresponding to each depth of the area by the model. The problems that in-well conditions are difficult to accurately predict before drilling under existing complex well conditions, and mud leakage is prone to occurring are solved.

The invention discloses an intelligent identification and auxiliary compilation system for a regional power grid maintenance plan, and belongs to the technical field of electric power maintenance. Thesystem comprises an advanced application layer, a service support layer, a basic management layer and a data storage layer which are in information interconnection in sequence, wherein the basic management layer comprises an overhaul database, an overhaul knowledge base, a network communication module, a process management module, an authority management module, a graphic conversion module, a model conversion module and a visual interaction module, the service support layer comprises an online network modeling module, a state estimation processing module, a custom expert system and an integrated optimization algorithm, the advanced application layer comprises an overhaul plan optimization auxiliary decision module and a network publishing and query statistics module, an overhaul plan is input into the basic management layer through the visual interaction module, and a result is output through the advanced application layer after being optimized and identified by the service support layer. According to the invention, the rationality of equipment maintenance plan arrangement can be effectively improved, repeated power failure is avoided, and the equipment utilization rate is improved.

A generation method of a power generation scheme for preventing sustained damage of a hydropower station group includes the steps of 1, determining an optimization target and constraint conditions and establishing a statin optimizing and scheduling module; 2, considering uncertainty of hydrological forecasting and conversion of forecasting runoff; 3, calculating potential output of each phase; 4, determining a pattern of power generation constraint rules; 5, acquiring optimized power generation constraint rules and a power generation scheme set by means of an optimization algorithm; and 6, establishing an evaluation index system to evaluate optimal scheduling results. The generation method has the advantages that sustainability of the power generation process is ensured, a minimum of damage is ensured, and the simple and universal power generation constraint scheduling rules are provided to guide optimization of hydropower stations; the influence of hydrological forecasting errors upon scheduling results is fully considered, the power generation constraint scheduling rules are optimized by means of forms of energy, and more accurate decision basis is provided for decision makers.

The invention discloses a method for measuring enterprise management efficiency based on compound optimization analysis, comprising the following steps: A. collecting enterprise operation data, performing preprocessing on the collected data, removing irrelevant attributes, and eliminating relevant high-branch attributes; B. Cluster and classify the preprocessed data to obtain the enterprise operation data after clustering and classification; C. Extract data features from the data after clustering and classification to obtain the data after feature extraction; D. Finally, realize the enterprise operation data through comprehensive measurement Measures of management efficiency. The measurement method of the present invention is simple to operate, has high measurement efficiency, and improves enterprise management efficiency; among them, the data clustering and classification method adopted reduces the time spent on manual labeling of data sets, makes data classification more accurate, and improves data efficiency. Utilization rate; the data feature extraction method adopted can effectively extract the characteristics of enterprise data, which is convenient for subsequent comprehensive measurement.

The invention provides an arch bridge suspender state evaluation method considering an environment and a cable body state, which comprises the following steps of: firstly, finding out main factors influencing the normal use state of a suspender, listing the found factors as evaluation indexes, determining the index weight of each evaluation index by adopting an analytic hierarchy process, then detecting each evaluation index of the suspender, and determining the state of the suspender according to the index weight of each evaluation index. The method comprises the following steps: detecting the weight of each evaluation index, scoring each evaluation index according to a detection result to obtain an index score value of each evaluation index, calculating an inhaul cable state evaluation index BCEI according to the index weight and the index score value of each evaluation index, and then evaluating the suspender state according to the calculated inhaul cable state evaluation index BCEI. According to the method provided by the invention, the defect of relatively single inhaul cable force evaluation in the prior art is overcome, the inhaul cable force, cable body wire breakage or corrosion, protective layer damage and anchor head corrosion influence factors are comprehensively considered, the inhaul cable is completely evaluated in multiple aspects, and meanwhile, the simplicity and accuracy of suspender evaluation are improved.

The invention discloses a scene modeling method suitable for planning operation of an integrated energy system, and the method comprises the steps: firstly collecting photovoltaic output data, wind power output data, electric load data, gas load data, cold load data and thermal load data of a researched region, carrying out the per-unit processing of the obtained data, arranging the data according to time, and generating a scene data set; then, adopting an improved LHS method to carry out sampling and correlation control processing on the scene data set, and obtaining a sample matrix; and finally, clustering the sample matrix by adopting an improved K-means method to form a specific probability scene which retains the characteristics of the original scene. The method overcomes the defects that in the prior art, scene description is inaccurate, and it is difficult to completely adapt to future planning and operation, lays a foundation for subsequent system planning design, operation scheduling and the like, avoids the problem that reliable operation of the system is affected by installation mismatching caused by inaccurate traditional scene prediction methods, and effectively improves the design and operation economy of the comprehensive energy system.

