Goods warehouse-in and warehouse-out path optimization method and system

Abstract

The invention provides a cargo warehouse-in and warehouse-out path optimization method and system, and relates to the technical field of logistics management, and the method comprises the steps: determining a plurality of monitoring point positions through traversing a warehouse-in and warehouse-out region, and laying a plurality of monitoring devices according to the plurality of monitoring point positions; collecting the warehouse-in and warehouse-out areas through a plurality of monitoring devices to obtain a plurality of monitoring data sets; performing video fusion and splicing on the plurality of monitoring data sets to generate panoramic video data of the warehouse-in and warehouse-out area; performing real-time monitoring on the target goods based on the panoramic video data, and obtaining a first warehouse-in and warehouse-out path of the target goods; performing path analysis according to the first in-out warehouse path, optimizing the first in-out warehouse path according to an analysis result, and determining a second in-out warehouse path; and based on the warehouse-in and warehouse-out area, the target goods are intelligently conveyed according to the second warehouse-in and warehouse-out path. According to the invention, the technical problem of low efficiency and flexibility of cargo warehouse-in and warehouse-out path planning in the prior art is solved, and efficient and flexible cargo warehouse-in and warehouse-out path planning is realized.

Description

Technical Field

[0001] The present application relates to the technical field of logistics management, and in particular to a method and system for optimizing cargo in-and-out warehouse paths. Background Art

[0002] In the context of globalization and the rapid development of e-commerce, logistics management has become an important cornerstone supporting the operation of the modern economy. As a key link in the logistics chain, the entry and exit of goods directly affects the smoothness of the supply chain and the operational efficiency of the enterprise. However, the traditional management system of goods entry and exit has exposed many problems in the face of growing industry competition and complex logistics environment.

[0003] First, the limitations of traditional monitoring systems lie in their fixed camera layout and limited field of view, which lead to blind spots in the warehouse. These blind spots not only hinder the accurate grasp of the real-time status of goods, but also increase the risk of goods being lost or misplaced, seriously restricting the overall efficiency of warehouse safety management. Secondly, in terms of path planning, traditional manual planning methods rely on experience and judgment, lack flexibility and foresight, and are prone to unreasonable path planning, especially during peak hours or emergencies, which in turn causes traffic congestion and reduced operating efficiency inside the warehouse. This inefficient path planning model cannot meet the needs of modern logistics for rapid response and high throughput. Summary of the invention

[0004] The present application provides a method and system for optimizing the path of goods entering and leaving the warehouse, which solves the technical problem that the prior art cannot fully monitor the entry and exit of goods in real time and dynamically adjust the path planning due to the existence of monitoring blind spots, and realizes real-time optimization of the path of goods entering and leaving the warehouse, ensuring the efficiency and dynamic adaptability of path planning.

[0005] In view of the above problems, on the one hand, the present application provides a method for optimizing the path of goods entering and leaving the warehouse, and the method includes: traversing the entry and exit area to determine multiple monitoring points, and deploying multiple monitoring devices according to the multiple monitoring points, and there is a corresponding relationship between the multiple monitoring points and the multiple monitoring devices; collecting data on the entry and exit area through the multiple monitoring devices to obtain multiple monitoring data sets; performing video fusion and splicing on the multiple monitoring data sets to generate panoramic video data of the entry and exit area; real-time monitoring of target goods based on the panoramic video data to obtain a first entry and exit path of the target goods; performing path analysis according to the first entry and exit path, optimizing the first entry and exit path according to the analysis result, and determining a second entry and exit path; based on the entry and exit area, intelligently transporting the target goods according to the second entry and exit path.

[0006] On the other hand, the present application also provides a cargo in-and-out warehouse path optimization system, the system comprising: a monitoring point determination module, the monitoring point determination module is used to traverse the in-and-out warehouse area to determine multiple monitoring points, and multiple monitoring devices are deployed according to the multiple monitoring points, and there is a corresponding relationship between the multiple monitoring points and the multiple monitoring devices; a monitoring data acquisition module, the monitoring data acquisition module is used to collect data from the in-and-out warehouse area through the multiple monitoring devices to obtain multiple monitoring data sets; a video fusion module, the video fusion module is used to perform video fusion and splicing on the multiple monitoring data sets to generate panoramic video data of the in-and-out warehouse area; a first path acquisition module, the first path acquisition module is used to perform real-time monitoring of the target cargo based on the panoramic video data, and obtain a first in-and-out warehouse path of the target cargo; a second path determination module, the second path determination module is used to perform path analysis according to the first in-and-out warehouse path, optimize the first in-and-out warehouse path according to the analysis result, and determine a second in-and-out warehouse path; an intelligent transportation module, the intelligent transportation module is used to intelligently transport the target cargo according to the second in-and-out warehouse path based on the in-and-out warehouse area.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] The multiple monitoring points are determined by traversing the in-and-out area, and multiple monitoring devices are deployed according to the multiple monitoring points. The in-and-out area is collected through the multiple monitoring devices to obtain multiple monitoring data sets, and the multiple monitoring data sets are subjected to video fusion and splicing to generate panoramic video data of the in-and-out area, thereby achieving all-round seamless monitoring of the in-and-out area and eliminating monitoring blind spots. The target goods are monitored in real time based on the panoramic video data, and the first in-and-out path of the target goods is obtained. The path analysis is performed based on the first in-and-out path, and the first in-and-out path is optimized based on the analysis results, and the second in-and-out path is determined. Based on the in-and-out area, the target goods are intelligently transported according to the second in-and-out path, so that the goods in-and-out path is automatically analyzed and optimized according to the real-time monitoring data, the efficiency and accuracy of path planning are improved, and the efficient and safe completion of goods in-and-out operations is ensured.

