Warehouse management method, system, equipment and medium based on industrial Internet of Things

Abstract

The present application discloses a warehouse management method, system, device and medium based on the industrial Internet of Things, and relates to the technical field of the industrial Internet of Things. The method includes: obtaining the perception data of the warehouse, and generating a three-dimensional model of the warehouse according to the perception data; obtaining the environmental monitoring data of each monitoring point and the environmental source parameter data corresponding to each environmental source, and determining the environmental parameter data inside the warehouse according to the three-dimensional model, the environmental monitoring data and the environmental source parameter data; dividing the warehouse into at least one storage area according to the environmental parameter data; obtaining the product parameter data of the product to be stored, and determining the target storage area of ​​the product to be stored according to the product parameter data and the at least one storage area; generating a target transport instruction according to the target storage area to transport the product to be stored to the target storage area. The present application has the effect of improving the intelligent level of warehouse management.

Description

Technical Field

[0001] The present application relates to the technical field of industrial Internet of Things, and in particular to a warehouse management method, system, equipment and medium based on the industrial Internet of Things. Background Art

[0002] As a core component of modern logistics and supply chain management, warehouse management efficiency is directly related to enterprise costs and service quality. At present, many companies still rely mainly on manual operation modes in warehouse management, including manual handling of products and manual operation using basic machines. Although this traditional method has adapted to past operational needs to a certain extent, its limitations in efficiency and flexibility have gradually emerged. Manual handling not only involves high labor costs, but is also prone to errors, delays and safety hazards, while machine operation often requires additional manual intervention, making it difficult to achieve true automation.

[0003] In the current industry environment, despite the rapid development of information technology and automation equipment, many companies have not been able to effectively integrate these technologies into warehouse management. The lack of intelligence has caused a large number of companies to still rely on traditional manual records and manual operations in real-time data collection, cargo tracking, and inventory management. This inefficient management model limits the timeliness and accuracy of data, making it impossible for the decision-making process to fully rely on real operational data.

[0004] More importantly, the lagging level of informatization makes it impossible for enterprises to fully monitor and optimize warehouse operations, making it difficult to respond to market changes and customer needs, resulting in low inventory turnover and long service response time. Although advanced technology applications have begun to gradually emerge in some fields, the popularization and in-depth application of these technologies are still insufficient. Therefore, under the above background, the current level of intelligentization, automation and informatization of warehouse management is low, and the overall efficiency of warehouse operations needs to be improved. Summary of the invention

[0005] In order to improve the level of intelligence in warehouse management, the present application provides a warehouse management method, system, equipment and medium based on the Industrial Internet of Things.

[0006] In the first aspect, the present application provides a warehouse management method based on the industrial Internet of Things, which adopts the following technical solutions:

[0007] The warehouse management method based on the industrial Internet of Things is applied to the industrial Internet of Things system, wherein the industrial Internet of Things system includes a management platform, a sensor network platform and an object platform which are sequentially connected in communication. The method is executed by the management platform and includes:

[0008] Acquire perception data of the warehouse, and generate a three-dimensional model of the warehouse according to the perception data;

[0009] Acquire environmental monitoring data of each monitoring point and environmental source parameter data corresponding to each environmental source, and determine the environmental parameter data inside the warehouse according to the three-dimensional model, the environmental monitoring data and the environmental source parameter data;

[0010] Dividing the warehouse into at least one storage area according to the environmental parameter data;

[0011] Acquire product parameter data of the product to be stored, and determine a target storage area for the product to be stored according to the product parameter data and the at least one storage area;

[0012] A target transport instruction is generated according to the target storage area to transport the products to be stored to the target storage area.

[0013] By adopting the above technical solution, the perception data of the warehouse is first obtained, and a three-dimensional model of the warehouse is generated according to the perception data, then the environmental monitoring data of each monitoring point and the environmental source parameter data corresponding to each environmental source are obtained, and the environmental parameter data inside the warehouse is determined according to the three-dimensional model, the environmental monitoring data and the environmental source parameter data, and then the warehouse is divided into at least one storage area according to the environmental parameter data, and then the product parameter data of the product to be stored is obtained, and the target storage area for the product to be stored is determined according to the product parameter data and at least one storage area, and finally a target transportation instruction is generated according to the target storage area to transport the product to be stored to the target storage area; through the above method, the intelligent division of the warehouse area and the automatic transportation of the product to be stored are realized, which improves the intelligence, automation and information level of warehouse management, and thus improves the overall efficiency of warehouse operation.

[0014] Optionally, the perception data includes image data and scan data, and the step of generating the three-dimensional model of the warehouse according to the perception data includes:

[0015] Performing a corresponding preprocessing operation on the image data according to the image data to obtain first preprocessing data;

[0016] Performing a corresponding preprocessing operation on the scan data according to the scan data to obtain second preprocessing data;

[0017] Performing data fusion on the first preprocessed data and the second preprocessed data according to the first preprocessed data and the second preprocessed data to obtain corresponding fused data;

[0018] Three-dimensional reconstruction is performed based on the fused data to obtain a three-dimensional model of the warehouse.

[0019] By adopting the above technical solution, in order to generate a three-dimensional model of the warehouse, first, a corresponding preprocessing operation is performed on the image data according to the image data to obtain first preprocessed data, and then a corresponding preprocessing operation is performed on the scan data according to the scan data to obtain second preprocessed data, and then the first preprocessed data and the second preprocessed data are fused according to the first preprocessed data and the second preprocessed data to obtain corresponding fused data, and finally three-dimensional reconstruction is performed based on the fused data to obtain the three-dimensional model of the warehouse.

