Intelligent monitoring and early warning of unmanned warehouse and cloud management method thereof

Abstract

The application provides a kind of intelligent monitoring early warning of unmanned warehouse and its cloud management method, it is related to warehousing intelligent technology field, including intelligent perception network layer, fusion processing hub data layer, intelligent early warning decision engine layer and cloud management application layer, data acquisition is carried out by setting intelligent perception network layer, then by fusion processing hub data layer to multi-source heterogeneous data is time-space alignment and semantic fusion, constructs unified real-time three-dimensional scene model, again by intelligent early warning decision engine layer carries out scene reasoning and risk assessment, realizes the active early warning and automatic linkage of risk Disposal, finally through cloud management application layer carries out global visual display and is based on historical data self-learning optimization operation strategy;The application utilizes the closed loop architecture of multilayer cooperation, realizes the whole process intelligent management from real-time perception, accurate understanding to automatic decision and continuous optimization to physical warehouse, significantly improves the safety, efficiency and self-level of warehousing operation.

Description

Technical Field

[0001] This invention relates to the field of intelligent warehousing technology, and in particular to an intelligent monitoring and early warning system for unmanned warehouses and its cloud management method. Background Technology

[0002] An unmanned warehouse is a system that achieves a high degree of automation and intelligence in the entire warehousing process through the Internet of Things, artificial intelligence, robotics and big data. It uses automated equipment and sensor networks to perform physical operations and uses intelligent monitoring and early warning systems to identify safety hazards and abnormal states in real time.

[0003] However, existing unmanned warehouse monitoring and management technologies typically rely on decentralized and single-function sensing systems, mainly using two-dimensional video monitoring, which has visual blind spots and cannot obtain effective information about some hidden areas deep in the shelves and at the bottom of the equipment. Furthermore, it lacks the ability to monitor the internal physical state of materials, resulting in a single and incomplete environmental perception dimension.

[0004] At the data processing level, data from different sensors are often heterogeneous in format and have inconsistent spatiotemporal references, making it difficult to effectively correlate and fuse them. This makes it impossible for upper-layer applications to obtain a globally consistent, machine-understandable field model, and decision-making relies on one-sided judgments based on fragmented information.

[0005] Moreover, the early warning mechanisms are mostly passive response modes, which rely heavily on manual monitoring of the screen to detect anomalies. After the alarm is triggered, manual intervention is still required to verify and manually execute the handling process, resulting in a slow response and potentially missing critical handling opportunities in emergency situations.

[0006] Meanwhile, the system's management interface is mostly a simple stacking of two-dimensional charts and video images, making the information presentation unintuitive and lacking a three-dimensional display corresponding to the physical space. Furthermore, the system's operation strategies usually rely on fixed rules and human experience, and cannot learn and optimize autonomously based on historical operating data. This makes it difficult to continuously improve the overall system's operating efficiency after it reaches a certain level, resulting in limited intelligence. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of existing technologies and provide an intelligent monitoring and early warning system for unmanned warehouses and its cloud management method.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: an intelligent monitoring and early warning system for unmanned warehouses and its cloud management method, comprising:

[0009] The intelligent sensing network layer includes a full-domain static sensing unit for establishing a three-dimensional sensing network covering the entire warehouse, a dynamic mobile inspection unit for mobile blind spot filling nodes to perform close-range scanning and identification of fixed monitoring blind spots, and an environmental penetration sensing unit for sensing the internal environmental conditions.

[0010] The fusion processing central data layer includes a spatiotemporal synchronization alignment unit for unifying point cloud, depth image, spectrum and thermal data, and a multimodal 3D reconstruction unit for fusing geometric point cloud, visual texture and thermal radiation value.

[0011] The intelligent early warning decision engine layer includes a digital twin scenario computing unit for constructing and applying a virtual mapping model and reasoning about the real-time status of the physical warehouse, and a graded early warning handling unit for triggering graded alarms and executing corresponding contingency plans based on risk assessment results.

[0012] The cloud management application layer includes a global visualization cockpit unit for integrating and displaying multi-dimensional operational information of the entire warehouse, and a strategy self-learning optimization unit for analyzing historical data to generate operational optimization strategies and drive continuous system iteration.

[0013] In a preferred embodiment, the global static perception unit utilizes the parallax calculation of a multi-view stereo vision camera to actively acquire the three-dimensional coordinates of millions of points in the environment to form a point cloud;

[0014] The dynamic mobile inspection unit acquires three-dimensional images and depth information of the depths of the shelves and the bottom of the equipment by mounting a color depth camera, a lightweight lidar and a sonar sensor on a mobile robot platform and changing the observation position and angle.

[0015] The environmental penetration sensing unit uses the changes in the spectral characteristics after microwaves interact with materials to reflect the moisture and density state, thermal radiation imaging to reflect the temperature distribution, and wireless sensor network monitoring of temperature and humidity data to establish a quantitative and qualitative relationship model of the changes in physical field characteristic parameters, which is used for the detection and imaging of the internal state.

