1078 results about "Equipment state" patented technology

The invention provides an AI-based laboratory equipment scheduling optimization method and system, and the method comprises the steps: firstly obtaining a state monitoring data set containing the characteristics of equipment operation power consumption, idle time length, environment interference factors and the like in real time, and then carrying out the multi-dimensional analysis of the state monitoring data set; generating an availability evaluation index set containing characteristics of equipment load fluctuation, maintenance period prediction, compatibility matching and the like, and an experiment task priority queue, performing cross decision analysis on the availability evaluation index set and the experiment task priority queue based on a preset dynamic resource allocation model, and obtaining a scheduling strategy set containing a task allocation path, a cooperative operation rule and a conflict resolution mechanism; scheduling strategy parameters are calibrated according to experimental task operation log data, an optimized execution instruction set is generated, instructions are fed back to an equipment control system to adjust the equipment state, a dynamic resource allocation model is iteratively updated according to execution feedback data, and efficient scheduling optimization of laboratory equipment is achieved.

The invention belongs to the technical field of power equipment, and discloses a power equipment fault early warning method based on multi-source data fusion, and the method comprises the steps: constructing multi-dimensional feature association through multi-modal data time-space association collection and hierarchical fusion driven by a knowledge graph; a space-time weight matrix is used for correcting sampling deviation, fault mechanism knowledge is combined to strengthen key feature contribution degree, false alarm and missing alarm caused by data isolation are effectively avoided, early recognition of hidden defects of equipment is realized, and global perception capability of early warning is improved. A meta-learning enhanced cross-equipment early warning model and reinforcement learning dynamic threshold decision are adopted, cross-equipment rapid adaptation under a small number of samples is realized through a ''meta-micro'' double-circulation mechanism, and a nonlinear law of fault evolution can be accurately described by combining a three-dimensional dynamic threshold matrix to balance an equipment state, an environment and an operation and maintenance strategy. The model generalization problem of different types of equipment in a complex environment is solved, and the adaptability to scenes such as load fluctuation and environment sudden change is improved.

The invention relates to the technical field of power equipment state monitoring, and discloses a power equipment state evaluation and early warning method and system. The method comprises the following steps: collecting multi-source monitoring data of power equipment, and obtaining an equipment state data set by adopting a collaborative preprocessing method; a multi-dimensional feature extraction method is adopted to extract feature parameters reflecting the operation state and the degradation degree of the equipment; constructing an equipment health degree evaluation model, and obtaining the equipment health degree through a multi-time scale evaluation method; predicting a future deterioration trend and state transition time; establishing a grading early warning decision-making mechanism to realize early warning of the state of the power equipment; and identifying factors of equipment state degradation by adopting a root cause analysis method, and generating operation and maintenance decision suggestions according to historical cases. According to the invention, the health state of the power equipment can be accurately evaluated, and degradation trend prediction and fault early warning are realized.

The invention relates to the field of electric power material quality detection, and discloses a test scheduling system for detection task collaboration and data acquisition, which comprises a task construction module, a state collaboration module, a graph reasoning module and a data acquisition module. And the task construction module generates a standardized test task packet including a task identifier, a project code, a target equipment identifier, an environment requirement parameter and a two-dimensional code according to the test rule base and the resource configuration state, and pushes the standardized test task packet to corresponding test equipment through a Web Service interface. And the state collaboration module receives an equipment state feedback event, constructs an event time sequence flow graph based on the task identifier and generates a task state sequence with a timestamp. The atlas reasoning module takes the state sequence and the environmental parameters as input, constructs a test atlas structure and generates an optimization execution path. And the data acquisition module controls the test equipment to complete a detection task according to the path, acquires test data and environmental parameters, and encapsulates the test data and the environmental parameters to form a structured task data packet, thereby realizing data collection and task tracing.

