A state monitoring and intelligent goods storage and access system for an automated stereoscopic warehouse

Abstract

The application discloses a kind of state monitoring and goods intelligent access system of automated stereoscopic warehouse, comprising: state monitoring module group, microcontroller and intelligent access optimization module;Wherein, state monitoring module group includes mutually independent RFID ultra-high frequency module, code scanner module, shipment monitoring module, positioning module and environmental monitoring module;Microcontroller is electrically connected with each module in state monitoring module group respectively, for receiving and processing the data collected by each module in state monitoring module group, and carries out data interaction with host computer;Intelligent access optimization module is deployed in host computer, and built-in library position allocation algorithm and path optimization model are used to dynamically generate and issue access operation instruction according to the data processed by microcontroller.The application carries out real-time monitoring of warehouse environment, goods position and state by multi-sensor fusion detection technology, and realizes intelligent dynamic allocation of library position and global optimization of access path.

Description

Technical Field

[0001] This invention relates to the field of automated warehousing and logistics technology, and more specifically to an automated three-dimensional warehouse status monitoring and intelligent cargo storage and retrieval system. Background Technology

[0002] As a core intelligent equipment in the warehousing and logistics field, automated storage and retrieval systems (AS / RS) are widely used in various industries such as manufacturing, logistics and distribution, and e-commerce warehousing due to their high space utilization, high degree of automation, and low human intervention. They have become an important part of the modern warehousing system.

[0003] However, existing automated storage and retrieval systems (AS / RS) still face several key technological bottlenecks in actual industrial operation, resulting in warehousing efficiency, status monitoring accuracy, and overall system stability failing to meet the operational requirements of modern, efficient warehousing. Specific technical issues are as follows:

[0004] 1. Warehouse status monitoring relies on a single sensing detection method, which has limited environmental adaptability and detection accuracy. For example, QR code recognition is easily affected by environmental factors such as light intensity and label damage. In dense shelving application scenarios, RFID identification technology has problems such as redundant label reading, duplicate identification, and invalid data, which cannot achieve accurate, real-time, and comprehensive monitoring of goods location, goods status, and warehouse environment.

[0005] 2. The planning of goods storage and retrieval routes mostly adopts static planning methods, which lacks dynamic collaborative optimization logic that matches the actual operation scenario. This results in long empty running distances of stacker cranes, long operation waiting times, high equipment start-up and shutdown frequency, low overall operating efficiency of stacker cranes, and long warehouse inbound and outbound operation cycles.

[0006] 3. The warehouse location allocation strategy does not comprehensively consider multiple core factors such as cargo turnover rate, weight distribution, and category relevance, which can easily lead to the shift of the shelf center of gravity and insufficient structural stability. At the same time, frequently accessed goods are often assigned to remote warehouse locations, further reducing the overall efficiency of warehouse inbound and outbound operations and creating a vicious cycle in warehousing operations.

[0007] Therefore, addressing the technical shortcomings of existing automated storage and retrieval systems, such as inaccurate monitoring, low storage and retrieval efficiency, and poor system stability, and comprehensively improving the intelligence, efficiency, and security of warehousing systems, is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0008] In view of the above problems, this invention is proposed to provide an automated warehouse status monitoring and intelligent cargo storage and retrieval system that overcomes or at least partially solves the above problems. It constructs a high-precision, multi-dimensional status monitoring system through multi-sensor fusion detection technology and combines intelligent optimization algorithms to achieve dynamic collaborative optimization of storage location allocation and storage and retrieval paths.

