564 results about "Control layer" patented technology

The invention relates to the field of industrial automation, in particular to a multi-camera vision stacking pose planning method and system for an intelligent loading and unloading robot. The method comprises the following steps: constructing a collaborative sensing system through a plurality of industrial cameras deployed in a target space, and collecting dynamic environment data of a stacking area in real time; generating a self-adaptive stacking pose based on a hierarchical pose planning mechanism: analyzing an initial pose parameter through a theme control layer and generating a preliminary placement instruction, and verifying the feasibility of the preliminary placement instruction based on environmental data of the collaborative awareness system through a visual verification layer, and a final placement coordinate is optimized by combining the accurate positioning layer with a virtual box matching algorithm. The problems that in the prior art, real-time environment sensing capacity is lacked, pose planning is disjointed with the actual environment, and dynamic change scenes cannot be processed are solved.

The invention provides a training and examination robot system based on a multi-modal large model, equipment and a computer program product, and aims to accelerate training transformation and upgrading and improve coping ability as basic appeals, and based on regular training work of teams, training and examination robot, sensor acquisition equipment and big data platform three-terminal equipment. A multi-modal data acquisition layer for realizing real-time attitude detection and motion capture by using an edge calculation algorithm, a data processing and analysis layer for finishing real-time image processing through localized edge calculation in a network-free or power-free environment, and system control functions for finishing intelligent decision making, evaluation, error correction and the like are provided; an intelligent decision-making and control layer of a training subject rewriting decision-making mechanism is established, cross-modal collaborative learning middleware is utilized to realize modal representation consistency while training subject transposition evaluation is realized, training and assessment operations of the same subject of multiple persons or different subjects of multiple persons are supported, and the training and assessment efficiency is remarkably improved.

The invention discloses an intelligent control system and method for a shield muck treatment site, and relates to the technical field of shield engineering muck treatment. In order to solve the problems of low efficiency and high pollution of the existing muck treatment mode, the invention provides the following technical scheme: the muck treatment system comprises a sensing layer, a transmission layer, a control layer and an application layer, and the sensing layer is mounted around a muck treatment site and is used for monitoring muck components, equipment operation states and environmental parameters in real time; the transmission layer transmits the data collected by the sensing layer to the control layer; the control layer processes data of the sensing layer and automatically adjusts equipment operation parameters according to a preset control logic and an optimization algorithm; and the application layer analyzes and decides the data processed by the control layer. According to the system, intelligent monitoring and control are achieved, equipment faults are early warned in advance, the downtime is shortened, meanwhile, resource recovery and cyclic utilization are achieved, the construction cost and the environmental influence can be reduced, and the shield muck treatment efficiency, the resource recovery rate and the environmental protection performance are remarkably improved.

The invention discloses a process parameter switching method and device for multi-variety production, and a server side method comprises the steps: receiving a process parameter switching request sent by a manufacturing execution system used for monitoring a product production state, wherein the process parameter switching request carries production demand description information and a product batch model; according to the production demand description information, corresponding target process parameters are automatically obtained, the target process parameters are determined by adopting a preset comparison function, and the preset comparison function comprises a basic comparison term expression and a physical comparison term expression; pre-checking the target process parameters to obtain a checking result; and under the condition that the verification result indicates that the verification of the target process parameter is passed, binding the target process parameter to a product batch model, and issuing the product batch model to an equipment control layer. Therefore, by adopting the embodiment of the invention, the time required for searching and inputting the process parameters is avoided, so that the equipment can be switched to a new production task more quickly, and the production efficiency and the product quality are remarkably improved.

