Production scheduling method, system, equipment and medium based on industrial Internet of Things

Abstract

This application discloses a production scheduling method, system, device, and medium based on the Industrial Internet of Things (IIoT), relating to the technical field of the IIoT. The method comprises: acquiring image data of a target area, and acquiring dust spatial distribution data of the target area based on the image data; dividing the target area based on the dust spatial distribution data to obtain at least one concentration gradient area; determining at least one outbound area and at least one inbound area based on the dust concentration and a preset concentration; the concentration gradient area includes the at least one outbound area and the at least one inbound area; determining a target outbound device corresponding to the at least one outbound area and a target inbound area corresponding to the target outbound device based on the processing rate data, the dust concentration, and the preset concentration; and acquiring a production scheduling instruction based on the target outbound device and the target inbound area. This application has the effect of improving the safety of the production environment.

Description

Technical Field

[0001] The present application relates to the technical field of industrial Internet of Things, and in particular to production scheduling methods, systems, equipment, and media based on the industrial Internet of Things. Background Art

[0002] Dust is a widespread and significant environmental issue in modern industrial production. With increasing automation and mechanization, many factories generate large quantities of fine particulate matter during production. This dust not only impacts product quality but also poses a serious threat to the health and safety of factory workers. Dust is generated from a wide range of sources, including material processing, transportation, and storage. In particular, in industries such as wood processing, mining, chemicals, and pharmaceuticals, dust concentrations often exceed safety limits.

[0003] Dust has diverse components, including metal particles, non-metallic minerals, chemicals, and biological dust. When suspended in the air, these components can easily be inhaled by workers. Long-term exposure to dust can lead to respiratory illnesses, skin diseases, and even more serious occupational diseases. Furthermore, certain types of dust are highly flammable and explosive. Once mixed with air and reaching a certain concentration, they can cause fires or explosions, posing a significant safety hazard.

[0004] Despite the increasing number of dust control measures in place, dust concentrations remain elevated in many factories due to factors such as aging equipment, poor management, and inadequate workplace ventilation. Dust concentrations can rise rapidly during peak hours or during specific production processes, potentially exceeding monitoring limits. This poses a significant challenge to production safety. Current dust concentration monitoring data indicates that many factories lack sufficient safety and intelligent production processes, necessitating the implementation of more effective control measures to ensure employee health and workplace safety. Summary of the Invention

[0005] In order to improve the security of the production environment, this application provides a production scheduling method, system, equipment and medium based on the Industrial Internet of Things.

[0006] In the first aspect, this application provides a production scheduling method based on the Industrial Internet of Things, which adopts the following technical solutions:

[0007] A production scheduling method based on the Industrial Internet of Things 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 that are sequentially communicatively connected. The method is executed by the management platform and includes:

[0008] Acquire image data of a target area, and acquire dust spatial distribution data of the target area based on the image data;

[0009] Dividing the target area according to the dust spatial distribution data to obtain at least one concentration gradient area;

[0010] Obtaining the dust concentration of the at least one concentration gradient region, and determining at least one adjustment-out region and at least one adjustment-in region based on the dust concentration and a preset concentration; the concentration gradient region includes the at least one adjustment-out region and the at least one adjustment-in region;

[0011] Obtaining processing rate data corresponding to the at least one call-out area, the processing rate data including processing rates of each device in the at least one call-out area, and determining a target call-out device corresponding to the at least one call-out area and a target call-in area corresponding to the target call-out device based on the processing rate data, the dust concentration, and a preset concentration;

[0012] A production scheduling instruction is acquired according to the target outgoing equipment and the target incoming area, so as to transfer the target outgoing equipment corresponding to the at least one outgoing area into the target incoming area.

