435 results about "Assessment index" patented technology

The invention relates to the technical field of terrain wind resource assessment, in particular to a complex terrain wind resource assessment method and system based on CFD enhancement, and the method comprises the steps: processing terrain, weather and vegetation data, and generating a complex terrain feature vector; constructing bidirectional interaction between a mesoscale meteorological mode and computational fluid dynamics, and iteratively optimizing a flow field; fluid dynamic parameters are calculated through reinforcement learning automatic optimization; turbulence simulation is enhanced by combining the generative adversarial network with large eddy simulation; dynamically allocating resolution according to the grid priority of the multi-feature calculation; the lightweight model pre-judges errors, and after the errors reach the standard, evaluation indexes are calculated to complete wind resource evaluation; the system comprises a multi-source data fusion module, a dynamic coupling module, a parameter optimization module, a reinforced turbulence simulation module, an adaptive grid module and an error prediction and evaluation module, and all the modules cooperate to achieve full-process evaluation. The problems that in the prior art, one-way coupling precision is low, manual parameter selection is low in efficiency, turbulence simulation precision and efficiency are difficult to balance, and static grids are wasted are solved.

The invention relates to the technical field of tax data security, and discloses a data sharing privacy protection platform responding to tax digital requirements. The platform comprises a tax data privacy perception layer, a sensitive information positioning mapping layer, a cross-source data security fusion layer, a compliance sharing decision-making layer and an execution state feedback layer. The tax data privacy perception layer obtains multi-source tax information based on a dynamic differential privacy mechanism, generates an initial privacy risk heat map and segments a sensitive area; the sensitive information positioning mapping layer generates a data internal privacy distribution stereogram through a security perception array and time sequence analysis; the cross-source data security fusion layer establishes a space-time mapping relation and generates a privacy disclosure risk score; the compliance sharing decision-making layer converts the score into an executable sharing instruction; and the execution state feedback layer monitors data change, generates an evaluation index and dynamically adjusts a privacy protection strategy. According to the platform, the privacy protection of the whole tax data sharing process is realized, and the accuracy and dynamism of risk identification and strategy adaptation are improved.

The invention relates to a noise damping control system of an offshore wind turbine generator, and belongs to the technical field of offshore wind power generation. The coupling state evaluation module is used for determining a coupling state evaluation index through a preset deep neural network model based on the platform motion information, the vibration spectrum data and the sound pressure signal; the cooperative control strategy generation module is used for determining target rigidity and target damping according to the coupling state evaluation index and the vibration spectrum data; according to the invention, the coupling state evaluation module processes platform motion information, vibration spectrum data and sound pressure signals acquired by the data acquisition module through a preset deep neural network model; the preset model is endowed with strong nonlinear fitting capability through offline supervised learning of massive historical working condition data; and an accurate and quantitative risk basis which cannot be reached by the prior art is provided for subsequent control decisions.

The invention relates to the technical field of petroleum drilling engineering, and particularly discloses an analysis and prediction system based on petroleum drilling and production data, which realizes virtual reconstruction of a physical drilling system by constructing a digital mapping body, and adopts a multi-source sensor network to collect drill string vortex frequency and wellbore temperature field gradient data. Forming a time sequence feature set through time sequence alignment and fusion processing; performing differential transformation and modal decomposition on the feature set to extract transient response feature vectors and generate a risk distribution diagram; establishing a parameter-risk correlation model, and decoupling mechanical vibration and thermal stress interference by solving a physical equation to obtain a dynamic risk index; the risk indexes and the process parameters are mapped to a three-dimensional grid to construct a multi-dimensional feature space, a feature fusion grid is adopted to achieve information aggregation and reconstruction, and comprehensive risk assessment indexes are generated; a dynamic early warning threshold value is established according to the evaluation indexes, and drilling parameters are optimized in real time through closed-loop control.

The invention discloses a risk assessment method and system based on a Bayesian network and an evidence theory, and relates to the technical field of risk intelligent assessment, and the method comprises the steps: determining an assessment dimension and an assessment index of a high-investment low-income risk of an educational institution in MOOC learning according to a human-cargo-field model and an industry report; constructing a MOOC learning risk assessment index system according to the assessment dimensions and the assessment indexes; constructing a Bayesian network structure according to the MOOC learning risk assessment index system; obtaining questionnaire data and expert opinions according to the MOOC learning risk assessment index system, and determining Bayesian network parameters based on the questionnaire data and the expert opinions; and inputting the Bayesian network parameters into the Bayesian network structure to obtain an assessment result of the high-investment low-income risk, the assessment result including a risk prediction result, a key risk factor and a sensitivity analysis result. According to the method, the high-investment and low-income risk in MOOC learning can be accurately evaluated.

