2740 results about "Evaluation data" patented technology

A method for developing a generative AI system may include constructing a plurality of generative AI systems, wherein constructing the generative AI systems includes executing at least one modeling blueprint; providing a plurality of queries to each of the generative AI systems, the queries being part of an evaluation dataset; during processing of the queries by each generative AI system, monitoring values of one or more quantitative metrics; providing, for display by a user device, data indicating the values of the quantitative metrics for each generative AI system; and providing, for display by the user device, a recommendation regarding use or non-use of at least one generative AI system included in the plurality of generative AI systems.

The invention relates to the technical field of path planning, and discloses a path planning and control method and system for a container port automatic driving vehicle. The method comprises the following steps: acquiring environment data through a multi-source sensor, and obtaining a harbor district digital twinborn model through space-time registration fusion; a three-layer progressive planner is used for optimizing and generating a main and standby path scheme based on the model; predicting a dynamic target trajectory by using a time sequence attention network to obtain situation assessment; processing the evaluation data through a distributed predictive control algorithm to generate a control instruction; and monitoring a motion state through adaptive security constraints to form a security policy, and continuously optimizing by using deep reinforcement learning. According to the method, safe and reliable operation of the automatic driving vehicle is realized in a complex and changeable harbor district environment, and particularly, accurate path planning and control decisions can be made in the face of dynamic obstacles.

The invention discloses an artificial intelligence data privacy protection system based on a block chain and federated learning, and relates to the technical field of block chains and federated learning, and the system comprises a block chain module which employs a main chain-side chain double-chain architecture, a main chain stores a global model hash value and node reputation evaluation data, and a side chain module is used for storing node reputation evaluation data; the side chain stores the encrypted local model parameters through a fragmentation technology; the federated learning module comprises a dynamic difference privacy algorithm and a gradient ternary processing unit, and is used for adding noise to the gradient in a local training stage and converting the gradient into a ternary numerical format; the privacy protection module is used for integrating homomorphic encryption and zero-knowledge proof technologies and realizing ciphertext aggregation and verification of model parameters; and the malicious node detection module is used for identifying abnormal gradient update based on cosine similarity and Multi-Krum algorithm, and is linked with node reputation data in the block chain module. Through a system architecture and a privacy protection mechanism, the efficiency and performance of federal learning are improved while data privacy is ensured, and the method has a wide application prospect.

The invention discloses a casting surface treatment quality evaluation method and system, and relates to the technical field of intelligent manufacturing and quality control, and the method comprises the steps: carrying out the multi-modal data collection of a casting, and carrying out the preprocessing; analyzing the preprocessed data, and constructing a comprehensive quality evaluation data set; a quality evaluation model is constructed, abnormal conditions in surface processing are identified, and a quality evaluation result is generated; performing quality classification on the casting according to a quality evaluation result, generating an evaluation report, and providing quality state judgment and related optimization suggestions; the casting surface treatment process is adjusted and optimized, the overall manufacturing quality is improved, and continuous improvement of quality evaluation is achieved. According to the method, the detection precision of the casting surface treatment quality and the process optimization capability are improved. By adopting data-driven closed-loop control, quality abnormity can be accurately identified, optimization suggestions can be provided, intelligentization, stability improvement and long-term quality improvement of the manufacturing process are realized, the defect rate is effectively reduced, and the manufacturing consistency is improved.

The invention discloses a mechanical welding seam defect identification method, and relates to the technical field of welding quality intelligent detection and defect identification, and the method comprises the steps: S1, collecting welding seam surface image data, obtaining a standardized image data set, carrying out the feature extraction of the standardized image data set, and obtaining a feature extraction result; the method comprises the following steps: S1, extracting edge contours, texture distribution and gray change features of defects, and generating a high-dimensional feature vector containing space position coordinates and morphological features of the defects, S2, generating a weld quality evaluation database containing defect distribution uniformity and defect severity scores according to edge continuity parameters and gray uniformity parameters in the high-dimensional feature vector; according to the mechanical welding seam defect identification method, accurate identification, classification and positioning of welding seam defects can be achieved, comprehensive evaluation of the welding seam quality is given, and the intelligent level and reliability of welding quality control are effectively improved.

