684 results about "Cloud modeling" patented technology

The invention provides a parking-lot parking space predication and recommendation method based on a real-time traffic query and a cloud model. A system according to the parking-lot parking space predication and recommendation method comprises a real-time traffic query feedback module and an idle parking space predication module. At a parking-lot parking space predication aspect, a guiding meaning of a parking-lot idle parking space historical rule to predication is considered, real-time number change of parking spaces and in-future possible change factors are considered, thereby realizing self-adaptive predication on the number change of parking-lot parking spaces in short time, well preventing a predication error problem caused by a noise, and furthermore satisfying requirements for high predication accuracy and high real-time performance. The parking-lot parking space predication and recommendation method overcomes the problems of randomness, fuzziness and non-linearity in parking-lot parking space predication, greatly improving predication accuracy, facilitating settlement for a problem of high difficulty in vehicle parking in a city, alleviating traffic jams, facilitating balancing parking space utilization rates of the parking lots and improving parking space management efficiency of the parking lot.

The present invention provides a human body posture detection method and a device. The method comprises the steps of receiving the first depth image of a human body, converting the first depth image into a three-dimensional cloud model, carrying out optimized fitting on a human body posture bubble model obtained in advance and the three-dimensional cloud model, wherein the human body posture bubble model is obtained according to a human body skeleton joint detected in advance, obtaining the human body skeleton joint coordinate in the first depth image according to an optimized fitting result, and obtaining the posture of the human body according to the human body skeleton joint coordinate in the first depth image. By using the scheme provided by the invention, the processing calculation of image recognition and classification of each depth image is not needed, the human body processing efficiency is improved, and the computational resources are greatly saved.

The invention discloses a real-time evaluation method for the health state of a wind turbine unit. The method comprises the steps that firstly, on the basis of historical running data of the wind turbine unit, running work condition dividing of the wind turbine unit is achieved by applying a clustering technology, and standard state cloud models of the wind turbine unit under all work conditions are calculated; secondly, work condition identification is conducted on real-time data stream of the wind turbine unit through a stream-type clustering algorithm, and cloud models of the real-time states of the unit are calculated; thirdly, deviation values between the cloud models of the real-time states and the standard state cloud models are calculated and taken as health indexes of the wind turbine unit; lastly, the health state of the wind turbine unit is evaluated according to the magnitude of the health indexes. According to the method, the running states of the wind turbine unit are described through the cloud models, the health state and the development tendency of the wind turbine unit are acquired by introducing a time window method, therefore, the uncertainty of state monitoring information of the wind turbine unit is fully taken into account, the accuracy of an evaluation result is greatly improved, and powerful support can be provided for making a wind turbine unit maintenance plan.

The invention discloses a three-dimensional display system and method based on cloud computing. The system comprises a three-dimensional data acquisition module, a three-dimensional cloud model library, a rendering compression optimization module, an interactive display module and a big data analysis background. 3-D data of an object to be displayed is intelligently processed base on a cloud computing service, the original three-dimensional model and the corresponding link of the object to be displayed are obtained, and the original three-dimensional model and the corresponding link of the object to be displayed are mutually displayed according to the obtained link or the three-dimensional model of the object to be displayed after the optimization of rendering and compression, so that theobject to be displayed can interact with a user, and the interaction experience is good; by linking or interacting with the optimized three-dimensional model of the object to be displayed, the cross-platform display can be realized, and the multi-screen interaction and air imaging are also supported, so that the functions are more abundant; The system can analyze and count the user's behavior whena user viewing the interactive display content, so as to obtain the interesting points of different viewers for the convenience of business use, and to be more comprehensive. The system and method can be widely applied to the technical field of display.

The method discloses a method for sorting signals of a radar radiation source by using coverage in a complex dense environment and relates to a radar radiation source signal sorting method by using coverage, aiming at solving the problem of poor sorting ability of the prior art in the complex dense environment as well as in a serious overlapping condition of characteristic parameters. The method comprises the following steps of: choosing the characteristic parameters of a radar signal capable of being standardized as input characteristic vectors, initializing a radar signal sample parameter space; carrying out supervisory learning on the radar signal with parameter prior information in the radar signal sample parameter space by using a coverage algorithm; separating the radar signals of different kinds by using different fields by using the coverage algorithm; expressing the membership degree of the characteristic parameters of each radar signal to be sorted and identified in relative with each field by using a cloud model, and obtaining the membership value of the membership degree of the radar signal to be sorted and identified in relative with each field; and sorting the signal to be detected by using decision rules. The method for sorting signals of the radar radiation source by using coverage in the complex dense environment is suitable for sorting radar radiation source signals.

