A warehouse utilization evaluation method based on digital twin model

Abstract

The present invention discloses a method for evaluating storage utilization of a digital twin model, comprising the following steps: S1, collecting and modeling vertical warehouse space data; S2, constructing a digital twin vertical warehouse model based on the spatial data model; S3, evaluating and analyzing storage utilization based on the spatial data model, which uses spatial division and measurement methods to evaluate the utilization of the vertical warehouse space, and compares and analyzes the storage utilization rates of different areas to find out areas with insufficient or wasted space. The present invention uses spatial division and measurement methods to evaluate the utilization of the vertical warehouse space, which not only realizes the visual expression of space utilization, but also can calculate the storage utilization rate of the vertical warehouse from different dimensions such as shelf use, channel utilization, and overall space, so it can greatly improve the multi-dimensionality and precision of space utilization.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent warehousing and relates to a warehousing utilization evaluation method of a digital twin model. Background Art

[0002] At present, most of the intelligent analysis and management of vertical warehouse space still rely on relatively traditional technical means, and the modeling and visualization are relatively simple. The existing technology mainly uses two-dimensional planes or simple three-dimensional models to present the vertical warehouse space, which cannot achieve high-precision three-dimensional modeling and real perception, which also limits the depth and precision of spatial analysis. In addition, the traditional method does not realize automated spatial data collection, and the precision of spatial modeling is insufficient, so the analysis of storage utilization is relatively rough, especially in terms of storage utilization. The existing technology mainly conducts simple analysis from the perspective of overall space utilization, and does not realize detailed analysis from multiple dimensions such as shelf use, channel utilization, and storage utilization in different areas. The evaluation accuracy of space utilization is poor. Summary of the invention

[0003] The purpose of the present invention is to provide a warehouse utilization evaluation method based on a digital twin model, which is used to solve the problem that the prior art fails to realize detailed analysis of the warehouse utilization of different areas of the warehouse space from multiple dimensions, and the evaluation accuracy of space utilization is poor.

[0004] The storage utilization evaluation method of a digital twin model comprises the following steps:

[0005] S1. Collect and model the vertical library space data;

[0006] S2. Build a digital twin library model based on the spatial data model;

[0007] S3. Evaluate and analyze warehouse utilization based on the spatial data model. This step uses spatial division and measurement methods to evaluate the utilization of the vertical warehouse space, and compare and analyze the warehouse utilization rates in different areas to find out areas with insufficient or wasted space.

[0008] Preferably, step S3 comprises:

[0009] S3.1. Divide the evaluation space into units. According to the structural characteristics and management requirements of the vertical library space, divide the space into evaluation units of a certain size;

[0010] S3.2. Calculate the volume of the unit space, determine the spatial range of each evaluation unit on the digital twin library model, and calculate its spatial volume;

[0011] S3.3. Query all SKU products stored in the unit, query the list of all SKU products stored in each evaluation unit in the digital twin library model, obtain the volume data of each product, and sum up the volumes of different products to obtain the total volume of all products in the unit;

[0012] S3.4. Calculate the storage utilization rate by dividing the total product volume of each evaluation unit by the spatial volume of the unit to obtain the storage utilization rate;

[0013] S3.5. Analyze differences in storage utilization, compare storage utilization of different assessment units, identify areas with higher or lower utilization, and analyze the reasons for the differences;

[0014] S3.6. Evaluate the rationality of the space zoning scheme, whether the storage utilization rate of each zone is optimal, and whether there are areas of redundant space.

[0015] Preferably, step S3.1 includes: spatial division to store products of different types or attributes separately, dividing them into different spatial units by layer, delineating relatively independent but interrelated subspaces on the spatial model according to the spatial structure and management requirements, adding attribute information to each subspace, and intuitively presenting the division results through graphical expression; then continuously revising by soliciting expert opinions and field verification, and finally obtaining a high-quality spatial unit division plan for spatial analysis.