A cable health state assessment method and system. The method comprises: presetting assessment indicators for assessing the health state of a target cable, the assessment indicators comprising first-level indicators and second-level indicators, and the first-level indicators comprising an online monitoring indicator, a laying environment indicator, an offline test indicator, an apparatus information indicator and a daily inspection information indicator; acquiring parameterized data of the assessment indicators of the target cable, and preprocessing the parameterized data; on the basis of a preprocessing result, taking into account a first preset weight acquisation logic, so as to construct a comprehensive assessment matrix; and, on the basis of the comprehensive assessment matrix and actual parameter data of the target cable, taking into account a second comprehensive assessment indicator calculation logic, so as to determine the health state of the target cable. The cable health state assessment method and system consider assessment indicators in multiple aspects, and can accurately reflect health states of target cables by means of reasonable weight allocation and data processing modes.

The invention relates to a damage assessment method and device for a composite material structure and electronic equipment, and relates to the technical field of aircraft maintenance, and the method comprises the steps: obtaining an aircraft typical structure operation and maintenance database and an actual service environment database; extracting structure damage characteristics from the aircraft typical structure operation and maintenance database, and extracting service environment factor data of the aircraft from the actual service environment database; performing clustering analysis on the extracted service environment factor data to obtain a clustering analysis result, and performing correlation analysis on the extracted structure damage features and the clustering analysis result to obtain a correlation analysis result; and based on the correlation analysis result, constructing an evaluation index system, and performing damage evaluation on the composite material structure of the aircraft by using the evaluation index system. According to the scheme, various environmental factors and structural damage characteristics can be comprehensively considered, the service reliability and safety of the structure are evaluated more accurately, a more reliable basis is provided for structure maintenance decision, and potential risks for flight safety are reduced.

The invention discloses a land value evaluation method and system based on multi-source heterogeneous data fusion, and relates to the technical field of land economic evaluation. The invention discloses a land value evaluation method and system based on multi-source heterogeneous data fusion. The method comprises four steps of multi-source data acquisition and preprocessing, standardized evaluation index quantification, index weighted fusion and value calculation, and value distribution diagram generation and output. Firstly, four types of data of background status, planning, market feedback and social perception are acquired and preprocessed; secondly, constructing a hierarchical index system to quantify indexes; determining a weight through an analytic hierarchy process and calculating a land value index according to a preset formula; and finally generating a value distribution diagram in a thermodynamic diagram form. The method realizes standardization, high efficiency and visualization of land value evaluation, can be applied to the fields of city updating, land market monitoring, government planning decision making and the like, and provides scientific support for related decision making.

The invention discloses an automobile air conditioner intelligent diagnosis and assessment method and system, and relates to the technical field of automobile air conditioner intelligent manufacturing. The intelligent diagnosis and assessment method and system for the automobile air conditioner comprises the steps that S1, multi-point temperature, air volume, airflow and environment data in the operation process of the air conditioner are collected and preprocessed, and a standardized vehicle-mounted state data set is constructed; s2, evaluating the comfort adjusting capacity and dynamically adjusting the opening degree of the air door in combination with the temperature and humidity air change and the stability approaching time; s3, the parameter recovery speed and the thermal load intensity are analyzed, and the air door switching rhythm is adjusted; s4, integrating comfort and environment adaptability evaluation results, and executing regulation and control resource redirection; and S5, jointly monitoring the environmental pressure and the regulation and control capability value, constructing a cross-scene scoring system, and marking strategy switching nodes. The problems that an existing air conditioning system lacks an assessment index with cooperation of a use scene and an environment as a core, and the control performance cannot be dynamically assessed in a cross-environment mode are solved.

The invention discloses a multi-source information fusion safety production risk intelligent early warning and disposal platform, which comprises a multi-source information acquisition module, an information preprocessing module, a multi-source information fusion module, an intelligent risk identification and early warning module, a disposal scheduling module and a data storage and interaction module, heterogeneous data such as sensor monitoring, video monitoring, equipment logs and manual reporting are collected and cleaned and standardized, and then a weighted fusion algorithm and a cross-modal deep learning model are adopted to realize multi-source data fusion; based on a preset risk assessment index system and the trained risk identification model, mapping risk levels by quantizing comprehensive risk scores, generating graded early warning information and pushing the graded early warning information in multiple channels; meanwhile, a disposal scheme is automatically matched, an instruction is issued, a closed loop is tracked, and data security is guaranteed in combination with distributed and block chain storage. Comprehensive perception, accurate identification, intelligent early warning and efficient disposal of safety production risks are achieved, and the intelligent and refined level of safety production management is remarkably improved.