[0009] To achieve the above objectives, the present invention adopts the following technical solution: This invention provides an automated warehouse status monitoring and intelligent cargo storage and retrieval system, comprising: a status monitoring module group, a microcontroller, and an intelligent storage and retrieval optimization module; The status monitoring module group includes an independent RFID UHF module, a barcode scanner module, a shipment monitoring module, a positioning module, and an environmental monitoring module. The RFID UHF module, barcode scanner module, and shipment monitoring module are all used to collect real-time information about goods in the warehouse. The positioning module is used to collect real-time location information about goods in the warehouse. The environmental monitoring module is used to collect real-time environmental information about the warehouse. The microcontroller is electrically connected to each module in the status monitoring module group, and is used to receive and process the data collected by each module in the status monitoring module group, and to interact with the host computer. The intelligent access optimization module is deployed on the host computer and has a built-in storage location allocation algorithm and path optimization model. It is used to dynamically generate and issue access operation instructions based on the data processed by the microcontroller.

[0010] Furthermore, the RFID UHF module consists of an UHF RFID reader, an UHF RFID antenna, and a cargo tag; the UHF RFID antenna is deployed on the shelf beams at warehouse inventory nodes and outbound nodes; the UHF RFID reader is deployed at the inbound and outbound platforms; and the cargo tag is affixed to the cargo or cargo pallet.

[0011] Furthermore, the barcode scanner module includes an industrial camera, a DCMI image acquisition interface, and a DMA storage unit; located at the loading platform or inbound / outbound station of the stacker crane, it is used to collect and identify cargo labels.

[0012] Furthermore, the shipment monitoring module consists of an infrared photocell sensor, a pressure sensor, and a signal conversion unit; it is deployed in the cargo carrying area of ​​each inbound and outbound station in the warehouse to perform dual detection of the presence and placement of goods at the station.

[0013] Furthermore, the positioning module is a UWB ultra-wideband positioning device, consisting of a main base station, two or more secondary base stations, and a cargo tag; the positioning module adopts a three-point positioning architecture of "main base station - secondary base station - cargo tag" to obtain the three-dimensional spatial coordinate data of the corresponding cargo.

[0014] Furthermore, the environmental monitoring module consists of an MQ-2 gas sensor, a DHT11 temperature and humidity sensor, a photodiode light sensor, a PR-ZS-BZ noise sensor, and an ADC analog-to-digital converter unit; it collects the combustible gas concentration, temperature and humidity, light intensity, and noise level of the storage environment, and converts them into digital raw data which are then transmitted to the microcontroller.

[0015] Furthermore, the microcontroller processes the received data, specifically including: The data collected by the RFID UHF module is subjected to multiple polling verifications; wherein, the multiple polling verifications specifically include: continuously polling the same area a preset number of times based on the UHF RFID reader to obtain multiple sets of raw tag data; performing data logic cleaning on the multiple sets of raw tag data, retaining only the valid tag information in the continuous polling, and outputting identity data including goods code, category and batch; Based on the data collected by the RFID UHF module and the barcode scanner module, the information of goods entering the warehouse is entered and the information of goods in the warehouse is verified; the data collected by the shipment monitoring module is used as the basis for determining the completion of goods leaving the warehouse; the location data of the goods is output through the positioning module and merged with the verified identity information of the goods to form "goods identity-location" association information; The sensor data collected by the environmental monitoring module is subjected to moving average noise reduction and outlier removal to construct a three-dimensional temperature and humidity field model of the reservoir area to identify local environmental anomalies.

[0016] Furthermore, the storage location allocation algorithm built into the intelligent storage optimization module specifically includes: Based on the constructed mathematical model of the total running distance of a single operation of a stacker crane, the total running distance from the target goods to each available storage location is calculated; the mathematical model of the total running distance of a single operation of a stacker crane is calculated by summing the horizontal row movement distance, the vertical column movement distance, and the vertical layer lifting distance of the stacker crane from the inbound / outbound platform to the target storage location. The storage location with the shortest total running distance is selected as the initial optimal storage location, and the initial optimal storage location is verified by multiple rigid constraints. The multiple rigid constraints include: the stacker crane performs a single independent operation for a single item, the target goods enter and leave the warehouse according to the order of the upper computer order, the category of the target goods matches the preset storage category of the storage location, and it is assumed that the overall center of gravity height of the shelf after the target goods are placed must be lower than a preset proportion of the total height of the shelf. If the initially selected optimal storage location meets the multiple rigid constraints, the target goods will be directly allocated to that storage location; if the multiple rigid constraints are not met, the storage location will be removed and a new vacant storage location with the second smallest total running distance will be selected for constraint verification. This process will be repeated until a target storage location that meets the multiple rigid constraints is selected, thus completing the intelligent dynamic allocation of storage locations.

