192results about How to "Improve relevance" patented technology

The invention discloses a shale oil geological engineering dessert evaluation method and device based on geological modeling, and the method comprises the steps: determining a shale oil facies classification scheme and shale oil dessert evaluation key parameter values according to the shale oil geological engineering logging interpretation data, determining the corresponding scores of different shale rocks, and obtaining the shale oil dessert evaluation key parameter values of the shale oil facies classification scheme and the shale oil dessert evaluation key parameter values of the shale oil facies classification scheme; and determining a target sweet spot evaluation parameter weight based on sweet spot evaluation attributes of the lithofacies three-dimensional model and actual well drilling productivity data, establishing a shale oil geological engineering integrated comprehensive sweet spot factor, and determining a shale oil geological engineering comprehensive sweet spot evaluation result based on the sweet spot factor and the target sweet spot evaluation parameter value. Through the technical scheme of the embodiment of the invention, comprehensive evaluation of shale oil geological engineering desserts is realized, complexity of shale oil geological features can be adapted, correlation between geological and engineering dessert evaluation parameters is improved, and fine evaluation of shale oil dessert space distribution is ensured.

The invention provides a tumor medical image classification method of zero sample evolution NAS based on double-index collaborative evaluation, which comprises the following steps: collecting tumor medical image data, and constructing a data set containing a tumor focus image and a normal tissue image; constructing an extended search space based on a cell structure, wherein the search space comprises a basic operation set, the cell structure and a network overall structure; steady-state evolution algorithm parameters are set, and steady-state evolution initialization is carried out to obtain an initial candidate architecture; taking a double-index comprehensive score calculated by the information rate and the FIM stability as a comprehensive index for evaluating the current candidate architecture, updating the candidate architecture through a steady-state evolutionary algorithm based on a dynamic optimization mechanism until the maximum evolutionary algebra is reached, and obtaining an optimal architecture; and training the optimal architecture by using the data set, and carrying out tumor medical image classification by using the trained optimal architecture. According to the method, the dependence on the annotated data is reduced, the consumption of computing resources is reduced, and meanwhile, the diagnosis efficiency and accuracy are improved.

The invention discloses an enhanced generation query method and system based on multi-granularity retrieval and reinforcement learning strategies. According to the method, semantic analysis is carried out on an original query by utilizing a large language model, the original query is decomposed into a plurality of sub-problems, and each sub-problem generates a query vector; in the multi-granularity vector knowledge base, respectively executing vector similarity retrieval on the query vector of each sub-problem to obtain a related document fragment vector of each sub-problem under each granularity, and further generating a multi-granularity vector set of each sub-problem; weighting the multi-granularity vector set of each sub-problem to obtain a comprehensive context information block, determining the weight of the multi-granularity vector set of each sub-problem through a multi-arm reinforcement learning strategy, and inputting the comprehensive context information block into a large language model to output a query result. According to the method, the retrieval accuracy is improved through semantic decomposition and multi-granularity knowledge collaborative retrieval, results are intelligently integrated through a self-learning mechanism, and lightweight self-adaptive optimization is achieved.

The invention provides a channel prediction method and system based on a multi-cycle feature convolutional network. According to the method, context sensing weighted input and a dynamic multi-model pool mechanism are introduced. A terminal device obtains environmental context features in real time, dynamically calculates the weight of each time step in a historical channel state information sequence based on the features, and generates a weighted optimized input sequence. Meanwhile, the network equipment pre-trains and maintains a plurality of multi-period feature convolutional network models specific to different environments, and the network can effectively capture the complex time-varying rules in the period and during the weeks in the channel, so that the accuracy of channel state information prediction is improved. And the terminal equipment immediately selects and activates the optimal sub-model in the model pool for prediction according to the real-time context features, and locally performs increment fine adjustment. According to the invention, through management of the data input end and rapid and adaptive switching of the prediction model end, the precision of channel prediction and the adaptive capacity to environmental sudden change are effectively improved.

A simulation method and related equipment for machining process training are disclosed. The method includes: acquiring engineering data and process data of the target workpiece; extracting features from the engineering data and combining them with the process data to construct a case resource library; performing semantic association analysis between the pre-set knowledge resource library and the machining cases in the case resource library to generate a process knowledge network; constructing a virtual training environment based on the process knowledge network; collecting interaction data of learners in the virtual training environment and evaluating the interaction data to obtain evaluation results; and generating guidance animations based on the evaluation results and the process knowledge network. This application can improve the learning efficiency of simulation training.

