66 results about "Indicator matrix" patented technology

The invention discloses a plant cultivation method based on intelligent data collection and cloud service technology. The method comprises: S1, collecting plant growth indicators, the watering amount,the fertilization amount and the fertilization type, forming an influence factor matrix X, and uploading to the cloud server, wherein the decision variables are the watering amount, the fertilizationamount and the fertilization type; S2, determining plant health indexes, forming an indicator matrix Y, performing training and testing by an Elman neural network, and establishing a plant cultivation model; S3, optimizing the plant cultivation model by using the MOPSO algorithm to obtain a set of optimal solutions for each decision variable and plant health indexes corresponding to the optimal solutions; and S4, predicting the real-time data by using the model in the S3 to obtain a recommended decision X<*>, sending the X<*> to a user terminal, displaying the recommended optimal watering amount, fertilizing amount and fertilizing type in the user interface, a user remotely operating a device control sensor through the user terminal to complete automatic watering and automatic fertilization.

The invention is a principal component analysis based capacity optimization planning method for a pumped storage power station, which is characterized in that it includes: establishing a comprehensive evaluation index system for the capacity planning of a pumped storage power station, generating a capacity planning scheme for a pumped storage power station, and constructing a comprehensive evaluation Index matrix, convert the comprehensive evaluation index matrix to the same trend, perform standardized transformation on X′, calculate the correlation coefficient matrix for the comprehensive evaluation index matrix after the standardized transformation, calculate the variance contribution rate and cumulative variance contribution rate of the principal components and select Principal components, calculation of the selected w principal components, calculation of factor loads, comprehensive evaluation of each alternative, and determination of the optimal planning capacity of the pumped storage power station. It has the advantages of scientific and reasonable method, simple and practical, fast calculation speed and higher precision.

The invention relates to an entropy weight method resistance based photovoltaic power station operation characteristic assessment method, and belongs to the technical field of photovoltaic power generation. The entropy weight method resistance based photovoltaic power station operation characteristic assessment method includes firstly, establishing an assessment indicator system taking electric quantity, energy consumption, efficiency, power generation hours and power station operation hours as indicators, and acquiring the related indictors through collection and calculation; preprocessing the related indicators acquired by calculation to acquire an average indicator matrix; calculating anti-entropies of the indicators according to the average indicator matrix; finally, calculating weights of the indicators according to the anti-entropies and calculating an assessed value of photovoltaic power station operation characteristics according to the weights to realize assessment of the photovoltaic power station operation characteristics. The entropy weight method resistance based photovoltaic power station operation characteristic assessment method has the advantages that a photovoltaic power station performance assessment mathematic model is created through establishment of the assessment indicator system for the photovoltaic power station operation characteristics, so that scientific guidance and shared power station experiences are provided for overall operation performance assessment of photovoltaic power stations, and development of operation and maintenance of the photovoltaic power stations is promoted.

The invention discloses a method and system for optimizing an electricity reliability improving object of an intelligent distribution network. The system includes a data acquisition module, an evaluation module and a storage module. The optimization method includes: establishing an intelligent distribution network electricity reliability improving demand degree evaluation index system; allowing the data acquisition module to acquire the related index data and store the related index data in the storage module; allowing the evaluation module to normalize and nondimensionalize the index values of all the objects to be optimized to form a normalized index matrix; calculating subjective and objective weight values of each index, and calculating a comprehensive weight value of each index; calculating a weighted normalized index matrix, determining positive and negative absolute ideal solutions, and calculating the grey relational degree of each object to be optimized; calculating the relative closeness degree of each object to be optimized relative to the positive ideal solution, optimizing the reliability improving object according to the relative closeness degree, and outputting an optimization result to the storage module. The method and system can comprehensively evaluate the real electricity consumption situation of the users in the intelligent distribution network, and guide the development of the electricity reliability improving project of the intelligent distribution network.

A method for modeling an image for multiple tasks includes providing an image with n image features, providing an indicator matrix which has m non-zero components corresponding to the m features selected from the n features, constructing a model of the image using the m selected features for each specific labeling task. There are a variable for a specific task to be performed on the image and a variable for a plurality of tasks to be performed on the image.

The invention discloses an electric energy meter manufacturer evaluation method based on a multi-attribute decision model, which relates to an evaluation method. The invention comprises the followingsteps: acquiring the original data of the electric energy meter and determining an evaluation index; Calculate each index value of the electric energy meter, construct the index matrix, form the decision matrix; Calculate the information entropy and Spearman grade correlation coefficient of each index value of watt-hour meter, determine the objective weight of the quality evaluation index according to CRITIC method, process the decision matrix and obtain the watt-hour meter supplier evaluation matrix; Calculate the positive ideal solution and negative ideal solution of the watt-hour meter supplier, and calculate the Euclidean distance from each watt-hour meter supplier to the positive ideal point vector and the negative ideal point vector. Calculate the relative approximation degree between each watt-hour meter supplier and the ideal solution, and determine the quality level of each watt-hour meter supplier from high to low. The technical scheme provides an objective and scientific basis for the electric energy meter quality evaluation and supervision of the power grid company.