[0017] Furthermore, the path optimization model built into the intelligent storage and retrieval optimization module transforms the multi-location storage and retrieval task of the stacker crane into a shortest path planning problem. The model aims to minimize the total operation time. Based on the three-dimensional motion characteristics of the stacker crane, it calculates the actual operation time between any two storage locations. It outputs a storage location access sequence ordered by time and the corresponding speed control command to drive the stacker crane to perform storage and retrieval operations.

[0018] As can be seen from the above technical solution, compared with the prior art, the present invention discloses an automated three-dimensional warehouse status monitoring and intelligent cargo storage and retrieval system, forming a fully intelligent warehousing operation system of "monitoring-decision-execution", which has the following beneficial effects: First, it significantly improves data accuracy and system reliability. Through deep cleaning, deduplication, filtering, and cross-validation of RFID, positioning, image, and sensor data using a microcontroller, interference from environmental noise, signal drift, and misjudgments by single sensors is effectively eliminated. This ensures that the cargo identification, location, and status information acquired by the host computer is accurate and reliable, preventing data errors from causing incorrect storage, retrieval, or equipment collisions at the source.

[0019] Secondly, it achieves a balance between intelligent and safe warehouse location allocation. The built-in warehouse location allocation algorithm not only pursues the shortest running distance for stacker cranes, but also innovatively introduces multiple rigid constraints such as "shelf center of gravity height limit" and "category matching". This improves storage and retrieval efficiency while strictly ensuring the physical stability of the shelving structure, preventing the risk of tipping over due to uneven distribution of goods, and achieving a dual optimization of efficiency and safety.

[0020] Furthermore, it significantly shortens the operation cycle and reduces energy consumption. The path optimization model, based on the three-dimensional composite motion characteristics of the stacker crane, intelligently plans the optimal access sequence in terms of time, fully utilizing the mechanism of simultaneous horizontal and vertical movement to avoid ineffective backtracking and repeated lifting and lowering. Compared to traditional fixed paths, it greatly reduces the idle time and acceleration / deceleration losses of a single operation, improves overall throughput, and extends equipment life.

[0021] Finally, the system's dynamic adaptability is enhanced. The module can dynamically generate instructions based on real-time orders and on-site conditions, flexibly responding to emergency order insertions or partial failures, ensuring that the warehousing system can maintain efficient and orderly operation under complex and changing conditions, and significantly improving the overall operational efficiency of smart logistics. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0023] Figure 1 This is a framework diagram of the automated warehouse status monitoring and intelligent cargo storage and retrieval system provided in this embodiment of the invention; Figure 2 This is a floor plan of the automated three-dimensional warehouse provided in an embodiment of the present invention; Among them, 1-high-rise rack, 2-stacker crane track, 3-stacker crane, 4-outbound conveyor, 5-shape detection device, 6-inbound conveyor, 7-on-machine control cabinet, 8-monitoring computer, 9-ground storage area, 10-ground control cabinet. Detailed Implementation

[0024] 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.

[0025] This invention discloses an automated storage and retrieval system for monitoring the status of an automated warehouse and for intelligent storage and retrieval of goods, referring to... Figure 1 As shown, it includes: a status monitoring module group, a microcontroller, and an intelligent access optimization module; The status monitoring module group includes independent RFID UHF module, barcode scanner module, shipment monitoring module, positioning module, and environmental monitoring module. The RFID UHF module, barcode scanner module, and shipment monitoring module are all used to collect real-time information about goods in the warehouse. The positioning module is used to collect real-time location information of goods in the warehouse. The environmental monitoring module is used to collect real-time environmental information in the warehouse. The microcontroller is electrically connected to each module in the status monitoring module group, and is used to receive and process the data collected by each module in the status monitoring module group, and to interact with the host computer. The intelligent storage and retrieval optimization module, deployed on the host computer, has a built-in storage location allocation algorithm and path optimization model. It is used to dynamically generate and issue storage and retrieval operation instructions based on the data processed by the microcontroller.

