1787 results about "Data entry" patented technology

The invention provides a scene reconstruction and semantic understanding method and system based on a visual large model, and relates to the field of computer vision and three-dimensional reconstruction, and the method comprises the steps: obtaining multi-view image data of a target scene, inputting a pre-training visual large model, and outputting a joint feature representation and attention weight matrix; generating three-dimensional coordinate values and semantic probability distribution of spatial sampling points in a three-dimensional space according to the joint feature representation, and constructing a spatial semantic field; clustering the spatial sampling points by using the attention weight matrix, performing semantic consistency enhancement, and converting the spatial sampling points into deterministic semantic tags; constructing a geometric optimization objective function, and adjusting three-dimensional coordinate values; and extracting continuous space sampling points with the same semantic tag to form an object boundary, constructing a scene topological graph, deducing a scene functional structure, and generating a navigation path. According to the method, the unification of accurate geometric reconstruction and deep semantic understanding of the scene is realized, the three-dimensional reconstruction precision and semantic analysis accuracy are improved, and reliable support is provided for intelligent navigation.

The invention relates to the field of discrete manufacturing intelligence, in particular to an intelligent decision-making and business collaboration method based on an OAG ontology and an LLM large model, which comprises the following steps: collecting historical data and business documents, and performing directional fine tuning on a basic large language model to form the LLM large model; the method comprises the following steps: analyzing discrete manufacturing scene original data and business documents, and constructing an OAG ontology library; receiving field data in real time through the LLM large model, updating the OAG ontology library, and performing feedback optimization on the LLM large model to form bidirectional feedback; integrating real-time data and historical data, inputting the data into an LLM large model, converting the data into business knowledge through layering of an OAG ontology library, and generating a main and standby decision scheme; and establishing an agent federated center, issuing a decision scheme, collecting execution data and feeding back an LLM large model, and realizing cross-scene collaboration and full-process data closed loop. According to the invention, through a technical path of a whole-process data closed loop, a core pain point that an existing LLM does not understand a business is effectively solved, and whole-link value conversion of discrete manufacturing data is realized.

The invention relates to the field of agricultural product cold-chain storage, and discloses an intelligent management monitoring system for agricultural product cold-chain storage and transportation, which comprises the following steps: synchronously sensing operation states and operation behaviors of agricultural product storage units and cold source equipment to form a data input sequence with operation association semantics; introducing a dynamic sampling mechanism based on operation event triggering into the data input sequence, and constructing a continuous evolution feature set reflecting local cold chain stability; performing time sequence analysis on a temperature and humidity parameter recovery track and a cooling capacity response rhythm presented in the continuous evolution characteristic set; delimiting a storage area corresponding to the risk evolution fragment as a key monitoring unit, and constructing an operation influence association model for describing a local cold chain constraint relationship; and performing linkage adjustment on the sensing node acquisition strategy, the monitoring update frequency and the cold source scheduling parameters under the constraint relation limited by the operation influence correlation model. The method has the advantage that the controllability of the cold chain operation process is improved.

The invention discloses an intelligent supervision method based on a BIM technology, and relates to the technical field of constructional engineering, and the method comprises the following steps: obtaining a BIM model corresponding to the constructional engineering, constructing a digital twinning mapping relation, and carrying out the construction of a digital twinning mapping relation based on a project type, construction complexity and historical supervision data; initializing a data entry time limit through an algorithm, and changing a synchronous threshold value and a quality parameter benchmark; and collecting real-time data, wherein the real-time data comprises process progress data, material entering data, design change information and environmental safety data of the construction site. According to the intelligent supervision method based on the BIM technology, digital and intelligent transformation of supervision work is realized through deep fusion of the BIM technology and multi-source data acquisition, the matching degree of data and an actual construction scene is improved, the accuracy of anomaly recognition is enhanced through a dynamic weight distribution strategy, and the accuracy of anomaly recognition is improved. The problem of dependence on fixed threshold judgment is effectively solved, and the manual intervention cost is reduced.

