381 results about "Industrial site" patented technology

The invention belongs to the technical field of robot control, particularly provides a force feedback teleoperation control system and method for the tail end of a mechanical arm of a quadruped robot, and aims at the key challenges that control errors are caused by communication time delay and soft obstacles are difficult to recognize in a dynamic environment. Modeling and judgment are conducted on the contact state of the tail end of the mechanical arm in advance, and feedforward control and buffer adjustment oriented to communication time delay are achieved. And meanwhile, a dynamic semantic map is constructed in combination with multi-source sensing information, and soft obstacle reasoning and path optimization are performed by fusing a tail end force sense change trend, so that the recognition and avoidance capabilities of the system in a complex and invisible obstacle environment are remarkably improved. The system has good perspectiveness, self-adaptability and high redundancy safety characteristics, is suitable for multi-task inspection operation of industrial sites such as a thermal power plant, and is especially suitable for a remote man-machine cooperative operation scene in a narrow space.

The invention relates to the technical field of remote monitoring, in particular to a remote monitoring system and method based on a cloud platform. The method comprises the following steps: acquiring multispectral video image data of an industrial site; analyzing the difference characteristics of visible light and infrared spectrum based on the original data of the multispectral video image, identifying the characteristic mode of electromagnetic interference, and generating spectrum difference interference fingerprint data; identifying an uninterfered area according to the spectrum difference interference fingerprint data to obtain spectrum reliability distribution data; extracting non-interfered spectrum section information based on the spectrum reliability distribution data; performing complementary fusion on the non-interfered spectrum section information and the interfered area to obtain texture enhanced video data; and analyzing the time-varying characteristic of electromagnetic interference by using the texture enhanced video data to obtain interference time sequence characteristic data. According to the invention, dynamic calibration of interference is realized by analyzing the deformation characteristic of the calibration frame, and the adaptive capacity of the system to a changing electromagnetic environment is remarkably improved.

The invention relates to the technical field of industrial equipment health monitoring and fault diagnosis, and discloses a centrifugal pump intelligent detection method and system based on multi-source data. The centrifugal pump intelligent detection method based on the multi-source data comprises the steps that centrifugal pump multi-source heterogeneous data are collected and preprocessed; extracting multi-scale features and constructing an optimal feature subset through hierarchical feature selection; constructing a multi-level dynamic Bayesian network to establish a probabilistic reasoning framework; calculating a feature credibility weight based on the signal quality, the feature stability and the diagnosis correlation; executing multi-source evidence fusion and Bayesian state reasoning; confidence self-calibration and parameter self-adaptive adjustment are realized through historical diagnosis result feedback; the method can effectively cope with the complex and changeable environment of an industrial site, can still keep stable diagnosis performance under the condition that the sensor part loses efficacy or the data quality is uneven, improves the accuracy and reliability of fault diagnosis, and provides powerful technical support for predictive maintenance of industrial equipment.

The invention relates to the technical field of vision, in particular to an object monitoring method based on a multi-camera joint calibration technology and a monitoring camera thereof. The method comprises the following steps: driving all cameras to synchronously shoot a calibration reference object with known geometric characteristics, and solving internal and external parameters of each camera and an accurate space pose relationship between the internal and external parameters by utilizing shot images to form a joint space relationship chain; controlling the camera array to synchronously shoot a target object, and correspondingly converting the two-dimensional image points acquired by the cameras into spatial point coordinates in a unified three-dimensional coordinate system by using the joint spatial relation chain to generate three-dimensional point cloud data of the target object; and processing the generated three-dimensional point cloud data, extracting key geometric features of the target object in real time, performing comparative analysis on the extracted features and a preset standard or a historical state, and outputting a state monitoring result of the target object. According to the invention, the influence of temperature drift and mechanical vibration on the measurement precision is effectively suppressed, and the stability of long-term monitoring of an industrial field is guaranteed.

The invention relates to an intelligent safety protection management method and system. The method comprises the following steps: acquiring behavior data and position data of personnel in an industrial site; analyzing the behavior data and the position data by using a pre-trained personnel behavior model to obtain a behavior recognition result; according to the behavior recognition result and the current operation state of the target equipment, judging a risk level corresponding to the personnel behavior; based on the risk level, a corresponding safety response strategy is matched in the edge computing node, a control instruction is generated according to the safety response strategy, and the control instruction is used for driving the target device to execute a corresponding response action; and the voice interaction module is used for acquiring voice input of an on-site operator, performing semantic recognition on the voice input to obtain a voice recognition result, and executing corresponding emergency control operation to cover a current control instruction when the voice recognition result meets a preset emergency instruction condition. The method has the effect of improving the accuracy of intelligent safety management in the industrial environment.