[0016] In one preferred implementation, the global static perception unit deploys sensor arrays at high points on the warehouse's top truss and both sides of the main aisle. These arrays work collaboratively via a time synchronization protocol to stitch together point cloud data in real time, describing the overall macroscopic geometry of the warehouse and performing three-dimensional spatial sampling. The dynamic mobile inspection unit integrates a color depth camera, lightweight LiDAR, and sonar sensors through a robot platform. It accepts task scheduling from the cloud and is dispatched to designated coordinates when the static unit detects an anomaly to perform close-range, multi-view fine scanning of the depths of the shelves and the bottom of the equipment. The environmental penetration perception unit uses the spectrum of microwave moisture sensors and density sensors to determine the material status, thermal imaging cameras to monitor temperature distribution, and wireless sensor networks to monitor temperature and humidity data.

[0017] In a preferred embodiment, the spatiotemporal synchronization alignment unit transforms the sensor data to a unified reference coordinate system and aligns it along the time axis through coordinate system transformation and time interpolation.

[0018] The multimodal 3D reconstruction unit is based on feature-level fusion and semantic segmentation of deep learning. It inputs the aligned geometric point cloud, visual texture, and thermal radiation value into the neural network for feature extraction and fusion.

[0019] In one preferred embodiment, the spatiotemporal synchronization alignment unit determines the position and attitude through sensor extrinsic parameter calibration, performs spatial alignment using a rigid body transformation matrix, and simultaneously adds a unified timestamp through a network time protocol. For data that arrives at different times, timestamp interpolation is used for prediction, providing a prerequisite for multi-source data fusion. The multimodal 3D reconstruction unit uses a point cloud processing network to voxelize the point cloud and associate image pixel features and thermal radiation values ​​with 3D points. After training, it outputs a semantic point cloud with category and attribute annotations, forming a high-quality data base for digital twins.

[0020] In one preferred implementation, the digital twin scenario computing unit constructs a virtual entity that is updated in real time through dynamic data-driven modeling and knowledge graph reasoning, and combines business data to understand event logic;

[0021] The hierarchical early warning and handling unit automatically executes the workflow based on the rule engine, encodes expert strategies into business rules, and monitors their triggering.

[0022] As a preferred implementation, the digital twin scenario calculation unit constructs a twin based on a real-time three-dimensional semantic scene, and simultaneously establishes a knowledge graph describing "warehouse objects-relationships-events" to infer fault scenarios based on the object's task status; the hierarchical early warning and response unit establishes a three-level rule base, and in the event of an emergency, automatically activates fire protection, locks access control, stops emergency equipment, and notifies the person in charge, and achieves linkage execution with various control systems through a standard application programming interface.

[0023] As a preferred implementation, the global visualization cockpit unit uses web-based graphics libraries and game engines to perform 3D graphics rendering and multi-source data binding, thereby achieving intuitive presentation of high-dimensional data.

[0024] The strategy self-learning optimization unit discovers optimization patterns from system operation data based on supervised learning and reinforcement learning using historical data.

[0025] As a preferred implementation, the global visualization cockpit unit uses relevant development tools to perform high-performance rendering of the digital twin model, and dynamically binds real-time status, alarms, and key performance indicators to the visualization model through a network instant communication protocol, realizing unified management of three-dimensional scene browsing, object attribute query, alarm spatial positioning, and data chart overlay on a single screen; the strategy self-learning optimization unit optimizes inspection paths by applying genetic algorithms and reinforcement learning, uses a time-series model of long short-term memory networks to predict equipment failures, and analyzes order data through association rule mining algorithms to optimize warehouse allocation, forming an autonomous closed loop of perception-analysis-decision-optimization.

[0026] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0027] This invention achieves comprehensive, multi-dimensional monitoring of a warehouse—from its macroscopic geometric structure to hidden corners and the internal physical state of materials—through the synergy of a full-domain static sensing unit, a dynamic mobile inspection unit, and an environmental penetration sensing unit in the intelligent sensing network layer. This solves the problems of visual blind spots and single sensing dimensions in traditional monitoring systems, improving the knowability of the environment and asset status. The spatiotemporal synchronization alignment unit and multimodal 3D reconstruction unit in the fusion processing central data layer transform multi-source heterogeneous raw data into a unified, accurate, and semantically rich 3D scene model through high-precision spatiotemporal alignment and deep learning fusion. This provides a single, reliable data source for upper-layer applications, fundamentally avoiding decision-making errors caused by data inconsistencies and misunderstandings, ensuring the accuracy and consistency of the entire system's perception of the world. The intelligent early warning decision engine layer's digital twin scenario calculation unit and hierarchical early warning processing unit... The placement unit, through the deep integration of dynamic twins and knowledge graphs, achieves understanding and risk prediction of complex business scenarios, transforming passive post-event alarms into proactive pre-event and in-event warnings and intelligent interventions. The automatic linkage control based on the rule engine can initiate fire protection and emergency stop within milliseconds, significantly improving emergency response speed and warehouse operation safety, while greatly reducing reliance on manual duty. The global visualization cockpit unit and strategy self-learning optimization unit in the cloud management application layer provide a unified interactive interface, enabling intuitive understanding of massive high-dimensional data and greatly improving management efficiency. The strategy self-learning unit continuously mines historical data through machine learning, enabling the system to autonomously optimize inspection paths, predict equipment failures, and intelligently allocate storage locations, driving continuous improvement in operational efficiency and forming an autonomous closed loop that can learn from experience and iterate itself.