[0026] This embodiment is applied to an automated storage and retrieval system (AS / RS), referencing... Figure 2The diagram shows a floor plan of an automated storage and retrieval system (AS / RS), including high-rise racks 1, stacker crane tracks 2, stacker crane 3, outbound conveyor 4, shape detection device 5, inbound conveyor 6, on-board control cabinet 7, monitoring computer 8, ground storage area 9, and ground control cabinet 10.

[0027] Specifically, high-rise rack 1: as the core storage carrier, it provides multi-level and multi-column dense storage space to maximize the use of vertical space to store goods.

[0028] Stacker crane track 2: laid on the floor and top of the aisle, providing high-precision horizontal travel guidance and support benchmark for the stacker crane.

[0029] Stacker crane 3: As the core actuator, it moves rapidly along three-dimensional space under the guidance of the track, automatically completing the storage, retrieval and handling of goods.

[0030] Outbound Conveyor 4: Responsible for continuously and smoothly transporting qualified goods from the entrance of the alley to the downstream sorting or shipping area.

[0031] Shape detection device 5: Located at the inbound node, it measures the length, width, height and weight of goods in real time, and intercepts oversized and overweight goods to protect the safety of shelves and equipment.

[0032] Warehouse conveyor 6: Responsible for transporting goods to be stored from the receiving area to the aisle entrance and for precise handover of goods with the stacker crane.

[0033] Onboard Control Cabinet 7: Installed on the stacker crane body, it integrates drive controllers and sensor interfaces, executes motion control commands in real time, and provides feedback on equipment status.

[0034] Monitoring Computer 8: As the central hub of the host computer, it runs the warehouse management system to coordinate and schedule tasks, optimize routes, and provide visual monitoring of the entire operation.

[0035] Ground storage area 9: Serves as a buffer zone for inbound and outbound goods, used for temporary storage of goods awaiting processing or for sorting and temporarily storing abnormal goods.

[0036] Ground control cabinet 10: Fixedly installed at the end of the tunnel, it centrally deploys the main power supply, RFID reader and network switch, providing energy and data hub support for the entire tunnel system.

[0037] In this embodiment, a status monitoring module group, a microcontroller, and an intelligent access optimization module are deployed. The status monitoring module group includes independent RFID UHF module, barcode scanner module, shipment monitoring module, positioning module, and environmental monitoring module.

[0038] The RFID UHF module consists of an UHF RFID reader / writer, an UHF RFID antenna, and cargo tags. Its function is to realize cargo tag identification, inventory data collection, and outbound cargo tag verification.

[0039] In this embodiment, UHF RFID antennas are evenly installed on the beams of each layer of the high-rise shelf 1, with an antenna spacing of ≤2 meters, to achieve comprehensive scanning of goods labels in the shelf area without blind spots; UHF RFID readers are deployed at the inbound and outbound stations; and goods labels are affixed to the goods or goods pallets.

[0040] This UHF RFID module, combined with a microcontroller, employs a combination of data filtering, redundant tag processing, and reader polling techniques. The reader polls at least three times. The polling process is as follows: the microcontroller checks the data buffer length in real time → the microcontroller sends a polling command to the RFID reader → the RFID reader collects the raw data of the cargo tags and transmits it back to the microcontroller → the microcontroller filters duplicate and invalid data and removes redundant tags from the raw data → it outputs valid cargo tag data without redundancy, including cargo code, category, and batch information, enabling accurate cargo identification and inventory data statistics. In this embodiment, the raw data collected by the UHF RFID module, after processing by the microcontroller, is correlated and matched with the cargo location data from the positioning module and uploaded to the host computer to form a "cargo identity-location" association.

