482 results about "Industrial yard" patented technology

The invention provides an intelligent production scheduling system and method for a coal industry scene, and the method comprises the steps: fusing multi-source production information (including equipment state information, coal quality index information and safety detection information) through a server, constructing a fault prediction model, and carrying out the fault prediction of the real-time production information; and generating a task list based on the fault prediction result, the real-time production information and the historical production information, matching a workflow and an optimal executor for each task in the task list, then sending the task to the PC terminal for execution, monitoring the execution progress of each task in real time, and urging the overdue task. And generating a corresponding report for each executed task based on the fused information, the fault prediction result, the real-time production information, the historical production information and the execution process information of each task, and sending the report to a display device for display. By adopting the method, the efficiency and the accuracy of production scheduling in a coal industry scene can be improved, the real-time performance of production scheduling can be ensured, and the statistics and the checking of the whole process link can be facilitated.

The invention discloses an intelligent detection and dynamic supplement regulation and control system for electroplating bath solution components, and belongs to the technical field of electroplating. The system comprises an electroplating bath body, a detection module, a data processing and control module and a regulation and control module. The detection module is used for detecting the metal ion concentration, the pH value, the temperature and the additive concentration of the bath solution on line in real time through a spectrum sensor, an electrochemical sensor and a physical parameter sensor; the data processing and control module adopts a PID and machine learning fused intelligent regulation and control model, predicts the component change trend according to the detection data and the process parameters, and generates a dynamic regulation and control instruction; the regulation and control module quantitatively supplements target components through a high-precision metering pump, and the closed-loop feedback correction module is combined to ensure that the components of the bath solution are stabilized within a preset threshold range. Multi-parameter synchronous detection and accurate regulation and control are achieved, the quality stability of the plating layer is improved, raw material consumption and labor cost are reduced, and the method can be widely applied to industrial scenes such as metal surface treatment.

The invention belongs to the technical field of industrial robot inspection, and discloses an instrument identification method for industrial robot inspection, an instrument identification system comprises a pose adjustment module, a lightweight detection module, an image double enhancement processing module and a double-branch instrument reading identification module, and the instrument identification method comprises the following steps: obtaining an instrument image, inputting the instrument image into an image dual-enhancement processing module to obtain a preprocessed instrument image; the preprocessed instrument image is input into a lightweight detection module, an instrument target detection result is output, the instrument type is judged according to the result, and an instrument reading result is output and recorded. According to the method, the improved lightweight target detection model and the adaptive image optimization processing algorithm are combined, the real-time performance and the high efficiency of instrument reading in an industrial scene are effectively improved, more intelligent and accurate instrument reading and industrial inspection can be provided, and powerful support is provided for information acquisition in a complex industrial environment.

The invention discloses a park multi-source heterogeneous data analysis method and system based on the industrial Internet of Things, and relates to the technical field of data analysis. The park multi-source heterogeneous data analysis method based on the industrial Internet of Things comprises the following steps: determining a suspected target according to image information of a target area; according to the moving trend of the suspected target, predicting a moving path of the suspected target, and obtaining feature information of the suspected target through supervision equipment on the moving path; according to the feature information and in combination with working data of the suspected target in a past preset time period, predicting an abnormal probability of an abnormal condition of the suspected target, and judging whether the abnormal probability exceeds a preset threshold value or not; and if the abnormal probability exceeds a preset threshold value, tracking the abnormal target. The technical problems that in the prior art, due to data islands and response mechanism lagging, accurate risk identification and dynamic management and control in a complex industrial scene are difficult to achieve, and false alarm or missing alarm is likely to occur are solved.

The invention discloses an industrial robot predictive maintenance system based on machine learning, and relates to the technical field of industrial robot maintenance. Comprising a data acquisition module, a preprocessing module, a feature engineering module, a hybrid prediction model unit, a meta-learning and domain adaptive module, a federated learning coordination module, a digital twin sample generation module and a dynamic decision engine unit. A model agnostic meta-learning algorithm is utilized to train a cross-brand universal feature extractor, distribution of different brand data is confused in combination with an adversarial field adaptive network, a'data gap 'caused by sensor parameter definition and sampling frequency difference in traditional modeling is broken through, model cooperative training is achieved on the premise that data of all brands are not out of the local, and the modeling efficiency is improved. A global model containing cross-brand fault generality is generated, and the problem of data islands caused by commercial secrecy requirements in an industrial scene is solved.