[0009] As mentioned above, this application combines panoramic video fusion technology with path optimization algorithm to conduct comprehensive and real-time monitoring of warehouses, enhance the visualization and understanding of internal warehouse activities, track and monitor the in and out paths of goods in real time, and perform path analysis and optimization to provide the optimal in and out paths, thereby improving warehousing efficiency and cargo turnover speed.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 A flow chart of a method for optimizing the path of goods entering and leaving a warehouse provided in an embodiment of the present application;

[0012] Figure 2 A schematic diagram of a process for generating panoramic video data of an inbound and outbound area in a method for optimizing a cargo inbound and outbound path provided in an embodiment of the present application;

[0013] Figure 3 A schematic diagram of a flow chart of performing path analysis according to a first inbound and outbound path in a method for optimizing a cargo inbound and outbound path provided in an embodiment of the present application;

[0014] Figure 4 A schematic diagram of the structure of a cargo in-and-out path optimization system provided in an embodiment of the present application.

[0015] Explanation of the reference numerals: monitoring point determination module 10 , monitoring data acquisition module 20 , video fusion module 30 , first path acquisition module 40 , second path determination module 50 , intelligent transportation module 60 . DETAILED DESCRIPTION

[0016] The embodiments of the present application provide a method and system for optimizing the paths of goods entering and leaving the warehouse, thereby solving the technical problem that the prior art cannot comprehensively monitor the entry and exit of goods in real time and dynamically adjust the path planning due to the existence of monitoring blind spots. This realizes real-time optimization of the paths of goods entering and leaving the warehouse, and ensures the efficiency and dynamic adaptability of path planning.

[0017] Embodiment 1, as Figure 1 As shown, an embodiment of the present application provides a method for optimizing the path of goods entering and leaving the warehouse, and the method includes:

[0018] Step S1: traverse the warehouse entry and exit area to determine a plurality of monitoring points, and deploy a plurality of monitoring devices according to the plurality of monitoring points, wherein the plurality of monitoring points correspond to the plurality of monitoring devices.

[0019] Specifically, conduct a comprehensive survey of the entire cargo in and out area to understand the specific layout, channels, shelf distribution, cargo storage location and other key information of the in and out area, and determine the specific location where the monitoring equipment needs to be set up, that is, the monitoring point. The selection of monitoring points should be able to cover the key areas and key operation links of the warehouse to the maximum extent, while minimizing the monitoring blind spots. They are usually key nodes in the process of cargo in and out of the warehouse, such as entrances, exits, cargo loading and unloading areas, important channel intersections, etc. After determining the monitoring points, install corresponding monitoring equipment at each point, such as cameras, sensors or other types of monitoring devices. When laying out monitoring equipment, factors such as the height, angle, coverage, stability and security, cable and network connections, and the field of view, resolution, and night vision of the equipment should be considered to ensure that all monitoring equipment can effectively capture the required information under different conditions. For example, the monitoring equipment should be placed at a height of 3-5 meters to ensure that the target object can be covered and the blind spots can be reduced, and the coverage of the monitoring equipment should be appropriately overlapped to avoid monitoring blind spots, and appropriate protective measures should be taken, such as lightning protection and electromagnetic interference protection.

[0020] Each monitoring point corresponds to at least one monitoring device to ensure that the area monitored by the point is effectively observed and recorded. In this way, a one-to-one correspondence is established between the monitoring point and the monitoring device, thus forming a comprehensive and effective monitoring system that seamlessly monitors the in and out areas of the warehouse in all directions.

[0021] Step S2: Collect data from the warehouse entry and exit areas through the multiple monitoring devices to obtain multiple monitoring data sets.

[0022] Specifically, the multiple monitoring devices deployed in step S1 will continuously capture or record the real-time situation in the area. For example, capture information such as the movement of goods, the activities of personnel, and the location of vehicles. Since multiple monitoring devices are deployed at multiple monitoring points, each monitoring device will generate its corresponding monitoring data set, and multiple monitoring data sets are obtained. These monitoring data sets may contain continuous video streams, static images, temperature readings, humidity data, etc., depending on the type of monitoring equipment used. Multiple monitoring data sets together constitute a comprehensive monitoring of the entire entry and exit area. By analyzing these data sets, the status and location of the goods can be monitored in real time, and abnormal situations such as lost or misplaced goods can be discovered in time. In addition, these data can also be used for historical analysis to help optimize future entry and exit processes and path planning.

