Warehouse inventory intelligent management method, device and terminal based on AI visual monitoring

Abstract

The embodiment of the invention relates to the technical field of data processing and AI vision, and provides a warehouse inventory intelligent management method and device based on AI vision monitoring and a terminal, and the method comprises the steps: obtaining inventory management basic data and external dynamic influence data of a to-be-monitored warehouse; based on an AI visual technology, according to the sample image data set and the label image data set, performing dynamic optimization processing on current inventory data in the inventory management basic data to obtain dynamically optimized inventory data; according to the inventory management basic data and the dynamic optimization inventory data, carrying out inventory structure evaluation to obtain inventory structure evaluation data; performing real-time dynamic adjustment processing on the inventory structure evaluation data according to the external dynamic influence data to obtain inventory dynamic adjustment data; according to the dynamic optimization inventory data, the inventory structure evaluation data and the inventory dynamic adjustment data, an inventory health evaluation report is generated, and the purpose of more accurate and more comprehensive warehouse inventory intelligent management is achieved.

Description

Technical Field

[0001] The present application relates to the fields of data processing technology and AI vision technology, and specifically to a warehouse inventory intelligent management method, device and terminal based on AI vision monitoring. Background Art

[0002] In the traditional warehousing management field, inventory counting is heavily dependent on manual work, especially for large warehouses, which often requires a lot of manpower and takes days or even weeks. It not only leads to interruption of warehouse operations, but also easily leads to omission or misrecording of the quantity of goods due to human negligence, such as fatigue and carelessness, or repeated or missed inventory counting due to the unstable storage location of goods, which leads to discrepancies between accounts and actuals, affecting the accuracy of enterprise cost accounting and profits. Therefore, how to achieve more accurate and comprehensive intelligent management of warehouse inventory has become a problem that needs to be solved urgently. Summary of the invention

[0003] The embodiments of the present application provide a warehouse inventory intelligent management method, device and terminal based on AI visual monitoring, which can generate a more accurate and comprehensive inventory health assessment report based on inventory management basic data and external dynamic influence data, which is conducive to achieving more accurate and comprehensive warehouse inventory intelligent management.

[0004] A first aspect of an embodiment of the present application provides a warehouse inventory intelligent management method based on AI visual monitoring, the method comprising: Obtain the basic inventory management data and external dynamic impact data of the warehouse to be monitored; Based on AI vision technology, according to the sample image data set and the label image data set, the current inventory data in the inventory management basic data is dynamically optimized to obtain dynamically optimized inventory data; Based on the basic inventory management data and the dynamically optimized inventory data, the inventory structure is evaluated to obtain the inventory structure evaluation data; Perform real-time dynamic adjustment processing on inventory structure assessment data according to external dynamic impact data to obtain inventory dynamic adjustment data; Generate an inventory health assessment report based on dynamically optimized inventory data, inventory structure assessment data and inventory dynamic adjustment data.

[0005] In this example, by obtaining the basic inventory management data and external dynamic influence data of the warehouse to be monitored, the current inventory data in the basic inventory management data can be dynamically optimized based on the sample image data set and the label image data set based on AI vision technology to obtain dynamically optimized inventory data, and inventory structure evaluation can be performed based on the basic inventory management data and the dynamically optimized inventory data to obtain inventory structure evaluation data, so that the inventory structure evaluation data can be dynamically adjusted in real time based on the external dynamic influence data to obtain inventory dynamic adjustment data, and then an inventory health evaluation report can be generated based on the dynamically optimized inventory data, the inventory structure evaluation data and the inventory dynamic adjustment data, which is conducive to achieving more accurate and comprehensive warehouse inventory intelligent management.

[0006] A second aspect of an embodiment of the present application provides a warehouse inventory intelligent management device based on AI visual monitoring, the device comprising: An acquisition unit, used to acquire basic inventory management data and external dynamic impact data of the warehouse to be monitored; A first processing unit is used to dynamically optimize the current inventory data in the inventory management basic data based on the AI ​​vision technology and according to the sample image data set and the label image data set to obtain dynamically optimized inventory data; A second processing unit is used to perform inventory structure evaluation according to the inventory management basic data and the dynamically optimized inventory data to obtain inventory structure evaluation data; A third processing unit is used to perform real-time dynamic adjustment processing on the inventory structure evaluation data according to the external dynamic impact data to obtain inventory dynamic adjustment data; The fourth processing unit is used to generate an inventory health assessment report according to the dynamically optimized inventory data, the inventory structure assessment data and the inventory dynamic adjustment data.

[0007] A third aspect of an embodiment of the present application provides a terminal, comprising a processor, an input device, an output device and a memory, wherein the processor, input device, output device and memory are interconnected, wherein the memory is used to store a computer program, the computer program comprises program instructions, and the processor is configured to call the program instructions to execute the step instructions in the first aspect of the embodiment of the present application.

[0008] The fourth aspect of the embodiments of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps described in the first aspect of the embodiments of the present application.

[0009] A fifth aspect of the embodiments of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in the first aspect of the embodiments of the present application. The computer program product may be a software installation package. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0011] Figure 1 A schematic diagram of the structure of a warehouse inventory intelligent management system is provided for an embodiment of the present application; Figure 2 A flowchart of a warehouse inventory intelligent management method based on AI visual monitoring is provided for an embodiment of the present application; Figure 3 A schematic diagram of the structure of a terminal provided in an embodiment of the present application; Figure 4 A structural schematic diagram of a warehouse inventory intelligent management device based on AI visual monitoring is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0012] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0013] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0014] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments.

[0015] In order to better understand the warehouse inventory intelligent management method based on AI visual monitoring provided in an embodiment of the present application, the scenario of applying the warehouse inventory intelligent management method based on AI visual monitoring is first briefly introduced below.

[0016] When managing the warehouse inventory, the most common way is to count and record the items. On the one hand, the traditional manual inventory counting work is extremely cumbersome. Large warehouses often need to invest a lot of manpower for each inventory, which takes several days or even weeks. Not only does it cause the warehouse operation to be interrupted, but it is also very easy to miss or misrecord the quantity of goods due to human negligence, such as fatigue and carelessness, or repeat or miss inventory due to the unstable storage location of goods, which leads to discrepancies between the accounts and the actual situation, affecting the accuracy of the company's cost accounting and profits. On the other hand, the procurement of materials lacks reasonable planning, lacks foresight, and is out of touch with the actual production of the company. It often happens that the purchased materials do not match the model, the quantity is too large, or they cannot be used due to process improvements, resulting in a large waste of funds. At the same time, the material reserves are not entirely reasonable. There is a phenomenon of overstocking and shortage of materials. Not only does it occupy a large amount of inventory funds, but it also increases the urgent materials due to the inability to grasp the inventory dynamics in a timely manner, pushing up logistics and procurement costs. In the final analysis, the traditional warehouse management system is outdated and lacks real-time data updates, making it difficult to respond to changes in market demand in a timely manner, hindering companies from capturing market opportunities and seriously restricting the improvement of their operational efficiency and market competitiveness. Therefore, it is urgent to introduce innovative management methods and technical means to solve this problem.

