640 results about "Equipment state" patented technology

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 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 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 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 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 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 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 belongs to the technical field of electrical equipment state monitoring, and particularly discloses an electrical equipment intelligent online monitoring system and method based on multi-parameter fusion. Comprising the following steps: synchronously acquiring a partial discharge parameter, a mechanical characteristic parameter and a temperature rise parameter during operation of equipment, performing signal preprocessing on the parameters respectively, and constructing a multi-dimensional feature vector for diagnosis; the multi-dimensional feature vector is combined with a time sequence correlation model, a trend correlation model and an intelligent diagnosis model to output the multi-dimensional feature vector, collaborative decision making is carried out on an analysis result through a D-S evidence theory, and an equipment state evaluation conclusion and early warning information are generated and output in a visual form; according to the method, the monitoring limitation of a single parameter is overcome, early warning can be realized while the diagnosis accuracy is improved, the operation and maintenance efficiency is further improved, and the comprehensive diagnosis after deep analysis is also helpful for operation and maintenance personnel to take corresponding measures in time.

The invention relates to the field of electrical power grid operation and maintenance, in particular to a differentiated operation and maintenance method and system based on real-time weak links of equipment. According to the method, online monitoring data and power supply reliability operation data of power distribution network equipment are collected in real time to form an equipment state multi-source data set; based on the data set, calculating an equipment health degree evaluation result by adopting a state evaluation model, and obtaining an importance degree evaluation result through an analytic hierarchy process in combination with the position and load importance of the equipment in the network topology; and inputting the two results into a preset operation and maintenance strategy matrix to match a corresponding operation and maintenance strategy type, and finally generating a differentiated operation and maintenance strategy instruction containing specific operation and maintenance measures, an execution time window and a resource allocation scheme. According to the method and the device, the problems of lack of equipment-level pertinence and unreasonable resource allocation of an operation and maintenance strategy in the prior art are effectively solved, and the operation and maintenance accuracy and the resource utilization efficiency are improved.

The invention provides an event processing method, system and equipment based on Internet of Things equipment and a medium, and belongs to the technical field of Internet of Things. The method comprises the following steps: receiving an Internet of Things equipment message through an MQTT Broker, identifying an event type according to a theme, and distributing the event type to a corresponding thread pool; concurrently analyzing the JSON load in each thread pool, and converting the JSON load into a structured data object; caching the data in a batch processing queue in a classified manner, and triggering batch processing based on a queue state; extracting batch data, generating an optimized SQL statement, executing writing, and processing exceptions; screening data based on a routing rule and asynchronously forwarding the data to message middleware; meanwhile, processing parameters are dynamically adjusted according to the equipment state data and the system load. According to the invention, efficient processing of Internet of Things equipment events is realized, and the system throughput, the resource utilization rate and the data processing real-time performance are improved.

The invention discloses a circulation processing method and system based on digital process congestion degree analysis, and relates to the technical field of digital process management. The method comprises the following steps: constructing a network model containing nodes and an association relationship, and representing a task flow path and a weight; real-time data such as to-be-processed task queues, equipment states and environment parameters are collected in a multi-source mode and subjected to cleaning standardization processing; based on static load, dynamic change, equipment reliability and environmental interference characteristics, predicting a future congestion index through an LSTM sequential network; setting a dynamic threshold by combining node importance and a real-time state, and matching a strategy mapping table to generate adjustment strategies such as task shunting and resource redistribution; feedback is monitored after execution, and model parameters are optimized. The system comprises a process modeling module, a data acquisition module, a congestion prediction module, a strategy generation module and an execution feedback module. Through full-process intelligent management, the congestion risk is avoided, and the improvement efficiency and the resource utilization rate are improved.

The invention discloses a computational analysis method applied to high-power power electronic networking equipment, and relates to the technical field of power systems. According to the method, broadband power grid impedance, equipment state and device working condition data are acquired through a sensing layer, and accurate input is provided through layered compression and priority transmission; the cooperative calculation layer introduces a power grid impedance correction coupling interference matrix, establishes an electrothermal coupling loss mapping relation, and realizes coupling interference quantification, loss real-time calculation and full life cycle prediction; the linkage optimization layer constructs a multi-objective optimization function, an optimal control parameter is solved through an efficient algorithm, and a segmented execution strategy is adopted to adapt to different working conditions; the closed-loop feedback layer collects operation feedback data, dynamically updates the model and optimizes the weight, and links fault black-start support and optimal power supply recovery path planning. According to the invention, multi-target collaborative optimization of system stability, operation efficiency, equipment service life and power supply reliability is realized, and technical support is provided for large-scale safe and efficient operation of high-power networking equipment.

