10160 results about "Evaluation result" patented technology

The invention relates to the technical field of image processing, and discloses a backlight effect image edge enhancement method based on intelligent identification, which comprises the following steps of: judging a field environment type; performing global optimization on the original image based on a set environment perception type enhancement mechanism according to the judged field environment type, and outputting a global pre-processing image; constructing a backlight area segmentation model for the globally preprocessed image; outputting a local enhanced image; designing a structure perception type local adaptive threshold algorithm for the local enhanced image, and outputting a binary image keeping structural continuity; extracting an edge image of the binarized image through an edge detection algorithm, and optimizing a topological structure of a contour in the binarized image; and comparing with a wood template processing standard feature library, outputting an edge quality evaluation result and feeding back to a processing control system. Defect detection and machining control depth linkage is achieved, passive detection is changed into active optimization, and the production efficiency and the yield are improved.

The invention provides a road bridge crack detection method and system, and the method comprises the steps: collecting a bridge surface multi-view image, and constructing a training data set containing crack feature labeling through quality screening and standardized labeling; preprocessing the image by using a multi-scale feature fused deep convolutional neural network and carrying out semantic segmentation, initially identifying a suspected crack region and generating a segmentation mask; and constructing a BeNNS proxy model based on the mask, and establishing a mapping relationship between the detection result and the bridge structure topology, the stress flow field and the service function chain so as to evaluate the result reliability. And inputting an evaluation result into a hybrid evaluation mechanism, performing online real-time detection and offline batch verification to optimize precision, and outputting a verified crack region. Finally, morphological analysis is conducted on the area, geometric parameters and danger levels of cracks are extracted and integrated to a bridge health monitoring system, a crack evolution tracking algorithm and an early warning mechanism are established, and dynamic tracking early warning is achieved. The problem of low detection precision in a complex environment can be solved.

The invention relates to the technical field of growth state evaluation, and discloses a crop growth state evaluation method and system based on multi-dimensional monitoring. The method comprises the steps of collecting multi-source remote sensing data of a farmland area according to a crop growth period, and performing topographic correction on the multi-source remote sensing data to obtain target vegetation data; based on the multi-source remote sensing data, farmland plot boundaries are extracted, and a farmland space association graph is constructed; inputting the target vegetation data and the farmland space association graph into an elevation perception graph convolutional network for elevation feature analysis, and calculating to obtain a crop abnormal growth index; and generating a growth state evaluation result based on the target vegetation data and the crop abnormal growth index. According to the method, crop growth abnormity caused by regional factors can be accurately identified, so that the accuracy of evaluation results under different terrain and environmental conditions is ensured.

The invention discloses an urban flood toughness evaluation method, apparatus and device, a medium and a computer program product. The method comprises the steps of obtaining hydro meteorological data of a to-be-evaluated region and geographic information data of an urban spatial form element layer; setting boundary conditions of a plurality of flood scenes according to the hydrometeorological data, constructing a multi-scene urban flood disaster simulation model in combination with geographic information data, and respectively generating flood inundation depth data of urban spatial form element layers of the plurality of flood scenes; and calculating a first toughness index, a second toughness index and a third toughness index of each flood scene so as to calculate a flood toughness comprehensive evaluation result of each flood scene of the to-be-evaluated region. According to the method, an evaluation system of multiple composite flood disaster scenes is established, different disaster factors and interaction thereof are comprehensively considered, and the effect of key space parameters in urban flood toughness is fully considered, so that the capability of coping with flood disasters in cities can be more comprehensively and accurately evaluated.