The invention discloses an LFM (linear frequency modulation) signal detecting method under a strong interference source environment, belonging to the technical field of the signal processing. The LFM signal detecting method comprises the following steps of: firstly, carrying out time domain separation on received multi-component signals and various interference source signals by employing a array receiving time domain complex blind separating technology, decomposing the signals into multiple paths of time domain receiving signals; then respectively judging the signal and the interference of each path of time domain receiving signals respectively; selecting a broadband Gaussian noise interference source signal according to the broadband receiving system of an electronic reconnaissance receiver and the signal spectrum width characteristic represented by a second-order central moment of the spectrum density function; extracting the similarity judgment of the signal spectrum sequence via a cloud model feature vector and selecting a co-frequency narrow-band interference source signal; and at last, carrying out detection and parameter estimation of the multi-component LFM signal on remaining time domain separating signals by Wigner-Hough conversion respectively. The method can be used for effectively extracting single-component linear frequency-modulating signals in the multi-component signals and performing the accurate parameter estimation on the single-component linear frequency-modulating signals.

A computer-based method and apparatus to facilitate the exchange of pathology studies for the purpose of providing a primary or secondary pathological diagnosis. A study consists of one or more lower-resolution images, the references to the corresponding higher-resolution images, associated image metadata, study metadata and patient metadata. The studies are exchanged from one organization (hospital, practice, or individual physician) to another organization through a set of interconnected dispatcher services. In a cloud model, a plurality of dispatchers may be connected through a Global Dispatcher, both facilitating the addition of new organizations to the cloud and allowing for the addressing of studies from any organization in the cloud to any organization, group or individual in the cloud. By this means, the originating organization may obtain the desired level of care through the selection of recipient organizations, groups and individuals according to the organization's existing criteria. Efficiency in diagnosis is improved through the addressing of a study to a plurality of qualified recipients, as the first recipient with an appropriate, available resource may review and provide a diagnosis for the study.

The invention provides a method and a system for inversion of soil moisture under vegetation cover based on ASAR and Hyperion data. The method comprises the following concrete steps: acquiring Hyperion hyperspectral image data in an experimental zone and extracting a vegetation area in the experimental zone; constructing a vegetation water content model based on a ratio index and an empirical coefficient; carrying out regressive calculation through combination of vegetation water content actually measured in the ground and an vegetation ratio index calculated on the basis of the Hyperion hyperspectral image data so as to obtain the value of the empirical coefficient; matching ASAR radar data needed by the hyperspectral image data; and inputting the matched hyperspectral image data and ASAR radar data, the vegetation water content model and the value of the empirical coefficient into a water cloud model and carrying out inversion calculation to obtain water content in soil under vegetation cover. The method and the system provided by the invention have specific physical bases and the advantages of a few input parameters, easiness, flexibility, easy operation and the like and enable measurement of the water content in soil under vegetation cover to be more accurate.

The invention discloses a man-machine cooperation path planning method for an agriculture mobile robot based on a cloud model. The method comprises the following steps: dynamically guiding an algorithm A*(DGA*) to perform man-machine cooperation path planning, combining professional knowledge, hobbies and the like of people to form an expert system, introducing online uncertainty reasoning based on the cloud model, and realizing a rapid path planning strategy. The invention achieves the following beneficial effects that the agriculture mobile robot is taken as a research object, a large amount of experimental data are processed at the cloud by using the online cloud reasoning method based on man-machine cooperation, the time delay is greatly reduced, and the real-time reliability of autonomous path planning of the agriculture mobile robot is improved. By combining the man-machine cooperation path planning method based on the cloud model and the algorithm DGA*, the subjective initiative of people is fully exerted, the autonomous walking requirement of the agriculture mobile robot in a complicated dynamic environment is guaranteed, and the agriculture mobile robot has excellent operating performance for avoiding static and burst/dynamic obstacles.

