24014 results about "Fully automated" patented technology

The invention discloses an unmanned aerial vehicle route automatic planning method for electric power inspection, and relates to the technical field of unmanned aerial vehicle inspection. Comprising the following steps: starting an unmanned aerial vehicle, carrying out environment perception initialization, calculating the total cruise mileage, evaluating the interference risk, carrying out real-time obstacle avoidance, dynamically optimizing an inspection route, carrying out energy monitoring management, generating a return flight strategy, recording an inspection task and carrying out adaptive learning. Through dynamic electromagnetic interference modeling, multi-modal fusion perception, self-adaptive risk decision and cloud collaborative learning, the problem of insufficient adaptability of a traditional electric power inspection unmanned aerial vehicle in a complex electromagnetic environment and a dynamic obstacle scene is solved, the safety and the inspection efficiency are improved, the robustness is enhanced, and the method is suitable for popularization and application. Intelligent upgrading is carried out through continuous learning and multi-machine cooperation, the overall operation and maintenance cost of the system is reduced, a high-reliability and full-automatic inspection solution is provided for intelligent power grid construction, and the industrial application value is remarkable.

The invention belongs to the technical field of information, and relates to a multi-level dynamic intelligent automatic data processing and information evaluation method and system. The method comprises the steps that a database environment operation data set is constructed, targeted training and fine adjustment are conducted on a large language model in combination with reinforcement learning, and full-automatic data analysis, query and information extraction of multiple tables in a database are achieved; a multi-level agent interaction and cooperation framework is established, a scheduling agent is responsible for global decision making and strategy planning, an operation agent is responsible for execution of specific tasks, and the scheduling agent and the operation agent share information and coordinate the tasks to form a highly cooperative whole; and establishing a cyclic supervision and error correction mechanism, scheduling the intelligent agent to learn a normative workflow example of the target working environment and track the state of the operation intelligent agent in real time, identifying the behavior of the operation intelligent agent deviating from a predetermined path, and taking corrective measures. According to the method, high efficiency, intelligence and automation of data processing are realized, and the method has remarkable practical value and innovative significance.

Embodiments of the present disclosure provide a report generation method and system, an electronic device, and a storage medium. The report generation method comprises: acquiring requirement text information of a report to be generated in a target application scenario; performing requirement intent recognition on the requirement text information, to acquire a report parameter matching the report to be generated; on the basis of the target application scenario, selecting and arranging a preset intelligent agent, to obtain an intelligent agent call chain; on the basis of the report parameter, performing chain-type calling on the preset intelligent agent in the intelligent agent call chain, to generate a report matching the requirement text information. The present method can automatically and quickly generate a report matching a user requirement, effectively increasing report generation efficiency. Using the present method to generate a report does not require manual data acquisition and analysis; efficiency is higher, and costs are lower.

The invention relates to the technical field of electric vehicles, and discloses a full-automatic AGV charging collaborative optimization system which comprises an energy consumption prediction module, a consensus mechanism design module and an AGV scheduling optimization module. The energy consumption prediction module is used for acquiring a pose topological feature vector and a potential margin attenuation gradient, constructing a multi-dimensional input vector, and generating a dynamic energy consumption prediction value based on an energy consumption track residual model of multi-physics field coupling; the consensus mechanism design module is used for constructing and verifying a dynamic consensus factor and providing a priority decision basis; the AGV scheduling optimization module is used for discretizing an operation space of a battery swap station, generating a space-time folding unit, constructing a space-time conflict resolution atlas, and generating an optimal battery swap sequence and a collision avoidance path; through multi-module cooperation, efficient cooperation of multi-AGV charging and battery swap is realized, and congestion of the battery swap station and resource waste are avoided.