The invention relates to the technical field of data processing, and discloses an automobile manufacturing industrial data distributed processing method and system based on digital twinning. The method comprises the following steps: monitoring and collecting production abnormal events, decision emergency degree parameters and simulation task types based on service states to generate a service priority evaluation data set, and performing priority ranking on a plurality of simulation tasks to form a dynamic priority queue and a resource demand matrix, the queue is input into a distributed simulation engine for CPU, memory and GPU load balancing processing to generate a node resource allocation scheme, and preemptive scheduling is executed to migrate low-priority tasks to idle nodes to form a task execution mapping table; and monitoring simulation progress and resource consumption through an adaptive scheduling mechanism to obtain scheduling performance feedback data, and updating the dynamic priority queue. According to the method and the device, the problem of lack of an intelligent scheduling mechanism during concurrent execution of multiple simulation tasks in the prior art is solved, and the response speed of the key simulation task and the utilization efficiency of computing resources are improved.

The invention relates to a cable online operation fault positioning, monitoring and early warning method and system based on a neural network algorithm, and the method comprises the steps: collecting the operation environment and state data of a cable through a multi-source sensor, and generating an original monitoring data set through noise reduction and time sequence alignment; aiming at the problem of periodic distortion caused by seasonal fluctuation of environment temperature and humidity, performing dynamic reference compensation to generate a reference data set without seasonal influence; aiming at a coupling effect of a dynamic load and temperature drift, calculating insulation performance evaluation data of temperature compensation through load-insulation correlation mapping; aiming at the problem of disconnection of waterproof sealing monitoring and fault positioning, a sealing degradation grade is generated based on regional high-humidity detection; aiming at the defect of a fixed threshold strategy, generating a fault coordinate and a risk probability in combination with spatial positioning and probability analysis; and finally, self-adaptive updating of the parameter library is realized through credibility verification.

The invention relates to the technical field of flexible circuit board production, in particular to a process optimization method and system for a flexible circuit board production line. The method comprises the following steps: acquiring characteristic data of a flexible circuit board material; executing a circuit board thermocuring action according to the material characteristic data of the flexible circuit board, performing thermal stress intensity evaluation, and generating stress hot spot distribution data; performing real-time curing temperature control feedback adjustment according to the stress hotspot distribution data to obtain circuit board curing effect evaluation data; detecting a real-time surface wettability index of the to-be-processed flexible circuit board in front of the laminating station; and fitting process parameter compensation is carried out based on the circuit board curing effect evaluation data and the real-time surface wettability index so as to realize curing-fitting process optimization of the flexible circuit board production line. According to the invention, intelligent optimization of the curing-laminating process of the flexible circuit board production line is realized, and the quality problems of uneven stress and overlarge deformation in the thermocuring-laminating process are effectively solved.

The invention discloses an Internet-based artificial intelligence power dispatching management system, which relates to the technical field of power system intelligent dispatching and comprises a distributed data acquisition module, a deep neural network module, a hierarchical reinforcement learning module, an intelligent execution module and a safety management module. The distributed data acquisition module acquires operation data of the power system through a multi-dimensional sensor array to form a feature data set; the deep neural network module processes the feature data set based on a double-layer cross validation mechanism to generate state evaluation data; the hierarchical reinforcement learning module constructs a multi-target power dispatching model and dynamically generates a control parameter matrix; and the intelligent execution module realizes parameter self-tuning through a built-in self-correction controller. According to the method, an improved Transform structure is designed, deep extraction is performed on the characteristics of the power system by fusing the space attention network and the time sequence attention network, and the model performance is optimized by adopting a three-stage strategy, so that the accuracy of state evaluation is improved.