The invention provides an automobile intelligent fault diagnosis and maintenance auxiliary method and system combining subjective and objective information and a cloud model. The method comprises steps of acquiring automobile objective original diagnosis data through an OBD diagnosis port; carrying out preprocessing on the data, judging whether fault codes are in the preprocessed data, and if yes, analyzing the fault codes and providing automobile maintenance assistance; if no, carrying out quantitative conversion on acquired automobile subjective uncertainty fault sign qualitative value through a cloud model method, and comparing the qualitative value with a standard value in an automobile system standard database, wherein the system automatically diagnoses automobile fault parts initially; diagnosing the automobile fault reason through a case-based reasoning method based on a normal cloud mode; and using the rule-based reasoning to finish diagnosis and elimination of automobile faults. According to the invention, automobile faults caused by a complex condition that an automobile has a fault without a fault code or by damage of non-electronic elements are solved; automobile fault diagnosis can be highly efficiently and rapidly finished; maintenance steps are normalized; and maintenance price is properly set.

The invention provides a method for establishing a data analysis model, and the method comprises the steps: learning and pre-training an initial cloud model at a server side through employing a deep neural network based on the current user data, and transmitting the initial cloud model to different users; the user constructs a user model of the user based on the received cloud model according to the data characteristics of the user, learns and trains the user model by adopting a deep neural network, and returns the trained user model to the server; the server fuses the received user model based on a preset period to obtain a new cloud model; and the user adjusts the own user model based on the received new cloud model, learns and trains by adopting the deep neural network at the same time,and returns the trained user model to the server. According to the model framework constructed based on the method, the problems of data islands and individuation can be solved by combining federatedlearning and homomorphic encryption technologies, a powerful machine learning model is constructed by summarizing data from different organizations, and the privacy of the user can also be protected.

The invention relates to a log multi-parameter oil-gas layer interpretation and evaluation method based on a radar map and a cloud model. The method comprises the steps of: obtaining interpretation and evaluation parameters by normal geological log, gas log, geochemical log, nuclear magnetic log and the like; integrating log interpretation databases; drawing the log multi-parameter oil-gas interpretation radar map according to different reservoir types in different areas by using the databases; and mapping the interpretation parameters onto the rays of the radar map. The radar map is divided into a good reservoir, a medium reservoir and a poor reservoir from inside to outside; the area of the radar map is taken as the characteristic quantity for finishing characteristic extraction; the area of the radar map is used to calculate a cloud boundary equation, and the cloud boundary equation is mapped on the manufactured cloud scale; the cloud model is the same as the radar map, namely the reservoir is divided into a dry layer area, a low-yield oil-gas layer area and an oil-gas layer area with industrial value from inside to outside. The intuitional and quantitative interpretation and evaluation of log multi-parameter oil-gas reservoirs is realized by the radar map and the cloud model. The interpretation coincidence rate can be increased, and the interpretation conclusion is represented by a graph, so that the log multi-parameter oil-gas layer interpretation and evaluation method is intuitional in expression and strong in applicability.

The invention belongs to the field of engineering risk evaluation, and particularly relates to an adjacent building safety quantitative evaluation method in tunnel construction. The adjacent building safety quantitative evaluation method in the tunnel construction mainly comprises the following steps of step 1, building an evaluation index system, step 2, building a standard extensible cloud model of evaluation indexes, step 3, computing and obtaining a weight matrix of the evaluation indexes through a subjective and objective weighting method, step 4, computing a relevancy matrix of a to-be-evaluated object through cloud relevancy computing, and obtaining a safety evaluation grade of the to-be-evaluated object through a weighted average method and combination of an evaluation index weighted value and the relevancy matrix, and step 5, measuring the reliability of safety evaluation grade. The adjacent building safety quantitative evaluation method in the tunnel construction combines advantages of a cloud model and an extensible theory, and provides effective decision support advices for protection and control of an adjacent building of different safety grades.