[0028] The entire system, through the tight coupling of a four-layer architecture and the bidirectional flow of data and instructions, seamlessly connects the real-time perception, accurate modeling, intelligent decision-making, automatic execution, and continuous optimization of the physical warehouse, significantly improving the warehouse's security level and operational efficiency. It also brings significant economic benefits by reducing reliance on manpower, preventing accident losses, and optimizing resource allocation. Attached Figure Description

[0029] Figure 1 A schematic diagram of the modules for an intelligent monitoring and early warning system and its cloud management method for an unmanned warehouse proposed in this invention;

[0030] Figure 2 A flowchart illustrating the intelligent monitoring and early warning system and its cloud management method for an unmanned warehouse proposed in this invention; Detailed Implementation

[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] Example 1

[0033] like Figure 1 As shown, the present invention provides a technical solution: an intelligent monitoring and early warning method for unmanned warehouses and its cloud management method, comprising: an intelligent sensing network layer, wherein the intelligent sensing network layer includes a full-domain static sensing unit for establishing a three-dimensional sensing network covering the entire warehouse, a dynamic mobile inspection unit for mobile blind spot filling nodes to perform close-range scanning and identification of fixed monitoring blind spots, and an environmental penetration sensing unit for sensing the internal environmental conditions.

[0034] The fusion processing central data layer includes a spatiotemporal synchronization alignment unit for unifying point cloud, depth image, spectrum and thermal data, and a multimodal 3D reconstruction unit for fusing geometric point cloud, visual texture and thermal radiation value.

[0035] The intelligent early warning decision engine layer includes a digital twin scenario computing unit for constructing and applying a virtual mapping model and reasoning about the real-time status of the physical warehouse, and a graded early warning handling unit for triggering graded alarms and executing corresponding contingency plans based on risk assessment results.

[0036] The cloud management application layer includes a global visualization cockpit unit for integrating and displaying multi-dimensional operational information of the entire warehouse, and a strategy self-learning optimization sheet for analyzing historical data to generate operational optimization strategies and drive continuous system iteration.

[0037] In this embodiment, a closed loop from physical perception to intelligent decision-making is achieved through a multi-layer collaborative architecture;

[0038] The intelligent perception network layer comprehensively utilizes multi-view stereo vision, multiple sensors mounted on mobile robot platforms, and special sensing technologies such as microwave and thermal imaging to collect raw three-dimensional spatial data and environmental condition data from three dimensions: macroscopic static coverage, dynamic fine blind spot filling, and internal state penetration, forming the sensory nerves of the system.

[0039] As the central system, the fusion processing central data layer aligns all perceived data to a unified benchmark in the spatiotemporal dimension through precise time protocols and coordinate system transformations. Subsequently, feature-level fusion and semantic segmentation are performed based on deep learning networks to generate a unified 3D scene model with rich semantic labels, providing a high-quality and understandable data foundation for the upper layers.

[0040] The intelligent early warning decision engine layer is driven by the real-time three-dimensional semantic scene output by the fusion layer to build and update the digital twin, and embeds knowledge graphs for scenario reasoning and risk assessment. Once anomalies and risks are identified, the corresponding level of early warning is automatically triggered through the preset rule engine, and the fire protection and access control are automatically handled through the standard application programming interface.

[0041] The cloud management application layer serves as the interaction and evolution interface. It uses 3D graphics technology to bind digital twin models with real-time operating data for immersive visualization. At the same time, it uses machine learning algorithms to conduct in-depth analysis of historical data, autonomously generate and distribute optimized inspection paths and predict equipment failure strategies, driving the continuous iteration and optimization of the entire system.

[0042] The system follows a closed loop where data flows from bottom to top and instructions and strategies are fed back from top to bottom. Each unit in the perception layer uploads the raw data streams it collects to the central data layer of the fusion processing hub in real time via a wireless network. After completing spatiotemporal alignment and multimodal reconstruction, the fusion layer pushes the structured semantic 3D scene model to the intelligent early warning decision engine layer through a high-speed data interface. The decision engine layer performs reasoning and decision-making based on this model, and the resulting early warning instructions and linkage control signals are distributed to various execution devices in the warehouse through a standard application programming interface. At the same time, it uploads key alarm events, status snapshots, and decision logs to the cloud management application layer. The cloud application layer not only receives and visualizes all the information from the lower layers, but also sends the optimization strategies generated by the strategy self-learning optimization unit after analyzing historical data back to the dynamic mobile inspection unit of the perception layer and the rule base of the decision engine layer through the task scheduling system, forming an intelligent autonomous system that continuously cycles and optimizes through perception, processing, decision-making, and learning.