The invention discloses a medical and educational informatization comprehensive supervision and diagnosis platform system based on multi-modal data fusion. A data acquisition module acquires multi-modal data from a medical and educational information system and preprocesses the multi-modal data; the data storage module constructs a hybrid architecture to classify and store data and establishes an index. The multi-modal data fusion module deeply fuses medical records, images and physiological monitoring data of medical treatment and teaching resources, learning behaviors and teaching evaluation data features of education by means of a feature extraction technology and a fusion algorithm. The supervision and diagnosis module constructs models of medical service quality evaluation, disease diagnosis assistance, teaching quality monitoring and the like based on fusion features to realize accurate analysis. The user interaction module provides interaction interfaces for different roles and supports operations such as data query and uploading. The platform system breaks the data barrier, fully mines the data value, provides powerful support for informatization management, decision making and service optimization in the medical and education fields, and promotes the innovative development of the two fields.

The invention relates to the technical field of task scheduling, and discloses a distributed task scheduling method, device and equipment for OpenHarmony Internet of Things equipment, and the method comprises the steps: carrying out the hardware parameter collection and equipment classification of the Internet of Things equipment in an OpenHarmony distributed network, and obtaining an equipment capability data set; performing task analysis and dependency analysis on the application program to obtain a task directed acyclic graph; carrying out calculation and resource allocation prediction on the equipment load to obtain an equipment resource reservation table; constructing a nonlinear optimization model, and solving to obtain an initial task allocation scheme; performing two-stage distributed reinforcement learning optimization to obtain a target task scheduling scheme; the target task scheduling scheme is deployed to the Internet of Things equipment, data collection and performance evaluation are carried out, and a performance evaluation data set is obtained.The efficient communication and cooperative computing between the equipment are achieved based on the distributed soft bus, the overhead of task migration and data transmission is reduced, and the overall performance of the system is improved.

The invention relates to the technical field of intelligent bid evaluation, and provides an intelligent multi-dimensional bid evaluation analysis and decision-making method based on big data, which comprises the following steps: acquiring original bid evaluation data from a multi-source heterogeneous data interface, and fusing through semantic role labeling and a timestamp alignment algorithm to generate a time-space association data set. And performing multi-level cleaning to generate a high-confidence bid evaluation data set. And extracting a multi-dimensional index based on the domain knowledge graph, generating a dynamic feature tensor, and dynamically allocating a weight by adopting a coupling attenuation weight model. And constructing a bidder association network, calculating a node influence score, detecting a potential bidding behavior and generating a risk correction coefficient. And injecting the real-time data stream into the dynamic feature tensor, updating the index weight, and generating a three-dimensional scoring vector through a multi-target aggregation decision algorithm. And performing Pareto optimization by using the asymmetric game equilibrium model, and outputting an optimal bid-winning party sequence and a risk early warning report. The bid evaluation efficiency and fairness can be improved, and the bid invitation risk is reduced.

The invention belongs to the technical field of three-dimensional measurement, and discloses a 3D laser line scanning system based on a point cloud positioning algorithm and a control method. The method comprises the following steps: acquiring environment initial point cloud data and scanning target information; carrying out feature extraction, constructing an environment feature description model, carrying out three-dimensional space partitioning processing, carrying out regional reflectivity feature analysis, and constructing a space reflection feature model; adaptive optimization configuration is carried out on the laser scanning parameters, and a scanning strategy is generated; scanning path dynamic planning is carried out on the scanning target information, scanning density distribution calculation is carried out, and a scanning path is generated; performing laser line scanning control by using the scanning path and the scanning strategy, and performing scanning data quality real-time monitoring to obtain scanning quality evaluation data; scanning parameter iterative optimization is carried out on the scanning quality evaluation data, point cloud data real-time fusion reconstruction is carried out, a high-precision three-dimensional model is generated, the modeling precision is improved, and meanwhile the system resource utilization efficiency is optimized.