The invention discloses an intelligent courtyard ecological collaborative management system and method. The system comprises a sensing layer, a control layer and an execution layer. The sensing layer is provided with a multi-mode sensor array, comprises an environment monitoring module, an equipment state monitoring module and a biological recognition module, can collect multi-mode data such as illumination, temperature and humidity, equipment operation parameters and plant growth states, and transmits the multi-mode data to the control layer through wireless communication. The control layer adopts a federated learning framework to fuse multi-modal data, extracts features through a deep residual network, generates an environment state matrix by using an attention mechanism, and operates a Markov decision model to output a decision control command and a response level. And the execution layer adjusts the on-off state and operation parameters of the regulation and control equipment according to the output, so that intelligent cooperative control of the irrigation system, the lighting equipment and the like is realized. According to the system, through multi-modal data acquisition and deep fusion analysis, comprehensive perception and accurate regulation and control of the ecological environment of the courtyard are realized, and the management efficiency and the resource utilization rate are improved.

The invention relates to a coal mine equipment cooperative control system based on digital twinning, and belongs to the field of coal mine intellectualization. According to the system, the equipment state, environmental parameters and fault data are acquired in real time through the data acquisition layer; the data processing layer realizes multi-source data fusion and fault prediction by using a deep fusion neural network and a space-time attention network; the digital twinborn layer constructs a three-dimensional dynamic model based on unified geodetic coordinate system coupling equipment and geodetic survey information; and the control layer adopts a distributed stream computing framework to execute collaborative scheduling, active disturbance rejection speed control and fault prediction compensation. The problems that in the prior art, spatial position expression is missing, collaborative delay is high, data are not synchronous, and complex working condition adaptability is poor are solved. The method has the beneficial effects that total-factor high-precision space coupling is realized, the collaborative delay bottleneck is overcome, the adaptability to complex working conditions is enhanced, a closed-loop intelligent optimization system is constructed, and full-chain transparent management and control of the coal mine are promoted.

The invention provides an energy efficiency optimization and predictive regulation and control method for a building electromechanical system based on digital twinning. The method comprises five layers of architectures including a physical entity layer, a data acquisition and interaction layer, a digital twinning model layer, an energy efficiency analysis and optimization layer and a predictive regulation and control layer. The dynamic digital twinborn body interacting and synchronizing with the physical electromechanical system in real time is constructed, fundamental transformation from passive lag control to prospective predictive control is achieved, a high-fidelity virtual model is used as a nondestructive testing platform, countless operation strategies can be safely and rapidly simulated and evaluated in a virtual space, and therefore accurate optimization is achieved, and the real-time dynamic digital twinborn body is obtained. The overall energy efficiency of the system is remarkably improved, the dilemma that the traditional method makes consideration and loses others is eliminated, and the leap-type improvement of the comprehensive operation quality is achieved.

The invention relates to the technical field of intelligent control of injection molding machines, in particular to an injection molding machine closed-loop mold adjustment control system and method based on multi-sensor fusion and a self-adaptive algorithm, and the system comprises a sensing layer which adopts a multi-sensor fusion closed-loop feedback architecture and comprises a pressure sensing array, a displacement sensing unit and a temperature sensing array. The data acquisition module is used for synchronously acquiring mold clamping force, position and temperature data of a mold, constructing a three-dimensional error compensation model and realizing dynamic correction of the parallelism of the mold through space force field analysis; the execution layer comprises a servo motor, a belt wheel and a ball screw and is used for adjusting the position of the template; a double-closed-loop control module is arranged in the control layer, the double-closed-loop control module comprises outer ring mold clamping force control and inner ring position control, mold clamping force deviation serves as input of an outer ring, position correction is generated through a self-adaptive PID algorithm, the inner ring converts the position correction into a servo motor rotation angle instruction, and therefore precise control over the mold clamping force is achieved.

The invention provides a flow and form decoupling cooperative processing method, and relates to the technical field of business flow management, and the method comprises the steps: constructing a layered architecture, separating a flow control layer from a form service layer, defining a flow node jump rule by the flow control layer based on a BPMN standard, and dynamically defining a form structure by the form service layer through JSON Schema; a form identifier is declarated in the process definition file, a corresponding form template is matched through a dynamic mapping service according to context information, and the context information comprises a process instance state, a user role, a device type and a service type; establishing a data sandbox isolation mechanism, generating a unique data space identifier for each process instance, and managing data access authority through a multi-dimensional isolation strategy of a tenant level, a service level and a process instance level; deploying an event bus communication mechanism, triggering two-way communication between a process engine and a form engine through a predefined event type, and ensuring the consistency of cross-system operation by adopting a transaction compensation mechanism.