The invention relates to the technical field of semiconductors, and discloses a full-temperature-zone polynomial digital temperature compensation method and system based on an SOI chip, and the method comprises the steps: obtaining the temperature frequency data of a preset temperature zone of a target SOI chip, fitting a full-temperature-zone curve, extracting a critical point of the temperature zone, dividing a segmented compensation interval according to the critical point, and obtaining the temperature frequency data of the preset temperature zone of the target SOI chip; the method comprises the following steps: calculating frequency drift amounts under different voltages to construct a full-automatic test framework, acquiring an environment temperature parameter and an instantaneous temperature value by using the framework, analyzing a temperature compensation vector of a segmented compensation interval, further monitoring a signal frequency interval output by a chip, extracting a phase noise parameter, calculating a frequency offset error amount with a preset value, and calculating the frequency offset error amount. And analyzing an error source, optimizing a polynomial compensation order, and collecting temperature drift suppression data to generate a full temperature region compensation report. According to the invention, the performance stability of the chip in a complex environment can be improved.

The invention discloses a data quality intelligent auditing system and method based on a dynamic rule base, and belongs to the technical field of data auditing, and the system comprises a rule base construction module which is used for analyzing business scene parameters through a scene analysis unit according to business scene demands and data type features to generate a rule configuration instruction; the multi-source monitoring engine module is connected to the rule base construction module and is used for collecting multi-source data in real time and loading corresponding checking rules; the automatic verification execution module is used for executing normalized quality verification on the multi-source data based on the verification rule base; and the feedback optimization module analyzes a rule hit rate and a false alarm rate in a verification result through a reinforcement learning algorithm, and dynamically iteratively updates a rule threshold value and a logic combination in the rule base. By constructing a full-automatic process of rule generation, execution, feedback and updating, the problems that a traditional system depends on manual intervention, response is slow, the industry average rule updating period is 3-7 days, and real-time updating is achieved through the scheme are solved.

The invention provides a complex task full-automatic processing method based on multi-agent cooperation and a related device, and relates to the technical field of artificial intelligence such as large language models, generative models, agents and task intelligent scheduling. The method comprises the following steps: interacting with a target user who puts forward an original task demand to obtain a complete task demand; the complete task demand is split into a plurality of sub-tasks at least comprising a complex sub-task, the complex sub-task refers to a sub-task needing at least two sub-agents to process according to a cooperation process, and a single sub-agent is used for processing a simple sub-task; issuing the complex sub-tasks to corresponding target execution intelligent units, and issuing the simple sub-tasks to corresponding target sub-agents; and summarizing sub-task execution results returned by each target execution intelligent unit and each target sub-agent. According to the method, the execution intelligent unit specially used for processing the complex sub-task is introduced, and the complex sub-task is processed more intensively according to the cooperation process through the at least two sub-agents contained in the execution intelligent unit, so that the task disassembling difficulty is reduced; and a better sub-task processing result can be obtained through the execution intelligent unit integrated by the multiple sub-agents.

The invention relates to a full-automatic injection molding optimization method, device and equipment based on digital twinning, and belongs to the technical field of intelligent manufacturing equipment. Inputting the sensor data into a digital twin model to construct equipment dynamic response simulation, and synchronously comparing the actual mode locking force with the offset of a preset pressure threshold; generating compensation data of mold locking pressure according to the offset, and dynamically correcting an acceleration curve of a servo motor in a glue injection stage on the basis of the displacement of a screw rod; in hot runner control, a thermal interference compensation coefficient is calculated according to a temperature difference value of adjacent temperature zones, and an output power gradient signal of each heating ring is adjusted; the corrected parameter information is transmitted to an equipment PLC through an industrial bus to execute control, and a compensation coefficient threshold value is iteratively updated according to a product weight fluctuation value in the next forming period; closed-loop data interaction of an equipment control system is achieved, and the injection molding process is optimized.

