31results about How to "Achieving Adaptiveness" patented technology

The application relates to the technical field of equipment health management, and discloses a battery replacement cabinet fault prediction and health management method and system. The method comprises the following steps: acquiring an original temperature sequence, an original humidity sequence, a battery module number and a battery replacement cabinet position number, performing abnormal value correction to obtain a corrected temperature sequence and a corrected humidity sequence; performing abnormal fluctuation point screening according to the corrected temperature sequence and the corrected humidity sequence to generate an abnormality judgment record; removing interference according to the abnormality judgment record to obtain a refined feature set; performing abnormal risk analysis according to the refined feature set to obtain an overheating risk report; generating an intervention instruction according to the overheating risk report to obtain an initial execution scheme; driving operation is performed according to the initial execution scheme to obtain an equipment operation log; and performing reliability evaluation according to the equipment operation log to obtain a reliability evaluation conclusion. The method can realize early warning and health management of battery replacement cabinet faults.

This invention belongs to the technical field of power equipment operation and maintenance, power distribution line construction, and high-altitude operation machinery. A pole-mounted transformer replacement device based on a truck-mounted crane is characterized by comprising a main frame and support system, a translational load-bearing system, a chain drive system, and a vertical lifting and centering system. The main frame and support system includes a main load-bearing frame, stabilizing legs, and lifting lugs. The translational load-bearing system includes a translational load-bearing platform and a linear guide rail slider assembly. The chain drive system mainly consists of a geared motor, a drive gear, a driven gear, a short transmission chain, a drive sprocket shaft, a bearing support seat, a long transmission chain, and a reducer mounting support. The vertical lifting and centering system includes a centering mechanism, a vertical lifting mechanism, and a vertical lifting support plate. The centering mechanism includes a horizontal drive motor, a horizontal push rod mechanism, and a guide bracket. This device can be transported by truck, deployed quickly, and is suitable for operation in confined and electrified environments.

The invention provides an enterprise space appeal matching method based on an AI model, and the method comprises the following steps: collecting the multi-source sensing data of an enterprise space, and building a structured time-space information matrix through an access control, energy consumption, position and personnel label sensing terminal; constructing a causal relationship graph by using a structural causal modeling algorithm, and identifying key causal factors directly related to the spatial appeal; causal invariant characterization is extracted based on a multi-environment invariant risk minimization method, and an intervention effect model is established in combination with controllable intervention variables such as partition adjustment, capacity limitation and use strategies; generating an anti-fact sample of the candidate space unit by using an anti-fact reasoning mechanism, and calculating a matching adaptation score; establishing a matching optimization model according to constraint conditions such as topology, capacity, safety and moving lines; according to the method, high-precision and interpretable matching distribution under the conditions of complex space resources and multi-dimensional appeals is realized, and the method has the effects of flexible deployment, high matching stability and sustainable optimization.

The invention discloses a multi-terminal parallel authentication authorization and equipment perception method for a key task communication system, which belongs to the technical field of key task communication, and specifically comprises the following steps: S1, configuration preparation: a server pre-configures a terminal initial configuration file for a terminal; the initial configuration file of the terminal comprises lt; m < cpt >-UE-idgt; element, lt; m < cpt >-UE-idgt; the elements comprise the next-level lt; the method comprises the following steps: carrying out Instance-ID-URNgt; an element; lt; the method comprises the following steps: carrying out Instance-ID-URNgt; the elements are instantiated to be unique identifiers by the configuration terminal; s2, configuration issuing and effectiveness: the server issues the initial configuration file of the terminal to the terminal, and the initial configuration file of the terminal is restarted to take effect through the terminal; s3, authentication and information reporting: the terminal initiates an authentication and service authorization request, wherein the request carries the result; the method comprises the following steps: carrying out Instance-ID-URNgt; an element; s4, service authorization and configuration issuing: the server receives the request, analyzes the terminal instantiation unique identifier, records analysis information, and determines whether to issue matched user configuration file data to the terminal; according to the invention, the terminal registration authentication process is deeply coupled with the terminal type and capability evaluation, and intelligent decision-making can be carried out based on terminal difference.