The invention discloses an equipment self-adaptive regulation and control method and system based on multi-mode perception, and relates to the technical field of equipment intelligent control. The method comprises the following steps: acquiring real-time operation parameters of medical equipment, and generating multi-modal equipment state data; performing depth state estimation based on the data and a pre-stored historical operation database, identifying a current equipment operation mode and a performance degradation trend, and generating a dynamic target set point interval; matching the dynamic target set point interval with a preset control strategy library to generate a self-adaptive control instruction set for driving an actuator group so as to achieve a target parameter; issuing an instruction set to drive an actuator group to execute regulation and control actions, and continuously collecting operation feedback data; and the feedback data is compared with the dynamic target set point interval to obtain the operation deviation, and the control instruction set is dynamically optimized by adopting a control algorithm based on the deviation, so that the self-adaptive monitoring and cooperative control of the operation state of the medical equipment are realized.

The invention relates to the technical field of power system automation, in particular to a monitoring and maintenance system and method for a transformer substation. The system comprises a quantitative classification module, a difference analysis module and a grading adjustment module. Equipment in a transformer substation is classified through the quantitative classification module, correction parameters such as aging and load loss are introduced for single-equipment monitoring through the difference analysis module, misjudgment and missed judgment caused by equipment state changes are reduced by combining multi-level threshold values, associated equipment is modeled through a GNN model, neighbor states are aggregated, the equipment health degree is output, and the equipment quality is improved. The method comprises the following steps of: quantifying a fault propagation probability, pre-warning cascading fault risks in advance, and adjusting a threshold value of a single device in a linkage manner while a hierarchical adjustment module takes risk response measures, so that single device monitoring is adaptively matched with an associated device risk state, full-coverage accurate monitoring is realized, and local and global risks are considered.

The invention relates to the technical field of equipment fault intelligent diagnosis, in particular to an equipment fault intelligent diagnosis method and system based on a Modbus protocol. The method comprises the following steps: acquiring real-time operation data from target industrial equipment through a Modbus protocol, dynamically adjusting an initial sampling frequency based on an equipment operation state, and performing multiple verification and compensation correction on the acquired data to obtain a stable data stream; performing multi-scale decomposition and feature enhancement processing on the stable data stream, extracting a time-frequency domain mixed feature set, and constructing a feature evolution trajectory; inputting the feature evolution trajectory into a double-branch diagnosis model integrating equipment state prediction and fault classification, and outputting an equipment health degree score and fault type probability distribution; and constructing a dynamic fault threshold curved surface, carrying out multi-dimensional fusion decision by combining the equipment health degree score and the fault type probability distribution, and generating a graded fault early warning and maintenance strategy. According to the invention, the accuracy, timeliness and adaptability of industrial equipment fault diagnosis can be greatly improved.

The invention relates to the technical field of power supply system management, and discloses a centralized management method and management system for a power supply system. The method comprises the following steps: monitoring a current fluctuation sequence and voltage offset data of each node of a power supply network in a continuous operation period, and collecting an equipment state log text and load change time sequence information; identifying a time point of abnormal sudden change of current fluctuation, and extracting a characteristic parameter set of a power supply quality reduction event in combination with a voltage offset data change amplitude; matching a historical abnormal event library, positioning key operation record fragments strongly related to load change in the log, and generating a mapping relation table of equipment operation behaviors and power supply quality fluctuation through semantic analysis and time sequence alignment; and correcting the parameters of the load prediction model, outputting and executing a power supply priority dynamic adjustment strategy, and triggering automatic calibration of equipment operation parameters. According to the method, comprehensive control of the power supply system is realized, abnormity can be identified and processed in time, association between operation and quality is clear, and stability and flexibility of operation of the power supply system are improved.

