149 results about "Intuitionistic fuzzy" patented technology

The invention relates to a brain MRI (magnetic resonance image) segmentation method based on an improved fuzzy C-means clustering algorithm. The method comprises steps that 1, initial classification is carried through utilizing the fuzzy C-means clustering algorithm; 2, the clustering quantity c, a fuzzy factor m, an algorithm iteration stop threshold Epsilon, the maximum iteration times max, a neighborhood window size and other artificial setting parameters are given; 3, a similarity matrix W of two pixels is calculated; 4, similarity rhoki of pixel pair types is calculated; 5, a membership matrix U is updated; 6, if ||U(t+1)-U(t)||<Epsilon, or t=max, iteration stops, U(t+1) is outputted, otherwise t=t+1, and the process turns to the step 4; and 7, for U(t+1), the maximum membership algorithm is employed to carry out deblurring operation, and label distribution is carried out to accomplish image segmentation. Through the method, three-portion optimization including improving a clustering center mode, introducing the partial space information and utilizing the intuitionistic fuzzy set information is accomplished, effects of noise resistance enhancement and segmentation precision improvement are realized, and an actual problem of high-precision segmentation for a brain MRI is solved.

The invention relates to a pornographic image recognizing method comprising: a first step of establishing a sample image database; a second step of extracting characteristics of sample images; a third step of training the characteristics extracted from the sample images to form a pornographic image classifier; a fourth step of extracting the characteristics of an image needing to be recognized; and a fifth step of inputting the characteristics of the image needing to be recognized in the pornographic image classifier, thus obtaining a recognition result. The image characteristics employ a color histogram characteristic vector consisting of determined values, fuzzy values and intuition values together. The invention further comprises a step of optimizing the image characteristics by genetic algorithm. When in use, the method can increase the image recognition accuracy and shorten the recognition time.

The invention relates to the field of pattern recognition, and discloses an unbalanced data classification method based on intuitive fuzzy integration and a system based on the method. The method comprises the following steps of: a) cleaning original data, and classifying original point-of-sale (POS) class samples according to intra-class positions to generate POS class artificial samples; b) training a base classifier by using different sample sets of inter-class approximate balance; c) converting the classification output equal utility of the base classifier into an intuitive fuzzy matrix; and d) integrating samples to be classified into the membership and the non-membership of the POS class and the negative (NEG) class by combining the weight of the base classifier, and making a classification decision. The invention has the advantages that: over learning is avoided by integrating over sampling and under sampling; the training samples of the base classifier are different, so that the difference of the base classifier is ensured; the base classifier is not specifically limited, so the method has good expandability; the intuitive fuzzy reasoning method quantitatively describes the uncertainty in classification so as to improve the performance of integrated learning; therefore, the system based on the method can better support the medical diagnosis decision and the like.

The invention discloses a method and a device for selecting a network based on a converged heterogeneous radio access network. The network selection method comprises the following steps of: transforming the attribute evaluation value of each attribute of the spare network to respective intuition fuzzy number according to the network selection state information and the heterogeneous radio converged network information; obtaining a fuzzy multi-attribute decision matrix according to the respective intuition fuzzy number and further obtaining a normalized decision matrix; then, obtaining the optimal attribute weight by using the maximum total difference policy of all the attributes between the attribute evaluation values of all the spare network; and weighting the normalized decision matrix by using the optimal attribute weight vector, processing the optimal normalized weighting fuzzy policy matrix based on a possibility and sorting to obtain the optimal network. By the network selection method and device, the stability of the network selection mechanism is improved without influence on the network selection accuracy.