[0017] In response to the above-mentioned technical problems, the present application provides a warehouse inventory intelligent management method based on AI visual monitoring. By acquiring the inventory management basic data and external dynamic influence data of the warehouse to be monitored, the current inventory data in the inventory management basic data can be dynamically optimized based on artificial intelligence (AI) visual technology according to a sample image data set and a label image data set to obtain dynamically optimized inventory data. The inventory structure evaluation can be performed based on the inventory management basic data and the dynamically optimized inventory data to obtain inventory structure evaluation data, so that the inventory structure evaluation data can be dynamically adjusted in real time according to the external dynamic influence data to obtain inventory dynamic adjustment data. Furthermore, an inventory health evaluation report can be generated based on the dynamically optimized inventory data, the inventory structure evaluation data and the inventory dynamic adjustment data, which is conducive to achieving the purpose of more accurate and comprehensive warehouse inventory intelligent management.

[0018] The warehouse inventory intelligent management method based on AI visual monitoring is applied to the warehouse inventory intelligent management system. Figure 1 The figure shows a schematic diagram of the structure of a warehouse inventory intelligent management system. Figure 1 As shown in the figure, the warehouse inventory intelligent management system includes: data acquisition module, inventory dynamic optimization processing module (including target damage identification module), inventory structure evaluation module, real-time dynamic adjustment module and inventory health evaluation module. Among them, the data acquisition module is used to collect the basic inventory management data (such as historical sales, current inventory, supply chain, cost-related data) and external dynamic impact data of the warehouse to be monitored, which can provide comprehensive data support for subsequent modules. The inventory dynamic optimization processing module can dynamically optimize the current inventory data with the help of AI visual technology. For example, it can accurately judge abnormalities such as item damage through the target damage identification module to correct the inventory data to reflect the actual inventory situation. The inventory structure evaluation module can evaluate the inventory structure from multiple dimensions (such as category, value, time, region, etc.) based on the basic inventory management data, generate inventory structure evaluation data, and judge the rationality of the inventory structure. The real-time dynamic adjustment module can adjust the inventory structure evaluation data in real time in combination with external dynamic impact data, so that the inventory structure can adapt to external changes such as the market and supply chain in a timely manner. The inventory health assessment module can comprehensively and dynamically optimize inventory data, inventory structure assessment data and inventory dynamic adjustment data, comprehensively assess the health status of warehouse inventory, generate inventory health assessment reports and provide improvement suggestions, etc. This application does not impose any restrictions on this.

[0019] See also Figure 2 , Figure 2 A flowchart of a warehouse inventory intelligent management method based on AI visual monitoring is provided for an embodiment of the present application. The method includes: S10: Obtain the basic inventory management data and external dynamic impact data of the warehouse to be monitored.

[0020] The warehouse to be monitored can be understood as any warehouse that has a demand for intelligent inventory management. The warehouse inventory intelligent management method based on AI visual monitoring provided by this application can be used to perform intelligent inventory management on the warehouse to be monitored, and this application does not limit this.

[0021] The basic data for inventory management can be understood as a set of basic information that warehouse inventory management relies on. The basic data for inventory management may include, but is not limited to, historical in-and-out warehouse data, current inventory data, supply chain inventory data, and inventory cost data, which are not limited in this application. The historical in-and-out warehouse data may be data including the in-and-out quantity, in-and-out time, and in-and-out reasons of various items in the warehouse over a period of time in the past, which may reflect the demand for historical items in the warehouse. The current inventory data may be data that records in real time the types, quantities, storage locations, and item status (such as whether they are intact) of existing items in the warehouse, which may show the current inventory status of the warehouse. The supply chain inventory data may be inventory data in the procurement process, as well as inventory data in logistics distribution, etc. Inventory cost data may be cost data related to warehouse inventory, such as inventory procurement cost, inventory storage cost, inventory transportation cost, inventory loss cost, etc., which may be used to calculate the economic cost of the inventory management process.

[0022] External dynamic impact data can be used to indicate real-time changing data from outside the warehouse that can affect inventory management, such as weather factors, such as humid weather may affect the placement of inventory, high temperature weather may affect the quality of inventory, etc., and changes in market demand, such as changes in consumer preferences may lead to inventory hoarding and thus affect the quality of inventory, etc. This application does not impose any restrictions on this.

[0023] S20: Based on AI vision technology, according to the sample image data set and the label image data set, the current inventory data in the inventory management basic data is dynamically optimized to obtain dynamically optimized inventory data.

[0024] Among them, AI vision technology can be understood as the use of computer vision algorithms, deep learning models and other technical means to enable computers to understand and analyze the content in images or videos to simulate human visual perception and comprehension capabilities, so as to achieve related technologies for tasks such as object recognition, detection, tracking and analysis.

[0025] The sample image data set may include one or more sample image data, which can be understood as image data of various types of monitoring items in the inventory used in the model training process. The sample image data in the sample image data set can be obtained by image acquisition devices in AI visual technology, such as cameras, etc., and can provide rich visual image information for the model, so that the model can learn the appearance characteristics, placement, quantity and other information of inventory monitoring items in various situations, so that the current inventory data can be better analyzed and processed in the subsequent process. This application does not limit this.

[0026] The label image data set may include one or more label image data, which can be understood as data after the relevant information of the inventory monitoring items in the sample image data is annotated. Among them, the label may include the category, quantity, status (such as intact or damaged, etc.), storage location, etc. of the monitored items, which is not limited in this application. The label image data can provide a supervision signal for the model, allowing the model to understand the true situation of the inventory monitoring items contained in the sample image data, so that the model can learn how to more accurately predict or identify the real information based on the sample image data, and then realize the dynamic optimization of the current inventory data in subsequent steps, which is not limited in this application.

[0027] The current inventory data can be used to indicate the actual inventory status data recorded in the current warehouse inventory intelligent management system, and can reflect the actual quantity, location, status, etc. of various items in the current inventory. Dynamically optimized inventory data can be understood as optimized inventory data obtained after dynamic optimization of the current inventory data based on AI vision technology. The dynamically optimized inventory data can be the data after optimizing and verifying the current inventory data by comprehensively considering factors such as the abnormal location, damage, and expiration of the monitored items. It can reflect the optimal status of the inventory in real time and can provide more scientific and reasonable decision-making support for intelligent inventory management, such as procurement plan formulation, inventory allocation, etc. This application does not impose any restrictions on this.

[0028] It should be understood that, based on AI vision technology, according to the sample image data set and the label image data set, the current inventory data in the inventory management basic data is dynamically optimized to obtain dynamically optimized inventory data, which refers to how to dynamically optimize the current inventory data through AI vision technology to obtain more accurate dynamically optimized inventory data. Among them, in step S20, that is, based on AI vision technology, according to the sample image data set and the label image data set, the current inventory data in the inventory management basic data is dynamically optimized to obtain dynamically optimized inventory data, which may include the following steps: S21: Based on the AI ​​vision technology, perform image acquisition and preprocessing on the items in the warehouse to be monitored to obtain a set of monitored item image data; S22: Based on the AI ​​vision technology, perform position anomaly recognition processing on each monitored object image data in the monitored object image data set to obtain out-of-position optimization data; S23: Based on the AI ​​vision technology, abnormal identification processing of damaged items is performed according to the sample image data set, the label image data set and the monitored item image data set to obtain optimized data of damaged items; S24: Based on the AI ​​vision technology, according to the off-site optimization data and the damaged item optimization data, the current inventory data in the inventory management basic data is reviewed and verified to obtain verified inventory data; S25: Based on the AI ​​vision technology, the verified inventory data is subjected to outbound optimization processing to obtain dynamically optimized inventory data.

[0029] Among them, the monitoring item image data set may include one or more monitoring item image data, which can be understood as the image data corresponding to the monitoring item obtained by photographing the monitoring item in the monitored warehouse through the image acquisition device involved in the AI ​​vision technology (such as a camera, a camera, etc.). The monitoring item image data may include the appearance shape, spatial position, placement status, packaging text information, etc. of the monitoring item, which can be understood as the original data for subsequent analysis and processing.