The invention discloses a method for establishing a high-voltage circuit breaker fault detection model, and the method comprises the following steps: collecting current, voltage, mechanical response, temperature and other multi-dimensional signals of a circuit breaker under different working conditions, and unifying the signals into standardized time sequence data; a nonlinear dynamic sparse identification method is utilized to establish a dynamic model for describing equipment state evolution, and sparse coefficients reflecting physical change rules are extracted from the dynamic model to serve as health features. And the features are fused with current monitoring data to generate a joint feature input vector, and a health prediction model based on a TabPFN architecture is introduced for training and discrimination. And finally, accurate prediction of the current health state or the potential fault of the circuit breaker is realized, and the model self-adaptive updating capability is realized. According to the method, physical modeling and data analysis are combined, so that the accuracy and interpretability of fault prediction are improved.

The invention belongs to the technical field of transformer substation fault diagnosis, and relates to a transformer substation equipment health diagnosis and operation and maintenance suggestion generation method, in the invention, a dynamic portrait of an equipment state is constructed through acquisition and preprocessing of multi-source heterogeneous data, comprehensive extraction and fusion modeling of equipment operation characteristics are realized, and on the basis, the equipment health diagnosis and operation and maintenance suggestion generation method is provided. A dynamic equipment association diagram is introduced, the health state of equipment can be comprehensively evaluated and a risk propagation path can be identified by describing an association relationship between the equipment and combining a risk propagation model, and finally, an interpretable operation and maintenance suggestion is automatically generated based on a case reasoning and strategy optimization method, so that the problems of insufficient data integration capability and high risk propagation efficiency of a traditional method are effectively solved. The risk assessment dimension is single; and the operation and maintenance suggestion generation lacks intelligent support.

The invention relates to the technical field of equipment monitoring, in particular to a method and a system for collecting and monitoring production data of automotive trim injection molding equipment. Comprising the following steps: firstly, acquiring real-time operation state data from injection molding equipment through a communication node, generating an equipment state data set, and constructing a parameter coupling model through a time sequence analysis method; when the temperature parameter fluctuation exceeds a preset threshold value, fusing the pressure data to generate optimal configuration, transmitting the optimal configuration to related equipment through an interactive network, and determining a linkage parameter set; thirdly, the linkage parameter set is processed through a classification method, a deviation index is obtained, a synchronization instruction is generated according to deviation, it is ensured that beats between the devices are consistent, and a unified production cycle is formed; and finally, through the prediction model, extracting production cycle data, predicting parameter fluctuation, adjusting equipment parameters, and determining final process configuration. The problems of beat deviation and parameter fluctuation between injection molding equipment in production are solved, the production process is optimized, and the production efficiency and the product quality are improved.

The invention discloses a primary equipment state monitoring method and system in a new energy power system, and the method comprises the following steps: deploying a multi-mode sensor array on primary equipment, and synchronously collecting a voltage signal, a current signal, a temperature signal, a vibration signal and an environment parameter; inputting the collected data into an edge computing node for preprocessing to obtain a multi-modal signal sequence; variational mode decomposition is carried out to form a multi-dimensional feature vector; inputting a long-short-term memory neural network model containing an attention mechanism, performing training and reasoning by using an AdamW optimizer and a cosine annealing learning rate strategy, and outputting the health degree of equipment; determining the weight of each monitoring index based on an analytic hierarchy process, and dividing the equipment into a plurality of state grades; the fault probability is obtained through fuzzy Petri net reasoning, and a corresponding early warning mechanism is triggered according to a preset threshold value. According to the invention, through multi-dimensional data fusion and intelligent analysis, the accuracy and real-time performance of state monitoring are significantly improved.

The invention discloses a power equipment dynamic health degree assessment and predictive maintenance method, and belongs to the technical field of railway power system operation and maintenance. The method comprises the following steps: constructing a parameterized digital twinborn body of power equipment, and collecting real-time operation data, resume data and environment data; based on the parameterized digital twins and the collected data, equipment health degree components are calculated through a multi-model cooperation method, and a comprehensive health index is generated through fusion; performing equipment life prediction and maintenance decision generation according to the comprehensive health index, and outputting an optimal maintenance strategy; and performing visual virtual rehearsal and augmented reality auxiliary execution on the optimal maintenance strategy to form a closed-loop maintenance system. According to the method, the problems of data and model separation, model static stiffness and health assessment deficiency in the prior art are solved, dynamic perception, accurate assessment and predictive maintenance of the equipment state are realized, and the operation and maintenance efficiency and the system reliability are improved.