The invention discloses an agent-based construction method and device of an intuitionistic fuzzy theory medical diagnosis model. With the adoption of a sensor technique and an artificial intelligence technology, remote and automatic acquisition and transmission of patient symptom are achieved; diagnosis analysis is carried out on the patient symptom by an intelligent medical diagnosis model built based on an intuitionistic fuzzy theory; the patient symptom is continuously updated and completed by a systematic knowledge module, namely a symptom-disease knowledge base and a disease-medicine knowledge base, and the diagnosis effect of the system is improved and enhanced; and on the condition that the existing medical staff, resources and technology are limited and the ageing population of China is faster, agent-based intelligent medical diagnosis system (AIMD) design is changed to ATM in a substantialization way and is installed in an aged community, convenient and high-efficiency medical services are provided for a patient through self-service terminal services, and the medical stress is effectively relieved. By the medical service mode, the medical clinic time is saved, and the medical cost is also reduced.

The invention discloses a power grid disaster real-time regulating and control device and method based on an intuition fuzzy rough set. The method includes the steps that historical disaster data in a database unit are called; the collected historical disaster data are reduced through the method based on the intuition fuzzy rough set, and then disaster grade evaluation rules of a power grid are obtained; disaster data of the power grid are collected in real time, and the current disaster grade of the power grid is determined according to the formed historical disaster grade evaluation rules; the data which are evaluated in real time are updated to a historical database and used for addition and modification of a power grid disaster knowledge base; power grid disaster emergency responses are conducted, namely, corresponding emergency restoration measures are taken according to different disaster grades to regulate and control the power grid. According to the system and method, the situation that the data size used in the power grid disaster evaluation process has fuzziness, randomness, uncertainty, redundancy and other characteristics is taken into account, the defect that internal relations and potential relations of data attributes can not be acquired through a traditional probability theory and other method is successfully overcome by the utilization of the method based on the intuition fuzzy rough set, and evaluation accuracy is improved.

The present invention discloses a lifting intuitionistic fuzzy tree based target tracking method. The method comprises: carrying out movement detection on a current video frame, and taking possible movement objects obtained from detection as observation results; associating the observation results with prediction results of targets, wherein the targets comprise reliable targets and temporary targets; carrying out trajectory management on unassociated observation results and prediction results, including carrying out on-line tracking on the unassociated prediction results of reliable targets so as to obtain candidate results, and using lifting intuitive fuzzy trees of the unassociated reliable targets to match the candidate results; and using associated results and matched results to obtain a trajectory of the target of the current frame, using the trajectory of the target of the current frame to predict, and updating the lifting intuitionistic fuzzy tree for the successfully associated or successfully matched reliable targets. The present invention further discloses a lifting intuitionistic fuzzy tree based target tracking apparatus. According to the method and apparatus disclosed by the present invention, performance of the target tracking in the case of the leak detection can be improved.

The invention discloses an unmanned combat aerial vehicle maneuvering gaming method with intuition fuzzy information. The method comprises steps of firstly, establishing a strategy set of an unmannedcombat aerial vehicle maneuvering decision; then, carrying out multi-attribute intuition fuzzy estimation on selectable maneuvering strategies according to the threatening size so as to obtain an intuition fuzzy payment matrix of unmanned combat aerial vehicle maneuvering gaming; establishing a unmanned combat aerial vehicle maneuvering gaming planning mode with intuition fuzzy information under an undetermined environment; and finally, using an improved differential evolution algorithm, solving the mode so as to obtain the optimal maneuvering gaming strategy of the unmanned aerial vehicle under the undetermined environment. According to the invention, the optimal decision problem of the unmanned combat aerial vehicle maneuvering under the undetermined environment is mainly solved.

The invention relates to an image segmentation method based on improved intuitionistic fuzzy C-means clustering, belonging to the image segmentation field. Firstly, an improved non-membership functionis proposed to generate intuitionistic fuzzy sets, and a method based on gray feature is proposed to determine the initial clustering center, which highlights the role of uncertainty in intuitionistic fuzzy sets and improves the robustness to noise. Secondly, the improved nonlinear function is used to map the data to the kernel space in order to measure the distance between the data points and the clustering centers more precisely. Then we introduce the local space-Gray level information, considering membership degree, gray level features and spatial position information. Finally, the intuitionistic fuzzy entropy in the objective function is improved, and the fuzziness and intuitionism of the intuitionistic fuzzy set are considered. The invention can effectively overcome the influence ofnoise and blur in the image on the algorithm, improves the segmentation performance of the algorithm, the pixel clustering performance and the robustness, is suitable for various different types of gray-scale images, and can obtain more accurate segmentation results.