The invention relates to the technical field of intelligent manufacturing and industrial Internet of Things, in particular to an intelligent factory equipment safety management system and method, and the system comprises an identity authentication module, a dynamic safety interaction module, an intelligent monitoring response module and a self-adaptive optimization module. An identity authentication module; through fusion of multi-dimensional information authentication, block chain decentralized storage, intelligent algorithm behavior analysis, encrypted communication and fine-grained authority control, multi-source data threat detection and reinforcement learning driven strategy optimization, a whole-process security system is constructed. The method comprises the steps of identity authentication, security interaction, monitoring response and adaptive optimization. The problems that in a traditional industrial scene, equipment identity authentication is fragile, data transmission is not safe, threat response lags and strategies are staticized are solved, high-safety authentication, dynamic encryption communication, real-time threat response and strategy self-optimization are achieved, and the equipment safety protection capacity and the system intelligence level are improved.

The invention discloses a multi-modal data fusion-based steel wire rope detection and real-time transmission method and system, and the method comprises the steps: starting a detection system, collecting a steel wire rope damage detection signal, collecting equipment position and operation state data, constructing an original data set, carrying out the preprocessing, building an incidence matrix, dividing a processing unit, and then extracting features, and forming an initial feature data set. And extracting damage features through a multi-branch network, strengthening the weight of a key region, fusing position and equipment state features through a multi-modal fusion module, generating fusion vectors, classifying and identifying, and outputting a structured detection result. And the acquisition equipment end performs grading processing on the data, distributes transmission channels according to priorities, dynamically adjusts parameters, adds integrity and time sequence identifiers, and the upper computer verifies the data integrity and triggers abnormal supplementary transmission. And the upper computer decodes the data, reconstructs the waveform, gives an alarm in real time in combination with preset parameters, and generates details of damage key information. Reliable technical support is provided for safe operation and maintenance of the steel wire rope in industrial scenes.

The utility model relates to the technical field of detection equipment, and provides multi-spectral detection equipment for defects in industrial places, which comprises a three-axis cradle head bracket, the detector outer shell is rotationally connected with the three-axis holder bracket, and the position adjustment of the detector outer shell is controlled by the three-axis holder bracket; the visible light lens I, the visible light lens II, the infrared lens and the ultraviolet lens are mounted on the detector outer shell, and lenses of the four lenses are positioned on the same side surface of the detector outer shell; and the visible light lens I, the visible light lens II, the infrared lens and the ultraviolet lens are distributed in a crossed manner. According to the technical scheme, visible light pictures and infrared or ultraviolet pictures under the same field angle can be collected, the four lenses are distributed in a crossed mode, coordinate transformation of the pictures can be achieved conveniently, information result fusion of visible light detection and infrared or ultraviolet detection is achieved, and the detection accuracy is improved. And the detection accuracy and reliability of industrial place defects and the like can be improved.

The invention discloses a lightweight industrial device surface defect detection method, system and equipment and a medium, and the method comprises the steps: obtaining any one of or a combination of two of an image and point cloud data of a normal sample in an industrial scene, and training a pre-established teacher model; constructing a student model, generating knowledge distillation through masking, migrating teacher features to a student network, and training the student model; performing channel sorting pruning on the trained student model to obtain a pruning model; and inputting a to-be-detected sample collected in real time into the pruning model, and judging whether the to-be-detected sample has defects or not. According to the method, the knowledge distillation technology and the model pruning technology are combined, through iterative optimization, the complexity of the model is greatly reduced while high precision is kept, and the limitation of a single lightweight method on a multi-modal defect detection task is effectively solved. According to the invention, the target of deploying a high-performance multi-modal defect detection model on edge equipment can be realized, and meanwhile, relatively high detection precision and modal loss robustness are kept.

The invention belongs to the technical field of three-dimensional scanning path planning and industrial automatic detection, and particularly relates to a full-coverage scanning path planning method for a large complex structural part. The revolutionary optimization of the three-dimensional scanning path of the large structural member is realized by constructing a comprehensive technical system of curvature self-adaptive sampling, pose collaborative optimization, a three-stage Weibull motion strategy, real-time collision detection and whole-process efficiency improvement. According to the scheme, geometric feature perception, motion continuity guarantee, algorithm adaptability enhancement and operation safety control are creatively integrated, a technical breakthrough is formed in three dimensions of non-blind area coverage, high motion efficiency and zero collision risk, and meanwhile, through parameterization design and a cross-platform compatible framework, the operation safety is improved. Deployment flexibility and system robustness in an industrial scene are remarkably improved, and a complete solution with high precision, high efficiency and high reliability is provided for automatic detection of a complex curved surface.

