4967results about How to "Save time and cost" patented technology

The invention relates to the field of natural language processing, and discloses an SQL statement generation method, system and device based on a large model. Respectively carrying out vectorization processing and document processing on the metadata and the metadata description text information from the database, and storing the metadata and the metadata description text information into a vector database and a full-text retrieval library; in response to the query instruction, respectively performing query matching on the vector database and the full-text retrieval library, screening matching results which are ranked in the front and are preset ranks according to the similarity from high to low, and determining an optimal SQL statement list and optimal metadata; on the basis of the current language of the query instruction, generating a cue word of the current language in combination with the optimal SQL statement list and the optimal metadata; the large model of the preset type is determined according to the language type of the cue word, the preset large model is called to conduct reasoning on the cue word, and the SQL statement is generated. Based on the large model and the generative AI technology, the accuracy of the generated SQL statement is improved, the use threshold of non-technical personnel is lowered, and the query efficiency of the SQL statement is also improved.

The invention discloses a multi-modal document structured processing and knowledge extraction method based on a large language model. The method comprises the following steps: S1, receiving an input multi-modal document; s2, performing preprocessing on the text data in the multi-mode document; s3, performing feature extraction on non-text data in the multi-mode document; s4, performing multi-modal data fusion on the preprocessed text data and the non-text data after feature extraction; s5, performing deep semantic analysis on the fused multi-modal data through a pre-trained improved BERT model, wherein the deep semantic analysis comprises key entity recognition, relation extraction and event detection; s6, based on a deep semantic analysis result, automatically constructing a knowledge graph by using the extracted information; and S7, outputting the data of the knowledge graph in a format for analysis or application. According to the method and the system, structured processing and knowledge extraction of unstructured and multi-modal documents are realized.

The invention discloses a soil fertility intelligent assessment method and system based on image recognition, and relates to the technical field of data processing, and the method comprises the steps: obtaining land image information of a to-be-assessed region, the land image information comprising a multispectral image, a thermal imaging image and an RGB image of the to-be-assessed region; carrying out image preprocessing on the land image information, and carrying out vegetation coverage and health assessment according to the multispectral image: speculating soil characteristics according to the RGB image: speculating the humidity change of a soil surface layer according to the temperature distribution of a thermal imaging image, and identifying a drought region and a region with sufficient moisture; integrating the high-resolution texture features of the RGB image, the spectral information of the multispectral image and the speculation result of the temperature distribution of the thermal imaging image to generate a comprehensive analysis result; and classifying soil types and evaluating fertility grades according to a comprehensive analysis result. The problem that in the prior art, in intelligent soil fertility evaluation, workers need to carry out on-site sampling examination is solved, and the technical effect of improving the soil fertility evaluation efficiency is achieved.

The invention discloses a weld joint key point recognition method, device and equipment, a medium and a program product, and the method comprises the steps: generating a data set based on training image data and annotation data, constructing an original recognition model, improving the original recognition model, and training and optimizing the improved original recognition model based on the data set. Obtaining a target recognition model; acquiring a depth image and an RGB image of the welding part through image acquisition equipment on the welding robot; the RGB image is input into the target recognition model for weld joint key point recognition, weld joint key point coordinates of the welding part are determined based on the recognition result and the depth image, accurate recognition of the weld joint key points is achieved, a welding robot is guided to accurately reach the weld joint key point coordinate positions for welding, key point recognition autonomy and adaptability are improved, and the welding quality is improved. And welding tasks with different requirements are effectively adapted, the welding efficiency is greatly improved while the welding quality is ensured, and the time cost is reduced.

The invention provides an intelligent cold chain transportation information management method, system and device and a storage medium, and the method comprises the steps: recognizing the type of transported goods and environment demands through a task recognition module, and determining and configuring a target sensor of a cold chain vehicle through a sensor configuration module; the target sensor collects environment data, the vehicle-mounted GPS module collects position information and a driving state, the RFID tag records a cargo loading state, and all data are sent to the central management platform. The platform constructs a multi-dimensional feature vector, the multi-dimensional feature vector is compressed into a low-dimensional feature representation through an encoder, a reconstruction data vector is obtained through reconstruction of a decoder, and a reconstruction error is calculated. And by analyzing the reconstruction error, the platform generates an analysis result, and the environment control equipment in the vehicle is automatically adjusted according to the result, so that the stability of cargo transportation is ensured.