[0023] Step S2 collects relevant data of the warehouse entry and exit areas in real time through monitoring devices distributed in various key locations to form multiple monitoring data sets. These data sets are the prerequisite for realizing panoramic video data generation, real-time monitoring and path planning optimization, and play an important role in improving the safety and efficiency of warehouse management.

[0024] Step S3: fusing and stitching the multiple monitoring data sets to generate panoramic video data of the entry and exit area.

[0025] Specifically, video fusion stitching is the process of merging multiple video sources into a single, seamless video. Panoramic video data refers to continuous video that can show the entire in-and-out area. The multiple monitoring data sets obtained in step S2 are calibrated and synchronized, and the monitoring data therein are image processed to eliminate or minimize the parallax and distortion caused by the camera position, angle and lens characteristics, so as to facilitate subsequent video stitching and fusion. For example, the lenses of each monitoring device are calibrated to ensure that the images they capture can be aligned in space, the video data of each device are synchronized to ensure that the timestamps of the video frames are consistent during the stitching process, the brightness and contrast are adjusted, and the color distortion is corrected so as to obtain a visually coherent picture after stitching. Then, an image stitching algorithm, such as feature matching, optical flow estimation or panoramic image construction technology, is applied to identify the overlapping parts between different video clips, and multiple video streams are fused into a seamless panoramic video to obtain panoramic video data of the in-and-out area.

[0026] The panoramic video data finally generated in step S3 provides a continuous monitoring picture with a wide viewing angle, which can display the entire activity situation in the entry and exit area, and provide comprehensive data support for real-time monitoring and path optimization.

[0027] Step S4: Real-time monitoring of the target goods is performed based on the panoramic video data to obtain a first inbound and outbound path of the target goods.

[0028] Specifically, the target goods refer to the goods that need to be entered and exited from the warehouse. Image recognition technology, such as deep learning target detection algorithm, is used to identify and locate the target goods in the panoramic video data. The movement of the goods in the video frame is tracked by target tracking algorithms, such as optical flow method and multi-target tracking algorithm, and the position of the goods in the continuous video frames is predicted and updated. As the target goods move in the video frame, the position information of each time point is recorded. This position information includes the coordinates of the goods on the two-dimensional plane and possible timestamps, which are used to construct the movement path of the goods. In the video frame, multiple identical goods or different goods may be detected. Using data association technology, such as similarity measurement based on appearance or motion, the detection frames belonging to the same goods are associated to ensure the continuity of the path, and the recorded position information is connected to form the first entry and exit path of the target goods. The entry and exit path refers to the route that the goods pass from entering the warehouse to leaving the warehouse, which can be represented as a series of line segments or curves, showing the specific movement trajectory of the goods in the warehouse. The first entry and exit path data obtained is stored for subsequent analysis and optimization.

[0029] Through the above steps, real-time monitoring of the movement of goods can be achieved, and accurate inbound and outbound path information can be obtained, which is convenient for subsequent analysis and optimization, and an accurate and reliable optimized path can be obtained.

[0030] Step S5: performing path analysis according to the first in-and-out path, optimizing the first in-and-out path according to the analysis result, and determining a second in-and-out path.

[0031] Specifically, data related to the first in-and-out path is collected, including the starting point of the path, the end point of the path, the degree of congestion on the path, etc. The first in-and-out path is analyzed, including the evaluation of factors such as path length, number of turns, congestion, waiting time, etc., to identify potential bottlenecks, conflict areas or inefficient paths. Based on the results of the path analysis, the specific links that can be improved are identified, and the first in-and-out path is optimized, for example, avoiding obstacles or replacing new paths. Based on the results of the path optimization, the second in-and-out path is determined, and the new second in-and-out path is tested to ensure that it can effectively solve the problems in the first in-and-out path and improve the overall handling efficiency. If new problems or potential room for improvement are found during the test, the system will enter the cycle of path analysis and optimization again until a satisfactory second in-and-out path is determined. The second in-and-out path refers to the final in-and-out path of the goods obtained after optimizing the first in-and-out path.

[0032] Through this process, warehouse managers can optimize the goods' inbound and outbound routes based on path evaluation, thereby improving overall warehousing efficiency and operational performance.

[0033] Step S6: Based on the storage-in and storage-out area, the target goods are intelligently transported along the second storage-in and storage-out path.

[0034] Specifically, the second inbound and outbound path is sent to the warehouse management system (WMS) and automatic handling systems, such as automated guided vehicles (AGVs) and unmanned guided vehicles (UGVs). The automatic handling equipment intelligently navigates and handles the goods according to the instructions of the second inbound and outbound path. During the cargo transportation process, multiple monitoring devices continuously monitor the location and status of the goods, as well as environmental changes in the warehouse. If an emergency or new optimization opportunity is encountered, the path and task allocation can be adjusted in real time to complete the inbound and outbound operations.

[0035] Through the above process, intelligent and automated goods entry and exit can be achieved, which greatly reduces manual intervention and improves the efficiency and accuracy of goods entry and exit.

[0036] Furthermore, step S1 of the embodiment of the present application further includes:

[0037] Step S11: constructing a three-dimensional model of the entry and exit area based on the spatial layout information of the entry and exit area.

