1565 results about "Acquisition apparatus" patented technology

The invention relates to a motor imagery ability evaluation and training enhancement system and method based on electroencephalogram and a medium, and belongs to the technical field of brain-computer interfaces. The system comprises an electroencephalogram acquisition device, a processing terminal and a display device. By collecting and analyzing electroencephalogram signals of a subject, the system extracts time-domain, frequency-domain and space-domain features by using a multi-feature fusion technology, so that accurate quantitative evaluation of motor imagery ability is realized. The evaluation core index is a lateral index. A built-in self-adaptive training module dynamically adjusts training difficulty and comprises a basic mode, a middle-level mode and a high-level mode, and personalized efficient training is ensured. The method comprises pre-training guidance, data acquisition and processing in formal training, and adaptive training adjustment based on an evaluation result. By combining multi-feature fusion and an adaptive training mechanism, an efficient and personalized solution is provided for evaluation and enhancement of motor imagery ability, so that the rehabilitation training effect of a motor imagery brain-computer interface system is more effectively improved.

The invention relates to the technical field of audio and video emergency transmission, and discloses an audio and video low-delay return method and system in an extreme environment. The method comprises the following steps: acquiring original multi-modal data of audio and video acquisition equipment in a target area, and analyzing a data state of the original multi-modal data; and meanwhile, available network transmission links are monitored, and the quality is evaluated. Self-adaptive coding parameters are generated in combination with the link quality and the data state, dynamic coding is executed on original data, and a coding stream suitable for redundant transmission is formed. And carrying out cooperative distribution transmission based on the network link set and the coded stream to generate return data. According to the generation process, a device and energy management instruction is formed and issued to the acquisition and relay device. According to the method, the network state and the content characteristics are optimized in a dynamic coding link in a collaborative manner, and the audio and video quality, the real-time performance and the system energy efficiency which are transmitted back in an extreme environment are improved through a feedback closed loop for transmitting a result to equipment management.

The invention provides a method and device for detecting vehicle falling into water, a vehicle and a storage medium, the method is applied to the technical field of vehicle falling into water detection, the method comprises the steps that M falling into water detection parameters are obtained through M collection devices of different types in the vehicle, the falling into water detection parameters are used for representing the falling into water state of the vehicle, and M is a positive integer larger than 1; determining M basic probability distribution values corresponding to the M collection devices according to the M water falling detection parameters; according to the M basic probability distribution values, the falling-into-water confidence coefficient of the vehicle is determined; and when the falling-into-water confidence is greater than or equal to the preset confidence, determining that the vehicle is in a falling-into-water state. According to the method, whether the vehicle falls into water or not can be identified by fusing the falling-into-water detection parameters collected by multiple sensors, the reliability and robustness of vehicle falling-into-water detection can be effectively improved, and misjudgment caused by identification of single equipment is avoided.

The invention discloses a general electromyographic signal processing method and system based on a large self-supervised model. The general electromyographic signal processing method comprises the following steps: step 1, acquiring a multi-source original multi-electrode channel EMG signal X from an electromyographic acquisition device; and finally, performing data unification processing, and finally converting into a space-time activity diagram with a fixed size of 224 * 224. On the basis of the space-time activity diagram and the fatigue state mark, constructing an AEMG for training according to heterogeneous unlabeled EMG data collected by a collection device; performing light-weight Adapter layer fine adjustment on the pre-trained large myoelectricity model to adapt to gesture recognition muscle force regression gait analysis or rehabilitation evaluation downstream tasks; aiming at the problem that the dimension and the structure of myoelectricity data are not matched due to different acquisition devices, acquisition parts and acquisition tasks, original signals are converted into space-time activity diagrams in a unified format through data unification processing, device differences are represented by combining a sensor embedding module, effective alignment of cross-source data is achieved, and the accuracy of the data is improved. And a basis is provided for large-scale data utilization.