The systems and methods disclosed herein receives artifacts generated using a first set of models within a multi-model superstructure. The multi-model superstructure includes a second set of models to test the first set of models. The multi-model superstructure dynamically routes the artifacts of the first set of models to one or more models of the second set of models by (i) determining a set of dimensions of the artifacts against which to evaluate the artifacts and (ii) identifying the models in the second set used to test the particular dimension. The second set of models then assesses each artifact against a set of assessment metrics. If an artifact fails to meet one or more assessment metrics, the second set of models generates actions to align the artifact with the set of assessment metrics.

The invention discloses a pollutant toxicity assessment method and device based on RAG enhancement and knowledge graph reasoning, equipment and a medium, and relates to the technical field of pollutant assessment, and the method comprises the steps: constructing a standardized toxicological knowledge graph through multi-source data collection and preprocessing; by combining RAG enhanced retrieval and knowledge graph reasoning, a toxicity reasoning path and an evaluation index are rapidly generated, meanwhile, interpretable content is provided, multi-source heterogeneous data fusion and self-adaption to spectral characteristic differences of different sites are achieved, the problems that traditional evaluation is low in efficiency, high in cost, insufficient in knowledge utilization and the like are effectively solved, rapid and accurate evaluation is achieved, and the evaluation efficiency is improved. And the credibility and the interpretability of the evaluation result are enhanced.

The invention belongs to the technical field of traffic management evaluation, and particularly discloses and provides an urban traffic toughness quantitative evaluation method based on multi-mode traffic collaboration, and the method comprises the steps: carrying out the time-space standardization processing of multi-source data of motor vehicles, public traffic, slow traffic and external disturbance events, and generating a standardized time-space data set; based on the data set, respectively calculating a comprehensive transportation efficiency index, an alternative path redundancy index and a connection connectivity index, and constructing a toughness evaluation index set; based on the historical sequence of each index, generating a dynamic combination weight through a subjective and objective combination weighting method; and based on the index set and the dynamic weight at the current moment, calculating a comprehensive toughness index and generating a time sequence thereof to output an evaluation result. According to the method, the defects that in the prior art, an isolated analysis single mode depends on a static model are effectively overcome, and accurate multi-dimensional quantitative evaluation of the real toughness level of the complex traffic system under disturbance is achieved.

The invention relates to a data evaluation method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the steps that a question is processed through a retrieval question-answering system, and an initial answer and first context information of the initial answer are obtained; performing reflection iteration processing on the question through a large language model to obtain a standard answer and second context information of the standard answer; and performing index calculation based on the initial answer, the first context information, the standard answer and the second context information to obtain a target evaluation index, and determining an answer evaluation result based on the target evaluation index. By adopting the method, the standard answer with high credibility can be generated after deep thinking and multiple rounds of reflection verification, and multiple comprehensive evaluation indexes are calculated based on the standard answer and the question, so that the evaluation accuracy is improved under the condition of reducing the evaluation cost.

The invention relates to a skill grade evaluation method and system for talent management. The skill grade evaluation method comprises the steps of obtaining target occupational information, corresponding occupational element information and assessment index information; constructing a multi-channel evaluation model based on the occupational element information and the assessment index information; obtaining target skill data and performing preliminary evaluation to generate a standardized evaluation result; obtaining target historical behavior data and extracting skill mode features and implicit capability features; inputting the skill mode features and the implicit capability features into a multi-channel evaluation model to generate a personalized evaluation result; in conclusion, according to the skill grade evaluation method and system for talent management provided by the invention, standardized evaluation is carried out on skill data by constructing a multi-channel evaluation model, and dynamic correction is carried out in combination with implicit features extracted from historical behavior data; the method has the advantages of dynamically capturing skill demand changes, identifying skill synergistic effects, mining recessive ability characteristics and providing timeliness occupational planning.