The invention discloses a contract evaluation method, system and device based on a large model and a medium, and the method comprises the steps: obtaining a target contract, carrying out the data preprocessing, obtaining the preprocessed contract data, and inputting the preprocessed contract data into a preset contract large language model; the preset contract large language model stores a vector knowledge base including coastal bulk cargo transportation knowledge, legal clauses and industry regulations; in a preset contract big language model, performing contract classification and contract summary extraction on the current preprocessed contract data by using a first model agent to obtain contract summary information; performing contract risk assessment on the current preprocessed contract data to obtain risk assessment information and / or risk avoidance suggestion information; performing benefit measurement and calculation on the current preprocessed contract by using a third model agent to obtain benefit measurement and calculation information; according to the method, the examination and evaluation of the large model in the coastal bulk cargo transportation contract are realized, and the purposes of saving the labor cost and improving the business turnover rate are achieved.

The invention discloses a method and device for automatically converting a cross-format document into a Markdown document and a form enhancement method and system. The Markdown document conversion method comprises the following steps: firstly, obtaining a document image; mining hidden structure information from the document image, wherein the hidden structure information comprises target elements and hierarchical relationships among the target elements; performing semantic content analysis on the document image based on the structure information to obtain a semantic content analysis result; extracting key information in the document image according to an analysis result; the key information is converted into a Markdown format, preliminary formatting is carried out in a form or list mode, and a preliminary Markdown form is obtained; and finally, optimizing the preliminary Markdown form to form a target Markdown form, so that the cross-format document is automatically converted into the Markdown. According to the method, a complex nested structure and a form with irregular layout in the document can be effectively identified and converted, and the integrity and readability of form information when the form is converted into the Markdown format are ensured.

A user behavior analysis system based on cloud side-end cooperation of a large language model is used for carrying out information collection, cloud side-end cooperation and user behavior analysis on mobile phones and computer equipment, and comprises an information collection module, a cloud side-end cooperation module and a user behavior analysis module. According to the invention, information acquisition is carried out on electronic data of mobile phone equipment, information privacy collaboration is carried out through the cloud side end module, and then user behavior analysis is carried out based on a large model, so that the defects of low collaboration efficiency, high user behavior identification false alarm rate and the like of an existing user behavior analysis system are overcome; the evidence obtaining efficiency and the user behavior analysis effect are effectively improved by introducing a cloud side-end cooperation and large language model, and the cloud side-end cooperation behavior analysis system which is high in efficiency, high in precision and light in weight is achieved.

The invention relates to the technical field of testing of structural components or equipment, in particular to a method and a device for testing mechanical durability of a wheel hub motor angle module and electronic equipment, and the method comprises the following steps: calculating the mechanical durability of the wheel hub motor angle module on the basis of actual driving data and whole vehicle parameters of a vehicle carrying the wheel hub motor angle module; a whole vehicle multi-body dynamics model and a whole vehicle multi-body dynamics-control joint simulation platform of a vehicle are constructed, a whole vehicle durability simulation working condition of the vehicle is established in combination with test field condition constraints, a test driving file is iterated based on a simulation analysis result, a tool clamp is designed by utilizing the rim size of a hub motor angle module and the position of a suspension mounting point, and a test result is obtained. Therefore, a target test bench is constructed, and the target test bench is used for executing the durability test task of the wheel hub motor angle module. Therefore, the technical problem that in the related technology, a vehicle carrying a wheel hub motor angle module can collect less data and does not have the real vehicle spectrum collection condition under the long-term and severe road condition, so that the durability test is difficult to carry out is solved.

The invention provides double-arm picking equipment for shelf-type planted shiitake mushrooms and an operation method thereof. The double-arm picking equipment comprises a moving platform, the upper portion of the moving platform is connected with a chassis, and a lifting device, a shiitake mushroom storage device and a shiitake mushroom picking device are arranged above the chassis. Through mechanical automatic mushroom stick fixing and mushroom picking, intelligent identification, picking and placing of mushroom sticks on a shelf and identification and picking of mushrooms on the mushroom sticks can be carried out at the same time, planting areas can be traversed automatically, the mushroom picking speed is increased, and labor and time cost is saved. Three-dimensional scanning is carried out through the depth camera vision system, the position and size of the mushroom sticks and the maturity of mushrooms can be accurately recognized, and it is ensured that the quality of picked mushrooms is high. The lifting device adjusts the height of the mechanical arm and cooperates with a visual system, picking and mushroom stick processing are more accurate, damage to mushrooms and mushroom sticks is reduced, mushroom picking automation is achieved, operators do not need to put physical labor heavily, and the workload is relieved.

