260 results about "Condition evaluation" patented technology

The invention relates to an intelligent detection and evaluation method and system for underground pipeline defects, and the method comprises the following steps: S1, collecting multi-modal data of an underground pipeline based on an inspection robot, and transmitting the multi-modal data to an intelligent detection and evaluation platform; s2, the intelligent detection and evaluation platform preprocesses the collected multi-modal data; s3, performing defect detection on the underground pipeline based on the preprocessed multi-modal data; s4, constructing a three-dimensional model of the underground pipeline, and superposing a defect detection result into the three-dimensional model; and S5, based on the three-dimensional model of the underground pipeline, performing intelligent evaluation on the underground pipeline, and generating a pipeline condition evaluation report. According to the invention, the efficiency and reliability of underground pipeline defect detection are effectively improved.

The invention relates to the technical field of road maintenance and management, in particular to a pavement internal disease analysis method and system based on artificial intelligence. Five core processes of multi-layer sensor network real-time data acquisition, multi-modal data fusion and deep learning feature extraction, dynamic environment and use mode combined disease prediction and analysis, intelligent prediction and decision support system, and real-time disease condition evaluation and report generation are carried out. Through the technical means, comprehensiveness and timeliness of data acquisition, accuracy of disease identification and prediction, scientificity of a maintenance strategy and optimization of resource allocation are realized; the finally generated disease current situation report provides a detailed disease distribution diagram, disease type analysis and disease severity evaluation, comprehensive, accurate and timely disease information is provided for maintenance personnel, and the efficiency and effect of pavement maintenance and management are remarkably improved.

The invention discloses a servo motor dynamic overload detection and protection method based on a heat accumulation model, which relates to the technical field of servo system fault protection application, and comprises the steps of constructing a heat accumulation equivalent model, dynamically detecting an overload state, executing a heat dissipation recovery strategy, implementing a multi-stage protection mechanism, self-adaptive environment compensation and a historical data learning function. Through discretization processing of overload curve data and a nonlinear acceleration calculation strategy, the problems of protection lag and false triggering caused by traditional single-parameter detection are avoided, it is ensured that the motor can respond in time in light load, heavy load and load fluctuation scenes, the burn-out risk of the motor is reduced, and through integration of an environment temperature sensor and a heat dissipation condition evaluation unit, the reliability of the motor is improved. The heat accumulation coefficient and the heat dissipation attenuation parameter are dynamically adjusted, the problem of model deviation caused by environment interference is solved, when heat dissipation deterioration and temperature feedback abnormity are detected, the heat dissipation recovery period is automatically prolonged, calculation logic is corrected, and the continuous operation reliability of the system is improved.

The invention discloses a digital management system based on a VR technology, and relates to the technical field of digital management, and the system comprises a task execution module which executes a scenario-type decision simulation task according to a task demand in a virtual scene, records the decision behavior and interaction data of a user in real time in the execution process, and sends the decision behavior and interaction data to a server; generating a preliminary feedback report according to the decision behavior of the user and the interaction data; and the execution condition evaluation module is used for extracting task evaluation indexes by adopting data mining on the basis of the preliminary feedback report, evaluating the task execution condition of the user by adopting an analytic hierarchy process on the basis of the task evaluation indexes, and dynamically adjusting the situational decision simulation task in the virtual scene according to an evaluation result. According to the invention, the execution condition evaluation module is utilized to carry out multi-dimensional deep analysis on the decision behavior and interaction data of the user, accurate feedback and guidance are provided, the improvement of personal ability is promoted, and the design and adjustment of subsequent tasks are optimized.

The invention discloses an agricultural product quality safety management system, and relates to the technical field of agricultural product quality safety monitoring. The agricultural product quality safety management system comprises a storage environment evaluation module; a qualification condition evaluation module; a safety condition evaluation module; a comprehensive quality evaluation module; and an environment adjusting module. The storage environment index is obtained by combining the to-be-managed agricultural product environment information and the preset environment information, then the qualification condition index is obtained by combining the to-be-managed agricultural product qualification information and the preset qualification information, and the safety standard index is obtained by combining the to-be-managed agricultural product safety information and the preset safety information. And finally, judging whether to adjust the environment by acquiring the comprehensive quality safety evaluation index, thereby achieving the effect of more accurately monitoring the quality safety in the agricultural product storage process, and solving the problem of inaccurate quality safety monitoring in the agricultural product storage process in the prior art.