[0013] By adopting the above technical solution, image data of the target area is obtained, and dust spatial distribution data of the target area is obtained based on the image data. Then, the target area is divided according to the dust spatial distribution data to obtain at least one concentration gradient area. Then, the dust concentration of at least one concentration gradient area is obtained, and at least one transfer-out area and at least one transfer-in area are determined based on the dust concentration and a preset concentration. The concentration gradient area includes at least one transfer-out area and at least one transfer-in area. Then, processing rate data corresponding to the at least one transfer-out area is obtained, and a target transfer-out device corresponding to the at least one transfer-out area and a target transfer-in area corresponding to the target transfer-out device are determined based on the processing rate data, the dust concentration, and the preset concentration. Finally, a production scheduling instruction is obtained based on the target transfer-out device and the target transfer-in area to transfer the target transfer-out device corresponding to the at least one transfer-out area to the target transfer-in area. Through the above method, during the processing process, each processing equipment can be moved and scheduled according to the dust concentration at each location in the target area to ensure that the dust concentration at each location in the target area is at a safe level, thereby improving the safety of the production environment, and improving the intelligent and automated level of production, and meeting the dust management and control requirements under specific production environments.

[0014] Optionally, the step of acquiring dust concentration data of the target area according to the image data includes:

[0015] performing a preprocessing operation on the image data according to the image data to obtain a corresponding preprocessed image;

[0016] Extracting features of dust particles based on the preprocessed image to obtain corresponding dust particle feature data;

[0017] Acquire historical image data, and perform data annotation on the historical image data to obtain corresponding model training data;

[0018] Dividing the model training data into a training set and a test set according to a preset ratio, and training a pre-selected deep learning model based on the model training data to obtain a trained deep learning model;

[0019] Testing and verifying the trained deep learning model based on the test set to obtain a dust particle recognition model;

[0020] The dust particle characteristic data is input into the dust particle recognition model to obtain the dust spatial distribution data of the target area.

[0021] By adopting the above technical solution, in order to obtain the dust spatial distribution data of the target area, the image data is first preprocessed according to the image data to obtain the corresponding preprocessed image, and then the dust particles are feature extracted according to the preprocessed image to obtain the corresponding dust particle feature data, and then the historical image data is obtained, and the historical image data is data labeled to obtain the corresponding model training data, and then the model training data is divided into a training set and a test set according to a preset ratio, and a pre-selected deep learning model is trained according to the model training data to obtain a trained deep learning model, and then the trained deep learning model is tested and verified according to the test set to obtain a dust particle recognition model, and finally the dust particle feature data is input into the dust particle recognition model to obtain the dust spatial distribution data of the target area.

[0022] Optionally, performing a preprocessing operation on the image data according to the image data to obtain a corresponding preprocessed image includes:

[0023] Performing denoising processing on the image data according to the image data to obtain image A;

[0024] Performing image enhancement on the image A according to the image A to obtain image B;

[0025] Image segmentation is performed on the image B according to the image B to obtain a corresponding preprocessed image.

[0026] By adopting the above technical solution, in order to obtain appropriate preprocessing, the image data is first denoised according to the image data to obtain image A, and then image A is enhanced according to image A to obtain image B, and finally image B is segmented according to image B to obtain the corresponding preprocessed image.

[0027] Optionally, the step of dividing the target area according to the dust spatial distribution data to obtain at least one concentration gradient area includes:

[0028] Extracting corresponding image features from the image data, the image features including color features, brightness features, and texture features, converting the image features into point cloud data based on the image features, and constructing a three-dimensional model of the target area based on the point cloud data;

[0029] Obtaining a preset grid division accuracy, and performing grid division on the target area according to the three-dimensional model and the grid division accuracy to obtain grid data corresponding to the three-dimensional model;

[0030] Determining dust particle data of each grid according to the dust spatial distribution data and the grid data; the dust particle data includes the number of dust particles and the size of dust particles;

[0031] Determining the dust particle density of each grid according to the dust particle data and the grid division accuracy, and performing cluster analysis on each grid according to the dust particle density to obtain a grid clustering result for each grid;

[0032] The grids are merged according to the grid clustering result to obtain at least one concentration gradient region.