The invention discloses an automatic single-yarn strength tester and a test method, and relates to the technical field of textile detection equipment, the automatic single-yarn strength tester comprises a single-yarn strength tester used for testing breaking strength, breaking elongation and breaking strength indexes of a single yarn and fiber pure spinning or blended yarn; and the translation mechanism is used for translating a plurality of distributed yarns and carrying out continuous high-efficiency test operation. According to the automatic single-yarn strength tester, a connecting piece is pulled through a tension spring, then a fastening piece and a limiting block are driven to conduct limiting installation on a limiting groove in a rubber pad, the rubber pad can be rapidly replaced after being abraded, the yarn clamping stability is ensured, and the testing accuracy is ensured; the good friction force of the clamping face can be recovered by replacing the rubber pad, it is ensured that the single yarn is tightly fixed in the clamping mechanism, the single yarn cannot move due to unstable clamping in the tensile test process, it is ensured that the test can be conducted smoothly, and the result is accurate.

The invention relates to the technical field of computer code upgrading, and discloses an automatic code upgrading method and device based on a compiler and a storage medium. The method comprises the steps that firstly, lexical analysis, grammatical analysis and semantic analysis are conducted on a source code, an abstract syntax tree is generated, then a new standard grammatical rule mapping table is constructed, and a new standard grammatical rule mapping table is generated; and traversing the abstract syntax tree, querying whether the nodes of the abstract syntax tree can be queried in the mapping table, outputting the character strings mapped to the queried nodes, and normally outputting codes conforming to node semantics for the nodes which are not queried. The abstract syntax tree of the original code is automatically traversed, and node matching and conversion are performed according to the syntax rule mapping table of the new standard, so that the need of manual intervention is greatly reduced, the automation degree of code migration from an old standard to the new standard is improved, a large amount of manpower and time cost is saved, and the migration problem caused by human errors is reduced.

The invention provides a fluororubber field knowledge graph construction method based on a large language model, and the method comprises the steps: obtaining process related knowledge data of the fluororubber field, and carrying out the classification, and obtaining the classification knowledge data of the fluororubber field; based on a preset ChatGLM model, performing knowledge extraction according to the fluororubber field classification knowledge data to extract key entities and relationships; carrying out deep analysis on the key entity and the relationship by utilizing semantic correlation and context association analysis, and enhancing the connectivity of the key entity and knowledge in the relationship through semantic similarity and association strength to obtain fused knowledge data; explaining and expanding the fused knowledge data by using a ChatGLM model to obtain expanded triple data; and performing knowledge fusion on the triple data, and performing graph storage by using a Neo4j database to obtain a knowledge graph. According to the method, the knowledge graph construction efficiency is remarkably improved through automatic knowledge extraction and processing of the large language model, and the time cost of manual labeling and data processing is reduced.

The invention relates to an anti-ant large-core-number fiber ribbon cable comprising a cable core and an outer sheath. The cable core includes loose tubes; and fiber ribbons are sleeved inside the loose tubes. The outer sheath is an outer nylon sheath; and a non-metallic reinforcement layer is arranged between the outer sheath and the cable core. According to the anti-ant large-core-number fiber ribbon cable provided by the invention, with the outer nylon sheath, ants can not bite the sheath, so that the damages on the optical cable by ant biting in a pipe at an area with a serious anti problem are prevented, the normal state of the cable is ensured, and the application region of the cable is extended. The cable based on a simple and reasonable structure has advantages of small cable diameter, high fiber density, large fiber capacity, and low duty ratio and can be laid in a pipe conveniently; and the bending performance is good. During branched connection, 4 to 24 fibers can be melted once, so that the construction time is saved and the manpower cost is lowered, and the construction efficiency is improved. On the basis of the full drying type dielectric structure, branched connection is realized conveniently and usage becomes convenient.

The invention provides an engine pipeline measurement method based on laser scanning, which belongs to a non-contact measurement technology, and comprises the following steps: before measurement, selecting a three-dimensional laser scanner, configuring and calibrating the three-dimensional laser scanner, carrying out a scanning test, adjusting a scanning environment, and then obtaining pipeline point cloud data; the outlier error of the pipeline point cloud data is denoised by using a KD-tree (K-dimension tree), and the outlier error of the pipeline point cloud data is denoised by using the KD-tree (K-dimension tree); de-noising system errors and random errors of the pipeline point cloud data by using a Gaussian filter; segmenting the point cloud data of the pipeline, and identifying and isolating all parts (linear segments, elbows and joints) of the pipeline; performing reverse modeling on the pipeline point cloud data to obtain a digital model of the pipeline; and comparing and analyzing the original point cloud of the pipeline and the pipeline model, and evaluating the error of pipeline measurement. According to the invention, the feature extraction and processing analysis of the pipeline point cloud are realized, the time cost of engine pipeline assembly is reduced, and the wear of the pipeline is reduced.