The invention provides a multi-band radio frequency power amplifier dynamic range adaptive test method and a system thereof. The method comprises the following steps: acquiring an output signal frequency spectrum of a tested radio frequency power amplifier, extracting characteristic frequency points including a center frequency and a bandwidth, matching the characteristic frequency points with a preset frequency band database, and automatically loading a test scheme of a corresponding frequency band. According to the method, the frequency bands are automatically matched through spectrum sensing, the test scheme is loaded, the multi-frequency-band characteristics can be rapidly adapted without manual parameter configuration, index missing test is avoided, and the switching efficiency is improved; dynamically optimizing test parameters by using a genetic algorithm, and accurately capturing a nonlinear distortion critical point while balancing coverage and efficiency; by combining multi-dimensional signal analysis and a deep learning model, the problem of missing detection of traditional single-domain analysis is solved, and the abnormal signal recognition capability is remarkably enhanced; through linear model correction, impedance dynamic matching and index threshold iteration, full automation and high precision of the test are realized, the environmental interference influence is effectively reduced, and a self-adaptive test system is constructed.

According to the capacity expansion and contraction method and system, the electronic equipment and the storage medium, the resource demand mode of the target cloud service is predicted by using the prediction model, full-automatic prediction of the resource demand is realized, manual participation is not needed, and the prediction model is trained by a large amount of data, so that the prediction efficiency is improved. Compared with manual work, accurate resource demand prediction data can be output, then automatic capacity expansion or capacity expansion is carried out on the target cloud service according to the predicted capacity expansion or capacity reduction strategy, full-automatic execution of capacity expansion and capacity reduction is achieved, the capacity expansion server and the capacity reduction server are determined based on the server resource occupation amount in the capacity expansion or capacity reduction strategy, and the capacity expansion or capacity reduction efficiency is improved. The full utilization of the resource data in the server is realized, and the service operation cost is reduced to a certain extent.

The invention provides a laboratory equipment access and configuration method and system based on dynamic protocol analysis, and the method comprises the steps: firstly, monitoring a physical connection signal of a port, automatically triggering communication link configuration, calling a preset protocol parameter based on an interface type, and obtaining first identification information of equipment; then, based on a preset protocol mapping table or a dynamic matching rule base, determining a protocol type, and based on the protocol type, performing segmented analysis on the original data stream, identifying an effective frame and checking integrity to obtain a field definition, an instruction template and a communication parameter for binding storage with the unique identifier of the equipment; and finally, automatically generating an instruction according to the communication configuration, performing communication interaction with laboratory equipment, and triggering retransmission or link recovery operation when the communication state is abnormal by monitoring the communication state in real time. In addition, the communication condition of the laboratory equipment is displayed in real time through the interaction platform. According to the method, the deployment cost is reduced through full-automatic identification configuration, and the communication stability is improved through communication link optimization and exception handling.

Disclosed in the present invention is a fully automated log analysis and fault handling system and method based on an NLP large model. The method comprises: collecting a service state, an operating environment, and log files; extracting and generating log summaries from the log files, extracting key information from the log summaries to obtain state information of a service process; if an error is fed back, the NLP large model performing fault analysis on the error by using the service state and the operating environment as prior knowledge, and recording upstream and downstream key information involved in the error; on the basis of the upstream and downstream key information involved in the error, finding a fault cause and providing a fault handling action; and then executing a plan on the basis of the fault handling action. Log collection of the present invention is not limited to a prescribed format, and an NLP large model using a service state and an operating environment as prior knowledge is introduced for fault analysis.

The invention provides a space projection error analysis-combined 4D millimeter wave radar and visual camera automatic calibration method. The method comprises the steps of constructing a sensor internal reference model; designing a composite multifunctional calibration board and a three-dimensional adjustable holder, performing rigid geometrical relationship modeling, and automatically associating physics-features-coordinates; collecting multi-frame synchronization frame pair data, and performing time sequence fine grit alignment and interpolation compensation to obtain a space-time alignment frame pair sequence; extracting cross-modal spatial constraints of the features and automatically screening and matching point pairs to obtain a high-confidence point pair set; performing point-point constraint and point-point error, point-plane error and pixel reprojection error minimization to obtain a standardized error term; and constructing an overall objective function, and performing spatial constraint and time deviation estimation to obtain corrected external parameters and synchronization deviation. According to the invention, through a full-automatic standardized process, the probability of artificial participation and error occurrence is greatly reduced; and multi-modal and multi-scene high-precision calibration is supported, and the flexibility and robustness are greatly improved.