The invention discloses a market supervision data asset management method and system based on compliance and privacy protection, and relates to the technical field of data asset management. According to the method, the privacy compliance strategy is generated according to the metadata label and is packaged into the data capsule, so that the binding of the data and the compliance strategy is realized; the privacy compliance knowledge graph is constructed, and semantic matching and compliance verification are performed in combination with a large language model, so that the semantic consistency and matching degree between a privacy compliance strategy and a supervision regulation are improved, and manual intervention is reduced; a privacy protection processing mechanism is called according to a compliance verification result, and a proper data protection technology is dynamically applied to reduce the risk of data leakage; anonymization processing parameters are optimized before data are published, data distortion processing is implemented, and the privacy disclosure risk is effectively controlled on the premise that data availability is guaranteed; and a data asset value dynamic evaluation model is constructed, the data comprehensive value and the risk level are evaluated, and refined and differentiated management of data assets is supported.

The invention relates to the technical field of display screen color adjustment, and discloses a display screen color adjustment method and system based on environment information, and the method comprises the steps: obtaining display content data, environment color data, environment light data and a display screen type of a display screen; calculating color perception data according to the environment color data and the display screen type, and performing data processing on the color perception data to obtain chromaticity feedback data; establishing a relationship between the ambient light data and user preferences through a support vector machine method to obtain user evaluation data; inputting the chromaticity feedback data and the user evaluation data into a color correction model to obtain color adjustment data; classifying the display content data and calculating a type weight index through a squirrel optimization algorithm to obtain a weight index; and generating a color adjustment instruction according to the color adjustment data and the weight index, and performing adaptive adjustment on the display content. The method solves the problems that the color adjustment of the display screen has high requirements on a light source environment and is strong in subjectivity.

The invention relates to the technical field of data packet transmission, and discloses a data packet transmission control method and device for a low-power-consumption dual-mode Bluetooth chip, and the method comprises the steps: obtaining a signal strength parameter, a power consumption parameter, a transmission rate parameter and an environment parameter of the dual-mode Bluetooth chip, inputting the parameters into a deep time convolution network, and carrying out the time sequence feature extraction, obtaining a transmission mode switching judgment result; calculating transmission state evaluation data; the chip transmitting power is adjusted in a grading mode, standby power consumption is controlled through a nanoamp LDO circuit, and a dynamic power control parameter is obtained; performing compression coding on the data packet to obtain a dual-mode transmission protocol configuration parameter; executing time sequence sampling, and constructing a dual-mode state feature sequence; the data packet delivery success rate, the end-to-end transmission time delay and the total power consumption of the system are calculated, transmission control parameter optimization is carried out through double-layer deep reinforcement learning, target transmission control parameters are obtained, and the optimal balance of the transmission performance and the power consumption of the dual-mode Bluetooth chip is achieved.

The invention relates to the technical field of glass detection, and discloses a method, a device and equipment for detecting glass after light sweeping. The method comprises the following steps: collecting multi-source original sensing data of glass to be detected, carrying out signal denoising, synchronous transformation and multi-scale glass feature extraction on the multi-source original sensing data, and carrying out feature combination, feature dimension reduction compression, region segmentation and region contour reconstruction on the multi-scale glass features to obtain an initial defect region set; performing spatial-temporal feature combination of multi-source features on the initial defect region set to obtain an enhanced defect feature set; and constructing a region similarity matrix corresponding to the enhanced defect feature set, performing defect category judgment and multi-dimensional glass quality scoring on the region similarity matrix to obtain glass quality evaluation data, performing process association on the glass quality evaluation data, and generating a final detection report of the to-be-detected glass. According to the method, the result accuracy of quality detection on the glass subjected to light sweeping is improved.