The invention relates to an industrial vision adaptive illumination compensation system and method based on Bayesian optimization, and the system comprises an environment perception module which is used for collecting the multi-dimensional environment state data of an industrial site in real time, and collecting an original image; the intelligent preprocessing module is used for enhancing the original image according to the parameter combination output by the Bayesian optimization module to generate an enhanced image; the model reasoning module is used for identifying and detecting the enhanced image through a bimodal algorithm to generate a detection result; the Bayesian optimization module is used for performing objective function calculation to generate an optimization objective score, performing iterative optimization according to the optimization objective score and a historical observation data set, generating a candidate parameter combination, and generating a global optimal parameter and a configuration template based on the candidate parameter combination and a current environment feature; and the industrial interface module is used for transmitting the global optimal parameter, the detection result and the multi-dimensional environment state data to an industrial control system. According to the invention, the efficiency and adaptability of illumination compensation are improved, and the data calculation amount is reduced.

The invention provides an HC-MOGA-based agent task collaborative scheduling method in an industrial internet, and aims to solve the key problems of low scheduling efficiency, poor system robustness and the like caused by heterogeneous types of equipment (subsequent modeling is an agent), complex task dependence and non-uniform resource distribution in an industrial site. According to the method, a heterogeneous agent scheduling model with task identification, resource matching and dependent modeling capabilities is constructed for a multi-stage operation task cooperatively completed by a data acquisition agent and an operation type agent in an industrial internet environment. An HC-MOGA (heterogeneous constraint multi-objective genetic algorithm) is provided, multi-dimensional chromosome coding, a layered mapping mechanism, a dual-stage variation strategy and a dynamic constraint repair mechanism are introduced, and a dual-objective optimization model fusing work completion income and comprehensive resource consumption and potential loss is established. The method is suitable for industrial internet typical application scenes such as intelligent manufacturing, automatic production lines and multi-agent task collaborative management.

The invention discloses an industrial data analysis system and method based on digital twinning and causal inference, and the system comprises a physical sensing layer which is used for collecting multi-source heterogeneous data of an industrial site; the digital twinborn platform layer is used for constructing and operating a virtual twinborn model corresponding to the physical entity; the intelligent analysis engine layer is integrated with a causal analysis module and a federal learning module which are associated; the application and interaction layer is used for visualizing the analysis result and issuing a control instruction; wherein the causal analysis module is used for constructing a causal graph based on the multi-source heterogeneous data and performing causal inference. Through federal learning, on the premise of protecting data privacy of all parties, cross-organization and cross-region collaborative modeling and knowledge sharing are realized.

The invention discloses an aluminum profile surface defect detection method based on a DMSA-Swindow-Unet network model, and relates to the technical field of industrial detection, and the method comprises the steps: carrying out the preprocessing of a collected aluminum profile surface image; dividing the image into image blocks by using a convolution operation with a predetermined step length, and performing linear projection to generate an embedded feature sequence; the features are input into a Swin Unet encoder module to extract multi-layer local and global features, and an alternate window attention and shift window attention mechanism is adopted by a multi-layer nested Swin Transform module; in the decoding process, coding features of corresponding scales are fused through jump connection; a DMSA module is configured after the jump connection and is used for enhancing the context information sensing capability after fusion; and step-by-step up-sampling is carried out through image block expansion, and a pixel-level defect segmentation image consistent with an original image in size is output. The method is high in detection precision, strong in robustness, good in adaptability and strong in deployability, and is suitable for automatic identification and intelligent quality inspection scenes of surface defects of the aluminum profile on an industrial site.

The invention discloses a quality detection system based on three-dimensional point cloud scanning and RGB image fusion, relates to the technical field of industrial automatic detection, and aims to solve the problem of high quality detection error rate caused by the fact that a detection result is easily interfered by visual angle change and illumination conditions in an existing detection method. According to the method, the color point cloud model under the unified coordinate system is constructed through space calibration and point pixel level registration, and the structural information and texture features of the workpiece are effectively reserved. Therefore, the problem that the quality detection error rate is high due to the fact that the detected object has curvature dramatic change, a strong reflection area or frequent shielding is solved. According to the method, the detection accuracy is improved, meanwhile, the robustness and the operation efficiency are high, the industrial workpiece detection requirements with complex curved surface structures and high surface quality requirements can be met, and the actual application requirements of an industrial site for an intelligent surface detection system are met.