The invention discloses an ancient coin image-based intelligent evaluation method and system, and the method comprises the steps: carrying out the type recognition of an ancient coin image through a deep learning model, and determining the type of a to-be-evaluated ancient coin; extracting casting identification information in the ancient coin image, and determining a casting bureau of the ancient coin by comparing with a casting bureau database; extracting texture features of the surface of the ancient coin, and determining a parent model used by the ancient coin by comparing the parent model texture database; extracting difference characteristics of different sub-money batches, and classifying the ancient coins into specific sub-money batches; and detecting a plurality of condition attributes of the ancient coin image, performing quantitative scoring on the condition attributes, and calculating the condition score of the ancient coin according to each quantitative score so as to realize intelligent evaluation of the ancient coin. According to the embodiment of the invention, type identification, casting bureau determination, parent model comparison, batch classification and condition evaluation of the ancient coins can be comprehensively realized, the accuracy and efficiency of ancient coin evaluation are improved, and the method has a wide application prospect.

The invention discloses an intelligent electric control valve operation optimization control method. The method comprises the steps that actual process parameters and the valve opening degree are collected, and the discharging flow is predicted; constructing a sample library, and reasoning and judging historical data according to expert experience knowledge; dividing working condition evaluation grades by using a cloud model of cloud entropy optimization, and integrating subjective weights and objective weights by combining a game theory and an expert scoring matrix to obtain a final evaluation result; if the current working condition does not conform to the expected working condition, searching data most similar to the expected working condition from a sample library by utilizing a c-means cloud clustering intelligent search strategy, and outputting a valve opening value corresponding to the data as an optimized operation mode; and performing optimization control on the valve electric actuating mechanism through a multi-rate model prediction intelligent control algorithm. According to the method, full-period self-adaptive optimization control from flow prediction, operation state evaluation and intelligent optimization of operation parameters to model prediction control is realized.

The invention discloses a landslide construction simulation method based on finite elements, particularly relates to the field of construction simulation, and realizes whole-process dynamic simulation through GTS-NX software. Firstly, a three-dimensional finite element model is established based on geological exploration data, a Mohr-Coulomb elastic-plastic constitutive model is adopted, and the bulk modulus and the shear modulus are calculated through the elastic modulus and the Poisson's ratio. The initial ground stress is simulated by adopting a self-weight stress field method, and the initial displacement influence is eliminated by adopting a displacement difference method. In construction process simulation, the unit passivation / activation technology is adopted to accurately reproduce the excavation and support time sequence, and cyclic calculation of excavation-stress release-support-balance is achieved. In nonlinear analysis, superposition of an elastic stress field and a plastic stress field is considered, and an unloading influence factor is introduced to correct anisotropy of a deformation modulus. A safety coefficient is calculated through a strength reduction method, a multi-working-condition evaluation system is established, a monitoring data fusion and parameter inversion mechanism is developed, and a three-level early warning system is formed.

The invention provides a highway bridge intelligent maintenance decision-making system and decision-making method. The system comprises a data acquisition module, a processing and storage module, a bridge condition evaluation module, a maintenance decision making module, an implementation and management module, and a monitoring and feedback module. The data acquisition module collects various types of data of bridges, environments, traffic and the like through multiple means such as fiber bragg gratings and vibration sensors. And the processing and storage module cleans the data by using a DBSCAN algorithm, processes the classified data by one-hot coding, and constructs distributed database storage based on a block chain. And the evaluation module adopts an Isomap algorithm to evaluate technical conditions and predict diseases. And the decision module generates a scheme by using a particle swarm optimization-simulated annealing algorithm, and analyzes and optimizes by using an analytic hierarchy process-grey clustering. And maintenance implementation is managed by means of a BIM model. The monitoring module collects data in real time and feeds back an adjustment strategy. The system can acquire data in real time through multiple sensors, accurately process data, accurately evaluate diseases, optimize maintenance decisions, dynamically adjust strategies, guarantee bridge safety and prolong the service life.

The invention provides a bridge state comprehensive evaluation method based on multi-source data fusion, which is suitable for the field of bridge health evaluation and comprises the steps of selecting multi-source data indexes, determining multi-source data evaluation scales, analyzing multi-source data, distributing weights, calculating comprehensive scores and evaluating bridge states. The method has the advantages of high accuracy, good predictability and the like, and can be widely applied to the field of bridge technical condition evaluation.