The invention relates to the technical field of data processing, and provides a trade big data processing and user behavior prediction method and system.The trade big data processing and user behavior prediction method comprises the steps that transaction data, browsing behavior data and feedback evaluation data of a user on a cross-border e-commerce platform are obtained to generate a user behavior pattern sequence, and then a user interaction graph is constructed; and carrying out edge weight updating and node label classification to obtain a high-dimensional relation graph, carrying out similarity partition processing to obtain a predicted popularity index graph, and predicting the future cross-border commodity interaction behavior of the target user group by using the predicted popularity index graph. By acquiring transaction data, browsing behavior data and feedback evaluation data of users and generating a prediction popularity index graph, multi-dimensional data are effectively and comprehensively processed, accurate prediction of cross-border commodity interaction behaviors of a target user group is realized, and the user experience is improved when a user intention deep relationship is processed and cross-time-period behavior evolution analysis is performed. The problems of low model dimension and insufficient prediction precision exist.

The invention relates to the technical field of medical diagnosis systems, in particular to an early gonitis diagnosis system and method based on multi-modal deep learning, a data collection module is used for collecting and preprocessing multi-modal data including knee joint X-ray image data, MRI (Magnetic Resonance Imaging) image data and clinical evaluation data; the model construction module constructs a deep learning model by combining a convolutional neural network and a Transform architecture, realizes mapping from multi-modal data to a clinical assessment phenotype label by adopting a feature automatic learning module and a potential relationship mining module, and integrates a Grad-CAM interpretability method for understanding a model prediction basis; diagnostic data are continuously collected to be fed back to the data collection module for model optimization, the convolutional network is good at capturing local image features, the Transform architecture is good at modeling a long-range dependency relationship, and the feature extraction and relationship modeling capabilities of the model are greatly improved through combination of the convolutional network and the Transform architecture.

The invention provides an interactive simulation teaching processing method and device for ship electromechanical guarantee, and relates to the technical field of interactive simulation teaching, and the method comprises the steps: building a ship electromechanical full-structure model in a virtual simulation platform, digitally mapping real equipment operation parameters into virtual parts with real-time response characteristics, and carrying out the real-time simulation of the virtual parts; synchronous acquisition and normalized calibration of operation instructions, equipment feedback and environment disturbance in a teaching scene are realized, and a unified teaching simulation data set is generated. State recognition, fault simulation and operation evaluation calculation are completed through multi-layer task scheduling, and real-time teaching feedback is generated and visually displayed on a terminal. Based on difference analysis of dynamic evaluation data and a standard operation template, operation deviation and abnormity are automatically identified, and a targeted guide prompt is generated. According to the invention, unified modeling and data linkage can be carried out on student operation behaviors, equipment dynamic responses and teaching feedback results, and the problems of teaching feedback lagging and lack of pertinence in operation correction are solved.

The invention provides an online learning energy concentration degree evaluation method combined with formability evaluation, and belongs to the technical field of online education, and the method comprises the steps: capturing eye movement data of a learner in an online learning process, carrying out feature extraction and time sequence analysis, and combining formability evaluation data to construct a deep neural network energy concentration degree evaluation model. And performing real-time dynamic evaluation on the energy concentration state of the learner by using multi-dimensional indexes such as attention continuity, visual concentration degree and cognitive input degree. Through a sliding time window technology and a specially-designed evaluation equation, the score of the energy concentration degree of each learning stage can be accurately calculated, and finally the overall energy concentration level of the learner is comprehensively output, so that data support is provided for personalized learning. According to the method, learning energy concentration degree monitoring and analysis support is provided, and the problem that it is difficult to continuously, objectively and comprehensively monitor the attention state of a learner on the premise that learning activities are not affected in the prior art is solved.

The invention discloses a power grid dispatching anti-error method and system based on topology analysis, and relates to the technical field of power grid dispatching anti-error, and the method comprises the following steps: building a remote signaling credibility evaluation model based on remote signaling evaluation data, and obtaining high-credibility remote signaling data; performing sequential logic verification based on high-credibility remote signaling data and multi-source data fusion to generate a power grid topology model; carrying out partition division on the power grid topology model based on spectral clustering and a state-driven edge weight method, and carrying out boundary equivalence processing to obtain a plurality of topology modules; and performing simulation scheduling operation on the plurality of topology modules based on a scheduling instruction dispatching order objectification analysis result, performing path traversal based on a depth-first search method, and performing anti-error analysis on the traversed path in combination with a reinforcement learning method. According to the power grid dispatching anti-error method combining remote signaling credibility evaluation, dynamic topology partition and reinforcement learning algorithm, the anti-error verification capability and the intelligent level of a power grid dispatching system are comprehensively improved.