The invention relates to an intelligent path planning technology, and discloses an industrial robot multi-target path planning method based on a quantum annealing algorithm, and the method comprises the steps: obtaining the environment information of an industrial site; path planning is carried out based on the environment information, and a quantum Hamiltonian is constructed according to an optimization target of a path planning problem; encoding each optional path in the path planning space by adopting quantum bits, and initializing the quantum bits to be in a uniform superposition state; quantum system energy is gradually reduced through a quantum annealing algorithm, and an optimal solution of quantum bits is searched; after quantum annealing is finished, a final path is extracted; carrying out smooth optimization processing on the extracted final path; and generating a motion instruction of the industrial robot according to the optimized final path. The invention further discloses a control system, the industrial robot and a computer readable storage medium. According to the method, the global optimality of path planning is improved while multi-target path planning is efficiently carried out on the industrial robot.

The invention provides an intelligent gateway data processing method for industrial multi-protocol adaptive conversion, which aims at the current situation that various heterogeneous communication protocols exist in an industrial site and is based on a complete process of protocol feature vector extraction, machine learning automatic identification, semantic level analysis and mapping, dynamic rule configuration and protocol frame reconstruction. By constructing a protocol feature model library, semantic contents of original data frames are automatically identified and analyzed, and accurate equivalent conversion of multi-protocol data is further realized in combination with a protocol conversion rule and a unified semantic model. The reconstruction function frame realizes frame structure assembly, data coding and check calculation, and ensures that an output frame completely conforms to a target protocol specification. The method has high compatibility, high expansibility and good real-time performance, and the industrial protocol intercommunication efficiency and the automation degree are remarkably improved.

The invention belongs to the technical field of industrial automation, and discloses an optimization application method and device for an OPC UA protocol in the industrial automation field, and the method comprises the steps: obtaining the multi-dimensional context information of an industrial site, analyzing the future communication demands and behavior trends of a target OPC UA node through a preset prediction model, obtaining predictive behavior features, and carrying out the optimization application of the OPC UA protocol. And according to the predictive behavior characteristics and the current context information, an OPC UA communication optimization strategy is dynamically generated and adjusted, the OPC UA communication optimization strategy comprises a data sampling period, a release interval, a message queue size, a data coding mode and a security strategy level, and the optimization strategy is applied to a communication session between an OPC UA server and a client. According to the method, OPC UA communication parameters are dynamically optimized through intelligent perception and predictive analysis, the data transmission efficiency can be remarkably improved, the system resource consumption is reduced, the real-time performance and reliability of communication are enhanced, and the method is particularly suitable for a complex and dynamic industrial automation environment.

The invention relates to the technical field of spectrum detection, in particular to an element quantitative analysis method, system and equipment and a medium. The method comprises the following steps: acquiring LIBS spectral data of a sample; preprocessing the LIBS spectral data to obtain target spectral data; according to the target spectral data, on the basis of an element spectral line standard library, the analysis element and the matrix element are calibrated, and a calibrated analysis element spectral line and a calibrated matrix element spectral line are obtained; according to the spectral line of the analysis element and the spectral line of the matrix element, calculating to obtain the plasma temperature of the analysis element by using a Boltzmann planar graph method; and inputting the analysis element plasma temperature and the analysis element spectral line into the element quantitative analysis model to obtain the predicted content of the analysis element. The method effectively solves the problems of low detection efficiency, complex operation, sensitive matrix effect and the like in the prior art, and is suitable for rapid and accurate quantitative analysis of stainless steel elements on an industrial site.

The invention belongs to the technical field of image processing, and particularly relates to a graphite ore grade detection method based on an improved YOLO11 model, C3k2-CAS and Detect-SEAM modules are introduced, the feature extraction capability is enhanced, the expression of different scales of ore textures is optimized, and the perception capability of the model for grade difference is improved, so that the detection precision is remarkably improved; the attention mechanism of the CAS module is adopted to replace the traditional multiplication operation, the calculation complexity and the model parameter quantity are greatly reduced while the detection performance is maintained, and the improved model is more efficient than a baseline model and is more suitable for edge device deployment; a plurality of data enhancement strategies are combined, so that the model can maintain high robustness and stable detection capability in a complex industrial environment; the trained optimization model can be efficiently deployed to graphite ore grade detection intelligent equipment, real-time and accurate industrial field detection is achieved, the mineral separation efficiency is greatly improved, and an efficient and reliable computer vision solution is provided for the intelligent mining industry.