The application relates to the field of battery life prediction, in particular to a multi-feature fuel cell life prediction method based on a long short-term memory network; the method comprises the following steps: collecting voltage attenuation data and collecting running state data to construct an original input data set; analyzing the running state data, calculating a correlation coefficient according to the voltage attenuation data and each running state data, analyzing the correlation coefficient, and obtaining an input feature set; processing the input feature set and the voltage attenuation data to construct a neural network training data structure; dividing the training data structure into a training set and a test set; constructing a network architecture solution space to be searched, analyzing the network architecture solution space, constructing a fitness function, and determining an optimal network architecture parameter combination based on the fitness function; and inputting the training set into the optimal network architecture parameter combination for training to generate a target detection prediction result. The application can improve the prediction accuracy of the voltage attenuation trend.

The application relates to a power distribution network voltage control method based on a reactive virtual power plant, which comprises the following steps: constructing a double-layer collaborative regulation model based on discrete and continuous reactive regulation equipment in the power distribution network under the management of the reactive virtual power plant, and establishing power distribution network physical constraints; under the power distribution network physical constraint condition, day-ahead scheduling converts the optimization control of the discrete reactive regulation equipment into a first multi-agent reinforcement learning of a discrete action space strategy for solving, and generates day-ahead scheduling planning to be issued to an intra-day correction layer; under the conditions that the power distribution network physical constraints and the day-ahead scheduling plan are taken as boundary constraints, intra-day correction converts the control of the continuous reactive regulation equipment into a second multi-agent reinforcement learning of a continuous action space strategy for solving, and generates intra-day correction instructions; the continuous reactive regulation equipment is adjusted based on the intra-day correction instructions to obtain node voltage in the power distribution network; the node voltage in the power distribution network is fed back to the day-ahead scheduling layer to realize closed-loop collaboration of voltage optimization regulation.

The application discloses a kind of vehicle-mounted Ethernet dynamic flow scheduling method and system, it is related to vehicle communication field, comprising: T1, the data stream feature registration and global view management of the equipment of access vehicle-mounted network are carried out;T2, time window is taken as scheduling unit, multiple time slots are divided in time window, and hierarchical time slot scheduling model is constructed;T3, based on flow quota, network data is monitored and flow shaping in real time, and burst flow is inhibited;T4, according to the trigger of high priority emergency data stream, time slot preemption and time window dynamic reconstruction are executed;T5, according to network transmission quality feedback, the bandwidth quota of each data stream is adaptively adjusted.Through the above scheme, time window and three-level time slot physically isolate burst flow, ensure that safety data bandwidth such as braking, steering is monopolized, significantly reduce the packet loss rate, realize key business strong guarantee.

The invention discloses a device cooperative control method and system based on a distributed system, a terminal and a storage medium, and the method comprises the steps: obtaining the operation state data and communication link information of a plurality of physical devices, mapping each physical device into a node in a dynamic topological graph, and carrying out the cooperative control of the plurality of physical devices based on the operation state data and the communication link information; constructing feature representation of the distributed system; inputting the feature representation into a collaborative decision-making model for topological adaptive processing and inter-device long-range dependency relationship modeling, and outputting collaborative decision-making instructions corresponding to the physical devices; distributing the collaborative decision instruction to corresponding physical equipment for execution, and collecting system-level efficiency feedback generated when the collaborative decision instruction is executed; and dynamically optimizing the collaborative decision model according to the efficiency feedback and a preset system-level resource constraint so as to continuously adapt to the topology and state change of the distributed system. According to the method, the self-adaption to the dynamic change network topology is realized, and accurate collaborative decision and efficient resource control can be carried out.

The utility model provides a nursing bracket for neurosurgery, which relates to the technical field of mechanical engineering and comprises a bracket, two supporting mechanisms are assembled at the top of the bracket, two rotation limiting mechanisms are assembled in the bracket, each supporting mechanism comprises a first supporting column, two supporting blocks and two arc-shaped plates, a first column rod is fixedly connected to the top of the first supporting column, a connecting rod is fixedly connected to the side end of each second supporting column, two column grooves are symmetrically formed in each second supporting column, and the side end of each column block is slidably connected into the corresponding column groove. According to the nursing bracket for the neurosurgery department, the traditional nursing bracket used for the neurosurgery department is improved, the adaptability of the traditional nursing bracket used for the neurosurgery department to arms of doctors with different body types is improved through an additional structure, and when the arms of the doctors are placed, the self-adaption effect on the arms of the doctors can be achieved simply through the supporting mechanism.