The invention discloses a video target multi-target tracking method and device and a storage medium. The video target multi-target tracking method comprises the following steps: obtaining a first image feature of a target object and a second image feature of an observation object; performing feature similarity calculation of N feature categories on the first image feature and the second image feature to obtain N groups of feature similarity results; screening the N groups of feature similarity results based on the feature similarity of the rough set, and fusing the screened results to obtain afeature fusion result; based on maximum entropy intuitionistic fuzzy clustering, performing association cost matrix calculation according to the feature fusion result to obtain an association cost matrix calculation result; judging whether the target object is associated with the observation object or not according to the association cost matrix calculation result, and if yes, updating a target model; and if not, performing target trajectory management on the target object.

An embodiment mode of the invention discloses an intuitionistic fuzzy association method for ADS-B monitoring data and radar tracks. The method comprises the steps of carrying out association pretreatment on the ADS-B monitoring data and radar tracks to obtain a plurality of primary association radar tracks, obtaining fuzzy intuitionistic indexes corresponding to association attributes between the ADS-B monitoring data and the primary radar tracks, obtaining fuzzy decision grades corresponding to the association attributes according to the fuzzy intuitionistic indexes, and carrying out weighing summation on the fuzzy intuitionistic indexes to obtain final associated radar tracks. The associated attributes comprise the distance, the speed, the speed difference and the course angle difference. The embodiment mode of the invention further discloses an intuitionistic fuzzy association device for the ADS-B monitoring data and the radar tracks. By means of the method, the intuitionistic fuzzy association method for the ADS-B monitoring data and the radar tracks can improve association accuracy between the ADS-B monitoring data and the radar tracks and is easy to achieve.

The invention relates to the technical field of digital image processing, and discloses an image enhancement method based on an intuitional fuzzy set so as to effectively improve the quality of a weak-edge noise image. Based on the intuitional fuzzy set theory, a new intuitional fuzzy operator is provided; then, through self-adaptation operation on a membership plane, the membership of pixel points in a foreground area is increased, and the membership of pixel points in a background area is decreased; finally, through inverse transformation, a high-quality image is acquired. Specific information of the image is selectively highlighted or restrained, so that the image is more suitable for visual characteristics of humans or recognition functions of other systems.

The invention discloses a network selection method based on intuitionistic fuzzy set multi-attribute decision-making. The method comprises the following steps: determining an intuitionistic fuzzy set decision-making matrix; converting each attribute value in the intuitionistic fuzzy set decision-making matrix into a trapezoidal fuzzy number to obtain a trapezoidal fuzzy number decision-making matrix, and simultaneously obtaining a membership function of each trapezoidal fuzzy number; by use of a distance formula of an intuitionistic fuzzy set, converting the distance relation of the intuitionistic fuzzy set into an area relation of the membership function of a corresponding trapezoidal fuzzy number set, and according to the trapezoidal fuzzy number decision-making matrix, obtaining a total deviation between each network to be selected and all other networks to be selected under each attribute; based on a deviation maximum idea, establishing a weight model of attributes, and according to a total deviation value, obtaining a weight value of each attribute; and by use of an intuitionistic fuzzy set weighed averaging operator (IFWA), calculating integrated attribute values of the networks to be selected, and selecting an optimum network from the networks to be selected. According to the invention, the problem of network selection in a heterogeneous network environment can be effectively solved.