The invention discloses a switch equipment performance aging intelligent diagnosis method and device based on multi-parameter fusion, relates to the technical field of power equipment abnormal defect diagnosis, and solves the problem of low precision of aging judgment of switch equipment depending on a single temperature and humidity threshold value or a single gas parameter in the prior art. The method comprises the following steps: acquiring internal and external humiture and internal gas characteristic parameters of equipment; constructing multi-channel standardized monitoring data through time synchronization, filtering and de-noising and exception elimination processing; calculating an absolute humidity difference inside and outside the cabinet, constructing a humidity expectation model, and obtaining a standardized residual error; performing offset detection and trend smoothing processing on the standardized residual error to obtain a cumulant and a smoothing trend quantity, and fusing the cumulant and the smoothing trend quantity to calculate an offset index; outputting a moisture seepage risk score, and triggering early warning when the moisture seepage risk score and the characteristic gas concentration exceed a threshold value. According to the invention, real-time high-precision evaluation of the aging state of the equipment is realized, early hidden dangers are effectively identified, and the safety and reliability of a power system are improved.

The invention relates to an acquisition equipment parameter determination method and equipment for improving welding seam image quality, and the method comprises the steps: determining the current parameters of acquisition equipment, and obtaining a welding seam region image under the current parameter condition; high-brightness pixel points and low-brightness pixel points in the welding seam area image are extracted to determine the high-brightness pixel proportion, the low-brightness pixel proportion and the high-brightness pixel distribution divergence; according to the high-brightness pixel proportion, the low-brightness pixel proportion and the high-brightness pixel distribution divergence, whether the welding seam area image is qualified or not is judged; if yes, taking the current parameter as a determined parameter of the acquisition equipment; and if not, correcting the current parameter to obtain the updated current parameter, and returning to determine the current parameter until the determined parameter of the acquisition equipment is obtained. The problems that in the prior art, under the complex illumination working condition, proper collection equipment parameters cannot be determined to ensure clear presentation and stable extraction of a welding seam feature area, then the robustness and positioning precision of a welding seam tracking system are affected, and the technical adaptability is reduced are solved.

The invention relates to the technical field of computers, and discloses an agricultural and sideline food whole-process information tracing method and system, and the method comprises the steps: deploying sensing and collection equipment in all links of production, logistics, storage and sales, and generating a standardized event record with a time-space stamp and a digital signature; executing data cleaning, event modeling and map construction through a centralized platform to form a multi-dimensional tracing network with commodities as nodes and operation events as edges; and in response to user query, dynamically generating a visual tracing path according to time, geography, responsibility subject or environmental parameter dimensions. The system comprises a production end acquisition subsystem, a logistics end acquisition subsystem, a storage end acquisition subsystem, a sales end acquisition subsystem and a central data fusion platform, and a permission control and credibility scoring mechanism is arranged in the platform. According to the method, through event-driven atlas and multi-source data fusion, full-chain information reversible restoration, multi-condition filtering query and compliance automatic judgment are realized, and the authenticity, integrity and decision support capability of tracing are remarkably improved.

The invention discloses a standard system applicability evaluation method and system based on multi-source data, and relates to the field of standardization, and the method comprises the steps: collecting the historical operation data of equipment, and dividing the equipment into an experiment group equipment group and a control group equipment group; the method comprises the following steps: preprocessing historical operation data of equipment, and respectively constructing a performance index value sequence for quantitatively reflecting the achievement condition of a target standard for a technical target for an experimental group equipment group and a control group equipment group; and on the basis of the performance index numerical value sequences of the experimental group equipment group and the control group equipment group, a net effect value and statistical confidence of a target standard are calculated by adopting a double difference model, and a unit model and a geographical climate region are added into the double difference model as fixed effect control variables. According to the method, the corrected net effect value and the intermediary centrality score are fused into the comprehensive index, so that multi-dimensional dynamic evaluation combining data driving, causal identification and knowledge value of standard applicability is realized.

The invention relates to the technical field of power system state monitoring and operation and maintenance, and discloses a power distribution network equipment state intelligent sensing and predictive maintenance decision-making method, system, equipment and medium, and the method comprises the steps: collecting equipment operation data, monitoring data and environment data through a power distribution automation system, an online monitoring device, an environment sensor and other channels; performing time synchronization and quality verification on the multi-source data; extracting multi-dimensional characteristic parameters reflecting the running state of the equipment, and performing normalization processing and fusion analysis on the characteristics; establishing an equipment state evaluation model by using a deep learning algorithm, and carrying out quantitative scoring on the current health condition of the equipment; constructing a time sequence prediction model, and predicting the future degradation trend and residual life of the equipment; and establishing a multi-objective optimization model, and generating an optimal maintenance decision scheme. The whole process is from data acquisition, state perception and fault prediction to decision optimization, and intelligent management of power distribution network equipment is realized.