[0043] Example 2

[0044] like Figure 2 As shown, the global static perception unit deploys sensor arrays at the highest points on the warehouse top truss and both sides of the main passage. Through a time synchronization protocol, it works together to stitch point cloud data in real time, describe the macroscopic geometric shape of the entire warehouse, perform three-dimensional spatial sampling, and actively acquire the three-dimensional coordinates of millions of points in the environment to form a point cloud by using the parallax calculation of multi-view stereo vision cameras.

[0045] The dynamic mobile inspection unit mounts a color depth camera, a lightweight lidar, and a sonar sensor on a mobile robot platform and accepts cloud-based task scheduling. When the static unit detects an anomaly, it is dispatched to a designated coordinate to perform close-range, multi-view fine scanning of the depth of the shelf and the bottom of the equipment to obtain three-dimensional images and depth information.

[0046] The environmental penetration sensing unit determines the material state by deploying microwave moisture sensors and density sensors to measure the spectrum, a thermal imaging camera to monitor temperature distribution, and a wireless sensor network to monitor temperature and humidity data. The changes in the spectrum characteristics after the interaction between microwaves and materials reflect the moisture and density state, thermal radiation imaging reflects the temperature distribution, and the wireless sensor network monitors temperature and humidity data to establish a quantitative and qualitative relationship model of the changes in physical field characteristic parameters, which is used for the detection and imaging of the internal state.

[0047] In this embodiment, the global static perception unit actively transmits and receives light signals using the parallax calculation principle of a multi-view stereo vision camera. It calculates pixel parallax from images from different perspectives using triangulation, thereby actively acquiring the three-dimensional coordinates of millions of points in the environment to form a dense point cloud. A coverage network is formed by deploying sensor arrays at high points on the warehouse top truss and both sides of the main passage. Each sensor node achieves microsecond-level time coordination through a precise time synchronization protocol to ensure that the collected point cloud data has a consistent spatiotemporal reference. The point cloud data is then stitched together in real time using a point cloud registration algorithm to form a basic digital chassis describing the macroscopic geometry of the entire warehouse. This achieves high-frequency, high-precision three-dimensional spatial sampling of a large-scale warehouse environment, providing a static spatial reference for the entire system.

[0048] The dynamic mobile inspection unit mounts the sensing module on a mobile robot platform and uses the principle of robot motion control to continuously change the observation position and angle, bypassing fixed obstructions. It combines multi-sensor fusion technology to obtain three-dimensional images and depth information of the area. When there are blind spots in the fixed sensor network, it can be scheduled to perform multi-view scanning, which significantly improves the ability to perceive the depths of the shelves and the bottom of the equipment.

[0049] The environmental penetration sensing unit is based on the principle of interaction between physical fields and matter. By establishing a quantitative and qualitative relationship model between changes in physical field characteristic parameters and the invisible state inside the target, it can detect and image the harsh environmental conditions inside the warehouse, expand the sensing dimension, and enable the system to not only sense geometric shape, but also monitor material status and environmental parameters, so as to fully grasp the warehouse operation conditions.

[0050] The sensor array inside the global static perception unit is connected via a wireless network and runs a precise time synchronization protocol to maintain time consistency. The raw point cloud data collected is initially stitched together and then uploaded in real time to the fusion processing central data layer via a high-speed data interface. When the global static perception unit detects an anomaly during macroscopic scanning, it reports the anomaly event along with its coordinate information to the cloud management application layer. Subsequently, the task scheduling system of the cloud management application layer generates an inspection task and sends a scheduling command to the dynamic mobile inspection unit via a wireless communication network. After receiving the command, the robot platform of the dynamic mobile inspection unit autonomously navigates to the designated coordinates and starts its onboard sensors to perform a fine scan. The scanned data is also uploaded to the fusion processing central data layer. Various sensors in the environmental penetration perception unit collect spectrum, image, and temperature and humidity data through a low-power wireless protocol and aggregate them to the gateway. Then, they are uniformly uploaded to the fusion processing central data layer via the network for further processing, achieving comprehensive perception coverage from macroscopic to microscopic and from external to internal.

[0051] Example 3

[0052] like Figure 2 As shown, the spatiotemporal synchronization alignment unit determines the position and attitude through sensor extrinsic parameter calibration, performs spatial alignment using rigid body transformation matrix, and simultaneously adds a unified timestamp through network time protocol. It also uses timestamp interpolation to predict non-simultaneous data, providing a prerequisite for multi-source data fusion. Through coordinate system transformation and time interpolation, the sensor data is transformed into a unified reference coordinate system and aligned on the time axis.

[0053] The multimodal 3D reconstruction unit uses a point cloud processing network to voxelize the point cloud and associate image pixel features and thermal radiation values ​​with 3D points. After training, it outputs a semantic point cloud with category and attribute annotations, forming a high-quality data base for digital twins. Based on deep learning feature-level fusion and semantic segmentation, the aligned geometric point cloud, visual texture, and thermal radiation values ​​are input into the neural network for feature extraction and fusion.

