682 results about "Assessment index" patented technology

The invention provides a pressure-bearing equipment damage mode identification and risk assessment method and system, and relates to the technical field of safety engineering, and the method comprises the steps: collecting multi-source sensor data and image data, inputting the data into a deep neural network after preprocessing and feature extraction, extracting spatial features through a convolutional layer, and extracting time sequence features through a recurrent neural network. And using the attention mechanism to fuse the features to identify an injury pattern. And then, constructing a multi-level evaluation index system, performing combined weighting by adopting an analytic hierarchy process and an entropy weight method, inputting weights into an improved Bayesian network model based on a D-S evidence theory, dynamically updating a conditional probability table by the model by utilizing a deep neural network and a fuzzy inference rule, and finally obtaining a risk evaluation result. According to the invention, the damage mode of the pressure-bearing equipment can be effectively identified, risk assessment is carried out, and assessment precision and reliability are improved.

The invention provides an evaluation method, system and device for a large language model and a storage medium, and the method comprises the steps: constructing a sample data set which comprises input data and corresponding reference answers, inputting the input data into an evaluated model, and generating an output result; a multi-dimensional evaluation index system is designed according to task requirements, a dynamic weight is allocated to each evaluation index, each evaluation index is provided with scoring standard description, and the evaluation indexes comprise at least two items of context correlation, term consistency, language fluency and expression accuracy; combining the input data, the reference answer, the output result and the scoring standard description into a standardized input instruction, and calling an evaluation model to perform multi-dimensional scoring on the standardized input instruction to generate an evaluation result; and analyzing the evaluation result according to a preset threshold value, and generating a structured feedback suggestion containing an improvement direction. According to the method, rapid optimization and iteration of the large language model can be effectively supported.

The invention relates to the technical field of computer control, and discloses an automatic computer control method and system based on artificial intelligence. The method comprises the following steps: firstly, acquiring multi-source heterogeneous sensor data, including environmental parameters, equipment running states and operation instruction logs; then, an environment state evolution model is constructed, an equipment health degree evaluation index is generated, and an operation instruction log is analyzed; and then, inputting the results into a multi-modal decision model to generate a dynamic control instruction set, constructing a control strategy topological graph through an adaptive optimization algorithm, and outputting an equipment regulation and control scheme. The system correspondingly comprises a multi-source data acquisition module, an environment modeling module, a health assessment module and the like. Multi-source data can be effectively processed, the health condition of equipment can be accurately evaluated, a control instruction and a regulation and control scheme can be intelligently generated, the intelligent level of computer control is improved, and the reliability and the operation efficiency of the system are improved.

A fuzzy cloud-based risk assessment method for reservoir operation schemes, including: collecting basic data of target reservoirs; constructing optimal operation model of conventional reservoirs to obtain model calculation results; acquiring change process of operation characteristics during whole operating period according to model calculation results, and constructing multi-level reservoir multi-dimensional risk assessment index system; dividing different risk level intervals according to multi-level reservoir multi-dimensional risk assessment index system; obtaining large number of sample data xij of reservoir risk index under corresponding working conditions by multiple parallel calculations to conduct risk assessment; calculating cloud digital features of different indexes by inverse cloud generator; and weighting indexes in index layer and criterion layer by Analytic Hierarchy Process to obtain multi-dimensional risk assessment results. In order to promote sustainable development and utilization of water resources and enhance level of reservoir risk management, scientific evaluation of reservoir risk is conducted by comprehensively considering various uncertainties.

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 relates to an intelligent power security situation assessment method based on a multi-source sensor network, and the method comprises the steps: obtaining multi-dimensional data from a power system through the multi-source sensor network, and constructing an original data set containing time sequence features; preprocessing and denoising the original data set, and extracting dynamic change characteristics of key parameters to obtain a cleaned parameter change sequence; based on the cleaned parameter change sequence, analyzing an interaction relationship among the key parameters, and obtaining spectrum feature data; judging whether a resonance risk exists in the system or not according to the frequency spectrum characteristic data in combination with a resonance phenomenon theoretical model, and generating a risk assessment report; for the risk assessment report, identifying unstable factors and extracting a high-risk parameter combination, and generating unstable factor distribution feature data; and according to the system stability evaluation index, judging whether the power grid operation state is close to an unstable boundary, and generating a risk early warning signal and a parameter adjustment suggestion.