The invention provides an industrial scene automatic monitoring method, system and product based on multi-modal data, and relates to the technical field of industrial scene monitoring, and the method comprises the steps: obtaining environment multi-modal data in an industrial scene, carrying out the anomaly detection of each device in the industrial scene based on the environment multi-modal data, and obtaining the abnormal data of each device in the industrial scene; outputting an environment perception result in a natural language form; obtaining operation data corresponding to each device based on the environment perception result, obtaining a real-time correlation coefficient between different operation parameters of each device during operation, and outputting a parameter correlation result during device operation in a natural language form; and aligning time information of the environment multi-modal data and the operation data, describing an industrial scene state based on an environment perception result and a parameter association result, and outputting an industrial scene monitoring result including an environment state, an equipment state and an overall trend in a natural language form. According to the invention, the problems of low efficiency, easy omission and the like caused by dependence on workers in industrial scene monitoring are solved.

The invention discloses a multi-agent resource scheduling method based on a large language model in an industrial scene, and the method comprises the following steps: 1, setting initial scheduling parameters and user input of a multi-agent scheduling system; 2, the planning agent performs intention recognition, feature value extraction and coding; and 3, the scheduling agent receives the scheduling coding information, selects a scheduling algorithm tool and generates a scheduling scheme. And 4, the fault detection agent receives the initial scheduling scheme and receives key parameters of the working agent. And 5, the fault detection agent judges that a fault exists and optimization exists, if yes, the scheduling agent is fed back, and the step 3 is executed again, and if not, an optimal scheduling result is output. According to the method, a multi-agent architecture driven by a large language model is adopted, and a dynamic algorithm tool selection mechanism and a prompt project are introduced. Minimization of task total time consumption and maximization of fault detection accuracy are taken as optimization objectives, and adaptivity of industrial scene resource scheduling and system reliability are remarkably improved.

The invention provides a task processing method and device based on an industrial large model, a medium and a product, and relates to the technical field of industrial automation. The method comprises the following steps: constructing a multi-source data set based on an industrial scene, and training a plurality of adapters corresponding to the multi-source data set by adopting an adaptive low-rank adaptation algorithm; according to the learnable weight, fusing the plurality of adapters to obtain a fine-tuned industrial large model; and inputting an operation and maintenance task of the intelligent equipment in the industrial scene into the fine-tuned industrial large model, outputting an operation and maintenance strategy of the intelligent equipment, and controlling the intelligent equipment in the industrial scene to execute the operation and maintenance strategy. According to the method, efficient fine tuning and capability integration of an industrial large model in a multi-source heterogeneous data scene are achieved by introducing a self-adaptive low-rank adaptation algorithm and a multi-task adapter fusion framework, the fusion conflict problem of multi-source heterogeneous data is solved, efficient reasoning of full-process intelligent interaction of the air compressor is achieved, and the intelligent interaction of the air compressor is achieved. And the stability and the precision of the large industrial model in a complex industrial task are improved.

The invention relates to a self-adaptive vehicle washing system for an engineering vehicle and a control method. According to the self-adaptive vehicle washing system for the engineering vehicle and the control method, the existence, contour and dirt characteristics of the vehicle are automatically detected through the sensing recognition system, the cleaning logic is autonomously executed in combination with the intelligent control system, vehicle guiding, equipment starting and stopping and washing operation do not need to be manually intervened, the cleaning efficiency is improved, and the working efficiency is improved. And continuous operation requirements of industrial scenes such as metallurgy and mines are met. The spray washing system adopts a top spray head and a lateral spray head which can move longitudinally and transversely to be matched with a fixed chassis spray head, and contours of different vehicles can be accurately covered; according to the method, grating sensing data and an image recognition result are fused through a lightweight convolutional neural network, the vehicle type and the dirt adhesion level are accurately judged, the water pressure, the nozzle angle and the cleaning time are dynamically adjusted, optimal matching of cleaning parameters is achieved, the water consumption and energy consumption are reduced, and meanwhile a cleaning program is continuously optimized through machine learning.