[0020] Optionally, the environmental monitoring data includes temperature monitoring data and wind speed monitoring data, the environmental parameter data includes temperature parameter data and wind speed parameter data, the environmental source includes a heat source and a wind source, the environmental source parameter data includes heat source parameter data corresponding to the heat source and wind source parameter data corresponding to the wind source, and the step of determining the environmental parameter data inside the warehouse according to the three-dimensional model, the environmental monitoring data and the environmental source parameter data includes:

[0021] Acquire model training data, the model training data including historical temperature monitoring data, historical wind speed monitoring data, historical heat source parameter data corresponding to the heat source, and historical wind source parameter data of the wind source, and divide the model training data into a training set and a validation set according to a preset ratio;

[0022] Training the pre-built environmental parameter model according to the training set to obtain a pre-trained environmental parameter model;

[0023] Verifying the pre-trained environmental parameter model according to the verification set to obtain a pre-generated environmental parameter model;

[0024] Determine the feature points in the warehouse according to a preset coordinate collection method and the three-dimensional model, and generate corresponding coordinate point sets according to the feature point coordinates corresponding to the feature points;

[0025] The coordinates of the feature points in the coordinate point set are input into the pre-generated environmental parameter model to obtain the temperature parameter data and the wind speed parameter data of each of the feature points.

[0026] By adopting the above technical solution, in order to obtain the temperature parameter data and wind speed parameter data inside the warehouse, the model training data is first obtained, and the model training data includes historical temperature monitoring data, historical wind speed monitoring data, historical heat source parameter data corresponding to the heat source, and historical wind source parameter data of the wind source, and the model training data is divided into a training set and a verification set according to a preset ratio, and then the pre-constructed environmental parameter model is trained according to the training set to obtain a pre-trained environmental parameter model, and then the pre-trained environmental parameter model is verified according to the verification set to obtain a pre-generated environmental parameter model, and then the feature point coordinates corresponding to the feature points in the warehouse are determined according to the preset coordinate acquisition method and the three-dimensional model, and the corresponding coordinate point set is generated according to the feature point coordinates, and finally the feature point coordinates in the coordinate point set are input into the pre-generated environmental parameter model to obtain the temperature parameter data and wind speed parameter data of each feature point.

[0027] Optionally, the environmental parameter data includes illumination data, the environmental source includes a light source, the environmental source parameter data includes light source parameter data corresponding to the light source, and the step of determining the environmental parameter data inside the warehouse according to the three-dimensional model, the environmental monitoring data and the environmental source parameter data includes:

[0028] Determining the light source type of each of the light sources in the warehouse according to the environmental source parameter data, and determining the illumination calculation model of each of the light sources according to the light source type;

[0029] Acquire coordinate data of each of the light sources according to the three-dimensional model, and determine distance data between each of the light sources and the feature point according to the coordinate data and the coordinate point set;

[0030] For each of the feature points, determine a single theoretical illuminance of each light source irradiating the feature point according to the distance data and the illumination calculation model, and determine a theoretical illuminance of the feature point according to the single theoretical illuminance;

[0031] The corrected illumination of each of the feature points is determined according to the theoretical illumination, and the corrected illumination is used as illumination data inside the warehouse.

[0032] By adopting the above technical scheme, the light source type of each light source in the warehouse is first determined according to the environmental source parameter data, and the illumination calculation model of each light source is determined according to the light source type, and then the coordinate data of each light source is obtained according to the three-dimensional model, and the distance data between each light source and the feature point is determined according to the coordinate data and the coordinate point set, and then for each feature point, the single theoretical illuminance of each light source irradiating the feature point is determined according to the distance data and the illumination calculation model, and the theoretical illuminance of the feature point is determined according to the single theoretical illuminance, and finally the corrected illuminance of each feature point is determined according to the theoretical illuminance, and the corrected illuminance is used as the illumination data inside the warehouse.

[0033] Optionally, the step of dividing the warehouse into at least one storage area according to the environmental parameter data includes:

[0034] Determine the maximum temperature and the minimum temperature according to the temperature parameter data of each characteristic point, determine the maximum wind speed and the minimum wind speed according to the wind speed parameter data of each characteristic point, and determine the maximum illumination and the minimum illumination according to the illumination of each characteristic point;

[0035] Determine a temperature division threshold value according to the maximum temperature value and the minimum temperature value, determine a wind speed division threshold value according to the maximum wind speed value and the minimum wind speed value, and determine an illumination division threshold value according to the maximum illumination value and the minimum illumination value;

[0036] Constructing an environmental feature vector of each feature point according to the temperature parameter data, wind speed parameter data and illumination of each feature point, and performing cluster analysis on the internal space of the warehouse according to the environmental feature vector, the temperature division threshold, the wind speed division threshold and the illumination division threshold to obtain a corresponding clustering result;

[0037] The warehouse is divided into at least one storage area according to the clustering result.

[0038] By adopting the above technical scheme, in order to realize the division of the internal area of ​​the warehouse, the maximum temperature and the minimum temperature are first determined according to the temperature parameter data of each feature point, and the maximum wind speed and the minimum wind speed are determined according to the wind speed parameter data of each feature point, and the maximum illuminance and the minimum illuminance are determined according to the illuminance of each feature point, and then the temperature division threshold is determined according to the maximum temperature and the minimum temperature, and the wind speed division threshold is determined according to the maximum wind speed and the minimum wind speed, and the illuminance division threshold is determined according to the maximum illuminance and the minimum illuminance, and then the environmental feature vector of each feature point is constructed according to the temperature parameter data, wind speed parameter data and illuminance of each feature point, and the internal space of the warehouse is clustered according to the environmental feature vector, the temperature division threshold, the wind speed division threshold and the illuminance division threshold to obtain the corresponding clustering results, and finally the warehouse is divided into at least one storage area according to the clustering results.

[0039] Optionally, the step of determining a target storage area for the product to be stored according to the product parameter data and the at least one storage area includes:

[0040] Determine the temperature storage condition, wind speed storage condition and light storage condition of the product to be stored according to the product parameter data;

[0041] Determine the regional environmental parameters of the at least one storage area according to the clustering result, and determine at least one area to be selected from the at least one storage area according to the regional environmental parameters, the temperature preservation condition, the wind speed preservation condition, and the light preservation condition;

[0042] Acquire the starting transport position of the product to be stored, and determine the transport distance between the at least one area to be selected and the starting transport position according to the at least one area to be selected and the three-dimensional model;

[0043] For the at least one area to be selected, the area to be selected corresponding to the minimum transportation distance is used as the target storage area.

