3D modeling method and system based on logistics park and warehoused materials

Abstract

The application provides a 3D modeling method and system based on a logistics park and warehouse materials, relates to the technical field of 3D modeling, and solves the technical problem of low warehouse data accuracy of warehouse materials in the logistics park. The method comprises the following steps: in response to a 3D modeling instruction for the materials, a drone in the logistics park sends a broadcast signal in the form of a broadcast; an electronic tag receives the broadcast signal and sends a feedback signal for the broadcast signal according to the to-be-modeled data; the feedback signal contains target warehouse data conforming to the to-be-modeled data and a material identifier of target materials corresponding to the electronic tag; a scanning device arranged on the drone scans all the electronic tags in the logistics park to receive the feedback signal; and a data processing system corresponding to the drone performs 3D modeling of the situation of each target material in the logistics park based on the target warehouse data in the feedback signal and the material identifier, to obtain a 3D model of the warehouse materials.

Description

Technical Field

[0001] This application relates to the field of 3D modeling technology, and in particular to a 3D modeling method and system based on logistics parks and warehouse materials. Background Technology

[0002] Currently, information management platforms are used to manage the status data of stored materials within logistics parks. For example, the warehouse management system (WMS) on these platforms centrally manages the entire process of receiving, allocating storage locations, and verifying outbound shipments. Automated storage systems (AS / RS) integrate stacker cranes, conveyor lines, and intelligent control systems to achieve high-density storage and precise retrieval. Combined with automated sorting lines and intelligent packaging systems, this supports high-frequency order processing. However, the accuracy of current storage data for materials within logistics parks is relatively low. Summary of the Invention

[0003] The purpose of this invention is to provide a 3D modeling method and system based on logistics parks and warehouse materials, so as to solve the technical problem of low accuracy of warehouse data for warehouse materials in logistics parks.

[0004] Firstly, this application provides a 3D modeling method based on logistics parks and warehoused materials, wherein all materials in the logistics park are equipped with electronic tags; the electronic tags contain corresponding warehouse data for the materials in the logistics park, the warehouse data including the contents of the materials, the time the materials entered the logistics park, the warehouse type of the materials in the logistics park, the storage method of the materials in the logistics park, and the storage location of the materials in the logistics park; the method includes:

[0005] In response to a 3D modeling instruction for the materials, the drone in the logistics park broadcasts a signal; the broadcast signal contains data to be modeled, which includes at least one of the following: the content of the item to be modeled, the materials to be modeled that entered the logistics park within a target time, the storage type to be modeled in the logistics park, the materials to be modeled that are stored in the logistics park in a target storage manner, and the materials to be modeled that are stored at a target storage location.

[0006] The electronic tag receives the broadcast signal and sends a feedback signal to the broadcast signal based on the data to be modeled; the feedback signal includes target warehouse data that matches the data to be modeled and the material identifier of the target material corresponding to the electronic tag;

[0007] The scanning device mounted on the drone scans all the electronic tags in the logistics park to receive the feedback signal;

[0008] The data processing system corresponding to the drone performs 3D modeling of the warehouse materials based on the target warehouse data and the material identification in the feedback signal, thereby obtaining a 3D model of the warehouse materials.

[0009] In one possible implementation, the electronic tag receives the broadcast signal and, based on the data to be modeled, sends a feedback signal to the broadcast signal, including:

[0010] The electronic tag receives the data to be modeled from the broadcast signal;

[0011] The electronic tag filters target warehouse data that matches the data to be modeled from the warehouse data it has set.

[0012] The electronic tag sends a feedback signal to the broadcast signal based on the target warehouse data.

[0013] In one possible implementation, the electronic tag is equipped with a gyroscope; the contents of the materials correspond to anti-damage movement standard data; the method further includes:

[0014] The actual vibration data of the material is monitored using the gyroscope.

[0015] The damage monitoring results of the materials are analyzed based on the actual vibration data and the corresponding anti-damage movement standard data.

[0016] In response to a damage detection command for the materials, the drone broadcasts a detection signal.

[0017] The electronic tag receives the detection signal and sends a response signal to the detection signal; the response signal contains the monitoring result of the degree of damage of the material corresponding to the electronic tag;

[0018] The scanning device scans all the electronic tags in the logistics park to receive the response signal.