The invention discloses a fractal mask bilinear interpolation-based checkerboard angular point positioning method, which comprises the following steps of S1, acquiring a checkerboard image by using image acquisition equipment, and extracting candidate angular points in the checkerboard image; s2, determining a direction angle of the edge of the local area by using an edge detection algorithm and a polar coordinate transformation and clustering algorithm; s3, based on the direction angle of the edge of the local area, using a gray integral method and a least square method to preliminarily position the coordinates of candidate angular points; s4, in the neighborhood of the preliminarily positioned candidate angular point coordinates, obtaining a sub-pixel-level gray value by using fractal mask bilinear interpolation, and calculating local gradient distribution; s5, dynamically generating a weight mask based on a fractal theory, constructing a central point minimum error function according to a corner local gradient consistency principle, iteratively optimizing candidate corner coordinates, and outputting a sub-pixel level position; according to the method, the sub-pixel-level accurate positioning of the checkerboard angular points is realized by fusing the edge detection, the gray integration and the iterative optimization of the fractal theory.

The invention discloses a product production process scheduling method and system based on intelligent manufacturing, and belongs to the technical field of intelligent manufacturing. According to the method, rapid local rescheduling is realized through fault influence domain identification, a three-level response strategy and real-time equipment health assessment. When an equipment fault signal is detected, a to-be-executed task on the fault equipment is identified, a subsequent task chain is traced through the technological process dependency relationship, the boundary of a fault influence domain is determined, and the influence domain relates to 10%-20% of tasks in the whole production plan. And selecting a corresponding response strategy according to the predicted repair time, adopting a task delay strategy when the predicted repair time is less than 30 minutes, performing local redistribution on tasks in the influence domain when the predicted repair time is less than 30 minutes to 2 hours, and expanding the influence domain to the whole process for comprehensive optimization when the predicted repair time is more than 2 hours. Meanwhile, monitoring data such as equipment vibration, temperature, current and rotating speed are collected to calculate a health degree score, and 5%-10% of productivity redundancy is reserved for high-risk equipment.

The invention discloses a vibration monitoring and displacement identification method for a towering steel structure based on machine vision, and relates to the technical field of towering steel structures. Comprising the following steps: S1, visual acquisition and reference calibration: arranging visual acquisition equipment in the height direction and the circumferential direction of the towering steel structure according to mechanical characteristics of a vertical stiffness mutation region and a dynamic response sensitive part of a geometric asymmetric distribution characteristic of the structure; a three-dimensional coordinate system is established based on a structure installation reference surface, fixed reference monitoring points are arranged in a tower foot symmetric area and a mast antenna root, three-dimensional coordinate data of the reference points are synchronously collected, the collection range completely covers a node concentration area and a key stress part of a structure transparent area, and a continuous image sequence of the structure surface is captured in real time; the resolution and the sampling frequency of the visual acquisition equipment are determined according to the inherent frequency of the structure and the vibration response characteristics, and calibration is completed through reference point coordinates after the equipment is installed; s2, carrying out image preprocessing and feature enhancement; S3, carrying out feature point extraction and trajectory tracking; S4, carrying out vibration displacement calculation and twin fusion; and S5, carrying out multi-source verification and structural state linkage treatment.