[0033] By adopting the above technical solution, in order to obtain at least one concentration gradient region, corresponding image features are first extracted from the image data. The image features include color features, brightness features, and texture features. The image features are then converted into point cloud data based on the image features. A three-dimensional model of the target region is constructed based on the point cloud data. A pre-set grid division accuracy is then obtained, and the target region is grid-divided according to the three-dimensional model and the grid division accuracy to obtain grid data corresponding to the three-dimensional model. Dust particle data for each grid is then determined based on the dust spatial distribution data and the grid data. The dust particle data includes the number of dust particles and the size of dust particles. The dust particle density of each grid is then determined based on the dust particle data and the grid division accuracy. Cluster analysis is then performed on each grid based on the dust particle density to obtain grid clustering results for each grid. Finally, the grids are merged based on the grid clustering results to obtain at least one concentration gradient region.

[0034] Optionally, the step of determining at least one transfer-out area and at least one transfer-in area according to the dust concentration and a preset concentration includes:

[0035] For each of the at least one concentration gradient region, obtaining a target dust concentration of the concentration gradient region according to the dust concentration, and determining whether the target dust concentration is greater than the preset concentration according to the target dust concentration and a preset concentration;

[0036] If so, the concentration gradient region is used as the call-out region;

[0037] If not, the concentration gradient region is used as the input region.

[0038] By adopting the above technical solution, in order to determine the transfer-out area and the transfer-in area, for each concentration gradient area in at least one concentration gradient area, the target dust concentration of the concentration gradient area is obtained according to the dust concentration, and it is judged whether the target dust concentration is greater than the preset concentration according to the target dust concentration and the preset concentration. If the target dust concentration is greater than the preset concentration, the concentration gradient area is used as the transfer-out area; if the target dust concentration is not greater than the preset concentration, the concentration gradient area is used as the transfer-out area.

[0039] Optionally, the step of determining a target call-out device corresponding to the at least one call-out area and a target call-in area corresponding to the target call-out device according to the processing rate data, the dust concentration, and a preset concentration includes:

[0040] For each of the at least one call-out area, determining a processing rate ratio corresponding to each of the processing equipment in the call-out area according to the processing rate data;

[0041] determining a single dust concentration corresponding to each of the processing equipment in the transfer-out area according to the processing rate ratio and the dust concentration;

[0042] Obtaining the dust concentration of the adjusted-out area corresponding to each adjusted-out area according to the dust concentration, and determining the dust concentration difference of the adjusted-out area corresponding to each adjusted-out area according to the dust concentration of the adjusted-out area and the preset concentration;

[0043] Screening each of the processing equipment in each of the transfer-out areas according to the difference between the single dust concentration and the dust concentration in the transfer-out area, and obtaining target transfer-out equipment corresponding to each of the transfer-out areas;

[0044] A target incoming area corresponding to the target outgoing device is determined according to the target outgoing device, the dust concentration and a preset concentration.

[0045] By adopting the above technical solution, in order to determine the target call-in area corresponding to the target call-out equipment, for each call-out area in at least one call-out area, the processing rate ratio corresponding to each processing equipment in the call-out area is determined according to the processing rate data, and then the single dust concentration corresponding to each processing equipment in the call-out area is determined according to the processing rate ratio and the dust concentration, and then the call-out area dust concentration corresponding to each call-out area is obtained according to the dust concentration, and the call-out area dust concentration difference corresponding to each call-out area is determined according to the call-out area dust concentration and the preset concentration, and then the processing equipment in each call-out area is screened according to the single dust concentration and the call-out area dust concentration difference to obtain the target call-out equipment corresponding to each call-out area, and finally the target call-in area corresponding to the target call-out equipment is determined according to the target call-out equipment, dust concentration and preset concentration.

[0046] Optionally, the step of determining the target incoming area corresponding to the target outgoing device according to the target outgoing device, the dust concentration, and a preset concentration includes:

[0047] For each of the at least one transferred-in area, obtaining a corresponding transferred-in area dust concentration according to the dust concentration, and determining a corresponding transferred-in area dust concentration difference according to the transferred-in area dust concentration and the preset concentration;

[0048] The target dust concentration corresponding to each target outgoing device is obtained according to the single dust concentration, and the target incoming area corresponding to each target outgoing device is determined from the at least one area to be incoming according to the difference between the target dust concentration and the dust concentration of the incoming area.