[0019] In one possible implementation, the step of analyzing the damage monitoring results of the material based on the actual vibration data and the corresponding damage prevention movement standard data includes:

[0020] Based on the actual vibration data and the corresponding damage prevention movement standard data of the material, the damage monitoring results of the material are analyzed using the following formula:

[0021] RMS= Where x(t) is the actual vibration acceleration value of the material monitored by the gyroscope at time t, T is the monitoring time period of the gyroscope, and RMS represents the actual vibration intensity of the material.

[0022] = ;in, This refers to the maximum range of vibration variation of the material.

[0023] DamageIndex = w 1× + w 2× + w 3×∑( );

[0024] in, This indicates the maximum allowed RMS value. The maximum permissible peak value of the vibration amplitude of the material. It is the vibration amplitude value at frequency f. This represents the maximum permissible amplitude at a specific critical frequency f. w 1. w 2. w 3 represents the specified weighting coefficients for each parameter, and DamageIndex represents the monitoring result of the damage level of the material.

[0025] In one possible implementation, after the scanning device scans all the electronic tags in the logistics park to receive the response signal, the method further includes:

[0026] The data processing system corresponding to the drone determines the materials to be screened from all the materials based on the damage monitoring results in the response signal, controls the drone to go to the location of the materials to be screened, and controls the robotic arm set on the drone to unseal the packaging of the materials to be screened.

[0027] The data processing system controls the image acquisition device installed on the drone to acquire images of the items in the materials to be screened, and identifies whether the items in the materials to be screened are damaged based on the images.

[0028] In one possible implementation, the method further includes:

[0029] In response to a retrieval command for the first material, the drone sends a retrieval signal; wherein the retrieval signal includes retrieval conditions, the retrieval conditions including at least one of the following: the contents of the item to be retrieved, the material that entered the logistics park within a first time period, the material of a first storage type, the material stored in the logistics park in a first storage manner, and the material stored at a first storage location.

[0030] The electronic tag receives the extraction conditions from the extraction signal, and determines whether its corresponding materials match the extraction conditions based on the extraction conditions and its own set storage data; the electronic tag is equipped with an indicator light;

[0031] If the material corresponding to the electronic tag matches the extraction conditions, then the indicator light set in the tag will be activated.

[0032] The image acquisition device on the drone identifies the first electronic tag that is running the indicator light, controls the drone to fly to the location of the first electronic tag, and controls the robotic arm on the drone to extract the first material corresponding to the first electronic tag from multiple materials.

[0033] In one possible implementation, after the image acquisition device mounted on the drone identifies the first electronic tag activating the indicator light, the following is also included:

[0034] The first warehousing data stored in the first electronic tag is compared with the model warehousing data of the virtual material corresponding to the first electronic tag in the 3D model of the warehousing material.

[0035] If a discrepancy is detected between the first storage data and the model storage data, the 3D model of the stored materials is corrected based on the first storage data to keep the 3D model of the stored materials updated.

[0036] Secondly, this application provides a 3D modeling system based on logistics parks and warehoused materials. All materials in the logistics park are equipped with electronic tags. Each electronic tag contains corresponding warehouse data for the material within the logistics park. This warehouse data includes the item content of the material, the time the material entered the logistics park, the warehouse type of the material within the logistics park, the storage method of the material within the logistics park, and the storage location of the material within the logistics park. The 3D modeling system based on logistics parks and warehoused materials includes:

[0037] The first issuing module is used to respond to a 3D modeling instruction for the materials, wherein the drone in the logistics park broadcasts a broadcast signal; the broadcast signal contains data to be modeled, which includes at least one of the following: the content of the item to be modeled, the materials to be modeled that enter the logistics park within a target time, the storage type to be modeled in the logistics park, the materials to be modeled that are stored in the logistics park in a target storage manner, and the materials to be modeled that are stored at a target storage location.

[0038] The second transmitting module is used for the electronic tag to receive the broadcast signal and to transmit a feedback signal to the broadcast signal according to the data to be modeled; the feedback signal includes target warehouse data that matches the data to be modeled and the material identifier of the target material corresponding to the electronic tag;

[0039] The scanning module is used by the scanning device installed on the drone to scan all the electronic tags in the logistics park in order to receive the feedback signal;

[0040] The modeling module is used by the data processing system corresponding to the UAV to perform 3D modeling of the storage materials in the logistics park based on the target storage data and the material identification in the feedback signal, so as to obtain the 3D model of the storage materials.

[0041] Thirdly, this application also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program that can run on the processor, and the processor executes the computer program to implement the method described in the first aspect above.