[0038] Step S12: traverse the three-dimensional model of the warehouse entry and exit area, and define a plurality of initial monitoring points according to the monitoring range.

[0039] Step S13: screening the multiple initial monitoring points according to the monitoring overlap range to determine the multiple monitoring points.

[0040] Specifically, spatial layout information refers to the relative position and size of various elements in the warehouse entry and exit area, such as the height of the shelf, the width of the aisle, etc. The three-dimensional model is a digital presentation of the spatial layout information, which can reflect the actual layout of the warehouse entry and exit area. Use laser scanners, drone photogrammetry or other three-dimensional modeling technologies to conduct detailed measurements and data collection of the warehouse entry and exit area to obtain the spatial layout information of the warehouse entry and exit area. Based on the spatial layout information of the warehouse entry and exit area, a three-dimensional model of the warehouse entry and exit area is constructed. This three-dimensional model contains information such as the actual size of the warehouse, shelf layout, aisle location, entrances and exits. The construction of the three-dimensional model can use three-dimensional modeling software or technology, such as CAD (computer-aided design) or 3D scanning, to create an accurate digital copy.

[0041] The monitoring range refers to the area where the monitoring equipment can effectively capture images or data. The initial monitoring points are possible locations for monitoring equipment installation based on preliminary analysis. Virtual traversal is performed on the 3D model to simulate the location of camera installation. According to the monitoring requirements, the different viewing angles and angles of the model are analyzed to set the initial monitoring points. The selection of these initial monitoring points takes into account the key areas that need to be monitored, such as shelf aisles, loading and unloading areas, entrances and exits, etc.

[0042] The monitoring overlap range refers to the part of the field of view of two or more monitoring devices that overlap each other. Screening refers to selecting the most suitable one or a group of options from the initial options based on certain standards and conditions. The screening process can be achieved through an optimization algorithm. Analyze the monitoring overlap range between the initial monitoring points. If two or more points have a large overlap in monitoring, then select one of the points as the final monitoring point to avoid waste of resources and redundancy of monitoring images. In this way, it can be ensured that each area is covered by at least one monitoring point, while optimizing the layout of the monitoring network.

[0043] Through the above process, the rationality of monitoring points and the effectiveness of the monitoring system can be ensured, which ensures that the monitoring system can meet the actual monitoring needs and improves the design efficiency of the monitoring system. The final monitoring points will be used for the actual deployment of monitoring equipment, providing a basis for subsequent monitoring data collection, video fusion splicing, real-time monitoring and path optimization.

[0044] Further, such as Figure 2 As shown, step S3 of the embodiment of the present application also includes:

[0045] Step S31: Analyze the multiple monitoring data sets according to the three-dimensional model of the warehouse entry and exit area to obtain multiple data space relationships.

[0046] Step S32: extracting multiple video frames based on the multiple monitoring data sets.

[0047] Step S33: determining alignment parameters and splicing parameters of the multiple video frames according to the multiple data space relationships.

[0048] Step S34: performing video fusion and splicing on the multiple monitoring data sets according to the alignment parameters and the splicing parameters to obtain the panoramic video data.

[0049] Preferably, step S31 of the embodiment of the present application further includes:

[0050] Step S311: extract multiple feature points of the multiple monitoring data sets, traverse the three-dimensional model of the warehouse entry and exit area based on the multiple feature points for registration, and generate multiple spatial registration data sets.

[0051] Step S312: performing calculations based on the multiple spatial registration data sets to generate multiple spatial position data, multiple spatial motion trajectories, and multiple spatial interaction data.

[0052] Step S313: determining the spatial relationship of the multiple data according to the multiple spatial position data, the multiple spatial motion trajectories, and the multiple spatial interaction data.

[0053] Specifically, feature points are easily identifiable and unique points in an image, such as corner points, edge points, etc., which are used in the subsequent spatial registration process. Feature points are extracted from multiple monitoring data sets, and based on the extracted multiple feature points, the three-dimensional model of the entry and exit area is traversed for registration. Registration is the process of aligning image data with the three-dimensional model space. By comparing the image feature points and the corresponding points in the three-dimensional model, multiple spatial registration data sets are generated. The spatial registration data set contains the location information of the feature points in the three-dimensional model, as well as their mapping relationship with the corresponding points in the monitoring data set.

[0054] Spatial position data refers to the coordinates of the feature points in each monitoring data set in three-dimensional space. Spatial motion trajectory refers to the movement path of an object in three-dimensional space at different time points. Spatial interaction data involves the interaction and relationship between different objects or people, such as distance, direction, and speed. The spatial position data of each object or feature point is obtained by using the principles of geometry and kinematics to calculate based on multiple spatial registration data sets. At the same time, the spatial motion trajectory is generated by tracking the movement of these points over time. The interaction between multiple objects in the monitoring data set is analyzed to generate spatial interaction data, such as proximity between objects, collision detection, etc., and finally multiple spatial position data, multiple spatial motion trajectories, and multiple spatial interaction data are obtained.