[0054] In this embodiment, the spatiotemporal synchronization alignment unit determines the precise position and attitude parameters of each sensor through sensor extrinsic parameter calibration technology, and uses a rigid body transformation matrix to transform the raw data from different sensors into a unified global reference coordinate system to achieve spatial alignment. At the same time, it uses the network time protocol to give all data streams a unified timestamp, and uses a timestamp interpolation algorithm to predict the alignment on the time axis for data that arrives at different times due to transmission delays. This eliminates the benchmark differences of heterogeneous data in the spatiotemporal dimension, provides spatiotemporal prerequisites for subsequent multi-source data fusion, and ensures the reliability of the data foundation for subsequent processing.

[0055] The multimodal 3D reconstruction unit is based on the feature-level fusion and semantic segmentation principles of deep learning. It inputs the aligned multimodal data into a trained point cloud processing neural network. The point cloud processing neural network voxels the point cloud for a structured representation, and then extracts and fuses features and thermal radiation values ​​from image pixels through convolution operations, associating them with corresponding points in 3D space. Finally, it outputs a semantic point cloud with category and attribute labels, generating a high-quality 3D scene model rich in semantic information. This directly constitutes the core data base of the digital twin, enabling the upper-layer system to understand and reason about the complex state of the warehouse.

[0056] The spatiotemporal synchronization alignment unit receives the raw sensing data stream uploaded from the intelligent sensing network layer through a high-speed data interface. After completing the spatiotemporal alignment process internally, it pushes the aligned multimodal data packets to the multimodal 3D reconstruction unit in real time through the internal high-speed data channel. After receiving the aligned data, the multimodal 3D reconstruction unit processes it using a neural network model deployed on a high-performance computing server. The generated semantic point cloud model is then uploaded to the digital twin scenario computing unit of the intelligent early warning decision engine layer through a low-latency network protocol for the construction and updating of the digital twin.

[0057] Example 4

[0058] like Figure 2 As shown, the digital twin scenario computing unit constructs a twin based on a real-time three-dimensional semantic scene, and simultaneously establishes a knowledge graph describing "warehouse object-relationship-event". It infers fault scenarios based on the object's task status, and constructs a real-time updated virtual entity through dynamic data-driven modeling and knowledge graph reasoning, and combines business data to understand event logic.

[0059] The hierarchical early warning and response unit automatically executes the workflow based on the rule engine, encodes expert strategies into business rules and monitors their triggering. By establishing a three-level rule base, it automatically activates fire protection, locks access control, stops emergency equipment, and notifies the person in charge in the event of an emergency. It achieves linkage execution with various control systems through a standard application programming interface.

[0060] In this embodiment, the digital twin scenario computing unit is based on dynamic data-driven modeling and knowledge graph reasoning. It constructs a dynamically updated virtual twin based on the real-time three-dimensional semantic scene provided by the fusion processing central data layer. At the same time, it establishes a knowledge graph describing "warehouse object-relationship-event". Using the graph reasoning algorithm, it infers potential faults and abnormal scenarios based on the real-time state of the object and the task context, realizing a deep understanding and scenario awareness of the physical warehouse. This enables the system to identify complex event logic and predict risks from massive amounts of data.

[0061] The hierarchical early warning and response unit automatically executes the workflow based on the rule engine. It encodes expert experience into executable business rules and builds a three-level rule base. It continuously monitors the inference results output by the digital twin scenario computing unit. Once the rule conditions are matched, the corresponding level of early warning response is automatically triggered. In emergency situations, it automatically starts the fire protection system, locks access control, stops relevant equipment, and notifies the person in charge through the notification system, realizing emergency response and handling, and greatly improving warehouse safety and operational efficiency.

[0062] The digital twin scenario computing unit acquires semantic point cloud data in real time from the multimodal 3D reconstruction unit of the fusion processing hub data layer via a high-speed data interface. This data serves as input for building and updating the twin. The unit also receives business strategy updates and historical data feedback from the cloud management application layer. After completing scenario reasoning, the digital twin scenario computing unit sends event signals containing risk assessment results to the hierarchical early warning and response unit via an event-driven architecture. The hierarchical early warning and response unit matches the received event signals with a rule base. If an early warning is triggered, it sends control commands to the external control systems of the fire controller, access control system, and equipment emergency stop device in the warehouse via a standard application programming interface to execute coordinated responses. Simultaneously, it uploads the details of the early warning event and the response action log to the global visualization cockpit unit of the cloud management application layer for real-time display and recording via network protocol. This data is then fed back to the strategy self-learning optimization unit for strategy optimization, forming a closed-loop decision flow from scenario awareness to automatic execution.