[0030] The off-site optimization data can be understood as the optimization data obtained after the position anomaly recognition processing is performed on the image data of each monitored object. Specifically, it can be determined whether the monitored object is in the correct position by using AI visual technology, such as identifying the position information of the monitored object in the image data of the monitored object based on AI visual technology, and further comparing it with the preset normal position, such as whether the height is inconsistent, whether the height exceeds the limit, whether the area is abnormal, whether the stacking stability is unbalanced, and whether there is abnormal displacement and other position abnormalities, so that the corresponding off-site optimization data can be further generated, and this application does not limit this.

[0031] It should be understood that performing position abnormality identification processing on each monitoring item image data in the monitoring item image data set to obtain out-of-position optimization data refers to how to perform position abnormality identification processing on the monitoring item image data and make position optimization suggestions for the items with abnormal positions to obtain out-of-position optimization data. Among them, step S22, that is, performing position abnormality identification processing on each monitoring item image data in the monitoring item image data set to obtain out-of-position optimization data, may include the following steps: S221: constructing a three-dimensional model of the object according to each monitored object image data in the monitored object image data set to obtain a three-dimensional model of the monitored object; S222: performing height abnormality monitoring processing according to the three-dimensional model of the monitored object to obtain height abnormality data; S223: performing regional abnormality monitoring processing according to the three-dimensional model of the monitored object to obtain regional abnormality data; S224: performing stacking stability detection processing according to the three-dimensional model of the monitored object to obtain stability imbalance data; S225: performing real-time dynamic position tracking processing according to the three-dimensional model of the monitored object to obtain abnormal displacement data; S226: Perform an abnormal fusion process according to the height abnormal data, the regional abnormal data, the stability imbalance data and the abnormal displacement data to obtain ex-situ optimized data.

[0032] The three-dimensional model of the monitored object can be used to indicate the three-dimensional model obtained after the three-dimensional model of the object is constructed based on each monitored object image data. The three-dimensional model of the monitored object can be understood as a digital representation of the shape, size, position and other characteristics of the object in three-dimensional space, which can more intuitively and accurately reflect the spatial information of the monitored object.

[0033] It should be understood that performing a three-dimensional model construction process on each monitored object image data in the monitored object image data set to obtain a three-dimensional model of the monitored object refers to a process of performing a three-dimensional model construction process on each monitored object image data to obtain a three-dimensional model of the monitored object. Among them, step S221, that is, performing a three-dimensional model construction process on each monitored object image data in the monitored object image data set to obtain a three-dimensional model of the monitored object, may include the following steps: S2211: Acquire, from the monitored object image data set, a first monitored object image data subset photographed by a first photographing device and a second monitored object image data subset photographed by a second photographing device; S2212: Acquire first plane coordinate data of a first imaging point from the first monitored object image data subset, and acquire second plane coordinate data of the first imaging point from the second monitored object image data subset; S2213: Determine the first three-dimensional coordinates of the first imaging point according to the first plane coordinate data, the second plane coordinate data, the first optical center coordinates of the first photographing device, the first focal length of the first photographing device, the second optical center coordinates of the second photographing device, and the second focal length of the second photographing device; S2214: Acquire third plane coordinate data of a second imaging point from the first monitored object image data subset, and acquire fourth plane coordinate data of the second imaging point from the second monitored object image data subset; S2215: Determine the second three-dimensional coordinates of the second imaging point according to the third plane coordinate data, the fourth plane coordinate data, the first optical center coordinates of the first photographing device, the first focal length of the first photographing device, the second optical center coordinates of the second photographing device, and the second focal length of the second photographing device; S2216: Obtain a three-dimensional model of the monitored object according to the first three-dimensional coordinates and the second three-dimensional coordinates.

[0034] The first photographic device may be any photographic device that performs image acquisition and preprocessing on the items in the warehouse to be monitored. The second photographic device may be any photographic device other than the first photographic device that performs image acquisition and preprocessing on the items in the warehouse to be monitored. In other words, the first photographic device and the second photographic device are different photographic devices.

[0035] The first monitoring item image data subset may include one or more first monitoring item image data, which may be understood as the monitoring item image data obtained after the first photographic device performs image acquisition and preprocessing on the items in the warehouse to be monitored. The second monitoring item image data subset may include one or more second monitoring item image data, which may be understood as the monitoring item image data obtained after the second photographic device performs image acquisition and preprocessing on the items in the warehouse to be monitored.

[0036] The first imaging point may be any imaging point that has been photographed by both the first photographic device and the second photographic device. The first plane coordinate data may be understood as the plane coordinate data obtained after the first photographic device photographs the first imaging point. The second plane coordinate data may be understood as the plane coordinate data obtained after the second photographic device photographs the first imaging point. It should be noted that a coordinate system may be established with a preset point (such as the upper left corner endpoint or the lower left corner endpoint) on the first photographic device as the origin (such as referred to as the first origin), thereby determining the first plane coordinate data based on the first origin. Optionally, a coordinate system may be established with a preset point (such as the upper left corner endpoint or the lower left corner endpoint) on the second photographic device as the origin (such as referred to as the second origin), thereby determining the second plane coordinate data based on the second origin, and the present application does not impose any restrictions on this.

[0037] The optical center is a special point in the lens of a photographic device, which can be understood as the central reference point where light converges after passing through the lens of the photographic device. The first optical center coordinates can be understood as the coordinates corresponding to the optical center of the first photographic device. The second optical center coordinates can be understood as the coordinates corresponding to the optical center of the second photographic device. The focal length is the focal length of the lens of the photographic device, which can reflect the optical characteristics of the lens of the photographic device. The first focal length is the focal length of the lens of the first photographic device, and the second focal length is the focal length of the lens of the second photographic device.

[0038] According to the first plane coordinate data obtained by the first photographing device shooting the first imaging point, the second plane coordinate data obtained by the second photographing device shooting the first imaging point, the first optical center coordinates of the first photographing device and the second optical center coordinates of the second photographing device, the first three-dimensional coordinates of the first imaging point can be further determined using the triangulation principle. In other words, the first three-dimensional coordinates can be understood as the three-dimensional coordinates corresponding to the first imaging point in the space jointly photographed by the first photographing device and the second photographing device.

[0039] Optionally, the process of determining the first three-dimensional coordinates of the first imaging point according to the first plane coordinate data, the second plane coordinate data, the first optical center coordinates of the first photographic device, the first focal length of the first photographic device, the second optical center coordinates of the second photographic device, and the second focal length of the second photographic device may refer to the following formula: in, The horizontal coordinate in the first plane coordinate data can be represented; A first focal length of the first photographic device may be indicated; A horizontal coordinate that can represent a first three-dimensional coordinate of a first imaging point; The abscissa may represent the coordinates of the first optical center of the first photographing device; A depth coordinate that may represent a first three-dimensional coordinate of a first imaging point; A depth coordinate that can represent a first optical center coordinate of the first photographing device; The vertical coordinate in the first plane coordinate data can be represented; A ordinate that may represent a first three-dimensional coordinate of a first imaging point; A ordinate that can represent the coordinates of the first optical center of the first photographic device; The horizontal coordinate in the second plane coordinate data can be represented; A second focal length of a second photographic device may be indicated; A horizontal coordinate that can represent the coordinates of the second optical center of the second photographic device; A depth coordinate that can represent a second optical center coordinate of a second photographic device; The ordinate in the second plane coordinate data may be represented; The ordinate of the second optical center coordinate of the second photographic device can be represented by , the above formula can be used to construct a system of equations to solve The specific value of is not limited in this application.