The invention discloses a parameter-adaptive excitation PID (Proportion Integration Differentiation) control method, system and equipment and a readable storage medium, and the method comprises two control links: one control link is a main control link, namely, when the terminal voltage of a synchronous motor is changed, a variable parameter compensation link is set before conventional series / parallel PID control, and a variable parameter compensation link is set before conventional series / parallel PID control; according to the compensation link, the series / parallel PID parameters can be dynamically compensated according to the working points of the synchronous motor, parameter self-adaption and optimal control are achieved, and the reactive response speeds of the synchronous motor at all the working points can tend to be consistent under the condition that the zero pole of the system is not changed; and the second step is a parameter limiting link, that is, when the deviation is smaller than a deviation given value, PID parameters are attenuated in time, so that the terminal voltage of the synchronous motor is fast and stable, and the running stability of the synchronous motor can be ensured while the dynamic reactive response speed is effectively improved.

This invention relates to the fields of unmanned aerial vehicles (UAVs) and computer vision technology, and particularly to a UAV-based method for classifying bridge components. The technical problem is that existing point cloud-based automatic classification methods for bridge components suffer from issues such as scarce samples, high annotation costs, insufficient model generalization ability, and low processing efficiency. The technical solution is a UAV-based method for classifying bridge components, comprising a data acquisition step, a data augmentation step, a data preprocessing step, a model training step, and a component classification step. In the model training step, preprocessed 3D point cloud data and its corresponding component category labels are used to train a deep learning-based point cloud semantic segmentation model, enabling the model to learn the feature representations of different bridge components. This invention uses a systematic data augmentation strategy to simulate real-world acquisition interference, combined with an efficient point cloud deep learning model, to achieve accurate, robust, and efficient classification of various bridge components.

The embodiment of the invention provides a vehicle control method, a vehicle and a computer readable storage medium, and the method comprises the steps: obtaining state perception data of the vehicle, the state perception data comprising physiological signal data and operation behavior data of a vital sign object in the vehicle; performing feature extraction on the state sensing data to obtain multi-modal feature data; state identification is carried out based on the multi-mode characteristic data, a state prediction result is obtained, the state prediction result is used for determining an auxiliary control mode corresponding to the vehicle, and the auxiliary control mode is used for adjusting driving auxiliary function parameters and cabin environment response of the vehicle; and controlling the vehicle by using the state prediction result. According to the invention, the technical problems of single monitoring means, insufficient sensing accuracy and delayed safety response in the prior art are solved.

The application belongs to the cross field of mobile communication technology and artificial intelligence predictive maintenance, and discloses a maintenance method and device of a relay communication equipment of a mobile communication system, the device comprises a cross-domain data sensing module, which is used for synchronously collecting physical state data and communication service quality data; a context self-adaptive analysis module, which is used for generating an environmental interference context factor based on the communication service quality data, and dynamically adjusting the analysis algorithm parameters of the physical state data by using the factor; a fusion prediction and decision module, which is used for combining the context self-adaptive physical state characteristics and the communication service quality data to perform fault prediction; and a closed-loop self-optimization module, which is used for quantifying the feedback of the execution effect of the maintenance task to automatically optimize the prediction model. The application helps to significantly improve the signal-to-noise ratio and accuracy of fault prediction under complex environments, realizes the evolution from passive prediction to intelligent self-adaptation, and guarantees the high reliability of the communication system under extreme working conditions.

The application discloses a gymnastics training auxiliary protection device and a use method thereof, and belongs to the field of gymnastics training devices.The device comprises a pad cover, a sponge pad, a support frame, a connecting frame, a fixing frame, a buffer spring and a buffer piece; one end of each of the connecting frames is connected with a connecting slot; one end of each of the fixing frames is connected with a fixing slot; one end of each of the buffer springs is fixedly connected with one end of each of the connecting frames away from the sponge pad, and the other end of each of the buffer springs is fixedly connected with one end of each of the fixing frames away from the support frame; when an athlete jumps and somersaults from a high altitude and lands, a high-intensity impact is generated, the buffer springs in the corresponding stress areas of the sponge pad are compressed and deformed, the instantaneous impact force is absorbed, the buffer springs in the non-stress areas remain unchanged and do not deform as a whole, the self-adapting effect of heavy pressure and large buffer and light pressure and stable support is realized.