A subjective trust quantification evaluation method based on the cloud model theory which is applied under a network environment in a network activity based on interpersonal relationship is mainly used for solving the trust decision problem of a trust body during the interpersonal interactive process in the network environment which aims at profit or is nonprofit. The technical points of the invention are that: 1. comprehensive quantification processing and evaluating are carried out according to the historical subjective credit rating data of a trust object and aiming at the nondeterminacy of subjective data; 2. the comprehensive quantification processing and evaluating of the credit rating change of the object are carried out according to the subjective credit rating changing data of the trust object; 3. the trust decision is accomplished based on the quantification data obtained in the steps of 1 and 2. The subjective trust quantification evaluation method of thte invention can effectively solve the nondeterminacy problem of the subjective trust quantification evaluation and upgrades the successful rate of online trading or cooperation and the satisfaction of the trust body.

The invention provides a ground settlement risk grade evaluation method based on a cloud model and a data field. According to the method, a PS point is optimized through a cloud model algorithm; then, the data field is used for carrying out automatic clustering on the settlement speed, the population density and the economic density; the obtained settlement speed classification grade, the population density lattice grade or the economic density lattice grade are overlapped; and a ground settlement risk grading chart is generated. The evaluation method adopted by the invention considers the uncertainty of ground settlement risks and the relevance among the ground settlement risks; the influence on the ground settlement by factors such as hazard factor risks, hazard-affected body vulnerability and hazard-formative environment is integrated; the evaluation method is more scientific, reliable and accurate; and a reliable basis is provided for the ground settlement risk grading.

A cooperative game method and trapezium-cloud model-based power distribution network operation state fuzzy and comprehensive evaluation method is characterized by comprising the steps of considering the purposiveness, the systematicness, the operability, the independence, the significance and the dynamism, and constructing a multi-dimension hierarchical power distribution network index system based on the safety, the high quality, the economy, the Intelligence, the environmental protection property and the sustainability; utilizing a weight method, an entropy weight method, a anti-entropy weight method and a compatibility matrix analysis method to calculate the power distribution network index weights separately; carrying out the cooperative game on the determined weights to obtain an optimal combined weight; carrying out the variable weight correction on the combined weight to obtain a final weight value; dividing the evaluation grades of a power distribution network to obtain an evaluation set; calculating a trapezium-cloud model parameter that each index corresponds to each evaluation grade, utilizing a trapezium-cloud model to calculate the membership degree values of the indexes of the to-be-evaluated power distribution network, and calculating a membership degree vector and an evaluation result of the whole power distribution network layer by layer and upwards on a fuzzyand comprehensive evaluation frame.

The invention discloses a device for on-line monitoring and evaluating state of an air blower. The device comprises a data collection and operation module, a master controller module and a communication interface module, wherein the data collection and operation module is used for collecting parameter information of vibration of the air blower, temperature, voltage, electrical current, pressure, and the like; vibration signals of the air flow are decomposed by adopting a method of multiwavelet; early failure characteristic signals are extracted; performance evaluation is performed by adopting state performance evaluation algorithm based on a cloud model; the collected signals of the temperature, the pressure, and the like are distinguished and displayed by the master controller; and the device is equipped with a plurality of communication interfaces, so that data exchange between information and factory control net and manufacturing execution system can be realized. Through adoption of the device, performance state of the air flow can be evaluated accurately, so that reasonable maintenance for the air flow is realized.

The invention discloses an urban water disaster risk prediction method based on an RBF (radial basis function) neural network-cloud model. The method includes (1) determining evaluation factors, levels and the indicator range under corresponding levels; (2) determining an expectation Ex and an entropy En of the cloud model; (3) determining the weight of each evaluation factor according to measured values of the evaluation factors and the indicator range of each level; (4) training the RBF neural network, finishing model establishment for the RBF neural network, inputting the measured values of the evaluation factors of the cloud model to the trained RBF neural network to perform simulated prediction, and obtaining a prediction value of each evaluation factor; and (5) substituting the prediction value of each evaluation factor to the integrated cloud model to allow the integrated cloud model to calculate corresponding certainty degree of the prediction value of each evaluation factor belonging to each risk level and multiply the corresponding weight to obtain integrated risk level distribution. The urban water disaster risk prediction method is visualized and reliable and strong in operability, and accuracy of prediction is improved.