The invention relates to the technical field of spiral flatcar machine control, and discloses an automatic operation control method and system for a spiral flatcar machine. The method comprises the following steps: according to a spiral motion characteristic parameter set and a working condition prediction matrix, carrying out n equal division processing on a transmission area of the spiral flatcar machine according to a spiral direction to obtain n self-adaptive data acquisition control units and a multi-dimensional state monitoring matrix; performing feature extraction processing on the multi-dimensional state monitoring matrix to obtain a working condition evaluation vector and a dynamic deviation feature set; performing iterative optimization processing on the working condition evaluation vector and the dynamic deviation feature set to obtain a real-time feedback control instruction set and a dynamic compensation parameter matrix; and according to the real-time feedback control instruction set and the dynamic compensation parameter matrix, intelligent closed-loop control is carried out on the spiral flatcar machine, and real-time control data and a process feature mapping set are obtained. According to the invention, high-precision automatic control of the spiral flatcar machine in the continuous loading operation process is realized, and the material loading uniformity and the operation efficiency are ensured.

The invention relates to a bridge technical condition evaluation method and system. The method comprises the steps that bridge basic information input by a user is acquired; generating bridge structure information according to the bridge basic information; determining a disease type and a disease index of each part according to a bridge disease library; according to the disease type and the disease index of each part, calculating the score of each part deducted according to the disease; calculating the score of each part type in each structure according to the score of each part deducted according to the disease; redistributing the weight of each part type in each structure, and obtaining the technical condition score of each structure based on the score of each part type in each structure; performing weighted calculation on the technical condition score of each structure to obtain an overall technical condition score of the bridge; and determining a technical condition grade according to the overall technical condition score of the bridge. According to the invention, automatic evaluation of the bridge state is realized, and the efficiency and accuracy of bridge maintenance and management are greatly improved.

The invention provides an electric motor coach whole vehicle OTA upgrading control method based on a BMS and a VCU, a temperature control dynamic threshold value and a multi-sensor weighted scoring algorithm are adopted, the BMS adaptively adjusts an SOC threshold value according to the battery temperature, the VCU synthesizes the vehicle speed, a hand brake and other states to generate upgrading condition evaluation, a user portrait database and a logistic regression model are combined, and the whole vehicle OTA upgrading control method based on the BMS and the VCU is obtained. Differentially pushing popup windows and predicting user cancel behaviors, implementing a block verification and low-temperature delay write-in strategy, dynamically optimizing remaining time display and low-bandwidth mode switching by a VCU, constructing a multi-stage rollback strategy library, automatically selecting a rollback path according to failure reasons, and cooperating with a safe restart mechanism of key state linkage; and in combination with NLP analysis user feedback driving system iteration, the low-temperature environment compatibility, the user interaction experience and the upgrading success rate are effectively improved, and a closed-loop optimized OTA upgrading ecological system is formed.

According to the clinical nutrition intelligent management system and method disclosed by the invention, nutrition status evaluation is carried out by applying a nutrition comprehensive evaluation formula, the standardization and objectivity of evaluation are greatly improved, the formula quantifies multiple key factors and gives weights, different evaluators carry out calculation based on the same information and formula, and the consistency of evaluation results is ensured; besides, the comprehensive nutrition evaluation formula comprehensively considers the body mass index, the disease-related nutrition influence coefficient, the nutrition-related index comprehensive score, the diet health degree score and other aspects adjusted by age and gender, accurately reflects the actual nutrition condition of the patient, and solves the problem of insufficient accuracy of traditional evaluation. And the nutrition plan generation module can customize nutrition plans for patients according to the nutrition comprehensive evaluation indexes in combination with a preset rule base, so that individual requirements of different patients are fully met, and a scientific, accurate and personalized solution is provided for clinical nutrition management.

An apparatus for determining a patient survival profile using artificial intelligence-enabled electrocardiogram (ECG), the apparatus includes a processor and a memory containing instructions configuring the processor to receive a plurality of patient profiles, wherein each patient profile includes ECG data, define a plurality of cohort labels, wherein defining the plurality of cohort labels includes generating a condition score for each patient profile of the plurality of patient profiles and defining the plurality of cohort labels as a function of the condition score, assign the plurality of cohort labels to the plurality of patient profiles, generate condition training data by correlating the plurality of patient profiles with a plurality of condition identifiers, generate a condition evaluation model and a machine-learning algorithm using the condition training data, determine a patient survival profile for a user-inputted patient profile using the condition evaluation model, and display the patient survival profile at a display device.