[0044] By adopting the above technical scheme, in order to determine the target storage area, the temperature preservation conditions, wind speed preservation conditions and light preservation conditions of the products to be stored are first determined according to the product parameter data, and then the regional environmental parameters of at least one storage area are determined according to the clustering results, and at least one to-be-selected area is determined from at least one storage area according to the regional environmental parameters, temperature preservation conditions, wind speed preservation conditions and light preservation conditions, and then the starting transportation position of the products to be stored is obtained, and the transportation distance between at least one to-be-selected area and the starting transportation position is determined according to at least one to-be-selected area and the three-dimensional model, and finally, for at least one to-be-selected area, the to-be-selected area corresponding to the smallest transportation distance is used as the target storage area.

[0045] Optionally, the step of determining at least one area to be selected from the at least one storage area according to the regional environmental parameters, the temperature storage condition, the wind speed storage condition and the light storage condition includes:

[0046] Determine regional temperature data, regional wind speed data, and regional lighting data of the at least one storage area according to the regional environmental parameters;

[0047] Determine a temperature matching value of the at least one storage area according to the regional temperature data and the temperature preservation condition, determine a wind speed matching value of the at least one storage area according to the regional wind speed data and the wind speed preservation condition, and determine an illumination matching value of the at least one storage area according to the regional illumination data and the illumination preservation condition;

[0048] For each storage area, the comprehensive matching value of the storage area is determined according to the preset temperature weight, the preset wind speed weight, the preset light weight, the temperature matching value, the wind speed matching value and the illumination matching value, and it is judged whether the comprehensive matching value is greater than the preset value. If so, the storage area is selected as the area to be selected.

[0049] By adopting the above technical scheme, in order to determine the area to be selected, the regional temperature data, regional wind speed data and regional lighting data of at least one storage area are first determined according to the regional environmental parameters, and then the temperature matching value of at least one storage area is determined according to the regional temperature data and the temperature preservation conditions, and the wind speed matching value of at least one storage area is determined according to the regional wind speed data and the wind speed preservation conditions, and the illumination matching value of at least one storage area is determined according to the regional illumination data and the illumination preservation conditions. Finally, for each storage area, the comprehensive matching value of the storage area is determined according to the preset temperature weight, the preset wind speed weight, the preset illumination weight, the temperature matching value, the wind speed matching value and the illumination matching value, and it is judged whether the comprehensive matching value is within the preset range. If the comprehensive matching value is within the preset range, the storage area is used as the area to be selected.

[0050] On the second aspect, this application also provides a warehouse management system based on industrial Internet of Things, which adopts the following technical solutions:

[0051] The warehouse management system based on the industrial Internet of Things includes a management platform, a sensor network platform and an object platform which are sequentially connected in communication, and the management platform is configured with:

[0052] A three-dimensional model generation module, used to obtain the perception data of the warehouse and generate a three-dimensional model of the warehouse according to the perception data;

[0053] An environmental parameter data generation module, used to obtain environmental monitoring data of each monitoring point and environmental source parameter data corresponding to each environmental source, and determine the environmental parameter data inside the warehouse according to the three-dimensional model, the environmental monitoring data and the environmental source parameter data;

[0054] A storage area division module, used for dividing the warehouse into at least one storage area according to the environmental parameter data;

[0055] A target storage area determination module, used to obtain product parameter data of the product to be stored, and determine the target storage area of ​​the product to be stored according to the product parameter data and the at least one storage area;

[0056] The transport module is used to generate a target transport instruction according to the target storage area to transport the products to be stored to the target storage area.

[0057] In a third aspect, the present application also provides a computer device, which adopts the following technical solution:

[0058] A computer device comprises a memory and a processor, wherein the memory stores a computer program executable on the processor, and the processor implements the method described in the first aspect when executing the computer program.

[0059] In a fourth aspect, the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0060] A computer-readable storage medium stores a computer program that can be loaded by a processor and execute the method described in the first aspect.

[0061] To summarize, the present application includes at least the following beneficial technical effects: first, the perception data of the warehouse is obtained, and a three-dimensional model of the warehouse is generated based on the perception data, then the environmental monitoring data of each monitoring point and the environmental source parameter data corresponding to each environmental source are obtained, and the environmental parameter data inside the warehouse is determined based on the three-dimensional model, the environmental monitoring data and the environmental source parameter data, and then the warehouse is divided into at least one storage area based on the environmental parameter data, and then the product parameter data of the products to be stored is obtained, and the target storage area for the products to be stored is determined based on the product parameter data and at least one storage area, and finally, a target handling instruction is generated based on the target storage area to transport the products to be stored to the target storage area; through the above method, the intelligent division of warehouse areas and the automatic handling of products to be stored are realized, which improves the intelligence, automation and informatization level of warehouse management, thereby improving the overall efficiency of warehouse operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a schematic diagram of the overall process of an embodiment of the present application.

[0063] Figure 2 It is a structural diagram of one application scenario of the system of an embodiment of the present application.

[0064] Figure 3 It is a structural diagram of another application scenario of the system of an embodiment of the present application.

[0065] Figure 4 It is a structural block diagram of the computer device of the present application. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical solutions and advantages of this application more clear, the following Figure 1-Figure 4 It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0067] The embodiment of the present application discloses a warehouse management method based on industrial Internet of Things.

[0068] Reference Figure 1 The invention discloses a warehouse management method based on industrial Internet of Things, which is applied to an industrial Internet of Things system. The industrial Internet of Things system includes a management platform, a sensor network platform and an object platform which are sequentially connected in communication. The method is executed by the management platform, and includes:

[0069] Step S11, acquiring the perception data of the warehouse, and generating a three-dimensional model of the warehouse according to the perception data.

[0070] Step S12, obtaining the environmental monitoring data of each monitoring point and the environmental source parameter data corresponding to each environmental source, and determining the environmental parameter data inside the warehouse according to the three-dimensional model, the environmental monitoring data and the environmental source parameter data.

