87results about How to "Improve enforceability" patented technology

The invention discloses a fermentation strain yield prediction method based on artificial intelligence, particularly relates to the field of biological fermentation production, and is used for solving the problems that in the prior art, due to the fact that the yield is difficult to directly map in concentration prediction, monitoring indexes are normal, the final yield deviates from expectation, and reliable strain comparison and production management decision cannot be supported. A historical batch process observation sequence and a material account sequence are aligned to generate a conversion efficiency characterization sequence to form a yield learning sample training yield prediction model, and the conversion efficiency characterization sequence is generated on batches and input into the model to obtain a yield prediction value. And calculating an accounting volume trajectory consistency parameter and a conversion contribution steady-state proportion parameter, inputting the parameters into a coefficient analysis model to obtain a decision credible coefficient, generating a yield risk judgment result for a yield prediction value based on the decision credible coefficient, and realizing a new-batch online prediction record.

The invention discloses an automatic driving test and evaluation method and device based on a large language model, and solves the problems that the pertinence of a test scene is insufficient, the result understanding depends on manpower, the evaluation interpretation is weak and the like in a traditional method. By means of semantic analysis, logical reasoning and domain knowledge support of a large language model and in combination with an automatic driving test domain knowledge graph, generation of a structured test portrait of a tested system, construction of a test demand list, generation of a semantic test scene description file, interpretable test result attribution, multi-dimensional comprehensive evaluation and report output are completed in sequence. And a test portrait is updated through report feedback to form a closed loop. The method is suitable for function verification and performance evaluation of an automatic driving simulation test, can also be used for post-processing analysis of real vehicle test data, realizes automation of a whole test process and interpretability of an evaluation result, and improves test precision and efficiency.

The invention belongs to the crossing field of network space security, software engineering and artificial intelligence, and discloses a dynamic and static cooperative vulnerability full-link intelligent analysis and verification method based on a large language model and an MCP protocol, and the method comprises the steps: S1, task initialization and context construction; s2, target code deep analysis and static taint preanalysis are carried out; s3, carrying out Fuzzing Harness directional generation and iterative optimization on the basis of the stain paths; s4, performing dynamic and static collaborative sandbox execution and dynamic stain tracking; and S5, vulnerability accessibility judgment and report output of multi-source data fusion are carried out. According to the method, a dynamic and static combination mechanism is adopted, vulnerability analysis and verification can be automatically completed for software target codes, and accurate technical support is provided for vulnerability risk grading and repairing priority ranking.

The invention provides a Bayesian-based aviation equipment residual life uncertainty prediction method and system. The method comprises the following steps: S1, constructing an observation function equation and a state parameter description equation of residual life of key or important systems, modules and parts of aviation equipment; s2, constructing an agent model; s3, solving joint posterior probability distribution and performing residual life prediction; and S4, according to the prediction probability distribution of the remaining life of the aviation equipment, generating an optimization control instruction or a maintenance instruction for the aviation equipment, and acting the optimization control instruction or the maintenance instruction on the aviation equipment. The method provided by the invention not only provides a theoretical basis for solving the problem of prediction, analysis and prediction of the service life of the aviation equipment, but also clearly defines how information and uncertainty thereof flow and convert between a physical world and a digital world, and provides a brand new thought for prediction of the uncertainty of the residual service life of the aviation equipment.

The application discloses a kind of pumped storage power station multi-objective optimization scheduling method and system of water conservancy and hydropower engineering.The method comprises: obtaining the power grid regulation demand and power station operating state in scheduling window;Generation head interval under the unit operating condition reachable domain atlas, representing unit reachable operating condition and operating condition switching relationship;The regulation demand is split into regulation fragment, to find operating condition path in atlas with target response time as backtracking starting point and record path interruption event;Accordingly determine regulation gap and its physical constraint reason;Generation pre-compensation action access atlas, find compensation operating condition path again;Combined with two kinds of paths, generate multi-unit operating condition switching sequence and confirmable power scheduling trajectory.The present application can judge regulation demand bearing capacity and constraint reason in advance, correct scheduling trajectory in advance, and improve power station response certainty.