The invention belongs to the technical field of agricultural sensing data management and control, and particularly relates to a vision-based multi-modal data fusion intelligent monitoring culture method and system, and the method comprises the steps: obtaining the growth image data and environmental parameter data of a strain based on a multi-sensor information source; identifying the growth image data through a deep learning algorithm, and outputting an image identification result; performing fusion processing on the image recognition result and the environment parameter data through a multi-data fusion algorithm to generate a comprehensive environment evaluation index; importing the comprehensive environment evaluation index and the abnormal state information into a growth environment control model, generating an environment regulation and control instruction conforming to a preset strain growth index, and executing the environment regulation and control instruction through an environment regulation and control module; the system can be used for realizing comprehensive environment monitoring, accurately evaluating the strain growth environment condition, and avoiding errors and one-sidedness caused by a single data source, so that culture environment parameters are dynamically adjusted.

The invention provides an active transformer area voltage out-of-limit early warning and photovoltaic bearing capacity evaluation method, which comprises the following steps: acquiring multi-source data of a power distribution transformer area, and generating a standardized multi-source time sequence data set through time alignment processing, data quality evaluation and feature fusion; extracting voltage out-of-limit features based on the time sequence similarity matrix and the node influence matrix; the periodicity, volatility and randomness characteristics of photovoltaic output are analyzed, a coupling relation matrix and a sensitivity vector are established, and an influence evaluation model is constructed; performing out-of-limit early warning based on an early warning index system and a deep learning model; and establishing a multi-dimensional evaluation index system for carrying out bearing capacity evaluation in combination with power flow analysis and the dynamic influence factors. Through multi-source data fusion and multi-dimensional feature analysis, the accuracy of voltage out-of-limit early warning is improved, accurate evaluation of the photovoltaic bearing capacity is realized, and effective technical support is provided for safe operation of the power distribution network.

The invention relates to the technical field of engineering cost management, in particular to an artificial intelligence-based cost whole-process management method and system, and the method comprises the steps: collecting environment data, market data and project data, and carrying out the feature extraction; calculating the weight of each modal data through a self-attention mechanism, and obtaining a fused feature vector; the knowledge graph, the graph convolutional network and TransE are combined to generate a knowledge graph feature vector, a self-adaptive cost prediction model is constructed, and a prediction cost is output and implemented; fusing a graph attention network, a structure causal model and an improved fuzzy comprehensive evaluation method, constructing a risk assessment model, outputting a risk grade score, and automatically generating and adjusting an optimal risk coping strategy; constructing a low-code intelligent collaboration platform; and based on an evaluation index result, optimizing model parameters and platform configuration by using a genetic algorithm. According to the invention, the risk factors are accurately analyzed through the risk assessment model based on fusion of multiple technologies, and the assessment accuracy is improved.

The invention relates to the technical field of risk management, in particular to a multi-scale urban rainstorm flood risk assessment method. The method comprises the following steps: acquiring a regional basic data set; performing data preprocessing on the regional basic data set to generate a regional basic preprocessed data set; constructing a rainstorm flood risk assessment index system based on the regional basic preprocessing data set to obtain an urban scale flood assessment index, and performing urban scale rainstorm flood risk assessment to obtain an urban scale rainstorm flood risk assessment result; according to the method, the index information is obtained from the open source data, and the defects that in previous researches, part of index data is poor in spatial precision and long in time are overcome; the multi-scale urban rainstorm flood risk assessment method comprehensively considering the urban scale and the block scale is provided, and the accuracy, scientificity and comprehensiveness of urban rainstorm flood risk assessment can be improved.

The invention provides an equipment state data analysis method and system applied to automated electrical equipment, and the method comprises the steps: obtaining a historical operation data set of target equipment, and constructing a multi-level state analysis model based on the historical operation data set, the multi-level state analysis model comprises at least one first analysis level and at least one second analysis level, key operation parameters are extracted from the real-time operation data flow of the target equipment through an operation parameter screening strategy in the first analysis level, and a parameter screening result is generated according to the key operation parameters; inputting a parameter screening result into a state evaluation strategy in a second analysis hierarchy, and generating a state evaluation index of the target equipment in combination with a dynamic threshold range corresponding to the key operation parameter; and outputting a real-time operation state grade of the target equipment according to a comparison result of the state evaluation index and a preset state threshold value. According to the invention, efficient monitoring and accurate early warning of the equipment health state can be realized.

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 discloses a digital twinning-based high-altitude falling risk state assessment technology for building workers and a mapping method and system. The method comprises the following steps: extracting aqueduct building engineering site space element information and personnel positions by an IFC model, and respectively inputting the information and the positions into an indoor navigation model and a three-dimensional digital model; historical risk data of an aqueduct construction engineering site are collected to analyze a risk assessment index and a preset risk threshold value; constructing a digital twinborn model according to the risk assessment index, the risk threshold value, the indoor navigation model and the three-dimensional digital model; real-time construction site environment data, worker behavior data and equipment state data are collected to update and simulate the digital twin model in real time; evaluating the risk range of the high-altitude falling risk by combining the updated and simulated results with a risk evaluation algorithm; and performing mapping association on the digital twinborn model through the construction site environment data, the worker behavior data and the risk range, performing visual interface presentation on the mapping association part, and designing a risk early warning trigger threshold.