The invention discloses a Yolov8-Seg-based sand filter working condition evaluation method, relates to the technical field of industrial sewage treatment, and solves the technical problems that an existing sand filter working condition evaluation model is relatively large, the operation speed is low and the recognition efficiency is relatively low. Comprising the following steps: 1, acquiring an inspection image; 2, preprocessing the inspection image to obtain an original image; 3, reconstructing the original image based on the hexagonal pyramid model to obtain an input image; inputting the input image into the image recognition model to obtain a recognition result image; wherein the image recognition model is constructed based on a Yov8-seg model improved by a GSA module; 4, evaluating the working condition of the sand filter according to the identification result map; corresponding image defects can be recognized more easily after the inspection images collected under different illumination and different environment conditions are correspondingly processed, and the recognition precision of the network is improved; and thus, the accuracy of evaluating the working state of the sand filter is improved.

The invention discloses a quality inspection system for a production process of an amomum tsao-ko mixture for guiding qi downward and dredging collaterals, and aims to realize comprehensive monitoring and control of the production process through multiple modules and improve the product quality and the production efficiency. The image data acquisition module is used for acquiring raw material and production process images and spectral analysis images. The timing module records and judges the production process time. The multi-source data acquisition module collects production environment data and raw material weight. The raw material proportion control module monitors a raw material proportion. And the data processing module is used for analyzing and processing images and environment data in the production process. And the user interaction module provides an interface for real-time quality detection results and user modification commands. Through comprehensive data acquisition and processing, intelligent proportion control, accurate time management and efficient environmental condition evaluation, the production quality and safety of the amomum tsao-ko mixture for guiding qi downward and dredging collaterals are remarkably improved, human errors are reduced, and the production efficiency is improved.

The invention relates to a safety protection method based on dynamic monitoring of circulating flow of heat source equipment. The method comprises the following steps: collecting pipeline pressure data, circulating water flow data and circulating water temperature data in a circulating pipeline of heat source equipment through a multi-source sensor; inputting the circulation pipeline data into a preset heat source equipment state model to perform working condition evaluation so as to generate an equipment working condition report; performing fault feature extraction on the equipment working condition report, and identifying an abnormal operation mode according to the extracted fault features; in response to the identified abnormal operation mode, determining a fault type matched with the abnormal operation mode according to a preset fault type library; and based on the determined fault type, generating a corresponding control instruction by using a preset risk-instruction mapping rule. According to the invention, real-time monitoring of the operation state of the heat source equipment, automatic identification of abnormal modes and active safety protection control are realized.

The invention provides an operation condition evaluation method and system for a water supply system. Belongs to the technical field of water supply system intelligent management. The method comprises the following steps: acquiring real-time data of a water supply system through various types of sensors and monitoring equipment arranged in each link of the water supply system; meanwhile, collecting non-real-time data of the water supply system to form a multi-dimensional heterogeneous water supply system data set; aiming at data of different types and sources, adopting a self-adaptive preprocessing strategy to carry out data preprocessing; and generating a standard multi-dimensional heterogeneous water supply system data set. The spatio-temporal data analysis technology is adopted, spatio-temporal characteristics in water supply system data are deeply excavated, and the operation condition and dynamic change of the water supply system can be reflected more accurately.

The invention provides a children practice situation evaluation method, medium and system, and belongs to the technical field of deep learning models.The children practice situation evaluation method includes the steps that children and standard playing audio signals are collected and preprocessed into time-frequency representation, a time window sequence is constructed to calculate window similarity, beat mark points are extracted to form a rhythm sequence, and the rhythm sequence is obtained; a rhythm perception analysis model is input to generate a rhythm feature representation vector, a multi-dimensional deviation vector is formed by applying multi-level time model analysis, a rhythm deviation tolerance range matrix is constructed, deviation similarity is calculated, and finally a rhythm performance score is output through a rhythm evaluation neural network model. According to the method, a convolutional neural network, a long-short term memory network and an attention mechanism are fused, and comprehensive quantitative evaluation of the playing rhythm performance of the children is realized through large-scale data set training and expert scoring verification.

