48 results about "Rough set" patented technology

The invention relates to the technical field of foundation pit detection, and discloses a deep foundation pit deformation prediction method and system based on neural network and rough set classification, and the method comprises the following steps: data collection and preprocessing; carrying out attribute reduction based on a rough set theory; constructing and training a model based on an improved attention mechanism recurrent neural network (A-RNN); carrying out credibility verification and dynamic feedback adjustment; and outputting a result and an early warning response. By integrating multi-parameter monitoring data of displacement, soil pressure, underground water level, support stress and the like, driving factors of deformation of the deep foundation pit are comprehensively captured, misjudgment caused by data deviation of a single sensor is reduced, the combination of bidirectional LSTM and an attention mechanism effectively captures local features of long-term dependence and key time points in time sequence data, and the accuracy of deep foundation pit deformation detection is improved. And the accuracy of deformation prediction is obviously improved.

The invention provides an image noise mark feature selection method and system, a storage medium and a computer. The method comprises the steps of obtaining a to-be-processed image noise mark data set; embedding a sample set in the image noise mark data set into a multi-granularity fuzzy cluster to construct a dynamic fuzzy membership evaluation matrix; dynamically evolving a multi-level high-precision granular ball cluster; obtaining mark distribution with high identification degree; constructing a rough perception feature evaluation framework based on granular ball topology driving, extracting decision equivalence classes by combining rough set upper and lower approximation and extended positive domain models, and determining and measuring the contribution degree of each feature to a decision system by fusing multi-granular-ball decision boundary information based on a dependency degree quantitative model; a particle and ball structure consistency verification mechanism is introduced, and multi-level evaluation is carried out on the importance of the features through dependency and consistency. According to the method, the optimal feature subset with strong anti-noise performance and high discrimination capability is obtained, and stable and efficient input support is provided for a subsequent image noise mark learning model.

The present invention relates to the technical field of wheel-rail tribology. Specifically disclosed is a wheel-rail material rolling contact fatigue damage prediction method based on different evaluation indices. The method comprises the following steps: on the basis of a wheel-rail shakedown map, carrying out wheel-rail rolling contact fatigue simulation tests, so as to obtain damage states and shakedown limit curve equations of wheel / rail materials with different shear yield strengths under different contact parameters; on the basis of a rough set theory, calculating the importance and relative weights of different evaluation indices; establishing a comprehensive evaluation index cloud model, respectively calculating comprehensive certainty degrees of an index attribute value to be evaluated belonging to different damage states, and using a maximum-value rule for the comprehensive certainty degrees to evaluate and predict rolling contact fatigue damage states; and using comprehensive evaluation index attribute values to perform validation, so as to finally determine a prediction result of a selected wheel-rail material rolling contact fatigue damage state under a condition to be subjected to prediction. The method in the present invention only depends on existing engineering data, such that the subjectivity of a damage state prediction process can be greatly reduced.

The invention relates to the technical field of concrete durability evaluation, in particular to a compound green concrete salt freezing resistance durability evaluation system under a frozen soil environment, which comprises a data input unit, a decision information table storage unit, a rough set analysis processing unit and an evaluation output unit, the data input unit receives a sample data set containing freeze-thaw cycle times, dry-wet cycle times, freeze-thaw-dry-wet cycle times, long-term soaking time, composite salt concentration, gas content and fly ash mixing amount, and the decision information table storage unit stores a decision information table constructed based on a rough set theory. The influence factor combination and the relative dynamic elastic modulus percentage are associated, the rough set analysis processing unit establishes a mapping relation through attribute reduction and rule extraction and quantifies the weight of each factor, and the evaluation output unit outputs a relative dynamic elastic modulus predicted value, durability grade classification and key factor contribution sorting. And the salt freezing resistance durability of the concrete under the multi-factor coupling effect is accurately evaluated.