The invention discloses a DIKWP-driven individualized brain-map interactive feedback system, which is used for neural rehabilitation and brain-computer interface training. The system obtains brain activity data of a patient through electroencephalogram acquisition equipment, constructs a personal brain-semantic map in combination with cognitive evaluation, and establishes a mapping relation between semantic units and brain region responses. In the training process, the DIKWP semantic analysis module performs multi-layer analysis on indexes such as reaction time, accuracy and intention achievement, generates multi-mode instant feedback such as visual sense, auditory sense or tactile sense, and performs directional reinforcement on a weak semantic domain. The system has a dynamic target optimization capability, and the difficulty can be automatically adjusted according to training performance; when attention distraction, emotion abnormity or semantic deviation is detected, a safety intervention mechanism is automatically triggered, and training effectiveness and safety are guaranteed. According to the method, closed-loop individualized rehabilitation interaction is realized, the adaptation degree and efficiency of brain-computer interface training are remarkably improved, and the method is suitable for various rehabilitation scenes such as languages, movement and cognition and has a good industrial application prospect.

The invention discloses an equipment health state operation and maintenance method based on multi-channel perception, and relates to the technical field of equipment health monitoring, and the method comprises the steps: collecting the perception data of equipment in a steady-state operation stage and a dynamic working condition transition stage; according to the method, time domain segmentation marking is carried out on sensing data of a dynamic working condition transition section, transient response parameters and parameter change rate characteristics of all segments are extracted, a dynamic working condition characteristic library is established, early degradation signs are found by identifying the consistent trend of parameter slow change characteristics and interval shrinkage, and self-adaptive operation and maintenance of the equipment health state are achieved.

The invention provides a high-precision live-action three-dimensional reconstruction method for building heritage, and relates to the technical field of data processing, and the method comprises the steps: obtaining image data, and dividing a to-be-reconstructed region into a plurality of sub-regions according to the overall structure of the building heritage; carrying out point cloud acquisition on each sub-region by utilizing acquisition equipment, adopting a first sampling interval in a conventional region, and dynamically shortening to a second sampling interval in an engraved concave or narrow space region; performing coarse registration processing, and executing iterative correction by using the feature corresponding points of the overlapping region to generate a first sub-region point cloud model; quality detection is carried out, and a missing area with the point cloud density not meeting the density judgment standard is determined; extracting local edge contour and gray gradient information in combination with the image data, and projecting the local edge contour and the gray gradient information to the missing area to generate a compensation point; carrying out weighted fusion processing, and executing curved surface interpolation in a fusion region; performing boundary feature alignment and global splicing to obtain a three-dimensional reconstruction model; according to the invention, the accuracy of three-dimensional reconstruction is improved.

The invention relates to a power grid equipment dispatching control method and device based on artificial intelligence, equipment and a medium, and the method comprises the steps: collecting equipment operation data through a multi-mode sensing network, and generating a noise reduction data set through wavelet noise reduction processing; fusing electrical, thermodynamic and mechanical characteristics to calculate a real-time aging factor; utilizing a matrix transformation function to dynamically correct equipment factory parameters to generate current operation parameters; an optimal power distribution path is constructed through graph neural network topology analysis, and a control instruction set is generated; and updating the mapping function by combining the historical aging factor sequence based on the deviation quantized value of the execution feedback result and the theoretical parameter. According to the method, the problems of parameter drift and scheduling model mismatch caused by equipment aging are solved, real-time sensing of the aging state, dynamic parameter correction and closed-loop optimization are achieved, and the accuracy and safety of power grid scheduling are remarkably improved.

The invention discloses an underwater target volume measurement method, device and equipment based on three-dimensional reconstruction and a medium, and belongs to the technical field of photogrammetry, underwater image processing and point cloud volume calculation cross, and the method comprises the steps: carrying out the video data collection of an underwater target through video collection equipment, and obtaining an original image with image format data; the underwater image enhancement method model based on depth estimation performs image enhancement on the original image to obtain an underwater enhanced image; reconstructing a real scene containing a target based on the underwater enhanced image; and based on the three-dimensional reconstruction point cloud, segmenting to obtain the to-be-measured target, and obtaining the volume parameter of the underwater target. According to the method, a three-dimensional reconstruction technology is utilized, a real target structure is restored, the measurement accuracy of the underwater target defect volume is improved, a reference standard is provided for later repair, the maintenance cost is reduced, and the operation safety of a hydraulic structure is guaranteed.