The invention discloses a method and system for evaluating power consumption condition performance of an electric power user. The method comprises the following steps: solving first level index weights subjective weights of second level indexes corresponding to first level indexes by using an improved G1 algorithm; calculating correction weights of the second level indexes by using a modified intuitionistic fuzzy entropy method and the subjective weights; calculating performance evaluation scores of the first level indexes according to the correction weights by using a TOPSIS method based on an intuitionistic fuzzy set; and calculating an estimated value of power consumption condition performance of the electric power user according to the performance evaluation scores and the first level index weights. According to the method, based on the hierarchical comprehensive assessment method, the first level index weights are solved by using the improved G1 algorithm, the second level index weights are calculated by using the modified intuitionistic fuzzy entropy method, twice weighting integration is carried out on the index values of the levels, thereby reducing the influence of single weighting to the performance evaluation result, and expert experience and objective data are combined to reflect the subjective and objective evaluation information, which ensures the validity and the rationality of the performance evaluation.

The invention relates to a method using an improved intuitionistic fuzzy clustering algorithm to segment an infrared ship image. The method includes: using a threshold obtained by an image variance matrix and a Chebyshev inequality to determine the possible area of a ship, and using the center of the possible area as the preset ship center; using the improved intuitionistic fuzzy clustering algorithm to segment the infrared ship image. The method has the advantages that the ship central position is considered, and the ship central positon is used to build the distance measurement from pixels to different clustering centers; the regular terms built by area variance is used to guide neighborhood information into a target function; the improved algorithm sufficiently considers the influence of the ship central position on data clustering, reasonably utilizes the neighboring information, and accordingly the infrared ship image segmentation result is improved evidently, and the method is promising in market prospect and high in application value.

The present invention discloses an intuitionistic fuzzy random forest based target tracking method. The method comprises: carrying out movement detection on a current video frame, and taking possible movement objects obtained from detection as observation results; associating the observation results with prediction results of targets, wherein the targets comprise reliable targets and temporary targets; carrying out trajectory management on unassociated observation results and prediction results, including carrying out on-line tracking on the unassociated prediction results of reliable targets so as to obtain candidate results, and using intuitive fuzzy random forests of the unassociated reliable targets to match the candidate results; and using associated results and matched results to obtain a trajectory of the target of the current frame, using the trajectory of the target of the current frame to predict, and updating the intuitionistic fuzzy random forest for the successfully associated or successfully matched reliable targets. The present invention further discloses an intuitionistic fuzzy random forest based target tracking apparatus. According to the method and apparatus disclosed by the present invention, performance of the target tracking in the case of the leak detection can be improved.

The invention belongs to the field of multi-attribute decision making, and discloses a method and system for interval intuitionistic fuzzy multiple attribute decision-making base on improved entropy and score function. Aiming at the popular problem of supplier selection, the fuzzy entropy of uncertainty and hesitation is used to determine the weight of each index under the condition that the weight of supplier attribute is completely unknown, and the objective weighting method is used to correct the deviation caused by the agent preference. The new score function is used to sort the scheme set, and the final supplier selection scheme is obtained. The invention utilizes an improved method to calculate the attribute weight according to the contribution degree of the attribute to the decisionscheme. In view of the limitation of the existing score function, a new score function is proposed. The weight of the attribute is correctly and reasonably calculated, and the score function makes upthe problem the other sorting functions fail to sort certain interval numbers to a certain extent. When the invention selects suppliers for enterprises, the invention provides a more objective and reasonable method.

The invention discloses an infrared and weak visible light image fusion method based on a Cauchy ambiguity function, Firstly, the improved guiding filter is used to enhance the visibility of the darkregion of the weak visible light image. Secondly, the infrared image and the enhanced weak visible light image are decomposed by NSST to obtain the low frequency and high frequency components of the infrared image and enhanced weak visible light image. Secondly, a Cauchy membership function is constructed by using an intuitionistic fuzzy set to fuse the low frequency components of infrared image and enhanced weak visible image, and the high frequency components of infrared image and enhanced weak visible image are fused by using an adaptive two-channel pulse emission cortex model. Finally, thefused low-frequency and high-frequency components are reconstructed by non-downsampled shear wave inverse transform, and the final fused image is obtained. The invention can more effectively extractthe detail information of the image and obtain better fusion effect.