The invention provides a dynamic energy-saving control method and device for garden landscape lighting, and the method comprises the steps: obtaining comprehensive monitoring data, such as multi-point environment illumination, people flow distribution and time period characteristics, and generating a partition lighting demand matrix through multi-dimensional fusion processing; identifying a contradictory region between the lighting demand and the energy consumption limitation, generating a conflict level mark based on energy efficiency analysis, and reversely deducing minimum power configuration; constructing a dynamic energy-saving constraint condition, carrying out boundary testing, generating a flexible energy-saving boundary, and determining an energy consumption control range; establishing a hierarchical control architecture of a main control layer and an auxiliary control layer, and generating a basic dimming instruction sequence and a supplementary control rule; analyzing an illumination influence relation of adjacent areas to establish a linkage mechanism, and optimizing operation parameters through cooperative control; the illumination power is dynamically adjusted by adopting an envelope modulation technology, a periodic fluctuation feature is extracted to identify an optimal energy efficiency mode, a self-adaptive control strategy is constructed, and intelligent dynamic energy-saving control of garden landscape illumination is realized.

The invention provides a trailing suction dredger loading operation parameter optimization real-time recommendation system and method, and relates to the technical field of trailing suction dredger dredging engineering.The system comprises a data collection layer, a real-time calculation layer, an intelligent recommendation layer and a feedback control layer, the data collection layer is used for collecting trailing suction dredger loading operation parameters in real time, and the real-time calculation layer is used for calculating the real-time calculation layer; the real-time calculation layer can calculate energy consumption and yield in real time based on edge calculation equipment, the intelligent recommendation layer can generate a Pareto optimal solution set through a multi-objective optimization algorithm, and the dynamic weight adjustment module adjusts energy consumption and yield weight coefficients in real time according to the construction stage. The feedback control layer issues the collected loading operation parameters of the trailing suction dredger to an execution mechanism in real time through a PLC, and monitors the operation state of equipment to update a historical database, so that collaborative optimization of energy consumption and yield can be achieved, and the optimization precision is continuously improved through historical data.

The invention relates to the technical field of medical informatization and intelligent scheduling, and particularly discloses a ward nursing process prediction and dynamic scheduling method and system.The ward nursing process prediction and dynamic scheduling method comprises the steps that firstly, a digital twinborn simulation environment synchronized with a physical ward in real time is established, and a nursing process is simulated through a multi-agent network; carrying out multi-time sequence deduction based on the environment, generating a prospective scheduling instruction set, and issuing and executing the prospective scheduling instruction set; calculating a prediction mismatch degree index by collecting actual execution data and comparing the actual execution data with a plurality of dimensions of an expected state; when the index exceeds a dynamic threshold value, a meta control layer is triggered to carry out self-adaptive adjustment on simulation environment parameters, and the self-adaptive adjustment comprises reconstruction of an agent behavior logic weight or switching of a scene deduction mode; and deducing and generating an updated instruction set again based on the adjusted environment to form closed-loop optimization.

The invention provides a tire crane automatic control system and method based on embedded edge calculation, and relates to the technical field of port automation, the tire crane automatic control system comprises a sensing and detecting layer used for collecting motion states and operation environment information of a cart, a trolley and a lifting appliance of a tire crane in real time; the core control layer is used for processing sensor data in real time and generating a control instruction; the action execution layer comprises a PLC gateway, a main control PLC and a driving motor, the PLC gateway communicates with the core control layer and is used for receiving a control instruction, and the main control PLC drives the motor to execute corresponding actions of a cart, a trolley and a lifting appliance; and the task management layer comprises an intelligent remote control console, is in communication connection with the core control layer, and is used for issuing the operation task instruction to the core control layer and receiving the system state data from the core control layer. Unmanned operation of the tire crane can be achieved, and a complete technical base is provided for port automation.