[0071] It is understandable that in a warehouse, environmental sources usually include heat sources, wind sources, light sources, etc. Common heat sources in a warehouse include equipment that generates heat during operation, air conditioners, etc.; wind sources in a warehouse usually include fans, air conditioners, and open windows; light sources in a warehouse usually include natural light sources and artificial light sources. Natural light sources include natural light that shines into the warehouse from windows, and artificial light sources include various lamps in the warehouse.

[0072] Step S13, dividing the warehouse into at least one storage area according to the environmental parameter data.

[0073] Step S14, obtaining product parameter data of the product to be stored, and determining a target storage area for the product to be stored based on the product parameter data and at least one storage area.

[0074] Step S15, generating a target transport instruction according to the target storage area to transport the products to be stored to the target storage area.

[0075] In the above implementation, the perception data of the warehouse is first obtained, and a three-dimensional model of the warehouse is generated based on the perception data. Then, the environmental monitoring data of each monitoring point and the environmental source parameter data corresponding to each environmental source are obtained, and the environmental parameter data inside the warehouse is determined based on the three-dimensional model, the environmental monitoring data and the environmental source parameter data. Then, the warehouse is divided into at least one storage area based on the environmental parameter data. Then, the product parameter data of the products to be stored in the warehouse are obtained, and the target storage area for the products to be stored in the warehouse is determined based on the product parameter data and at least one storage area. Finally, a target transportation instruction is generated based on the target storage area to transport the products to be stored in the warehouse to the target storage area. Through the above method, the intelligent division of warehouse areas and the automatic transportation of products to be stored in the warehouse are realized, which improves the intelligence, automation and informatization level of warehouse management, thereby improving the overall efficiency of warehouse operations.

[0076] As a further implementation of the management method, the sensed data includes image data and scan data, and the step of generating a three-dimensional model of the warehouse according to the sensed data includes:

[0077] Step S21, performing corresponding preprocessing operations on the image data according to the image data to obtain first preprocessing data.

[0078] It should be noted that in step S21, the preprocessing operation includes operations such as image denoising, image enhancement, image scaling, and image cropping.

[0079] Step S22, performing corresponding preprocessing operations on the scanned data according to the scanned data to obtain second preprocessed data.

[0080] It should be noted that in step S22, the preprocessing operations include denoising, data alignment, missing value filling, feature extraction and other operations.

[0081] Step S23: fusing the first preprocessed data and the second preprocessed data according to the first preprocessed data and the second preprocessed data to obtain corresponding fused data.

[0082] Step S24, performing three-dimensional reconstruction based on the fused data to obtain a three-dimensional model of the warehouse.

[0083] In the above embodiment, in order to generate a three-dimensional model of the warehouse, the image data is first preprocessed accordingly to obtain first preprocessed data, and the scan data is preprocessed accordingly to obtain second preprocessed data, and then the first preprocessed data and the second preprocessed data are fused according to the first preprocessed data and the second preprocessed data to obtain corresponding fused data, and finally three-dimensional reconstruction is performed based on the fused data to obtain the three-dimensional model of the warehouse.

[0084] As a further implementation method of the management method, the environmental monitoring data includes temperature monitoring data and wind speed monitoring data, the environmental parameter data includes temperature parameter data and wind speed parameter data, the environmental source includes a heat source and a wind source, and the environmental source parameter data includes heat source parameter data corresponding to the heat source and wind source parameter data corresponding to the wind source. The step of determining the environmental parameter data inside the warehouse according to the three-dimensional model, the environmental monitoring data and the environmental source parameter data includes:

[0085] Step S31, obtaining model training data, the model training data including historical temperature monitoring data, historical wind speed monitoring data, historical heat source parameter data corresponding to the heat source and historical wind source parameter data of the wind source, and dividing the model training data into a training set and a validation set according to a preset ratio.

[0086] It is understandable that the preset ratio is usually set according to the actual situation, for example, the ratio of the training set to the validation set is set to 7:3 or 8:2.

[0087] Step S32, training the pre-built environmental parameter model according to the training set to obtain a pre-trained environmental parameter model.

[0088] Step S33, verifying the pre-trained environmental parameter model according to the verification set to obtain a pre-generated environmental parameter model.

[0089] Step S34, determining feature points in the warehouse according to a preset coordinate collection method and a three-dimensional model, and generating corresponding coordinate point sets according to feature point coordinates corresponding to the feature points.

[0090] It should be noted that in this embodiment, in order to collect feature points, the internal space of the warehouse is usually divided into several three-dimensional spaces according to the three-dimensional model of the warehouse. The shapes of these three-dimensional spaces are usually rectangles, prisms and other shapes with significant features. Then, based on the shapes of these three-dimensional spaces, these three-dimensional spaces are divided into grids, and feature points are collected according to the intersections, center points or other important positions of the network.

[0091] Step S35, inputting the coordinates of the feature points in the coordinate point set into the pre-generated environmental parameter model to obtain the temperature parameter data and wind speed parameter data of each feature point.

[0092] In the above embodiment, in order to obtain the temperature parameter data and wind speed parameter data inside the warehouse, the model training data is first obtained, and the model training data includes historical temperature monitoring data, historical wind speed monitoring data, historical heat source parameter data corresponding to the heat source, and historical wind source parameter data of the wind source, and the model training data is divided into a training set and a verification set according to a preset ratio, and then the pre-constructed environmental parameter model is trained according to the training set to obtain a pre-trained environmental parameter model, and then the pre-trained environmental parameter model is verified according to the verification set to obtain a pre-generated environmental parameter model, and then the feature point coordinates corresponding to the feature points in the warehouse are determined according to the preset coordinate acquisition method and the three-dimensional model, and the corresponding coordinate point set is generated according to the feature point coordinates, and finally the feature point coordinates in the coordinate point set are input into the pre-generated environmental parameter model to obtain the temperature parameter data and wind speed parameter data of each feature point.

[0093] As a further implementation of the management method, the environmental parameter data includes illumination data, the environmental source includes a light source, and the environmental source parameter data includes light source parameter data corresponding to the light source. The step of determining the environmental parameter data inside the warehouse according to the three-dimensional model, the environmental monitoring data and the environmental source parameter data includes:

[0094] Step S41, determining the light source type of each light source in the warehouse according to the environmental source parameter data, and determining the illumination calculation model of each light source according to the light source type.