[0055] The multiple spatial position data, multiple spatial motion trajectories and multiple spatial interaction data are comprehensively analyzed to determine the data space relationship between the monitoring data sets and between the data sets and the three-dimensional model. The data space relationship describes the spatial correspondence between different monitoring data sets, including their relative positions in the three-dimensional space, perspective differences and coverage, etc. It is the key to the subsequent video fusion and stitching, ensuring the spatial coherence and accuracy of the stitched panoramic video.

[0056] A video frame is a single still image in a video, and a collection of multiple video frames constitutes a complete video stream. A series of representative video frames are extracted from each surveillance dataset. These video frames should be able to represent the content of the entire dataset and are usually synchronized in time to facilitate subsequent processing.

[0057] Alignment parameters refer to parameters used to adjust the position and direction of video frames, including translation, rotation and scaling parameters, etc. Stitching parameters refer to parameters used to combine aligned video frames, including stitching order and transition effects, etc. Based on the data spatial relationship obtained in step S31, image processing algorithms such as feature matching, perspective transformation and image blending techniques are used to calculate alignment parameters and stitching parameters to ensure smooth transition between video frames.

[0058] According to the alignment parameters and stitching parameters determined in the above steps, image processing techniques such as image stitching, color correction and perspective transformation are used to perform video fusion and stitching on multiple monitoring data sets, and the extracted video frames are fused into a continuous panoramic video.

[0059] Through the above steps, the conversion from the original monitoring data set to the panoramic video data is realized, providing a comprehensive view that can monitor the entire entry and exit area in real time, and providing detailed data reference for subsequent data analysis.

[0060] Further, such as Figure 3 As shown, step S5 of the embodiment of the present application also includes:

[0061] Step S51: extracting the path starting point and the path ending point according to the first storage entry and exit path.

[0062] Step S52: Mapping the first inbound and outbound path to the three-dimensional model of the inbound and outbound area based on the path starting point and the path end point to generate an initial three-dimensional path for the target goods.

[0063] Step S53: Calculate the path smoothness according to the curvature of the initial three-dimensional path, perform path evaluation on the initial three-dimensional path in combination with the path length, and generate a storage entry and exit path score.

[0064] Step S54: performing obstacle detection based on the in-and-out path score and the panoramic video data, and adding the first detection result to the analysis result for output.

[0065] Preferably, step S54 of the embodiment of the present application further includes:

[0066] Step S541: When the entry / exit path score is lower than a preset threshold, the initial three-dimensional path is traversed according to the panoramic video data to determine whether there are obstacles on the initial three-dimensional path.

[0067] Step S542: If the obstacle exists in the initial three-dimensional path, an avoidance analysis is performed on the obstacle to generate an avoidance analysis result.

[0068] Step S543: Evaluate the avoidance cost according to the avoidance analysis result, and generate a replacement instruction when the avoidance cost is greater than or equal to a preset cost value, and add it to the analysis result.

[0069] Step S544: When the avoidance cost is less than the preset cost value, an avoidance instruction is generated and added to the analysis result.

[0070] Specifically, the path start point and path end point are extracted from the first inbound and outbound path. The path start point is the location where the goods start to move, and the path end point is the target location where the goods move. Based on the path start point and path end point, the two-dimensional path point data is converted into coordinates in three-dimensional space and mapped into the three-dimensional model of the inbound and outbound area to generate an initial three-dimensional path of the target goods connecting the start point and the end point. The curvature of the initial three-dimensional path is calculated to evaluate the smoothness of the path. Curvature is an indicator of the degree of curvature of the path. The larger the curvature, the higher the degree of curvature of the path. Path smoothness refers to the geometric smoothness of the path, which affects the smoothness of the movement of the handling equipment. At the same time, the path length is combined for path evaluation to generate an inbound and outbound path score. This score reflects the efficiency and feasibility of the path. The smoother the path and the shorter the length, the higher the score.

[0071] According to actual needs and historical data analysis, a threshold for the inbound and outbound path score is pre-defined in the system. When the score of the inbound and outbound path is lower than the preset threshold, it indicates that there may be problems with the original path, such as being too long, too tortuous, or having potential obstacle risks. At this time, the obstacle detection process is started, the initial three-dimensional path is traversed, and the panoramic video data is used to identify obstacles on the path. The purpose of this step is to ensure that there will be no transportation failure or damage to the goods due to unforeseen obstacles during the actual handling process.

[0072] Once an obstacle is found on the initial 3D path, an avoidance analysis is performed. The size, shape, and location of the obstacle as well as the size and maneuverability of the handling equipment are considered to determine the best avoidance route. The results of the avoidance analysis will guide the handling equipment on how to bypass the obstacle to ensure that the goods reach the destination safely. A cost value is pre-defined according to actual needs to represent the maximum avoidance cost that can be expected. The additional time, distance, and energy consumption required to avoid the obstacle based on the above avoidance analysis results are calculated. If the avoidance cost is greater than the preset cost value, that is, the negative impact of avoiding the obstacle exceeds the expected tolerance range, a change instruction is generated to instruct the handling equipment to find another path. On the contrary, if the avoidance cost is less than the preset cost value, the detour is considered acceptable and an avoidance instruction is generated. This instruction will be added to the analysis results to guide the handling equipment to operate along the new path.