The invention discloses an under-driven dexterous hand grabbing pose generation method based on deep learning, and belongs to the technical field of robot operation and intelligent grabbing. According to the method, the prior knowledge of a large language model LLM to real capture is introduced into a data set, and a reasonable capture pose is realized by using object semantic-geometric joint features of a point cloud encoder Point Transform and the LLM. According to the method, three types of grabbing strategy classification models for the under-actuated five-finger dexterous hand are constructed, including two-finger kneading, three-finger kneading and five-finger grabbing, and are used for restraining and guiding generation of subsequent fine grabbing postures. According to the selected grabbing strategy, a network is generated to predict six-dimensional poses and angles of all joints of the dexterous hand, and a grabbing posture matched with the shape of the object is obtained. According to the method, two constraint loss items are additionally added to realize grabbing quality optimization. The method can be suitable for intelligent grabbing tasks of industrial robots, service robots and precision operation equipment under the condition that negative grabbing of samples and complex post-processing optimization are not needed.

The present application relates to the technical field of unmanned aerial vehicle navigation, and relates to an unmanned aerial vehicle inspection path planning method, system, device and storage medium; wherein the unmanned aerial vehicle inspection path planning method comprises: obtaining a digital elevation model, a digital surface model of a plateau forest area and laser point cloud data of the plateau forest area; determining a three-dimensional voxel space of the plateau forest area based on the digital elevation model, the digital surface model and the laser point cloud data, and determining an unmanned aerial vehicle accessible region by performing accessibility determination on the three-dimensional voxel space based on a vertical safety margin; performing three-dimensional path search from a starting point to an ending point in the unmanned aerial vehicle accessible region by using an improved algorithm; performing smoothing processing on the searched three-dimensional path, and imposing a maximum turning radius constraint and a maximum climb rate constraint to generate a final three-dimensional track. The present application can improve the safety and executability of the planned route of the unmanned aerial vehicle.

This invention relates to the field of energy storage system control technology, and discloses an intelligent control and power regulation method for a string-type grid-connected energy storage system, comprising: S1, acquiring grid status and mode-defined demand; S2, acquiring cluster-level topology mapping; S3, collecting cluster state variables of each energy storage cluster; S4, determining capacity estimates and uncertainty metrics, and shrinking or correcting capacity boundary terms; S5, performing inter-cluster collaborative allocation based on support demand and available capacity to obtain support shares; S6, mapping control parameters and controlling converters; S7, event-triggered reconfiguration and reallocation. By introducing a safe availability capacity assessment mechanism based on cluster-level capacity estimation, uncertainty metrics, and boundary shrinkage, the method achieves precise on-demand supply of grid support while meeting current, voltage, and temperature constraints, significantly reducing the risks of overload, exceeding limits, and misjudgment, and improving system safety and support effectiveness.

The invention discloses a bearing capacity self-adaptive scheduling system and method for a high-proportion new energy power grid, and relates to the technical field of power system scheduling. The system adopts a three-layer structure: a multi-dimensional bearing capacity state generation layer constructs a power grid digital twinborn body which can be updated in a rolling manner, models bearing capacity into a multi-dimensional bearing capacity state variable and maps the multi-dimensional bearing capacity state variable into a bearing capacity constraint parameter, and constructs an operation feasible region; a rolling scheduling optimization decision-making layer establishes a rolling scheduling optimization model of cross-time scale coupling under the uncertainty of new energy output, and generates a scheduling scheme meeting the operation feasible region constraint and the consistency constraint; and the online safety check and feedback layer performs online safety check by adopting an agent model based on the digital twinborn body, outputs a corrected bearing capacity constraint parameter to update the operation feasible region and trigger re-solving, and issues an execution instruction and writes back operation feedback for continuous updating after the check is passed. According to the method, the real-time performance and the scheduling performability of the security boundary description can be improved, and the new energy consumption capability is improved.

The invention discloses a working condition self-adaption-based guide vane connecting rod stress trend diagnosis method, and relates to the technical field of intelligent diagnosis of hydroelectric generating sets, and the method comprises the steps: constructing an optical fiber stress monitoring system, setting an optical fiber grating sensor for a guide vane connecting rod, collecting a stress signal in real time, and collecting operation parameters; the method comprises the following steps: collecting multi-working-condition data within one month of normal operation after overhaul of a unit, calculating a correlation matrix between guide vane connecting rod stress and operation parameters, and establishing a stress working condition self-adaptive analysis model; and calculating a maximum value, a minimum value, an average value and a standard deviation of stress based on rolling statistics, determining a normal range of stress fluctuation, inputting real-time acquired data into a diagnosis algorithm, judging a stress deviation trend according to the reference model, outputting an alarm level, and updating the reference model. According to the method, the accuracy and timeliness of anomaly recognition are improved, the pertinence and the performability of maintenance decision making are enhanced, and the operation safety and the service life management effect of the guide vane connecting rod of the hydraulic unit are improved.