[0041] In this embodiment, a barcode scanner module is installed at the core position of the loading platform or the inbound / outbound station of the stacker crane 3. It collects and identifies the cargo labels through a barcode scanner camera with a resolution of ≥5 million pixels, a DCMI image acquisition interface, and a DMA storage unit, so as to achieve accurate QR code recognition throughout the stacker crane operation process.

[0042] The barcode scanner module employs a DCMI image acquisition + DMA dual-buffered storage processing method, integrating illumination compensation and incomplete code recognition algorithms. It boasts a high recognition success rate even under abnormal conditions such as partial QR code damage or blurring. The core execution flow is as follows: hardware initialization → DCMI image acquisition interface acquires raw image data of the goods label → DMA dual-buffered storage unit enables high-speed, lossless storage of image data and its transmission back to the microcontroller. The microcontroller calls the ZBAR QR code recognition library to decode the image data, perform illumination compensation, and incomplete code restoration → outputs accurate decoded goods label data, including information such as goods specifications, weight, and warehousing time, achieving refined goods information collection.

[0043] The data processed by the barcode scanner module is cross-validated by the microcontroller and the cargo tag data of the RFID UHF module to remove inconsistent data and upload it to the host computer to form a complete cargo information file.

[0044] The shipment monitoring module consists of an infrared photocell sensor, a pressure sensor, and a signal conversion unit. In this embodiment, it is deployed in the cargo carrying area of ​​each inbound and outbound station in the warehouse. The infrared photocell sensors are symmetrically installed on both sides of the station, and the pressure sensor is embedded in the core position of the station's carrying surface. Its function is to realize dual detection of the presence or absence of goods on the station and their placement status, so as to avoid goods being missed, misplaced, or missed outbound inspections.

[0045] The infrared photodiode sensor detects raw data by feeding back the level signal on its OUT pin; a low level indicates no obstruction, and a high level indicates an obstruction. The pressure sensor collects raw data on the platform's load pressure. Both types of raw data are transmitted synchronously to the microcontroller. The microcontroller performs cross-validation processing on the level signal and pressure data. When the infrared photodiode detects a high level and the pressure data is greater than or equal to the preset weight threshold for the goods, the goods are determined to be in place and properly positioned. When the infrared photodiode detects a low level and the pressure data is close to 0, the goods are determined to be out of place. All other situations are considered abnormal states. The microcontroller outputs a precise status result: goods in place / out of place / abnormal.

[0046] The detection results of the shipment monitoring module are processed by the microcontroller and fed back to the host computer in real time, serving as the basis for determining the start and stop of stacker crane operations and the completion of outbound shipment.

[0047] The positioning module in this embodiment is a UWB (Ultra-Wideband) positioning device; it consists of one main base station, no fewer than two secondary base stations, and several tags. The main base station is deployed in the warehouse control room, and the secondary base stations are evenly deployed on the columns around the high-rise shelves (spacing ≤ 15 meters). The tags are attached one-to-one to the stacker crane's loading platform and the surface of the goods carrier. The function is to realize real-time three-dimensional spatial positioning and trajectory tracking of the stacker crane and goods in the warehouse, providing location data for path planning and storage location allocation.

[0048] The positioning module adopts a three-point positioning architecture of main base station-secondary base station-tag. The positioning process is as follows: the main base station communicates with the microcontroller and receives ranging commands, and issues ranging commands to the secondary base station → the secondary base station performs real-time ultra-wideband ranging with the tag, collects raw distance data, and calculates the target's three-dimensional coordinate prototype data → the prototype data is sent back to the microcontroller. The microcontroller performs calibration correction and error compensation processing on the coordinate data → outputs stacker crane / cargo three-dimensional spatial coordinate data with an accuracy of ≤±5cm, achieving dynamic, continuous, and accurate positioning.