[0016] Preferably, the step S3.1 specifically includes the following steps:

[0017] S3.1.1. Select the partitioning method based on the spatial characteristics and select the partitioning elements that can clearly define the unit range;

[0018] S3.1.2. Set the unit quantity range according to the spatial area, conduct trial division on the spatial model, evaluate whether the unit boundaries and area meet the requirements, and adjust the method and correct the quantity and conduct trial division again if necessary;

[0019] S3.1.3. When the test division effect meets the requirements, confirm the final division plan of the spatial unit, add attribute information to each unit and express it graphically;

[0020] S3.1.4. Invite experts to review the division plan and, if necessary, conduct field verification to determine the practicality and effectiveness of the plan. Continue to revise and improve the plan until high-quality spatial unit division results are obtained.

[0021] Preferably, the division method is a structural division method, a product division method or a mixed division method.

[0022] Preferably, the utilization efficiency of each space unit is evaluated by a warehouse utilization algorithm, different utilization standards are set according to the unit height, and the utilization of each unit is compared with the standard. Units above the standard are rated as efficiently utilized, and units below the standard are rated as inefficiently utilized.

[0023] Preferably, each space unit is scored according to the utilization evaluation results, and the utilization of the space unit from low to high corresponds to the score from few to many. The space units are sorted from high to low according to the score, and the areas with high and low utilization efficiency are found out. The space units with high and low scores are selected, and their unit properties, product types and layouts are analyzed to find out the factors affecting the utilization rate. The experience of the high-scoring units is extended to the space units in similar conditions, and the shortcomings of the low-scoring units need to be improved.

[0024] Preferably, based on the analysis results, improvement plans are proposed for low-scoring units to improve warehouse utilization by adjusting shelf spacing, changing storage layout, and clearing out useless equipment.

[0025] Preferably, the step S1 collects the spatial data inside the vertical library and models the data in a certain format to form a digital vertical library spatial model, which specifically includes the following sub-steps:

[0026] S1.1. Select spatial data acquisition equipment;

[0027] S1.2, define the scope and granularity of data collection;

[0028] S1.3, collect spatial data;

[0029] S1.4. Normalize the collected data to a standard format;

[0030] S1.5. Perform data filtering, registration and modeling.

[0031] Preferably, step S2 integrates the spatial model with the structure of the three-dimensional warehouse, shelf layout, logistics operation data and other information to form a three-dimensional warehouse digital twin model containing spatial and business information, which specifically includes the following sub-steps:

[0032] S2.1. Collect relevant business data of the library;

[0033] S2.2. Establish shelf and SKU product database;

[0034] S2.3, construct a vertical warehouse structure model and further form a digital model including the vertical warehouse space geometry structure;

[0035] S2.4, introduce shelf and SKU product models into the spatial digital model;

[0036] S2.5, build a work process model;

[0037] S2.6. Realize virtual roaming and interaction of the model.

[0038] The present invention has the following advantages:

[0039] 1. This solution uses space division and measurement methods to evaluate the utilization rate of vertical warehouse space. It not only realizes the visualization of space utilization, but also calculates the storage utilization rate of vertical warehouse from different dimensions such as shelf use, channel utilization, and overall space. The existing technology mainly stays at the rough analysis of the overall storage utilization rate. This solution has greatly improved the multi-dimensionality and precision of space utilization. On the other hand, this solution compares and analyzes the storage utilization rate of different areas, and can find out the areas with insufficient or wasted space, thereby providing decision support for space optimization and transformation.

[0040] 2. The effect of spatial unit division in this scheme comes from careful design and repeated verification, constant comparison between the spatial model and the site, and integration of expert opinions and management requirements, so as to successfully complete the high-quality spatial unit division work. The quality of the division results directly affects the depth and accuracy of spatial analysis. This scheme achieves high-quality spatial unit division through a carefully designed division method, which can maximize the effectiveness in subsequent work and realize the spatial analysis and optimization of digital twin warehouses.

[0041] 3. The present invention realizes the visualization of space utilization. Traditional methods usually only provide static space plans or data reports, which are not intuitive enough. This solution uses three-dimensional visualization to highly intuitively express the distribution of storage utilization, dynamic monitoring results, and simulation effects of space transformation plans, which is conducive to the formulation of relevant decisions.

[0042] 4. The present invention obtains a true three-dimensional spatial model based on high-precision spatial data modeling. Compared with traditional methods, which usually can only obtain two-dimensional spatial plan or elevation drawings, and cannot intuitively reflect the three-dimensional structure of the space, this solution constructs a true three-dimensional spatial model, including spatial geometric characteristics, shelf layout, and three-dimensional models of key equipment and facilities, making the expression of spatial information more realistic and reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is a basic flow chart of a warehouse utilization evaluation method of a digital twin model of the present invention.