The invention discloses a flood type landslide disaster monitoring and early warning method, and belongs to the technical field of geological disaster prediction. The method comprises the following steps: step 1, acquiring a flood type landslide disaster case in which a rainstorm event and a landslide event coincide in time and space, collecting landslide factor data, environment data, monitoring data and historical landslide data, and constructing a historical database; step 2, constructing and training a Bayesian network model based on a historical database; step 3, acquiring landslide monitoring data and preprocessing the landslide monitoring data; step 4, training an LSTM time sequence prediction model; 5, inputting the real-time monitoring data into the LSTM model, and predicting to obtain future landslide monitoring data; and step 6, inputting future monitoring data into the Bayesian network to obtain a slope instability probability based on a future trend. According to the invention, through organic fusion of the Bayesian network and the LSTM, dynamic prediction and real-time response of the landslide instability probability are realized, and timeliness, accuracy and robustness of early warning are significantly improved.

A set of images of a scene re received. Each image includes temporal data and spatial data relating to the scene. Based on the spatial data of each image, three-dimensional (3D) Gaussian splatting data is generated. The temporal data of each image and the 3D Gaussian splatting data are inputted to a neural network to generate spatial-temporal 3D Gaussian embeddings. Offset data based on the spatial-temporal 3D Gaussian embeddings is generated. The video of the scene is rendered based on the 3D Gaussian splatting data and the offset data, allowing for improved rendering of video of the scene.

The invention discloses an intelligent early warning method and system for snow-melting flood. The method comprises the following steps: constructing digital twin integrating snow-melting confluence and hydrological hydrodynamic force; collecting multi-source monitoring information, and forming driving data through quality control and spatio-temporal interpolation; driving data are input into digital twinning, and model parameters and states are updated through data assimilation; simulating snow-melting runoff, water level and flow to obtain snow-melting flood probability prediction; the method comprises the following steps of: solving a river reservoir gate pump scheduling scheme by using a multi-objective evolutionary algorithm by taking reservoir outlet flow, gate opening and pump station starting and stopping as decision variables, calculating a water level, flow and a submerging range in digital twinning, and selecting a target scheme according to an evaluation rule to generate a scheduling instruction and graded early warning information; monitoring data is collected during scheduling execution and compared with probability prediction, and when the deviation exceeds a threshold value, data assimilation and scheduling scheme updating are triggered. According to the invention, the snow-melting flood forecasting precision and flood control scheduling collaboration are improved.

The invention relates to the field of data analysis, and discloses a financial statement dynamic analysis system based on multi-agent collaboration, which comprises a data input module, a preprocessing module, a data analysis module and a diagnosis output module, the data analysis module performs multi-dimensional parallel analysis from numerical trend, text semantics and business rules through cooperative work of a time sequence prediction agent, a text evaluation agent and a rule reasoning agent, and provides reference support by relying on a knowledge base; and the diagnosis output module synthesizes conclusions of the agents to generate a comprehensive financial risk index and judges a risk state. According to the method, the deterioration trend intensity value is calculated by monitoring the comprehensive financial risk index in the continuous report period, and the analysis granularity of the intelligent agent is evaluated according to the dynamic regulation and control text. Besides, for the identified significant risks, the system can automatically position key financial indexes and associated risk roots through an iterative positioning method in cooperation with numerical anomaly and text risk events.

The invention relates to the field of smart cities, in particular to an urban landscape semantic segmentation method and system based on a large model, and the method comprises the steps: obtaining street view image data of a target geographic region; inputting the streetscape image data into a preset image semantic segmentation model, analyzing the streetscape image data through the image semantic segmentation model, and identifying various predefined environmental element categories contained in each image; calculating an urban landscape quantitative index based on the recognition result of the environmental element category; associating the calculated value of the urban landscape quantitative index to a corresponding road section or node in the road network spatial data corresponding to the target geographic area; generating a road network space visualization layer representing the value of the index value in a color grading manner based on the result after association; and according to a preset index threshold value, identifying the spatial road sections or nodes meeting the early warning conditions in the visual layer and generating prompt information. And the scientificity and comparability of an analysis result are ensured.