[0049] During movement, the tags maintain continuous wireless communication with the main and secondary base stations. After the positioning data is processed by the microcontroller, it is associated with the cargo identification data of the RFID UHF module and the cargo tag data of the barcode scanner module, and uploaded to the host computer to build a real-time dynamic map of the warehouse "cargo-location-stacking crane".

[0050] The environmental monitoring module consists of an MQ-2 gas sensor, a DHT11 temperature and humidity sensor, a photodiode light sensor, a PR-ZS-BZ noise sensor, and an ADC analog-to-digital converter. It is installed as sensor nodes in the four corner pillars of the warehouse and in the middle area of ​​the shelves to achieve full-area monitoring of the warehouse environment without blind spots. Its function is to collect combustible gas concentration, temperature and humidity, light intensity, and noise levels in the warehouse environment in real time, realize early warning of environmental anomalies, and ensure the safety of the warehouse environment and the normal operation of equipment.

[0051] In this embodiment, each sensor collects analog raw data of the corresponding environmental parameters, which is then converted into digital raw data by the ADC analog-to-digital converter and transmitted to the microcontroller. The microcontroller uses a multi-sampling mean filtering algorithm to reduce noise and remove interference from the digital raw data, eliminating abnormal fluctuation data, and outputting standardized warehouse environmental parameter data, including combustible gas concentration (ppm), ambient temperature (°C), ambient humidity (%RH), light intensity (lx), and noise level (dB). When any parameter exceeds a preset safety threshold, the microcontroller triggers a dual alarm on both the local and host computers.

[0052] After the microcontroller transmits the processed data to the host computer, the intelligent storage and retrieval optimization module on the host computer dynamically generates and issues storage and retrieval operation instructions based on the storage location allocation algorithm and path optimization model.

[0053] In this embodiment, the intelligent storage and retrieval optimization module achieves intelligent storage and retrieval of goods by coordinating and dynamically adjusting storage location allocation optimization and retrieval route optimization.

[0054] The specific steps for optimizing warehouse location allocation are as follows: The core principles of three-dimensional allocation are: prioritizing cargo turnover rate, balancing shelf weight, and clustering related product categories. The optimal solution for warehouse location is calculated by combining the three-dimensional running distance model of stacker cranes.

[0055] Regarding the distance model calculation method, this embodiment establishes a mathematical model for the total running distance of a single operation of the stacker crane: Si = SH + SL + SJ, where SH is the horizontal row movement distance of the stacker crane from the inbound / outbound platform to the target storage location, SL is the vertical column movement distance, and SJ is the vertical layer lifting distance. The system traverses all available storage locations in real time, calculates the Si value from the target goods to each available storage location, and selects the storage location with the smallest Si value as the initial optimal storage location.

[0056] Next, constraints are set and storage locations are dynamically allocated. In this embodiment, multiple rigid constraints are applied to verify the initially selected optimal storage location. The constraints include: the stacker crane performs single-item independent operations; goods are strictly put into and taken out of the warehouse according to the order of the host computer's orders; the category of goods completely matches the preset storage category of the storage location; and the overall center of gravity height of the rack does not exceed 60% of the total height of the rack.

[0057] If the initially selected storage location meets all the constraints, the storage location is directly allocated; if it does not meet the constraints, the storage location is removed and a new empty storage location with the Si smallest value is selected for constraint verification. This process is repeated until the final target storage location that meets all the principles and constraints is selected, thus completing the intelligent dynamic allocation of storage locations.

[0058] The specific steps for optimizing the pickup route in this embodiment are as follows: The system receives a batch of order lists, parses the order contents, and extracts the key attributes of each task: goods type, target storage location coordinates, priority label, and time window constraints. Then, it classifies the storage locations based on their 3D coordinates; it uses a path optimization model to read the (X,Y,Z) 3D coordinates of all storage locations in the task pool, and employs a spatial clustering algorithm to divide the scattered storage locations into several "job clusters."