This invention relates to the field of rhinitis ointment formulation and discloses a method for controlling the operation of process parameters in the formulation process. First, an initial demulsification risk temperature difference threshold is calculated based on the HLB value of the emulsifier and the difference between the relative heat capacity of oil and water. Then, the actual temperatures of the oil and water phases are collected in real time, and the real-time temperature difference and its rate of change are calculated. The current temperature difference curve is matched with the temperature difference characteristic curves of historical demulsification batches, and the risk judgment conditions are dynamically adjusted based on the matching degree and the rate of change. When a demulsification risk is determined, a temperature compensation value is calculated based on the difference between the real-time temperature difference and the current batch risk threshold, and the rate of change. The heating actuator is driven until the real-time temperature difference is less than or equal to the current batch risk threshold. The above steps are continuously executed during the constant temperature waiting phase. This invention achieves adaptive identification of demulsification risk through shape matching and dynamic threshold adjustment, and combines temperature compensation with the rate of change to reduce the probability of demulsification before emulsification.

This invention relates to the field of intelligent cockpit and vehicle thermal management technology, specifically to a multi-sensor fusion in-vehicle adaptive temperature control method and system. The method includes: synchronously acquiring multi-source heterogeneous signals within the vehicle; preprocessing them through filtering and format standardization; using Kalman filtering to perform time synchronization and state estimation, generating a fused feature vector; identifying the current operating condition label, calculating the predicted average voting value, and outputting the target predicted average voting value; calculating the basic control quantity through a feedback controller and the disturbance compensation control quantity through a feedforward controller, and adding them to obtain the total control quantity; decomposing the total control quantity into compressor speed commands, fan airflow commands, damper position commands, and expansion valve opening commands, driving actuator actions, and outputting the temperature control execution result. This invention solves the problems of air conditioning control lag, large temperature fluctuations, and unintelligent mode switching, achieving high-precision temperature control and seamless adaptive adjustment, significantly improving driving and passenger thermal comfort.

The invention discloses an unmanned aerial vehicle inspection parking path planning method and system based on machine learning. The method comprises the following steps: collecting and screening a target image; dividing grids according to the target image and acquiring brightness data of each grid; dividing an outdoor scene area and an indoor scene area according to the brightness data of the grid; performing corresponding path planning on different scene areas to obtain an aerial photography path; performing multi-index comprehensive evaluation on the aerial photography path to obtain a qualified aerial photography path; evaluating an air route image and recording an inferior image during routing inspection aerial photography; taking the inferior image as feedback input, and generating a new post-endurance shooting path; and continuing routing inspection aerial photography and updating to obtain an optimal aerial photography path meeting the air route quality.

This invention relates to an artificial intelligence-based data inspection quality control method, system, device, and medium. The method includes: cleaning and determining the distribution of raw inspection data to generate a qualified dataset; adaptively learning key model parameters such as the effective data range and optimal smoothing coefficient through iterative calculation and simulation optimization based on the dataset and its distribution type; training a long short-term memory neural network time-series prediction model using the qualified dataset, and setting quality control limits by combining the learned benchmark dispersion to construct a real-time quality monitoring model; inputting real-time inspection samples into the model, generating alarm signals by calculating an exponentially weighted moving average monitoring statistic and comparing it with the control limits; iteratively optimizing the model and parameters based on historical alarm signals and new data to achieve continuous self-updating of quality control, enabling automatic learning of quality control parameters and intelligent optimization of the model, and improving the real-time monitoring capability of minor system deviations during the inspection process.

This application discloses a multi-service broadcast optical reception scheduling method and system based on wavelength allocation, relating to the field of optical network communication technology. The method includes: for broadcast WDM multi-service concurrent reception scenarios, jointly modeling the service request category, time limit, and data size constraints with wavelength resource occupancy status and interference risk; firstly, dynamically grouping available wavelengths based on service category load prediction to achieve adaptive resource boundaries according to service attributes; then constructing a comprehensive priority index including the latest reception time limit, service category, and interference risk of candidate wavelengths, performing ordered cross-priority scheduling, and arranging reception time slots on the target wavelength to generate a wavelength scheduling table and a reception scheduling table; finally, driving the receiver to tune and receive according to time slots using scheduling commands. This improves wavelength utilization while reducing reception conflicts and latency jitter and strengthening critical service assurance.

The invention discloses a man-machine cooperative flight multi-dimensional cognitive state quantitative evaluation method for a low-altitude aircraft, and the method specifically comprises the steps: synchronously collecting the electroencephalogram, eye movement, skin electricity, heart rate and other multi-dimensional physiological data of a driver and the flight data of the aircraft; based on coupling of electroencephalogram, eye movement and flight data, a quantitative model of the driver three-dimensional space cognitive load is constructed; for specific emergencies, on the basis of analysis of event electroencephalogram related potential components and in combination with driver sight line, the duration of a key instrument is obtained again, and a situational awareness damage index is generated; and fusing the indexes with task management pressure indexes obtained based on the skin and the heart rate to obtain a multi-dimensional cognitive state comprehensive evaluation result, and dynamically adjusting man-machine interaction logic or an automatic driving takeover strategy according to the multi-dimensional cognitive state comprehensive evaluation result. According to the method, the cognitive state of the low-altitude aircraft driver in the complex three-dimensional environment can be objectively and quantitatively evaluated, and technical support is provided for man-machine collaborative optimization and definition of the safety boundary of the low-altitude aircraft.