This invention discloses a method for multi-source data fusion and power prediction of wind turbine units, belonging to the field of power prediction technology. It addresses the technical problems of poor multi-source data fusion and power prediction analysis in existing solutions. By filtering and retaining key features through mutual information, redundant information can be effectively reduced. Dynamic weighting via an attention mechanism makes the fused features more adaptable to real-time operating conditions. Differential weight adjustments for wind speed, terrain, and equipment status ensure high relevance of the fused features even in complex scenarios. Spatial feature extraction captures local spatial correlations, temporal features capture temporal dependencies, and random forests handle nonlinear mappings, solving the problem of insufficient generalization ability of single models. The attention mechanism automatically assigns weights to different time steps and features, avoiding complex mathematical processes such as matrix operations and noise covariance estimation.

This invention discloses a method for constructing large-scale model applications based on configurable workflows and domain knowledge bases, comprising the following steps: S1, constructing a domain knowledge base, establishing a semantic graph, and generating a knowledge embedding dataset; S2, defining a configurable workflow, setting jump rules based on a semantic flow language, and binding task semantic tags; S3, configuring a Prompt adaptive generation mechanism, combining semantic tags and the knowledge embedding dataset to generate Prompt input text; S4, calling the knowledge embedding dataset, retrieving semantic fragments, and embedding them into the Prompt to form an enhanced Prompt input; S5, inputting the large language model to obtain return results and metrics, and performing jump judgment; S6, scheduling large language model service instances, dynamically selecting service interfaces based on metrics and task status; S7, processing process termination nodes, organizing output results, and recording logs and call data. This invention achieves intelligent scheduling and application construction of large-scale models based on workflows and knowledge bases.

The invention relates to the technical field of micro-terrain intelligent identification, in particular to a power transmission line icing micro-terrain intelligent identification method and system based on icing simulation. Comprising the following steps: S1, determining position data of a power transmission line, and obtaining remote sensing data covering the power transmission line according to the position data; s2, establishing an icing micro-topography database, and performing micro-topography classification on the icing micro-topography database; the defects in the prior art are effectively overcome through multi-source data fusion and intelligent modeling, a multi-source fusion scheme of SAR data, multispectral data, DEM data and meteorological data is adopted, comprehensive extraction of three-dimensional features of terrains, airflow and water vapor is achieved in combination with accurate position data of a power transmission line management end, and the accuracy of the three-dimensional features of the terrains, the airflow and the water vapor is improved. The problems of single feature dimension and incomplete data coverage in the prior art are solved, richer basic data support is provided for microtopography recognition, and the relevance between the features and the icing mechanism is remarkably improved.

The application relates to the technical field of power systems, and discloses a new energy power source virtual equivalent modeling method, a new energy power source virtual equivalent modeling system, a storage medium and equipment, the method comprising the following steps: establishing a virtual equivalent model of a new energy station, wherein the virtual equivalent model is composed of an equivalent potential and an equivalent impedance in series; determining parameter constraint conditions of the virtual equivalent model, wherein the parameter constraint conditions comprise: setting the amplitude of the equivalent potential to be the same as the amplitude of an AC system potential on the opposite side of a power grid line; setting the impedance angle of the equivalent impedance to be equal to the impedance angle of the power grid line; collecting measured voltage and measured current at a power grid protection point in real time; establishing a variable relationship equation group in polar coordinate form based on the parameter constraint conditions of the virtual equivalent model; and solving the variable relationship equation group to obtain dynamic parameters of the virtual equivalent model. The application can realize accurate equivalent modeling of the new energy station, thereby accurately reflecting the dynamic characteristics of the new energy power source.

The invention provides a multi-round dialogue intention classification method and device, and the method comprises the steps: defining and extracting a single-valued slot which is a core keyword contained in an FAQ intention; constructing intention clusters based on the single-valued slots, wherein the intention clusters of the same entity type and the intention clusters of the same single-valued slots are constructed; a single-valued slot inheriting slot mechanism is introduced into multiple rounds of dialogues, and different dynamic score adding strategies are performed on the intention clusters of the same entity type and the intention clusters with the same single-valued slot based on the recognition result that the hit intention has the single-valued slot; and performing intention classification again according to the intention score after score addition. The intention recognition accuracy and effectiveness can be improved.