The invention relates to the technical field of automation and intelligent operation and maintenance of a power system, in particular to a knowledge graph-fused reinforcement learning switching operation anti-error verification method, which systematically extracts a multi-dimensional anti-error rule covering an operation sequence, an equipment state and an electrical safety distance by constructing an operation ticket knowledge graph, and improves the accuracy of the operation ticket knowledge graph. The defect that a traditional single-station anti-error system is incomplete in rule coverage is overcome, meanwhile, in combination with deep mining of a reinforcement learning model on historical operation data, implicit anti-error rules can be automatically extracted, illegal scenes which are not covered by a traditional rule base are supplemented, overall-process and multi-level accurate verification of switching operation is achieved, and the verification efficiency is improved. And the risks of misoperation and missing detection are greatly reduced.

The invention discloses a load decomposition method based on fusion feature data enhancement, and the method comprises the steps: synchronously collecting the low-frequency power data of a bus end of an electrical loop of a building and the low-frequency power data of all electric equipment ends, and generating a confusion power sequence of similar equipment through Beta distribution mixing, so as to enhance the recognition capability of a model for power overlapping features; based on the power time sequence data, extracting a mutation feature, an equipment state feature and a time coding feature to construct a multi-dimensional feature vector; a CNN-BiLSTM double-branch neural network is adopted, spatial-temporal characteristics are fused through a dynamic weight attention mechanism, and equipment state classification and power decomposition tasks are jointly optimized. In practical application, bus end power data is input, and the operation state and power distribution of each electric device are obtained. According to the method, an adversarial training strategy and a gating feature fusion mechanism are innovatively introduced, the load decomposition performance in a complex power utilization scene is remarkably improved, and the method is particularly suitable for identification and power prediction of equipment with similar rated power.

The invention relates to the technical field of substation operation and maintenance, in particular to a robot inspection path optimization method and system applied to an extra-high voltage substation, and the method comprises the steps: triggering the dynamic optimization of an inspection path in response to the detected equipment operation state change or inspection environment change of the extra-high voltage substation; based on a preset transformer substation environment space model and real-time equipment states and environment parameters collected in real time, a first target inspection path is generated through path cost analysis, and simulation verification and fine adjustment are carried out; when interference risks, positioning deviation overrun and / or multi-machine conflicts exist in execution of the first target inspection path, path planning is updated based on local environment information collected by the robot in real time, and a second target inspection path is generated; and executing the second target inspection path. According to the invention, the equipment state and the environment change can be responded in real time, the inspection path can be automatically adjusted, key inspection tasks are prevented from being omitted, and the continuity of the inspection process is ensured.

The invention provides an equipment operation state prediction method and system based on digital twinning. The method comprises the following steps: firstly, constructing a multi-physics field digital twinning model comprising an equipment independent model and a coupling relation between equipment; a target stress event that causes damage to the device is identified by processing multi-modal sensor data in real time. Once identified, simulation deduction is performed in the twin model, the simulation simulates the propagation, transformation and accumulation process of the stress in the device network based on the dynamically adjusted stress conduction weight, and calculates the accumulated stress of each associated device. And finally, converting the accumulated stress into an equivalent aging increment of each device in combination with a preset damage model, updating a health state baseline of each device, and generating a prediction report of a future associated aging risk. According to the method, the conduction and accumulation effects of the stress in the equipment network are simulated, so that the spanning from monomer monitoring to system-level associated aging prediction is realized, and the accuracy of equipment state prediction is greatly improved.