[0073] Through the above steps, the system can evaluate the efficiency and safety of the path in real time based on the panoramic video data and three-dimensional model, and generate corresponding instructions based on the evaluation results to realize intelligent analysis and optimization of the in and out paths to ensure the efficiency and safety of warehouse operations.

[0074] Furthermore, step S5 of the embodiment of the present application further includes:

[0075] Step S55: When the analysis result is the replacement instruction, a plurality of candidate optimization paths are generated based on the path starting point, the path end point and the three-dimensional model of the storage area.

[0076] Step S56: Calculate the path smoothness of the multiple candidate optimization paths according to the curvatures of the multiple candidate optimization paths, perform path evaluation on the multiple candidate optimization paths in combination with the path lengths of the multiple candidate optimization paths, and generate path scores for the multiple candidate optimization paths.

[0077] Step S57: Compare the path scores of the multiple candidate optimization paths with the entry and exit path scores, extract the candidate optimization paths with higher scores than the entry and exit paths, perform obstacle detection on the candidate optimization paths with higher scores than the entry and exit paths in combination with the panoramic video data, and generate the second entry and exit path according to the second detection result.

[0078] Specifically, candidate optimization paths refer to multiple possible path options that the system can generate under existing conditions. When the analysis results indicate that the path needs to be changed, multiple candidate optimization paths are generated using a path planning algorithm based on the known starting and ending points of the path and the three-dimensional model of the warehouse. These paths will try to avoid known obstacles while considering the physical structure inside the warehouse. Each candidate optimization path is evaluated in detail to generate a corresponding path score. The path score of the candidate optimization path is compared with the original in-and-out path score. Any candidate path with a higher score than the original path will be extracted as a potential optimization option. Obstacle detection is performed on these high-scoring candidate paths in combination with panoramic video data to obtain a second detection result. The second detection result is a result reflecting the safety of the candidate path after obstacle detection on the candidate path. Based on the second detection result, an optimal path is selected as the second in-and-out path.

[0079] Through the above process, multiple candidate paths are intelligently generated and evaluated, and finally an optimized path that ensures both efficiency and safety is selected, which improves the flexibility and reliability of warehouse operations while also improving the overall efficiency of cargo handling.

[0080] In summary, the method for optimizing the cargo in-and-out path provided by the embodiment of the present application has the following technical effects:

[0081] The multiple monitoring points are determined by traversing the in-and-out area, and multiple monitoring devices are deployed according to the multiple monitoring points. The in-and-out area is collected through the multiple monitoring devices to obtain multiple monitoring data sets, and the multiple monitoring data sets are subjected to video fusion and splicing to generate panoramic video data of the in-and-out area, thereby realizing all-round seamless monitoring of the in-and-out area and eliminating monitoring blind spots. Based on the panoramic video data, the target goods are monitored in real time, the first in-and-out path of the target goods is obtained, the first in-and-out path is analyzed and evaluated, and obstacle detection is performed. The first in-and-out path is optimized according to the analysis results, and avoidance instructions or replacement instructions are generated to determine the second in-and-out path. Based on the in-and-out area, the target goods are intelligently transported according to the second in-and-out path, so that the goods in-and-out path is automatically analyzed and optimized according to the real-time monitoring data, and the optimal path with the highest efficiency and avoiding obstacles is found, the efficiency and accuracy of path planning are improved, and the efficient and safe completion of goods in-and-out operations is ensured.

[0082] As described above, the embodiments of the present application combine panoramic video fusion technology with path optimization algorithm to conduct comprehensive and real-time monitoring of the warehouse, analyze and monitor the flow of goods, and generate the goods' in and out paths; and according to the real-time situation of the in and out areas, continuously perform path analysis and optimization, provide the optimal in and out paths, realize efficient and flexible goods in and out path planning, and improve warehousing efficiency and goods circulation speed.

[0083] Embodiment 2, as Figure 4 As shown, an embodiment of the present application provides a cargo in-and-out path optimization system, the system comprising:

[0084] The monitoring point determination module 10 is used to traverse the storage area to determine multiple monitoring points, and deploy multiple monitoring devices according to the multiple monitoring points. There is a corresponding relationship between the multiple monitoring points and the multiple monitoring devices.

[0085] The monitoring data collection module 20 is used to collect data from the warehouse entry and exit area through the multiple monitoring devices to obtain multiple monitoring data sets.

[0086] The video fusion module 30 is used to perform video fusion and splicing on the multiple monitoring data sets to generate panoramic video data of the entry and exit area.

[0087] The first path acquisition module 40 is used to monitor the target goods in real time based on the panoramic video data to obtain a first in-and-out path of the target goods.

[0088] The second path determination module 50 is used to perform path analysis according to the first in-and-out path, optimize the first in-and-out path according to the analysis result, and determine a second in-and-out path.

[0089] The intelligent transport module 60 is used to intelligently transport the target goods along the second inbound and outbound path based on the inbound and outbound area.

[0090] Furthermore, the monitoring point determination module 10 of the embodiment of the present application is also used to perform the following steps:

[0091] A three-dimensional model of the entry and exit area is constructed based on the spatial layout information of the entry and exit area.