The invention discloses a roadside sensing unit optimization deployment method under partial vehicle-road cooperation conditions, and belongs to the technical field of roadside sensing unit optimization deployment, and the method comprises the steps: building traffic scenes, and obtaining a true value probability occupation grid of each traffic scene through multi-time-step continuous simulation; carrying out sensing parametric modeling on the roadside sensing unit and the intelligent networked automobile, establishing a ray tracing algorithm considering the shielding effect, and obtaining an observable probability occupation grid under a vehicle-road collaborative sensing condition through the ray tracing algorithm considering the shielding effect; a cross entropy of a true value probability occupancy grid and an observable probability occupancy grid is used as a traffic flow monitoring-oriented vehicle-road collaborative perception substitution evaluation index, and optimization deployment is carried out on a roadside perception unit; a specific complete technical framework is provided for introducing POG into the field of RPU optimization layout, and the problem that in the prior art, influences on ICV are insufficiently considered when RPU optimization layout modeling is carried out is solved.

The invention relates to the field of constructional engineering, and discloses an assembly type reinforced concrete-steel structure foundation pit supporting system convenient to disassemble and a construction technology, and the assembly type reinforced concrete-steel structure foundation pit supporting system comprises a top beam, a waist beam, a supporting pile and an inner supporting system; the inner supporting system is formed by assembling a standard steel-concrete beam section main support and a steel member auxiliary support in cooperation with a jack adjusting system. The invention further provides a new system on-site assembly construction process, namely detailed steps of assembly construction from the uppermost layer to the lower layer step by step. The fabricated structure integrates the advantages of two fabricated structures, is suitable for foundation pit supporting with large stress and strict deformation control, and has the advantages of being high in construction speed, short in construction period, low in construction cost, convenient to dismantle and capable of completely recycling most of inner supporting components compared with a traditional cast-in-place steel-concrete foundation pit supporting system.

The invention discloses a PCB production process management system and method based on intelligent tracing, and relates to the technical field of intelligent tracing. Production links and a parallel line of each link are obtained in a process line by using production characteristics; constructing a flow line framework of a PCB generation process; the correlation degree between the production data in the adjacent production links is calculated, the correlation degree of different production data in all the adjacent production links is judged, and the production data with the maximum correlation degree serves as transmission data of the corresponding production link; automatically learning a Bayesian network structure through maximum likelihood estimation to obtain a potential causal path of each piece of transmission data; generating a chained quality evaluation model; and after the real-time PCB quality is judged to be unqualified by utilizing a qualified quality threshold, performing reverse tracing by utilizing a potential causal path of each production link in the chain type quality evaluation model.

The invention belongs to the technical field of machine learning, and discloses an automatic quality inspection method for annotation information, electronic equipment and a storage medium, and the method comprises the steps: obtaining annotation information on a to-be-trained image; wherein the annotation information comprises coordinates of each annotation frame; the to-be-trained image and the corresponding labeling information are input into a trained large model, the trained large model is used for extracting the feature information of the to-be-trained image to obtain a feature map of the to-be-trained image, and according to the coordinates of all the labeling boxes, the feature map is cut in an equal proportion to obtain picture features corresponding to all the labeling boxes; and carrying out clustering analysis on the extracted picture features corresponding to each labeling box to identify abnormal labels and realize automatic quality inspection of the labeling information. According to the method, the quality inspection efficiency and accuracy of the annotation information can be improved, the dependence on manual auditing can be reduced, and a large amount of labor cost and time cost are saved.

The invention discloses a data quality problem deep analysis and traceability system and method, and relates to the technical field of data processing, and the system comprises a data collection module, a data monitoring module, a knowledge graph construction module, a machine learning analysis module and a traceability analysis module. According to the system, the data is closely connected with the business knowledge, the source and the processing flow through the data association network constructed based on the knowledge graph technology, when a data quality problem occurs, the source and the propagation path of problem data can be rapidly tracked in the complex knowledge graph, and meanwhile, the data quality is improved. According to the abnormal data association analysis driven by machine learning, the internal relation between the seemingly isolated abnormal data can be mined, the whole view of the data quality problem is presented from the global perspective, the defect that the potential systematic problem is ignored due to the fact that only single-point anomaly is processed is avoided, and the data quality problem can be fundamentally solved.