The invention discloses a medical examination index reduction method based on rough set positive domain and information entropy synthesis, is suitable for high-dimensional data attribute reduction and knowledge discovery in scenes such as medical data analysis, and provides a new reduction strategy by fusing two perspectives of a rough set theory and an information theory method. The comprehensive importance degree of the attributes is obtained through a weighted average mode, and the weight is determined by the ratio of the positive domain to the boundary domain. Iterative reduction is carried out according to the comprehensive importance, deterministic and non-deterministic elements are reasonably considered, the problem of feasibility of attribute reduction of a large-scale fuzzy information decision system is solved, and a more accurate reduction set is obtained. Key diagnosis indexes are conveniently focused, the diagnosis efficiency and accuracy are improved, the clinical interpretability is enhanced, unnecessary detection items are reduced, and the effects of optimizing data, improving the diagnosis efficiency and saving the medical cost are achieved.

The present invention discloses a medical test index simplification method based on the combination of rough set positive domain and information entropy, which is suitable for high-dimensional data attribute simplification and knowledge discovery in scenarios such as medical data analysis. By integrating the two perspectives of rough set theory and information theory methods, a new simplification strategy is proposed, which obtains the comprehensive importance of attributes by weighted average, and its weight is determined by the ratio of the positive domain to the boundary domain. It performs iterative simplification according to the comprehensive importance, reasonably takes into account the deterministic and non-deterministic elements, solves the feasibility problem of attribute simplification in large-scale fuzzy information decision-making systems, and obtains a more accurate simplified set. It is convenient to focus on key diagnostic indicators, improve diagnostic efficiency and accuracy, enhance clinical interpretability, reduce unnecessary test items, and play a role in optimizing data, improving diagnostic efficiency, and saving medical costs.

The invention relates to the technical field of medical data mining and mode recognition, in particular to a special child early disease prediction method based on double-layer granular ball knowledge representation BGBK and three-way role arbitration TRA. The method comprises the following steps: firstly, converting original special child data into multi-granularity granular ball representation through an unsupervised granular ball generation algorithm; a BGBK structure is constructed, and coarse-grained particle ball information and fine-grained sample information are fused; a three-boundary neighborhood rough set theory is utilized to endow semantic roles to the granular balls, wherein the semantic roles comprise a core region, an abnormal boundary and a transition boundary; designing a TRA strategy, and calculating a sample abnormal score in combination with the role influence factor; and finally, constructing an abnormal factor by fusing the abnormal scores under the multi-attribute subspace, and realizing accurate early prediction of the special child disease. The method can effectively process complex special child data, has the advantages of high detection precision, strong robustness, good interpretability and the like, and significantly improves the accuracy of early prediction of special child diseases.

The invention relates to the technical field of multi-source information processing, in particular to a multi-source information decision fusion method and system for a multi-granularity multi-view rough set, and the method comprises the following steps: obtaining multi-source heterogeneous data of an object to be decided, constructing a multi-source decision information table through cleaning and standardization processing, and dividing the information table into a plurality of view subsets; and constructing a multi-granularity hierarchical structure based on the view subset, calculating rough set approximation precision and attribute dependency under different granularity hierarchies, and generating a local decision rule set. And performing weighted aggregation on the local decision rule set by using a multi-source information fusion algorithm, and constructing decision evaluation parameters based on consistency measurement. According to the method, the problems of one-sided information under a single view angle and detail loss under a single granularity are effectively solved through the calculation of the multi-view and multi-granularity collaborative mechanism reverse driving decision cost, and the accuracy and robustness of decision analysis under a complex and uncertain environment are remarkably improved based on the feedback adjustment of the dynamic weight; and deep mining and efficient fusion of multi-source information are realized.