The invention discloses a multi-dimensional evaluation method for credential environment migration, and particularly relates to the field of evaluation. According to the method, an evaluation index system covering infrastructure, an application system, data resources and security compliance is constructed, and comprehensive scanning is realized through instruction set detection, binary analysis and equal security compliance inspection; dynamically calculating the weight by adopting an AHP-entropy weight hybrid model, and triggering weight rebalance based on the volatility; through combination of static scanning and a dynamic probe, the compatibility, performance and safety differences are quantitatively analyzed; and finally, generating a visual risk assessment report, integrating data of each stage to calculate a compatibility score, a performance loss rate and a security level, providing instruction level difference comparison, a performance flame graph and a vulnerability attack path interaction view, and outputting a migration suggestion comprising a priority reconstruction list, an NEON instruction optimization scheme and a staged reinforcement plan.

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 provides an overseas public opinion security situation intelligent perception method and system fused with big data, and relates to the technical field of public opinion analysis, and the method comprises the steps: obtaining overseas public opinion data, extracting multi-dimensional features, employing a bidirectional cross attention mechanism to construct an adaptive weight matrix, generating a fusion feature matrix through multi-head time sequence convolution and a dynamic graph structure attention mechanism, constructing an evaluation index system, determining a weight based on information entropy and an evidence theory, determining an early warning level based on a multi-criterion decision, and generating management and control suggestions and early warning information.

The invention discloses a multi-dimensional candidate matching evaluation method and system, and relates to the technical field of human resource management. The method comprises the following steps: constructing an evaluation index system containing multiple dimensions according to a target post demand; collecting multi-dimensional data of candidates, wherein the multi-dimensional data comprises personal backgrounds, skill levels, character traits and occupational experiences; based on the evaluation index system, performing standardization processing on the multi-dimensional data of candidates; calculating the score of each dimension in the evaluation index system according to the standardized multi-dimensional data; calculating a matching weight of each dimension through a preset weight distribution model; and according to the score and the matching weight of each dimension in the obtained evaluation index system, calculating the comprehensive matching degree of the candidate and the target post, and outputting a matching degree analysis report. Through multi-dimensional comprehensive evaluation, a weight distribution model and a personalized matching degree analysis report, more objective and scientific candidate matching evaluation is provided, and the accuracy of recruitment decision is improved.

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.

The invention relates to the technical field of building engineering, and provides a building construction progress digital monitoring method and system. The method comprises the following steps: collecting three-dimensional point location data of a construction area, and comparing the three-dimensional point location data with a BIM model to extract a progress deviation; associating the construction log to a BIM component node and setting a first evaluation index; associating the material records to BIM component nodes, and setting second evaluation indexes; drawing up a resource map of construction process logic in combination with the deviation and the two indexes; and based on the atlas, according to a weight attenuation coefficient, multi-scale assessment of cumulative influence is carried out by taking efficiency as a feature, and resource compensation reminding is carried out in combination with dynamic threshold adjustment. The technical problem that progress deviation is difficult to accurately recognize due to data updating lagging and construction process logic disjunction in traditional building construction progress monitoring is solved, real-time accurate analysis of the construction progress is achieved through intelligent comparison of multi-source data fusion and the BIM model, resource allocation is dynamically optimized in combination with multi-dimensional evaluation indexes, and the construction progress monitoring efficiency is improved. And the construction management efficiency is improved.

The invention provides a quality evaluation method for a geological three-dimensional model, and the method comprises the steps: S11, carrying out the analysis of a historical geological project document, extracting evaluation indexes and data demands, and determining related modeling source data according to the type and target of a geological project; s12, learning an evaluation scheme of a historical successful geological project document, and generating a three-dimensional geological model evaluation scheme containing a verification data range and method; s13, monitoring and analyzing the modeling process of the three-dimensional geologic model based on the three-dimensional geologic model evaluation scheme; s14, determining the weight of each evaluation index in the three-dimensional geologic model evaluation scheme so as to determine the quality grade of the three-dimensional geologic model, and continuously adjusting the weight distribution of the evaluation indexes; and S15, according to the quality grade of the three-dimensional geologic model, determining whether the three-dimensional geologic model meets the acceptance requirement or not. The invention provides a systematic evaluation method for the quality evaluation of the three-dimensional geologic model, thereby promoting the application and development of the three-dimensional geologic model in various fields.