The invention discloses an intelligent self-adaptive real-time task scheduling method and system based on multi-dimensional optimization. The method comprises the following steps: S1, inputting a task; s2, a DAG task is decomposed into independent sequential sub-tasks; s3, dynamically calculating the priorities of the tasks by adopting a multi-factor dynamic priority scheduling module, and updating the priorities of the tasks; s4, dynamically adjusting an acceleration factor by adopting a self-adaptive acceleration factor management module; s5, vertex correlation analysis is carried out, and a variance of processor load imbalance is calculated by adopting a load balanced distribution algorithm and is minimized; s6, in combination with static analysis and dynamic inspection, a hybrid schedulability judgment module is adopted to judge the tasks, and judgment results are fused; and S7, fusing decisions by adopting a decision fusion algorithm, obtaining a scheduling strategy through a scheduling strategy coordinator, executing the task, and monitoring the task in real time. Real-time monitoring and real-time optimization scheduling are realized, and the utilization rate of resources is improved.

The invention discloses a financing service intelligent recommendation method based on big data, electronic equipment and a computer readable medium. The method comprises the following steps: receiving an enterprise financing application; obtaining related data of the enterprise, dynamically evaluating the credit of the enterprise, and if the credit score of the enterprise reaches a preset value, continuing to execute a financing recommendation process; the financing recommendation process comprises the following steps: preliminarily screening out financial products matched with enterprise financing demands, and scoring the financial products according to a preset recommendation rule; and sorting the financial products according to the scores, selecting at least one financial product according to the ranking, pushing enterprise financing application data to a financial institution corresponding to the selected financial product and obtaining a pre-credit result, and if data of successful pre-credit exists, analyzing the pre-credit result, selecting a target financial product and pushing the target financial product to an enterprise client. The financing service intelligent recommendation method can intelligently recommend financial service products accurately matched with enterprise financing requirements for enterprises.

The invention provides an automatic software testing method and system based on a visual attention mechanism, and relates to the technical field of automatic testing, and the method comprises the steps: obtaining an initial interface of to-be-tested software, carrying out the preprocessing, inputting an enhanced interface image into an interface detection model, and recognizing control information through the analysis of the enhanced interface image; inputting the control information into a pre-trained attention mechanism model, generating semantic association and context information between controls based on the control information, determining an execution sequence and execution parameters of test actions, sequentially simulating manual test actions, and performing test operation on the controls according to the execution parameters; interface feedback information of the to-be-tested software is obtained, text content in the interface feedback information is extracted based on a character recognition algorithm, semantics corresponding to the text content are determined in combination with a pre-constructed semantic understanding model and compared with a preset expected result, and a test conclusion corresponding to the test operation is generated.

The invention discloses a water conservancy project dam detection method and device based on an unmanned aerial vehicle and a medium, belongs to the technical field of water conservancy project dams, and aims to solve the problems that an existing water conservancy project dam often depends on manual inspection, the detection efficiency is low, the labor cost is high, missing detection and misjudgment are likely to occur, and the detection efficiency is high. And intelligent monitoring and early warning of dam risks are not facilitated. The method comprises the steps of collecting a real-time detection image of a dam based on a preset unmanned aerial vehicle flight plan; performing risk identification processing related to dam main body characteristics on the real-time detection image to obtain dam main body characteristic information; through preset dam laser radar data, carrying out calculation processing related to dam appearance characteristics on the real-time detection image to obtain dam appearance characteristic information; and according to the reflectivity data of the preset multispectral image, carrying out index judgment processing on dam environment characteristics on the real-time detection image to obtain dam environment characteristic information.

The invention relates to an intelligent electric meter reading method and system, and the method comprises the steps: enabling a high-definition camera to shoot an intelligent electric meter according to a preset meter reading time interval, obtaining an electric meter image, and uploading the electric meter image to an edge computing device through the Internet of Things; the high-definition camera is installed right in front of a dial plate of the intelligent electric meter. The edge computing device carries out image preprocessing on the electric meter image and transmits the electric meter image after image preprocessing to a server; the server recognizes the received electric meter images by adopting a plurality of image recognition models based on a deep learning algorithm, compares and verifies the electric meter readings obtained by recognition of the plurality of image recognition models, and if the comparison and verification are passed, sends the electric meter readings to the server; if yes, the current ammeter reading of the intelligent ammeter is obtained according to the ammeter reading obtained through identification processing of various image identification models; and the server performs associated storage according to the ammeter number of the intelligent ammeter and the current ammeter reading. According to the invention, the meter reading efficiency can be improved.