[0059] Then, collaborative planning of outbound and inbound routes is performed. For the categorized task clusters, the model executes compound job planning: Prioritize matching "inbound point" with adjacent "outbound point" to form a compound cycle of "take one, store one" or "store one, take one" to avoid stacker crane returning empty.

[0060] If a perfect match cannot be achieved, a hybrid scheduling strategy is adopted, inserting pure inbound tasks and pure outbound tasks into the optimal gap.

[0061] At this stage, the model is substituted into the three-dimensional motion time formula, and the access sequence with the shortest global time is calculated through a heuristic algorithm.

[0062] After receiving the optimized access sequence and speed control instructions, the onboard control cabinet controls the stacker crane to operate cyclically according to the planned path, completing all tasks for the batch order.

[0063] This embodiment adopts a multi-module collaborative distributed architecture, consisting of an RFID UHF module, a positioning module, a barcode scanner module, a shipment monitoring module, an environmental monitoring module, a microcontroller, and an intelligent storage and retrieval optimization module. The microcontroller is electrically connected to each functional module, serving as the core control unit to realize data acquisition, data processing, data interaction, and operational status feedback. The overall standardized operating process of the system is as follows: the host computer issues precise operation instructions through the intelligent storage and retrieval optimization module → the stacker crane receives the instructions and executes the corresponding actions → the RFID UHF module scans the cargo tags and completes the tag authenticity verification → the stacker crane executes the core cargo storage and retrieval operation → each functional module feeds back operation data and status data to the microcontroller in real time → the microcontroller summarizes all data and uploads it to the host computer through the intelligent storage and retrieval optimization module to complete data synchronization. The following core objectives are achieved: 1. Enable multi-dimensional, high-precision, real-time monitoring of warehouse environment parameters, real-time location of goods, and physical status of goods, thereby improving the accuracy, reliability, and real-time performance of monitoring data.

[0064] 2. Enables intelligent dynamic allocation of storage locations and global optimization of storage and retrieval paths, significantly reducing stacker crane operation time and empty travel distance, and improving the overall efficiency of warehouse inbound and outbound operations.

[0065] 3. Optimize the overall weight distribution of the shelving to reduce system operation risks such as shelving tipping and equipment failure, thereby achieving a comprehensive improvement in the stability and safety of the warehousing system.

[0066] 4. Implement modular design and flexible adaptation of each functional module to meet the application scenarios of automated warehouses of different scales and with different operational needs, and enhance the industrial application value of the technical solution.

[0067] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0068] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A status monitoring and intelligent cargo storage and retrieval system for an automated three-dimensional warehouse, characterized in that, include: Status monitoring module group, microcontroller and intelligent access optimization module; The status monitoring module group includes an independent RFID UHF module, a barcode scanner module, a shipment monitoring module, a positioning module, and an environmental monitoring module. The RFID UHF module, barcode scanner module, and shipment monitoring module are all used to collect real-time information about goods in the warehouse. The positioning module is used to collect real-time location information about goods in the warehouse. The environmental monitoring module is used to collect real-time environmental information about the warehouse. The microcontroller is electrically connected to each module in the status monitoring module group, and is used to receive and process the data collected by each module in the status monitoring module group, and to interact with the host computer. The intelligent access optimization module is deployed on the host computer and has a built-in storage location allocation algorithm and path optimization model. It is used to dynamically generate and issue access operation instructions based on the data processed by the microcontroller.

2. The system as described in claim 1, characterized in that, The RFID UHF module consists of an UHF RFID reader, an UHF RFID antenna, and a cargo tag. The UHF RFID antenna is deployed on the shelf beams at warehouse inventory nodes and outbound nodes. The UHF RFID reader is deployed at the inbound and outbound platforms. The cargo tag is affixed to the cargo or cargo pallet.