The invention discloses an industrial robot inspection system based on digital twinning and an obstacle avoidance method, and relates to the field of industrial automation. The system comprises a multi-source sensing and data acquisition module, a digital twin construction and synchronization module, an intelligent inspection planning and state evaluation module, a dynamic obstacle avoidance and path re-planning module and an intelligent early warning and decision support module. The method comprises the following steps of: acquiring working related data of the industrial robot in real time, constructing and dynamically updating a digital twin virtual environment, planning an inspection path, evaluating an equipment state, generating an obstacle avoidance strategy, triggering early warning and outputting a decision suggestion. According to the method, efficient monitoring and intelligent management and control of an industrial scene are realized, an innovative scheme is provided for safe and stable operation of industrial production, and the industrial automation management level is effectively improved.

The invention relates to the technical field of hydraulic engineering and computer application, and particularly provides an industrial water demand prediction method based on big data mining and physical mechanism dual-drive, and the method comprises the steps: carrying out the space-time alignment and feature anchoring of a multi-source database by taking an industrial water full-cycle element as an index; establishing an inter-index nonlinear semantic association network; multi-dimensional indexes in the nonlinear semantic association network are input into the dynamic entropy weight redundancy elimination model, redundant features are eliminated, and an industrial water sensitive factor set is generated; constructing an industrial water constitutive equation with explicit interpretability in combination with physical correlation characteristics of the core control chain; and performing bidirectional embedding with a social economic parameter prediction module to form a double-engine prediction architecture which takes a physical mechanism equation as a core driving force and takes a real-time data stream as a dynamic corrector, thereby realizing mechanism-data dual-drive prediction of industrial water demand evolution. According to the method, the problem that a prediction result of a traditional data-driven model in an industrial scene is not traceable is solved.

The invention belongs to the field of information processing, discloses an industrial multi-modal data semantic alignment method based on vector space and topological constraints, and aims to solve the problem that multi-modal data semantic segmentation in an industrial scene is difficult to unify and associate. The method comprises the following steps: performing feature extraction and structured analysis on modal data, and mapping the modal data to a unified semantic vector space through a projection layer; on the basis of vector similarity matching, topological structure constraints derived from process drawings and the like are introduced, neighbor relation verification is carried out on candidate entities, context logic verification is carried out in combination with a large model, and therefore the accuracy of cross-source entity alignment is remarkably improved; mixed retrieval is supported by establishing an efficient vector database index, and incremental updating of the knowledge graph is achieved through large-model-assisted reasoning and rule base verification. Vector semantics and specific structural dependency of an industrial system are effectively fused, and high-precision and evolvable cross-modal semantic alignment and intelligent association analysis are achieved.

The invention relates to the technical field of industrial robot dynamic path planning, and particularly discloses an AI-based industrial robot dynamic path planning method and system. Comprising the steps of fusion perception data set generation, dynamic parameter set construction, trajectory prediction input signal generation, obstacle motion trajectory prediction, collision risk map generation, path optimization objective function generation, dynamic path re-planning execution and joint control instruction conversion. According to the method, a perception data set is fused in real time, environment changes and the dynamic state of the robot are captured, a dynamic parameter set is generated, a collision risk map is constructed, meanwhile, the weight is optimized by dynamically adjusting a path, a target function is generated to execute re-planning, and finally a re-planning output path is obtained; dependence of a traditional method on a static map is avoided, efficient operation in a changeable industrial scene is ensured, safety and efficiency are balanced, an active safety barrier is provided for an industrial robot, and the efficiency and quality of an industrial automation process are optimized.

The invention relates to the technical field of industrial data analysis, and discloses an artificial intelligence-based industrial equipment operation data analysis method and system, equipment and a medium, and the method comprises the steps: carrying out the dynamic noise reduction processing of the multi-source sensor data of the industrial equipment, and dynamically adjusting the size of a filtering window; performing cross-modal feature alignment on the multi-sensor data after noise reduction, and respectively extracting time-frequency domain features; constructing a lightweight state evaluation model based on the equipment maintenance knowledge base, performing equipment state evaluation on the fused features, and outputting a confidence index; performing multi-stage verification on the evaluation result, and dynamically correcting the confidence index according to the verification result; and triggering a control instruction in a grading manner according to the corrected confidence index, and completing closed-loop response from data analysis to equipment control. The method breaks through the technical bottleneck of a traditional method in a dynamic industrial scene on the premise of ensuring privacy security (data are not delivered out of a factory), and provides a standardized solution for predictive maintenance and intelligent control.