[0044] Figure 2 This is a flow chart of step S3 in the present invention.

[0045] Figure 3 This is a flow chart of dividing the evaluation space units in step S3 of the present invention.

[0046] Figure 4This is a system architecture diagram of a digital twin warehouse space analysis system that applies the present invention. DETAILED DESCRIPTION

[0047] The specific implementation modes of the present invention are further explained in detail below by describing the embodiments with reference to the accompanying drawings, so as to help those skilled in the art to have a more complete, accurate and in-depth understanding of the inventive concept and technical solution of the present invention.

[0048] like Figure 1-4 As shown, the present invention discloses a warehouse utilization evaluation method of a digital twin model, comprising the following steps.

[0049] S1. Collect and model the vertical warehouse space data.

[0050] This step collects the spatial data inside the warehouse through sensors, 3D laser scanning and other means, and models these data in a certain format to form a digital warehouse space model, which is the basis for realizing the digital twin warehouse. This step specifically includes the following sub-steps.

[0051] S1.1. Select spatial data acquisition equipment. This step collects spatial data through 3D laser scanners, depth of field cameras, RFID sensors, etc. 3D laser scanners collect point cloud data with high accuracy and fast speed, but the cost is high; depth of field cameras are low-cost but greatly affected by light; RFID sensors can monitor space utilization in real time, and combined use can achieve ideal results. In comparison, traditional methods mostly use manual surveying or two-dimensional drawing modeling to obtain spatial data, which has low accuracy and efficiency.

[0052] S1.2. Define the scope and granularity of data collection. The vertical warehouse has a large space, so it is necessary to define the specific area and sampling granularity for data collection according to the purpose of analysis. Usually, sampling can be carried out according to the slot area, channel area and elevated area of ​​the vertical warehouse. The spacing of the above sampling is recommended to be 0.1-0.5 meters, and the number of sampling points should not be less than 5 million per 100 square meters. Sufficient sampling points can provide enough data to establish a model that meets the accuracy requirements.

[0053] S1.3. Collect spatial data. On the basis of defining the data collection scope and sampling granularity, use the selected equipment to scan and collect the library space to obtain point cloud data, image data, RFID sensing data, etc. The collection workload is large and usually takes several days to three weeks. The above sampling point settings can collect enough three-dimensional data to establish the corresponding three-dimensional space model. Traditional methods can usually only obtain two-dimensional spatial plan or elevation drawings, and cannot intuitively reflect the three-dimensional structure of the space.

[0054] S1.4. Normalize the collected data to a standard format. The data collected by different devices are in different formats, so they need to be normalized and converted to a standard data format, such as point cloud data converted to .ply format, .las format, etc., image data converted to .png or .jpg format, RFID data converted to Excel tables, etc. This is conducive to subsequent data filtering, registration and modeling work.

[0055] S1.5. Perform data filtering, registration and modeling. Perform pre-processing such as filtering, denoising, registration and alignment on the collected data. Then use 3D modeling software (such as Revit, Navisworks) to build the pre-processed data into a three-dimensional space model, including spatial geometry and equipment models such as shelves. For key facilities, more detailed models can be made by increasing sampling points to obtain more and more detailed data, and other areas can be appropriately streamlined to reduce the amount of data collection and modeling calculations.

[0056] As can be seen from the above, the digital twin method uses 3D laser scanning, RFID sensing and other means to achieve high-precision automatic collection, which greatly improves the speed and accuracy of data collection. Traditional methods can usually only obtain two-dimensional spatial plans or elevations, and cannot intuitively reflect the three-dimensional structure of the space. In addition, the digital twin method can construct a true three-dimensional spatial model, which includes spatial geometric characteristics, shelf layout, and three-dimensional models of key equipment and facilities, making the expression of spatial information more realistic and reliable.

[0057] S2. Build a digital twin library model based on the spatial data model.