The invention discloses an intelligent travel text system based on python deep learning, and relates to the technical field of python deep learning, and the system comprises the following components: a data collection module which is used for automatically collecting comment data about a specific travel destination from various channels of various travel websites, social media platforms and online travel forums, according to the method, massive comments and evaluation data about a specific tourism destination can be automatically collected from multiple channels, the accuracy and consistency of analysis data are ensured through fine data preprocessing, fine adjustment is performed by applying an advanced deep learning sentiment analysis algorithm in combination with a specific corpus in the tourism field, and the accuracy and consistency of data analysis are ensured. The system can accurately judge the emotional tendency of tourists and deeply mine key opinions and suggestions in comments, and the deep emotional mining ability not only helps tourism management departments and scenic spots to quickly understand real feedback of the tourists, but also can accurately identify satisfaction distribution and main concerns of the tourists on services.

The invention discloses an intelligent operation risk analysis method and system based on deep learning, and relates to the technical field of power plant management, and the method comprises the steps: collecting the operation original data of power plant equipment in real time; carrying out data fusion on the collected data, and extracting equipment evaluation data; analyzing the equipment evaluation data through the long-short-term memory neural network model, and predicting an equipment fault based on an analysis result; based on the equipment fault prediction result, generating an equipment scheduling scheme by using a particle swarm optimization algorithm, and optimizing the scheduling scheme; and converting the optimized equipment scheduling scheme into a control instruction, and generating and executing equipment scheduling through a low-code development platform. According to the intelligent operation risk analysis method based on deep learning, a scheduling scheme can be optimized according to the health state and the fault risk of the equipment, the operation efficiency of the equipment is improved, a repair and maintenance plan is effectively arranged, and the problem of excessive maintenance or repair lag is avoided.

The invention relates to the technical field of CT scanning parameter optimization, in particular to a CT scanning parameter optimization method and system based on reinforcement learning, and the method comprises the steps: obtaining basic information of a patient, scanning part information and initial CT scanning parameters; obtaining a CT image quality evaluation data set; constructing a reinforcement learning environment based on the basic information of the patient, the scanning part information and the initial CT scanning parameters; using the CT image quality evaluation data set to train a reinforcement learning model; according to the reinforcement learning model, optimized CT scanning parameters are generated; according to the method, the optimized CT scanning parameters are output and used for executing CT scanning, the system can generate customized optimal scanning parameters for each patient by taking the individual features of the patient as a part of a reinforcement learning environment, the accuracy and safety of CT examination are greatly improved, multi-target collaborative optimization is achieved, and the method is suitable for popularization and application. The optimal balance point can be found among a plurality of targets such as image quality, noise level and radiation dose.

The invention provides a state monitoring and fault early warning method for a maglev vehicle system, relates to the technical field of urban rail transit data processing, and solves the problem of how to effectively combine an empirical threshold mode and a machine learning technology and give full play to a combination effect. The method comprises the steps of collecting operation data of a magnetic levitation vehicle system, performing corresponding data preprocessing operation, performing analysis and calculation in a mode of constructing a mathematical model, and evaluating the health state of the magnetic levitation vehicle system; constructing and training a fault prediction model, and inputting the evaluation data into the trained fault prediction model to obtain potential fault data of the magnetic levitation vehicle system; and generating early warning information based on the potential fault data, retrieving an expert experience library by using the early warning information, generating an intelligent decision result, and feeding back the intelligent decision result to the operation and maintenance personnel. According to the method, machine learning and expert experience are effectively combined, so that the real-time performance and the accuracy of fault early warning are improved.