The invention belongs to the technical field of new energy automatic optimization, and discloses a fault diagnosis and optimization method based on a rough set and a random forest, and related equipment. The fault diagnosis and optimization method based on the rough set and the random forest comprises the following steps: inputting multi-source heterogeneous operation data into a constructed fault diagnosis model, and outputting early warning information, the early warning information comprising a fault category; wherein the fault diagnosis model training method comprises the steps of performing knowledge reduction on a multi-dimensional data set by adopting a rough set algorithm, inputting an equipment operation sample set after knowledge reduction into a random forest model, performing multi-target optimization analysis through a big data platform based on early warning information and real-time operation data, and generating decision support scheme data; according to the method, a safe and executable operation and maintenance strategy and an operation instruction can be generated in real time by utilizing a multi-objective optimization technology, and the closed-loop technical bottleneck from data fusion, intelligent diagnosis to dynamic optimization is effectively broken through.

The invention provides a multi-dimensional quality and efficiency evaluation method and device for cluster and field power grid projects, and the method comprises the steps: taking a project cluster and a project field as research objects, obtaining a project data set corresponding to a power grid project cluster / field, fully considering the relevance between projects, determining the key evaluation dimension and evaluation index of the project cluster / field, and achieving the quality and efficiency evaluation of the power grid project cluster / field. A comprehensive quality and efficiency evaluation index system is constructed by combining existing data setting specific indexes and calculation formulas thereof, the index weight of each evaluation index is determined according to a rough set theory, the objectivity of weight distribution of each evaluation index is improved, and comprehensive evaluation of project clusters and fields is achieved.

The invention relates to the technical field of data fusion, and discloses a multi-sensor data fusion method, device and equipment and a storage medium, and the method comprises the steps: carrying out the synchronization of laser radar data, depth image data and ultrasonic distance data, obtaining a first sensor data set, and constructing an obstacle behavior prediction model; performing Bayesian posterior probability calculation on a second sensor data set acquired in real time according to the obstacle behavior prediction model to obtain a fusion trajectory prediction result and an uncertainty value; performing neighborhood rough set rule reduction based on the fusion trajectory prediction result to obtain a target obstacle avoidance decision rule group; according to the method, the prediction uncertainty is quantified into the risk coefficient to dynamically adjust the conservative degree of the obstacle avoidance strategy, the coupling analysis of the prediction confidence and the control strategy is realized, and the dynamic obstacle avoidance execution instruction is generated. And the cleaning efficiency and the obstacle avoidance safety are effectively balanced.

The invention discloses a leg posture analysis and rating system for a football player in a ball kicking process, and the system comprises a data collection unit which collects four-stage kinematics parameters of kicking, kicking and stretching, backward swinging, forward swinging and ball touching through a three-dimensional motion capture system and a high-speed camera; the data processing unit is used for processing parameters and extracting characteristic parameters of each stage; the expert evaluation interface is used for receiving evaluation data of multiple experts on different ball kicking technology types on the multi-score influence factors; the weight calculation unit is used for calculating an influence factor attribute weight by using a Bayesian method based on expert data; the rating decision matrix construction unit is used for constructing a comprehensive matrix in combination with a weight rough projection method; the multi-angle analysis unit is used for performing multi-angle risk rating by fusing a rough set theory and an approximate ideal solution sorting method; and the result output unit is used for outputting the posture quality grade and the training suggestion. According to the method, the actual requirements of football training on precision, standardization and comprehensiveness of posture analysis can be met.

The invention discloses a fatigue analysis and diagnosis method for key components of a circuit breaker operating mechanism. The fatigue analysis and diagnosis method comprises the following steps: (1) collecting vibration signals in the operating process of a circuit breaker; and (2) tracing the energy storage spring deformation, and finely dividing vibration signals accompanying the operation process into three sub-stages: a tripping trigger stage, an energy storage spring deformation stage and a closing buffer stage. And (3) carrying out adaptive variation mode extraction (AVME) by using the signal in the deformation stage of the energy storage spring, so as to obtain an expected mode. And (4) extracting typical time-frequency domain characteristics of an expected mode as condition attributes of a theoretical information table of key components of the operating mechanism, taking whether fatigue occurs as decision attributes, and establishing a fault original decision information table. And (5) reducing the fault original decision information table by using a rough set theory to obtain a fault reduction decision information table. And (6) training and learning a support vector machine (SVM), and constructing a fault diagnosis model for the key components of the circuit breaker operating mechanism. Experiments show that the method has high accuracy and practicability in the field of circuit breaker fault identification and evaluation.