The invention discloses a geotechnical engineering catastrophe dynamic real-time intelligent early warning method and system based on displacement monitoring, and the method comprises the steps: collecting the real-time displacement data and related parameters of a geotechnical engineering region through displacement monitoring equipment, and transmitting the real-time displacement data and related parameters to a bank slope stability evolution analysis platform to establish a multi-dimensional database; calling a fractured rock mass effective stress analysis model to calculate rock mass effective stress distribution characteristics, and inputting a multi-grid sliding surface stress calculation algorithm to obtain a potential sliding surface stress state and a critical threshold value; inputting the sliding surface stress parameters and the displacement data into a multi-field coupling analysis learning prediction model to obtain a displacement evolution trend and a catastrophe risk coefficient; and finally, real-time early warning information is generated and fed back through the catastrophe dynamic real-time intelligent early warning module. The method improves the real-time performance and accuracy of early warning, can effectively guarantee the safety of geotechnical engineering, and is suitable for catastrophe early warning scenes of geotechnical engineering such as bank slopes and foundation pits.

The invention provides a production control method and system based on SPC alarm linkage process locking, and relates to the technical field of quality management and control, and the method comprises the steps: deploying an SPC sensing node used for collecting technological parameters at a cutting edge position where a machining tool of production equipment is in contact with a workpiece, or at a contact point where a probe of online measurement equipment is in contact with the surface of the workpiece; the method comprises the following steps: acquiring process parameter data of a key production process in real time through an SPC sensing node deployed at a cutting edge position or a contact point; inputting the process parameter data as real-time data or small sample expansion data into an SPC judgment engine; and performing statistical process control analysis on the real-time data or the small sample expansion data through an SPC judgment engine so as to model data clusters which are densely distributed and have stable parameters in the data into a virtual reference curved surface representing a normal state domain. According to the invention, the accuracy of abnormity determination can be improved, and timely management and control of quality abnormity and minimization of production loss can be realized.

The invention discloses a submarine cable construction ship extremely-short-term motion response prediction method and system, and relates to the technical field of ship motion control in ocean engineering. Comprising the following steps: acquiring six-degree-of-freedom time sequence data based on a ship-borne inertial measurement unit of a submarine cable construction ship, and constructing a data set; the data set is preprocessed; constructing a model CNN-RSBU-BiLSTM (Convolutional Residual Shrinkage-Bidirectional Long Short Term Memory) with fusion of the convolutional residual shrinkage; the model CNN-RSBU-BiLSTM is trained by means of the data set obtained after preprocessing is carried out on the model CNN-RSBU-BiLSTM; and inputting the six-degree-of-freedom time sequence data acquired and preprocessed in real time into the trained model, and outputting an extremely short-term six-degree-of-freedom motion response prediction result of the submarine cable construction ship. According to the method, the multi-scale feature extraction capability of the one-dimensional convolutional neural network, the noise reduction enhancement effect of the residual shrinkage building unit and the digging capability of the bidirectional long-short-term memory network on the time sequence dependency relationship are combined, so that accurate prediction of the extremely-short-term six-degree-of-freedom motion response of the submarine cable construction ship is realized, and the prediction accuracy is effectively improved.

The invention provides a dry film photoresist preparation data analysis method and system, and the method comprises the steps: obtaining the time sequence process data of the whole process of dry film photoresist preparation, carrying out the preprocessing of the time sequence process data, inputting the time sequence process data into a TCN-NP model, outputting the quality index prediction result of the dry film photoresist, constructing a stage type small sample network, and carrying out the prediction of the quality index of the dry film photoresist. Performing fault identification on the time sequence process data through a stage type small sample network to obtain a fault diagnosis result, and generating a process parameter optimization instruction according to a quality index prediction result and the fault diagnosis result in combination with a hierarchical reinforcement learning framework; through the full-flow integrated scheme including time sequence data processing, quality prediction and uncertainty quantification, small sample fault identification and collaborative optimization decision making, the problems of low process control precision and blind optimization decision making in the dry film photoresist preparation process are solved, so that the stability of the photoresist preparation quality is improved.

The invention relates to the technical field of intelligent medical auxiliary diagnosis and neural function automatic evaluation, in particular to an NIHSS score automatic evaluation system for a stroke patient. Comprising a multi-mode induction and perception unit which is used as a front-end data entry and is used for collecting patient response in real time to generate a video stream containing depth and color information and a synchronous audio stream; the dynamic reference calibration unit is used for extracting kinematic characteristics to construct an individualized nerve reference template; the neural motion spectrum decomposition unit is used for generating a spectrum pathological feature vector for distinguishing myasthenia and ataxia; the opposite-side image rejection analysis unit is used for generating compensation and driving confidence for representing a real nerve driving intention; the cross-modal reflection analysis unit is used for generating a sensory pathway integrity index according to the nerve conduction velocity difference; and the collaborative scoring decision engine is used for mapping the multi-modal features into standardized NIHSS scores. According to the method, the interference of age and basic physique on scoring is effectively eliminated, and a high-precision comparison reference can be provided for subsequent abnormal judgment of the affected side.