[0042] Fourthly, this application also provides a computer-readable storage medium storing computer-executable instructions that, when invoked and executed by a processor, cause the processor to perform the method described in the first aspect above.

[0043] This application brings the following beneficial effects:

[0044] This application provides a 3D modeling method and system based on logistics parks and warehoused materials. All materials in the logistics park are equipped with electronic tags, which contain corresponding warehousing data. This data includes the material's contents, the time the material entered the logistics park, the storage type of the material, the storage method, and the storage location. The method responds to 3D modeling commands for the materials. Unmanned aerial vehicles (UAVs) in the logistics park broadcast signals containing data to be modeled. This data includes at least one of the following: the contents of the material to be modeled, the material entering the logistics park within a target time, the storage type, the material stored in the logistics park in a target storage method, and the material stored at a target storage location. The electronic tags... The system receives broadcast signals and sends feedback signals based on the data to be modeled. These feedback signals include target warehousing data matching the data to be modeled and the material identifiers of the target materials corresponding to the electronic tags. Scanning equipment on the drone scans all electronic tags in the logistics park to receive the feedback signals. The drone's data processing system then uses the target warehousing data and material identifiers from the feedback signals to create a 3D model of each target material within the logistics park. This solution, through the collaboration of drones and electronic tags, enables real-time updates and acquisition of accurate warehousing data, including information such as item contents, entry time, storage type, method, and location. This makes inventory records more accurate, thereby improving the precision of warehousing data within the logistics park and solving the technical problem of low accuracy in warehousing data within logistics parks. Furthermore, based on the unique electronic tags of each material, the system can filter based on specific attributes (such as entry time and storage type) and visually display the specific distribution of materials in the warehouse through 3D modeling, greatly enhancing the efficiency of material tracking and management.

[0045] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0047] Figure 1A flowchart illustrating the 3D modeling method based on logistics parks and warehouse materials provided in this application embodiment;

[0048] Figure 2 An example of a 3D model of warehouse materials in the 3D modeling method based on logistics parks and warehouse materials provided in the embodiments of this application;

[0049] Figure 3 A schematic diagram of the structure of a 3D modeling device based on logistics parks and warehouse materials provided in this application embodiment;

[0050] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0052] The terms "comprising" and "having," and any variations thereof, used in the embodiments of this application, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the steps or units listed, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0053] Currently, the accuracy of warehousing data for stored goods within logistics parks is relatively low. Therefore, this application provides a 3D modeling method and system based on logistics parks and stored goods, which can solve the technical problem of low accuracy in warehousing data for stored goods within logistics parks.

[0054] The embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0055] Figure 1 This is a flowchart illustrating a 3D modeling method based on logistics parks and warehoused materials, provided in an embodiment of this application. All materials in the logistics park are equipped with electronic tags; each electronic tag contains corresponding warehouse data for the material within the logistics park. This data includes the material's contents, the time the material entered the logistics park, the warehouse type, the storage method, and the storage location. Figure 1 As shown, the method includes:

[0056] In step S110, in response to the 3D modeling instruction for the materials, the drones in the logistics park broadcast a signal.

[0057] The broadcast signal contains data to be modeled, which includes at least one of the following: the content of the item to be modeled, the materials to be modeled that enter the logistics park within the target time, the storage type to be modeled in the logistics park, the materials to be modeled that are stored in the logistics park in the target storage manner, and the materials to be modeled that are stored at the target storage location.

[0058] For example, a control center or mobile application issues a 3D modeling command for a specific material to the system. Upon receiving the command, the system selects the most suitable drone for the task based on the material's location and characteristics, and sends the command to the selected drone. Based on the material's location and surrounding environment, the system calculates the optimal flight path to avoid obstacles and efficiently complete the task. The drone automatically takes off according to a preset program and flies along the planned path to the target location. Upon arrival, the drone uses its onboard sensors to photograph or scan the material from multiple angles, collecting necessary data for 3D modeling. Some advanced systems can perform preliminary data processing directly on the drone, improving efficiency. The collected data is then integrated into a unified format and a broadcast signal is generated. For broadcasting, the drone transmits a signal containing 3D model data or other relevant information via its communication module.

[0059] In step S120, the electronic tag receives the broadcast signal and sends a feedback signal to the broadcast signal based on the data to be modeled.

[0060] The feedback signal includes target warehouse data that matches the data to be modeled and the material identification of the target material corresponding to the electronic tag.