The invention relates to the field of industrial internet information security, in particular to an industrial equipment identity authentication and access authority management method and system based on cloud-side collaboration.The method comprises the steps that equipment identity characteristics are collected in real time through an edge authentication gateway, and multi-factor dynamic authentication is carried out; uploading the authentication data to a cloud by adopting a hierarchical encryption transmission strategy; consensus evidence storage is carried out on equipment identity hash and authority change records at a cloud end by utilizing a block chain technology, a global authority strategy is generated based on evidence storage data and authority use data of each edge node through federated learning, and authority distribution is dynamically adjusted in combination with an equipment state and a production scene; the authority state of the equipment is visually displayed through the digital twinborn body, and authority adjustment is automatically executed based on the intelligent contract when the trigger condition is met; and the efficiency of industrial equipment authentication and authority management is improved.

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

The invention discloses an electrical complete equipment state monitoring and early warning system based on multi-parameter intelligent sensing, belongs to the technical field of electrical equipment state monitoring, and can clearly point out a specific fault mode (such as contact resistance increasing overheating) and a possible position (such as an A-phase bus connection point). Through multi-parameter time sequence feature fusion and an intelligent diagnosis model, deep mining of a fault source is realized, the operation and maintenance efficiency is greatly improved, and the maintenance work is turned from blind troubleshooting to accurate disposal. Weak precursor signals of early and slow faults can be captured by extracting depth time sequence characteristics (such as temperature rise rate and dominant frequency offset rate) strongly related to a fault evolution mechanism. The AI model is utilized to learn complex modes of these precursor, and early warning can be given out before the equipment performance is obviously degraded, so that predictive maintenance of'nipping in advance 'is realized, and unplanned shutdown and major accidents are effectively avoided.

The invention relates to a production scheduling and equipment maintenance collaborative optimization method based on a Stackelberg game, and the method comprises the steps: building a Stackelberg game model, taking a production scheduling party as a leader, and taking an equipment maintenance party as a follower; constructing a production leader agent and a maintenance follower agent; executing two-stage reverse training based on meta learning: firstly training a meta maintenance follower agent to enable the agent to quickly adapt to any production strategy and generate an optimal response; taking the current to-be-trained production leader agent as a reference fine tuning element agent to obtain a paired maintenance follower agent, and training the paired production leader agent under the support of the optimal response of the paired maintenance follower agent; and sequentially deciding according to input production task data and equipment state data by utilizing the trained agent pair, and outputting an approximate Stackelberg equilibrium solution to obtain a collaborative optimization result. Compared with the prior art, the method has the advantages of high efficiency, high reliability, accordance with actual working conditions and the like.

The invention relates to the technical field of fault maintenance, and particularly provides an intelligent decision-making system for fault maintenance of multi-type loading and unloading equipment of an ore wharf, which comprises an acquisition and transmission module, a cleaning and extraction module, a fault diagnosis module, a fault pre-control module, a graded early warning module, a resource scheduling module and a system maintenance module. The full-dimensional monitoring of the equipment state can sensitively capture tiny abnormities in the operation of the equipment, find early fault symptoms of bearing wear and hydraulic oil degradation in advance, change the problem of high omission ratio of traditional manual inspection, strive for sufficient processing time for equipment maintenance, enable the equipment maintenance to be changed from dependence on artificial experience to data support, and improve the equipment maintenance efficiency. And the diagnosis efficiency and the prediction accuracy are greatly improved. The problem of conflicts in traditional resource scheduling is effectively solved, reasonable configuration of maintenance resources is achieved, the fault processing period is shortened, the overall efficiency of operation and maintenance of wharf equipment is improved, accurate control over the equipment state is kept, and the method adapts to dynamic changes of operation scenes of multiple types of loading and unloading equipment of an ore wharf.