This application relates to a method, apparatus, storage medium, and electronic device for cross-platform migration of workflows. The method includes: parsing the native workflow file of the source platform to extract node information of each source node and the connection relationships between nodes; standardizing the node information and connection relationships of each source node according to the platform mapping table of the source platform, and constructing an intermediate representation based on the standardized node information and connection relationships; transforming each intermediate node in the intermediate representation into a target node corresponding to the target platform according to the capability characteristics and multi-level node transformation strategy of the target platform; adapting and reconstructing the connection relationships between the target nodes, and assembling them according to the workflow file structure of the target platform to generate a native workflow file that can be recognized and directly loaded and executed by the target platform. This application solves the technical problems of high cost and poor reliability in cross-platform workflow migration.

This invention relates to the field of trajectory planning technology, and discloses a robot trajectory optimization method, system, electronic device, and storage medium. The method includes: obtaining initial planned trajectories for different joints of the robot based on preset robot actions; mapping different joints of the robot and preset environmental obstacles into parameterized geometric primitives; constructing a cost functional for optimizing the initial planned trajectory; iteratively updating the trajectory using gradient descent on the cost functional until a preset termination condition is met, and controlling the robot's movement based on the updated trajectory. This invention maps robot joints and environmental obstacles into parameterized geometric primitives, transforming the calculation of the shortest distance and gradient from the reference trajectory of each point on the robot in the current frame to all obstacles in the environment into calculating the collision distance between parameterized geometric primitives using analytical distance formulas. This reduces the computational load and thus improves the efficiency of robot motion optimization.

The invention discloses a medicine supply chain intelligent management method based on big data processing, and the method comprises the steps: collecting multi-source data, and carrying out the preprocessing of the multi-source data to generate a standardized data set; constructing a unified entity set, and generating full-link tracing relation data; constructing a multivariable time sequence, and forming a variable Token; an improved iTransform model is constructed, and a structured result is obtained; outputting a scene set and a disposal record based on approximate nearest neighbor search; generating an executable instruction set according to the structured result, the scene set and the disposal record; and outputting to a service execution end, and generating an execution result and a tracing audit record. According to the invention, through combination of prediction and early warning of the improved iTransform model and similar scene retrieval of approximate nearest neighbor search, perceptible, traceable and executable closed-loop intelligent management of the medicine supply chain is realized.

The utility model discloses a hydrogen fuel cell carbon paper deformation detection device which comprises a universal testing machine, an experiment pressing block, an experiment cushion block, a polar plate carbon paper stacking structure, a video extensometer, a concentrated light source and a microscope, the polar plate carbon paper stacking structure is arranged between the experiment pressing block and the experiment cushion block, and the universal experiment machine is configured to provide continuously changing packaging force and uniformly transmit the packaging force to the polar plate carbon paper stacking structure through the experiment pressing block and the experiment cushion block, namely, the mechanical environment of carbon paper in a galvanic pile is simulated; the concentrated light source is configured to provide brightness, and the video extensometer and the microscope are configured to observe the form change of the carbon paper in the polar plate carbon paper stacking structure after continuous stress. The device simulates the in-pile mechanical environment through the universal testing machine, and directly observes the real form change of the carbon paper in the flow channel through the optical detection technology, thereby being beneficial to improving the cognition on the internal structure of the electric pile and guiding the design of the electric pile.

The invention provides a generation method of database query statements based on graphics. The generation method comprises the steps of obtaining a data query request for a target database; obtaining a database structure chart used for representing a target database structure, and performing graph extraction processing and character extraction processing on the database structure chart to obtain an extraction result, the extraction result at least comprises entity nodes in the database structure diagram, entity names and entity attribute names corresponding to the entity nodes, and an association relationship between the entity nodes; mapping the entity name into a data table name, mapping the entity attribute name into a field name, and mapping an association relationship between the entity nodes into an association relationship between data tables to obtain structure information of the target database; according to the data query request and the structure information, constructing a cue word at least comprising the data query request and the structure information; and inputting the cue word into an artificial intelligence model to obtain a database query statement.