[0061] In some embodiments, step S120 may specifically include the following steps: the electronic tag receives the data to be modeled in the broadcast signal; the electronic tag filters target warehouse data that matches the data to be modeled from its own set warehouse data according to the data to be modeled; the electronic tag sends a feedback signal to the broadcast signal based on the target warehouse data.

[0062] Electronic tags enable the system to quickly filter out target warehouse data that meets the requirements from a large amount of stored warehouse data based on the received data to be modeled. This means that the system can quickly locate information about relevant items or materials in massive amounts of information, greatly improving the efficiency and accuracy of data processing.

[0063] In step S130, the scanning device on the drone scans all the electronic tags in the logistics park to receive feedback signals.

[0064] For example, a drone takes off along a pre-set flight path and begins its scanning mission. During this process, the drone maintains stable flight to ensure the scanning equipment operates accurately. Once activated, the drone's scanning equipment begins scanning all electronic tags within the logistics park. These tags may be attached to goods, shelves, or other logistics facilities. The system broadcasts the data to be modeled to the electronic tags via some means (such as a wireless network). Upon receiving this signal, each electronic tag determines whether a match has been found based on its internally stored data and prepares the appropriate feedback information.

[0065] When an electronic tag determines that it meets the requirements of the data to be modeled, it can send a feedback signal. Scanning equipment on the drone receives these feedback signals and transmits them back to the central processing system. The received feedback signals contain important information about the item associated with the electronic tag. The central processing system aggregates and analyzes all received feedback signals to update inventory information, track cargo locations, or perform other administrative operations.

[0066] In step S140, the data processing system corresponding to the UAV performs 3D modeling of the warehouse materials based on the target warehouse data and material identification in the feedback signal, and obtains the 3D model of the warehouse materials.

[0067] The above-mentioned 3D model of stored materials, such as Figure 2 As shown in this embodiment, by combining drones and electronic tags, accurate warehousing data can be updated and acquired in real time, including information such as item contents, entry time, storage type, method, and location. This makes inventory records more accurate, thereby improving the accuracy of warehousing data for goods stored in the logistics park. Moreover, based on the unique electronic tag for each item, the system can filter based on specific attributes (such as entry time, storage type, etc.) and intuitively display the specific distribution of goods in the warehouse through 3D modeling, greatly enhancing the efficiency of goods tracking and management.

[0068] In some embodiments, the electronic tag is equipped with a gyroscope; the contents of the goods correspond to anti-damage movement standard data; the method may further include the following steps:

[0069] The drone monitors the actual vibration data of the materials using a gyroscope; it analyzes the damage level of the materials based on the actual vibration data and the corresponding damage prevention movement standard data; in response to the damage detection command for the materials, the drone broadcasts a detection signal; the electronic tag receives the detection signal and sends a response signal; the response signal contains the damage level monitoring result of the materials corresponding to the electronic tag; and the scanning device scans all electronic tags in the logistics park to receive the response signal.

[0070] By integrating gyroscopes into electronic tags, the actual vibration of materials during transportation and storage can be monitored in real time, ensuring that any abnormal vibrations that may cause damage are detected promptly. Furthermore, by combining the specific damage-resistant movement standard data for each item, the system can compare the actual vibration data to more accurately analyze and assess the likelihood or extent of damage, providing a basis for subsequent handling.

[0071] In some embodiments, the above-mentioned analysis of the damage monitoring results of materials based on actual vibration data and corresponding damage prevention movement standard data may specifically include the following steps:

[0072] Based on the actual vibration data and the corresponding damage prevention movement standard data of the materials, the monitoring results of the damage degree of the materials are analyzed using the following formula:

[0073] RMS= Where x(t) is the actual vibration acceleration value of the material detected by the gyroscope at time t, T is the monitoring time period of the gyroscope, and RMS represents the actual vibration intensity of the material.

[0074] = ;in, This refers to the maximum range of vibration variation of the material.

[0075] DamageIndex = w 1× + w 2× + w 3×∑( );

[0076] in, This indicates the maximum allowed RMS value. The maximum permissible peak value of the vibration amplitude of the material. It is the vibration amplitude value at frequency f. This represents the maximum permissible amplitude at a specific critical frequency f. w 1. w 2. w 3 represents the specified weighting coefficients for each parameter, and DamageIndex represents the monitoring results of the material damage level.