The invention discloses a multi-tenant management and control system and method for a big data platform, and relates to the technical field of data management, and the system comprises a data basic management and control module layer and a data processing collaborative management and control layer. The data basic management and control module layer comprises a big data component management module, a tenant management module, a unified login authentication module, a project management module, a data resource authority management module and a computing resource management module; the data processing collaborative management and control layer comprises a component health monitoring sub-module, a resource prediction distribution sub-module, a dynamic desensitization encryption sub-module, a task collaborative management sub-module and a data quality management and control sub-module; the problems of weak tenant basic management and control, low resource allocation efficiency and insufficient component reliability and data security management and control in the existing multi-tenant management and control technology are solved, and the defects of lack of cross-tenant cooperative capability, lack of task and data quality management and control and insufficient data security management and control caused by the problems are overcome.

The invention discloses a multi-mode perception collaboration and real-time synchronization system, which breaks through the limitation of vision / hearing in the prior art, covers vision, hearing, smell and movement, and realizes more accurate cooperation through a perception layer, a control layer and an execution layer. According to the invention, an immersive experience space full of shock and reality can be created, synchronous output of visual, auditory, tactile and olfactory signals is realized by means of high-precision equipment, and a series of cross-sensory signal superposition schemes are designed on the basis of response characteristics of human physiology and psychology to various sensory stimuli. Therefore, the overall perception of the experiencer to the space atmosphere is enhanced. Meanwhile, through weight distribution and conflict resolution, the system can flexibly adjust the sensing mode according to the environment and the user state. The multi-device cooperation error can be controlled at millisecond level and millimeter level, and the natural fusion of virtuality and reality is realized.

The invention provides a display module and a display device, and the display module comprises a display panel which comprises a plurality of pixel units arranged in an array; the light control device is arranged on the light emitting side of the display panel, the light control device comprises a first regulation and control layer and a second regulation and control layer which are sequentially arranged on the display panel in a stacked mode, the first regulation and control layer comprises a first refraction layer and a second refraction layer covering the first refraction layer, the first refraction layer comprises a plurality of lenses arranged in an array mode, and the second refraction layer comprises a second refraction layer covering the second refraction layer; the orthographic projection of each lens on the display panel at least covers two pixel units, the refractive index of the first refraction layer is larger than that of the second refraction layer, when the display module is in a three-dimensional display state, liquid crystal molecules in the second regulation and control layer do not deflect, and when the display module is in a two-dimensional display state, liquid crystal molecules in the second regulation and control layer do not deflect. And the second regulation and control layer is equivalent to a plurality of lens units arranged in one-to-one correspondence with the lenses. The display module provided by the embodiment of the invention can reduce view crosstalk in a three-dimensional display state.

The invention relates to the technical field of artificial intelligence and data engineering, in particular to an intelligent data development treatment system and method based on a large model, the system is composed of a data access layer, a large model treatment engine, an intelligent feature factory, a compliance control layer and an application interface layer, and all the modules cooperate to achieve intelligent transformation of the whole data development process; the method has the beneficial effects that the potential mode of the multi-source heterogeneous data can be automatically identified, for example, transaction records, user behavior data and system monitoring information are distinguished from mixed log files, and manual rule pre-definition is not needed. And dynamically adjusting the protection intensity according to the data use scene.