The invention relates to a semantic collaborative SLAM map fusion and splicing method based on a multi-modal large model. The method comprises the following steps: deploying a plurality of intelligent agents, collecting original data and preprocessing the original data to obtain multi-modal perception data; inputting the data into a local SLAM (Simultaneous Localization and Mapping) module, combining with a semantic consistency loss fine-tuning multi-modal large model, and constructing a local geometric and semantic map of each agent; fusing the features and mapping to obtain a local and global semantic feature map, and calculating relative poses between the intelligent agents to register the map; and finally, optimizing, reasoning and integrating the initial global map to form a final global semantic map with complete semantics and standard geometric accuracy. According to the method, the problems of insufficient precision, poor robustness, lack of semantic information, difficulty in adapting to dynamic scenes and the like in the existing map splicing process can be effectively solved, and the efficiency, the accuracy and the intelligent level of global map construction are effectively improved.

The invention discloses a fixed source carbon emission data quality comprehensive evaluation method and system, and relates to the technical field of carbon emission monitoring, and the method comprises the steps: S1, collecting dual-source monitoring and production data, carrying out the invalid recognition, emission amount accounting aggregation, working condition classification and correlation coefficient construction, and building a score working condition basic data set through variable coefficient detection; s2, calculating a correlation coefficient of the to-be-inspected valid data, and obtaining a valid data risk comprehensive score through multi-dimensional inspection scoring and in combination with an industry weight; s3, calculating an invalid data proportion as an invalid data risk score; and S4, performing two-dimensional risk rating on the valid data and the invalid data, and outputting a four-dimensional result. The system realizes the method through four layers of modules of data input, processing, risk evaluation and result output, and solves the technical problems that the existing data quality evaluation only pays attention to valid data, the inspection method is single, and a complete dual-source collaborative quality control and full-dimensional quality control scheme is lacked.

The invention discloses a high-quality AIGC art two-dimensional code generation method and system based on intelligent area positioning. The method comprises the following steps: scanning a source image uploaded by a user, and dividing the source image into a plurality of candidate areas; for each candidate area, calculating a multi-dimensional image characteristic value, generating an area suitability score, eliminating an unsuitable area through a smoothness threshold elimination rule, and determining the candidate area with the highest area suitability score as a target area; extracting a target area image segment from the source image, and inputting the image segment and to-be-coded two-dimensional code data into a generative image fusion process to generate an artistic two-dimensional code segment; and backfilling the artistic two-dimensional code segment to a corresponding position of the source image to obtain a complete artistic two-dimensional code image. According to the method, the optimal fusion area can be automatically identified and positioned before generation, the generation success rate and the scanning identification rate of the AIGC art two-dimensional code are remarkably improved, the calculation cost is reduced, and the unification of the visual effect and the functionality is ensured.

The invention relates to the technical field of power systems, and discloses a photovoltaic equipment fault diagnosis method, system and equipment and a medium. The method comprises the steps of obtaining a structured multi-mode fault data set based on a disclosed photovoltaic data set and collected multi-source heterogeneous data generated in the operation process of photovoltaic equipment; selecting an initial multi-modal large language model, and constructing a feature-oriented dynamic routing architecture according to a pre-training weight matrix of the initial multi-modal large language model; based on the structured multi-modal fault data set, utilizing a pre-constructed joint training loss function to carry out fine tuning training on the initial multi-modal large language model to obtain a target multi-modal large language model; and inputting the real-time operation data of the photovoltaic equipment into the target multi-mode large language model for reasoning to obtain a fault diagnosis result of the photovoltaic equipment. According to the invention, efficient and accurate diagnosis of the photovoltaic equipment fault is realized.