[0077] In this embodiment of the application, by analyzing the damage monitoring results of materials using the above formula, the damage monitoring results of materials can be made more accurate, thereby improving the accuracy of material damage monitoring.

[0078] In some embodiments, after the scanning device scans all electronic tags in the logistics park to receive a response signal, the method may further include the following steps:

[0079] The data processing system corresponding to the drone determines the materials to be screened from all materials based on the damage monitoring results in the reply signal, controls the drone to go to the location of the materials to be screened, and controls the robotic arm set on the drone to unseal the packaging of the materials to be screened.

[0080] The data processing system controls the image acquisition equipment set on the drone to acquire images of items in the materials to be screened, and identifies whether the items in the materials to be screened are damaged based on the images.

[0081] The data processing system automatically identifies materials requiring further inspection based on the damage level monitoring results from the feedback signals of electronic tags, eliminating the need for pre-judgment by humans and significantly saving time and labor costs. Furthermore, by controlling drones to travel to the location of the materials to be inspected and utilizing their robotic arms for packaging and unpacking operations, precise handling of specific materials is achieved, reducing errors or damage that could result from human intervention.

[0082] In some embodiments, the method may further include the following steps:

[0083] In response to the retrieval command for the first material, the drone sends out a retrieval signal; wherein the retrieval signal includes retrieval conditions, which include at least one of the following: the contents of the item to be retrieved, the material to be retrieved that entered the logistics park in the first time, the material to be retrieved that is of the first storage type, the material to be retrieved that is stored in the logistics park in the first storage method, and the material to be retrieved that is stored at the first storage location.

[0084] The electronic tag receives the extraction conditions from the extraction signal and determines whether its corresponding materials match the extraction conditions based on the extraction conditions and its own set storage data; the electronic tag is equipped with an indicator light;

[0085] If the material corresponding to the electronic tag matches the extraction conditions, the indicator light set in the tag will be activated.

[0086] The image acquisition device on the drone identifies the first electronic tag of the operation indicator light, controls the drone to fly to the location of the first electronic tag, and controls the robotic arm on the drone to extract the first material corresponding to the first electronic tag from multiple materials.

[0087] The retrieval signals emitted by the drones contain specific retrieval conditions, which the electronic tags use to intelligently match against stored warehouse data. This mechanism ensures that only materials meeting the retrieval requirements are selected, significantly improving the accuracy of retrieval. Furthermore, once the electronic tag determines that its corresponding material meets the conditions, it activates an indicator light. The drone's image acquisition equipment can recognize these indicator lights and guide the drone precisely to the location of the corresponding material. This method avoids the time wasted and potential errors associated with manual material searches, improving operational efficiency. Moreover, the entire retrieval process is almost entirely automated, from sending the retrieval command and matching conditions to the final retrieval of the material, all completed autonomously by the system. This not only significantly shortens processing time but also reduces manpower requirements, demonstrating its high efficiency, especially when dealing with large quantities of materials.

[0088] In some embodiments, after the image acquisition device mounted on the aforementioned drone identifies the first electronic tag of the operation indicator light, the method may further include the following steps:

[0089] Compare the first warehousing data stored in the first electronic tag with the model warehousing data of the virtual material corresponding to the first electronic tag in the 3D model of the warehousing material;

[0090] If the comparison detects a discrepancy between the first storage data and the model storage data, the 3D model of the stored materials is corrected based on the first storage data to keep the 3D model of the stored materials updated.

[0091] By comparing and correcting actual warehouse data (first-level warehouse data) with data in the virtual 3D model, consistency between the two can be ensured, which greatly reduces erroneous decisions or operational errors caused by data inconsistencies. Furthermore, the automated comparison and correction process can quickly identify and correct data discrepancies without manual intervention, thereby improving the overall efficiency and accuracy of inventory management.

[0092] Regarding the intelligent warehousing aspect of the aforementioned 3D model of stored materials, users can manage and view various storage location data within a 3D visualization environment. Storage location occupancy status: Supports real-time display and viewing of various storage location occupancy statuses through various charts, including the total number of storage locations and current usage rate. Material storage location occupancy ranking: Supports real-time display of storage location occupancy rankings and the usage percentage of various types of items through various charts. Storage location utilization status: Supports real-time display of various storage location usage trends through various charts, such as daily, weekly, monthly, and yearly usage trends. The system visualizes storage location management, allowing users to view the storage location of goods in each storage location within the 3D scene. It supports quick querying and locating of the specific location of goods and supports fuzzy search functionality. Shelf area status display: Occupied storage locations are displayed in red, while vacant locations are displayed in green. Clicking on a row or column will expand the display. Clicking on a storage location pallet displays basic information about the goods stored in that location (item, sub-warehouse attributes, storage age, purchase order number). Clicking on the goods will display the pallet number, box number, and corresponding barcode for the terminal materials.