[0040] Furthermore, the second imaging point may be any imaging point other than the first imaging point that has been photographed by both the first photographing device and the second photographing device. The third plane coordinate data may be understood as the plane coordinate data obtained after the first photographing device photographs the second imaging point. The fourth plane coordinate data may be understood as the plane coordinate data obtained after the second photographing device photographs the second imaging point.

[0041] Optionally, relevant content of determining the second three-dimensional coordinates of the second imaging point is determined based on the third plane coordinate data, the fourth plane coordinate data, the first optical center coordinates of the first photographic device, the first focal length of the first photographic device, the second optical center coordinates of the second photographic device and the second focal length of the second photographic device. Please refer to the detailed description of determining the first three-dimensional coordinates mentioned above, and the present application will not repeat them here.

[0042] Optionally, this application uses the example of constructing a three-dimensional model of a monitored object by determining the three-dimensional coordinates of the first imaging point and the second imaging point, which does not constitute a limitation on this application. Optionally, the three-dimensional coordinates of other imaging points (such as the third imaging point, the fourth imaging point, etc.) can be further determined, so that the three-dimensional model of the monitored object can be further constructed through the three-dimensional coordinates of the first imaging point, the second imaging point, the third imaging point, and the fourth imaging point, etc., and this application does not limit this.

[0043] By acquiring the three-dimensional coordinates of multiple imaging points (such as the first imaging point and the second imaging point), the discrete three-dimensional coordinate points can be further used for interpolation, fitting and other operations to gradually construct a three-dimensional model of the monitored object. It is understandable that the above three-dimensional coordinate points can constitute the point cloud data of the monitored object, and the surface data of the monitored object can be further generated based on the point cloud data, so that the three-dimensional model of the monitored object can be obtained, and this application does not limit this.

[0044] It can be understood that the above-mentioned monitoring object image data set may include monitoring object image data subsets taken by various photographic devices at different times. For the monitoring object image data subset at each time, the three-dimensional coordinate points of the monitoring object corresponding to each time can be generated. The three-dimensional coordinate points of the monitoring object corresponding to each time are merged according to the time sequence, and the three-dimensional model of the monitoring object that changes in time sequence can be obtained. The present application does not impose any restrictions on this.

[0045] Height anomaly data can be understood as height anomaly data obtained after height anomaly monitoring based on the three-dimensional model of the monitored object. Specifically, the height of each monitored object can be monitored based on the three-dimensional model of the monitored object, such as by setting a conventional height interval threshold to determine whether there is an obvious height inconsistency between the same type of objects, or by setting a height threshold to determine whether the height of the objects is over the limit, etc., so as to obtain height anomaly data indicating that there is an obvious height inconsistency between the same type of objects, or that the height of the objects is over the limit.

[0046] Regional anomaly data can be understood as regional anomaly data obtained after regional anomaly monitoring processing is performed based on the three-dimensional model of the monitored item. Specifically, regional monitoring can be performed on each monitored item based on the three-dimensional model of the monitored item. Specifically, the conventional placement position of the monitored item can be preset, and the position of the monitored item in the three-dimensional model can be located, so as to further compare whether the monitored position of the monitored item is consistent with the conventional placement position to determine whether the monitored item is in the area where it should be located. For example, by obtaining the conventional placement position of the monitored item (that is, the area where it should be located), such as when monitored item A is conventionally placed in the first area of ​​shelf C, and item A is monitored at a position outside the first area of ​​shelf C, it can be determined that the monitored item A is a regional anomaly, and this application does not impose any restrictions on this.

[0047] The stability imbalance data can be understood as the stability abnormality data obtained after the stacking stability detection process is performed based on the three-dimensional model of the monitored object. Specifically, the stacking stability detection can be performed on each monitored object based on the three-dimensional model of the monitored object. Specifically, the center of gravity position of the stacked objects can be calculated by the three-dimensional model of the monitored objects to further determine whether each monitored object is prone to collapse or fall, so as to further evaluate the stability of the objects when they are stacked together. This application does not limit this.

[0048] Abnormal displacement data can be understood as abnormal displacement data obtained after real-time dynamic position tracking processing based on the three-dimensional model of the monitored object. Specifically, real-time dynamic position tracking can be performed on each monitored object based on the three-dimensional model of the monitored object, that is, the real-time changes of the monitored object can be obtained through the three-dimensional model of the monitored object to continuously track the position changes of the monitored object in space. For example, the displacement of the monitored object B can be discovered in time by monitoring the spatial data of the detected object B corresponding to time t1 in the three-dimensional model of the monitored object and the spatial data of the detected object B corresponding to time t2. If unauthorized movement is found, it can be determined that the monitored object B has abnormal displacement data between time t1 and time t2, and this application does not limit this.

[0049] Furthermore, the highly abnormal data, regional abnormal data, stability imbalance data and abnormal displacement data obtained through monitoring are comprehensively processed, such as being integrated through lists to clearly reflect the abnormalities of each position in the table. Optionally, different levels of priority warnings can be given to different position abnormal data, such as stability imbalance data can be set as a higher priority warning, and highly abnormal data can be set as a lower priority warning, so as to finally obtain ex situ optimization data, which is not restricted in this application.

[0050] Optionally, the obtained highly abnormal data, regional abnormal data, stability imbalance data and abnormal displacement data can be converted accordingly (such as all expressed in the form of scoring values), and the converted data can be further weighted and summed to obtain a numerical value that can be used to evaluate the overall degree of dislocation, and by setting a dislocation threshold, an dislocation warning can be issued when the dislocation threshold is exceeded, thereby better avoiding the dislocation of the monitored items. This application does not impose any restrictions on this.

[0051] The damaged item optimization data can be understood as the optimized data obtained after performing damage anomaly recognition processing on each monitored item image data. Specifically, the initial damage recognition module can be trained to obtain a more accurate target damage recognition module, so that damage recognition processing can be performed on each monitored item image data based on the target damage recognition module, and then the above-mentioned damaged item optimization data can be further obtained, and this application does not limit this.

[0052] It should be understood that performing abnormal identification processing of damaged items based on the sample image data set, the label image data set and the monitored item image data set to obtain the damaged item optimization data refers to how to perform abnormal identification processing of damaged items on the monitored item image data, and make optimization suggestions for the damaged items, etc., to obtain the damaged item optimization data. Among them, step S23, that is, performing abnormal identification processing of damaged items based on the sample image data set, the label image data set and the monitored item image data set to obtain the damaged item optimization data, may include the following steps: S231: using an initial damage recognition module to perform appearance damage recognition processing on each sample image data in the sample image data set to obtain an appearance damaged object image data set; S232: using an initial damage recognition module to perform quality damage recognition processing on each sample image data in the sample image data set to obtain a quality damaged object image data set; S233: using an initial damage recognition module to perform expiration recognition processing on each sample image data in the sample image data set to obtain an expired item image data set; S234: constructing a damage recognition loss function according to the image data set of the appearance-damaged item, the image data set of the quality-damaged item, the image data set of the expired item, and the label image data set; S235: Based on the damage identification loss function, the initial damage identification module is trained to obtain a target damage identification module; S236: using a target damage identification module to perform damage identification processing on each monitored object image data in the monitored object image data set to obtain a target damaged object image data set; S231: performing anomaly fusion processing on each target damaged object image data in the target damaged object image data set to obtain damaged object optimized data.