The invention discloses an ecological resource background condition evaluation method, which comprises the following steps of: constructing an ecological resource background evaluation index system, and selecting 25 secondary indexes from five aspects of quantity, quality, function, landscape pattern and management and protection according to the accessibility and representativeness of data to construct an ecological resource condition evaluation index system; basic data investigation: based on an ecological resource classification system, comprehensively investigating the area, the structure, the quality and the management condition of each type of ecological land within the evaluation time period, and obtaining evaluation basic data; evaluation index calculation: calculating various evaluation index values according to the evaluation system; and evaluating the background condition of the ecological resources, determining the weight of each evaluation index according to the index parameters, standardizing the evaluation indexes, and carrying out weighted summation on the standardized evaluation index values to obtain a comprehensive evaluation value of the condition of the ecological resources. According to the invention, comprehensive monitoring of ecological resource conditions is realized, and the accuracy of ecological condition assessment results is greatly improved.

The invention relates to the technical field of medical health monitoring and neural networks, and provides a chronic obstructive pulmonary disease patient leaving hospital monitoring system which comprises the steps that multi-source data of a patient is acquired, and the multi-source data at least comprises daily health monitoring data and electronic medical record data; preprocessing and feature extraction are conducted on the multi-source data through a data processing module, health feature data of the patient are obtained, and the health feature data are features related to the chronic obstructive pulmonary disease condition selected from the multi-source data and comprise features based on respiratory functions, features based on lifestyles and behaviors and features based on complications and complications; and features based on drug use; integrating and updating the health feature data through a patient health condition evaluation module, and performing clustering analysis on the integrated and updated real-time health data by adopting a density-based clustering algorithm DBSCAN to obtain a health portrait of the patient; the health portrait is analyzed through a medical large model analysis module, an illness state evaluation result and a health management suggestion are obtained, and a deep learning model designed by combining a convolutional neural network CNN with a multi-modal model architecture of an attention mechanism Transform is used in the medical large model analysis module; remote monitoring, interactive communication and personalized intervention operation are executed through the remote medical intervention module according to the illness state evaluation result.

The invention provides an energy storage system operation evaluation and fault early warning method, and relates to the technical field of energy storage systems, and the method comprises the steps: carrying out the historical data back calculation of a platform database, and carrying out the summarization to obtain an energy storage system unuploaded signal data set; determining a battery management system evaluation index set, an energy storage converter evaluation index set and a thermal management system evaluation index set; daily change condition evaluation and summarization are carried out on the non-uploaded signal data set according to the evaluation index set, and energy storage system operation daily state data are obtained; establishing a data threshold value, and performing fault data marking and early warning on the energy storage system operation day state data based on the data threshold value; and establishing an operation trend prediction model according to the energy storage system operation day state data, and carrying out risk data prediction and early warning. The technical problem that the prior art mainly depends on real-time data and an alarm mechanism to carry out operation state monitoring and is difficult to effectively prevent potential faults is solved, and the operation safety of the energy storage system is improved.

The invention relates to a manned spacecraft on-orbit health state evaluation method, which comprises the steps of manned spacecraft on-orbit state evaluation: evaluating the on-orbit state of manned spacecraft key equipment according to telemetry parameter evolution characteristics representing manned spacecraft function indexes; evaluating the technical capability of the manned spacecraft: before the manned spacecraft executes a key on-orbit task, evaluating whether a cabin sealing function, an attitude control function, an orbit control function, a docking function, and a mechanical arm and a pressure relief and recompression device related to execution of the key task are normal or not; carrying out task evaluation on the manned spacecraft: carrying out task execution condition evaluation on a key on-orbit task during the on-orbit operation period of the manned spacecraft; reliability and safety evaluation: evaluating the on-orbit safety, reliability, maintainability and life condition of the manned spacecraft, and identifying internal and external risks possibly existing in the long-term on-orbit operation period; and guarantee condition evaluation: evaluating whether the ground flight control team guarantee condition meets the flight control task requirement or not.

The invention discloses an aircraft echelon comprehensive evaluation model, which relates to the technical field of aircraft use condition evaluation and comprises the following indexes: an index I: a service life reserve and maintenance stop rate index Q1; the index is used for evaluating the conditions that the total remaining service life of aircrafts of the same type is not lower than 40% and the maintenance stopping rate is not higher than 15% within a certain time period. The aircraft echelon control and optimization model algorithm is designed by using the achievement of the aircraft echelon control and optimization theory, and the aircraft echelon control and optimization model algorithm is designed according to the aircraft echelon control and optimization strategy in combination with the actual situation of the aircraft echelon of each unit of the flight fleet by using the aircraft echelon control and optimization tool. According to the method, an aircraft overhaul echelon scheme, an aircraft annual echelon use scheme, an aircraft month echelon use scheme and an aircraft flight day echelon use scheme can be automatically generated, a manager can conveniently control and use aircraft echelon, the problems of centralized overhaul, centralized regular inspection, centralized life reaching and the like of aircraft equipment are solved, and the use effect is good.