A graphical indicator comprising a plurality of first header blocks, a plurality of second header blocks and a plurality of data blocks for forming an indicator matrix is provided. Each of the first and second header blocks has a header graphical micro-unit, and each of the data blocks has a data graphical micro-unit. An array area is formed by the second header blocks and the data blocks. A first virtual line and a second virtual line are respectively formed by virtual centers of the first and second header blocks, and an included angle between the first and second virtual lines is less than 90 degrees.

The invention discloses an operation state monitoring method for an intelligent vehicle road system. The method comprises the steps of: building an index matrix from the dimensions of an intersection,a road segment and a road network to obtain an operation state index set; aiming at the characteristics of multiple index parameters and different dimensions, adopting a threshold value method to carry out normalization processing on numerical values; determining the dispersion degree of each index by using an entropy weight method, and calculating the weight of the indexes; measuring the calculation deviation between the index values by adopting a weighted total root-mean-square deviation calculation method to reflect the evaluation accuracy; and performing combined weighting on the index set by using entropy weight-weighted total root-mean-square deviation calculation; importing the index set into a deep reinforcement learning neural network, performing multi-layer convolution by usinga gating structure, extracting a feature value, obtaining an optimal index set by using the reinforcement learning network and training a maximized objective function, and performing comprehensive analysis on an IVIS operation condition. Through the scheme, the possibility of absolute concentration of the indexes is reasonably avoided, and the accuracy of the index set is greatly improved.

The patent discloses a running condition monitoring method of an intelligent vehicle-road system. The method comprises the steps that an index matrix is established based on dimensions such as intersections, road sections and road nets, and a running condition index set is obtained; aiming at a characteristic that a lot of index parameters exist and dimensions are different, values are normalizedthrough a threshold method; an entropy weight method is used for judging a dispersion degree of each index and calculating a weight occupied by each index; a calculation method of weighting total root-mean-square deviations is used for measuring calculation deviations between the index values and reflect accuracy of measurement; calculation of entropy weight-weighting total root-mean-square deviations is used for combined empowerment of the index set; and the index set is import into a deep reinforcement learning neural network, a gate control structure is used for multi-layer convolution, characteristic values are extracted, the reinforcement leaning network is applied, an optimal index set is obtained through training of a maximized target function, and comprehensive analysis of IVIS running conditions is realized. By the scheme, possibilities in an index absolute set are avoided rationally, and accuracy of the index set is greatly increased.

The invention discloses a gas field overall layout evaluation method based on multi-level fuzzy comprehensive evaluation. The method comprises the following steps: (1) taking technical economy, dependable conditions and safety production as first-level indexes, refining each first-level index into a second-level index, and establishing a multi-level analysis evaluation index system; (2) determining the weight of each secondary index by using an AHP analytic hierarchy process; (3) constructing an evaluation index matrix; (4) standardizing the evaluation index matrix to obtain a fuzzy relative membership matrix R; and (5) introducing the weight of the secondary index into fuzzy comprehensive evaluation calculation, calculating the relative membership degree of each scheme, and finally determining the optimal scheme of the overall layout of a gas field according to the maximum membership degree principle. The evaluation method has objectivity and provides a certain theoretical basis for comparison and selection of the overall layout scheme of the gas field.

The invention provides a variety identification method and system based on component structure characteristics, and relates to the technical field of variety identification, and the method comprises the steps: obtaining the content data of a target component substance contained in a sample to be identified; Converting the content data into component data of relative percentage closed structure characteristics; Establishing a sample index matrix according to the component data; Converting the sample index matrix into an Euclidean space to obtain a new sample index matrix; And identifying the variety of the to-be-identified sample according to the new sample index matrix to obtain the identification accuracy of the to-be-identified sample. According to the variety identification method and system based on the component structure characteristics provided by the embodiment of the invention, the substance varieties can be identified more objectively and reliably, and the identification accuracy is effectively improved.

The invention relates to a comprehensive performance evaluation method of a numerical control machine tool based on an improved pull-out method, which belongs to the technical field of performance evaluation of a numerical control machine tool. The linear proportional method is used to standardize the data of various performance indicators of the machine tool. The subjective weights of indicators at all levels are determined based on the ordinal relationship analysis method, the two objective weights of indicators at all levels are determined by the entropy weight method and the mean square error method, and the subjective and objective weight information is synthesized based on the principle of vector Pearson correlation coefficient. The comprehensive weight is used to weight the index, the final weight coefficient of the index is determined by the positive definite matrix corresponding to the weighted index matrix, and the comprehensive evaluation value of the three-level index is obtained based on the linear weighted evaluation function. Finally, a similar method is used to calculate the comprehensive evaluation value of the large system layer by layer. The invention is used for the comprehensive performance evaluation of various numerical control machine tools and the horizontal comparison of the specific performance of different machine tools, and provides a scientific and operable evaluation method and process for the comprehensive performance evaluation of the machine tools.