The invention discloses an automatic parameter adjustment test method for an electromagnetic angular displacement sensor, and the method comprises the steps: injecting a standard angle signal into a to-be-tested sensor, and collecting an output response signal; calculating a deviation value of the sensor parameter based on the output response signal and the standard angle signal, and judging whether the deviation value exceeds a preset threshold value or not; if yes, based on the deviation value, a control instruction used for compensation is generated through a self-adaptive control algorithm, and the mechanical zero position of the to-be-detected sensor or the relative position of an internal electromechanical conversion element is adjusted; based on the adjusted output response signal, the above steps are repeatedly executed, and sensor parameters are iteratively updated; and when the deviation value is lower than a preset threshold value, judging that the output precision meets a preset requirement, storing the current parameter, and completing the parameter adjustment process of the to-be-detected sensor. By constructing a closed-loop control flow of'detection-analysis-adjustment-verification 'and an adaptive control algorithm, full-automatic, high-precision and high-efficiency calibration of the zero position, sensitivity and excitation signal parameters of the electromagnetic angular displacement sensor is realized.

The invention relates to the technical field of intelligent control, in particular to a full-automatic plug-in testing machine control system and method, and the system comprises a multi-dimensional data collection module, an abnormal state recognition module, an action termination judgment module, a parameter correction module and a trajectory optimization execution module. According to the invention, based on multi-sensor array fusion, multi-physical quantity cooperative acquisition in equipment operation is realized, vibration, temperature, path and plugging force data are comprehensively calibrated by using a time axis, and through spectral analysis and three-dimensional deviation comparison, fine abnormity and trend change are dynamically revealed, and operation fluctuation and potential abnormity are identified in real time. According to the method, the reasonable interval of the insertion and extraction force is automatically calculated, the action duration and path trend are adjusted, the operation parameters are continuously corrected, equipment abnormity early warning, accurate intervention and multi-parameter linkage adjustment are achieved in the whole process, the test continuity, the execution sensitivity and the prevention and control capacity are improved, misoperation is effectively restrained, abrasion is reduced, and the high-density intelligent test requirement is met.

The invention discloses an AI low-code development component automatic generation method based on a knowledge graph, and relates to the technical field of AI low-code development, and the method comprises the following specific steps: constructing an AI development knowledge graph: constructing a knowledge graph containing a five-layer structure, collecting and preprocessing multi-source data, and extracting various entities and association relationships, so as to obtain an AI development knowledge graph; constructing an ontology structure, importing the ontology structure into a database, and forming a final knowledge graph through manual auditing and machine reasoning optimization; according to the method, the multi-dimensional AI development knowledge graph is constructed, user requirements are analyzed in combination with the optimized natural language processing model, the component configuration scheme is generated by introducing the multi-level reasoning mechanism, and finally the standardized low-code component is automatically generated, so that full-automatic conversion from the user requirements to the low-code component is realized, and the user experience is improved. The defect that an existing low-code platform depends on manual component configuration is overcome, the development period is greatly shortened, and the development cost and the technical threshold are reduced.

The invention provides a full-automatic stacking method and system.The method is completed on the basis of a stacking robot, physical parameter information of goods to be stacked is obtained, a single-layer stacking reference scheme is generated on the basis of the physical parameter information and stacking environment parameters, and a final stacking scheme is generated on the basis of the single-layer stacking reference scheme; based on the area where the goods are located and the target stacking area, an optimal operation path is planned for the stacking robot, and automatic stacking operation is executed according to the final stacking scheme; the optimal stacking scheme can be automatically generated according to the physical characteristics of different cargos, and the method is suitable for stacking operation of cargos of various specifications and has wide application prospects.