This invention relates to the field of medical image processing technology, specifically to an automatic segmentation and recognition method and system for cancer images based on the fusion of multimodal images. The method includes: acquiring a multimodal medical image set containing computed tomography (CT) images, magnetic resonance imaging (MRI) images, and positron emission tomography (PET) images; generating spatially aligned data blocks through three-dimensional spatial registration and voxel alignment; extracting fusion features using an improved encoder-decoder network optimized based on the characteristics and spatial dependencies of different modal channels; obtaining enhanced semantic feature maps through multi-level context-aware processing; identifying suspected cancer regions through a region candidate generation network; and simultaneously outputting pixel-level segmentation masks and cancer type probabilities through a segmentation network, ultimately generating a three-dimensional segmentation and recognition result. This method can accurately capture multimodal-specific features, clearly distinguish the boundaries between lesions and normal tissue, simplify the processing flow, reduce errors, and achieve accurate and efficient segmentation and recognition of cancer lesions.

The invention relates to the technical field of vehicles, and discloses a driving behavior detection method and device, electronic equipment and a medium, and the detection method comprises the steps: determining a peak frequency and a high-frequency energy ratio of an accelerator pedal opening degree in a current time period according to the accelerator pedal opening degree corresponding to each moment in the current time period of a target vehicle; the high-frequency energy ratio is the ratio of the frequency band energy when the opening frequency of the accelerator pedal is greater than the preset frequency to the total energy; determining the change frequency of positive and negative changes of the accelerator pedal opening change rate of the target vehicle in the current time period, the change amplitude of the accelerator pedal opening during each positive and negative change and the jerking amplitude of the target vehicle; and determining the current driving behavior of the target vehicle according to the peak frequency, the high-frequency energy ratio, the change times, the change amplitude, the pause amplitude and the predicted collision time between the target vehicle and the front vehicle at the current moment, thereby optimizing a feedback mechanism of the vehicle.

The invention discloses a building digital information security management method and system based on a block chain, and the method comprises the steps: collecting building BIM parameters through distributed nodes, constructing an initial information block, extracting feature vectors through a graph convolutional neural network, carrying out the verification through a block chain consensus mechanism, storing the feature vectors in a specific structure, and constructing a security evaluation model in combination with the parameters. And when the threshold value is exceeded, triggering the smart contract to execute operation. The system comprises a parameter acquisition unit, a feature extraction unit, a consensus verification unit, a chain storage unit, a security assessment unit, an intelligent contract execution unit and other associated units, and all the units operate cooperatively. By dynamically adjusting a storage and verification mechanism, building component parameters are utilized to construct a model, the integrity of feature extraction and the storage security are enhanced, the problems of insufficient feature characterization and lack of dynamic management in the prior art are solved, and the method is suitable for building full-life-cycle information security management.

The application relates to the technical field of semantic analysis, in particular to a customer group evolution prediction method based on large model semantic analysis, which comprises the following steps: obtaining historical unstructured data of users of a streaming media platform, performing semantic analysis on multiple time points by a large model to generate semantic vectors; processing the semantic vectors by a loop module to generate a historical semantic sequence; training a semantic evolution model based on the sequence; in an offline batch processing stage, calculating future semantic vectors of all users in advance by using the evolution model, and constructing a semantic index; when a seed customer group is received online, calculating a customer group centroid vector representing a future trend for the seed customer group; performing approximate neighbor search on the centroid vector in the predictive semantic index, and outputting a predicted evolved customer group. The application improves prediction accuracy through future-to-future matching, and ensures low delay and feasibility of the evolution prediction function through an offline precalculation architecture.

This invention provides an intelligent analysis system for photoelectric detection spectral data based on deep learning, belonging to the fields of photoelectric detection, spectral data analysis, and deep learning application technology. The system includes core units such as a spectral data acquisition unit, a preprocessing unit, a feature extraction unit, and a deep learning analysis unit, as well as auxiliary units for anomaly detection and data storage, forming a closed-loop architecture of acquisition-processing-analysis-output-optimization. It improves data quality through step-by-step preprocessing algorithms, reduces computational load by adaptively selecting key features, deeply mines spectral patterns using a model that integrates convolutional neural networks and attention mechanisms, achieves rapid scenario adaptation through transfer learning, and dynamically optimizes model parameters based on a feedback mechanism. The system solves the problems of low accuracy, poor adaptability, and long training cycles in traditional spectral analysis, achieving accurate, efficient, and intelligent spectral data analysis, and is suitable for various photoelectric detection scenarios such as qualitative and quantitative analysis of substances and environmental monitoring.