[0053] Among them, the initial damage identification module can be an initial tool for preliminarily identifying and analyzing the image data of the object according to a pre-set model or program based on artificial intelligence or machine learning algorithms to identify various possible damage conditions of the object. It is understandable that the parameters of the initial damage identification module can be randomly set, and can be further optimized and adjusted in the subsequent training process to obtain parameters that better match the damage identification task, and then further obtain a more accurate target damage identification module, and this application does not limit this.

[0054] The image data set of damaged-appearance items may include one or more image data of damaged-appearance items, and the image data of damaged-appearance items may be image data that is screened out after the initial damage recognition module performs appearance damage recognition processing on each sample image data in the sample image data set, and shows that the monitored items in the sample image data have appearance damage. Appearance damage may refer to damage phenomena that occur in the physical appearance of the monitored items, and the scope of recognition may include but is not limited to appearance deformation (such as shape change), opening (such as cracked opening on the surface), crack (such as crack on the surface) or flatulence (such as flatulence of vacuumed items), etc., and this application does not limit this.

[0055] The quality damaged item image data set may include one or more quality damaged item image data, and the quality damaged item image data may be image data that is screened out after the initial damage recognition module performs quality damage recognition processing on each sample image data in the sample image data set, showing that the monitored item in the sample image data has quality damage. Quality damage may refer to the quality damage phenomenon of the monitored item, and the scope of recognition may include but is not limited to leakage (such as internal substances leaking out), packaging soaking (such as the packaging being soaked due to moisture, etc.), etc. Optionally, AI vision technology can be further combined with Internet of Things technology, such as identifying weight abnormalities through weight sensors (such as the actual weight does not match the standard weight), or identifying temperature abnormalities through temperature sensors, or identifying humidity abnormalities through humidity sensors, etc., so as to more comprehensively analyze and judge the quality damage of each monitored item image data, and this application does not impose any restrictions on this.

[0056] The expired item image data set may include one or more expired item image data, and the expired item image data may be image data that is screened out after the initial damage recognition module performs expiration recognition processing on each sample image data in the sample image data set, and shows that the monitored item in the sample image data is expired. Specifically, information related to the expiration date of the monitored item, such as identification information such as the production date and shelf life, can be identified based on AI vision to determine whether the monitored item is expired, and this application does not limit this.

[0057] The damage identification loss function may be a loss function for performing abnormal identification processing of damaged items, constructed based on the image data set of damaged items, the image data set of quality damaged items, the image data set of expired items, and the label image data set. The damage identification loss function may be used to measure the difference between the prediction result of the initial damage identification module and the real label in the label image data set, and optimize the parameters of the damage identification module by minimizing the difference, thereby improving the damage identification accuracy of the damage identification module.

[0058] Optionally, the damage identification loss function involved in this application can be constructed based on the multi-classification cross entropy loss function. Optionally, the process of constructing the damage identification loss function based on the image data set of the appearance damaged items, the image data set of the quality damaged items, the image data set of the expired items and the label image data set can be seen in the following formula: in, The damage identification loss function can be expressed as; It can represent the number of monitored object image data in the monitored object image data set, that is, the number of samples; Can represent the index of the sample; Indicates the damage category. This application uses the three damage categories (namely, appearance damage, quality damage, and expiration) as an example for illustration, which does not constitute a limitation on this application; An index that can represent the damage category; It can be said that the i-th sample data (i.e., the monitored object image data) is the true label of the j-th type of damage; Indicates the operation of performing logarithmic operations; It can represent the predicted probability that the i-th sample data (i.e., the monitored item image data) is damaged by the j-th category.

[0059] It should be noted that in the process of training the initial damage identification module, the parameters of the initial damage identification module can be calculated first, and then the damage identification loss function can be further calculated based on the parameters or data of the initial damage identification module (such as the parameters of the initial damage identification module itself, or the result data obtained by calculating using the initial damage identification module), that is, the parameters or data of the above-mentioned initial damage identification module are substituted into the damage identification loss function to calculate and obtain the damage identification loss value, so that when the damage identification loss value is less than the preset loss threshold, that is, at this time, the damage identification loss function converges, the training of the initial damage identification module is completed, and then the target damage identification module is obtained. This application does not impose any restrictions on this.

[0060] The target damage recognition module can be understood as a module that can more accurately identify the damage of an item after the initial damage recognition module is trained and optimized by the damage recognition loss function. The target damaged item image data set may include one or more target damaged item image data, and the target damaged item image data can be understood as image data containing item damage information obtained after the target damage recognition module is used to perform damage recognition processing on each monitored item image data. It can be understood that the target damaged item image data is more accurate and reliable than the recognition result obtained based on the initial damage recognition module.

[0061] Further, each target damaged item image data in the target damaged item image data set is subjected to abnormal fusion processing, such as by list integration to clearly reflect each damage abnormality and damage degree in the table, so as to obtain damaged item optimization data. Optionally, different damage abnormalities can be given different degrees of priority warning, such as expired items can be set as a higher priority warning, and appearance damage can be set as a lower priority warning, so as to finally obtain damaged item optimization data, which is not limited in this application.

[0062] After obtaining the out-of-position optimization data and damaged item optimization data, the current inventory data in the inventory management basic data can be optimized and compared and verified based on the out-of-position optimization data and damaged item optimization data. On the one hand, the data with abnormalities (such as out-of-position abnormalities and damage abnormalities) can be optimized, and on the other hand, the quantity information of the monitored items in the current inventory data can be checked and verified for accuracy, thereby correcting the inventory data deviation caused by abnormal position or damage of the items, and further verifying the quantity in the current inventory data to obtain verified inventory data. Verified inventory data can be understood as inventory data that has been reviewed and verified, with position abnormalities and damage abnormalities corrected and quantity verified, which can more accurately reflect the actual quantity, location and status of the monitored items in the inventory.

[0063] Outbound optimization processing can be understood as the use of AI visual technology to perform relevant identification of monitored items, such as expiration identification, fragility identification, etc., to further determine the related operations of the outbound priority of monitored items. Specifically, taking expiration identification as an example, it is possible to identify information such as the shelf life and production date on the monitored items, and further combine it with the current time to analyze the remaining validity period of the monitored items, so as to determine the outbound priority for the monitored items. For example, for monitored items with a shorter remaining validity period, priority outbound can be indicated, and for monitored items with a longer remaining validity period, it can be indicated not to be temporarily outbound. This application does not impose any restrictions on this.

[0064] Based on AI vision technology, the current inventory data is dynamically optimized and processed. Image acquisition and analysis can be used to accurately identify abnormal location, damage status, expiration date and other information of items, so that inventory data can be updated in real time, such as correcting inventory deviations caused by misplacement, damage, expiration, etc. of items, and the order of item delivery can be reasonably planned. This can significantly improve the accuracy and timeliness of inventory data, which is conducive to providing a reliable basis for subsequent procurement, allocation, sales and other decisions, and can effectively reduce inventory costs, improve inventory turnover, and enhance the competitiveness of enterprises in the market.

[0065] S30: performing inventory structure evaluation according to the inventory management basic data and the dynamically optimized inventory data to obtain inventory structure evaluation data.

[0066] Inventory structure evaluation data can be understood as the evaluation of the inventory structure of various commodities in the inventory based on the basic inventory management data and the dynamically optimized inventory data, such as the evaluation data obtained after analyzing and evaluating the characteristics such as category proportion, value distribution, inventory time distribution, and regional distribution. The inventory structure evaluation data can be used to judge the rationality of the inventory structure.