The invention discloses an intention understanding tool calling method based on a large model function call technology, and the method comprises the following steps: S1, obtaining a natural language instruction of a user, and carrying out the preprocessing of the natural language instruction; s2, based on the structured instruction fragment and the context dialogue information, constructing a semantic vector for intention recognition, and inputting the semantic vector into a large language model; s3, screening function description information from a function definition library according to an intention recognition result; s4, extracting parameter content from the semantic vector under the constraint of a parameter limiting condition; s5, constructing a call request format conforming to a function call interface specification, and executing a corresponding tool call task; and S6, receiving a function calling result, generating a natural language response text, and outputting the natural language response text to a user interface to complete an instruction response closed loop. According to the method, full-automatic semantic mapping from a natural language instruction to tool function calling is realized, and the analysis accuracy and the calling success rate are improved.

The invention provides a medicine dispensing machine which comprises a frame, and a medicine bottle box for storing ampoule bottles and penicillin bottles, a liquid bag box for storing liquid bags and a suction and injection device hanging frame for conveying suction and injection devices are arranged in the frame. An ampoule bottle opening device for opening an ampoule bottle, a penicillin bottle cover opening device for opening a bottle cover of the penicillin bottle, a penicillin bottle liquid medicine mixing device for mixing and uniformly shaking liquid medicine injected into the ampoule bottle, and a marking device for marking a liquid bag injected with the mixed liquid medicine are arranged in the frame; the frame is internally provided with a sucking and injecting manipulator which is used for clamping a sucking and injecting device and sucking and injecting liquid medicine of an ampoule bottle into a penicillin bottle, all the devices are transferred through a transferring manipulator, full-automatic medicine dispensing is carried out, and the medicine dispensing efficiency is improved.

The invention belongs to the technical field of physical and chemical analysis, and particularly discloses a high-precision full-automatic titration system and method based on fusion of a spectrum sensor and a PLC, a multi-channel spectrum sensor is adopted to capture fine spectrum characteristics of solution color change in real time, and the flow rate of titration liquid is dynamically adjusted through a PLC embedded PID control algorithm, so that the titration accuracy is improved. The method realizes submilliliter-level precision control, constructs a real-time compensation mechanism in combination with temperature, humidity, pH and other environmental parameters, improves the accuracy and robustness of titration end point judgment, automatically updates an end point judgment threshold and control parameters in combination with an AI self-learning module, realizes self-adaptive adjustment and sustainable evolution of the system, and improves the accuracy and robustness of titration end point judgment. The device has the characteristics of modular structure, high detection precision, high control response and high operation stability.

The invention belongs to the technical field of automatic assembly, and discloses an automatic assembly system and method for a large nut of a water turbine ball valve, and the system comprises a mechanical arm, an end effector and an intelligent nut tightening control system. The end effector integrates an electromagnet adsorption mechanism, a servo drive rotating mechanism, a floating compensation mechanism and a visual recognition mechanism, nuts of different specifications are grabbed in a self-adaptive mode through an electromagnet, submillimeter-level accurate positioning is achieved through a laser galvanometer stereo camera, and the assembling posture is automatically adjusted in combination with the floating compensation mechanism. The intelligent control system adopts a staged tightening strategy, performs real-time monitoring through a torque sensor, and has a dual protection mechanism. The hydraulic turbine ball valve large nut assembling machine achieves full-automatic assembling of hydraulic turbine ball valve large nuts, has the advantages of being high in assembling efficiency, high in positioning precision, high in adaptability, reliable in quality and the like, solves the problems that manual assembling is large in labor intensity, many in potential safety hazards, poor in quality consistency and the like, and remarkably improves the assembling quality and efficiency of key parts of a hydropower station.