The invention relates to a port storage yard container storage time prediction method and device based on a self-attention mechanism, and the method comprises the steps: calculating the correlation degree between each characteristic variable and the storage time through gray correlation degree analysis; feature variables with high correlation are screened out based on the correlation degree to serve as prediction input, a complex dependency relationship and long program column features among modeling variables of the multi-head self-attention network are introduced, nonlinear mapping and fitting are achieved in combination with a full-connection network, and therefore the accuracy of a prediction result is effectively improved; in the model training process, a loss function with measurement monotonous consistency is designed, a constraint model keeps monotonous sequence consistency of output results on the premise that prediction precision is not lost, and the container turnover and secondary transfer risks caused by prediction sequence deviation in storage yard operation are reduced. And the model prediction performance is comprehensively evaluated from two dimensions of prediction precision and monotone consistency. According to the method, prediction precision and application practicability are both considered, and reliable time information can be provided for storage optimization of the container in the storage yard.

The application provides a kind of dynamic road pattern-based road health state monitoring method, system and equipment, its method includes: based on the grating array sensing network in road section obtains the initial dynamic road pattern of road under vehicle excitation;Obtain the reference dynamic road pattern of road in reference state, based on reference dynamic road pattern, initial dynamic road pattern is normalized, and dynamic road pattern is obtained;Dynamic road pattern is the state response variation curve of structural excitation along the road mileage position variation and change characteristic;Based on dynamic road pattern and reference dynamic road pattern, determine whether there is non-normal pavement damage, roadbed emptying and non-uniform settlement and other diseases in road.The application uses the whole vibration or strain response of road to construct the dynamic road pattern, considers the overall structural characteristics of road, improves the relevance of dynamic road pattern and road health state, and then can realize the purpose of accurately monitoring the road health state based on dynamic road pattern, improves the accuracy and real-time performance of road health state monitoring.

This invention relates to the field of intelligent inspection and 3D visual reconstruction technology for automotive parts, specifically a method for 3D image reconstruction of lightweight internal high-pressure molded parts for automobiles. The method includes: acquiring a sequence of structured light images, multispectral reflectance texture images, and a priori simulation model of the target object, and completing spatial coordinate registration; dynamically allocating edge extraction weights based on the local curvature gradient of the 2D image to generate a non-uniform density 3D point cloud; constructing a surface reconstruction energy function using surface reflectance variation data as a penalty term, fitting the point cloud to the 3D surface, and generating a target 3D mesh model; fusing 3D geometric features, 2D texture features, and the priori simulation model, and outputting the wall thickness reduction rate and residual stress distribution via a graph neural network; generating and overlaying a risk heat map, and outputting the evaluation decision results. This invention extends from geometric reconstruction to risk semantic reconstruction, improving the comprehensiveness and reliability of online inspection of internal high-pressure molded parts.

The invention provides a commodity recommendation method and system based on consumer behaviors, and the method comprises the steps: firstly extracting explicit behavior features and implicit preference features of consumers in different context scenes, and determining node preference indexes of consumption behaviors in different context scenes; generating a short-term intention chain and a long-term preference chain of the consumer according to the node preference index, and further constructing an element feature map reflecting consumer consumption behavior preference according to a mapping association relationship between the short-term intention chain and the long-term preference chain; extracting the preference weight of each preference node in the element characteristic spectrum under different context scenes; and detecting the matching degree between the element characteristic spectrum and the current consumption behavior characteristics, and when the matching degree is lower than a preset threshold value, performing preference updating on the element characteristic spectrum based on the preference weight corresponding to each preference node and the current consumption behavior characteristics, thereby generating a commodity recommendation list. By adopting the scheme of the invention, personalized commodity recommendation based on different context scenes can be realized in an internet e-commerce platform.

The application provides a medical question and answer method and system based on cooperative dual-source retrieval enhancement generation, which comprises the following steps: according to an input original medical question and candidate items, performing task self-adaptive query rewriting through a large language model to generate differential diagnosis keywords, entity enhanced queries and hypothetical medical abstracts; for the differential diagnosis keywords, performing an iterative network retrieval with a reflection mechanism to obtain network candidate evidence; for the entity enhanced queries and the hypothetical medical abstracts, performing a hybrid retrieval in a local medical knowledge base to obtain local candidate evidence; aggregating and deduplicating the network candidate evidence and the local candidate evidence, obtaining the aggregated heterogeneous evidence, performing deep semantic correlation scoring through a cross-encoder model, screening out a target evidence set, and splicing the original medical question as context input into a generative large language model to generate a final medical answer. The application improves the accuracy, timeliness and explainability of the medical question and answer system.