This invention discloses a collaborative batch scheduling method and system for dual production lines in tire production, specifically for pre-fabricated rubber packs. Relating to the fields of intelligent manufacturing and production scheduling, the technical solution involves: acquiring basic tire production data; converting tire demand into rubber pack demand based on the mapping relationship between tire type and rubber pack type; generating predicted demand for each rubber pack type within a scheduling time window; establishing a collaborative batch scheduling model for both the rubber pack and tire production lines based on the predicted demand and the basic data; constructing a set of candidate scheduling schemes; and performing joint decoding, evaluation, and feedback correction on a unified time axis to output the optimal scheduling scheme. The beneficial effects of this invention are: achieving collaborative optimization between pre-fabricated rubber packs and tire production; reducing inventory and material shortages; improving equipment utilization and on-time order delivery capabilities; and enhancing the system's adaptability to demand fluctuations and resource changes.

The present application relates to the technical field of software performance monitoring and artificial intelligence, in particular to a low-code platform performance diagnosis method and system based on multi-AI provider cooperation, the method of the present application receives batch page performance audit results, extracts page quantity, average performance score, common performance problems, performance budget and page distribution data; reads low-code platform component library description, visual building rules and configuration anti-pattern knowledge, constructs diagnostic prompt words containing low-code domain context and structured output constraints; in the automatic provider mode, cascade calling is performed in the order of Azure OpenAI, Anthropic and OpenAI, and different structured output constraint strategies are adopted for the interface capabilities of different AI providers; after field integrity verification and format standardization processing of the returned results, a performance diagnosis result file is generated persistently. The present application improves the availability, output stability and executability of low-code configuration level optimization suggestions of AI performance diagnosis service.

The invention discloses an intelligent vehicle test driving sales interaction method and device, and the method comprises the steps: obtaining the natural language input data of a user in response to the interaction triggering operation of the user for a target vehicle; analyzing a user demand tag set representing the car purchase intention and preference dimension of the user; screening a to-be-displayed target vehicle function set from a displayable function set of the target vehicle based on the user demand tag set, and determining a target experience scene set matched with the target vehicle function set according to a preset function scene mapping relationship; and acquiring the current position of the target vehicle as a route starting point, determining road network data based on the map data, and positioning a scene section corresponding to the target experience scene set in the road network data so as to plan and generate a personalized test driving route capable of covering the target experience scene set on the premise of meeting a preset route constraint condition. Therefore, the active test driving experience organization driven by the vehicle purchase intention and preference of the user is realized.

The application provides a signal timing and networked automatic driving vehicle speed hierarchical collaborative control method based on reinforcement learning, and relates to the technical field of intelligent traffic control and vehicle-road collaborative control, and comprises the following steps: obtaining the stop-and-drive vehicle state information on the import lane served by each phase at each signal cycle boundary, and generating a reference timing scheme of the next cycle by a cycle timing intelligent agent based on the stop-and-drive vehicle state information; obtaining a refined discharge window; and outputting a continuous longitudinal acceleration control instruction to a target networked automatic driving vehicle by a networked automatic driving vehicle speed intelligent agent according to the refined discharge window, so that the arrival time of the target networked automatic driving vehicle matches the discharge window, and the collaborative control of traffic signal timing and vehicle speed is realized.

The invention discloses a multi-level agent collaborative decision-making method and system, and the method comprises the steps: carrying out the semantic analysis of an energy-saving standard document, extracting a standard knowledge unit, carrying out the semantic matching of an urban energy consumption target and the standard knowledge unit, and generating a correlation graph; a high-competition target group is identified through target competition degree analysis, collaborative agent pairs are screened through resource complementation degree calculation, and a building-level decision network is constructed; identifying low-load high-potential equipment and performing capability activation through equipment capability scanning and load potential evaluation; conflict intention double-layer features are extracted in combination with conflict detection, a conflict root is traced through a causal chain, and a mediation rule is established; improving the consensus degree of weak consensus nodes by adopting a consensus obstacle traceability and enhancement strategy; and finally, a hierarchical decision scheduling graph is formed through decision dependency analysis, multi-level collaborative decision scheduling is performed to generate a collaborative decision instruction, and comprehensive and intelligent multi-agent collaborative decision support is provided for a city-level energy-saving and carbon-reducing system.