The invention relates to the technical field of landscape evaluation, and discloses an urban green land landscape evaluation method based on a large model, and the method comprises the steps: carrying out the real-time analysis and standardization processing of a streetscape image, IoT environment data, satellite vegetation coverage data and RTK high-precision positioning information of a target city district, extracting core landscape elements, building cross-scene semantic mapping, and carrying out the calculation of the cross-scene semantic mapping. Transmitting the standardized data to a central database and deploying edge nodes; extracting a multi-dimensional landscape index by adopting an improved semantic segmentation model, dynamically adjusting an index weight in combination with regional features and seasonal changes, and classifying and correcting deviation data by edge nodes; a visual evaluation result is generated based on a cooperative computing architecture and a digital twinborn model, a landscape space to be promoted is identified, and optimization suggestions are generated; and obtaining planner feedback information, updating the model, the weight rule and the suggestion generation strategy, and forming a closed loop iteration mechanism. According to the method, the dynamic and practical evaluation requirements of the current urban green land landscape can be met.

The invention discloses a multi-modal data driven general report generation method and system based on a large model, and belongs to the technical field of intelligent report generation. Firstly, texts, images and sensor data related to a report theme are obtained and subjected to standardized preprocessing; analyzing the report generation instruction, and matching and querying a task modal mapping library matching modal configuration scheme according to a task demand; quantitatively evaluating the data quality of each modal, dynamically calculating the final decision weight of each modal in combination with the basic weight, and distributing the final decision weight to a corresponding processing path to form dominant, supplementary and reference data; inputting the dominant data and the supplementary data into a multi-modal model for analysis to obtain a preliminary conclusion with confidence score, performing consistency judgment, if no conflict exists, performing fusion to form a comprehensive conclusion, and if the conflict exists, combining a quality evaluation result and an arbitration rule to complete conflict judgment; and finally, inputting the comprehensive conclusion and the reference data into a large language model to generate a report text, and outputting a complete report after typesetting and proofreading.

The invention relates to the technical field of medical rehabilitation, and discloses a lower limb weight-bearing gait rehabilitation training system which comprises a data acquisition module, a data processing and analysis module, a patient individualized modeling module, an intelligent decision and control module, a rehabilitation execution module and a man-machine interaction and medical information interface module which are in communication connection through a network. The data acquisition module is used for acquiring multi-modal data of a patient in real time, and the multi-modal data comprises static sign data, dynamic physiological parameters, kinematics and dynamics parameters and non-motion physiological and psychological state data; and the data processing and analysis module is used for carrying out preprocessing, feature extraction and deep analysis on the original data, and outputting a structured patient individualized feature vector and an evaluation result. According to the invention, a patient three-dimensional skeletal muscle digital twinborn model is constructed through the patient individualized modeling module, and in combination with a continuous learning intelligent model library, body sign differences of different patients can be accurately adapted.

The invention relates to the technical field of digital twinning, and provides a digital twinning-based river slope stability real-time monitoring and early warning method and system, and the method comprises the steps: constructing a coupled digital twinning initial model; based on the multi-source heterogeneous monitoring data set, processing the coupled digital twinborn initial model through an inversion analysis and state estimation algorithm to obtain a digital twinborn optimization model; processing the multi-source heterogeneous monitoring data set through an edge computing node to obtain edge preprocessing data, inputting the edge preprocessing data and a digital twin optimization model into a cloud computing cluster, and processing through a multi-physics field coupling analysis algorithm to obtain a slope stability evaluation result data set; and processing the slope stability assessment result data set through a multi-index fusion algorithm to obtain a comprehensive risk score, and processing the comprehensive risk score based on a graded early warning threshold to obtain multi-level risk early warning information and an engineering disposal scheme. According to the invention, high-precision real-time monitoring, dynamic risk assessment and graded early warning of the stability of the river slope are realized.

