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 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 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.

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 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.