The invention relates to a zero sample Text2SQL (Structured Query Language) generation method based on structure awareness and adaptive routing, belonging to the technical field of natural language processing and data query, comprising the following steps of: based on user query, judging the structure complexity of a user query problem by adopting a mode of combining lightweight prompt and complexity adaptive routing decision; dynamically selecting different query generation paths; aiming at a simple path, directly inputting user query and a database mode into a large language model to generate a query statement; for a complex path, converting the user query into a structured intermediate representation, and inputting the user query and the structured intermediate representation into a large language model to form a candidate SQL (Structured Query Language) set; and selecting one of the candidate SQL sets as a final SQL. According to the method, under the zero sample condition, the structure correctness and the semantic consistency are ensured, the reasoning cost is effectively controlled, and the overall efficiency of the system is improved.

The invention relates to the technical field of smart power grids, in particular to a model data dual-drive virtual power plant regulation and control potential evaluation method, which comprises the following steps: S1, acquiring a real-time operation data set and an inherent characteristic parameter set of each distributed resource in a virtual power plant; s2, determining a static reference regulation and control range for each type of resources; s3, calculating the real-time state deviation degree of the ideal performance state according to the current real-time operation data set; s4, dynamically correcting the static reference regulation and control range, and generating a real-time adjustable range of the corresponding resource; and S5, evaluating to obtain a collaborative overall regulation potential value and a collaborative time-phased regulation potential curve of the virtual power plant. According to the method, by fusing resource characteristic modeling and real-time state analysis, dynamic evaluation and time-phased prediction of the collaborative regulation and control capability of the virtual power plant are realized, and the accuracy and the performability of regulation and control decision making are remarkably improved.

The application discloses a charging and discharging strategy scheduling method and system of an electric vehicle, relates to the technical field of vehicle network interaction and intelligent power grid scheduling, and comprises the following steps: acquiring current micro power grid area real-time scheduling related data; aggregating the first two layers of parameters of a Critic network in an initial scheduling model of all vehicles in the area to obtain global parameters; updating corresponding parts of local models of the vehicles based on the global parameters to form a collaborative scheduling model; after the models generate charging and discharging strategies based on real-time data, the charging and discharging strategies of each vehicle are output after competitive optimization according to the willingness of users to participate; and the application realizes collaborative optimization while fully protecting the privacy of users by aggregating model parameters instead of sharing original data; the willingness driving mechanism is introduced to improve the acceptability and execution rate of the scheduling scheme; and the adaptive scheduling capability of the system to dynamic environments such as electricity price fluctuation and uncertain user behavior is enhanced by combining federated learning and reinforcement learning.

This invention belongs to the field of intelligent manufacturing and industrial intelligent optimization technology, specifically involving a task scheduling method for marine diesel engine assembly workshops based on near-end strategy optimization. First, a transportation time matrix TT is generated based on the workshop grid layout and predetermined tracks. A unified model simultaneously characterizes process pre-processes, workstation mutual exclusion, transportation loop trips, capacity, and just-in-time delivery constraints. Then, an event-driven simulation environment is constructed, introducing a near-end strategy optimization (PPO) agent. A "state-rule action-reward" mechanism is used to reschedule online for disturbances such as transportation delays, path conflicts, and congestion, outputting scheduling instructions including start time, workstation allocation, transportation resource assignment, and route sequence. This invention can shorten the maximum completion time while ensuring on-time delivery, reducing empty runs and ineffective waiting, and is suitable for transportation-coupled and highly constrained assembly manufacturing scenarios.

The application provides a power distribution operation weather risk dynamic early warning method and system. The method comprises the following steps: querying weather forecast data according to a current operation position, time-aligning the weather forecast data and weather detection data to obtain a synchronous weather data set, extracting features and performing weighted fusion on the synchronous weather data set according to a fusion weight coefficient to obtain weather characteristic values, converting the weather characteristic values into initial risk values according to a numerical mapping rule, performing weighted summation on the initial risk values according to a risk weight coefficient to obtain a basic weather risk value, performing scene correction on the basic weather risk value according to power distribution operation data and the current operation position to obtain a comprehensive risk value, and matching the comprehensive risk value with a risk and instruction relationship to obtain a risk grade and an early warning instruction. Through multi-source weather data synchronous fusion, power distribution operation scene correction and hierarchical mapping early warning, the application realizes scene-based early warning of weather risks, so as to improve the safety of power distribution operations.