The invention discloses a big data fire safety monitoring management method and system, and the method comprises the steps: sequentially carrying out the cleaning, denoising and normalization processing of collected data, and obtaining standard multi-modal data; extracting an environment trend feature, a spatial distribution feature and a time sequence period feature of the standard multi-modal data by using an improved multi-scale convolutional neural network, and aligning and fusing the features to obtain feature multi-modal data; establishing a risk state assessment model based on a CTHMM continuous time hidden Markov model, defining the safety state of the system as a hidden state, taking the feature multi-modal data as an observation sequence, and outputting a fire risk assessment index; and based on the fire risk assessment index, grading the fire risk by using a hierarchical decision-making mechanism, and triggering an early warning mechanism of a corresponding level according to the risk level. Fire-fighting management is converted from passive disposal to active early warning, and the probability of fire occurrence is remarkably reduced.

The invention provides a power distribution network health state assessment method and device, equipment and a storage medium, and the method comprises the steps: building a power distribution network model on a power system, and obtaining the operation data of a power distribution network; calculating a power distribution network health state evaluation index according to the power distribution network operation data; secondly, according to the power distribution network health state evaluation indexes, constructing a power distribution network health state evaluation data set; inputting the power distribution network health state evaluation data set into the improved TabPFN model, and evaluating the health state of the power distribution network to obtain a power distribution network health state evaluation result; and carrying out interpretability analysis on the power distribution network health state evaluation result by utilizing a Shapril value additive interpretation theory to obtain an interpretability analysis result. Thus, power distribution network health state evaluation based on the improved TabPFN model can be used immediately after being opened, the reasoning speed is high, precision is high, the model evaluation result is subjected to interpretable analysis in combination with the SHAP theory, the problem that a deep learning model is difficult to interpret is solved, and transparency and credibility of the model are improved.

The invention provides a multi-dimensional and dynamic digital assessment system and assessment method. Comprising a support module (comprising an index knowledge base, an algorithm operation base and an evaluation model base), a multi-source index fusion center (for constructing a three-dimensional data model and integrating a multi-dimensional data resource pool), a dynamic rule configuration engine (for realizing flexible configuration through a visual interface, a model adaptation mechanism and double-track verification) and a core business module (for managing assessment indexes, evaluating indexes and evaluating indexes). And evaluating the model and plan). The support module lays a basic resource layer, a fusion center constructs a data base, a configuration engine realizes flexible verification, the business module drives full-process execution, and full-process digital management from index creation, model configuration to examination execution is realized. The problems of iteration lag, multi-scene adaptation redundancy, weak data verification and the like of a traditional system are solved, index legality is guaranteed through static grammar detection and dynamic simulation operation, data traceability is achieved through a temporary database and full-link recording, and the dynamic response capacity, scene compatibility and data reliability of the assessment system are improved.

The invention relates to the technical field of data processing, in particular to a personnel assessment system and method based on big data. The method comprises the following steps: collecting and preprocessing employee behavior data in an assessment period to obtain preprocessed data, and constructing a standardized behavior data unit sequence; performing semantic feature extraction based on behavior events in the standardized behavior data unit sequence, generating enhanced behavior events, performing classification and aggregation on the enhanced behavior events, and constructing an employee behavior map; based on the employee behavior map, generating behavior embedding vectors of the employees, and performing weighted aggregation on the behavior embedding vectors of the employees to obtain behavior aggregation vectors; generating an initial performance score based on the behavior aggregation vector, and performing personalized correction on the initial performance score to obtain a final performance score; and performing personnel assessment based on the final performance score. The problems that a traditional personnel assessment method lacks unstructured data utilization ability, assessment index post adaptability is insufficient and time correlation modeling is lacked are solved.

The present application provides a method for assessing the value of data assets. The method is applied to a scenario where a commercial bank performs service recommendation or service delivery. The method comprises: acquiring data asset data to be assessed inputted by a user, said data asset data comprising data assets to be assessed and an association relationship between said data assets, and said data assets comprising a plurality of data table assets; generating a corresponding data asset graph on the basis of said data asset data; determining an assessment indicator for said data assets on the basis of the data asset graph, the assessment indicator comprising at least one of the following indicators: an information content indicator for the data table assets, a data transmission efficiency indicator for the data table assets, and a data flow impact indicator for the data table assets; and determining the respective value of the plurality of data table assets on the basis of the assessment indicator for said data assets. The present solution can improve the accuracy of assessing the value of data assets.