The invention belongs to the technical field of machine state prediction, and particularly relates to a fault diagnosis method and system based on a coding and decoding attention mechanism enhanced pulse neural network, and the method comprises the steps: collecting a device vibration signal, and segmenting the device vibration signal into time sequence segments; converting each fragment into a two-dimensional time-frequency image by using S transformation to construct a data set; the image is input into a lightweight hybrid network model to extract deep features, the model is composed of a pulse convolution encoder and a pulse efficient additive attention mechanism encoder which are alternately cascaded, and the pulse convolution encoder extracts local features through pulse neurons and depth separable convolution; the global context dependency is modeled by adopting an efficient additive attention mechanism with low linear complexity; and finally, fault classification is completed based on deep features. Through fusion of pulse calculation and an efficient attention mechanism, the feature extraction capability and diagnosis precision are improved while the parameter quantity of the model is remarkably reduced, and the method is particularly suitable for being deployed on edge equipment with limited resources to realize fault diagnosis.

The invention relates to an energy efficiency model establishing and dynamic updating method based on mechanism and data driving fusion. The method comprises the following steps: establishing an equipment mechanism energy efficiency model; collecting real-time operation data of the equipment and preprocessing the real-time operation data; extracting actual measurement energy efficiency of the equipment from the preprocessed real-time operation data, and obtaining mechanism prediction energy efficiency based on an equipment mechanism energy efficiency model; calculating a difference value between the actual measurement energy efficiency and the mechanism prediction energy efficiency as an energy efficiency deviation; extracting and screening performance degradation characteristics from the historical operation data; establishing a nonlinear mapping relation between the energy efficiency deviation and the screened performance degradation characteristics by adopting a machine learning algorithm, namely an equipment data driving correction model; and fusing the initial mechanism energy efficiency model and the data driving model to obtain an equipment energy efficiency fusion model, and training and optimizing the equipment energy efficiency fusion model. According to the method, self-adaption and automatic establishment and updating of the energy efficiency model can be achieved, and it is ensured that the energy efficiency model keeps high precision and high robustness in the whole life cycle.

The invention relates to the technical field of industrial equipment health assessment, in particular to an equipment full-life-cycle health assessment method and system driven by multi-source heterogeneous data. The method comprises the following steps: collecting multi-source heterogeneous data of equipment, and constructing a life cycle health state time sequence diagram; constructing an equipment standard health path based on the standard data of the equipment; constructing a historical health path of the equipment based on the multi-source heterogeneous data; constructing an equipment forward prediction health path based on the historical health path and the multi-source heterogeneous data of the current life cycle stage; defining a virtual decommissioning final-state node of the equipment, and constructing a reverse causal inverse deduction health path of the equipment; using a four-path health deviation mode evaluation mechanism to analyze deviation mode distribution and health evaluation results of the equipment; the invention realizes a method for comprehensively evaluating the health state of the equipment in each stage of the full life cycle and the influence of the health state on the retirement result.

The invention provides three-dimensional vehicle-mounted navigation equipment and a navigation method based on the equipment. The method comprises the following steps: carrying out equipment initialization and reference calibration, arranging communication equipment on a vehicle roof, arranging acquisition equipment on a vehicle head grid, and arranging a calibration and calculation module in a vehicle cabin; synchronously acquiring multi-source scene and body state data by using hardware; identifying an operation scene through a CNN-LSTM hybrid model, and dynamically allocating a sensor fusion weight based on a reinforcement learning algorithm; a BP neural network is utilized to construct nonlinear mapping of the vibration frequency and the positioning error, and a dynamic error value is calculated; the dynamic error is substituted into extended Kalman filtering for real-time correction, and vibration influence is eliminated; the method comprises the following steps: pre-judging a GNSS signal trend by using an LSTM model, and initializing a backup module in advance to realize multi-mode seamless switching; dynamically adjusting map updating frequency according to environment change and compressing data by using an octree; and outputting a three-dimensional navigation instruction on the display screen. The problems of high-precision positioning and smooth switching of the engineering vehicle under strong vibration and complex scenes are effectively solved.