The invention discloses a vector quantization based long-term intuitionistic fuzzy time series prediction method. The method includes the steps of A, intuitionistic fuzzification preprocessing of serial data and B, vector quantization based long-term intuitionistic fuzzy time series prediction. A long-term intuitionistic fuzzy time series prediction model built by the method expands single output of the serial data into multiple outputs, a predicated value is converted from a scalar into a vector, and accordingly long-term predication performance of a time series system is improved to a great extent. A sliding window mechanism is introduced into the method, fuzzy change characteristics of the serial data are acquired accurately and rapidly; a discourse domain internal is divided dynamically by the aid of an IFCM (intuitionistic fuzzy C-means) algorithm to be more close to the actual uncertain data distribution; by the aid of vector quantization based long-term time range prediction, the problems of zero matching of intuitionistic fuzzy rules and system error accumulation can be well solved; through example verification and result analysis, the model has good predication performance.

The invention particularly discloses a fuzzy QoS (Quality of Service) selection method for web services in combination with expert opinions and user preference. The fuzzy QoS selection method comprises the following steps that: 1. performance situations and importance degrees of all QoS indexes are described by using fuzzy language variables based on an intuitionistic fuzzy set; 2. all experts evaluate the importance degree each QoS index and the performance situation of each QoS index of each service by using the fuzzy language variables, and evaluations of all experts are combined to obtain the comprehensive opinions of all experts on the QoS indexes; 3. users raise requirements on the performance situation and the importance degree of each QoS index by using the fuzzy language variables, and the comprehensive opinions of all experts on the QoS indexes are combined to obtain the all opinions on the QoS indexes; and 4. QoS comprehensive rankings of services are obtained according to the overall opinions on the QoS indexes. The fuzzy QoS selection method for web services in combination with expert opinions and user preference has the advantages that not only can the fuzzy evaluations of the experts be combined, but also the user preference is considered, and QoS selections which contain the expert opinions and respect the user preference are obtained by enabling the user preference to act on the expert opinions.

The invention discloses an improved FMEA method based on an interval intuitionistic fuzzy set and a mixed multi-criterion decision model. In the first stage, an interval intuitionistic fuzzy network analysis method is used to determine the weight of risk factors, firstly, the network structure diagram of risk factors and sub-risk factors is formed, the interval intuitionistic fuzzy pair comparisonmatrix is constructed and the aggregation degree is calculated, then the local ranking vector is calculated to obtain the initial super-matrix and weighted super-matrix, and finally the weights of all sub-risk factors are determined. In the second stage, an interval intuitionistic fuzzy comprehensive proportional evaluation method is utilized to determine the risk order of all failure modes, theset interval intuitionistic fuzzy risk assessment matrix is constructed for the first time, then the matrix is standardized and weighted to calculate the total risk and risk degree of each failure mode, and finally the ranking of all failure modes is determined. The fuzziness and uncertainty of expert language evaluation is overcome effectively. the hierarchy and interrelation of risk factors areconsidered. Thus, the risk ranking of failure modes is more accurate.

A power grid risk comprehensive assessment method based on improved power betweenness is used for objectively evaluating power grid risks. The technical scheme is that the method first calculates improved power betweenness of each node and line; then the improved power bewteenness is introduced into a power grid fault consequence severity index, thereby obtaining a power grid fault consequence severity index based on improved power betweenness; then an intuition fuzzy analytic hierarchy process is adopted to make a comprehensive assessment of a fault severity index, and an evaluation result and a power grid fault probability are combined, thereby obtaining a final power grid risk value; and finally, power grid risks are accurately assessed according to the final power grid risk value. The power grid risk comprehensive assessment method based improved power betweenness introduces the improved power betweenness into the power grid fault consequence severity index, so that each index contains consideration of influence on a load side in a calculation process, and the overall fault severity evaluation result is relatively reasonable. The power grid risk comprehensive assessment method based improved power betweenness can comprehensively, objectively and accurately make an evaluation of power grid risks.