[0095] It should be noted that in a warehouse, light sources usually include lighting fixtures and natural light that enters the warehouse from windows; light source types usually include point light sources, parallel light sources and surface light sources. Light bulbs and LED lights in the warehouse are usually regarded as point light sources; natural light or sunlight that enters the warehouse from windows are usually regarded as parallel light sources; lighting fixtures such as panel lights are usually regarded as surface light sources.

[0096] It should be further explained that for point light sources, the calculation formula is:

[0097]

[0098] in, E Indicates illumination, I Indicates the light intensity of the light source. d Indicates the distance between the point light source and the receiving surface.

[0099] For parallel light sources, the calculation formula is:

[0100]

[0101] in, E Indicates illumination, I Indicates the light intensity of the light source. In a parallel light source, the light source is a surface light source. Represents the angle between the light ray and the normal of the receiving surface.

[0102] For surface light sources, the calculation formula is:

[0103]

[0104] in, E Indicates illumination, Represents the light intensity distribution function, usually provided by the light source specification; θ represents the angle between the light and the normal of the receiving surface, d Represents the distance between the point light source and the receiving surface, It represents the azimuth, that is, the rotation angle of the light in the plane perpendicular to the normal of the receiving surface. dA represents a small area element on the receiving surface, which is used for integral calculation.

[0105] Step S42, acquiring coordinate data of each light source according to the three-dimensional model, and determining distance data between each light source and the feature point according to the coordinate data and the coordinate point set.

[0106] Step S43: for each feature point, determine the single theoretical illumination of each light source irradiating the feature point according to the distance data and the illumination calculation model, and determine the theoretical illumination of the feature point according to the single theoretical illumination.

[0107] It can be understood that, for a specific feature point, the theoretical illuminance of the feature point is obtained by summing up the single theoretical illuminances.

[0108] Step S44, determining the corrected illumination of each feature point according to the theoretical illumination, and using the corrected illumination as illumination data inside the warehouse.

[0109] It should be noted that for a specific feature point, the illumination of the point is usually affected by factors such as humidity and air dust in the warehouse, so the theoretical illumination needs to be corrected.

[0110] In the above implementation, the light source type of each light source in the warehouse is first determined according to the environmental source parameter data, and the illumination calculation model of each light source is determined according to the light source type. Then, the coordinate data of each light source is obtained according to the three-dimensional model, and the distance data between each light source and the feature point is determined according to the coordinate data and the coordinate point set. Then, for each feature point, the single theoretical illuminance of each light source irradiating the feature point is determined according to the distance data and the illumination calculation model, and the theoretical illumination of the feature point is determined according to the single theoretical illuminance. Finally, the corrected illuminance of each feature point is determined according to the theoretical illuminance, and the corrected illuminance is used as the illumination data inside the warehouse.

[0111] As a further implementation of the management method, the step of dividing the warehouse into at least one storage area according to the environmental parameter data includes:

[0112] Step S51, determining the maximum temperature and the minimum temperature according to the temperature parameter data of each feature point, determining the maximum wind speed and the minimum wind speed according to the wind speed parameter data of each feature point, and determining the maximum illumination and the minimum illumination according to the illumination of each feature point.

[0113] Step S52, determining a temperature division threshold according to the maximum temperature value and the minimum temperature value, determining a wind speed division threshold according to the maximum wind speed value and the minimum wind speed value, and determining an illumination division threshold according to the maximum illumination value and the minimum illumination value.

[0114] Step S53, constructing the environmental feature vector of each feature point according to the temperature parameter data, wind speed parameter data and illumination of each feature point, and performing cluster analysis on the internal space of the warehouse according to the environmental feature vector, temperature division threshold, wind speed division threshold and illumination division threshold to obtain the corresponding clustering result.

[0115] Step S54, dividing the warehouse into at least one storage area according to the clustering result.

[0116] In the above embodiment, in order to realize the division of the internal area of ​​the warehouse, the maximum temperature and the minimum temperature are first determined according to the temperature parameter data of each feature point, and the maximum wind speed and the minimum wind speed are determined according to the wind speed parameter data of each feature point, and the maximum illuminance and the minimum illuminance are determined according to the illuminance of each feature point. Then, the temperature division threshold is determined according to the maximum temperature and the minimum temperature, and the wind speed division threshold is determined according to the maximum wind speed and the minimum wind speed, and the illuminance division threshold is determined according to the maximum illuminance and the minimum illuminance. Then, the environmental feature vector of each feature point is constructed according to the temperature parameter data, wind speed parameter data and illuminance of each feature point, and the internal space of the warehouse is clustered according to the environmental feature vector, the temperature division threshold, the wind speed division threshold and the illuminance division threshold to obtain the corresponding clustering results, and finally, the warehouse is divided into at least one storage area according to the clustering results.

[0117] As a further implementation of the management method, the step of determining a target storage area for the product to be stored according to the product parameter data and at least one storage area includes:

[0118] Step S61, determining the temperature storage conditions, wind speed storage conditions and light storage conditions of the products to be stored according to the product parameter data.

[0119] Step S62, determining regional environmental parameters of at least one storage area according to the clustering results, and determining at least one area to be selected from at least one storage area according to the regional environmental parameters, temperature preservation conditions, wind speed preservation conditions and light preservation conditions.

[0120] Step S63, obtaining the starting transport position of the product to be stored, and determining the transport distance between the at least one area to be selected and the starting transport position according to the at least one area to be selected and the three-dimensional model.

[0121] Step S64: for at least one area to be selected, the area to be selected corresponding to the minimum transportation distance is used as the target storage area.

[0122] In the above embodiment, in order to determine the target storage area, the temperature preservation conditions, wind speed preservation conditions and light preservation conditions of the products to be stored are first determined based on the product parameter data, and then the regional environmental parameters of at least one storage area are determined based on the clustering results, and at least one to-be-selected area is determined from at least one storage area based on the regional environmental parameters, temperature preservation conditions, wind speed preservation conditions and light preservation conditions, and then the starting transportation position of the products to be stored is obtained, and the transportation distance between at least one to-be-selected area and the starting transportation position is determined based on at least one to-be-selected area and the three-dimensional model, and finally, for at least one to-be-selected area, the to-be-selected area corresponding to the smallest transportation distance is used as the target storage area.