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 provides an urban park risk toughness assessment method and system based on probabilistic reasoning, and the method comprises the steps: carrying out the recognition of risk factors of a park target region, and building a risk toughness assessment index system suitable for a park; constructing a park risk toughness assessment Bayesian network model according to the risk toughness assessment index system and the dependency relationship of the influence factors in the system; determining prior information of the conditional probability of a root node in the Bayesian network by adopting a fuzzy Bayesian method, and initializing model parameters to obtain the prior probability of the root node of the Bayesian network model; the non-root node conditional probability of the Bayesian network model is calculated, and the risk transfer relation between the nodes is quantified; and based on the actual situation of the park, performing reasoning calculation on the target node and the related father node to obtain a park risk toughness evaluation result. The method can scientifically evaluate the toughness characteristics of different areas, especially parks, in a city, and provides a decision basis for improving the toughness of the city.

The invention particularly relates to a meteorological equipment health degree assessment method based on regression and fuzzy comprehensive analysis. The method comprises the following steps: constructing a meteorological equipment health degree assessment index system; the method comprises the steps of collecting multi-source data of meteorological equipment, and completing data preprocessing through missing value filling and abnormal value correction; performing feature extraction on the multi-level indexes by adopting regression analysis, screening out core parameters strongly related to the health degree, and quantifying priorities; combining an analytic hierarchy process, integrating expert experience and feature importance results, constructing a hierarchical judgment matrix, and calculating the dynamic weight of each hierarchy index; and based on a fuzzy comprehensive evaluation method, establishing a multi-dimensional comment set and a membership function for the quantitative index and the qualitative index, fusing the weight matrix and the membership data, and outputting a quantitative score of the health state of the meteorological equipment. According to the method, the accuracy of health assessment of the meteorological equipment in severe environments such as extremely cold environments can be realized, and reliable technical support is provided for intelligent operation and maintenance of the meteorological equipment.

The invention discloses a target threat assessment method based on a large language model, and the method comprises the following steps: carrying out the modeling of a confrontation environment, integrating multi-dimensional information, and determining a plurality of threat assessment targets; generating an evaluation index through the large language model; evaluating the generated evaluation indexes in combination with expert knowledge, if the evaluation indexes do not meet the requirements, further generating the evaluation indexes by the large language model, and if the evaluation indexes meet the requirements, generating an evaluation matrix by the large language model; performing evaluation by combining an evaluation matrix symmetrically formed by expert knowledge, if the evaluation matrix does not meet requirements, further generating the evaluation matrix by the large language model, and if the evaluation matrix meets the requirements, calculating the distance between each target and the positive and negative ideal solutions; and performing threat level sorting on the target according to the distance. By optimizing the decision process, more efficient threat assessment in a complex confrontation environment is realized, and decision support with higher reference value is provided for decision makers.

The invention discloses a black land water and soil resource bearing capacity evaluation system based on big data, which relates to the technical field of agricultural production and comprises a data acquisition module, a data preprocessing module, an evaluation index calculation module, a bearing capacity evaluation model module and a result analysis and display module. The data acquisition module is used for acquiring soil data, water resource data and social economic data of black land. According to the evaluation system for the bearing capacity of the water and soil resources of the black land, through integration of multi-source heterogeneous data, comprehensive and accurate evaluation of the bearing capacity of the water and soil resources of the black land is achieved, high-density deployment and real-time updating of the data acquisition module are achieved, the temporal-spatial resolution and reliability of an evaluation result are ensured, and the evaluation accuracy is improved. The problem of insufficient data acquisition density in the prior art is effectively solved, a data preprocessing and evaluation model construction technology is adopted, the dynamic adaptability and prediction precision of an evaluation model are improved, and visual and understandable chart display and decision support information are provided.

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 relates to a science and technology project evaluation method and system, and relates to the technical field of multi-index decision and intelligent modeling. Comprising the steps of collecting multi-dimensional data of finance, intellectual property, teams, historical projects and the like of science and technology projects; constructing an evaluation index system; constructing a cognitive map by taking the indexes as nodes and combining statistical correlation and expert rules; a graph attention network (GAT) is utilized to train the graph, and structural causal weights among indexes are extracted; fusing the structure weight and the original fuzzy relation matrix to form a structure enhanced fuzzy relation matrix; and in combination with a weight vector determined by a fuzzy analytic hierarchy process (FAHP), performing fuzzy weighted comprehensive evaluation, and outputting a project grade and an interpretable result. According to the method, the accuracy, self-adaptability and interpretability of science and technology project evaluation are effectively improved, and the method is suitable for complex project evaluation tasks in a multi-source heterogeneous data environment.

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.