The invention relates to the technical field of kelp production intelligent management, in particular to a kelp drying production process full-automatic management system based on artificial intelligence. The system comprises the following modules: a kelp characteristic sensing module, a drying parameter planning module, an intelligent intervention adjustment module, a production parameter intelligent optimization module and a production visual management module, a double-line hanging multi-temperature-zone double-layer tunnel type drying structure is adopted, and a drying tunnel is divided into a plurality of temperature zones; different temperatures can be set for each temperature zone according to the kelp drying requirements, the temperature, humidity and kelp drying state of each temperature zone are monitored in real time, dynamic zoning and staged regulation and control are performed according to preset drying parameters, kelp automatic production operation parameters are intelligently learned, automatic production parameter feedback is performed, and the kelp drying efficiency is improved. The automatic control of kelp drying production and the management of the kelp drying production process are realized. According to the invention, full-automatic management of the kelp drying production process is realized through dynamic zoning and staged regulation and control.

The invention relates to the technical field of automatic data analysis, in particular to an intelligent data report generation method and system based on an MCP protocol. The method comprises the following steps: constructing a semantic intention vector through a context semantic structure according to an obtained natural language instruction of a user; constructing a semantic task path graph based on the semantic intention vector by utilizing a knowledge graph; according to the constructed semantic task path diagram, performing intention path and tool capability mapping based on an MCP protocol; constructing a task flow based on an MCP protocol and performing task scheduling optimization; carrying out data source unified modeling based on the constructed task flow, and generating a stable and controllable calculation task flow; and visual expression generation from data to graphs is realized through an intelligent recommendation and structural modeling mechanism. According to the method, a semantic-driven MCP plug-in tool chain and a chart capability component index are constructed, so that a full-automatic closed loop from a natural language intention to data analysis and chart generation is realized, and meanwhile, the use threshold of non-technical users is reduced.

The invention relates to the field of crude oil heating, in particular to a full-automatic electric heating system and device based on the integrated heat storage technology, and the system comprises a temperature scale module, a heating module and a heat storage module. The heating module is provided with all temperature control nodes of the oil gathering pipeline, target temperature difference compensation, pipe section heat loss compensation and a flow velocity correction factor are integrated, and adaptive electric heating power of all the temperature control nodes of the oil gathering pipeline is generated through a reverse cascade feedback control algorithm. The heat storage module continuously collects temperature field characteristics, operation condition characteristics and medium thermal attributes of the oil gathering pipeline, judges whether heat storage operation is triggered or not through multi-dimensional coupling evaluation, distributes heat storage media through a priority algorithm, drives a valve to switch and execute adaptive heat storage operation, and achieves precise temperature field regulation and control and energy efficiency optimization of the oil gathering pipeline. And stable crude oil transportation is guaranteed.

The invention relates to the related technical field of semiconductor wafers, in particular to a full-automatic wafer film thickness detection method and system based on spectral reflection, and the method comprises the steps: obtaining a wafer surface reflection spectral signal, and extracting spectral feature information and wavelength parameters; identifying a polarization characteristic parameter and an interface interference characteristic parameter, introducing the wavelength parameter as an adjusting variable, determining a wavelength dependence term, and generating a global film thickness distribution map, thereby solving the problems that in a detection scene with a multi-layer film or a rough interface, the influence of an incident light polarization state on a reflection signal is not fully considered, the reflection signal characteristic extraction is distorted, and the detection accuracy is poor. According to the method, the technical problem that the film thickness obtained through reflection characteristic backstepping deviates from the actual film thickness is solved, polarization characteristic parameters and interface interference characteristic parameters are recognized, the thickness of each detection point is determined after the film refractive index is corrected, the thickness detection precision is effectively improved, and the influence of spectral signal noise and interface interference is reduced; the technical effect of accurately presenting the global film thickness distribution of the wafer film is achieved.

The invention relates to the technical field of particulate matter sampling analysis, and discloses a full-automatic multi-point sampling system and method for particulate matters in a large flue. The system comprises an analysis module for acquiring airflow data through an airflow parameter sensor; the arrangement module is used for optimizing sampling point positions according to the airflow data; the monitoring module monitors particulate matter distribution in real time; the acquisition module is used for acquiring samples by adopting a gradient constant-speed strategy; the generation module is used for analyzing the sample to generate a component characteristic spectrum; and the fusion module comprehensively analyzes and generates a detection report. According to the application, full-automatic multi-point sampling and comprehensive analysis of the particulate matters in the large flue are realized, the defects of strong dependence on manual operation, limited sampling point positions, incapability of adapting to a complex flue environment and the like in a traditional sampling method are overcome, and the accuracy, representativeness and working efficiency of particulate matter sampling are improved.