[0058] In order to improve the depth and precision of modeling and spatial analysis, as well as the accuracy of space utilization assessment, it is necessary to further build a digital twin warehouse model based on the spatial data model. This step integrates the spatial model with the warehouse structure, shelf layout, logistics operation data and other information to form a digital twin model of the three-dimensional warehouse containing spatial and business information, which is the prerequisite for subsequent detailed analysis.

[0059] S2.1. Collect business data related to the vertical warehouse. Business data includes vertical warehouse structure data (aisle width, inter-layer height, etc.), shelf layout data (number of shelves, specifications, dimensions, load capacity, etc.), SKU product data (volume, weight, storage requirements, etc.) and operation process data (operation path, processing time, etc.).

[0060] S2.2. Establish a shelf and SKU (the smallest unit of goods shipped out of the warehouse) product database. Based on the collected shelf layout data and SKU product data, build a related database or data set to store the spatial attributes of each shelf and product, and provide data support for modeling space utilization and operation processes.

[0061] S2.3. Construct a vertical warehouse structural model and further form a digital model containing the vertical warehouse spatial geometric structure. Based on the spatial digital model, extract the main spatial structure (such as passages, stair platforms, elevator shafts, etc.) and establish a structural model. The structural model and the spatial model are overlapped to form a digital model containing the spatial geometric structure.

[0062] S2.4, introduce shelf and SKU product models into the spatial digital model. According to the shelf layout data and SKU product database, place shelf and product models in the spatial digital model, and specify the spatial dimensions, center of gravity position and other information of each model. This step realizes the combination of spatial model, shelf model and product model.

[0063] S2.5. Construct an operation process model. Based on the collected operation process data, on the digital model integrating the spatial geometry and shelf layout, establish process models for different operations, such as the inbound process model, outbound process model, and shelf process model. The process model associates each process step with the spatial elements.

[0064] S2.6. Realize virtual roaming and interaction of the model. After obtaining the digital twin library model, develop corresponding algorithms or systems to realize virtual roaming and interaction of the model, such as realizing free movement and observation in the model space, selecting a certain product to obtain its attribute information, triggering a certain operation process to simulate the effect, etc. This is conducive to verifying the accuracy and application effect of the model.

[0065] S3. Evaluate and analyze warehouse utilization based on spatial data models.

[0066] This step uses space division and measurement methods to evaluate the utilization rate of the vertical warehouse space, and compares and analyzes the storage utilization rates of different areas to find out areas where space is underutilized or wasted. This can provide decision support for space optimization and transformation.

[0067] S3.1. Divide the evaluation space into units. According to the structural characteristics and management requirements of the vertical warehouse space, divide the space into evaluation units of a certain size, such as dividing it into different space units by layer, with each unit area of ​​50-100 square meters. This is conducive to distinguishing the changes in storage utilization in different areas.

[0068] Spatial unit division is the process of building basic units for spatial analysis. It selects appropriate division methods and elements according to spatial structure, management requirements, etc., divides relatively independent but interrelated subspaces on the spatial model, adds attribute information to each subspace, and intuitively presents the division results through graphical expression. After that, it is continuously revised by soliciting expert opinions and field verification, and finally obtains a high-quality spatial unit division scheme for spatial analysis. The quality of spatial unit division directly determines the depth and accuracy of subsequent spatial analysis, so its implementation process requires careful design and repeated verification. The specific steps are as follows.

[0069] S3.1.1. Select a partitioning method based on spatial characteristics, such as structural partitioning, product partitioning, or mixed partitioning, and select partitioning elements that can clearly define the unit range.

[0070] S3.1.2. Set the range of unit quantity according to the spatial area, conduct trial division on the spatial model, evaluate whether the unit boundaries and area meet the requirements, and adjust the method and correct the quantity if necessary and conduct trial division again.

[0071] S3.1.3. When the trial division effect meets the requirements, confirm the final division plan of the space unit, add attribute information to each unit and express it graphically to facilitate management personnel to understand and apply it.

[0072] S3.1.4. Invite experts to review the division plan, raise questions or suggestions for improvement during use, and conduct field verification when necessary to determine the practicality and effectiveness of the plan, and continuously revise and improve it until high-quality spatial unit division results are obtained.