The application discloses a kind of based on adaptive high-dimensional data local differential privacy protection method, using rough set theory and mutual information, the data of different relevance and sensitivity are clustered grouping, different grouping is used different random response probability, permanent random response and temporary random response are carried out.Local differential privacy processing data, using SNE method for dimension reduction and adaptive sampling gradient optimization in server end, with low-dimensional data joint probability distribution approximates the joint distribution of all attributes, according to the sampling strategy and the approximate joint distribution constitutes release dataset.The application solves the problem of high-dimensional data with dimension disaster and local differential privacy combination and maximum degree maintaining data relevance, and more reasonably allocates privacy budget according to the different data sensitivity to some extent, improves data availability and reduces communication cost.

The present invention discloses a method and system for dividing operation modes of a high-proportion distributed new energy power grid. The method obtains and pre-processes the operation data of the power grid, then coordinates the factors affecting the operation mode of the power system, and obtains the characteristics of each factor through logical analysis; defines the characteristic quantity expression corresponding to the characteristics of each factor, calculates the characteristic quantity of each factor and normalizes it; clusters each factor according to the normalized characteristic quantity of each factor, obtains several typical scenes of the factor, and extracts the typical curve of each typical scene of each factor; combines the permutations and combinations of the typical curves of the typical scenes of various factors in actual daily operation, and obtains the normalized operation mode set of the factors actually existing in the power grid; clusters the normalized operation modes of each factor according to the weighted characteristic vector, and obtains a rough set of power grid operation modes, and obtains the final power grid operation mode set through aggregation, merging, calibration and supplementation.

The invention discloses a garment process parameter adjustable design method and system, particularly relates to the technical field of data processing of computer-aided process design, and is used for solving the problem that an existing digital design method does not consider actual production fluctuation, so that the suitability of a process parameter scheme in actual production fails. The method comprises the following steps: acquiring process fluctuation related data of a garment production end, extracting process fluctuation characteristic parameters and fluctuation tolerance thresholds thereof, grouping initial process parameter adjustment schemes, and establishing hierarchical association with the fluctuation characteristic parameters to judge whether the schemes meet all threshold constraints or not; if not, the adjustment amount of each process parameter is analyzed and determined based on the rough set theory, the fluctuation conduction effect of the adjustment amount is evaluated, and finally, collaborative correction and iterative verification are carried out on the initial scheme according to the adjustment amount and the conduction influence degree to generate a final process parameter adjustment scheme meeting all fluctuation tolerance threshold constraints; and the conversion of process parameter design from ideal condition calculation to actual production fluctuation adaptation is realized.

The application provides a rough set data classification training method and system based on a blockchain, relates to the technical field of the blockchain, and can be applied to the financial field and other fields.The method comprises the following steps: constructing a data matrix by means of a coordination coupling algorithm according to user data of a blockchain client; obtaining a sample data set by selecting the data matrix, constructing an interference data set according to the sample data set and a preset Gaussian noise; constructing a fuzzy decision system according to the interference data set, extracting an attribute reduction core of the fuzzy decision system with respect to data attributes by means of data attribute classification training; and performing attribute reduction on the interference data set by means of the attribute reduction core to obtain training data.