The invention discloses a node credibility evaluation method, system, equipment and medium for electric power data governance, and belongs to the technical field of electric power data node evaluation.The method comprises the steps that a multi-dimensional index system for evaluating the credibility of electric power data nodes is constructed, index data are collected, and the credibility of the electric power data nodes is evaluated through a dynamic combination weighting method on the basis of the multi-dimensional index data; calculating the optimal combination weight of the evaluation indexes, performing normalization and forward processing on the multi-dimensional index data, performing weighted aggregation on the processed multi-dimensional index data by using the optimal combination weight, performing calculation to obtain direct credibility, constructing a node association graph, and based on the node association graph and the direct credibility, performing calculation to obtain direct credibility; and calculating the indirect credibility of each node through a trust propagation model, carrying out fusion to obtain comprehensive credibility, setting a credibility threshold, and matching a processing scheme. According to the method, comprehensive quantitative evaluation and closed-loop management of the credibility of the power data nodes are realized, and the perspectiveness of data quality management and control and equipment operation and maintenance is effectively improved.

The invention discloses a production command agent test and evaluation method, device and equipment and a medium, and belongs to the technical field of agents, and the method comprises the steps: combining any evaluation dimension in an evaluation dimension set with any test scene type in a test scene type set, and obtaining an evaluation factor combination set; performing parameter matching in an agent configuration parameter pool according to the evaluation dimension and the test scene type corresponding to the evaluation factor combination, and generating a plurality of agent configuration parameter groups corresponding to the evaluation factor combination; the production command agents corresponding to the agent configuration parameter sets are evaluated and tested, and evaluation index values corresponding to the agent configuration parameter sets are obtained; and according to the evaluation factor combination set, the agent configuration parameter group and the evaluation index value, generating total evaluation test information, and according to the total test information, testing and evaluating the to-be-evaluated production command agent. Therefore, by implementing the method, the evaluation reliability of the production command agent can be improved.

The invention relates to the technical field of employee evaluation, in particular to a high-risk industry multi-dimensional dynamic weight employee evaluation method, which comprises the following steps of: obtaining quantitative scores of three-level indexes of employees and normalizing the quantitative scores by constructing a multi-level evaluation index system; inputting the standardized score vector into a Transform-based weight learning model, dynamically learning an index weight through a self-attention mechanism, carrying out step-by-step aggregation through a multi-layer perceptron, and outputting a comprehensive score of the employee on each first-level index; inputting the comprehensive score and the historical background information into a pre-established large language model, and performing fusion analysis according to a structured template to generate a personalized evaluation text; and finally, based on the score, the text and the employee information, automatically synthesizing a visual comprehensive evaluation report containing the radar map, the information bar and the text. According to the invention, objectiveness, individuation and operability of employee evaluation are realized.

A transformer fault severity assessment method based on oil chromatographic analysis and characteristic quantification belongs to the technical field of power equipment fault diagnosis, and comprises the following steps: regularly collecting a transformer oil sample, detecting concentration data of at least one characteristic gas in oil, forming a time sequence data sequence, the method comprises the following steps: constructing a multi-dimensional input feature set as the input of an XGBoost classification model, adjusting and optimizing model parameters, training the model to output a fault type identification result, adopting k-means and SMOTE algorithms to obtain the upper bound and the lower bound of the concentration of each feature gas in a normal state and in each fault type, adopting an SHAP algorithm to calculate the Shapley value of each input feature, and obtaining the fault type identification result. And taking the absolute value of the Shapley value as the importance weight of the feature to the corresponding fault type, and carrying out weighted summation on the difference value to obtain a comprehensive evaluation index. Through the improvement of the algorithm, the fault type and severity of the transformer can be accurately evaluated.

The invention provides a highway reconstruction and extension project traffic safety risk assessment method and system during holidays and festivals, and relates to the technical field of traffic safety risk assessment. The method comprises the steps that sample data of a road reconstruction and extension project are acquired, and the sample data comprise traffic time sequence data and holiday and festival identification data; performing data splicing on the traffic time sequence data and the holiday and festival identification data to obtain two-dimensional sample data; constructing an integrated long-short-term memory neural network model; taking the two-dimensional sample data as training data, and training the integrated long-short-term memory neural network model; predicting traffic volume data of the next day through the trained integrated long and short term memory neural network model to obtain traffic volume prediction data of the next day; setting a traffic safety risk assessment index system; and according to the traffic safety risk assessment index system and the next-day traffic volume prediction data, determining a traffic safety risk level caused by the highway reconstruction and extension project by adopting a cloud model theory.