The invention relates to the technical field of mine safety monitoring, and discloses a mine high-temperature heat damage monitoring system based on multi-mode Internet of Things data, and the system comprises a prediction and control integrated system architecture module which constructs a prediction and control integrated system architecture, and builds a three-layer PCIS architecture of a perception layer, a prediction layer and a control layer; the thermal field and flow field combined optimizer module is used for constructing a thermal field and flow field combined optimizer and realizing direct coupling of a prediction model and a control algorithm; the self-adaptive anti-interference control mechanism module is used for implementing a self-adaptive anti-interference control mechanism and realizing system fault tolerance through a fault mode library and dynamic reconstruction; the performance self-optimization module is used for implementing system performance self-optimization and continuously improving the system performance through incremental learning and knowledge migration; according to the method, through multi-modal data fusion and the space-time diagram convolutional network, the heat damage prediction precision is improved, the prediction time range is expanded, and more sufficient response time is provided for heat damage prevention and control.

The invention discloses a cigarette cut tobacco batch production circulation control system and method based on single-point condition judgment, and the system comprises a work order parameter verification module which is used for receiving a work order issued by a scheduling system, and carrying out the work order information integrity verification, process parameter compliance verification and equipment suitability verification; the regular expression configuration module is used for providing a visual interface and configuring a regular judgment rule set of equipment starting conditions, ending conditions and abnormal ending conditions; and the single-point judgment execution mechanism is embedded into the PLC control layer, acquires equipment parameters in real time, dynamically evaluates regular conditions based on a finite-state machine model, and triggers an equipment start-stop instruction. According to the system, the cigarette cut tobacco batch switching time is remarkably shortened, and the work order circulation efficiency is improved; the manual intervention frequency is further reduced, and the misoperation rate is further reduced; and the problem of production interruption caused by disconnection of the equipment state and the process parameters is solved.

The invention relates to the technical field of industrial compressed air system intelligent control, in particular to a digital air compression station intelligent control method and system based on AI self-learning, and the system comprises a data collection layer, an AI prediction layer, an optimization control layer and an execution layer. The data acquisition layer realizes accurate acquisition and preprocessing of multi-dimensional data through a three-stage high-precision sensor network and an edge computing node; the AI prediction layer adopts an XGBoost and LSTM mixed model to be combined with an online learning mechanism to realize accurate prediction of the gas consumption demand and the energy efficiency ratio within 1-24 hours; the optimization control layer completes air compressor operation combination optimization and pressure dynamic control based on an improved NSGA-II algorithm and self-adaptive PID adjustment; the execution layer realizes control instruction landing through frequency converter and cluster cooperative scheduling. Meanwhile, an equipment health degree monitoring module, a multi-source data fusion correction module and a waste heat-gas consumption collaborative optimization module are innovatively introduced, and the problems that a traditional air compression station is low in energy efficiency, poor in self-adaption, insufficient in prediction precision and lack of equipment management are solved.

The invention relates to the field of power distribution network graph optimization, in particular to a power distribution network planning graph visibility optimization method, which comprises a layered architecture module, a technical module, a data integration module, a data processing module, a data evaluation module, a visualization algorithm module and a user interaction module. The layered architecture module is divided into a presentation layer, a control layer, a business logic layer and a data access layer through a modern software system; the presentation layer is responsible for interacting with a user and providing a user interface; the control layer is used for processing user input, the processing speed and the response time of the system can be effectively improved and the maintenance cost can be effectively reduced by setting the modules to be mutually connected for use and improving the system performance and architecture, and the maintenance cost of the system is reduced and the availability of the system is enhanced through modular design and automatic deployment. The stability and reliability of the system are improved, uninterrupted operation of key services is ensured, technical innovation is promoted, and a theoretical basis and practical guidance are provided for technical innovation in related fields.

The invention relates to the technical field of aluminum decorative strip coating, and discloses an aluminum decorative strip color coating optimization system based on automatic control, which comprises a material pretreatment layer, a coating control layer and a quality detection layer. The material pretreatment layer is used for treating the surface of the aluminum decorative strip through surface cleaning, base material activation, roughness detection and adhesive force test; the coating control layer realizes coating control by utilizing a color blending unit, a spraying robot cluster, a dynamic temperature control module and a distributed controller in combination with a real-time color matching algorithm and a self-adaptive path planning mechanism; and the quality detection layer adopts an image analysis technology and a deep learning algorithm to construct a coating quality feature model, and color difference detection, defect positioning and process parameter compensation decision are realized. According to the system, automation, precision and intellectualization of the aluminum decoration strip coating process are achieved, the coating quality and the production efficiency are improved, and the system is suitable for the field of aluminum decoration strip color coating.