This paper discloses a method for constructing a usability evaluation index system for civil aircraft cockpit display interfaces. First, the EPIC model is used to analyze pilots' human-machine interface interaction cognition, extracting usability indicators in four dimensions. The usability indicators are then preliminarily screened based on the pilots' experience and knowledge, thereby constructing a usability evaluation index system for civil aircraft cockpit display interfaces. Secondly, a hierarchical clustering algorithm and a rough set information entropy algorithm are combined to propose an HC-CEBARKNC algorithm to cluster and simplify the established index system, obtaining a clustered reduced set of indicators for each dimension. Thirdly, the reliability of the clustered reduced set of indicators for each dimension is verified using the SVM classification accuracy, thereby constructing a reasonable usability evaluation index system for civil aircraft cockpit display interfaces. Finally, an improved formula based on rough set conditional information entropy is used to calculate the weights of each indicator in the index system. This method addresses the problem that the index system for civil aircraft cockpit display interface usability research is not comprehensive enough.

Embodiments of the invention provide a robot process data processing method and apparatus. The method comprises the steps of obtaining a process knowledge graph and a process parameter weight of a robot; key process parameters are determined according to the process parameter weights, the process knowledge graph is mapped into low-dimensional vectors according to a graph embedding algorithm, and node similarity calculation is performed on vectorization features of the key process parameters and process parameter vectors in the low-dimensional vectors of the process knowledge graph; extracting data features associated with the key process parameters from a process knowledge graph according to a similarity result to construct a process connected sub-graph; the method comprises the following steps: extracting process sequence data from a historical process database, performing association rule mining on the process sequence data based on conditional confidence and a rough set algorithm, storing the association rule as a graph structure, performing bidirectional linking on the graph structure and a process communication sub-graph, and determining a corresponding dynamic process rule base to perform process processing. The process programming efficiency and accuracy of the process robot can be improved.

The invention discloses an efficient cleaning method for super-large-scale multi-source heterogeneous data, which comprises the following steps: collecting the multi-source heterogeneous data, and carrying out image, audio and text classification on the multi-source heterogeneous data; respectively extracting an image feature vector, an audio feature vector and a text feature vector, and respectively performing a first round of importance reduction based on a rough set theory; splicing the image feature vector, the audio feature vector and the text feature vector after the first round of importance reduction, and performing a second round of importance reduction based on a rough set theory; a feature set based on images, audios and texts is obtained, and cleaning is achieved. According to the method, the scheme that the strategy of'modal reduction-fusion-global reduction 'is adopted, and engineering practicability and calculation efficiency are extremely high is adopted, the data dimension can be effectively reduced in a staged mode, calculation disasters caused by one-time processing of ultra-high-dimensional data are avoided, and meanwhile contribution in each modal can be better understood.

The present invention provides an intelligent maintenance scientific decision-making system for highway pavement and its decision-making method. The data collection and preprocessing module collects multi-source data on the public network and the highway management intranet, and uses specific algorithms to clean, integrate, and reduce the dimension. Based on the Django framework of Python, a road automatic detection system is built. The rough set theory is used to determine the index weights and calculate the PQI and MQI values, and a highway technical condition assessment report is generated. The pavement performance prediction module integrates multi-source data and uses a composite kernel function and a Gaussian process regression model to predict the pavement performance. The maintenance decision-making module determines the maintenance priority through ANP, generates a maintenance requirement list in combination with the hidden Markov model, and then uses the cuckoo search algorithm to generate a fund allocation plan. The maintenance benefit evaluation module uses the grey ideal solution method and the super-efficiency DEA model to evaluate the maintenance benefit. Finally, using the WebGL-based 3D visualization technology and the Echarts-based interactive query and analysis function, the road condition information display and auxiliary decision-making are realized.