The invention discloses a steel-concrete hybrid girder bridge operation monitoring and static and dynamic analysis method based on multi-source data fusion, which comprises the steps of 1, structural response data acquisition, 2, multi-source data fusion processing, 3, building a plurality of operation state evaluation models based on parameters acquired by a sensor, and 4, carrying out static and dynamic analysis on the operation state evaluation models. 4, performing static analysis, dynamic analysis and static and dynamic collaborative analysis on the girder bridge based on the comprehensive feature set after multi-source data fusion, and 5, providing data support for the static and dynamic collaborative analysis by the operation state evaluation model through the comprehensive feature set, and meanwhile, evaluating the operation state of the girder bridge. Reversely supplementing the operation state evaluation model with the structural mechanical response characteristics excavated in the static analysis and dynamic analysis processes to form a data closed loop; the method has the advantages that through data sharing and feature interaction, evaluation result cross validation and multi-scale analysis linkage, cooperation of an operation state evaluation model and static and dynamic collaborative analysis is achieved, and a data closed-loop and multi-dimensional evaluation system is formed.

The invention discloses a dynamic time sequence event equipment system adversarial network construction and recommendation method and device, and relates to the field of killing network design, and the method comprises the steps: constructing an initial detection-command and control-strike warning network; a dynamic time sequence event on the battlefield is continuously monitored, when the event type of the dynamic time sequence event is an equipment state event, the initial detection-command-strike warning network is updated, and when the event type of the dynamic time sequence event is a chained event, a closed detection-command-strike link set containing an enemy target is generated; the closed detection-command-strike link set comprises a plurality of killing chains; based on the multi-dimensional evaluation index system, evaluating each killing chain to obtain an evaluation result corresponding to each killing chain; and according to the evaluation results corresponding to all the killing chains, recommending an optimal interception scheme of an air defense and anti-guide interception action. The method overcomes the problems of large calculation dimension and slow response speed of an existing method.

The invention relates to the technical field of photovoltaic power generation, in particular to a flexible photovoltaic intelligent monitoring and management system and method based on the Internet of Things, multi-source heterogeneous data are comprehensively collected through deployed multiple types of Internet of Things sensor nodes, the data are uploaded to a cloud platform after being cleaned and standardized through edge nodes, a big data processing architecture integrated with flow and batch is adopted, and the intelligent monitoring and management system and method based on the Internet of Things are established. The method comprises the following steps: performing real-time analysis and state judgment on a real-time data stream, performing deep batch processing and feature mining on historical data, extracting high-order features such as a performance attenuation trend and an abnormal mode, fusing real-time and historical features, and realizing comprehensive scoring of a health state of a component and accurate prediction of residual life by utilizing a machine learning model. And based on an evaluation result and a preset knowledge base, automatically generating a differentiated precise operation and maintenance instruction, and issuing and executing the differentiated precise operation and maintenance instruction to form closed-loop management. According to the invention, the monitoring depth and breadth of the flexible photovoltaic system are effectively improved, the conversion from passive alarm to active predictive maintenance is realized, and the operation reliability of the system is significantly enhanced.

The invention relates to a multi-source data fusion-based urban physical examination evaluation index calculation method and system. The method comprises the steps of extracting a multi-source data sequence; identifying a data source of the urban physical examination index set, and extracting an independent time sequence data sequence; calculating the information entropy of the independent time sequence data sequence, and distributing a basic fusion weight; calculating a dynamic state evaluation value of the independent time sequence data sequence, and performing weighted fusion on the basic fusion weight and the dynamic state evaluation value to obtain a comprehensive state evaluation value; obtaining a distribution variance of the basic fusion weight, inputting the distribution variance into the uncertainty quantification model, and obtaining an index calculation result containing uncertainty measurement; the real-time performance of the evaluation result is enhanced through an aging attenuation mechanism, and the latest state of the city system is accurately reflected; the output uncertainty measurement index provides a quantitative basis of result credibility for a decision maker, and the decision risk caused by a data fusion error is reduced.