[0123] As a further implementation of the management method, the step of determining at least one area to be selected from at least one storage area according to regional environmental parameters, temperature storage conditions, wind speed storage conditions and light storage conditions includes:

[0124] Step S71, determining regional temperature data, regional wind speed data and regional lighting data of at least one storage area according to regional environmental parameters.

[0125] It should be noted that, in the present embodiment, the regional temperature data includes the regional average temperature and the regional temperature range, the regional wind speed data includes the regional average wind speed and the regional wind speed range, and the regional illumination data includes the regional average illuminance and the regional illuminance range; the regional average temperature is calculated from the temperature of each characteristic point in the region, the regional average wind speed is calculated from the wind speed of each characteristic point in the region, and the regional average illuminance is calculated from the illuminance of each characteristic point in the region; the regional temperature range is obtained from the highest temperature and the lowest temperature of the characteristic point in the region, the regional wind speed range is obtained from the highest wind speed and the lowest wind speed of the characteristic point in the region, and the regional illumination range is obtained from the highest illuminance and the lowest illuminance of the characteristic point in the region. In addition, the regional temperature data, the regional wind speed data, and the regional illumination data can also be obtained based on the environmental monitoring data of each monitoring point in the region.

[0126] Step S72, determine the temperature matching value of at least one storage area based on the regional temperature data and the temperature preservation conditions, determine the wind speed matching value of at least one storage area based on the regional wind speed data and the wind speed preservation conditions, and determine the illumination matching value of at least one storage area based on the regional lighting data and the lighting preservation conditions.

[0127] It should be noted that the temperature preservation condition includes the temperature preservation range, the wind speed preservation condition includes the wind speed preservation range, and the light preservation condition includes the illuminance preservation range; in this embodiment, in order to simplify the calculation process of the temperature matching value, the wind speed matching value, and the illuminance matching value, the regional temperature data only considers the regional average temperature, the regional wind speed data only considers the regional average wind speed, and the regional light data only considers the regional average illuminance. The calculation logic of the temperature matching value, the wind speed matching value, and the illuminance matching value can be consistent. The calculation logic of the temperature matching value is taken as an example below: Assuming that the temperature preservation range is - The average temperature of the region is , then the temperature matching value of this area T The calculation formula is:

[0128]

[0129] Step S73, for each storage area, determine the comprehensive matching value of the storage area according to the preset temperature weight, preset wind speed weight, preset light weight, temperature matching value, wind speed matching value and illumination matching value, and judge whether the comprehensive matching value is within the preset range. If so, the storage area is selected as the area to be selected.

[0130] Specifically, for each storage area, the comprehensive matching value of the storage area is determined according to the preset temperature weight, the preset wind speed weight, the preset light weight, the temperature matching value, the wind speed matching value and the illumination matching value, and it is judged whether the comprehensive matching value is within the preset range. If the comprehensive matching value is within the preset range, the storage area is selected as the area to be selected; if the comprehensive matching value is not within the preset range, the storage area is not selected as the area to be selected.

[0131] In the above embodiment, in order to determine the area to be selected, the regional temperature data, regional wind speed data and regional lighting data of at least one storage area are first determined according to the regional environmental parameters, and then the temperature matching value of at least one storage area is determined according to the regional temperature data and the temperature preservation conditions, and the wind speed matching value of at least one storage area is determined according to the regional wind speed data and the wind speed preservation conditions, and the illumination matching value of at least one storage area is determined according to the regional lighting data and the lighting preservation conditions. Finally, for each storage area, the comprehensive matching value of the storage area is determined according to the preset temperature weight, the preset wind speed weight, the preset lighting weight, the temperature matching value, the wind speed matching value and the illumination matching value, and it is judged whether the comprehensive matching value is within the preset range. If the comprehensive matching value is within the preset range, the storage area is used as the area to be selected.

[0132] The embodiments of the present application also disclose a warehouse management system based on the industrial Internet of Things.

[0133] refer to Figure 2 The warehouse management system based on the industrial Internet of Things includes a management platform, a sensor network platform and an object platform which are connected in sequence. The management platform is configured with:

[0134] A three-dimensional model generation module is used to obtain the perception data of the warehouse and generate a three-dimensional model of the warehouse based on the perception data;

[0135] The environmental parameter data generation module is used to obtain the environmental monitoring data of each monitoring point and the environmental source parameter data corresponding to each environmental source, and determine the environmental parameter data inside the warehouse based on the three-dimensional model, the environmental monitoring data and the environmental source parameter data;

[0136] A storage area division module, used to divide the warehouse into at least one storage area according to environmental parameter data;

[0137] A target storage area determination module is used to obtain product parameter data of the product to be stored, and determine the target storage area of ​​the product to be stored according to the product parameter data and at least one storage area;

[0138] The transport module is used to generate target transport instructions according to the target storage area to transport the products to be stored to the target storage area.

[0139] The overall framework of another application scenario of the warehouse management system based on industrial Internet of Things of this application is as follows Figure 3 As shown, it may include a user platform, a service platform, a management platform, a sensor network platform and an object platform that interact in sequence, forming a five-platform architecture based on the industrial Internet of Things. Among them, the management platform includes a three-dimensional model generation module, an environmental parameter data generation module, a storage area division module, a target storage area determination module and a handling module, and the sensor network platform includes several sensor network sub-platforms, each of which is provided with a sensor sub-database.

[0140] Specifically, in another application scenario mentioned above, the warehouse management system based on industrial Internet of Things includes a management platform, which is configured to: obtain the perception data of the warehouse, and generate a three-dimensional model of the warehouse based on the perception data; obtain the environmental monitoring data of each monitoring point and the environmental source parameter data corresponding to each environmental source, and determine the environmental parameter data inside the warehouse based on the three-dimensional model, the environmental monitoring data and the environmental source parameter data; divide the warehouse into at least one storage area based on the environmental parameter data; obtain the product parameter data of the products to be stored, and determine the target storage area for the products to be stored based on the product parameter data and at least one storage area; generate target transportation instructions based on the target storage area to transport the products to be stored to the target storage area.