This invention provides a method and system for fire emergency response plan simulation and verification based on case-based reasoning. The method includes establishing a structured historical fire case database, calculating the mixed similarity between the current scenario and historical cases based on rough set theory and cloud models, and selecting similar source cases; adapting the handling strategies of the source cases to the model by constraining the model to generate an initial plan to be verified; establishing a generalized stochastic Petri net model representing the evolution of the disaster-affected state and the interaction of rescue resources, mapping the initial plan to be verified to the transition rate and initial identifier in the database within the model, performing concurrent conflict detection and temporal logic deduction, and evaluating transient performance indicators; if the target rescue success indicator does not reach a preset threshold, iteratively correcting the resource scheduling parameters of the plan based on the sensitivity analysis results of key transitions, and repeating the deduction steps until the termination condition is met, outputting the target fire emergency response plan.

The present invention belongs to the technical field of unmanned aerial vehicles, and particularly relates to a multi-unmanned aerial vehicle task allocation method based on a three-way evolutionary algorithm, including establishing an uncertain multi-unmanned aerial vehicle task allocation model based on rough sets; using the three-way decision idea to design a three-way evolutionary algorithm to solve the task allocation model and obtain an optimal multi-unmanned aerial vehicle task allocation scheme; the present invention comprehensively considers three uncertain indexes of resource cost, voyage cost, and task reward, constructs a more comprehensive allocation model, and at the same time designs a three-way evolutionary algorithm, which improves the solving speed and solving accuracy of the traditional evolutionary algorithm and also improves in terms of robustness.

The invention relates to the technical field of state evaluation, and relates to a health state evaluation method for each subsystem of a rapier loom. The method comprises the following steps: 1, selecting feature parameters, extracting features based on wavelet transform, enhancing abnormal data based on GAN, and constructing a health state model; 2, performing attribute reduction and rule extraction on the evaluation indexes by using a rough set theory, and constructing an initial confidence rule base of each subsystem; 3, fusing the activated rules through an evidence reasoning algorithm, and outputting the confidence coefficient of each subsystem under each health state level; 4, data distribution stratified sampling is carried out to process missing data, and secondary fusion is carried out on all reasoning conclusions by combining a plurality of reasoning results and utilizing an ER algorithm to obtain a final reasoning result; 5, reasoning the error as a target function, and optimizing the parameters of the belief rule base by using a WOA algorithm with interpretability constraint; and step 6, updating the optimized rule base by the incremental rough set, and reserving recent data by using a sliding window, so that the model continuously learns new data. And the evaluation precision and reliability are obviously improved.

The invention discloses a scientific and technological achievement innovation degree evaluation method based on fuzzy comprehensive evaluation. The method comprises the steps that heterogeneous data such as technical indexes and market applications of scientific and technological achievements are collected and stored through a heterogeneous data fuzzy calculation platform; a coupling rough set fuzzy evaluation model is used for screening core evaluation indexes, and index fuzzy membership is optimized through a dynamic PSO fuzzy membership optimization algorithm; performing multi-source information fusion on the optimized membership value by using an evidence theory fuzzy fusion algorithm, and constructing a fuzzy comprehensive evaluation model in combination with a core index system to generate a preliminary evaluation result; and finally, calling historical and industrial standard data to verify the preliminary result through the platform, and outputting a final evaluation result. According to the method, advantages of various algorithms and platforms are integrated, the whole evaluation process is covered, the problems that an existing method is inaccurate in index screening, insufficient in data fusion, insufficient in evaluation precision and the like are solved, systematicness, objectivity and precision of evaluation are improved, and reliable support is provided for scientific and technological achievement innovation degree evaluation.

The invention discloses a nuclide identification method based on KNN classification. The nuclide identification method comprises the following steps: 1) performing dimension reduction on original high-dimensional gamma energy spectrum data of a training sample by using a principal component analysis (PCA); 2) performing attribute reduction on gamma energy spectrum features of the training sample after PCA dimension reduction through a neighborhood rough set, and rejecting redundant features and retaining a feature set with the most discriminant ability through heuristic search based on attribute dependency; and 3) utilizing the feature set obtained in the step 2) to realize rapid nuclide identification of the to-be-detected sample through a K-nearest neighbor classification method. According to the method, PCA dimension reduction, attribute reduction and KNN classification methods are fused, high recognition precision can be kept, meanwhile, better calculation efficiency is achieved, and the method is suitable for portable equipment with limited resources to achieve real-time and accurate nuclide recognition.