The invention discloses a multi-target threat assessment method suitable for a high dynamic environment, which relates to high-level information fusion and decision problems of a sensor network. Basedon the entropy measurement method and dynamic intuitionistic fuzzy method, the attributes of battlefield targets are reasonably weighted, and attribute information of multiple successive time of eachtarget is selected, according to the importance of target information in threat assessment, the time series weights of target information are calculated by the normal distribution method, and the target information at multiple times is weighted synthetically according to the time series weights; on the basis, the dynamic intuitionistic fuzzy multi-target threat estimation model is established, and the final dynamic threat assessment result, i.e., the threat degree ranking of multi-targets, is calculated. The method not only considers the current time target information, but also can effectively fuse the preorder time target related information, so it can dynamically reflect the continuously changing battlefield situation, and make the multi-target threat assessment effect more scientificand reasonable under the highly dynamic environment.

The invention discloses an on-line sequence data prediction method and device. The prediction method includes: constructing an interval type-2 intuitionistic loop fuzzy neural network model; and training the interval type-2 intuitionistic loop fuzzy neural network model. The special procedure is as follows: recursively updating an average and an standard deviation to standardize sequence data samples; selecting an intuitionistic fuzzy set fuzzy rule on the basis of a dynamic clustering method of recursive computation of an approximate density center; inputting the standardized sequence data samples into the interval type-2 intuitionistic loop fuzzy neural network model with the selected intuitionistic fuzzy set fuzzy rule, outputting a sequence data prediction result, and determining whether the sequence data prediction result meets a preset precision requirement or not; completing training of the interval type-2 intuitionistic loop fuzzy neural network model; otherwise, updating an output weight, a feedback weight, an intuitionistic fuzzy set fuzzy rule center and a point difference until the output prediction result reaches the preset accuracy requirement; and inputting on-line sequence data into the trained model to obtain a prediction result.

The invention belongs to the technical field of radar radiation source signal identification and evaluation in electronic countermeasure, and discloses a radar radiation source signal identification efficiency evaluation method and system. The method comprises the following steps: firstly, analyzing attributes with relevance characteristics under an evaluation criterion by adopting an intuitionistic fuzzy network analysis method to obtain corresponding attribute weights; Secondly, on the basis of the interval hesitant fuzzy thought, selecting an identification rate test result more reasonably,determining elements in an interval hesitant fuzzy initial decision matrix, and providing more accurate original data for a subsequent decision process; And finally, on the basis of the interval hesitant fuzzy thought, achieving evaluation of the radar radiation source signal recognition efficiency by using a cancellation and selection conversion method and combining an approximation ideal sorting method. The method can reasonably and effectively solve the problem of mutual independence of attributes, and can avoid low-efficiency assessment caused by doping of human factors and incapability of fully assessing all recognition schemes in a decision-making method.

The invention discloses an image segmentation method based on intuitionistic fuzzy C-means clustering. The method mainly solves the problems that image segmentation is sensitive to noise and is proneto falling into local optimum, and the number of clusters needs to be set. According to the scheme, an intuitionistic fuzzy set based on noise robustness is constructed based on a switch mean value strategy to perform curve fitting on gray value distribution of an image, all peak points of a fitting curve are screened to serve as an initial value range set of a clustering center, and the maximum number of the clustering centers is counted; on the basis, position information and gray scale information of pixels are utilized to construct a linear weighting function coefficient in an intuitionistic fuzzy objective function, and a membership matrix U is obtained; and according to the grading distance index evaluation index evaluation U, an optimal membership matrix is obtained, and an error detection strategy is used to screen error pixels for correct classification. According to the method, the noise robustness is enhanced, the number of image clusters can be adaptively determined, and the method can be used for image recognition and computer vision preprocessing.