[0141] Through the interaction between the various functional platforms of the industrial Internet of Things-based warehouse management system based on the above three platforms or five platforms, a complete closed-loop information operation logic is established, ensuring the orderly operation of perception information and control information and realizing intelligent equipment management.

[0142] The warehouse management system based on industrial Internet of Things of the present invention can implement any one of the warehouse management methods based on industrial Internet of Things, and the specific working process of the warehouse management system based on industrial Internet of Things of the present invention can refer to the corresponding process in the above-mentioned warehouse management method based on industrial Internet of Things.

[0143] The embodiment of the present application also discloses a computer device.

[0144] refer to Figure 4A computer device includes a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, any one of the above-mentioned warehouse management methods based on industrial Internet of Things is implemented.

[0145] The embodiment of the present application also discloses a computer-readable storage medium.

[0146] A computer-readable storage medium stores a computer program that can be loaded by a processor and execute any one of the above-mentioned warehouse management methods based on industrial Internet of Things.

[0147] Among them, computer-readable storage media can be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus or device; the program code contained on the computer-readable medium can be transmitted using any appropriate medium, including but not limited to wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0148] The above are all preferred embodiments of the present application, and are not intended to limit the protection scope of the present application. Any feature disclosed in this specification (including the abstract and drawings), unless otherwise stated, can be replaced by other equivalent or alternative features with similar purposes. That is, unless otherwise stated, each feature is only an example of a series of equivalent or similar features.

Claims

1. A warehouse management method based on industrial Internet of Things, characterized in that: Applied to an industrial Internet of Things system, the industrial Internet of Things system includes a management platform, a sensor network platform and an object platform that are sequentially communicatively connected, and the method is executed by the management platform, including: Acquire perception data of the warehouse, and generate a three-dimensional model of the warehouse according to the perception data; Acquire environmental monitoring data of each monitoring point and environmental source parameter data corresponding to each environmental source, and determine the environmental parameter data inside the warehouse according to the three-dimensional model, the environmental monitoring data and the environmental source parameter data; Dividing the warehouse into at least one storage area according to the environmental parameter data; Acquire product parameter data of the product to be stored, and determine a target storage area for the product to be stored according to the product parameter data and the at least one storage area; Generate a target transport instruction according to the target storage area to transport the product to be stored to the target storage area; The environmental monitoring data includes temperature monitoring data and wind speed monitoring data, the environmental parameter data includes temperature parameter data and wind speed parameter data, the environmental source includes a heat source and a wind source, the environmental source parameter data includes heat source parameter data corresponding to the heat source and wind source parameter data corresponding to the wind source, and the step of determining the environmental parameter data inside the warehouse according to the three-dimensional model, the environmental monitoring data and the environmental source parameter data includes: Acquire model training data, the model training data including historical temperature monitoring data, historical wind speed monitoring data, historical heat source parameter data corresponding to the heat source, and historical wind source parameter data of the wind source, and divide the model training data into a training set and a validation set according to a preset ratio; Training the pre-built environmental parameter model according to the training set to obtain a pre-trained environmental parameter model; Verifying the pre-trained environmental parameter model according to the verification set to obtain a pre-generated environmental parameter model; Determine the feature points in the warehouse according to a preset coordinate collection method and the three-dimensional model, and generate corresponding coordinate point sets according to the feature point coordinates corresponding to the feature points; Inputting the characteristic point coordinates, the temperature monitoring data, the wind speed monitoring data, the heat source parameter data and the wind source parameter data in the coordinate point set into the pre-generated environmental parameter model to obtain the temperature parameter data and the wind speed parameter data of each characteristic point; The environmental parameter data includes illumination data, the environmental source includes a light source, the environmental source parameter data includes light source parameter data corresponding to the light source, and the step of determining the environmental parameter data inside the warehouse according to the three-dimensional model, the environmental monitoring data and the environmental source parameter data includes: Determining the light source type of each of the light sources in the warehouse according to the environmental source parameter data, and determining the illumination calculation model of each of the light sources according to the light source type; Acquire coordinate data of each of the light sources according to the three-dimensional model, and determine distance data between each of the light sources and the feature point according to the coordinate data and the coordinate point set; For each of the feature points, determine a single theoretical illuminance of each light source irradiating the feature point according to the distance data and the illumination calculation model, and determine a theoretical illuminance of the feature point according to the single theoretical illuminance; Determining a corrected illumination of each of the feature points according to the theoretical illumination, and using the corrected illumination as illumination data inside the warehouse; The step of dividing the warehouse into at least one storage area according to the environmental parameter data comprises: Determine the maximum temperature and the minimum temperature according to the temperature parameter data of each characteristic point, determine the maximum wind speed and the minimum wind speed according to the wind speed parameter data of each characteristic point, and determine the maximum illumination and the minimum illumination according to the illumination of each characteristic point; Determine a temperature division threshold value according to the maximum temperature value and the minimum temperature value, determine a wind speed division threshold value according to the maximum wind speed value and the minimum wind speed value, and determine an illumination division threshold value according to the maximum illumination value and the minimum illumination value; Constructing an environmental feature vector of each feature point according to the temperature parameter data, wind speed parameter data and illumination of each feature point, and performing cluster analysis on the internal space of the warehouse according to the environmental feature vector, the temperature division threshold, the wind speed division threshold and the illumination division threshold to obtain a corresponding clustering result; The warehouse is divided into at least one storage area according to the clustering result.

2. The warehouse management method based on industrial Internet of Things according to claim 1 is characterized in that: The perception data includes image data and scan data, and the step of generating the three-dimensional model of the warehouse according to the perception data includes: Performing a corresponding preprocessing operation on the image data according to the image data to obtain first preprocessing data; Performing a corresponding preprocessing operation on the scan data according to the scan data to obtain second preprocessing data; Performing data fusion on the first preprocessed data and the second preprocessed data according to the first preprocessed data and the second preprocessed data to obtain corresponding fused data; Three-dimensional reconstruction is performed based on the fused data to obtain a three-dimensional model of the warehouse.