The invention discloses an internet hospital hierarchical diagnosis and treatment and resource scheduling method and system, and the method comprises the following steps: responding to a diagnosis and treatment request sent by a user, and obtaining diagnosis and treatment data containing the chief complaint information of a patient; inputting the diagnosis and treatment data into an illness state evaluation model for feature extraction, and outputting a feature vector representing the illness state of the patient; the feature vector at least comprises a first dimension parameter representing an emergency degree of an illness state and a second dimension parameter representing a complexity degree of the illness state; determining a target resource node in a hierarchical medical resource database based on the feature vector; and generating a resource scheduling instruction according to the target resource node, and establishing a communication link between the user terminal and a server corresponding to the target resource node. According to the method, a two-dimensional evaluation system of urgency and complexity is introduced, and a dynamic threshold adjustment mechanism based on supply and demand prediction is combined, so that the utilization efficiency of medical resources and the accuracy of triage matching are remarkably improved.

Systems and methods for executing an IAQ analysis of a building. One system includes a controller including memory and one or more processors configured to obtain IAQ data from one or more sensors within the building, wherein the IAQ data is associated with at least one of a plurality of environment species, obtain BAS data, identify one or more unknown parameters from the IAQ data and BAS data of two or more of the plurality of environment species, estimate the one or more unknown parameters based on inputting the IAQ data and the BAS data into an optimization model, and wherein the optimization model analyzes predicted concentrations of the plurality of environment species subject to the two or more of the plurality of environment species evolving according to a single-species concentration model, and provide the estimated one or more unknown parameters to one or more predictive models.

The invention discloses an AI visual identification-based landscaping pest and disease damage intelligent monitoring and early warning system, and particularly relates to the technical field of AI visual identification pest and disease damage, and the system comprises the steps: obtaining time sequence visual image data, space-time labels and environment sensing data of a target garden area; inputting the time sequence visual image data into a pre-trained disease and insect pest AI identification model, and outputting an identification result; calculating initial occurrence density and local diffusion trend parameters of disease and pest species, and generating a spatial distribution thermodynamic diagram; constructing a multi-factor risk assessment model; dynamically generating a graded early warning signal; according to the invention, the landscaping pest and disease damage intelligent monitoring and early warning system is constructed through the multi-source heterogeneous data acquisition module, the feature extraction and identification module, the data aggregation and space association module, the multi-factor risk assessment and time sequence prediction module and the dynamic early warning and decision support module; the problems of single monitoring dimension, weak data fusion and space association capability, fragmentation of decision support information and the like are solved.

A memory controller is coupled via at least one data path to plural memory regions for distributing and storing plural data entries. The memory controller includes parity generating circuitry configured to: perform logical operations on the plural data entries, based on an order in which the plural data entries are transmitted to the plural memory regions, to generate a parity entry; and add location information of the plural data entries, stored in the plural memory regions, into the parity entry.

The purpose of the present invention is to achieve an ozone water concentration estimation device which is capable of monitoring ozone water concentration with a simpler configuration than before even if impurities are present in ozone water. This ozone water concentration estimation device comprises a first data acquisition unit, an estimation unit, and a data output unit. The first data acquisition unit acquires first generation environment data including at least one of the ozone gas concentration of an ozone gas that is supplied to the inside of an ozone water generation tower, the ozone gas flow rate of the ozone gas, the water temperature of the ozone water that is retained in the ozone water generation tower, the pH of the ozone water, and the retention amount of the ozone water. The estimation unit estimates, from the first generation environment data, an ozone water concentration estimate value, which is an estimated value of the ozone water concentration in the ozone water at the time of measurement of the first generation environment data input from the first data acquisition unit, using a numerical model that estimates the ozone water concentration, which is the concentration of ozone dissolved in the ozone water at the time of measurement of the first generation environment data. The data output unit outputs the ozone water concentration estimate value.

The invention discloses an automatic focusing method and system of a cell slide scanner, and relates to the related field of optical equipment focusing technology.The method comprises the steps that a slide is previewed and scanned, a multi-mode preview image and physical height sensing data are synchronously collected and preprocessed, and then the pre-processed image is obtained; the multi-modal preview image and the corresponding height data are input into a pre-training focus prediction model, a focusing strategy graph aligned with a slide coordinate space is output, and the graph comprises the predicted optimal focus offset, the focusing function recommendation identifier, the focus verification confidence coefficient and the search radius of each view point; according to the focusing strategy graph, a final focus is determined through feedforward positioning, limited verification and conditional adjustment, a scanner objective table is controlled to carry out formal scanning at the final focus position, and a complete scanning image of the cell slide is collected. The problems that a traditional focusing method is long in time consumption and poor in adaptability to complex samples with low contrast, high background fluorescence and scratch pollution are solved, and the adaptive capacity of the focusing process is improved.