The invention discloses a cooperative generation and closed-loop checking method, system and device for primary and secondary device operation tickets of an electric power system and a storage medium, and the cooperative generation and closed-loop checking method for the primary and secondary device operation tickets comprises the steps: constructing a primary device ticket forming rule tree, and according to a dynamic binding relationship between a primary device rule and a secondary device rule chain, establishing a secondary device ticket forming rule tree; according to the method, intelligent alternate generation of primary and secondary operation steps is realized, and in the invoicing process, consistency checking of primary and secondary equipment state logic is synchronously carried out: through a forward reasoning and reverse tracking mechanism, whether a primary and secondary equipment state mapping relation before and after each step of operation is correct is judged in real time, and an alarm is given for an inconsistent condition. Therefore, self-verification of safety logic is completed while the operation order is generated, a complete closed loop of generation-simulation-check is formed, and the accuracy of the operation order and the safety of field operation are remarkably improved.

The invention discloses a five-prevention locking control method and system based on multi-source state fusion verification, and relates to the technical field of five-prevention locks.The method comprises the steps that an operation ticket is generated by responding to a target operation task input by an operator, and the operation ticket is issued to a computer key; in response to an unlocking operation of an operator using a computer key, determining a current operation step, and before the current operation step is executed, determining whether the current state of the current operation equipment meets the equipment state requirement or not; according to the current operation step, determining whether the operation state of the operator meets the operation requirement or not; if the current state of the current operation equipment meets the equipment state requirement and the operation state of the operator meets the operation requirement, the current operation step is permitted to be executed, the safety and operation normalization of the five-prevention system are improved, and the technical problem that in the prior art, potential safety hazards are likely to be caused by manual errors is solved.

The application provides a kind of intelligent building environment adaptive regulation and control method based on digital twinning and AI, it is related to artificial intelligence technical field, including the following steps: based on time alignment after multi-source system data, extract dynamic characteristics to construct dynamic feature set;Multi-source system data is input to digital twinning, with the current building equipment state as boundary condition, iteratively simulates the key environmental parameters of multiple time steps in the future, and outputs multi-step prediction sequence;Again input model predictive controller, with the minimum overall energy consumption in the prediction period as the optimization goal, with the key environmental parameters not exceeding the comfort interval as the constraint condition, the equipment control instruction sequence in the first preset time in the future is solved;The first equipment control instruction in the equipment control instruction sequence is issued to the on-site actuator to realize the adaptive regulation and control of building environment, solve the disadvantage that the environment parameter is over-standard and then responds passively, significantly improve the rapid response capability of the system to sudden environmental changes.

The invention discloses a remote intelligent control system and control method for a crane, and relates to the technical field of crane control, the system comprises the following components: a physical entity layer, a digital twinborn layer, a 5G transmission layer, a VR interaction layer and a virtual-real calibration layer; according to the method, an equipment state predictive modeling technology is adopted through a digital twinborn layer, historical operation data of a crane are collected, an equipment state database is constructed, key features related to equipment faults are extracted, and an equipment state prediction model is constructed based on a long-short-term memory network; according to the method, the change trend of equipment state parameters in the future 72 hours can be predicted, when the predicted state parameters approach or exceed a fault threshold value, the system can automatically mark corresponding parts in the digital twin, generate maintenance suggestions and synchronize the maintenance suggestions to a VR interaction layer to prompt an operator, and the maintenance suggestions are displayed in the VR interaction layer. According to the predictive maintenance mechanism, equipment faults can be early warned and processed in advance, the non-planned downtime is shortened, and the operation and maintenance cost is reduced.

The invention relates to the technical field of numerical control machine tool intelligent manufacturing and industrial data collection, in particular to a machining data collection method and system applied to a numerical control machine tool, and the method comprises the following steps: S1, the classification and access scheme design of the numerical control machine tool: dividing the numerical control machine tool into a class I intelligent new machine tool, a class II semi-intelligent machine tool and a class III old dumb equipment according to the digitization capability, a classification access scheme of combination of a general protocol adapter, a serial port-Ethernet converter and a non-intrusive sensor is respectively adopted to realize data access of heterogeneous and old equipment; uniform access of 10 + brand numerical control machine tools is supported, the data intercommunication rate of heterogeneous equipment reaches 100%, the problem of'information isolated island 'is solved, six types of equipment states are recognized, and the OEE calculation precision is improved to + / -2% and is improved by 5 times compared with a traditional method.