3. The warehouse management method based on industrial Internet of Things according to claim 2 is characterized in that: The step of determining the target storage area of ​​the product to be stored according to the product parameter data and the at least one storage area comprises: Determine the temperature storage condition, wind speed storage condition and light storage condition of the product to be stored according to the product parameter data; Determine the regional environmental parameters of the at least one storage area according to the clustering result, and determine at least one area to be selected from the at least one storage area according to the regional environmental parameters, the temperature preservation condition, the wind speed preservation condition, and the light preservation condition; Acquire the starting transport position of the product to be stored, and determine the transport distance between the at least one area to be selected and the starting transport position according to the at least one area to be selected and the three-dimensional model; For the at least one area to be selected, the area to be selected corresponding to the minimum transportation distance is used as the target storage area.

4. The warehouse management method based on industrial Internet of Things according to claim 3 is characterized in that: The step of determining at least one area to be selected from the at least one storage area according to the regional environmental parameters, the temperature storage condition, the wind speed storage condition and the light storage condition comprises: Determine regional temperature data, regional wind speed data, and regional lighting data of the at least one storage area according to the regional environmental parameters; Determine a temperature matching value of the at least one storage area according to the regional temperature data and the temperature preservation condition, determine a wind speed matching value of the at least one storage area according to the regional wind speed data and the wind speed preservation condition, and determine an illumination matching value of the at least one storage area according to the regional illumination data and the illumination preservation condition; For each storage area, the comprehensive matching value of the storage area is determined according to the preset temperature weight, the preset wind speed weight, the preset light weight, the temperature matching value, the wind speed matching value and the illumination matching value, and it is judged whether the comprehensive matching value is greater than the preset value. If so, the storage area is selected as the area to be selected.

5. The warehouse management system based on industrial Internet of Things is characterized by: It includes a management platform, a sensor network platform and an object platform which are connected in sequence, and the management platform is configured with: A three-dimensional model generation module, used to obtain the perception data of the warehouse and generate a three-dimensional model of the warehouse according to the perception data; An environmental parameter data generation module, used to obtain environmental monitoring data of each monitoring point and environmental source parameter data corresponding to each environmental source, and determine the environmental parameter data inside the warehouse according to the three-dimensional model, the environmental monitoring data and the environmental source parameter data; A storage area division module, used for dividing the warehouse into at least one storage area according to the environmental parameter data; A target storage area determination module, used to obtain product parameter data of the product to be stored, and determine the target storage area of ​​the product to be stored according to the product parameter data and the at least one storage area; A transport module, used for generating a target transport instruction according to the target storage area, so as to transport the products to be stored to the target storage area; The environmental monitoring data includes temperature monitoring data and wind speed monitoring data, the environmental parameter data includes temperature parameter data and wind speed parameter data, the environmental source includes a heat source and a wind source, the environmental source parameter data includes heat source parameter data corresponding to the heat source and wind source parameter data corresponding to the wind source, and the step of determining the environmental parameter data inside the warehouse according to the three-dimensional model, the environmental monitoring data and the environmental source parameter data includes: Acquire model training data, the model training data including historical temperature monitoring data, historical wind speed monitoring data, historical heat source parameter data corresponding to the heat source, and historical wind source parameter data of the wind source, and divide the model training data into a training set and a validation set according to a preset ratio; Training the pre-built environmental parameter model according to the training set to obtain a pre-trained environmental parameter model; Verifying the pre-trained environmental parameter model according to the verification set to obtain a pre-generated environmental parameter model; Determine the feature points in the warehouse according to a preset coordinate collection method and the three-dimensional model, and generate corresponding coordinate point sets according to the feature point coordinates corresponding to the feature points; Inputting the characteristic point coordinates, the temperature monitoring data, the wind speed monitoring data, the heat source parameter data and the wind source parameter data in the coordinate point set into the pre-generated environmental parameter model to obtain the temperature parameter data and the wind speed parameter data of each characteristic point; The environmental parameter data includes illumination data, the environmental source includes a light source, the environmental source parameter data includes light source parameter data corresponding to the light source, and the step of determining the environmental parameter data inside the warehouse according to the three-dimensional model, the environmental monitoring data and the environmental source parameter data includes: Determining the light source type of each of the light sources in the warehouse according to the environmental source parameter data, and determining the illumination calculation model of each of the light sources according to the light source type; Acquire coordinate data of each of the light sources according to the three-dimensional model, and determine distance data between each of the light sources and the feature point according to the coordinate data and the coordinate point set; For each of the feature points, determine a single theoretical illuminance of each light source irradiating the feature point according to the distance data and the illumination calculation model, and determine a theoretical illuminance of the feature point according to the single theoretical illuminance; Determining a corrected illumination of each of the feature points according to the theoretical illumination, and using the corrected illumination as illumination data inside the warehouse; The step of dividing the warehouse into at least one storage area according to the environmental parameter data comprises: Determine the maximum temperature and the minimum temperature according to the temperature parameter data of each characteristic point, determine the maximum wind speed and the minimum wind speed according to the wind speed parameter data of each characteristic point, and determine the maximum illumination and the minimum illumination according to the illumination of each characteristic point; Determine a temperature division threshold value according to the maximum temperature value and the minimum temperature value, determine a wind speed division threshold value according to the maximum wind speed value and the minimum wind speed value, and determine an illumination division threshold value according to the maximum illumination value and the minimum illumination value; Constructing an environmental feature vector of each feature point according to the temperature parameter data, wind speed parameter data and illumination of each feature point, and performing cluster analysis on the internal space of the warehouse according to the environmental feature vector, the temperature division threshold, the wind speed division threshold and the illumination division threshold to obtain a corresponding clustering result; The warehouse is divided into at least one storage area according to the clustering result.

6. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program executable on the processor, and the processor implements the method according to any one of claims 1 to 4 when executing the computer program.

7. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute the method according to any one of claims 1 to 4.

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