The invention discloses an intelligent inspection and fault prediction system based on computing power service, and belongs to the technical field of power grid inspection. The instruction analysis module analyzes the power grid dispatching instruction and determines target equipment and an expected state path; the topology network construction module constructs an equipment operation intention topology network and marks key nodes; the task screening module generates a sensing set containing equipment state recognition and robot movement control tasks according to the environment data, and screens out tasks outputting key node state parameters as a core task set; the computing power distribution module calculates the minimum computing power of the core task according to the weight of the key node and the environmental interference coefficient, and triggers non-core task degradation when the minimum computing power is insufficient; the fault prediction module inputs equipment state parameters output by the core task into a prediction network to generate an abnormal coefficient and a maintenance sequence; the real-time performance of key tasks is guaranteed by dynamically allocating computing power resources, and the operation and maintenance efficiency and reliability of the power grid are improved.

The invention discloses a smart home equipment state monitoring and abnormity early warning method and system, and relates to the technical field of smart home equipment state monitoring and abnormity early warning, and the method comprises the steps: constructing a dynamic space-time topological graph between home equipment; monitoring state data of each node device in the topological graph in real time; identifying a source abnormal node in the topological graph based on the state data and a preset abnormal rule; by taking the source abnormal node as input, executing fault propagation simulation deduction in combination with the direction and the weight of the edge to obtain a risk equipment node set and a fault propagation path; and based on the set and the propagation path, generating diagnosis early warning information used for positioning a fault root cause and representing a propagation link. According to the invention, through dynamic topology modeling, equipment physical connection and a logic dependency relationship are uniformly represented, root causes can be quickly locked and potential affected equipment can be predicted when multiple equipment are abnormal at the same time, prospective early warning of hidden cascading failures is realized, and operation and maintenance efficiency and system reliability are improved through a visual report.

The invention discloses a machine fault maintenance method and system based on multi-source data fusion. The method comprises the following steps: S1, collecting a vibration time sequence signal, a temperature time sequence signal, an equipment state parameter signal and a historical maintenance text signal of a machine; s2, generating vibration and temperature feature vector signals, and generating text feature vector signals; s3, constructing a multi-source feature dynamic fusion module, and splicing the vibration and temperature feature vector signal, the equipment state parameter signal and the text feature vector signal; s4, inputting the weighted fusion feature signal into a fault diagnosis model, and outputting a preliminary diagnosis signal containing fault type prediction and corresponding confidence; and S5, constructing a decision arbitration module, and when the uncertainty quantization signal is lower than a preset threshold value, outputting a preliminary diagnosis signal as a final maintenance decision signal. According to the multi-source data fusion-based machine fault maintenance method and system, the problems of low fault prediction accuracy and unreliable maintenance decision of industrial equipment can be solved.

The invention discloses an equipment fault prediction method based on industrial causal logic. The method comprises the following steps: acquiring an unbalanced equipment monitoring data set; missing value processing is carried out on the unbalanced equipment monitoring data set, and a causal relation graph is established; quantifying a causal relationship and calculating a causal weight matrix; constructing a causal constraint generative adversarial network, taking a causal weight matrix as an attention weight to generate a synthetic fault sample, combining the synthetic fault sample with an original fault sample to form a balanced fault sample set, and finally combining the balanced fault sample set with a normal sample set to form a balanced data set; and training a classifier based on the balanced data set and outputting a fault prediction result. According to the method, by identifying and utilizing the physical causal relationship in the equipment data, it is ensured that the generated synthetic equipment state sample strictly follows the engineering logic, unreasonable engineering samples generated by a traditional method are avoided, the accuracy of equipment fault prediction is remarkably improved, and therefore the equipment shutdown loss caused by false detection and missing detection is reduced.