[0093] For warehouse operation management, in a 3D visualization environment, users can manage forklift positioning and operator data: It supports real-time reconstruction of forklift operation positioning in the 3D scene, displaying forklift operation status, market data, etc.; it supports forklift positioning management, marking the specific location of all forklifts in the warehouse; and it supports statistical analysis of forklift utilization rate, vacancy rate, etc., in chart form. For equipment status, it supports marking the spatial distribution of all video surveillance equipment within the park in the 3D environment. It supports real-time video switching and zooming of individual cameras. It supports association with other system devices outside the video surveillance subsystem, searching for nearby video surveillance points, and allowing users to click on the found surveillance cameras to view real-time monitoring footage. Equipment operation and alarms: It supports displaying the operating status of all video surveillance equipment within the park using different colors (e.g., red for alarms). It supports displaying statistics on the number of video devices and alarms in an information panel. It supports linkage with AI video surveillance in the 3D scene, statistical analysis of AI alarm type proportions, and retrieval of a detailed alarm list. By deploying AI video surveillance, six monitoring algorithms are set up to detect "not wearing a safety helmet, materials placed on the line, obstructing the road, perimeter intrusion, open flame, and smoking" to achieve 24 / 7 automatic monitoring and alarm.

[0094] The intelligent management system integrates overview, warehousing, security, vehicle, and environmental monitoring modules to automate and streamline the logistics park. It precisely controls materials, monitors safety in real time, optimizes vehicle scheduling, and intelligently adjusts the environment, significantly improving the park's operational efficiency and safety levels.

[0095] This solution calculates and displays relevant professional indicators for warehousing and logistics, and refines the management dimensions based on material categories and different sub-warehouses, thereby improving the level of refined management of the park, optimizing material management methods, and realizing the visible sharing of idle resources.

[0096] The digital twin platform integrates five modules: overview, warehousing, security, vehicle, and environmental monitoring. The intelligent warehousing module displays indicators such as warehouse age and sub-warehouse inventory percentages. The intelligent security module incorporates AI-powered video analytics. Through integration with AI video analytics technology, real-time intelligent analysis and violation warnings of surveillance videos are possible, enabling intelligent online supervision. This solution can reduce park operating costs by 800,000 yuan, improve overall operational efficiency by 40%, and increase resource and equipment utilization efficiency by 20%.

[0097] Figure 3 A schematic diagram of a 3D modeling device based on logistics parks and warehouse materials is provided. All materials in the logistics park are equipped with electronic tags; each electronic tag contains corresponding warehouse data for the material within the logistics park. This warehouse data includes the item's contents, the time the material entered the logistics park, the warehouse type the material belongs to, the storage method the material uses, and the storage location of the material within the logistics park. Figure 3 As shown, the 3D modeling device 300 based on logistics parks and warehouse materials includes:

[0098] The first issuing module 301 is used to respond to a 3D modeling instruction for the materials, wherein the drone in the logistics park broadcasts a broadcast signal; the broadcast signal contains data to be modeled, which includes at least one of the following: the content of the item to be modeled, the materials to be modeled that enter the logistics park within a target time, the storage type to be modeled in the logistics park, the materials to be modeled that are stored in the logistics park in a target storage manner, and the materials to be modeled that are stored at a target storage location.

[0099] The second transmitting module 302 is used for the electronic tag to receive the broadcast signal and to transmit a feedback signal to the broadcast signal according to the data to be modeled; the feedback signal includes target warehouse data that matches the data to be modeled and the material identifier of the target material corresponding to the electronic tag;

[0100] The scanning module 303 is used by the scanning device installed on the drone to scan all the electronic tags in the logistics park in order to receive the feedback signal;

[0101] The modeling module 304 is used by the data processing system corresponding to the UAV to perform 3D modeling of the storage materials in the logistics park based on the target storage data and the material identification in the feedback signal, so as to obtain the 3D model of the storage materials.