3. The system as described in claim 1, characterized in that, The barcode scanner module includes an industrial camera, a DCMI image acquisition interface, and a DMA storage unit; it is located at the loading platform or inbound / outbound station of the stacker crane and is used to collect and identify cargo labels.

4. The system as described in claim 1, characterized in that, The shipment monitoring module consists of an infrared photocell sensor, a pressure sensor, and a signal conversion unit; it is deployed in the cargo carrying area of ​​each inbound and outbound station in the warehouse to perform dual detection of the presence and placement of goods at the station.

5. The system as described in claim 1, characterized in that, The positioning module is a UWB ultra-wideband positioning device, consisting of a main base station, two or more secondary base stations, and a cargo tag. The positioning module adopts a three-point positioning architecture of "main base station - secondary base station - cargo tag" to obtain the three-dimensional spatial coordinate data of the corresponding cargo.

6. The system as described in claim 1, characterized in that, The environmental monitoring module consists of an MQ-2 gas sensor, a DHT11 temperature and humidity sensor, a photodiode light sensor, a PR-ZS-BZ noise sensor, and an ADC analog-to-digital converter unit; it collects combustible gas concentration, temperature and humidity, light intensity, and noise levels in the storage environment, and converts them into digital raw data that is transmitted to the microcontroller.

7. The system as described in claims 2-6, characterized in that, The microcontroller processes the received data, specifically including: The data collected by the RFID UHF module is subjected to multiple polling verifications; wherein, the multiple polling verifications specifically include: continuously polling the same area a preset number of times based on the UHF RFID reader to obtain multiple sets of raw tag data; performing data logic cleaning on the multiple sets of raw tag data, retaining only the valid tag information in the continuous polling, and outputting identity data including goods code, category and batch; Based on the data collected by the RFID UHF module and the barcode scanner module, the information of goods entering the warehouse is entered and the information of goods in the warehouse is verified; the data collected by the shipment monitoring module is used as the basis for determining the completion of goods leaving the warehouse; the location data of the goods is output by the positioning module and merged with the verified identity information of the goods to form "goods identity-location" association information. The sensor data collected by the environmental monitoring module is subjected to moving average noise reduction and outlier removal to construct a three-dimensional temperature and humidity field model of the reservoir area to identify local environmental anomalies.

8. The system as described in claim 7, characterized in that, The storage location allocation algorithm built into the intelligent storage optimization module specifically includes: Based on the constructed mathematical model of the total running distance of a single operation of a stacker crane, the total running distance from the target goods to each available storage location is calculated; the mathematical model of the total running distance of a single operation of a stacker crane is calculated by summing the horizontal row movement distance, the vertical column movement distance, and the vertical layer lifting distance of the stacker crane from the inbound / outbound platform to the target storage location. The storage location with the shortest total running distance is selected as the initial optimal storage location, and the initial optimal storage location is verified by multiple rigid constraints. The multiple rigid constraints include: the stacker crane performs a single independent operation for a single item, the target goods enter and leave the warehouse according to the order of the upper computer order, the category of the target goods matches the preset storage category of the storage location, and it is assumed that the overall center of gravity height of the shelf after the target goods are placed must be lower than a preset proportion of the total height of the shelf. If the initially selected optimal storage location meets the multiple rigid constraints, the target goods will be directly allocated to that storage location; if the multiple rigid constraints are not met, the storage location will be removed and a new vacant storage location with the second smallest total running distance will be selected for constraint verification. This process will be repeated until a target storage location that meets the multiple rigid constraints is selected, thus completing the intelligent dynamic allocation of storage locations.

9. The system as described in claim 8, characterized in that, The intelligent storage and retrieval optimization module has a built-in path optimization model that transforms the multi-location storage and retrieval task of the stacker crane into a shortest path planning problem. The model aims to minimize the total operation time. Based on the three-dimensional motion characteristics of the stacker crane, it calculates the actual operation time between any two storage locations. It outputs a storage location access sequence ordered by time and the corresponding speed control command to drive the stacker crane to perform storage and retrieval operations.

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