The invention discloses a vehicle load estimation method and device and a storage medium. The method comprises the steps that first load estimation is constructed based on a vehicle longitudinal dynamics model and extended Kalman filtering; constructing a vehicle operation condition evaluation coefficient based on the vehicle operation condition influence factor, and constructing second load estimation according to the vehicle operation condition evaluation coefficient, a vehicle longitudinal dynamic model and a recursive least square method; a fused load estimate is constructed based on the vehicle dynamics parameter, the first load estimate, and the second load estimate, and a current vehicle load estimate is output with the fused load estimate and the current vehicle operating parameter. The method has the beneficial effects that the accuracy of load estimation in any running state of the vehicle is ensured, and meanwhile, the random influence of environmental noise on the recursive least square method is compensated through the vehicle running working condition influence factors, so that the accuracy of load estimation is further improved; and fusion load estimation is carried out by using the recursive least square method, so that the calculation complexity is relatively low, and the calculation power demand is reduced.

The invention discloses a personalized semantic evaluation system combined with a knowledge graph, and relates to the technical field of educational evaluation, and the system comprises the steps: obtaining a pre-constructed mathematical knowledge graph and a preset mathematical knowledge point set, and determining a target knowledge point frame; obtaining a test paper sequence and a test paper standard answer sequence of the target user in historical time, and performing matching analysis to obtain a test question score set sequence; performing test question semantic analysis, and determining a test knowledge point set sequence; respectively mapping and inputting the test knowledge point set sequence and the test question score set into a target knowledge point framework to obtain a test knowledge point score framework sequence; and performing iterative association interaction analysis, determining a target interaction test knowledge point score framework, performing analysis, and determining a target mastery degree evaluation result. The technical problem that the knowledge point mastering degree of a user cannot be accurately evaluated in the prior art is solved, and the technical effect of providing an accurate basis for learning condition evaluation is achieved.

Methods, apparatuses, systems, and computer-readable media for identifying and executing one or more interactive condition evaluation tests and collecting and analyzing user behavior data to generate an output are provided. In some examples, user information may be received and one or more interactive condition evaluation tests may be identified. An instruction may be transmitted to a computing device of a user and executed on the computing device to enable functionality of one or more sensors that may be used in the identified tests. Upon initiating a test, data may be collected from the one or more sensors. The collected sensor data may be transmitted to the system and processed using one or more machine learning datasets. Additionally, user behavior data may be collected and processed using one or more machine learning datasets. The sensor data, the user behavior data, and other data may be used together to generate an output.

The invention discloses an intelligent OTA upgrading method and device, a terminal and a storage medium in the technical field of remote wireless upgrading, and aims to solve the problems that in the prior art, mode selection is carried out only according to three factors of upgrading package volume, function importance and memory space, and more flexible coping strategies are lacked for some special conditions; and the upgrading success rate is relatively low. The method comprises the steps of obtaining update information and an update type; inputting the update information into a corresponding preset first fuzzy set and a membership function, and determining the membership of the update information in the corresponding first fuzzy set; determining a second fuzzy set of each fuzzy rule activation degree by adopting a Mamdani reasoning method; according to the method, the selection of the upgrading mode and the control of the upgrading process are further optimized by introducing technical means such as vehicle real-time state monitoring and network condition evaluation and using a fuzzy rule strategy, and the method is suitable for different special conditions, has flexibility and improves the success rate of OTA upgrading and the user experience.

The invention discloses a method for screening marine container ship port berthing assistance based on a group relative strategy, and belongs to the technical field of artificial intelligence and ship automatic berthing assistance. The method comprises the following steps: constructing a docking environment situation cache data set, constructing a marine container ship port docking assistance model, performing preliminary environment understanding on docking environment situation cache data, writing the data into a shared context, and adaptively calling a tool library module by using an intelligent agent to obtain an updated shared context; a scheme generation and evaluation tool module is used for obtaining candidate berthing schemes and safety and feasibility scores of the candidate berthing schemes, convergence judgment is carried out on the candidate berthing schemes and the safety and feasibility scores of the candidate berthing schemes, and a final berthing assistance scheme is obtained through a termination condition evaluation module. Through the optimized large language model and the self-adaptive tool arrangement mechanism, the tool calling accuracy and the scheme convergence efficiency are improved, and safe and efficient marine container ship docking operation is achieved.