[0102] The 3D modeling device based on logistics parks and warehouse materials provided in this application embodiment has the same technical features as the 3D modeling method based on logistics parks and warehouse materials provided in the above embodiment, so it can also solve the same technical problems and achieve the same technical effects.

[0103] An electronic device provided in this application embodiment, such as Figure 4 As shown, the electronic device 400 includes a processor 402 and a memory 401. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements the steps of the method provided in the above embodiments.

[0104] See Figure 4 The electronic device also includes a bus 403 and a communication interface 404. The processor 402, the communication interface 404 and the memory 401 are connected via the bus 403. The processor 402 is used to execute executable modules, such as computer programs, stored in the memory 401.

[0105] The memory 401 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 404 (which can be wired or wireless), such as the Internet, wide area network, local area network, or metropolitan area network.

[0106] Bus 403 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0107] The memory 401 is used to store programs. After receiving an execution instruction, the processor 402 executes the program. The method executed by the apparatus defined by the process disclosed in any of the preceding embodiments of this application can be applied to the processor 402 or implemented by the processor 402.

[0108] Processor 402 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 402 or by instructions in software form. The processor 402 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory 401, and processor 402 reads the information from memory 401 and, in conjunction with its hardware, completes the steps of the above method.

[0109] Corresponding to the above-described 3D modeling method based on logistics parks and warehouse materials, this application embodiment also provides a computer-readable storage medium storing computer-executable instructions. When the computer-executable instructions are invoked and executed by a processor, the computer-executable instructions cause the processor to perform the steps of the above-described 3D modeling method based on logistics parks and warehouse materials.

[0110] The 3D modeling device for logistics parks and warehouse materials provided in this application embodiment can be specific hardware on the device or software or firmware installed on the device. The implementation principle and technical effects of the device provided in this application embodiment are the same as those in the foregoing method embodiments. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the foregoing method embodiments. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can all be referred to the corresponding processes in the above method embodiments, and will not be repeated here.

[0111] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0112] For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0113] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0114] In addition, the functional units in the embodiments provided in this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0115] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the 3D modeling method based on logistics parks and warehouse materials described in the various embodiments of this application. The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0116] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0117] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application. All should be covered within the protection scope of this application. Therefore, the protection scope of this application should be determined by the protection scope of the claims.

Claims

1. A 3D modeling method based on logistics parks and warehouse materials, characterized in that, All goods in the logistics park are equipped with electronic tags; each electronic tag contains corresponding warehousing data for the goods within the logistics park, including the contents of the goods, the time the goods entered the logistics park, the storage type of the goods, the storage method of the goods, and the storage location of the goods within the logistics park; the method includes: In response to a 3D modeling instruction for the materials, the drone in the logistics park broadcasts a signal; the broadcast signal contains data to be modeled, which includes at least one of the following: the content of the item to be modeled, the materials to be modeled that entered the logistics park within a target time, the storage type to be modeled in the logistics park, the materials to be modeled that are stored in the logistics park in a target storage manner, and the materials to be modeled that are stored at a target storage location. The electronic tag receives the broadcast signal and sends a feedback signal to the broadcast signal based on the data to be modeled; the feedback signal includes target warehouse data that matches the data to be modeled and the material identifier of the target material corresponding to the electronic tag; The scanning device mounted on the drone scans all the electronic tags in the logistics park to receive the feedback signal; The data processing system corresponding to the drone performs 3D modeling of the warehouse materials based on the target warehouse data and the material identification in the feedback signal, thereby obtaining a 3D model of the warehouse materials.

2. The method according to claim 1, characterized in that, The electronic tag receives the broadcast signal and sends a feedback signal to the broadcast signal based on the data to be modeled, including: The electronic tag receives the data to be modeled from the broadcast signal; The electronic tag filters target warehouse data that matches the data to be modeled from the warehouse data it has set. The electronic tag sends a feedback signal to the broadcast signal based on the target warehouse data.

3. The method according to claim 1, characterized in that, The electronic tag is equipped with a gyroscope; the contents of the materials correspond to anti-damage movement standard data; the method further includes: The actual vibration data of the material is monitored using the gyroscope. The damage monitoring results of the materials are analyzed based on the actual vibration data and the corresponding anti-damage movement standard data. In response to a damage detection command for the materials, the drone broadcasts a detection signal. The electronic tag receives the detection signal and sends a response signal to the detection signal; the response signal contains the monitoring result of the degree of damage of the material corresponding to the electronic tag; The scanning device scans all the electronic tags in the logistics park to receive the response signal.