The invention discloses an equipment voiceprint noise reduction monitoring method fusing empirical mode decomposition and dual-channel U-Net, and relates to the technical field of power equipment state monitoring and voiceprint signal processing. The method comprises the following steps: acquiring an original voiceprint signal when equipment runs, and decomposing the signal into a plurality of intrinsic mode functions by using empirical mode decomposition to realize preliminary separation of noise and useful signals; constructing a dual-channel U-Net network, performing deep extraction on time domain features and frequency domain features of an intrinsic mode function, and introducing an ECA channel attention mechanism to strengthen fusion of effective features; designing a self-adaptive loss function, and optimizing the noise suppression capability of the network on different equipment and under different working conditions; and the noise-reduced voiceprint signal is used for equipment state evaluation to realize early recognition of an abnormal state. The method can be applied to an on-line monitoring system of power equipment, improves the signal-to-noise ratio of the voiceprint signal and the state evaluation accuracy, and has an important value for guaranteeing the stable operation of a power system.

The invention provides a hybrid model fault early warning method and system based on time sequence prediction and fuzzy logic, and the method comprises the steps: collecting operation and maintenance data of equipment in a continuous operation period, constructing an equipment state feature set, carrying out the feature correlation analysis of the equipment state feature set, generating an equipment state potential vector which reflects a dynamic coupling relation between parameters, and carrying out the fault early warning of a hybrid model. Calling a pre-trained fuzzy logic reasoning model to carry out fuzzy rule matching, and generating a fuzzy state set containing a multi-dimensional fuzzy subset; performing space-time correlation processing on the fuzzy state set to generate a multi-dimensional decision cloud picture, and performing spatial reconstruction on the multi-dimensional decision cloud picture through a quantization weight distribution mechanism to obtain an optimized decision matrix; and generating a fault early warning signal containing a fault early warning level and fault position positioning information based on the confidence distribution characteristics corresponding to the fault types. According to the invention, the accuracy and timeliness of fault early warning can be improved, and reliable decision support is provided for equipment operation and maintenance management.

The invention relates to an equipment predictive maintenance management method and system based on multi-feature fusion, belongs to the technical field of equipment health management, and is used for solving the problems that existing sensor data is easily disturbed and distorted, and a potential causal structure of an equipment degradation path is difficult to reveal due to lack of a comprehensive modeling framework. The method comprises the steps of collecting multi-source data in real time, extracting a causal contribution degree of the data to a fault to generate a causal significance feature value, generating a three-dimensional health feature vector through an anti-fact neural network model in combination with a physical failure model, fusing the two to obtain an equipment state risk feature matrix, inputting the model to output a risk score, and obtaining an equipment state risk result. And finally, dynamically adjusting the monitoring frequency and the maintenance level and iteratively optimizing the strategy. According to the method, data interference can be filtered out, multi-class reasoning mechanisms are deeply fused, the equipment degradation law is accurately revealed, and full-life-cycle self-adaptive maintenance is achieved.

The invention belongs to the technical field of intelligent safety supervision, and particularly relates to a coal mine personnel safety behavior analysis method, which synchronously associates a management instruction, personnel behavior data, an equipment state and environment parameters according to timestamps through a management platform, obtains the current position of a personnel in real time, dynamically judges whether the personnel moves correctly or not, and improves the safety of the personnel. When a person approaches each maintenance position, automatically checking whether the maintenance position has omission or not, if the omission is found, generating a rechecking instruction in real time and pausing subsequent operation authorization, if the omission is not found, carrying out consistency checking on the operation sequence, the operation video content and the standard regulation, and if the checking does not reach the standard, carrying out rechecking on the operation video content and the standard regulation; if yes, second-level early warning is triggered immediately, a recheck mechanism is started, subsequent operation authorization is paused by dynamically monitoring operation compliance and combining authority control, personnel are prevented from entering a subsequent maintenance position with an illegal state, and the problem of multi-position continuous violation which can only be traced afterwards in an existing method is thoroughly solved.