The invention provides a bearing fault variable working condition diagnosis method based on a multi-scale convolutional network and MAML, and relates to the technical field of equipment fault diagnosis. The method comprises the following steps: firstly, introducing fast Fourier transform to pre-process an original time domain vibration signal; secondly, a fault diagnosis model based on a multi-scale convolutional network and MAML is applied; and then, an internal and external circulation updating method based on model-independent element learning is adopted, so that the model can quickly adapt to a new task, and a relatively good prediction effect can be achieved only through a small amount of fine adjustment. According to the method, multi-scale feature extraction and meta-learning are creatively combined, the generalization ability of the model under variable working conditions is remarkably improved, the problem that a traditional fault diagnosis method depends on a single working condition and a large sample size is effectively solved, and the method is particularly suitable for small-sample and multi-working-condition bearing fault diagnosis scenes on an industrial site.

The invention provides a rotary kiln coal injection quantity prediction method and device based on multi-modal data dynamic gating and a readable storage medium thereof, and aims to solve the problems of unstable quality of multi-modal data and non-stability of time sequence data in an industrial field. Multi-modal feature adaptive fusion is realized by evaluating modal confidence; monitoring hidden state clustering center displacement in combination with a hidden state clustering deconstruction mechanism, and recognizing working condition change points in combination with a dynamic threshold value to realize adaptive segmentation of time series data; and constructing hierarchical features through local and global attention, and finally outputting a predicted value of the coal injection quantity. According to the method, the accuracy, robustness and real-time performance of prediction are improved, and support is provided for industrial intelligent control.

The invention discloses an industrial anomaly detection method and device based on cloud-edge cooperation. The method comprises the steps that an industrial site configures a cloud server comprising a large model and an edge client comprising an anomaly detection model; a camera is used for collecting image data of products on the industrial production line in real time and marking the image data; using the image data to train an anomaly detection model at an edge client; the trained anomaly detection model parameters are uploaded to a cloud server for aggregation, an integrated soft label is generated through cooperation of the large model and the multiple small models, and the integrated soft label is used for performing parameter updating on the anomaly detection model of the edge client; training and updating of the anomaly detection model are cycled, and real-time collected image data are detected in parallel. According to the method, the generalization ability of the large model and the local experience of the small model are fused, the collective intelligence of the model is enhanced, the accuracy, the stability and the cross-production-line generalization ability of anomaly detection on the industrial assembly line are remarkably improved, and the real-time requirement under the condition that edge device resources are limited is met.

The invention provides a multi-modal fusion causal analysis method and platform for industrial safety production, and the method comprises the steps: carrying out the preprocessing of multi-modal key data from an industrial site, and generating a standardized time series data matrix; based on the standardized time sequence data matrix, combining industrial physical rules and operation process semantics to construct an industrial safety causal atlas; based on a self-supervised learning mechanism, optimizing the structure and edge weight of the industrial safety causal atlas by using the standardized time sequence data matrix to obtain an optimized causal reasoning model; and performing path-level risk activation judgment and accident causal traceability by using a real-time standardized time sequence data matrix constructed by the causal reasoning model and real-time inflow multi-modal key data to generate a risk alarm and traceability report. According to the method, the perspectiveness, the accuracy and the interpretability of industrial safety monitoring are remarkably improved.

The invention discloses a metal dust risk dynamic assessment and grading early warning method based on the Internet of Things, and particularly relates to the technical field of metal dust monitoring. Multi-type sensor nodes are deployed on an industrial site, metal dust concentration, environmental parameters and equipment state information are collected, after edge calculation preprocessing, dust particle dynamic behavior characteristics and high-frequency signal abnormal characteristics are extracted, and the dust particle dynamic behavior characteristics and the high-frequency signal abnormal characteristics are calculated based on the change trend and the coupling relation. A judgment model is constructed to recognize the reliability state of the sensor under different interference working conditions in real time, when it is detected that the sensor has a potential misalignment risk, the system can automatically conduct data correction or redundant path switching and dynamically update an early warning result, and the method effectively improves the dust monitoring stability and early warning accuracy under the complex working conditions and improves the safety of dust monitoring. And the identification and prevention capability of the system on the silent potential safety hazard is obviously enhanced.