The invention relates to a radiographic image quality evaluation and enhancement method and system based on deep learning. The method comprises the following steps: constructing a multi-modal radiographic image quality evaluation data set containing noise types, artifact levels and diagnostic availability labels; designing a double-branch deep neural network architecture containing a quality evaluation branch and an image enhancement branch, the quality evaluation branch fusing global and local features to evaluate image quality, and the image enhancement branch realizing local enhancement of a focus area; coordinating a loss function optimization process by adopting a self-adaptive joint training strategy; and deploying an online correction module to iteratively optimize the enhancement parameters. The system comprises an image acquisition interface module, a preprocessing module, a quality evaluation engine module, an intelligent enhancement module, a post-processing module and the like. The technology can accurately evaluate the quality of the radiation image, effectively enhance the image, reduce noise and artifact interference and improve the diagnosis availability, and has important application value in the field of medical image processing.

The invention relates to the technical field of safety simulation training drilling, in particular to an intelligent assessment and evaluation method for mine accident emergency risk avoiding, which comprises the following steps of: S1, acquiring multi-dimensional data in an emergency risk avoiding capability drilling process of a student; s2, performing data preprocessing on the multi-dimensional data, and constructing an evaluation data set; s3, constructing an emergency risk avoiding capability evaluation system, wherein the system comprises evaluation items and distribution weights of the evaluation items; s4, establishing a weight evaluation model, and dynamically adjusting the distribution weight of the evaluation item in combination with the environmental data; and S5, scoring is completed according to the emergency risk avoiding capability evaluation system, and a personalized evaluation report is generated. According to the invention, comprehensive, objective and real-time evaluation of the emergency risk avoiding capability of the miner is realized, and the influence of human factors on the evaluation result can be reduced; the method can flexibly adapt to different environments and scenes through dynamic adjustment of the evaluation model, provides personalized evaluation reports and improvement suggestions, and effectively assists students in improving the emergency risk avoiding capability.

The invention relates to the technical field of ship lock slope monitoring, in particular to a ship lock slope displacement monitoring and early warning method and system based on hull operation influence. The method comprises the following steps: carrying out ship lock slope multi-source data real-time acquisition on ship lock slope area nodes to obtain real-time ship lock slope multi-source displacement factor data; ship lock slope displacement prediction processing is carried out based on the real-time ship lock slope multi-source displacement factor data, and ship lock slope displacement prediction data is generated; based on the ship lock slope displacement prediction data, time sequence space evolution trend analysis and time sequence space evolution trend analysis of ship lock slope displacement are carried out, and space-time evolution ship lock slope deformation data are generated; and performing ship lock slope displacement monitoring and early warning state evaluation processing based on the space-time evolution ship lock slope deformation data to generate ship lock slope displacement monitoring and early warning state evaluation data. According to the ship lock slope displacement monitoring system and method, accurate monitoring of ship lock slope displacement is achieved by analyzing multiple factors related to the ship lock slope.

The invention discloses a rehabilitation assisting method and device suitable for cognitive disorder treatment and a medium, and relates to medical rehabilitation. A tablet personal computer, a millimeter wave radar and the like are included, artificial intelligence technologies such as signal processing and deep learning are utilized, and rehabilitation training of patients with mild cognitive impairment caused by brain injury and cerebral apoplexy is assisted. Basic information, medical history and current cognitive conditions of the patient are recorded and collected through the tablet computer, and a rehabilitation file is generated; based on the cognitive disorder type and severity of the patient, a rehabilitation training plan is intelligently made; a patient performs rehabilitation training through a gamified training interface, and the physiological state in the training process is monitored in real time in a non-contact mode based on millimeter wave radar; a deep learning model is used for analyzing training data of the patient, and rehabilitation progress is evaluated; training and evaluation data are summarized, and a subsequent rehabilitation scheme is optimized. Full-process automatic management from patient information collection, training plan making, training implementation, rehabilitation evaluation to data analysis report generation can be rapidly and accurately achieved, and the cognitive rehabilitation efficiency and effect are improved.