The invention discloses a lower guide shoe temperature adaptive prediction method based on machine learning, and relates to the technical field of hydroelectric generating set operation and maintenance monitoring and intelligent monitoring, and the method comprises the steps: collecting and synchronizing multi-source data in real time, and screening feature subsets through XGBoost feature importance; dividing the operation state into a plurality of working condition subclasses, and training an XGBoost submodel for each working condition subclass; constructing an integrator, outputting a multi-step predicted value, and generating a confidence interval and a confidence index based on residual distribution or integration difference; judging an abnormal risk according to a multi-level alarm rule; and deploying a sliding window residual error and concept drift detection strategy to detect distribution drift or residual error abnormity. According to the method, sliding window residual monitoring, concept drift detection and integrated uncertainty evaluation are introduced, so that the stability and reliability of temperature prediction of the hydroelectric generating set are improved. And the accuracy of temperature early warning is improved based on the confidence interval and the multi-level warning rule.

This utility model discloses a metal cylinder-shaped suction cup in the field of automobile manufacturing technology. A support shaft is fixedly installed on the surface of the fixing block. The bottom end of the support shaft is rotatably connected to a disc via a ball joint. Connecting blocks are symmetrically fixedly installed on both sides of the fixing block. Telescopic components are symmetrically fixedly installed on both sides of the bottom of the connecting blocks. The top of the telescopic component is fixedly connected to the bottom of the connecting block via a top seat. A cavity is formed on the inner side of the top seat, and a spring is installed on the inner side of the cavity. Through the telescopic component, when the spring is compressed, the suction cup retracts inward to automatically adjust the angle. When stretched, the suction cup expands outward to make the disc horizontal. This adapts to the surface of products with different shapes and ensures good contact and self-adaptability. The suction cup can automatically adjust the contact state according to the shape of the product surface to ensure stable adsorption and release.

The invention relates to the technical field of data processing, in particular to an exhibition and display interaction logic processing method and system and a storage medium. Comprising the steps of collecting a user interaction request and a historical access log to form an initial execution path set; constructing a branch model, and identifying a high-frequency condition judgment sequence; if the sequence is not matched with the operation data, optimizing to obtain an adjusted condition judgment rule; extracting real-time features of the user interaction request from the operation data to generate feature vector representation, and traversing the adjusted rule to determine an optimal execution path; comparing the optimal path with the initial path set to obtain a path optimization index and generate a resource allocation adjustment scheme; when the response delay is reduced, the condition judgment rule is updated to form a final interaction processing mechanism; follow-up interaction requests are processed accordingly, and the execution efficiency and the dynamic adaptation capacity are evaluated. The problems that an existing exhibition and display system is fixed in interaction logic and difficult to adaptively optimize are solved, and dynamic optimization and efficient response of exhibition and display interaction logic are achieved.

This invention discloses a method and system for determining the spatial relationship of potential hazards based on hierarchical early warning, belonging to the field of computer data processing technology. It includes acquiring historical risk data and real-time environmental data, delineating virtual electronic warning zones, and generating dynamic virtual warning zone data; collecting video stream data and identifying and locating potential hazard targets, generating hazard target identification data, and performing spatial matching analysis in conjunction with the dynamic virtual warning zone data to generate an intrusion determination result; if an intrusion occurs, calculating the three-dimensional spatial clearance distance between the hazard target and the live conductor, and outputting a hierarchical alarm result based on multiple key risk factors; if no intrusion occurs, analyzing and predicting the target movement status and location based on the hazard target identification data to generate an out-of-zone early warning result. This invention employs a data processing method that combines dynamic zoning with quantitative analysis and trend prediction of potential hazard targets both inside and outside the zones, enabling intelligent, precise, and forward-looking risk early warning.