[0063] Example 5

[0064] like Figure 2 As shown, the global visualization cockpit unit is based on web graphics library and game engine for 3D graphics rendering and multi-source data binding, realizing intuitive presentation of high-dimensional data. By using relevant development tools, the digital twin model is rendered with high performance, and real-time status, alarms, and key performance indicators are dynamically bound to the visualization model through network instant communication protocol, realizing one-screen management of 3D scene browsing, object attribute query, alarm spatial positioning, and data chart overlay.

[0065] The strategy self-learning optimization unit discovers optimization patterns from system operation data based on supervised learning and reinforcement learning of historical data. It optimizes inspection paths by applying genetic algorithms and reinforcement learning, uses a time series model of long short-term memory network to predict equipment failures, and analyzes order data through association rule mining algorithms to optimize warehouse allocation, forming an autonomous closed loop of perception-analysis-decision-optimization.

[0066] In this embodiment, the global visualization cockpit unit uses web-based graphics libraries and game engines to render 3D graphics and dynamically bind multi-source data. It uses relevant development tools to perform high-performance rendering of the digital twin model and uses network instant messaging protocols to associate real-time status, alarm information and key performance indicator data with the visualization model in real time. This enables unified management of 3D scene browsing, object attribute query, alarm spatial positioning and data chart overlay on one screen, providing managers with an intuitive and immersive global monitoring and operation interface.

[0067] The strategy self-learning optimization unit is based on supervised learning and reinforcement learning of historical data. It optimizes inspection paths by applying genetic algorithms and reinforcement learning, uses a time series model of long short-term memory network to predict equipment failures, and analyzes order data through association rule mining algorithms to optimize warehouse allocation. It learns autonomously from system operation data and generates operation optimization strategies, driving the entire system to form an autonomous closed loop of perception-analysis-decision-optimization, and achieving continuous iteration and performance improvement.

[0068] The global visualization cockpit unit receives real-time updated twin models and scenario data from the digital twin scenario computing unit of the intelligent early warning decision engine layer via a network-like real-time communication protocol. Simultaneously, it obtains alarm event streams from the hierarchical early warning and handling unit and dynamically binds these multi-source data to the 3D rendering model for visualization. It also receives optimization suggestions and analysis results from the strategy self-learning optimization unit to enhance the displayed content. The strategy self-learning optimization unit collects historical operational data from the fusion processing hub data layer, the intelligent early warning decision engine layer, and the global visualization cockpit unit through a database interface. The optimization strategy generated after running machine learning algorithms is distributed to the dynamic mobile inspection unit of the intelligent perception network layer through the task scheduling system. It also updates the fault prediction model parameters to the digital twin scenario computing unit of the intelligent early warning decision engine layer, forming a two-way communication closed loop where data is collected from the bottom layer, analyzed and optimized in the cloud, and then fed back to the bottom layer for execution.

[0069] Working principle:

[0070] like Figure 1-2 As shown, an intelligent monitoring and management system for unmanned warehouses, centered on digital twins and driven by a closed-loop perception-decision-optimization model, operates through the collaborative operation of four core levels:

[0071] The intelligent sensing network layer's global static sensing unit utilizes a multi-view stereo vision camera array deployed at high points in the warehouse. Through parallax calculation, it generates and stitches together a large-scale 3D point cloud in real time, constructing a macroscopic static digital chassis covering the entire area, providing a basic spatial reference for the system. The dynamic mobile inspection unit acts as a mobile blind spot filling node. Upon receiving scheduling instructions from the upper layer, it drives a robot equipped with multiple sensors to designated blind spots for detailed scanning, acquiring details of hidden areas that cannot be observed from a fixed perspective. The environmental penetration sensing unit uses microwave and thermal imaging sensors, based on the principle of interaction between physical fields and matter, to detect the internal state of materials and the temperature and humidity of the environment, thereby sensing... The dimension is expanded from geometric space to physical state; then, all raw multi-source heterogeneous data from the intelligent perception network layer is transmitted in real time to the fusion processing central data layer. The spatiotemporal synchronization alignment unit performs precise spatiotemporal alignment of all data through sensor calibration, rigid body transformation, and network time protocol to eliminate baseline differences. Subsequently, the multimodal 3D reconstruction unit performs feature-level fusion and semantic segmentation of the aligned point cloud, image, and thermal radiation based on a deep learning network, outputting a real-time 3D semantic scene model with category and attribute labels. Then, the intelligent early warning decision engine layer is driven by the 3D semantic scene model, and the digital twin scenario computing unit dynamically updates the virtual warehouse twin and embeds it. A knowledge graph describing "object-relationship-event" relationships is used for scenario reasoning to understand complex business logic and predict risks. Once an abnormal scenario is identified, the hierarchical early warning and response unit automatically triggers corresponding early warnings based on a pre-set three-level rule base. It then links with the external control system via a standard application programming interface to execute emergency stops, lockouts, and automatic response actions, achieving a rapid closed loop from perception to response. The cloud-based management application layer serves as the global interaction and autonomy hub. The global visualization cockpit unit dynamically binds multi-dimensional data through a high-performance 3D rendering engine, providing a monitoring interface. Simultaneously, the strategy self-learning optimization unit continuously analyzes accumulated historical operational data, applying reinforcement learning and long short-term memory. The network algorithm autonomously optimizes inspection paths, predicts equipment failures, and adjusts storage location allocation strategies. These optimization strategies are then distributed to the dynamic mobile inspection units in the perception layer and updated in the decision-making layer's rule base through a task scheduling system, thereby driving the entire system to continuously iterate and optimize itself. This allows the raw perception data to be fused and processed from the bottom up to form an understandable semantic world model, driving decision-making and automatic control. Meanwhile, decision logs and historical data are uploaded to the cloud for analysis and learning, while the optimization strategies and scheduling instructions generated in the cloud are fed back to the perception and decision-making units from the top down. Ultimately, this builds an intelligent warehouse management ecosystem that can perceive in real time, understand accurately, handle automatically, and continuously evolve.