[0092] The three-dimensional model of the storage area is traversed, and a plurality of initial monitoring points are defined according to the monitoring range.

[0093] The multiple initial monitoring points are screened according to the monitoring overlap range to determine the multiple monitoring points.

[0094] Furthermore, the video fusion module 30 in the embodiment of the present application is also used to perform the following steps:

[0095] The plurality of monitoring data sets are analyzed according to the three-dimensional model of the warehouse entry and exit area to obtain a plurality of data spatial relationships.

[0096] A plurality of video frames are extracted based on the plurality of monitoring data sets.

[0097] Alignment parameters and splicing parameters of the multiple video frames are determined according to the multiple data space relationships.

[0098] The multiple monitoring data sets are subjected to video fusion and splicing according to the alignment parameters and the splicing parameters to obtain the panoramic video data.

[0099] Furthermore, the video fusion module 30 in the embodiment of the present application is also used to perform the following steps:

[0100] Extract multiple feature points of the multiple monitoring data sets, traverse the three-dimensional model of the warehouse entry and exit area based on the multiple feature points to perform registration, and generate multiple spatial registration data sets.

[0101] Calculation is performed based on the multiple spatial registration data sets to generate multiple spatial position data, multiple spatial motion trajectories, and multiple spatial interaction data.

[0102] The multiple data spatial relationships are determined according to the multiple spatial position data, the multiple spatial motion trajectories, and the multiple spatial interaction data.

[0103] Furthermore, the second path determination module 50 in the embodiment of the present application is further configured to perform the following steps:

[0104] A path starting point and a path end point are extracted according to the first storage entry and exit path.

[0105] The first inbound and outbound path is mapped to the three-dimensional model of the inbound and outbound area based on the path starting point and the path end point to generate an initial three-dimensional path of the target goods.

[0106] The path smoothness is calculated according to the curvature of the initial three-dimensional path, and the initial three-dimensional path is evaluated in combination with the path length to generate an inbound and outbound path score.

[0107] Obstacle detection is performed based on the in-and-out path score and in combination with the panoramic video data, and the first detection result is added to the analysis result for output.

[0108] Furthermore, the second path determination module 50 in the embodiment of the present application is further configured to perform the following steps:

[0109] When the entry / exit path score is lower than a preset threshold, the initial three-dimensional path is traversed according to the panoramic video data to determine whether there are obstacles on the initial three-dimensional path.

[0110] If the obstacle exists in the initial three-dimensional path, an avoidance analysis is performed on the obstacle to generate an avoidance analysis result.

[0111] The avoidance cost is evaluated according to the avoidance analysis result, and when the avoidance cost is greater than or equal to a preset cost value, a replacement instruction is generated and added to the analysis result.

[0112] When the avoidance cost is less than the preset cost value, an avoidance instruction is generated and added to the analysis result.

[0113] Furthermore, the second path determination module 50 in the embodiment of the present application is further configured to perform the following steps:

[0114] When the analysis result is the replacement instruction, a plurality of candidate optimization paths are generated based on the path starting point, the path end point and the three-dimensional model of the storage area.

[0115] The path smoothness of the multiple candidate optimization paths is calculated according to the curvatures of the multiple candidate optimization paths, and the multiple candidate optimization paths are evaluated in combination with the path lengths of the multiple candidate optimization paths to generate path scores of the multiple candidate optimization paths.

[0116] The path scores of the multiple candidate optimization paths are compared with the entry and exit path scores, and the candidate optimization paths with higher scores than the entry and exit paths are extracted. Obstacle detection is performed on the candidate optimization paths with higher scores than the entry and exit paths in combination with the panoramic video data, and the second entry and exit path is generated according to the second detection result.

[0117] Through the above-mentioned detailed description of a method for optimizing the path of goods entering and leaving the warehouse, those skilled in the art can clearly understand a system for optimizing the path of goods entering and leaving the warehouse in this embodiment. For the system disclosed in the second embodiment, since it corresponds to the method disclosed in the first embodiment and has corresponding functional modules and beneficial effects, the relevant parts can be referred to the description of the method part.

[0118] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing the path of goods entering and leaving the warehouse, characterized in that: The method comprises: Traversing the warehouse entry and exit area to determine a plurality of monitoring points, and deploying a plurality of monitoring devices according to the plurality of monitoring points, wherein the plurality of monitoring points and the plurality of monitoring devices have a corresponding relationship; Collecting data from the warehouse entry and exit areas through the multiple monitoring devices to obtain multiple monitoring data sets; Performing video fusion and splicing on the multiple monitoring data sets to generate panoramic video data of the entry and exit area; Based on the panoramic video data, the target goods are monitored in real time to obtain a first inbound and outbound path of the target goods; Performing a path analysis according to the first inbound and outbound path, optimizing the first inbound and outbound path according to the analysis result, and determining a second inbound and outbound path; Based on the inbound and outbound areas, the target goods are intelligently transported along the second inbound and outbound path.