The invention provides an artificial intelligence-based optical coating quality monitoring method, which comprises the following steps of: 1, acquiring visual image data of the surface of a coating substrate in real time by using a common industrial camera in an optical coating process; 2, preprocessing the collected image data, and extracting visual feature information representing the state of the film layer; 3, inputting the extracted feature information into a pre-trained artificial intelligence analysis model for simulating artificial experience judgment; step 4, outputting an evaluation result of the current coating quality by the artificial intelligence analysis model, wherein the evaluation result comprises identification of film layer uniformity, thickness trend and potential defects; and 5, generating a process adjusting instruction according to the evaluation result, feeding back the process adjusting instruction to a control system of the coating equipment, and dynamically adjusting process parameters to realize closed-loop optimization control.

The invention discloses a real-time behavior monitoring and dynamic optimization system for an advertisement putting strategy, and relates to the technical field of advertisement putting and user behavior analysis. A real-time multi-source user behavior data acquisition module acquires data; the distributed storage and indexing module supports fragmented storage, capacity expansion and multi-dimensional indexing; the behavior feature extraction and analysis module extracts features and identifies a time sequence rule; the putting effect evaluation module compares data through an index system and positions an effect short board; the user portrait dynamic updating module adjusts the label weight according to the latest data; the strategy optimization decision module optimizes parameters in combination with the portrait and the evaluation result; the execution scheduling module issues an instruction and monitors a state; the historical data mining and model training module is used for mining rules and iteratively training the model; and the visual interaction configuration module provides an interface and supports parameter configuration and data, effect and optimization suggestion display. According to the invention, full-scene real-time monitoring and cross-platform identity association of user behaviors are realized, and support is provided for refined advertisement marketing.

The invention discloses an ocean multi-mode environment perception and intelligent ship navigation decision-making method based on a double-branch vision-semantic encoder. The method comprises the following steps: S1, acquiring a multi-source data image containing a ship and a surrounding environment thereof from an existing public maritime data set or platform; s2, training a double-branch vision-semantic encoder by using the multi-source data image, and inputting a to-be-processed image extracted in real time into a multi-modal feature matrix in the trained double-branch vision-semantic encoder; s3, based on the multi-modal feature matrix, obtaining positioning information of the ship and surrounding environment elements, and constructing a dynamic security domain model; and S4, in combination with the dynamic security domain model and the multi-ship relative position relationship, carrying out quantitative evaluation on the navigation risk, and generating a self-adaptive navigation strategy based on an evaluation result. According to the invention, high-precision ship positioning and environment element identification under complex weather and illumination conditions are realized by using all-weather characteristics and multi-scale visual feature coding of SAR imaging.

The embodiment of the invention provides an intelligent agent memory indexing method and system based on intention recognition. The method is applied to the technical field of artificial intelligence and comprises the steps of obtaining real-time question-answer data, and performing preliminary intention classification on the real-time question-answer data by utilizing a domain knowledge rule library; extracting a structured description from the real-time question and answer data after the preliminary intention classification, performing deep intention analysis in stages, and outputting a standardized intention description text; according to the standardized intention description text, acquiring an Agent operation context, performing multi-dimensional retrieval to obtain an adaptive strategy, executing the adaptive strategy, and returning a strategy evaluation result; according to a strategy evaluation result, carrying out microscopic feedback and macroscopic feedback to update a strategy library; the Agent operation context is obtained through the following steps that semantic features of a standardized intention description text are captured, and the Agent operation context corresponding to the deep semantic features is recorded based on a fine-grained metadata labeling system. According to the invention, a complete closed loop from intention identification to strategy multiplexing to strategy optimization is realized.

The invention discloses a campus face recognition abnormal behavior monitoring method and system based on artificial intelligence, and the method comprises the steps: collecting the video stream data of a person in a campus in real time, and recognizing the behavior characteristics of the person in a video frame in real time through employing a lightweight target detection algorithm; extracting facial features of the personnel by using a pre-trained face recognition model, and recognizing identity information of the personnel; generating a personnel behavior semantic description vector with a spatio-temporal context based on the behavior characteristics and the identity information; inputting the personnel behavior semantic description vector into a dynamic early warning threshold engine, and outputting an abnormal behavior probability and risk level evaluation result; and triggering an early warning information pushing mechanism according to the abnormal behavior probability and the risk level evaluation result, and sending early warning information to a corresponding responsible person terminal. By using the embodiment of the invention, the accurate and automatic association of the abnormal behavior and the personnel identity can be realized, and the early warning accuracy, the handling response speed and the system adaptive ability of campus safety monitoring are improved.