[0073] Therefore, the effect of spatial unit division comes from careful design and repeated verification. It is necessary to constantly compare the spatial model with the site, and integrate expert opinions and management requirements to successfully complete the high-quality spatial unit division work. The quality of the division results directly affects the depth and accuracy of spatial analysis. Although the workload is large, it is also crucial as the foundation of spatial analysis. Only high-quality spatial unit division can play the greatest role in subsequent work and realize the spatial analysis and optimization of digital twin warehouses.

[0074] S3.2. Calculate the volume of the unit space. Determine the spatial scope of each evaluation unit on the digital twin warehouse model and calculate its spatial volume, that is, the total volume enclosed by the unit in three dimensions, in cubic meters. This volume should exclude the volume of the main structure of the warehouse (staircase, elevator shaft, etc.).

[0075] S3.3. Query all SKU products stored in the unit. In the digital twin library model, query the list of all SKU products stored in each evaluation unit, obtain the volume data of each product, and sum up the volumes of different products to obtain the total volume of all products in the unit.

[0076] S3.4. Calculate the storage utilization rate. Divide the total product volume of each evaluation unit by the spatial volume of the unit to obtain the storage utilization rate, which is usually expressed as a percentage. If the utilization rate of a unit is low, it indicates that the space in the area is underutilized or has a large potential for waste.

[0077] S3.5. Analyze differences in storage utilization. Compare storage utilization rates of different evaluation units, identify areas with higher or lower utilization rates, and analyze the reasons for the differences. Experience from high-utilization areas can be replicated in low-utilization areas, which is conducive to the overall improvement of space optimization.

[0078] S3.6. Evaluate the rationality of the space partitioning scheme. If the warehouse adopts a space partitioning scheme to store products of different types or attributes separately, it is possible to evaluate whether the storage utilization rate of each partition is optimal and whether there are areas with redundant space, which can provide a basis for optimizing the space partitioning scheme.

[0079] The above warehouse utilization analysis evaluates the utilization efficiency of each space unit through the warehouse utilization algorithm. Different utilization standards are set according to the unit height, such as 70% for low floors, 85% for middle floors, and 90% for high floors. The utilization rate of each unit is compared with the standard. Units above the standard are rated as efficient, and units below the standard are rated as inefficient.

[0080] Scoring and sorting: Based on the utilization evaluation results, give each space unit a score of 1-5. 1 point represents inefficient utilization and 5 points represent efficient utilization. And sort the space units from high to low according to the score to find out the areas with high and low utilization efficiency.

[0081] Analysis results: Select high- and low-scoring space units, analyze their unit attributes, product types, and layouts, and find out the factors that affect utilization. The experience of high-scoring units can be promoted, and the shortcomings of low-scoring units need to be improved.

[0082] Propose improvement plans: Based on the analysis results, propose improvement plans for low-scoring units, such as adjusting shelf spacing, changing storage layout, clearing useless equipment, etc., to improve storage utilization. The plan needs to take into account product characteristics and operating procedures to ensure the feasibility of implementation.

[0083] In this way, this solution uses space division and measurement methods to evaluate the utilization rate of the vertical warehouse space, which not only realizes the visualization of space utilization, but also can calculate the storage utilization rate of the vertical warehouse from different dimensions such as shelf use, channel utilization, and overall space. The existing technology mainly stays in the rough analysis of the overall storage utilization rate. This solution has a significant improvement in the multi-dimensionality and precision of space utilization. On the other hand, this solution compares and analyzes the storage utilization rate of different areas, and can find out the areas where space is underutilized or wasted, thereby providing decision support for space optimization and transformation.

[0084] The present invention is described above by way of example in conjunction with the accompanying drawings. It is obvious that the specific implementation of the present invention is not limited to the above-mentioned method. As long as various non-substantial improvements are made using the inventive concept and technical solution of the present invention, or the inventive concept and technical solution are directly applied to other occasions without improvement, they are all within the protection scope of the present invention.

Claims

1. A warehouse utilization evaluation method based on a digital twin model, characterized by: The following steps are involved: S1. Collect and model the vertical library space data; S2. Build a digital twin library model based on the spatial data model; S3. Evaluation and analysis of storage utilization based on the spatial data model. This step uses spatial division and measurement methods to evaluate the utilization of the vertical warehouse space, and compares and analyzes the storage utilization rates of different areas to find out areas where space is underutilized or wasted; The step S3 comprises: S3.