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 controller programming method and device based on an equipment model, equipment, a medium and a product, the equipment model is used as a unified semantic anchor point, heterogeneous characteristics of physical equipment are converted into computable structured description, hardware adaptation and business logic are decoupled through a driving framework, a large model focuses on generation of a high-value control strategy, and the control strategy is optimized. Semantic drift and logic illusion of a program generation large model are remarkably inhibited by relying on double constraints of equipment document data and a knowledge graph, finally, an executable drive program meeting strict requirements of an industrial site is output through compiling verification of a closed loop, and due to the fact that manual development is not needed, development efficiency is improved, development cost is reduced, and development efficiency is improved. The equipment model provides a cross-protocol and cross-manufacturer standardized interface contract, so that the problem of poor reusability in traditional development is solved, and the reusability is improved.

The present invention belongs to the technical field of mechanical fault diagnosis and signal processing, and specifically relates to a bearing fault diagnosis method, system, equipment and medium based on a multi-channel analog filter feature network. The method constructs a multi-channel analog filter feature extraction network, uses multiple parallel bandpass filter channels to extract bearing vibration signal characteristics, and combines an analog neural network classifier to perform fault classification. Signal preprocessing includes removing DC components, amplitude normalization and segmented processing; the filter passband parameters and classifier parameters are jointly optimized through a particle swarm algorithm, with classification accuracy and decision confidence as fitness functions. The system includes signal acquisition, preprocessing, multi-channel filtering, feature extraction, analog neural network classification and result display modules, achieving low-power, high-real-time bearing fault diagnosis. The present invention solves the problems of high power consumption and large delay of traditional digital processing methods, and is suitable for long-term monitoring and large-scale deployment in industrial sites.

The invention relates to the technical field of equipment state monitoring, in particular to an emergency cut-off valve bolt looseness detection method and system based on multi-modal fusion. The method specifically comprises the following steps of multi-source data synchronous acquisition, accelerometer installation and data processing, environmental adaptability preprocessing, bolt area optical flow feature extraction, Kalman filtering multi-source fusion, bolt looseness detection and loose bolt positioning. According to the emergency cut-off valve bolt looseness detection method based on multi-modal fusion, online, automatic and real-time accurate monitoring and early warning are achieved, the reliability and the anti-jamming capability of the system are remarkably improved, a high-precision and quantifiable objective diagnosis result is provided, good economical efficiency and large-scale deployment feasibility are achieved, and the method is suitable for popularization and application. The industrial pain point that a traditional contact type sensor scheme is difficult to apply to an industrial site with a large number of bolts in a large scale due to too high cost and technical complexity is effectively solved.

The invention relates to an industrial process soft measurement method, in particular to an industrial process soft measurement method based on a hierarchical space-time enhanced quality related network. Performing normalization processing on the original data; a multi-head hierarchical time enhancement module (MHTE) is designed, local time characteristics are extracted through cross convolution, global time characteristic extraction is guided, and local and global characteristics are fused; a stacked encoder framework is constructed; introducing a mixing quality regularization mechanism combining a maximum information coefficient (MIC) and a Jaccard similarity coefficient (JSC); and optimizing the model through layer-by-layer pre-training and regression fine tuning. And the trained soft measurement model is utilized to carry out online quality prediction, and real-time monitoring and production strategy adjustment are carried out, so that the product quality and the production efficiency are improved, and green production is promoted. Simulation experiments prove that the method can efficiently extract dynamic spatio-temporal characteristics, and has high prediction precision and stability.

The invention discloses an early warning grade evaluation method based on an urban pollution industrial site, relates to the technical field of industrial site early warning evaluation, and solves the technical problems of incomplete overall evaluation and lack of systematic analysis process and data processing correction caused by analysis of a single evaluation index. According to the method, pollution factor indexes are integrated in a three-dimensional mode through the pollutant standard exceeding multiple mean value, the specific pollutant toxicity coefficient and the pollution plume diffusion area proportion, the pollution intensity is reflected, toxicity hazards and spatial diffusion risks are considered, the soil pollution area is calculated through the Kriging interpolation method, the groundwater pollution plume area is predicted through the MODFLOW model, and the groundwater pollution plume area is predicted through the MODFLOW model. The overlapping area is processed through a maximum area method, repeated statistics is avoided, the accuracy of pollution range data is improved, different threshold values are set, standardized matching of comprehensive indexes and early warning levels is achieved, corresponding management and control measures are defined for all the early warning levels, abstract comprehensive indexes are converted into specific practical operation guidance, and hierarchical management and control can be conveniently carried out.