The invention discloses a state diagnosis and early warning system and method for power switch equipment, and belongs to the technical field of power equipment monitoring. According to the system, multi-dimensional sensing data of temperature, vibration, partial discharge and the like of the switch cabinet are uniformly collected and locally processed through an edge calculation module of a built-in multi-protocol Internet of Things gateway. On one hand, the edge calculation module carries out real-time state judgment based on a multi-dimensional threshold value; and on the other hand, the trend diagnosis module analyzes historical data by using an XGBoost algorithm to realize trend prediction and early anomaly recognition. And the comprehensive diagnosis module fuses the two types of results and dynamically adjusts the state level so as to realize accurate early warning. According to the method, comprehensive perception and on-site intelligent analysis are realized, the problems of one-sided perception, response delay, insufficient intelligence and data island existing in a traditional scheme are effectively solved, and the real-time performance, diagnosis accuracy and operation and maintenance efficiency of equipment state monitoring are remarkably improved.

The invention relates to the technical field of intelligent manufacturing, in particular to a welding production line robot rhythm analysis method and a computer program product, and the method comprises the following steps: extracting production rhythm data of a station by integrating multi-source heterogeneous signals such as program operation, material in-place and equipment state; constructing production time sequence characteristics reflecting a system coupling relation by combining robot operation records and upstream and downstream station data; then positioning key influence factors by using a beat anomaly analysis model; and finally, generating a feasible production rhythm optimization scheme based on an analysis result and a production constraint condition. According to the method, the abnormal time period and the key root cause can be positioned from the complex time sequence data through multi-source signal fusion and time sequence feature construction, so that the automation level of abnormality diagnosis and the accuracy of root cause positioning are improved, coupling evaluation is performed on an optimization scheme and a preset constraint condition, the feasibility of the proposed scheme is ensured, and the accuracy of root cause positioning is improved. And the overall production efficiency and stability of a welding production line are effectively improved.

The invention provides a PCIe link energy-saving regulation and control method, device and equipment and a storage medium, and relates to the technical field of computers, and the method comprises the steps: collecting target parameters such as a physical layer signal quality parameter, a transmission layer flow parameter and an equipment state parameter; predicting the bandwidth demand in the future time period based on the target parameter; generating a link configuration instruction in combination with a quality of service (QoS) priority strategy and a thermal constraint condition; and finally, the width, the rate and the error correction mechanism enabling state of the PCIe link are adjusted according to the instruction. According to the method, through dynamic bandwidth demand prediction, QoS priority and thermal constraint integration and link parameter adaptive adjustment, 5ms-level burst load response, multi-device global bandwidth optimization and cooperative improvement of energy efficiency and link utilization rate in a high-noise / heat dissipation limited scene are realized.

The invention relates to an intelligent control method and system for a double-table-board laser cutting machine in the field of new-generation information technology, and the method comprises the steps: collecting complex workpiece parameters and processing duration data of each task in the double-table-board laser cutting machine in real time, and carrying out the preliminary sorting of an initial task sequence through a genetic algorithm, thereby obtaining a preliminarily optimized task distribution sequence; for a low-power operation scheme, a cooperative operation time table is generated in combination with the balanced task allocation sequence, potential energy waste points are extracted from the time table, and a refined energy management sequence is obtained by fusing task duration data through an information processing link; monitoring a real-time equipment state, if an idle state is detected, switching to low-power operation, and determining a final task coordination and energy consumption control model by continuously updating sequence parameters; and for the final task cooperation and energy consumption control model, historical operation data is obtained for verification, and if a verification result shows that the production efficiency is improved, an optimized double-table-board laser cutting machine operation protocol is output.

The invention relates to an equipment health degree assessment method and system based on industrial equipment electrical characteristics, and the method comprises the steps: collecting a current signal and a voltage signal of target industrial equipment, and carrying out the anti-aliasing filtering and power frequency interference suppression, and obtaining regular waveform data; respectively extracting a time domain feature, a frequency domain feature and a time-frequency domain feature in the regular waveform data, and carrying out vectorization splicing to generate a multi-dimensional feature vector; a plurality of continuous multi-dimensional feature vectors arranged according to a time sequence are constructed into a feature sequence through a preset health degree evaluation model; and the equipment health index is compared with a preset early warning threshold value in real time, and when it is judged that the equipment health index is continuously lower than the early warning threshold value, an equipment health early warning signal is output. According to the method, the equipment state can be comprehensively represented through multi-dimensional feature fusion, precise health index calculation is achieved in combination with the health degree evaluation model, early warning is achieved through a dynamic threshold mechanism, and the accuracy and timeliness of equipment fault prediction are remarkably improved.