The invention discloses a document data entry method, electronic equipment, a storage medium and a program product, and relates to the technical field of document recognition, and the document data entry method comprises the following steps: obtaining a to-be-recognized document containing at least one to-be-recognized page; the document to be recognized is recognized through the optical character recognition technology, a first recognition result and a target page are obtained, and the target page is a page to be recognized containing non-text elements; identifying the target page through the target large language model to obtain a second identification result; and inputting the first recognition result and the second recognition result into the target form according to the similarity between the form field of the preset target form and the first recognition result and the second recognition result, and obtaining the input target form. Through cooperative work of the OCR and the large language model, synchronous and efficient recognition of text and non-text information is achieved, and the accuracy and the automation level of document recognition and document data entry are improved in combination with an intelligent matching entry mechanism.

An image processing apparatus that can decide whether to perform temperature control on a desired object in an image and have a degree of freedom in a control range of a presented temperature. The image processing apparatus includes an image data input unit that inputs image data of the image, an object detection unit that detects the object in the image based on the image data, a type identification unit that identifies a type of the object detected by the object detection unit, a control determination unit that determines whether to perform temperature control to an object occupied area that is occupied by the object in the image based on an identification result of the type identification unit, and a temperature decision unit that decides a temperature of the object occupied area when it is determined to perform the temperature control as a determination result of the control determination unit.

The invention discloses an ancient building settlement situation prediction method and system based on decomposition integration, and belongs to the technical field of cultural relic settlement monitoring and deep learning crossing. The method comprises the following steps: acquiring multi-source settlement monitoring data of an InSAR, a GNSS, a traditional sensor and the like, and generating continuous time sequence characteristic data by preprocessing a data format, complementing missing values and performing standardization; inputting the processed data into a pre-training neural network model based on a Transform architecture, capturing a long time sequence dependency relationship by using a multi-head attention mechanism of the pre-training neural network model, and outputting a future settlement predicted value; and setting a grading early warning threshold according to the type of the ancient building, generating early warning information in combination with a prediction result, and outputting a visual chart containing comparison between a true value and a predicted value. According to the method, efficient processing and long-time-sequence accurate prediction of settlement data are realized, and reliable technical support is provided for early discovery and disposal of settlement diseases of cultural relics and buildings.

A crop phenotype data fusion method and apparatus, an electronic device and a storage medium are provided. The method includes: inputting target data corresponding to the three-dimensional first to-be-fused crop phenotype data among the to-be-fused crop phenotype data into a weight adjustment model; acquiring a weight value corresponding to each first to-be-fused crop phenotype datum output by the weight adjustment model; and performing data fusion on the plurality of to-be-fused crop phenotype data based on the weight value corresponding to each first to-be-fused crop phenotype data and intrinsic and extrinsic parameter data of the crop phenotype sensors that collect the plurality of to-be-fused crop phenotype data. The target data includes environmental data when collecting crop phenotype data, relative position information between the crop phenotype sensors and the crop, and the target parameter values of the crop phenotype data. The target parameter values include signal-to-noise ratios and / or feature entropies.

The invention provides an online course MOOC learning prediction method based on heterogeneous feature fusion, and belongs to the field of computer-aided intelligent education. The method comprises the following steps: acquiring an MOOC data set, and preprocessing the data set to obtain a test set; constructing an IHFNet network comprising a multi-agent adaptive static feature selector module, a hierarchical time sequence feature extractor module and a heterogeneous feature fusion module; a multi-agent adaptive static feature selector module screens key static features; the hierarchical time sequence feature extractor module extracts behavior time sequence features with high discrimination ability; the heterogeneous feature fusion module carries out adaptive fusion on the key static features and the behavior time sequence features and carries out classification prediction; an IHFNet network is trained; and collecting MOOC data of a to-be-predicted learner, and inputting the MOOC data to the trained IHFNet network for learning risk prediction. According to the invention, modeling is carried out by fusing the static features of the learner and the behavior time sequence features, the feature representation ability of the learner is enhanced, and the learning risk prediction effect is improved.

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.