The invention discloses an insulin pump safety reminding system based on remote communication, and the system comprises an insulin pump terminal which is used for collecting equipment operation parameters, and receiving and executing a verified operation instruction; the patient monitoring terminal is bridged with a data transfer interface, a local data encryption cache interface, a graded early warning display interface and a remote man-machine interaction interface of the insulin pump terminal and the hospital comprehensive management platform; the graded authorization user terminal receives early warning information related to the insulin pump terminal, checks the state of a patient and initiates a remote operation request; the hospital comprehensive management platform is in communication connection with each terminal, manages each terminal, performs data aggregation and intelligent analysis, generates early warning information based on a multi-dimensional rule, and performs authority management and auditing and multi-source data fusion application; and the safety communication and control module is used for guaranteeing data transmission safety, operation instruction compliance and system emergency fault-tolerant operation. According to the invention, cross-time-space safety protection and active early warning can be realized, the treatment safety and management efficiency are improved, and the method is suitable for medical and pension institutions at all levels.

The invention belongs to the technical field of three-dimensional modeling, and particularly discloses a pipeline three-dimensional modeling method based on adaptive statistical filtering and ground constraint poses, and the method comprises the steps: controlling a collection device to move along a target pipeline, and collecting the original point cloud data of the target pipeline and the movement distance information of the collection device; on the basis of the moving distance information, continuous pose changes of the collection equipment in the target pipeline are calculated, and a ground constraint pose sequence is generated; taking a matching relationship between the original point cloud data as a constraint between pose nodes of the ground constraint pose sequence, and obtaining an optimized ground constraint pose sequence; on the basis of the optimized ground constraint pose sequence, registering the original point cloud data to obtain a pipeline point cloud model; filtering the pipeline point cloud model; and generating a three-dimensional model of the target pipeline based on the filtered pipeline point cloud model. The method can improve the precision of the generated three-dimensional model.

The invention discloses a multi-device collaborative integrated kitchen management system, and relates to the technical field of intelligent kitchen control, and the system comprises the steps: analyzing an order or menu request into a task graph, building dependence, and adding a capability demand, resource mutual exclusion, a quality window, a risk level and migration window, and a check point strategy label for a task node; the edge arrangement gateway collects the equipment state, the resource occupation and the network health degree, and reschedules and generates a main plan and a shadow plan under the constraint of a rolling window and a freezing window; a main plan is converted into an intention type transaction to be issued, idempotent processing is carried out according to a transaction identifier, feeding, discharging and valve opening are carried out, token reservation-consumption is carried out, and account checking and verification are carried out; generating a process check point according to a check point strategy, and executing thermal migration, compensation or security degradation and account checking synchronization according to a migration window and a shadow plan when the process check point is abnormal; repeated actions and state drifting caused by re-delivery are reduced, and the stability of continuous meal delivery is guaranteed.

The invention discloses an engineering construction digital intelligent supervision management and control system, and relates to the technical field of engineering construction supervision. The system comprises an information acquisition module which is used for acquiring equipment state, image and environment data; the data fusion layer module is used for carrying out cleaning, alignment and Kalman filtering fusion on the multi-source data to generate a multi-modal construction data flow; a progress calculation and prediction unit, a deviation judgment unit and a digital modeling unit are arranged in the analysis monitoring module, and the analysis monitoring module is used for updating a progress state based on an XGBoost model, analyzing a node dependency relationship by using a graph neural network, calculating a deviation propagation risk coefficient and generating a hierarchical deviation signal in combination with three-level judgment conditions; and the management and control layer module executes hierarchical response according to the level of the deviation signal, and feeds back execution effect data to the front-end module to realize model optimization and closed-loop iteration. According to the invention, intelligent perception, risk prediction and adaptive management and control of the construction process are realized, and the real-time performance, accuracy and autonomy of project supervision are improved.

The invention discloses a vehicle body gluing method and device based on three-dimensional visual guidance, and the method comprises the steps: obtaining a to-be-glued image of a to-be-glued region of a vehicle body through a three-dimensional visual image collection device in response to the arrival of the to-be-glued vehicle body at a gluing station, carrying out the image recognition of the to-be-glued image, obtaining the first coordinate information of a positioning hole, and obtaining the first coordinate information of the positioning hole; based on the first coordinate information, second coordinate information is generated, and according to the preset positioning hole coordinate information and the second coordinate information, the first vehicle body deviation of the to-be-glued vehicle body is calculated; and performing deviation correction on the preset gluing track according to the first vehicle body deviation to obtain a target gluing track. And the gluing robot is used for gluing the to-be-glued vehicle body based on the target gluing track. According to the method, the to-be-glued image is collected through the three-dimensional visual image collecting device, the vehicle body deviation is obtained based on the actual positioning hole coordinate information and the preset positioning hole coordinate information, then the gluing track is corrected, and the accuracy of the gluing track is improved.