4. The method according to claim 3, characterized in that, The step of analyzing the damage monitoring results of the material based on the actual vibration data and the corresponding damage prevention movement standard data includes: Based on the actual vibration data and the corresponding damage prevention movement standard data of the material, the damage monitoring results of the material are analyzed using the following formula: RMS= Where x(t) is the actual vibration acceleration value of the material monitored by the gyroscope at time t, T is the monitoring time period of the gyroscope, and RMS represents the actual vibration intensity of the material. = ;in, This refers to the maximum range of vibration variation of the material. DamageIndex = w 1× + w 2× + w 3×∑（ ）； in, This indicates the maximum allowed RMS value. The maximum permissible peak value of the vibration amplitude of the material. It is the vibration amplitude value at frequency f. The maximum permissible amplitude at frequency f. w 1. w 2. w 3 represents the specified weighting coefficients for each parameter, and DamageIndex represents the monitoring result of the damage level of the material.

5. The method according to claim 3, characterized in that, After the scanning device scans all the electronic tags in the logistics park to receive the response signal, the method further includes: The data processing system corresponding to the drone determines the materials to be screened from all the materials based on the damage monitoring results in the response signal, controls the drone to go to the location of the materials to be screened, and controls the robotic arm set on the drone to unseal the packaging of the materials to be screened. The data processing system controls the image acquisition device installed on the drone to acquire images of the items in the materials to be screened, and identifies whether the items in the materials to be screened are damaged based on the images.

6. The method according to claim 1, characterized in that, The method further includes: In response to a retrieval command for the first material, the drone sends a retrieval signal; wherein the retrieval signal includes retrieval conditions, the retrieval conditions including at least one of the following: the contents of the item to be retrieved, the material that entered the logistics park within a first time period, the material of a first storage type, the material stored in the logistics park in a first storage manner, and the material stored at a first storage location. The electronic tag receives the extraction conditions from the extraction signal, and determines whether its corresponding materials match the extraction conditions based on the extraction conditions and its own set storage data; the electronic tag is equipped with an indicator light; If the material corresponding to the electronic tag matches the extraction conditions, then the indicator light set in the tag will be activated. The image acquisition device on the drone identifies the first electronic tag that is running the indicator light, controls the drone to fly to the location of the first electronic tag, and controls the robotic arm on the drone to extract the first material corresponding to the first electronic tag from multiple materials.

7. The method according to claim 6, characterized in that, After the image acquisition device mounted on the drone identifies the first electronic tag that is activating the indicator light, the system further includes: The first warehousing data stored in the first electronic tag is compared with the model warehousing data of the virtual material corresponding to the first electronic tag in the 3D model of the warehousing material. If a discrepancy is detected between the first storage data and the model storage data, the 3D model of the stored materials is corrected based on the first storage data to keep the 3D model of the stored materials updated.

8. A 3D modeling system based on logistics parks and warehouse materials, characterized in that, All materials in the logistics park are equipped with electronic tags; the electronic tags contain the corresponding storage data of the materials in the logistics park, including the contents of the materials, the time the materials entered the logistics park, the storage type of the materials in the logistics park, the storage method of the materials in the logistics park, and the storage location of the materials in the logistics park. The 3D modeling system based on logistics parks and warehouse materials includes: The first issuing module is used to respond to a 3D modeling instruction for the materials, wherein the drone in the logistics park broadcasts a broadcast signal; the broadcast signal contains data to be modeled, which includes at least one of the following: the content of the item to be modeled, the materials to be modeled that enter the logistics park within a target time, the storage type to be modeled in the logistics park, the materials to be modeled that are stored in the logistics park in a target storage manner, and the materials to be modeled that are stored at a target storage location. The second transmitting module is used for the electronic tag to receive the broadcast signal and to transmit a feedback signal to the broadcast signal according to the data to be modeled; the feedback signal includes target warehouse data that matches the data to be modeled and the material identifier of the target material corresponding to the electronic tag; The scanning module is used by the scanning device installed on the drone to scan all the electronic tags in the logistics park in order to receive the feedback signal; The modeling module is used by the data processing system corresponding to the UAV to perform 3D modeling of the storage materials in the logistics park based on the target storage data and the material identification in the feedback signal, so as to obtain the 3D model of the storage materials.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions that, when invoked and executed by a processor, cause the processor to perform the method according to any one of claims 1 to 7.

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