The invention is suitable for the technical field of data asset management, and provides a data asset management system based on a block chain and big data analysis, and the system comprises a first terminal which carries out the storage of the ownership information of data assets through a block chain network, and generates a storage data package; generating an evaluation result based on a preset technical index analysis model; writing the hash value of the evaluation result into transaction data of the main chain of the block chain, and generating an evidence storage voucher matched with the block chain transaction; an interaction data sequence is packaged for identity signature, encryption and compression to generate a target code stream, and the target code stream is sent to the second terminal; the second terminal decrypts the encrypted data in the target code stream by using a private key, decompresses the data by using a compression algorithm matched with the first terminal, and restores the data into an interactive data sequence; verifying the access authority and the transaction condition of the evidence storage data packet; checking the anchoring state of the evidence storage voucher on the main chain of the block chain; and the data transaction is completed, the data asset state report is generated, and the data element marketization configuration efficiency is improved.

The invention discloses a guideboard surface deformation detection method and system based on a dynamic video sequence. The method comprises the following steps: acquiring road video data with a timestamp and position information; a traffic sign is automatically identified from the road video data, and geometric anomaly detection is carried out; a time sequence tracking sequence is established for the guideboard, and whether continuous abnormity exists in the guideboard is determined through multi-frame analysis; if yes, performing local texture consistency and local contour geometric analysis on the guideboard to obtain a fusion analysis result; performing state evaluation on supporting equipment corresponding to the guideboard to obtain a state evaluation result of the guideboard supporting equipment; and determining the final surface deformation state and the corresponding grade of the guideboard based on the fusion analysis result and the state evaluation result of the guideboard supporting equipment. By implementing the method provided by the invention, automatic detection and quantitative analysis of the bending or deformation condition of the road sign can be realized without manual intervention, and early warning information can be output in time.

The invention discloses a document generation system and method based on multi-agent collaboration, and the method comprises the steps: understanding and extracting the high-level semantic information of a text input by a user, and dynamically generating a problem to guide the user to define a demand, so as to form a detailed document demand; recognizing the theme and key elements of the document from the detailed document requirements; the optimal sequence of task execution is determined based on a shortest path algorithm, potential conflicts in task execution are predicted and solved through parallel processing and a search algorithm, and optimized task planning is formed through feedback circulation; generating a document outline according to user requirements, retrieving associated contents of document chapters so as to write chapter contents, and performing grammar and format verification to form a preliminary document; defining a document quality evaluation index according to user requirements and industry standards, performing document quality evaluation on the preliminary document, and feeding back the document quality evaluation result to the user; and generating a complete final document and presenting the complete final document to the user. According to the invention, the document generation quality is improved.

The invention relates to the technical field of medicine supply chain management, in particular to a medicine supply and marketing evaluation management device and system, and the device comprises a data collection module, an intelligent evaluation module, a dynamic adjustment module and a supervision integration module. The intelligent evaluation module is used for generating a medicine supply and demand balance evaluation result and a risk early warning index by adopting a supply and demand prediction model based on machine learning and combining historical supply and marketing data and real-time market demand fluctuation characteristics; and the dynamic adjustment module is used for starting an intelligent scheduling algorithm and generating a dynamic adjustment strategy when the evaluation result shows that the supply and demand imbalance risk value exceeds the preset threshold value. According to the invention, by constructing a full-link dynamic closed-loop management mechanism, the emergency drug prediction period is automatically shortened to realize rapid allocation when a public health accident occurs suddenly, and the problems of information asymmetry and slow emergency response of a traditional supply chain are solved.