The invention discloses an industrial virtual power plant flexible resource capacity aggregation method based on artificial intelligence, and belongs to the field of power system scheduling and energy management, and the method comprises the following steps: S1, generating a dynamic model parameter containing a constraint boundary and an output prediction curve; s2, scoring the generated dynamic model parameters containing the constraint boundary and the output prediction curve result, and performing priority ranking according to the scoring result; s3, based on a priority ranking result, generating an optimal flexibility resource aggregation capacity allocation scheme; s4, determining a feasible declaration scheme of the industrial virtual power plant; and S5, dynamically updating the steps S2, S3 and S4 by using the real-time operation data. By the adoption of the industrial virtual power plant flexible resource capacity aggregation method based on artificial intelligence, accurate quantification and efficient aggregation of the industrial virtual power plant flexible resource capacity are achieved by fusing the AI algorithm of multi-dimensional feature extraction and industrial scene constraint modeling.

The invention discloses a carbon emission monitoring method and system, and the method comprises the steps: carrying out the partitioning of a multivariable time sequence related to the change of carbon emission, and obtaining subsequence blocks of different scales; performing trend-seasonal term decomposition on the sub-sequence blocks to obtain seasonal terms and trend terms corresponding to the sub-sequence blocks with different scales; dual clustering feature enhancement processing is carried out on the season items and the trend items corresponding to the sub-sequence blocks of different scales, a clustering result is obtained, and dual clustering comprises time dimension clustering and channel dimension clustering; performing space-time cooperative attention prediction based on a clustering result to obtain a carbon emission prediction value; according to the method, the calculation complexity is reduced while the carbon emission prediction reliability is enhanced, and the strict requirements of an industrial scene for real-time reasoning and low resource occupation are met.

The invention discloses an industrial safety detection method combining time alignment and motion consistency, and belongs to the technical field of industrial safety monitoring. According to the method, through seven steps of multi-camera data acquisition and preprocessing, target feature extraction, coordinate projection, instantaneous speed estimation, multi-dimensional similarity fusion, soft matching optimization and model training and target trajectory generation, the problem of space-time inconsistency during multi-camera cooperative detection in an industrial scene is solved. A camera clock offset parameter is innovatively introduced to correct a cross-camera time difference, appearance, geometry and time prior multi-dimensional similarity are fused, a multi-loss function optimization model is constructed, and cross-camera target accurate matching and long-time trajectory tracking are realized. High-precision technical support is provided for personnel safety monitoring and equipment abnormal behavior detection in an industrial scene, and the industrial production safety protection capability is improved.

The invention relates to a desensitization method, system, equipment and medium for industrial data, and the method comprises the steps: building and dynamically maintaining an industrial field standard data variable rule table, and defining a desensitization strategy and a sensitivity level matched with specific industrial variables such as temperature, rotating speed and the like in physical meanings, time scenes and node levels of the specific industrial variables; the method comprises the following steps: adding a multi-dimensional identifier to original industrial data, establishing a six-dimensional binding relation model of data variable type-time dimension-node hierarchy-cluster role-sensitive level-rule entry, driving the data to execute a time-coordinated stepped desensitization process in four node hierarchies of a workshop, an enterprise, a park and a cluster, according to the method, refined access control and data decryption fusing time, space and role dimensions are realized according to the model and a full-link tracing log, and the problems that rules and industrial scenes are disjointed, dynamic time adjustment is lacked, cross-node collaboration is insufficient and authority control coarse granularity is caused in the prior art are effectively solved.

The invention discloses a data processing method and device, electronic equipment and a nonvolatile storage medium. The method comprises the steps that a technological process corresponding to an industrial machining task is determined, and the technological process comprises multiple machining procedures; a first parameter value corresponding to the machining procedure is determined, and the first parameter value is used for representing the importance degree of the machining procedure; determining industrial data corresponding to the processing procedure, and determining historical congestion times of the industrial data; the priority of the industrial data corresponding to the processing procedure is determined according to the first parameter value and the historical congestion frequency corresponding to the processing procedure, transmission scheduling is carried out on the industrial data according to the priority in the execution process of the industrial processing task, and the larger the first parameter value is, the higher the priority is, the larger the historical congestion frequency is, and the higher the priority is. According to the method and the device, the technical problem that the increasingly complicated intelligent industrial scene requirement is difficult to meet by a mode of carrying out industrial data communication scheduling through a fixed rule in the related technology is solved.