[0072] The operation includes the following steps:

[0073] S1: The intelligent sensing network layer initiates all-round data collection. The full-domain static sensing unit uses a multi-view stereo vision camera array deployed on the top and high points of the warehouse to generate and stitch together a large-scale three-dimensional point cloud in real time based on the parallax calculation principle, thus constructing a macro digital chassis covering the entire warehouse. At the same time, the dynamic mobile inspection unit acts as a mobile blind spot filling node, driving the robot to perform close-range fine scanning in fixed monitoring blind spots under the task scheduling in the cloud. The environmental penetration sensing unit uses microwave and thermal imaging sensors to detect the internal state of materials and the temperature and humidity of the environment. All raw sensing data is uploaded to the fusion processing central data layer in real time through the wireless network.

[0074] S2: The central data layer of the fusion processing performs standardization and semantic processing on multi-source heterogeneous data. The spatiotemporal synchronization alignment unit unifies all perceived data to a global spatiotemporal reference through sensor extrinsic parameter calibration, rigid body transformation matrix and network time protocol. Subsequently, the multimodal 3D reconstruction unit performs feature-level fusion and semantic segmentation on the aligned point cloud, image and thermal radiation data based on deep learning network, and outputs a real-time 3D semantic scene model with category and attribute labels, forming a high-quality data base for digital twins, and pushes it to the intelligent early warning decision engine layer through high-speed data interface;

[0075] S3: The intelligent early warning decision engine layer is based on semantic model-driven intelligent decision-making. The digital twin scenario computing unit dynamically updates the virtual warehouse twin based on real-time three-dimensional semantic scenarios and embeds a knowledge graph describing "warehouse objects - relationships - events" for scenario reasoning and risk assessment. Once an anomaly and risk are identified, the hierarchical early warning and handling unit automatically triggers the corresponding level of early warning according to the preset three-level rule base, and links the external control system through the standard application programming interface to perform automatic handling. At the same time, the early warning event and handling log are uploaded to the cloud management application layer.

[0076] S4: The cloud management application layer realizes global monitoring and interaction. The global visualization cockpit unit uses a web graphics library and game engine to perform high-performance rendering and dynamic binding of digital twin models with multi-dimensional data such as real-time status, alarm information, and key performance indicators. It provides a unified management interface with three-dimensional scene browsing, object attribute query, alarm spatial positioning, and data chart overlay, supporting immersive monitoring and operation by management personnel.

[0077] S5: The strategy self-learning optimization unit drives the system's autonomous closed loop. By analyzing historical operating data, it applies genetic algorithms, reinforcement learning, and long short-term memory networks to optimize inspection paths, predict equipment failures, and adjust storage location allocation. The generated optimization strategies are then sent back to the dynamic mobile inspection unit in the intelligent perception network layer and the rule base in the intelligent early warning decision engine layer through the task scheduling system. This forms a continuous iterative cycle of perception, analysis, decision-making, and optimization, enabling the entire system to evolve autonomously and improve its performance.

[0078] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments for application in other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for intelligent monitoring and early warning of unmanned warehouses and its cloud management, characterized in that: include: The intelligent sensing network layer includes a full-domain static sensing unit for establishing a three-dimensional sensing network covering the entire warehouse, a dynamic mobile inspection unit for mobile blind spot filling nodes to perform close-range scanning and identification of fixed monitoring blind spots, and an environmental penetration sensing unit for sensing the internal environmental conditions. The fusion processing central data layer includes a spatiotemporal synchronization alignment unit for unifying point cloud, depth image, spectrum and thermal data, and a multimodal 3D reconstruction unit for fusing geometric point cloud, visual texture and thermal radiation value. The intelligent early warning decision engine layer includes a digital twin scenario computing unit for constructing and applying a virtual mapping model and reasoning about the real-time status of the physical warehouse, and a graded early warning handling unit for triggering graded alarms and executing corresponding contingency plans based on risk assessment results. The cloud management application layer includes a global visualization cockpit unit for integrating and displaying multi-dimensional operational information of the entire warehouse, and a strategy self-learning optimization unit for analyzing historical data to generate operational optimization strategies and drive continuous system iteration.