2. A method for optimizing cargo in-and-out paths according to claim 1, characterized in that: Traverse the in-and-out areas to determine multiple monitoring points, including: Construct a three-dimensional model of the in-and-out area based on the spatial layout information of the in-and-out area; Traversing the three-dimensional model of the warehouse entry and exit area, and defining a plurality of initial monitoring points according to the monitoring range; The multiple initial monitoring points are screened according to the monitoring overlap range to determine the multiple monitoring points.

3. A method for optimizing the path of goods entering and leaving the warehouse as claimed in claim 2, characterized in that: The plurality of monitoring data sets are subjected to video fusion and splicing to generate panoramic video data of the entry and exit area, the method comprising: Analyzing the plurality of monitoring data sets according to the three-dimensional model of the warehouse entry and exit area to obtain a plurality of data spatial relationships; extracting a plurality of video frames based on the plurality of monitoring data sets; Determining alignment parameters and splicing parameters of the plurality of video frames according to the plurality of data spatial relationships; The multiple monitoring data sets are subjected to video fusion and splicing according to the alignment parameters and the splicing parameters to obtain the panoramic video data.

4. A method for optimizing the path of goods entering and leaving the warehouse as claimed in claim 3, characterized in that: The plurality of monitoring data sets are analyzed according to the three-dimensional model of the warehouse entry and exit area to obtain a plurality of data spatial relationships, the method comprising: Extracting multiple feature points of the multiple monitoring data sets, traversing the three-dimensional model of the warehouse entry and exit area based on the multiple feature points for registration, and generating multiple spatial registration data sets; Perform calculations based on the multiple spatial registration data sets to generate multiple spatial position data, multiple spatial motion trajectories, and multiple spatial interaction data; The multiple data spatial relationships are determined according to the multiple spatial position data, the multiple spatial motion trajectories, and the multiple spatial interaction data.

5. A method for optimizing the path of goods entering and leaving a warehouse as claimed in claim 2, characterized in that: Performing path analysis according to the first inbound and outbound path, the method includes: Extracting a path starting point and a path end point according to the first inbound and outbound path; Mapping the first inbound and outbound path to the three-dimensional model of the inbound and outbound area based on the path starting point and the path end point to generate an initial three-dimensional path for the target goods; Calculating the path smoothness according to the curvature of the initial three-dimensional path, performing path evaluation on the initial three-dimensional path in combination with the path length, and generating a storage entry and exit path score; Obstacle detection is performed based on the in-and-out path score and in combination with the panoramic video data, and the first detection result is added to the analysis result for output.

6. A method for optimizing the path of goods entering and leaving the warehouse as claimed in claim 5, characterized in that: According to the in-and-out path score, obstacle detection is performed in combination with the panoramic video data, and the first detection result is added to the analysis result for output, the method comprising: When the in-and-out path score is lower than a preset threshold, the initial three-dimensional path is traversed according to the panoramic video data to determine whether there are obstacles on the initial three-dimensional path; If the obstacle exists in the initial three-dimensional path, performing avoidance analysis on the obstacle to generate an avoidance analysis result; Evaluate the avoidance cost according to the avoidance analysis result, and generate a replacement instruction when the avoidance cost is greater than or equal to a preset cost value, and add the replacement instruction to the analysis result; When the avoidance cost is less than the preset cost value, an avoidance instruction is generated and added to the analysis result.

7. A method for optimizing the path of goods entering and leaving a warehouse as claimed in claim 6, characterized in that: The first inbound and outbound path is optimized according to the analysis result, the method comprising: When the analysis result is the replacement instruction, a plurality of candidate optimization paths are generated based on the path starting point, the path end point and the three-dimensional model of the storage area; Calculating the path smoothness of the multiple candidate optimization paths according to the curvatures of the multiple candidate optimization paths, performing path evaluation on the multiple candidate optimization paths in combination with the path lengths of the multiple candidate optimization paths, and generating path scores for the multiple candidate optimization paths; The path scores of the multiple candidate optimization paths are compared with the entry and exit path scores, and the candidate optimization paths with higher scores than the entry and exit paths are extracted. Obstacle detection is performed on the candidate optimization paths with higher scores than the entry and exit paths in combination with the panoramic video data, and the second entry and exit path is generated according to the second detection result.

8. A cargo in-and-out path optimization system, characterized in that: The system comprises: A monitoring point determination module, the monitoring point determination module is used to traverse the storage entry and exit area to determine multiple monitoring points, and to deploy multiple monitoring devices according to the multiple monitoring points, and there is a corresponding relationship between the multiple monitoring points and the multiple monitoring devices; A monitoring data collection module, the monitoring data collection module is used to collect data from the warehouse entry and exit areas through the multiple monitoring devices to obtain multiple monitoring data sets; A video fusion module, the video fusion module is used to perform video fusion and splicing on the multiple monitoring data sets to generate panoramic video data of the entry and exit area; A first path acquisition module, the first path acquisition module is used to monitor the target goods in real time based on the panoramic video data, and acquire a first inbound and outbound path of the target goods; A second path determination module, the second path determination module is used to perform path analysis according to the first in-and-out path, optimize the first in-and-out path according to the analysis result, and determine a second in-and-out path; An intelligent transportation module is used to intelligently transport the target goods along the second inbound and outbound path based on the inbound and outbound area.

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