[0067] Specifically, this application uses the inventory structure evaluation including the evaluation of category structure, value structure, time structure and regional structure as an example for explanation, which does not constitute a limitation on this application. Optionally, the basic inventory management data and dynamic optimization inventory data can be classified and summarized according to the category of goods. For example, the inventory goods can be divided into categories such as food, daily necessities, and electronic products, and the proportion of each category in the total inventory can be further calculated, so as to obtain the category structure evaluation data (that is, the category proportion).

[0068] Optionally, the inventory can be classified according to the value of the goods. The ABC classification method can be used to calculate the proportion of A, B, and C goods in the inventory and related indicators (that is, value distribution). Among them, for the ABC classification method, it can generally be divided according to the following rules: Category A goods: cumulative sales account for about 70%-80%, and the number of varieties accounts for about 10%-20%; Category B goods: cumulative sales account for about 15%-25%, and the number of varieties accounts for about 20%-30%; Category C goods: cumulative sales account for about 5%-15%, and the number of varieties accounts for about 60%-70%. This application does not impose any restrictions on this.

[0069] Optionally, the inventory can be classified according to time factors such as the time of entry or shelf life of the goods, for example, into recently entered goods, medium-term entered goods and long-term unsold goods; or divided into near-expiry goods, medium-term shelf life goods and long-expiry goods according to the shelf life; and further calculate the proportion of goods in different time intervals in the inventory and related indicators (that is, inventory time distribution).

[0070] Optionally, you can classify the inventory based on the storage area of ​​the goods, such as different warehouses, different shelf areas, etc., and further calculate the inventory share and related indicators of each area (that is, regional distribution).

[0071] Specifically, certain key indicators can be further calculated to better evaluate the inventory structure. For example, the sales cost and average inventory amount in a certain period can be obtained from the basic inventory management data to calculate the inventory turnover rate; the actual sales quantity and inventory supply quantity in a certain period can be counted to calculate the inventory fulfillment rate; the standards for stagnant inventory can be pre-defined, such as long-term unsold goods (for example, goods that have not been sold for more than 90 days), to further count the amount or quantity of stagnant inventory and calculate its proportion in the total inventory, etc. This application does not impose any restrictions on this.

[0072] Furthermore, the proportion data and key indicator data of the above dimensions can be summarized and organized. Specifically, a table can be used for summary and data analysis and interpretation (such as observing the proportion of each dimension, the trend of key indicators and the difference with historical data or industry standards; summarizing the overall characteristics of the inventory structure, finding out the existing problems and potential optimization points), so as to obtain inventory structure evaluation data. It is understandable that the inventory structure evaluation data may include an overall overview of the inventory structure, a detailed analysis of each dimension, the calculation results of key indicators and comparative analysis with historical data or industry standards, existing problems and improvement suggestions, etc., and this application does not limit this.

[0073] S40: Performing real-time dynamic adjustment processing on the inventory structure evaluation data according to the external dynamic impact data to obtain inventory dynamic adjustment data.

[0074] Inventory dynamic adjustment data can be understood as the dynamic adjustment data obtained after real-time dynamic adjustment of inventory structure evaluation data according to external dynamic impact data. It should be noted that the data obtained after real-time adjustment of inventory structure evaluation data in combination with external dynamic impact data can further enable the inventory structure to better adapt to changes in external factors such as market changes and supply chain fluctuations, thereby maintaining the optimal inventory status.

[0075] Specifically, by sorting out external dynamic influencing data, such as analyzing market demand fluctuation data, supplier supply capacity data, competitor strategy data and / or macroeconomic and policy data, and further analyzing the impact of each data on the inventory structure, such as the impact on category structure (for example, according to market demand fluctuations, when the demand for a certain category increases, the inventory of this category can be increased), the impact on value structure (for example, when the economy is down, consumers tend to buy cost-effective goods, then the inventory of Class B medium-value goods can be appropriately increased, and the proportion of Class A high-end goods inventory can be reduced), the impact on time structure (for example, in autumn, the inventory of down jackets that have been recently warehoused needs to be increased in advance, and the turnover of summer clothing inventory needs to be accelerated to reduce long-term inventory backlogs), and the impact on regional structure (taking into account regional differences in policies and regulations, such as some regions have strong subsidy policies for Class C products, and the inventory of Class C products in this region can be appropriately increased), etc., so that dynamic inventory adjustment data can be obtained, and this application does not impose any restrictions on this.

[0076] Optionally, an inventory dynamic adjustment strategy can be further formulated based on the inventory dynamic adjustment data to more comprehensively optimize the inventory quantity, inventory layout and inventory turnover, and real-time monitoring and feedback can be performed during the adjustment process to further adjust the strategy in order to obtain inventory dynamic adjustment data that is more appropriate and more in line with the current situation. This application does not impose any restrictions on this.

[0077] S50: Generate an inventory health assessment report according to the dynamically optimized inventory data, the inventory structure assessment data and the inventory dynamic adjustment data.

[0078] The inventory health assessment report can be understood as an assessment report further generated based on the dynamically optimized inventory data, the inventory structure assessment data, and the inventory dynamic adjustment data to indicate the overall health status of the warehouse inventory. Optionally, the inventory health assessment report may include, but is not limited to, evaluations of the accuracy of the inventory, the rationality of the inventory structure, the adaptability of the inventory to external changes, and improvement suggestions for existing problems, etc., which are not limited in this application.

[0079] Specifically, an overview of the current inventory status can be achieved based on the dynamic optimization inventory data and inventory structure assessment data, and the inventory dynamic adjustment data can be further interpreted to obtain countermeasures and adjusted data changes, so as to further demonstrate the effectiveness of the dynamic adjustment, and then obtain the above-mentioned inventory health assessment report. Optionally, a scoring system can also be established to score the inventory health status based on dimensions such as inventory accuracy, structural rationality, and dynamic adjustment timeliness, and give a conclusion on the health status. Optionally, the potential risks in inventory management can also be pointed out, and specific improvement measures can be proposed for the problems and risks found, which are not limited in this application.

[0080] It can be seen that in the above scheme, by obtaining the basic inventory management data and external dynamic influence data of the warehouse to be monitored, the current inventory data in the basic inventory management data can be dynamically optimized based on AI vision technology to obtain dynamically optimized inventory data, and the inventory structure evaluation can be performed based on the basic inventory management data and the dynamically optimized inventory data to obtain inventory structure evaluation data, so that the inventory structure evaluation data can be adjusted in real time according to the external dynamic influence data to obtain inventory dynamic adjustment data, and then an inventory health evaluation report can be generated based on the dynamically optimized inventory data, the inventory structure evaluation data and the inventory dynamic adjustment data, which is conducive to achieving more accurate and comprehensive warehouse inventory intelligent management purposes.

[0081] For the above embodiments, please refer to Figure 3 , Figure 3 A schematic diagram of the structure of a terminal provided in an embodiment of the present application, such as Figure 3 As shown, it includes a processor, an input device, an output device and a memory, which are connected to each other, wherein the memory is used to store a computer program, the computer program includes program instructions, the processor is configured to call the program instructions, and the program includes instructions for executing the following steps; Obtain the basic inventory management data and external dynamic impact data of the warehouse to be monitored; Based on AI vision technology, according to the sample image data set and the label image data set, the current inventory data in the inventory management basic data is dynamically optimized to obtain dynamically optimized inventory data; Performing inventory structure evaluation according to the inventory management basic data and the dynamically optimized inventory data to obtain inventory structure evaluation data; Performing real-time dynamic adjustment processing on the inventory structure evaluation data according to the external dynamic impact data to obtain inventory dynamic adjustment data; An inventory health assessment report is generated based on the dynamically optimized inventory data, the inventory structure assessment data and the inventory dynamic adjustment data.