This invention discloses a large-angle electrostatic adsorption compliant buffer device for impact adhesion tasks, comprising a buffer module and an adsorption module. The adsorption module is fixed to the buffer module by insulating silicone rubber. The buffer module includes a power supply module, an upper support plate, a tension spring, a buffer cavity, a telescopic rod, a damping ball head, and a lower support plate. The power supply module is fixed to the upper surface of the upper support plate, and the buffer cavity is connected to the lower surface of the upper support plate by screws. Both the lower and upper surfaces of the upper and lower support plates have spring grooves, and a tension spring is provided between the spring grooves of the upper and lower support plates. The telescopic rod cooperates with the buffer cavity and can slide along the central axis of the buffer cavity. One end of the telescopic rod is provided with a damping ball head, which cooperates with a universal damping ball joint fixed to the lower support plate. The adsorption module includes an electrostatic adsorption layer, an adhesive layer, a fixing plate, and a buffer fixing material. The adhesive layer is located on the outside of the electrostatic adsorption layer. This invention is applicable to target surfaces with various surface roughnesses and materials.

This invention relates to the field of image recognition technology, and more particularly to an AOI component misalignment identification method based on reverse-grabbing logic. The method includes: determining the reverse-grabbing detection box for each component under test to obtain background feature parameters for calculating misalignment sensitive information; determining the component test type based on the misalignment sensitive information; determining suspected edge features based on each suspected misaligned component; extracting identical good components to determine good edge features; determining whether there is an inconsistency between the suspected edge features and the corresponding good edge features; if there is an inconsistency, calculating the proportion of components with blackened areas and determining whether to analyze the goodness rate of components with blackened areas; and determining whether to trigger a secondary judgment of suspected misaligned components based on the component goodness rate based on the proportion of components with blackened areas. This invention improves the accuracy of AOI component misalignment identification and reduces the component false detection rate and missed detection rate.

This invention relates to the field of energy consumption monitoring technology, specifically to a building energy conservation assessment system and method based on energy consumption monitoring. The system includes an energy consumption sequence construction module, an energy efficiency status quantification module, an abnormal energy consumption element location module, and an energy conservation scheme evaluation module. By collecting building energy consumption data and driving data such as external environment and internal operation, a contextualized data sequence is constructed. A dynamic energy consumption benchmark model is established based on historical data. The energy efficiency health level is calculated and compared with a diagnostic threshold determined through nonparametric kernel density estimation to determine energy consumption anomalies. When anomalies are present, the contribution of each element in the context vector to the energy consumption deviation is quantified to locate the main anomaly source. An energy conservation scheme is generated based on the anomaly elements. The energy conservation effect is predicted through dynamic model simulation and compared with the threshold to evaluate the effectiveness of the scheme, thereby achieving accurate diagnosis and energy conservation optimization of building energy consumption.

The application relates to the technical field of adaptive testing, and particularly provides a WebUI adaptive testing system and method based on multi-agent cooperation and multi-modal perception, which comprises a multi-modal perception subsystem, an agent cooperative testing subsystem and an assertion and verification subsystem. The method comprises the following steps: adjusting element positioning strategies through a continuous interaction feedback mechanism for a multi-dimensional interface feature set, and outputting an element descriptor set; deploying a multi-agent cooperative decision unit comprising an interface understanding agent, a test logic agent and a data agent; outputting a semantic test script formed through a cooperative fusion mechanism; performing pixel-level difference calculation, identifying visual inconsistency areas through multi-scale image comparison; performing business-level logic difference, analyzing the consistency of interface element states and business rules, and outputting a hierarchical verification report. The application can maintain high coverage, low maintenance cost and reliable defect positioning capability of the test in an environment where pages frequently evolve.

This invention relates to the field of intelligent control technology for power plants, specifically to a power plant intelligent co-pilot platform architecture adaptation and debugging system and method. The power plant intelligent co-pilot platform architecture adaptation and debugging system includes an architecture adaptation layer, an intelligent debugging layer, a data interaction layer, a monitoring and early warning layer, and an operation and maintenance management layer, which sequentially establish communication connections. The architecture adaptation layer is used to parse heterogeneous communication protocols and adapt to devices of different brands and models, establishing standardized interface links. The intelligent debugging layer is used to automatically execute the adaptation and debugging process based on the standardized interface links and optimize the adaptation strategy based on the debugging results. The data interaction layer is used to achieve secure, real-time, and complete transmission of interactive data between layers during the adaptation and debugging process. The monitoring and early warning layer is used to monitor the debugging status in real time, locate fault points, and issue early warnings. The operation and maintenance management layer is used to record data throughout the entire adaptation and debugging process and provide contingency plans for complex adaptation scenarios.