The invention provides a visual system online parameter calibration method and system for industrial field dynamic measurement. The method comprises the steps that a plurality of calibration plates are arranged at calibration points; a two-dimensional rotating mechanism is used for driving a visual camera to rotate, and an image of each calibration plate is obtained; recording a rotation angle corresponding to the two-dimensional rotation mechanism when the image of the calibration plate is acquired each time; the physical coordinates and the image of each calibration plate and the rotation angles of the two dimensions of the two-dimensional rotation mechanism serve as self-calibration input data and are input into a visual imaging matrix formula, and pose parameters of the mounting base are obtained; and substituting the obtained pose parameters of the mounting base into a visual imaging matrix formula, calculating the pose calculation value of each calibration plate corresponding to the image of the calibration plate corresponding to each equation, and comparing the pose calculation value with the physical coordinates of the calibration plate until the distance between the pose calculation value and the physical coordinates is smaller than a preset value. The method can quickly calibrate the visual system, and is especially suitable for industrial sites with severe site environments.

The invention provides an industrial operation area multi-view video collaborative analysis method and system based on artificial intelligence. The method comprises the following steps: firstly, acquiring multi-view video data; secondly, based on the working area and the equipment layout, deducing a semantic association relationship among the multi-view video data; according to the semantic association relationship, dynamically adjusting the analysis priority of the multi-view video data through an artificial intelligence model to determine an analysis priority sequence; and according to the determined analysis priority sequence, executing interactive co-processing of the multi-view video data, and finally generating a co-analysis result. According to the technical scheme provided by the invention, the physical layout and the workflow of the industrial site are converted into the semantic association relationship, and the dynamic scheduling analysis resources and the interactive collaborative analysis are performed according to the semantic association relationship, so that the spanning from simple monitoring of a plurality of isolated video streams to intelligent and coherent understanding of the whole operation flow is realized; and the intelligent level and decision support capability of monitoring analysis are improved.

The invention discloses an intelligent identification and counting method, system and equipment for an industrial elevator and a medium, and the method comprises the steps: collecting an interference target negative sample, generating a diversified illumination scene sample, combining the interference target negative sample with the illumination scene sample, training and optimizing a detection model, deploying the trained and optimized detection model, and processing a video stream in real time. And judging the effective lifting behavior of the elevator, and intelligently counting the operation frequency of the elevator. According to the method, an adversarial training strategy and negative sample database construction are adopted, the discrimination capability of the model for an interference target is remarkably improved, the stable recognition performance of the system under the all-weather condition is ensured through developed illumination enhancement simulation, and the intelligent counting algorithm based on the state machine is used for realizing the intelligent counting of the interference target through a rigorous condition judgment mechanism. Accurate statistics of the work cycle of the elevator is achieved, a reliable automatic counting solution is provided for an industrial site, and the intelligent level of production management is greatly improved.

The invention discloses a femtosecond optical comb large-size precision absolute distance measurement system in a complex environment. Comprising a source module, a beam splitter, a third dichroic mirror, a fundamental frequency femtosecond optical comb reference detector, a frequency doubling femtosecond optical comb reference detector, a fourth dichroic mirror, a frequency doubling femtosecond optical comb measurement detector, a fundamental frequency femtosecond optical comb measurement detector, a phase discriminator, a ranging zero point mirror and a corner reflector. The source module comprises an atomic clock, a femtosecond optical comb, an optical fiber beam splitter, a signal generator, an electro-optical modulator, a lens, a frequency multiplication module, a first collimation beam expander, a second collimation beam expander, a first dichroic mirror and a second dichroic mirror. According to the scheme, the defects that in the prior art, the measurement range is insufficient, complex industrial sites are difficult to adapt, and the real-time performance is poor are overcome, and a new way is provided for breaking through the technical bottleneck of large-range, high-precision, dead-zone-free, rapid and real-time measurement in a complex environment; and a technical support is provided for engineering application of a femtosecond optical comb precision distance measurement technology and development of a subsequent measurement prototype.