The invention provides an industrial assembly action recognition and error correction training method based on deep learning, and the method comprises the steps: achieving the synchronous collection of the whole assembly process through the deployment of a multi-mode perception collection system; a standard assembly database is constructed, a three-dimensional simulation platform is introduced, random disturbance is applied through a parameterized digital human body model, field disturbance is simulated, and a virtual sample enhancement training set is generated; based on real and virtual samples, constructing a deep neural network model, fusing a graph convolutional network, an LSTM and a convolutional network to extract skeleton, trajectory and mechanical features, and realizing assembly action recognition, anomaly detection and key frame positioning; and an assembly quality scoring function is constructed through the track consistency, the attitude offset and the mechanical anomaly index, multi-stage error correction feedback is generated, and personalized training is guided. The method is high in recognition precision of errors in assembly actions of assembly operators, can achieve timely and intelligent feedback, and is suitable for assembly quality optimization and skill training in an industrial scene.

1. The name of the design product: a floor cleaning robot. 2. The use of the design product: automatic cleaning for industrial scenes. 3. The design points of the design product: in shape. 4. The picture or photo that best shows the design points: perspective view 1. 5. The bottom of the design product is not easily visible or not visible during use, and the top view is omitted.

The invention provides a prebaked anode flue gas carbon capture integrated system which comprises an integrated purification unit, a pollutant removal unit and a pressure swing adsorption carbon capture unit which are sequentially connected through a pipeline, flue gas is subjected to dust pre-removal and organic pollutant adsorption of the integrated purification unit and deep treatment of nitrogen and sulfur oxides of the pollutant removal unit, and the flue gas is subjected to carbon capture and carbon capture. According to the invention, the flue gas enters the pressure swing adsorption unit and then is efficiently captured by the pressure swing adsorption unit, and a continuous and synergistic treatment chain is formed in the whole process, so that the heat loss of the flue gas transmitted among the units is obviously reduced, and the energy waste caused by equipment dispersion in the traditional system is avoided; meanwhile, in the aspect of equipment layout, the modular integrated design greatly compresses the occupied area, efficient utilization of an industrial site is achieved, the flue gas treatment cost is reduced, and the flue gas treatment reliability is improved.

The invention discloses an industrial equipment fault diagnosis method and system under supplementary mark constraint, and belongs to the technical field of industrial intelligent operation and maintenance and fault prediction health management. The method comprises the steps of collecting and preprocessing equipment vibration signals; constructing a one-dimensional residual error-based double-end deep network as a complementary mark learning model; a joint probabilistic loss function is generated by using a complementary mark based on maximum likelihood estimation, so that the model learns fault features from a negative tag; and high-precision fault classification is realized by using the trained model. According to the method, the complementary mark learning normal form in weak supervised learning is applied to industrial equipment fault diagnosis, more than 90% of diagnosis accuracy is obtained on a plurality of standard bearing data sets such as CWRU and MFPT, the data marking threshold is remarkably reduced, the problems of scarcity of fault samples and difficulty in marking in an industrial scene are effectively solved, and the fault diagnosis accuracy is improved. The method has important practical application value.

The invention relates to the technical field of industrial equipment monitoring, in particular to an industrial equipment remote monitoring and early warning method based on data fusion, which comprises a multi-dimensional parameter dynamic correlation analysis module, an edge side distributed data processing unit, a self-adaptive threshold generator and an abnormal state early warning mechanism. Through capturing a multi-parameter nonlinear relationship, distributed data processing, dynamic threshold adjustment and a hierarchical early warning strategy, the monitoring real-time performance and accuracy are significantly improved, and the false alarm rate and the missing report rate are reduced. The method can effectively solve the problems of long monitoring period, high abnormity identification delay and the like, is suitable for large-scale industrial scenes, and has a wide application prospect.

The invention provides an industrial energy consumption monitoring and early warning system based on artificial intelligence. According to the system, the real-time performance and the intelligent level of energy consumption anomaly detection in an industrial scene are improved. The method comprises the following steps: firstly, acquiring equipment state data through a high-precision sensor group, and realizing nanosecond time synchronization based on an FPGA (Field Programmable Gate Array) to ensure time-space consistency of data; thirdly, a feature extraction and fusion mechanism is introduced into the edge end, anomaly recognition is achieved by combining an isolated forest and an auto-encoder model, and meanwhile an energy consumption prediction model is constructed based on historical data to generate a prediction curve; and finally, dynamically optimizing a detection threshold value by utilizing reinforcement learning, generating a scheduling strategy based on integer programming, and continuously optimizing model parameters and a control strategy by a closed-loop module. Through the integrated design of perception, analysis, prediction and optimization, the accuracy, predictability and response speed of energy consumption monitoring are remarkably improved, and the industrial deployment value is good.