The invention discloses a natural gas leakage fireball action range prediction method and device, and the method comprises the steps: correcting an existing observation factor and a transmission coefficient, building an observation factor function related to a radiated target observation elevation angle and an atmospheric transmission coefficient function, and building an improved fireball prediction model on the basis; and then solving the fireball action range corresponding to the preset damage degree under the optimal radiated target observation elevation angle. According to the method, errors caused by neglecting observation factors and transmission coefficients when a classical prediction model carries out hazard analysis are reduced, the accuracy of hazard radius calculation is improved, and the rationality of safety assessment is enhanced.

The invention discloses a method for quantitative evaluation of wellbore instability risk based on reliability theory, which comprises the following steps: S1, according to the basic data of the drilled stratum; S2, according to the input parameter uncertainty statistical table established in step S1 S3, establish the limit state model of wellbore collapse and wellbore fracture instability; S4, according to the wellbore instability limit state model described in step S3, respectively solve the wellbore collapse pressure value and wellbore fracture limited by the basic random variables Pressure value; S5. Given the mean value and coefficient of variation of wellbore pressure, and use Monte Carlo simulation to generate the value of wellbore pressure under the given mean value and coefficient of variation; S6, Statistical calculation results to obtain the quantification of the risk of wellbore collapse and rupture instability Evaluation results. The beneficial effects of the invention are that the risk of wellbore instability under the influence of parameter uncertainty can be quantitatively evaluated, and more accurate and effective decision-making basis can be provided for drilling technicians and construction personnel.

The invention relates to a reservoir flood control scheduling calculation method based on reservoir area and downstream risk decision-making feedback, which comprises the following steps: (1) collecting reservoir flood control scheduling information; Node period; (3) From the node period, count the backwater submersion loss E of the reservoir area according to the "downstream flood control safety" decision, and at the same time count the downstream inundation loss E' according to the "reservoir flood control safety" decision; (4) compare E and E', select the flood control compensation scheduling method. The method can quickly identify the flood damage in the reservoir area or downstream, and provide the management department with an acceptable loss decision-making scheme as an optimal scheme.

The invention relates to energy index calculation technology. The present invention aims to solve the problem that the current processing method using fixed output coefficients cannot truly reflect the impact of the difference in energy conversion efficiency of water turbines at different times on the output and power generation of the power station, the accuracy of energy indicators is lacking, and the energy indicators of current multi-hole multi-machine power stations In the calculation method, manual experience is generally used to allocate power generation reference flow between tunnels and machines. Because of the unclear calculation goals and principles and relatively subjective factors, it is difficult to ensure that the energy indicators between the schemes are consistent when designing the tunnel machine scheme. Based on the comparison basis, the reliability of the scheme is lacking. A calculation method for the energy index of multi-cavity and multi-machine hydraulic engineering is proposed. For other energy indicators, when the power generation flow of multi-hole and multi-machine hydropower projects is optimally allocated, the annual power generation of hydropower stations is the largest, which is suitable for multi-hole and multi-machine hydropower and water conservancy projects.

The embodiment of the invention provides a stratum fracture length real-time prediction method based on a kernel SMO algorithm, and belongs to the field of data mining and the field of drilling fluidleaking stoppage. The method comprises the steps: collecting historical drilling data, historical drilling crack length true values and real-time drilling data of a target block, and carrying out datapreprocessing; dividing the preprocessed historical drilling data into a training set and a test set, and determining the iteration number M and an allowable error e of a kernel SMO sensor in advance; generating parameter vectors by utilizing various parameters related to the crack length, initializing all the parameter vectors, and calculating a kernel matrix of the parameter vectors; generatinga new perceptron model according to the parameter vector, the kernel matrix, the number of iterations and the allowable error, testing the new model through the data of the test set, and regeneratinga model if the test result does not meet the precision requirement; and predicting the real-time drilling data by utilizing the new model meeting the precision requirement so as to achieve the effectof predicting the stratum fracture length in real time.