The invention relates to the technical field of port equipment evaluation, in particular to an optimization method for port large-scale machine equipment state evaluation, which comprises the following steps: S1, multi-source heterogeneous data collaborative acquisition; s2, data preprocessing and feature enhancement; s3, carrying out multi-modal feature fusion modeling; s4, state evaluation of transfer learning driving; s5, dynamic threshold early warning and residual life prediction; and S6, model optimization under a federated learning framework. According to the method, the early fault detection rate is changed from 82% to 95% through multi-modal fusion, the false alarm rate is reduced by 60%, the diagnosis precision is improved, a dynamic threshold value adapts to working condition fluctuation, the maintenance cost is optimized, the prediction error for the residual life is smaller than or equal to 15%, excessive maintenance is avoided, the replacement period of key components is prolonged by 30%, and accurate state evaluation can be carried out.

The invention provides a multi-source heterogeneous data fusion method and device. Equipment state data of a computer monitoring system, equipment ledger and historical operation and maintenance data of a production management system and environmental parameter data of a meteorological system are accessed in real time through a distributed data bus; performing standardization processing on the three types of heterogeneous data based on a unified spatio-temporal index model, and establishing a dynamic association model including a time dimension, a space dimension, an equipment dimension and a working condition dimension; performing causal relationship analysis on the cross-system data by using the dynamic association model to generate a multi-dimensional data association result related to the current accident; and according to a data association result, dynamically generating an emergency disposal scheme and pushing the emergency disposal scheme to a remote centralized control center so as to support real-time decision and disposal operation. According to the method, second-level fusion and intelligent correlation analysis of cross-system heterogeneous data can be realized, the diagnosis efficiency and emergency disposal accuracy of hydroelectric accidents are remarkably improved, the response time is effectively shortened, and the risk of repeated accidents is reduced.

The invention relates to an equipment state anomaly detection model processing method and an equipment state anomaly detection method. The method comprises the steps of obtaining a training sample set; inputting each training sample into a to-be-trained initial equipment state anomaly detection model to obtain reconstruction data corresponding to each training sample; obtaining a reconstruction error of each training sample according to each training sample and the reconstruction data corresponding to each training sample; according to the reconstruction error of each training sample, adjusting a loss function of a to-be-trained initial equipment state anomaly detection model to obtain a target loss function; according to the target loss function, optimizing model parameters of a to-be-trained initial equipment state anomaly detection model to obtain a target equipment state anomaly detection model; and the target equipment state anomaly detection model is used for identifying whether the equipment state of the to-be-detected equipment is abnormal or not, so that the equipment state anomaly detection precision is improved.

The invention provides a laser cutting production line task automatic allocation control method based on multi-device state coupling analysis, and belongs to the technical field of intelligent manufacturing and industrial automation. The method comprises the following steps: constructing a dynamic closed-loop control process through a central control scheduling system: receiving a task information packet of an MES; constructing an equipment state vector based on a real-time station state, and introducing a dynamic weight factor set to generate a weighted state vector; performing task triggering judgment through a coupling triggering judgment function in combination with the task dependency graph and historical task records; when the conditions are met, a task instruction is issued to the target station; and feeding back the state and updating the historical record after the task is completed. The invention further relates to AGV intelligent scheduling, visual positioning compensation, process parameter dynamic adjustment, predictive conflict detection, weight self-optimization and the like. According to the method, the production line cooperation efficiency is remarkably improved, manual intervention and system delay are reduced, and the method is suitable for an intelligent laser processing scene in which multiple devices run in parallel.

The invention relates to a suspension system equipment fault diagnosis method and system. The method comprises the following steps: constructing a digital twinborn model of the suspension system based on a three-dimensional modeling tool; acquiring operation data of equipment in the hanging system, and binding the operation data with the digital twinborn model; extracting features representing equipment states in the operation data, analyzing the features based on an intelligent analysis model, and generating a fault diagnosis result; and generating a fault early warning signal according to the fault diagnosis result, and displaying the fault early warning signal through the digital twinborn model. According to the method, the digital twinborn model and the intelligent analysis model are used for fault diagnosis, and the problems that equipment fault detection is difficult and fault positioning efficiency is low in a traditional hanging system can be solved.