The invention discloses an international Chinese education handwritten composition picture-oriented intelligent correcting and scoring platform, which is characterized in that the platform framework comprises a front-end display layer, a gateway control layer, a business service layer, a data processing layer and a basic storage layer, wherein the business service layer comprises a login module, a student management module, a teacher management module, a homework management module, a class management module and a correction module, and the correction module comprises image preprocessing, OCR recognition and proofreading, error correction sensitivity adjustment, intelligent correction marking, manual correction and result exporting. Compared with the prior art, the method has the advantages that the handwritten composition picture of a foreign learner is corrected by utilizing CLIP image recognition and a Qwen large language model, a fine-grained marking standard system and a correcting marking tool are developed, one-key intelligent correcting marking is realized, meanwhile, a correcting result can be reviewed and corrected for the second time, and the correctness of the handwritten composition picture of the foreign learner is improved. The correction accuracy is improved, high-quality labeling data can be accumulated to serve as training samples, and the performance of the intelligent correction model is continuously optimized.

The invention discloses a shield construction full-process management and control system and method based on intelligent scheduling, and relates to the technical field of tunnel construction, the system comprises a data acquisition layer, a data fusion and intelligent scheduling layer, an execution control layer and a man-machine interaction and visualization platform. The data acquisition layer acquires multi-source data of shield construction in real time; the data fusion and intelligent scheduling layer performs fusion processing on multi-source data, generates a comprehensive scheduling scheme based on an intelligent algorithm, and dynamically manages task priorities and security constraints; the execution control layer receives and executes the scheme, and cooperatively controls tunneling, transportation, assembly and vertical transportation equipment; the man-machine interaction and visualization platform realizes construction state visualization, scheduling display, abnormity alarm, remote control and multi-work-point data access and authority management. According to the invention, through a multi-layer collaborative architecture, full-process intelligent management and control of shield construction are realized, the data-driven decision-making capability, the equipment collaborative efficiency and the multi-project scheduling level are improved, and the construction efficiency is improved.

The invention discloses an intelligent agricultural big data system, and belongs to the field of agricultural big data, and the system comprises an environment monitoring module, a sensing layer deploys a sensor to collect original data, and a transmission layer is provided with an edge gateway which is connected with the sensor and carries out the transmission of the original data; the equipment layer is used for regulating and controlling irrigation, ventilation and light supplement schemes, and the control layer is used for optimizing MQTT message middleware to control the agricultural Internet of Things system; according to the data management module, a data collection layer obtains original data, a data processing layer processes the original data according to a three-layer data cleaning mechanism, and a data storage layer stores the processed data; according to the system management module, the data application layer constructs an interface covering Web, a mobile terminal and AR equipment, the data display layer generates a soil moisture thermodynamic diagram, a crop growth distribution diagram and an equipment operation state diagram, and intelligent management of agricultural production is ensured. The advanced Internet of Things technology is adopted, and the accuracy and the real-time performance of data acquisition are improved.

The invention relates to a low-altitude unmanned aerial vehicle assisted peak period urban intersection coordination control system and method, and belongs to the field of traffic control. The system comprises an equipment deployment layer, a sensing layer, a decision-making layer and a control layer. Based on infrastructures deployed by the equipment deployment layer, the unmanned aerial vehicle cruises, shoots and preliminarily analyzes an image, and transmits structured data to the edge computing equipment; the sensing layer determines intersection state information and transmits the intersection state information to the decision-making layer, and the decision-making layer generates a signal timing strategy and a regional scheduling scheme; and the control layer controls the signal lamp according to the information and feeds back an execution result, and the system can perform self-adaptive optimization and early warning. The method comprises the steps that the unmanned aerial vehicle acquires and processes an image, transmits data to a decision-making layer to optimize timing, issues a strategy to the signal lamp controller and feeds back an execution effect. According to the scheme, the sensing range and precision can be enhanced, the communication load is reduced, the signal timing is optimized, multi-intersection collaboration is supported, a closed-loop control system is constructed, deployment is flexible, and adaptability is high.