The invention discloses an electromagnetic interference risk assessment method based on expert knowledge and a neural network, and the method comprises the steps: building an electromagnetic interference risk assessment index system of a to-be-assessed object, bottom layer indexes of the index system comprise a power grid ripple coefficient, power grid peak signal conduction emission, power grid harmonic characteristics, electromagnetic emission characteristics, electromagnetic sensitivity characteristics, filtering characteristics and conduction and radiation safety margin. Establishing a potential interference source and potential sensitive equipment electromagnetic compatibility evaluation data set by using an expert knowledge method; and using the neural network to carry out electromagnetic interference risk assessment training and assessment of the to-be-assessed object. The method can give full play to the advantages of strong pertinence and high flexibility of expert knowledge and the advantages of strong ability of automatic knowledge acquisition and nonlinear mapping of a neural network, and realizes adaptive assessment of electromagnetic interference risks according to real-time acquisition and analysis of an electromagnetic environment; according to the method, manual parameter setting is not needed any more, the evaluation process is easier to implement, and the reliability of the evaluation result is higher.

The invention discloses a performance appraisal management method and system based on a performance appraisal index system, and belongs to the technical field of performance appraisal management, and the method comprises the steps: building a dynamic index library based on the performance appraisal index system of an enterprise, setting a weight and an applicable post range in an effective appraisal period for each index, and obtaining a dynamic index database; the method comprises the following steps: dynamically generating a basic assessment index and a general behavior index of each position according to an enterprise strategic target, collecting multi-dimensional information from multi-source heterogeneous data, establishing a performance multi-dimensional quantitative model, carrying out unified quantitative conversion on business data, subjective behaviors, subjective evaluation and business contributions, and carrying out quantitative analysis on the business data, subjective behaviors, subjective evaluation and business contributions. The business progress process and objective business data of each employee are calculated according to a unified quantitative conversion result, a performance report is generated, a salary system and a promotion system are called, performance appraisal personnel complete salary calculation and talent promotion decision making of the employees according to the performance report, objective and fair appraisal basis can be provided for enterprise performance management, and the enterprise performance management efficiency is improved. And the objective rationality of the performance evaluation result is improved.

The embodiment of the invention provides an insurance service risk early warning method and device, electronic equipment and a medium, belongs to the technical field of artificial intelligence, and is suitable for financial scenes. The method comprises the following steps: extracting evaluation index characteristics of evaluation index data; performing risk assessment according to the assessment index features to obtain assessment index risk data; performing risk causal reasoning on the insurance operation index data according to the evaluation index risk data, and performing risk analysis on index risk factor characteristics obtained by reasoning to obtain an index risk generation path; and carrying out operation risk early warning according to the evaluation index risk data, the index risk factor characteristics and the index risk generation path. According to the embodiment of the invention, through risk assessment, risk causal reasoning, risk analysis and operation risk early warning of the insurance operation indexes, the dynamic change of the insurance operation indexes can be self-adapted, the problem that the fixed feature weight cannot adapt to the dynamic change in the traditional method is effectively solved, and the risk early warning effect of the insurance business is improved.

The invention discloses a life cycle evaluation system and method and a computer program product, and belongs to the technical field of life cycle evaluation, and the life cycle evaluation system comprises a data acquisition module, an artificial intelligence prediction module, an environmental influence calculation module and a feedback and visualization module. The data acquisition module acquires original data in equipment operation; the artificial intelligence prediction module receives the original data and performs prediction to form resource flow data; the environment influence calculation module receives the original data and the resource flow data and converts the original data and the resource flow data into standardized life cycle evaluation indexes; and the feedback and visualization module displays the life cycle evaluation index and generates a parameter adjustment instruction. According to the invention, real-time environmental impact assessment and sustainable optimization in the hybrid manufacturing process are realized.

The invention relates to the technical field of image processing, in particular to a mine safety monitoring method and system for an open-pit mining area. The method comprises the following steps: firstly, constructing different sliding windows to slide on a wedge-shaped slope image, extracting periodic characteristics of a slope surface by using similarity evaluation indexes, and comprehensively evaluating the similarity of each region of the image; secondly, determining periods of the image in vertical and horizontal directions based on similarity evaluation indexes, and optimizing block grid parameters according to the periods, so that the algorithm processing scale is matched with the periodic characteristics of the image, and the pertinence and detail representation of image enhancement are improved; and finally, the crack risk evaluation index is calculated by using the enhanced image, so that the accuracy and reliability of crack risk evaluation are remarkably improved, and the precision and robustness of slope landslide disaster monitoring are enhanced.