The present application relates to the technical field of smart city, especially relates to a smart city bus stop board dynamic information accurate pushing method and system, wherein a query semantic representation structure is constructed by collecting query requests and scene state information, a joint understanding representation is obtained by modeling the scene state information multi-level semantic association, scene constraint multi-hop reasoning is executed in a bus knowledge association network, a candidate answer sequence is generated by filtering, an optimal answer is selected by calculating a scene adaptation score, pushing content is generated, interactive feedback is collected, feature fusion weights are adaptively adjusted to form a closed loop optimization, and accurate dynamic pushing of bus information and user experience improvement are realized.

This invention discloses a method, system, and storage medium for multivariate time-series feature extraction and grade prediction in flotation processes. The method includes the following steps: Step S1: Raw data input and encoding; encoding and structured input of time-series data composed of various process variables; Step S2: Extracting dynamic features; Step S3: Condition-guided encoding modeling; Step S4: Enhancing the saliency of key variables and important time segments; employing a multi-head attention mechanism to match key-value pairs generated from target-guided query vectors and multi-scale features; Step S5: Outputting a prediction module; performing feature fusion and nonlinear mapping on the attention mechanism output to output the predicted concentrate grade and recovery rate for future times. The system and storage medium are both based on the above method. This invention has advantages such as higher intelligence, better controllability, and improved prediction accuracy and model adaptability for key indicators in the flotation process.

This invention discloses a non-load-bearing front engine compartment structure, a frame, and a chassis. The front engine compartment structure includes two sets of wheel arch assemblies spaced apart along the width of the vehicle body. A first crossbeam and a second crossbeam are arranged parallel to each other and spaced apart between the two sets of wheel arch assemblies. Two hinge mounting members are arranged between the two wheel arch assemblies. The hinge mounting members are provided with mounting surfaces for mounting hood hinges. The structural strength of the wheel arch assembly itself is improved by the first and second structural beams. The parallel first and second crossbeams are fixed to the two sets of wheel arch assemblies respectively, further improving the correlation and stability between structural components and maintaining the structural strength of the wheel arch assemblies as a whole. The hinge mounting members are arranged on the wheel arch assemblies to provide the mounting base for the hood hinges. Under the premise of reliable overall structural strength, the additional hood mounting structure is avoided in the front engine compartment, thus improving overall reliability and space utilization.

The invention discloses a multi-model paper retrieval method oriented to academic questions and answers, which relates to the technical field of natural language processing and information retrieval, and comprises the following steps: firstly, constructing a unified corpus and a training data set, and respectively encoding by using models in a first model set and a second target model to generate a document vector set; screening difficult negative samples based on the initial retrieval result of the second model, constructing a comparative learning sample, performing fine adjustment on the comparative learning sample, and recoding a corpus; and taking the fine-tuned second target model and the models in the first model set as a model group, respectively performing target query and performing similarity retrieval by adopting each model in the model group to obtain a corresponding original similarity matrix, screening documents based on the original similarity matrix, and generating a first target document list of the target query. According to the method, the accuracy and robustness of academic literature retrieval can be effectively improved.

The invention discloses a system and method for advertising through an electronic game by using AI, and relates to the technical field of electronic game advertising. The method comprises the following steps: acquiring an image of an interaction area to generate real-time video stream data; performing computer vision analysis on the video stream, extracting non-identity group features of the current interactive group, and forming feature data including dominant age interval labels; taking the label as a query key, and performing matching query according to a dynamic content mapping rule base to determine a target advertisement visual element identifier; positioning a replaceable texture storage area in the game resource package according to the identifier; and on the basis of visual data corresponding to the identifier, the texture content of the region is replaced by an asynchronous rendering updating technology, so that a dynamic advertisement matched with the characteristics of the current crowd is generated and presented in real time in a game picture. According to the method and the device, in an offline anonymous scene, the advertisement in the game is dynamically and accurately updated according to the group characteristic sensed in real time, and the game performance is not influenced.