The invention is suitable for the field of sensor data analysis, and provides a sensor data analysis method and system based on artificial intelligence, and the method comprises the steps: collecting multi-dimensional historical sensor data of normal operation of equipment, and obtaining a standardized historical feature matrix through preprocessing and feature extraction; constructing an equipment health reference model based on the matrix, and establishing multi-dimensional feature dynamic association; collecting equipment data in real time, and extracting features according to the same process to obtain real-time feature vectors; combining the reference model with a known fault case library, constructing a fault evolution prediction model through adversarial training, and inputting real-time vectors to generate a future multivariable sensor data prediction sequence; and calculating a fault deterioration acceleration and a predicted time for reaching a safety threshold based on the prediction sequence, and comparing to generate a risk level signal and a maintenance strategy suggestion. The whole-process intelligent monitoring analysis of the equipment is realized, and the operation and maintenance accuracy and foresight are improved.

The invention relates to the technical field of fault diagnosis models, and discloses an adaptive AI fault diagnosis model fusing reinforcement learning and an industrial equipment predictive maintenance system. Comprising an industrial equipment data acquisition and preprocessing module, a reinforcement learning-fused adaptive fault diagnosis model module, an equipment health degree evaluation and residual life prediction module, a predictive maintenance decision and execution module and a system management and integration module, and the output end of the industrial equipment data acquisition and preprocessing module is electrically connected with the input end of a self-adaptive fault diagnosis model module fusing reinforcement learning. According to the system, data during equipment operation are effectively collected, the equipment health degree evaluation module is matched to convert the feature deviation degree into 0-100 visual scores, the health trend visualization unit displays changes through a broken line diagram and a thermodynamic diagram, fault risk points are marked, historical backtracking and multi-equipment comparison are supported, non-professional personnel can also rapidly judge the equipment state, and the equipment health degree evaluation module is matched to convert the feature deviation degree into 0-100 visual scores. And the equipment management efficiency is improved by 30%.

The invention relates to the technical field of industrial equipment automatic detection and fault diagnosis, in particular to a fault detection method and equipment for driving equipment and a medium. And the mobile robot and the cloud server perform cooperative execution. The method comprises the following steps: generating a global detection path based on mobile robot electric quantity, communication load and detection precision mapping; the mobile robot sequentially moves to each detection point; identifying the identification code and uploading the identification code to a cloud; the cloud dynamically generates a detection instruction set based on equipment historical data and issues the detection instruction set to the mobile robot; the mobile robot executes the instruction to acquire equipment state data and uploads the equipment state data; and the cloud end compares the data with a normal threshold value, and instructs the mobile robot to detect again or continue the task according to a result. According to the invention, through combination of intelligent path planning and cloud dynamic decision, high efficiency, accuracy and adaptive capability of driving equipment fault detection in a complex environment are realized.

The invention discloses an intelligent safety monitoring and early warning system and method for climbing operation, and the system comprises a sensing and data collection module which is used for obtaining the operation data of a climbing operator in real time through collection equipment, and transmitting the operation data to a data processing and analysis module; the data processing and analysis module is used for receiving the operation data, performing feature extraction on the data through a deep learning network to obtain key features, performing partition analysis on the climbing operation personnel in the operation area based on the key features, obtaining an analysis result and sending the analysis result to the early warning and feedback module; and the early warning and feedback module is used for receiving the analysis result, generating illegal operation early warning information, generating an early warning report according to the illegal operation early warning information and sending the early warning report to related personnel. According to the method, illegal behaviors and safety risks are recognized in time, early warning is performed in a polymorphic mode, and closed-loop management from operation task acquisition, process dynamic monitoring to post data tracing is formed through deep docking with a power plant two-ticket system, so that the accident response time is shortened.