The invention discloses a color image encryption method based on dynamic chaos and matrix convolution operation. According to the method, a cloud model and generalized Fibonacci are combined to propose a new composite chaotic system, dynamic random change of a chaotic sequence is realized, and scrambling transformation of pixel point coordinates is performed on spliced images of R, G and B components of a color image by using the chaotic sequence; then, the chaotic sequence value is used as a matrix convolution cloud side for alternately updating an input value and a pixel value of matrix convolution operation, and replacement transformation of an original pixel value is realized; and finally, positive and negative two-way exclusive OR operation is carried out on the replaced pixel value,a cloud model Fibonacci chaotic sequence and the pixel values of the front (rear) adjacent pixel points to realize linkage diffusion transformation of the pixel values of the whole pixel points causedby the change of any pixel value so as to generate encrypted images in sequence. The method has the characteristics of high encryption security, high anti-interference performance, high robustness and the like.

The invention discloses a three-dimensional visualization simulation method directed at boundary element analysis. The three-dimensional visualization simulation method comprises reading in result documents output after the boundary element analysis; establishing vertex buffers and index buffers, and recording node coordinate information and node numerical value information into the vertex buffers and unit organization information into the index buffers to generate grid models, entity models, cloud models and reference coordinate systems; and performing rendering, three-dimensional operation or other operation synchronously on the models and the reference coordinate systems. The method processes the result documents after the boundary element analysis, only fundamental information of construction models and clouds is needed to be input, no special requirement exists for formats, and common business engineering software of Ansys, Patran, Sesam and the like can be used for providing input information. The method has a flexible processing mechanism, wide applicability and good practical engineering application values.

The invention discloses an evaluation method applied to the field of risk analysis of a railway signal system. The method comprises the steps of firstly, formulating the levels of risk of the railway system, expressing a cloud model for evaluating the levels of risk by a forward cloud generator of a MATLAB simulation software, and generating a standard risk grade evaluation set cloud model figure; marking the failure mode of a function by an expert according to the formulated levels of risk to obtain a risk factor set, and converting the risk factor set into a comprehensive evaluation matrix R; utilizing an analytic hierarchy process to obtain risk factor set weight coefficients corresponding to all parameters in the risk factor set by the expert, and calculating a comprehensive evaluation result cloud model figure by fuzzy synthetic operator; comparing the comprehensive evaluation result cloud model figure with the standard risk grade evaluation set cloud model figure to obtain the level of risk of the comprehensive evaluation result. After the method is adopted, the risk evaluation result is more objective and accurate.

The invention discloses an improved water cloud model and a paddy parameter inversion method adopting the model. The canopy heterogeneity and phenological change thereof are considered in the construction of a planting area scene by the water cloud model; the secondary scattering between the plant and the underlying surface is considered in the scattering mechanism analysis process; a quantitative relation between the main scattering mechanism and the plant parameter is established. The model framework is more reasonable and is more suitable for the practical process of the growth and development of the crops, including paddy. The improved water cloud model is applied to the paddy parameter inversion method. On the basis of polarized SAR data, the contributions of different scattering mechanisms and the correlation thereof with different paddy parameters are fully considered. A polarized SAR paddy parameter inversion method considering the canopy heterogeneity and phenological change thereof is developed. According to the method, the inversion accuracy is high, the error is small, the parameters in the early phenological periods of the growth and development of the paddy can be accurately inversed and the monitoring for the growth of the paddy and the estimation for the yield are more true, accurate and reliable.

The invention discloses a comprehensive evaluation method of water quality based on a water pollution index method and cloud models, and relates to an improvement on the water pollution index method by using the cloud models based on the water pollution index method. The cloud models are combined with WPI values in the water pollution index method, a WPI value evaluation cloud model is obtained by a cloud parameter determination method, a single factor WPI value cloud model is obtained by a backward cloud generator, a normal cloud generator is used to generate a WPI value evaluation cloud picture and a single factor WPI value cloud picture to intuitively see the pollution status of each single factor; weights are combined, a comprehensive WPI value cloud model is obtained through a comprehensive cloud algorithm, and comprehensive evaluation grades of the water quality are intuitively displayed by a form of cloud droplets and determined, so that the problems that contributions of all pollution factors in comprehensive evaluation of the water quality are not considered in the water pollution index method and comparisons among different sections are difficultly realized are effectively solved.