The invention provides a supply and demand matching degree-based multi-target collaborative optimization scheduling system for a light storage building group, and the system comprises a sensing layer which is used for collecting the physical operation state data of the light storage building group in real time; the network layer is used for transmitting data to the control layer; the control layer is used for generating a global scheduling plan by taking maximization of the comprehensive supply and demand matching degree as an optimization target according to the supply and demand matching degree prediction information of the first scale and the time-of-use electricity price information of the power grid; according to the supply and demand matching degree prediction information of the second scale and the physical operation state data, performing rolling correction on the global scheduling plan to generate a correction instruction; according to the supply and demand matching degree prediction information of the third scale and the physical operation state data, performing local autonomous judgment on the correction instruction, and triggering a local response when supply and demand fluctuation is detected; and the application layer is used for displaying the data acquired by the sensing layer and / or the output data of the control layer, so that the self-balancing capability of the system is fundamentally improved.

The invention discloses an engineering construction digital intelligent supervision management and control system, and relates to the technical field of engineering construction supervision. The system comprises an information acquisition module which is used for acquiring equipment state, image and environment data; the data fusion layer module is used for carrying out cleaning, alignment and Kalman filtering fusion on the multi-source data to generate a multi-modal construction data flow; a progress calculation and prediction unit, a deviation judgment unit and a digital modeling unit are arranged in the analysis monitoring module, and the analysis monitoring module is used for updating a progress state based on an XGBoost model, analyzing a node dependency relationship by using a graph neural network, calculating a deviation propagation risk coefficient and generating a hierarchical deviation signal in combination with three-level judgment conditions; and the management and control layer module executes hierarchical response according to the level of the deviation signal, and feeds back execution effect data to the front-end module to realize model optimization and closed-loop iteration. According to the invention, intelligent perception, risk prediction and adaptive management and control of the construction process are realized, and the real-time performance, accuracy and autonomy of project supervision are improved.

A display device includes a substrate, a first metal layer on the substrate, a first barrier insulating layer on the first metal layer, an etching control layer on the first barrier insulating layer, a first contact hole passing through the substrate, the first barrier insulating layer, and the etching control layer, a second barrier insulating layer on the etching control layer and including a second contact hole, a fan-out line on the second barrier insulating layer and included in a second metal layer, a pad part inserted into the second contact hole and included in the second metal layer, the pad part integral with the fan-out line, a display layer on the fan-out line, and a flexible film under the substrate and inserted into the first contact hole to be electrically connected to the pad part. The first metal layer includes an etching mark adjacent to the first contact hole.

The invention relates to the technical field of remote monitoring, in particular to a TBM remote monitoring method based on a multi-level collaborative management architecture, which is applied to a remote comprehensive monitoring platform. The platform comprises a construction operation layer, a construction management and control layer and a decision support layer, each monitoring scene forms a local block chain ring by a plurality of construction surface nodes, and an integral block chain ring is integrally formed. The method comprises the steps of collecting construction state data, performing standardization processing, and inputting the data to an equipment risk identification model and a geological evaluation model for construction state analysis; based on local anomaly detection, deducing an anomaly propagation path, calculating the reverse coverage capability of each non-abnormal local circle, and determining a decision block chain circle; and performing node locking and decision switching through the abnormal isolation control smart contract, and generating a decision instruction based on a multi-objective optimization algorithm. According to the method, the abnormal self-healing capability and the dynamic decision-making efficiency of the TBM construction monitoring system are improved.