[0082] In this example, by obtaining the basic inventory management data and external dynamic influence data of the warehouse to be monitored, the current inventory data in the basic inventory management data can be dynamically optimized based on AI vision technology to obtain dynamically optimized inventory data, and inventory structure evaluation can be performed based on the basic inventory management data and the dynamically optimized inventory data to obtain inventory structure evaluation data, so that the inventory structure evaluation data can be dynamically adjusted in real time based on the external dynamic influence data to obtain inventory dynamic adjustment data, and then an inventory health evaluation report can be generated based on the dynamically optimized inventory data, the inventory structure evaluation data and the inventory dynamic adjustment data, which is conducive to achieving more accurate and comprehensive warehouse inventory intelligent management.

[0083] The above mainly introduces the scheme of the embodiment of the present application from the perspective of the execution process on the method side. It is understandable that in order to realize the above functions, the terminal includes a hardware structure and / or software module corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiments provided herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0084] The embodiment of the present application can divide the terminal into functional units according to the above method example. For example, each functional unit can be divided according to each function, or two or more functions can be integrated into one processing unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. It should be noted that the division of units in the embodiment of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0085] In line with the above, see Figure 4 , Figure 4 The present application provides a schematic diagram of a warehouse inventory intelligent management device based on AI visual monitoring. Figure 4 As shown, the device comprises: An acquisition unit 101 is used to acquire basic inventory management data and external dynamic impact data of the warehouse to be monitored; The first processing unit 102 is used to dynamically optimize the current inventory data in the inventory management basic data based on AI vision technology to obtain dynamically optimized inventory data; A second processing unit 103 is used to perform inventory structure evaluation according to the inventory management basic data and the dynamically optimized inventory data to obtain inventory structure evaluation data; The third processing unit 104 is used to perform real-time dynamic adjustment processing on the inventory structure evaluation data according to the external dynamic impact data to obtain inventory dynamic adjustment data; The fourth processing unit 105 is used to generate an inventory health assessment report according to the dynamically optimized inventory data, the inventory structure assessment data and the inventory dynamic adjustment data.

[0086] In a possible implementation, the first processing unit 102 is configured to dynamically optimize the current inventory data in the inventory management basic data based on the AI ​​vision technology and the sample image data set and the label image data set to obtain the dynamically optimized inventory data, specifically for: Based on the AI ​​vision technology, images of the items in the warehouse to be monitored are collected and preprocessed to obtain a set of monitored item image data; Based on the AI ​​vision technology, position anomaly recognition processing is performed according to the sample image data set, the label image data set and the monitored object image data set to obtain out-of-position optimization data; Based on the AI ​​vision technology, each monitored object image data in the monitored object image data set is subjected to an abnormal identification process for damaged objects to obtain optimized damaged object data; Based on the AI ​​vision technology, according to the off-site optimization data and the damaged item optimization data, the current inventory data in the inventory management basic data is reviewed and verified to obtain the verified inventory data; Based on the AI ​​vision technology, the verified inventory data is processed for outbound optimization to obtain dynamically optimized inventory data.

[0087] In a possible implementation, the first processing unit 102 is used to perform position anomaly recognition processing on each monitored object image data in the monitored object image data set to obtain out-of-position optimization data, specifically for: Performing object three-dimensional model construction processing according to each monitored object image data in the monitored object image data set to obtain a monitored object three-dimensional model; According to the three-dimensional model of the monitored object, a height abnormality monitoring process is performed to obtain height abnormality data; Performing regional abnormality monitoring processing according to the three-dimensional model of the monitored object to obtain regional abnormality data; Performing stacking stability detection processing according to the three-dimensional model of the monitored object to obtain stability imbalance data; According to the three-dimensional model of the monitored object, real-time dynamic position tracking processing is performed to obtain abnormal displacement data; Anomaly fusion processing is performed according to the height abnormality data, the regional abnormality data, the stability imbalance data and the abnormal displacement data to obtain ex-situ optimized data.

[0088] In a possible implementation, the first processing unit 102 is configured to perform object 3D model construction processing according to each monitored object image data in the monitored object image data set to obtain the monitored object 3D model, specifically for: From the monitored object image data set, obtain a first monitored object image data subset taken by a first photographing device and a second monitored object image data subset taken by a second photographing device; Acquire first plane coordinate data of a first imaging point from the first monitored object image data subset, and acquire second plane coordinate data of the first imaging point from the second monitored object image data subset; Determine the first three-dimensional coordinates of the first imaging point according to the first plane coordinate data, the second plane coordinate data, the first optical center coordinates of the first photographing device, the first focal length of the first photographing device, the second optical center coordinates of the second photographing device, and the second focal length of the second photographing device; Acquire third plane coordinate data of a second imaging point from the first monitored object image data subset, and acquire fourth plane coordinate data of the second imaging point from the second monitored object image data subset; Determine the second three-dimensional coordinates of the second imaging point according to the third plane coordinate data, the fourth plane coordinate data, the first optical center coordinates of the first photographing device, the first focal length of the first photographing device, the second optical center coordinates of the second photographing device, and the second focal length of the second photographing device; A three-dimensional model of the monitored object is obtained according to the first three-dimensional coordinates and the second three-dimensional coordinates.

[0089] In a possible implementation, the first processing unit 102 is configured to perform abnormal identification processing of damaged items according to the sample image data set, the label image data set and the monitored item image data set to obtain damaged item optimization data, specifically for: Using the initial damage recognition module, each sample image data in the sample image data set is processed for appearance damage recognition, so as to obtain an appearance damaged object image data set; Using an initial damage recognition module, performing quality damage recognition processing on each sample image data in the sample image data set to obtain a quality damaged item image data set; Using an initial damage recognition module, performing expiration recognition processing on each sample image data in the sample image data set to obtain an expired item image data set; Constructing a damage recognition loss function according to the image data set of the appearance-damaged items, the image data set of the quality-damaged items, the image data set of the expired items, and the label image data set; Based on the damage identification loss function, the initial damage identification module is trained to obtain a target damage identification module; Using a target damage identification module, performing damage identification processing on each monitored object image data in the monitored object image data set to obtain a target damaged object image data set; Anomaly fusion processing is performed on each target damaged object image data in the target damaged object image data set to obtain damaged object optimized data.

[0090] An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any one of the warehouse inventory intelligent management methods based on AI visual monitoring as recorded in the above method embodiments.

[0091] An embodiment of the present application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program enables a computer to execute part or all of the steps of any one of the warehouse inventory intelligent management methods based on AI visual monitoring as recorded in the above method embodiments.

[0092] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0093] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0094] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the device or unit can be electrical or other forms.

[0095] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0096] In addition, the functional units in the various embodiments of the application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware or in the form of software program modules.

[0097] If the integrated unit is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, disk or optical disk, etc., and other media that can store program codes.

[0098] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, which can include: a flash drive, a read-only memory, a random access memory, a magnetic disk or an optical disk, etc.

[0099] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A warehouse inventory intelligent management method based on AI visual monitoring, characterized in that: The method comprises: Obtain the basic inventory management data and external dynamic impact data of the warehouse to be monitored; Based on AI vision technology, according to the sample image data set and the label image data set, the current inventory data in the inventory management basic data is dynamically optimized to obtain dynamically optimized inventory data; Performing inventory structure evaluation based on the inventory management basic data and the dynamically optimized inventory data to obtain inventory structure evaluation data; Performing real-time dynamic adjustment processing on the inventory structure evaluation data according to the external dynamic impact data to obtain inventory dynamic adjustment data; An inventory health assessment report is generated based on the dynamically optimized inventory data, the inventory structure assessment data and the inventory dynamic adjustment data.