The invention belongs to the technical field of supporting structure performance evaluation, and particularly discloses and provides an NT-CEP pile wall anchor foundation pit supporting structure anti-overturning evaluation method which comprises the steps that in the whole construction period, the construction process is divided into a plurality of continuous load steps based on excavation and supporting procedures; under each load step, pile body strain, arc-shaped wall stress, anchor rod axial force and deep soil displacement are synchronously collected, and a multi-source response data set is constructed; constructing an NT-CEP pile-arc wall-anchor rod coupling model based on the design drawing; carrying out inversion analysis through the model, and solving the load sharing proportion and the overall anti-overturning safety coefficient of each component under each load step; and continuously identifying the abnormal component based on the load sharing proportion, and dynamically analyzing and outputting an anti-overturning capability evaluation index based on the change trend of the safety coefficient. According to the method, dynamic refined evaluation of the construction process is realized, and the authenticity and perspectiveness of an evaluation result are remarkably improved.

The embodiment of the invention relates to the technical field of data analysis, in particular to a national secret log auditing system which is characterized in that firstly, an operation behavior record sequence generated in the running process of a national secret application system is collected by the national secret log auditing system, and the sequence is composed of log entries containing identity verification information, resource access path information and state transition description information; performing context semantic association analysis on the operation behavior record sequence to obtain a semantic element extraction result and a semantic dependency relationship; then performing multi-level compliance verification based on a national secret security audit rule system, and generating a compliance judgment conclusion of a log entry level and a cross-entry-level abnormal behavior pattern recognition report; and finally, according to the judgment conclusion and the identification report, constructing a national secret log security auditing result set which comprises a risk level evaluation result, a violation evidence chain association graph and a security reinforcement strategy suggestion list, thereby effectively improving the accuracy and comprehensiveness of national secret log auditing.

The invention discloses an intelligent predictive maintenance system for an audio equipment fault, and the system comprises a data collection module which collects the internal sensor data during the operation of audio equipment, and outputs an audio signal feature parameter, an external environment parameter, and a historical operation log; the feature preprocessing module is used for carrying out standardization and noise reduction processing on the multi-source data based on a multi-modal feature fusion algorithm; the health state evaluation module outputs a health state evaluation result of the audio equipment in real time through a hybrid analysis model combining a convolutional neural network, a long and short-term memory network and an attention mechanism; the fault risk prediction module is used for performing real-time fault risk prediction according to the evaluation result and generating predictive maintenance decision parameters; and the maintenance decision and early warning module is used for outputting fault early warning information according to the prediction parameters and automatically generating maintenance operation suggestions when the early warning level reaches a preset condition. According to the invention, the operation reliability of the audio equipment can be effectively improved, and intelligent prediction and advanced maintenance of faults are realized.

The invention discloses a stamping die health state assessment method and system based on digital twinning, and relates to the technical field of die health state assessment, and the method comprises the following steps: a physical sensor network deployed on a stamping die collects die stamping process data in real time; constructing a finite element analysis simulation FEA model to simulate the working condition of the stamping die based on the geometric structure, the material attribute and the stamping process parameters of the die; according to the invention, the data of the die stamping process are collected in real time through the physical sensor network, and real-time calculation is carried out in combination with the finite element analysis simulation model, so that transient stress field, strain field and temperature field data of the die can be output in a short time, and real-time monitoring of the health state of the die is realized; by considering the degradation of the mold material performance along with the use time and the dynamic process of quantitative damage accumulation, the damage accumulation value is accurately calculated through the dynamic material performance database and the continuous damage mechanical model, and the accuracy of the evaluation result is improved.