2. The intelligent monitoring and early warning method for an unmanned warehouse and its cloud management method according to claim 1, characterized in that: The global static perception unit uses the parallax calculation of a multi-view stereo vision camera to actively acquire the three-dimensional coordinates of millions of points in the environment to form a point cloud; The dynamic mobile inspection unit acquires three-dimensional images and depth information of the depths of the shelves and the bottom of the equipment by mounting a color depth camera, a lightweight lidar and a sonar sensor on a mobile robot platform and changing the observation position and angle. The environmental penetration sensing unit uses the changes in the spectral characteristics after microwaves interact with materials to reflect the moisture and density state, thermal radiation imaging to reflect the temperature distribution, and wireless sensor network monitoring of temperature and humidity data to establish a quantitative and qualitative relationship model of the changes in physical field characteristic parameters, which is used for the detection and imaging of the internal state.

3. The intelligent monitoring and early warning system and its cloud management method for an unmanned warehouse according to claim 2, characterized in that: The full-domain static perception unit deploys sensor arrays at high points on the warehouse roof truss and both sides of the main passage. Working together through a time synchronization protocol, it stitches together point cloud data in real time to describe the macroscopic geometric shape of the entire warehouse and performs three-dimensional spatial sampling. The dynamic mobile inspection unit integrates a color depth camera, lightweight lidar, and sonar sensor through a robot platform and accepts task scheduling from the cloud. When the static unit detects an anomaly, it is dispatched to a designated coordinate to perform close-range, multi-angle fine scanning of the depth of the shelf and the bottom of the equipment. The environmental penetration sensing unit determines the material status by deploying microwave moisture sensors and density sensors to determine the spectrum, thermal imaging cameras to monitor temperature distribution, and wireless sensor networks to monitor temperature and humidity data.

4. The intelligent monitoring and early warning method for an unmanned warehouse and its cloud management method according to claim 1, characterized in that: The spatiotemporal synchronization alignment unit transforms sensor data into a unified reference coordinate system and aligns it along the time axis through coordinate system transformation and time interpolation. The multimodal 3D reconstruction unit is based on feature-level fusion and semantic segmentation of deep learning. It inputs the aligned geometric point cloud, visual texture, and thermal radiation value into the neural network for feature extraction and fusion.

5. The intelligent monitoring and early warning method for an unmanned warehouse and its cloud management method according to claim 4, characterized in that: The spatiotemporal synchronization alignment unit determines the position and attitude through sensor extrinsic parameter calibration, performs spatial alignment using a rigid body transformation matrix, and simultaneously adds a unified timestamp through a network time protocol. For data that arrives at different times, timestamp interpolation is used for prediction, providing a prerequisite for multi-source data fusion. The multimodal 3D reconstruction unit uses a point cloud processing network to voxelize the point cloud and associate image pixel features and thermal radiation values ​​with 3D points. After training, it outputs a semantic point cloud with category and attribute annotations, forming a high-quality data base for digital twins.

6. The intelligent monitoring and early warning method for an unmanned warehouse and its cloud management method according to claim 1, characterized in that: The digital twin scenario computing unit constructs real-time updated virtual entities and combines business data to understand event logic through dynamic data-driven modeling and knowledge graph reasoning. The hierarchical early warning and handling unit automatically executes the workflow based on the rule engine, encodes expert strategies into business rules, and monitors their triggering.

7. The intelligent monitoring and early warning method for an unmanned warehouse and its cloud management method according to claim 6, characterized in that: The digital twin scenario calculation unit constructs a twin based on real-time three-dimensional semantic scenes, and simultaneously establishes a knowledge graph describing "warehouse objects - relationships - events" to infer fault scenarios based on object task status; the hierarchical early warning and response unit establishes a three-level rule base, and in the event of an emergency, it automatically activates fire protection, locks access control, stops emergency equipment and notifies the person in charge, and achieves linkage execution with various control systems through standard application programming interfaces.

8. The intelligent monitoring and early warning method for an unmanned warehouse and its cloud management method according to claim 1, characterized in that: The global visualization cockpit unit uses web-based graphics libraries and game engines to render 3D graphics and bind multi-source data, enabling intuitive presentation of high-dimensional data. The strategy self-learning optimization unit discovers optimization patterns from system operation data based on supervised learning and reinforcement learning using historical data.

9. The intelligent monitoring and early warning method for an unmanned warehouse and its cloud management method according to claim 8, characterized in that: The global visualization cockpit unit uses relevant development tools to perform high-performance rendering of the digital twin model, and dynamically binds real-time status, alarms, and key performance indicators to the visualization model through network instant communication protocols, realizing unified management of 3D scene browsing, object attribute query, alarm spatial positioning, and data chart overlay on a single screen; the strategy self-learning optimization unit optimizes inspection paths by applying genetic algorithms and reinforcement learning, uses a time series model of long short-term memory networks to predict equipment failures, and analyzes order data through association rule mining algorithms to optimize warehouse allocation, forming an autonomous closed loop of perception-analysis-decision-optimization.

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