The application belongs to the field of air quality data processing, and particularly relates to an air quality data processing method based on neighborhood rough set attribute reduction. The method comprises the following steps: acquiring air quality sample data with multiple air attributes, and performing permutation and combination on each air attribute; using the dependency of the neighborhood rough set and the calculation of the introduced Shapley value, the importance of each air attribute is calculated, then a weighted neighborhood rough set is constructed according to the importance of the air attribute, the upper and lower approximation relationships of the weighted neighborhood are redefined, then the new dependency is defined, finally the reduced attribute subset greater than the set lower limit of the importance is calculated according to the new dependency; and the air quality data is optimized. The application can solve the problem of consistent air attribute weight in the existing air quality data processing process, and the air attribute obtained through the weighted calculation of the neighborhood rough set can make up for the deficiency of the neighborhood rough set model.

The invention belongs to the technical field of numerical control machining temperature monitoring, and particularly relates to a multi-measuring-point combined weighing numerical control machine tool temperature detection sensitive point selection method which comprises the following steps: S1, acquiring thermal error data of numerical control machine tool machining and temperature data of each measuring point; s2, constructing a multi-objective optimization model by taking the importance degree of the maximum measurement point combination to the thermal error and the minimum measurement point number as optimization objectives; s3, quantifying the importance Sig (Ti) of each measuring point Ti based on a neighborhood rough set method NRS; based on the importance Sig (Ti) of each measuring point Ti, calculating the probability pi of the measuring point Ti selected by each measuring point combination in the initial population; generating an initial population according to the probability pi that each measuring point Ti is selected by each measuring point combination in the initial population, the initial population comprising a preset number of measuring point combinations; and solving the multi-objective optimization model by using an ASNSGA-II algorithm to obtain a measurement point combination scheme. The method can effectively balance the importance degree and the number of measuring point combinations, and obtains a stable numerical control machine tool temperature detection sensitive point selection result with excellent comprehensive performance.

The invention discloses an electric power material green package evaluation method combining a network analysis method and a rough set theory, and belongs to the field of electric power material green package evaluation. According to the method, through grading the whole life cycle of typical electric power materials, green packaging related flow nodes of five stages of raw material acquisition, production and processing, transportation and distribution, use and waste treatment are determined, and a multi-level and all-around comprehensive evaluation index system is constructed. And establishing an index full-connectivity relation matrix by using a network analysis method, and realizing quantitative evaluation of green packaging by combining fuzzy mathematics and a weighted scoring algorithm. Furthermore, effectiveness verification and redundancy judgment are performed on the evaluation indexes by adopting a rough set theory, so that the scientificity and simplification of the system are improved. According to the method, a scientific decision basis can be provided for enterprise green purchasing and packaging management, and automatic evaluation and dynamic feedback of the electric power material packaging green degree are realized.

Embodiments of the present specification provide a credit determination method, device and equipment, wherein the method comprises: obtaining an initial rough set; wherein the initial rough set contains a plurality of original feature information of institutions; performing information reduction on the initial rough set by using a discernible matrix reduction algorithm to obtain an evidence information set; each feature information after information reduction serves as a separate evidence; obtaining basic probability assignment values of a recognition framework corresponding to each evidence in the evidence information set; and performing uncertain reasoning by using evidence theory according to the basic probability assignment values of the recognition framework corresponding to each evidence to obtain a representation value of each institution; wherein the representation value is used to represent the credit of the institution. In the embodiments of the present specification, the credit of each institution can be efficiently and accurately determined in the case that the original data amount of each institution is small, and the data exists uncertain or inaccurate knowledge expression.