1. Divide the evaluation space into units. According to the structural characteristics and management requirements of the vertical library space, divide the space into evaluation units of a certain size; S3.

2. Calculate the volume of the unit space, determine the spatial range of each evaluation unit on the digital twin library model, and calculate its spatial volume; S3.

3. Query all SKU products stored in the unit, query the list of all SKU products stored in each evaluation unit in the digital twin library model, obtain the volume data of each product, and sum up the volumes of different products to obtain the total volume of all products in the unit; S3.

4. Calculate the storage utilization rate by dividing the total product volume of each evaluation unit by the spatial volume of the unit to obtain the storage utilization rate; S3.

5. Analyze differences in storage utilization, compare storage utilization of different assessment units, identify areas with higher or lower utilization, and analyze the reasons for the differences; S3.

6. Evaluate the rationality of the space zoning scheme, whether the storage utilization rate of each zone is optimal, and whether there are areas with redundant space; The step S3.1 includes: spatial division, storing products of different types or attributes separately, dividing them into different spatial units by layer, demarcating relatively independent but interrelated subspaces on the spatial model according to the spatial structure and management requirements, adding attribute information to each subspace, and visually presenting the division results through graphical expression; then continuously revising by soliciting expert opinions and field verification, and finally obtaining a high-quality spatial unit division plan for spatial analysis; The step S3.1 specifically includes the following steps: S3.1.

1. Select the partitioning method based on the spatial characteristics and select the partitioning elements that can clearly define the unit range; S3.1.

2. Set the unit quantity range according to the spatial area, conduct trial division on the spatial model, evaluate whether the unit boundaries and area meet the requirements, and adjust the method and correct the quantity and conduct trial division again if necessary; S3.1.

3. When the test division effect meets the requirements, confirm the final division plan of the spatial unit, add attribute information to each unit and express it graphically; S3.1.

4. Invite experts to review the division plan and, if necessary, conduct field verification to determine the practicability and effectiveness of the plan, and continuously revise and improve it until a high-quality spatial unit division result is obtained; The step S1 collects the spatial data inside the vertical library and models the data in a certain format to form a digital vertical library spatial model, which specifically includes the following sub-steps: S1.

1. Select spatial data acquisition equipment; S1.2, define the scope and granularity of data collection; S1.3, collect spatial data; S1.

4. Normalize the collected data to a standard format; S1.5, perform data filtering, registration and modeling; The step S2 integrates the spatial model with the structure of the three-dimensional warehouse, shelf layout, logistics operation data and other information to form a digital twin model of the three-dimensional warehouse containing spatial and business information, which specifically includes the following sub-steps: S2.

1. Collect relevant business data of the library; S2.

2. Establish shelf and SKU product database; S2.3, construct a vertical warehouse structure model and further form a digital model including the vertical warehouse space geometry structure; S2.4, introduce shelf and SKU product models into the spatial digital model; S2.5, build a work process model; S2.

6. Realize virtual roaming and interaction of the model.

2. The method for evaluating storage utilization rate of a digital twin model according to claim 1, characterized in that: The division method is a structural division method, a product division method or a mixed division method.

3. The method for evaluating storage utilization rate of a digital twin model according to claim 1 or 2, characterized in that: The utilization efficiency of each space unit is evaluated through the warehouse utilization algorithm. Different utilization standards are set according to the unit height. The utilization of each unit is compared with the standard. Units above the standard are rated as efficient, and units below the standard are rated as inefficient.

4. The method for evaluating storage utilization rate of a digital twin model according to claim 3 is characterized in that: Based on the utilization evaluation results, each space unit is scored. The utilization rate of the space unit from low to high corresponds to the score from few to many. The space units are sorted from high to low according to the score to find out the areas with high and low utilization efficiency. The space units with high and low scores are selected, and their unit properties, product types and layout are analyzed to find out the factors affecting the utilization rate. The experience of high-scoring units is extended to space units in similar conditions, and the shortcomings of low-scoring units need to be improved.

5. The method for evaluating storage utilization rate of a digital twin model according to claim 4, characterized in that: Based on the analysis results, improvement plans are proposed for low-scoring units to improve warehouse utilization by adjusting shelf spacing, changing storage layout, and clearing out useless equipment.

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