The application relates to the technical field of power dispatch, in particular to a robust optimization dispatching method and system for multiple microgrids participating in an electricity auxiliary market. The method comprises the following steps: constructing a three-layer dispatching architecture; establishing a multi-market clearing model based on a supply-side price quota curve and a demand-side price quota curve; selecting the supply-side price quota curve or the demand-side price quota curve according to the net load state of the microgrid in the next time period t to participate in the bidding of the electricity auxiliary market, and obtaining the market result; establishing a power distribution optimization dispatching model of the active distribution network layer, wherein the power distribution optimization dispatching model takes minimizing the total operation cost of the distribution network system as the target; respectively establishing an optimization dispatching model of each microgrid, wherein the optimization dispatching model of each microgrid takes minimizing the operation cost of the microgrid as the target; and generating a real-time dispatching strategy of the microgrid according to the optimization dispatching model of each microgrid, and executing the real-time dispatching strategy of the microgrid in the next time period t.

The application discloses a kind of personalized intelligent recipe recommendation system based on user health information, comprising: user information management module, for obtaining and managing the health information of user;Energy calculation module, for calculating ideal body weight, body type classification, basal energy requirement, blood sugar adjusted energy requirement and meal nutrient target according to user information;Food database, for storing the nutrient composition and type attribute of food;Recipe generation module, for selecting food combination from food database according to meal type and nutrient target, and generating personalized recipe using multi-nutrient synergistic optimization intelligent algorithm.The present application is based on the principle of nutrition, on the basis of personalized health management, increase the precise energy calculation function based on ideal body weight and body type classification, realize precision and individualization in energy calculation, nutrient optimization, food selection and other multiple links.

The application discloses a terminal data transmission method based on AI optimization, relates to the technical field of intelligent communication of electric energy meters, and comprises the following steps: combining a labeled to-be-transmitted queue, a communication network state, a terminal resource state and time context information into a preliminary transmission state feature vector, and generating a transmission state feature vector through preprocessing; inputting the transmission state feature vector into a hierarchical constraint reinforcement learning model, outputting a scheduling type strategy through a high-level decision layer, outputting a parameter type strategy through a low-level execution layer under the upper-layer scheduling constraint, and performing feasibility determination on the two types of strategies by a constraint checker to output a feasible transmission strategy vector; and the feasible transmission strategy vector comprises a feasible scheduling type strategy and a feasible transmission type strategy; and the application realizes intelligent scheduling and stable execution of terminal data transmission of electric energy meters in a complex operation scenario, and improves the scene adaptability and executability of transmission decision.

The present application relates to the technical field of data processing, and provides a menu generation method and device based on multiple agents, a terminal, a storage medium and a computer program product, the method comprising: obtaining input information of a user, analyzing the input information, and determining demand information and dining scene information of the user; based on the recipe demand information and the dining scene information, determining supplementary information by using RAG technology; generating recipe information according to the recipe demand information, the dining scene information and the supplementary information; determining nutritional information of the recipe information; and optimizing the recipe information according to the nutritional demand information and the nutritional information to obtain target recipe information required as a menu generated based on multiple agents. The present application can combine user demand of multiple modal inputs and multiple scene constraints, generate an executable creative menu scheme that meets individual tastes and takes into account nutritional balance through a feedback mechanism and continuous learning ability, and improve user experience.

This invention discloses a method and system for personalized task recommendation for children's eye care, relating to the field of artificial intelligence recommendation technology. The method includes: acquiring children's behavioral data, preprocessing and splicing it to generate daily behavior vectors; normalizing the daily behavior vectors by setting boundaries to obtain normalized vectors; constructing a task library and generating tag vectors; constructing a Poincaré sphere space; mapping the normalized vectors and tag vectors to the Poincaré sphere space; calculating the vector matching degree; and generating a task recommendation list based on the matching degree for sending. Based on the recommendation list, task feedback is statistically analyzed; a dual loss function is constructed based on the task feedback; and a total loss function is constructed based on the dual loss function for iterative optimization to generate a final recommendation list as the basis for personalized task recommendation. This invention effectively improves the scientific rigor and feasibility of eye care task recommendation.