2. The intelligent warehouse inventory management method based on AI visual monitoring according to claim 1 is characterized in that: The method of dynamically optimizing the current inventory data in the inventory management basic data based on the AI ​​vision technology according to the sample image data set and the label image data set to obtain the dynamically optimized inventory data includes: Based on the AI ​​vision technology, images of the items in the warehouse to be monitored are collected and preprocessed to obtain a set of monitored item image data; Based on the AI ​​vision technology, each monitored object image data in the monitored object image data set is processed for position anomaly recognition to obtain out-of-position optimization data; Based on the AI ​​vision technology, abnormal identification processing of damaged items is performed according to the sample image data set, the label image data set and the monitored item image data set to obtain optimized data of damaged items; Based on the AI ​​vision technology, the current inventory data in the inventory management basic data is reviewed and verified according to the off-site optimization data and the damaged item optimization data to obtain verified inventory data; Based on the AI ​​vision technology, the verified inventory data is processed for outbound optimization to obtain dynamically optimized inventory data.

3. The intelligent warehouse inventory management method based on AI visual monitoring according to claim 2 is characterized in that: The performing position anomaly recognition processing on each monitoring object image data in the monitoring object image data set to obtain out-of-position optimization data includes: Performing object three-dimensional model construction processing according to each monitored object image data in the monitored object image data set to obtain a monitored object three-dimensional model; According to the three-dimensional model of the monitored object, a height abnormality monitoring process is performed to obtain height abnormality data; Performing regional abnormality monitoring processing according to the three-dimensional model of the monitored object to obtain regional abnormality data; Performing stacking stability detection processing according to the three-dimensional model of the monitored object to obtain stability imbalance data; According to the three-dimensional model of the monitored object, real-time dynamic position tracking processing is performed to obtain abnormal displacement data; Anomaly fusion processing is performed according to the height abnormality data, the regional abnormality data, the stability imbalance data and the abnormal displacement data to obtain ex-situ optimized data.

4. The intelligent warehouse inventory management method based on AI visual monitoring according to claim 3 is characterized in that: The step of constructing a three-dimensional model of an object according to each monitored object image data in the monitored object image data set to obtain a three-dimensional model of the monitored object includes: From the monitored object image data set, obtain a first monitored object image data subset taken by a first photographing device and a second monitored object image data subset taken by a second photographing device; Acquire first plane coordinate data of a first imaging point from the first monitored object image data subset, and acquire second plane coordinate data of the first imaging point from the second monitored object image data subset; Determine the first three-dimensional coordinates of the first imaging point according to the first plane coordinate data, the second plane coordinate data, the first optical center coordinates of the first photographing device, the first focal length of the first photographing device, the second optical center coordinates of the second photographing device, and the second focal length of the second photographing device; Acquire third plane coordinate data of a second imaging point from the first monitored object image data subset, and acquire fourth plane coordinate data of the second imaging point from the second monitored object image data subset; Determine the second three-dimensional coordinates of the second imaging point according to the third plane coordinate data, the fourth plane coordinate data, the first optical center coordinates of the first photographing device, the first focal length of the first photographing device, the second optical center coordinates of the second photographing device, and the second focal length of the second photographing device; A three-dimensional model of the monitored object is obtained according to the first three-dimensional coordinates and the second three-dimensional coordinates.

5. The method for intelligent warehouse inventory management based on AI visual monitoring according to claim 4 is characterized in that: The abnormal identification of damaged items is performed according to the sample image data set, the label image data set and the monitored item image data set to obtain the damaged item optimization data, including: Using the initial damage recognition module, each sample image data in the sample image data set is processed for appearance damage recognition, so as to obtain an appearance damaged object image data set; Using an initial damage recognition module, performing quality damage recognition processing on each sample image data in the sample image data set to obtain a quality damaged item image data set; Using an initial damage recognition module, performing expiration recognition processing on each sample image data in the sample image data set to obtain an expired item image data set; Constructing a damage recognition loss function according to the image data set of the appearance-damaged items, the image data set of the quality-damaged items, the image data set of the expired items, and the label image data set; Based on the damage identification loss function, the initial damage identification module is trained to obtain a target damage identification module; Using a target damage identification module, performing damage identification processing on each monitored object image data in the monitored object image data set to obtain a target damaged object image data set; Anomaly fusion processing is performed on each target damaged object image data in the target damaged object image data set to obtain damaged object optimized data.

6. An intelligent warehouse inventory management device based on AI visual monitoring, characterized in that: The device comprises: An acquisition unit, used to acquire basic inventory management data and external dynamic impact data of the warehouse to be monitored; A first processing unit is used to dynamically optimize the current inventory data in the inventory management basic data based on the AI ​​vision technology and according to the sample image data set and the label image data set to obtain dynamically optimized inventory data; A second processing unit is used to perform inventory structure evaluation based on the inventory management basic data and the dynamically optimized inventory data to obtain inventory structure evaluation data; A third processing unit is used to perform real-time dynamic adjustment processing on the inventory structure evaluation data according to the external dynamic impact data to obtain inventory dynamic adjustment data; The fourth processing unit is used to generate an inventory health assessment report according to the dynamically optimized inventory data, the inventory structure assessment data and the inventory dynamic adjustment data.

7. The intelligent warehouse inventory management device based on AI visual monitoring according to claim 6 is characterized in that: The first processing unit is used to dynamically optimize the current inventory data in the inventory management basic data based on the AI ​​vision technology and the sample image data set and the label image data set to obtain the dynamically optimized inventory data, specifically for: Based on the AI ​​vision technology, images of the items in the warehouse to be monitored are collected and preprocessed to obtain a set of monitored item image data; Based on the AI ​​vision technology, each monitored object image data in the monitored object image data set is processed for position anomaly recognition to obtain out-of-position optimization data; Based on the AI ​​vision technology, abnormal identification processing of damaged items is performed according to the sample image data set, the label image data set and the monitored item image data set to obtain optimized data of damaged items; Based on the AI ​​vision technology, the current inventory data in the inventory management basic data is reviewed and verified according to the off-site optimization data and the damaged item optimization data to obtain verified inventory data; Based on the AI ​​vision technology, the verified inventory data is processed for outbound optimization to obtain dynamically optimized inventory data.

8. The intelligent warehouse inventory management device based on AI visual monitoring according to claim 7 is characterized in that: The first processing unit is used to perform position anomaly recognition processing on each monitoring object image data in the monitoring object image data set to obtain out-of-position optimization data, specifically for: Performing object three-dimensional model construction processing according to each monitored object image data in the monitored object image data set to obtain a monitored object three-dimensional model; According to the three-dimensional model of the monitored object, a height abnormality monitoring process is performed to obtain height abnormality data; Performing regional abnormality monitoring processing according to the three-dimensional model of the monitored object to obtain regional abnormality data; Performing stacking stability detection processing according to the three-dimensional model of the monitored object to obtain stability imbalance data; According to the three-dimensional model of the monitored object, real-time dynamic position tracking processing is performed to obtain abnormal displacement data; Anomaly fusion processing is performed according to the height abnormality data, the regional abnormality data, the stability imbalance data and the abnormal displacement data to obtain ex-situ optimized data.

9. A terminal, characterized in that: The method comprises a processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program comprises program instructions, and the processor is configured to call the program instructions to execute the method according to any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 5.

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