The invention discloses a naval vessel formation dynamic air defense threat estimation method based on intuitionistic fuzzy TOPSIS, and relates to the field of naval vessel formation air defense decision. The method comprises the following steps of firstly standardizing different types of target information, and establishing an intuitionistic fuzzy decision matrix; secondly obtaining an initial weight of an evaluation index by using an intuitionistic fuzzy entropy weight method, and fusing subjective preference information of a commander to solve an index weight optimization model, so that a weight result better meets the requirements of a battlefield; then, determining a dynamic decision matrix by adopting intuitionistic fuzzy cross entropy and relative closeness, processing the multi-moment information by adopting a normal distribution method, establishing a weighted dynamic decision matrix to present the air defense threat situation dynamically; and finally obtaining a threat degreesorting result of the incoming targets by using a TOPSIS method. The method can be applied to the dynamic air defense threat estimation of a naval vessel formation in an uncertain sea battlefield environment.

The invention relates to a particle swarm and fuzzy means clustering based cell image segmentation method. On the basis of an intuitive fuzzy means clustering theory, an intuitive fuzzy membership degree form is improved aiming at cell images and combined with fractional order particle swarms to realize alternate optimization, and a morphological method is adopted for result improvement to realize accurate segmentation of the cell images. The method includes steps: firstly, selecting appropriate parameters, initial particle swarms and relevant data; secondly, starting alternate iterative optimization of the fractional order particle swarms and intuitive fuzzy clustering; thirdly, comparing results with standard fuzzy means clustering results, and selecting a morphological processing method through differential images; finally, subjecting result images to range conversion and watershed segmentation to obtain a final result. The particle swarm and fuzzy means clustering based cell image segmentation method can be widely applied to various cell image based application systems and has a promising market prospect and a high application value.

A method of manufacturing demand and device capability normalization modeling facing cloud manufacturing provided by the invention comprises the steps of: introducing a feature process route on the basis of establishing a device manufacturing capability model and a part manufacturing demand model, employing an intuitionistic fuzzy algorithm to perform matching of a device source set and a cloud task manufacturing demand, and constructing a manufacturing demand and device capability normalization model facing cloud manufacturing. Through combination of features of cloud manufacturing, the method of manufacturing demand and device capability normalization modeling facing cloud manufacturing integrally considers diversity of parts and devices, complexity of the manufacturing process, manpowerparticipation factors and quantifiable factors of the resource capability, performs feature modeling of the demand and the capability, and establishes a mapping relation of the cloud manufacturing task and the device capability, so that the efficiency and the capability of subsequent manufacturing resource intelligent search and cloud manufacturing service combination optimization matching, a cloud manufacturing service platform can rapidly and effectively select manufacturing resources satisfying the manufacturing demand from lots of manufacturing resources, and therefore, the manufacturingcost is reduced, product quality is improved, and efficient sharing and optimization configuration of the manufacturing resources are achieved.

The invention provides an evaluation method of an urban power distribution network reconstruction scheme. The method includes: establishinga distribution network reconstruction scheme comprehensive evaluation index system comprising indexes such as reliability, power supply capability, economy, DG admitting capability, disaster resistance capability, adaptability and coordination and a quantitative calculation method of each index; solving subjective and objective weights of each index by using an interval intuitionistic fuzzy analytic hierarchy process and a CRITIC method; and obtaining a comprehensive weight in combination with a minimum information identification principle, solving a comprehensive evaluation value of each transformation scheme in combination with TOPSIS, selecting an optimal transformation scheme according to a principle that the comprehensive evaluation value of the scheme is maximum, and handing over the optimal transformation scheme to related workers in the technical field for implementation. The index system constructed by the method is concise and easy to calculate, the overall performance of the distribution network reconstruction scheme can be comprehensively reflected from different angles, and the obtained evaluation result is high in identification degree, so that the decision result is more practical and credible, and has higher engineering application value.