The invention provides a multi-agent conflict-free path planning method and system based on an improved CBS algorithm. The method comprises the following steps: constructing an environment model; analyzing task starting points, task ending points and current state information of k intelligent agents from the received task request information, and judging whether all the intelligent agents need to re-plan paths or not by traversing task path sets of all the intelligent agents; when the path is planned again, forbidding points are added through the distance and the end point positions of the intelligent agents, a conflict-free path solver is called to obtain a path set of k intelligent agents, and the path set is cut off in space; comprehensively judging and processing the generated path; and selecting the shortest path which meets the safety requirement from the length by judging the same-direction or reverse-direction relationship between the paths, and issuing the shortest path. The method can be used in warehouse logistics and other complex environments, and the calculation speed and robustness of path planning are improved.

The invention discloses a line equipment defect labeling method based on an unmanned aerial vehicle power line image, and relates to the technical field of defect labeling, and the method comprises the following steps: obtaining a historical picture of a power transmission line, and carrying out the preprocessing of the historical picture; performing feature extraction on the preprocessed historical picture by using an image segmentation technology to obtain a line image feature value; manually marking defect pictures in the historical pictures to obtain the defect pictures, and marking the line image feature values of the defect pictures as defect image feature values; and taking the defect image feature value and the line image feature value as a training set, and performing model training by using a convolutional neural network to obtain a power line defect identification model. According to the defect identification algorithm, various defects of the power line equipment can be accurately detected and labeled, and the detection rate and accuracy of equipment faults are improved.

The invention provides a digital person business card interaction method, electronic equipment and a storage medium, and belongs to the technical field of data processing, and the method comprises the steps: responding to a business card viewing instruction, and outputting a target digital person business card associated with the business card viewing instruction; receiving an inquiry instruction based on the target digital person business card, and judging whether the inquiry instruction has a corresponding standard problem or not; if yes, inputting an information set associated with the standard question and the target digital person business card into a pre-trained information desensitization large model; if not, calling a preset search engine to search the inquiry instruction, determining a search data set based on a search result, and inputting the inquiry instruction, the target digital person business card and the search data set into a pre-trained information desensitization large model; and obtaining a desensitization answer output by the information desensitization large model, and outputting the desensitization answer. According to the method, the interaction efficiency of the recruitment process can be remarkably improved, the hysteresis of information transmission is reduced, and the personal information security is protected.

The invention provides a graphical construction and edge deployment method for configuration software development for a micro-service architecture, which comprises the following steps of: constructing an interface and logic of configuration software by adopting a graphical interface through operations such as drag-drop, connection and the like; establishing a graphical component library comprising a plurality of predefined micro-service components, wherein the predefined micro-service components comprise data acquisition, processing, storage, display and the like; the predefined micro-service component supports conversion of the graphical component into an executable micro-service code, and configuration software is constructed; deploying the micro-service of the configuration software at a position close to a data source in combination with an edge computing node deployment strategy; the edge deployment framework automatically completes deployment and starting of the micro-service according to the configuration file and supports automatic deployment, monitoring and upgrading of the micro-service; the states of the edge computing nodes are monitored through a graphical interface, and remote configuration and management are carried out; and performing real-time viewing, log analysis, troubleshooting and the like on the node state. The development efficiency is improved.

The invention discloses a remote sensing image change area detection method and device based on multi-modal prompt guidance, and the method comprises the steps: constructing a multi-modal remote sensing image change detection data set corresponding to a change detection field, the multi-modal remote sensing image change detection data set comprises remote sensing image data, change label range data and semantic category data of a change area; an initial remote sensing image change detection network based on multi-mode prompt guidance is established, and the initial remote sensing image change detection network is a change detection network established based on a detector-guider architecture; training the initial remote sensing image change detection network through the multi-modal remote sensing image change detection data set, and determining the remote sensing image change detection network based on the obtained model parameters; and inputting the multi-mode prompt into a remote sensing image change detection network, and obtaining a change detection result of the interested class of the remote sensing image.