The invention provides a code generation and evaluation method and system based on RAG and a multilevel decision tree, and the method comprises the steps: integrating project related design documents, and constructing a knowledge base capable of semantic retrieval through a vectorization technology; associating business demand description with related documents in the knowledge base based on an RAG technology, and performing demand semantic enhancement to generate a technology demand cue word; receiving the technical requirement cue word by adopting a large language model so as to generate a complete code conforming to business logic; constructing a four-level decision tree evaluation system, and sequentially executing code quality scanning, deployability verification, dynamic test verification and demand satisfaction verification through an evaluation assembly line to generate an evaluation result; and generating an optimization suggestion according to the evaluation result so as to trigger an iteration generation process when the code does not pass the verification, thereby solving the problems of disjunction between code generation and business requirements, low verification efficiency and insufficient iteration optimization.

The invention provides an intelligent inspection risk assessment method and system based on multi-sensor fusion, and the method comprises the steps: obtaining original multi-source data of a transformer substation, the original multi-source data comprises a binocular vision image, infrared thermal image data and environment sensor data, and carrying out the time-space calibration and preprocessing of the original multi-source data, obtaining a multi-source sensor data stream; carrying out feature extraction on the multi-source sensor data stream to obtain a multi-modal feature set, generating a refined semantic mask based on the multi-modal feature set, constructing an initial scene relation graph, calculating a risk level based on a multi-layer perceptron classifier, and generating a risk level evaluation result and a risk distribution graph; outputting a safety distance violation warning and a risk area identifier; and generating comprehensive risk early warning information based on the risk level assessment result, the risk distribution diagram, the safety distance violation warning and the risk area identifier.

The invention discloses a VLA model evaluation method and device, a storage medium and a program product. The method comprises the steps that an evaluation data set containing visual data, language data and action data is acquired; inputting the evaluation data set into a VLA model to be evaluated to obtain a corresponding model output result; based on the model output result, determining an index evaluation result of the VLA model corresponding to at least one evaluation dimension index; and determining a performance evaluation result of the VLA model according to each index evaluation result. Therefore, the comprehensive performance of the VLA model in multiple aspects of perception, prediction, decision making, planning, reasoning, interaction and the like can be comprehensively reflected.

The invention relates to a cross-attention mechanism optimization method and a cross-attention mechanism optimization system for multi-modal feature fine-grained alignment. The method comprises the following steps: constructing a space-time topological graph of a dynamic scene, and representing a space-time relationship among intelligent agents through nodes and various connecting edges; coding the heterogeneous spatio-temporal information by using a plurality of groups of parallel recurrent neural networks, and converting the heterogeneous spatio-temporal information into uniform dimension feature representation; establishing a multi-modal cross attention alignment component, and quantifying feature quality from multiple dimensions through a multi-criterion evaluation unit; an adaptive weight fusion system is adopted to dynamically integrate evaluation results, and a unified quality score is generated; constructing a progressive optimization architecture based on the quality score, and performing multi-task cooperative training on a double-layer attention mechanism by combining real-time sampling and a directional optimization strategy; and finally generating a movement decision. According to the method, fine-grained alignment and optimization of multi-modal features are realized, and the accuracy and adaptability of intelligent agent navigation and trajectory prediction in a complex dynamic environment are remarkably improved.

The invention discloses a mountain flexible photovoltaic support service state evaluation method and device, and aims to improve the operation safety and operation and maintenance efficiency of a photovoltaic power station in a complex mountain environment. Comprising the steps of establishing a digital twinborn model of the flexible photovoltaic support, deploying a multi-source heterogeneous sensor system, and collecting environmental load, structural response, material state and geological environment data in real time. Collected data are subjected to preprocessing, feature extraction and operation condition recognition, the data are input into a digital twinborn model for multi-physics coupling simulation analysis, and model parameters are inversed and corrected based on real-time monitoring data. And finally, based on the optimized digital twinborn model and real-time data, carrying out comprehensive evaluation on structure health, material aging, anchoring stability, performance degradation and the like, and generating risk early warning and maintenance strategy suggestions according to evaluation results. According to the method, the limitation of a traditional evaluation method in application of the mountain flexible photovoltaic support is overcome, and comprehensiveness, real-time performance and intelligentization of evaluation are achieved.