The application relates to the technical field of accurate load prediction of a virtual power plant, and provides a load accurate prediction method and system for multi-source heterogeneous data fusion of a virtual power plant, so as to solve the problem that the existing technology is difficult to adapt to the accurate load prediction demand of a virtual power plant for a short period. The application collects photovoltaic output, wind power fluctuation data of a virtual power plant, and real-time current and node voltage monitoring data of a distribution network, associates and labels the former two to obtain an associated data set, phase-calibrates the latter two to obtain a calibrated data set, and integrates the two data sets into a multi-source heterogeneous data set; then, fluctuation characteristics of various data are extracted, an XGBoost algorithm is used to screen out a core information feature set affecting load prediction accuracy, and finally, a GRU time sequence prediction model is used to generate a load prediction result in a preset time period, so that the multi-source heterogeneous data of a virtual power plant can be fused, feature screening and time sequence correlation processing are performed, and accurate load prediction in a preset time period can be realized.

The application relates to the technical field of point cloud data processing, and specifically discloses a UAV point cloud original piece data processing system with a grading naming function; the system comprises the following modules: a boundary division module generates a rectangular boundary suitable for a terrain and screens a to-be-graded original piece; a grading identification module constructs geometric constraints based on a projection distance, completes tower distribution through extreme value judgment and parallel scene processing rules; a naming adaptation module integrates multi-source data to construct a scene feature vector, divides a risk range through clustering, realizes differential naming by adopting multi-source filling and normalization sorting; a feedback checking module locates a preferential analysis interval through deviation analysis, constructs a space neighborhood group and judges a high-risk area, and replaces an original algorithm to complete grading renaming and closed-loop checking by adopting a space index-hash mapping method; the application realizes dynamic optimization of point cloud grading naming, improves grading precision and naming compliance, and provides data support for power transmission line operation and maintenance.

The invention discloses a deep learning model-based micro-nano structure and spectral characteristic relation measuring and calculating method, which comprises the following steps of: preparing micro-nano structure arrays containing multiple unit types, different sizes and periods, measuring and collecting reflection spectral data of each array at high throughput, and constructing a high-quality experimental spectral data set; and training a deep learning model by using the experimental data set to realize forward prediction of the experimental reflection spectrum corresponding to the geometric parameters of the micro-nano structure and reverse prediction of the geometric parameters and shape of the micro-nano structure corresponding to the target reflection spectrum. A multi-layer perceptron neural network model is adopted in forward prediction, and a convolutional neural network model is adopted in reverse prediction. According to the method, the difference between the simulated spectrum and the actual spectrum is effectively made up, and the efficiency and precision of micro-nano structure spectrum design are remarkably improved.

The present disclosure provides a transaction data processing method and device, the method comprising: receiving a transaction request sent by a user account; preprocessing original transaction data in the transaction request to obtain target transaction data; wherein the target transaction data comprises context data of the original transaction data; performing preliminary risk identification on the target transaction data according to a set risk identification rule to obtain a preliminary risk identification result; in response to the preliminary risk identification result indicating that the target transaction data is normal transaction data, performing secondary risk identification on the target transaction data using a risk identification model to obtain a secondary risk identification result; and in response to the secondary risk identification result indicating that the risk level of the target transaction data is uncertain, performing risk analysis on the target transaction data using a large language model to generate a risk analysis result of the target transaction data; through hierarchical and progressive risk identification and analysis of transaction data, the accuracy and comprehensiveness of risk judgment can be improved.

The invention discloses an evaluation method and system for an inner layer structure of a multifunctional integrated enclosure structure under an intermittent working condition, and the method comprises the steps: constructing a secondary evaluation index of the inner layer structure of the multifunctional integrated enclosure structure according to a building climate region to which an enclosure structure to be evaluated belongs, and integrating the building climate region into the construction of an index system, through a dynamic weight distribution technology, evaluation key points are automatically adjusted along with climate zone changes, the universality defect of traditional evaluation is overcome, dynamic heat and humidity coupling simulation and aging simulation are carried out on an inner layer structure of a multifunctional integrated enclosure structure under an intermittent working condition, the limitation of continuous steady-state working condition evaluation is broken through, simulation results are scored, and the evaluation accuracy is improved. And calculating a first-level evaluation index of the reaction comprehensive performance according to a climate region differentiation weight distribution result, and evaluating the inner layer structure of the enclosure structure according to the first-level evaluation index of the reaction comprehensive performance and a quantitative scoring result comprehensive result, so that the evaluation result is ensured to be matched with the regional suitability, and the evaluation result is more accurate.