The embodiment of the invention provides a drilling leakage crack width prediction method based on neural network data mining. The method comprises the steps that historical drilling data, a leakage stoppage case, an imaging logging real crack width and other data information of a target block are collected; data preprocessing is carried out on the collected data information, wherein the preprocessing content comprises data cleaning, integration and conversion; the preprocessed historical drilling data is used as input, the crack width is used as output, the imaging logging real crack width isused as a standard value, and a crack width prediction neural network model is obtained through supervision training and optimization; and the drilling instant data related to the target positive drilling well is imported into the neural network model, and the model automatically judges the corresponding well depth crack width at the moment. By utilizing the technical scheme provided by the embodiment of the invention, the crack width of the positive drilling well can be conveniently and accurately predicted in real time, so that decision support is provided for leakage stoppage constructionpersonnel, and the one-time leakage stoppage success rate of crack leakage is improved.

The invention provides a cultural and tourism economy operation data monitoring system architecture. The monitoring system architecture comprises a shared data platform, a supervision data platform and a key index extraction unit, wherein the data sharing platform captures cross-industry culture and tourism data, and uniformly stores the same type of field information of the cross-industry culture and tourism data through a plurality of preset tags; the supervision data platform provides process big data based on industry management; the key index extraction unit divides monitoring indexes according to the labels and the procedural big data, and integrates and analyzes culture and tourism data according to the monitoring indexes; the monitoring indexes comprise culture and tourism data applied in different industries under the corresponding preset labels. According to the method, multi-industry process data is applied in a targeted manner, common characteristics and attributes of culture and tourism market management are compatible, a proper index system is selected, a reasonable economic operation data monitoring system architecture is constructed, and the purposes of analyzing and monitoring the operation situation of the culture and tourism market are achieved.

The present invention discloses a regional power grid maintenance plan intelligent identification and auxiliary preparation method. It is the field of power maintenance technology. It includes the following steps: through the standard data resources in the power grid information physical fusion system, build a standardized basic database, and use fuzzy logic.According to the various constraints that affect intelligent reasoning, the maintenance knowledge base is formed;Equipment that needs to be maintained during the expiration; search for valid variables through the particle group algorithm to concentrate the basic data of each piece of maintenance, trigger the active rules mode, and use the regional power grid maintenance plan intelligent identification and auxiliary preparation system to complete the maintenance data of the maintenance data.Intelligent diagnosis analysis and adjustment.The present invention can effectively improve the rationality of the equipment maintenance plan, avoid repeated power outages, and improve the equipment utilization rate.

A generation method of a power generation scheme for preventing sustained damage of a hydropower station group includes the steps of 1, determining an optimization target and constraint conditions and establishing a statin optimizing and scheduling module; 2, considering uncertainty of hydrological forecasting and conversion of forecasting runoff; 3, calculating potential output of each phase; 4, determining a pattern of power generation constraint rules; 5, acquiring optimized power generation constraint rules and a power generation scheme set by means of an optimization algorithm; and 6, establishing an evaluation index system to evaluate optimal scheduling results. The generation method has the advantages that sustainability of the power generation process is ensured, a minimum of damage is ensured, and the simple and universal power generation constraint scheduling rules are provided to guide optimization of hydropower stations; the influence of hydrological forecasting errors upon scheduling results is fully considered, the power generation constraint scheduling rules are optimized by means of forms of energy, and more accurate decision basis is provided for decision makers.

The invention discloses a network transmission individual case risk index system evaluation method, which comprises the following steps of: 1, constructing a network transmission individual case riskindex system according to network transmission characteristics; 2, determining the connotation of one first-level index, five second-level indexes, twenty third-level indexes and three fourth-level indexes in the network sales individual case risk index system; 3, on the basis of the connotation of the risk indexes of the individual case sold by the network, constructing six mathematical models including a first-level index model and five second-level index models; 4, determining a calculation mode of 20 third-level indexes in the network transmission individual case risk index system; 5, determining a calculation mode of three four-level indexes in the network transmission individual case risk index system; 6, determining weights of five secondary indexes, twenty tertiary indexes and three quaternary indexes in the network sales individual case risk index system; and 7, determining a network transmission individual case risk evaluation method.

The invention discloses a reliability evaluation method for multi-level state monitoring data fusion, which specifically includes: according to the system and unit degradation law, determining the state of the system and the unit during the degradation process, and specifying the unit state combination corresponding to each state of the system; collecting system and the state monitoring data or information of the unit during service; update the current state probability of the unit in the system according to the multi-level state monitoring data or information; dynamically estimate the reliability of the multi-state system in the remaining service period. The method of the present invention combines the state monitoring data or information of the system level and the unit level in the multi-state system, and combines the logical combination relationship of the system and the unit state degradation law, and determines the current state of each unit in the multi-state system by constructing a Bayesian recursive model. state probability, so as to predict the future state and reliability of the system; at the same time, the method of the present invention considers the error of state monitoring data or information, making the method more general.