The invention discloses a spraying robot trajectory planning method based on depth camera scanning. The method comprises the steps that wall surface boundary polygon data, an initial spraying stroke set and a spraying dosage model parameter set are obtained; calculating a predicted coating thickness field, and performing difference calculation on the predicted coating thickness field and a preset target thickness to generate a thickness error field; performing connected domain clustering on the thickness error field to generate topological thickness error regions, and determining region type labels for the topological thickness error regions one by one; calling a matched editing operator from a discrete stroke editing operator library, performing geometric constraint verification, and generating a candidate editing scheme; and evaluating the candidate editing schemes based on a preset comprehensive scoring function, updating the initial spraying stroke set by using the scheme with the optimal score, obtaining an optimized spraying stroke set, and converting the optimized spraying stroke set into a robot control instruction. According to the method, the problem that global coverage and local thickness uniformity cannot be considered in traditional geometric planning is solved, and high-quality full-automatic spraying is achieved.

Presented herein are systems and methods that provide for automated analysis of medical images to determine a predicted disease status (e.g., prostate cancer status) and / or a value corresponding to predicted risk of the disease status for a subject. The approaches described herein leverage artificial intelligence (AI) to analyze intensities of voxels in a functional image, such as a PET image, and determine a risk and / or likelihood that a subject's disease, e.g., cancer, is aggressive. The approaches described herein can provide predictions of whether a subject that presents a localized disease has and / or will develop aggressive disease, such as metastatic cancer. These predictions are generated in a fully automated fashion and can be used alone, or in combination with other cancer diagnostic metrics (e.g., to corroborate predictions and assessments or highlight potential errors). As such, they represent a valuable tool in support of improved cancer diagnosis and treatment.

The invention discloses a full-automatic cable vibration high-precision measurement method based on a machine vision technology, and relates to the technical field of structural vibration measurement. The method comprises the following steps: installing and calibrating equipment, and ensuring that a cable plane coincides with an imaging plane; high-frame-rate video acquisition is carried out, and comparison verification is carried out in combination with an accelerometer; screening effective pixels based on the pixel intensity time sequence variance, and constructing a pixel intensity space-time matrix by using the time sequence of the pixels; sVD decomposition is adopted to extract an accumulated energy ratio gt; 90% of the main modes are reconstructed, and real signals are revealed; a plurality of inhaul cables are automatically positioned and coded through gradient features and Hough transform; sub-pixel-level vertical displacement is calculated by applying an optical flow method, and median filtering is combined to resist noise; fourier transform is carried out to obtain a spectrogram, and a fundamental frequency is identified; and calculating cable force based on a string pulling theorem. According to the method, remote, full-field and multi-target synchronous monitoring is realized, the displacement measurement precision reaches a sub-pixel level, and a high-precision and full-automatic solution is provided for health monitoring of structures such as bridges.

Fully automatic welding forming equipment for a special-shaped three-dimensional workpiece and a welding method is provided. The welding forming equipment includes a base station, a welding robot, a displacement worktable, an auxiliary robot, and a hopper. The welding robot, the auxiliary robot and the hopper are arranged on the base station, and the displacement worktable is arranged between the base stations. The displacement worktable adopts the structure of main and sub double frames and main and sub double rotary power, which has multiple degrees of freedom, and can flexibly adjust the position and angle of the welded parts, realize the welding requirements of any position and angle, and meet the welding operation requirements of complex special-shaped three-dimensional components without manual repair welding operation, and the multi-point welding can be realized through the cooperation of welding robot and auxiliary robot to improve the welding efficiency and quality.