[0049] By adopting the above technical solution, in order to determine the target call-in area corresponding to the target call-out device, for each call-in area in at least one call-in area, the call-in area dust concentration corresponding to the call-in area is obtained according to the dust concentration, and the call-in area dust concentration difference corresponding to the call-in area is determined according to the call-in area dust concentration and the preset concentration. Then, the target dust concentration corresponding to each target call-out device is obtained according to the single dust concentration, and the target call-in area corresponding to each target call-out device is determined from at least one area to be called in according to the target dust concentration and the call-in area dust concentration difference.

[0050] Secondly, this application also provides a production scheduling system based on the Industrial Internet of Things, which adopts the following technical solutions:

[0051] The production scheduling system based on the industrial Internet of Things includes a management platform, a sensor network platform, and an object platform that are communicatively connected in sequence. The management platform is configured with:

[0052] An image data processing module is used to obtain image data of a target area and obtain dust spatial distribution data of the target area based on the image data;

[0053] a target area division module, configured to divide the target area according to the dust spatial distribution data to obtain at least one concentration gradient area;

[0054] a scheduling area determination module, configured to obtain the dust concentration of the at least one concentration gradient area, and determine at least one dispatch-out area and at least one dispatch-in area based on the dust concentration and a preset concentration; the concentration gradient area includes the at least one dispatch-out area and the at least one dispatch-in area;

[0055] a call-out device determination module, configured to obtain processing rate data corresponding to the at least one call-out area, and determine a target call-out device corresponding to the at least one call-out area and a target call-in area corresponding to the target call-out device based on the processing rate data, the dust concentration, and a preset concentration;

[0056] The production scheduling module is used to obtain a production scheduling instruction according to the target outgoing device and the target incoming area, so as to transfer the at least one outgoing area into the target incoming area corresponding to the target outgoing device.

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

[0058] A computer device comprises a memory and a processor, wherein the memory stores a computer program that can be run 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 according to the first aspect.

[0061] In summary, the present application includes at least the following beneficial technical effects: acquiring image data of a target area, and acquiring dust spatial distribution data of the target area based on the image data, then dividing the target area according to the dust spatial distribution data to obtain at least one concentration gradient area, then acquiring the dust concentration of at least one concentration gradient area, and determining at least one transfer-out area and at least one transfer-in area based on the dust concentration and a preset concentration. The concentration gradient area includes at least one transfer-out area and at least one transfer-in area, then acquiring processing rate data corresponding to at least one transfer-out area, and determining a target transfer-out device corresponding to at least one transfer-out area and a target transfer-in area corresponding to the target transfer-out device based on the processing rate data, the dust concentration, and the preset concentration, and finally acquiring a production scheduling instruction based on the target transfer-out device and the target transfer-in area to transfer the target transfer-out device corresponding to at least one transfer-out area to the target transfer-in area; in the above manner, during the processing process, each processing equipment can be moved and scheduled according to the dust concentration at each location in the target area to ensure that the dust concentration at each location in the target area is at a safe level, thereby improving the safety of the production environment, and improving the intelligence and automation level of production, and meeting the dust management and control requirements under specific production environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a schematic diagram of the overall process of the 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 this application. DETAILED DESCRIPTION

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

[0067] The embodiments of the present application disclose a production scheduling method based on the Industrial Internet of Things.

[0068] Reference Figure 1 The production scheduling method based on the industrial Internet of Things is applied to the 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 connected in communication. The method is executed by the management platform and includes:

[0069] Step S11 : acquiring image data of a target area, and acquiring dust spatial distribution data of the target area based on the image data.

[0070] Step S12: Divide the target area according to the dust spatial distribution data to obtain at least one concentration gradient area.

[0071] Step S13: obtaining the dust concentration of at least one concentration gradient area, and determining at least one transfer-out area and at least one transfer-in area according to the dust concentration and a preset concentration.

[0072] The concentration gradient region includes at least one outgoing region and at least one incoming region.

[0073] It can be understood that the dust concentration at various locations in the concentration gradient area can be considered to be the same or similar, that is, for each concentration gradient area, the dust concentration in the concentration gradient area varies within a certain concentration range; the transfer-out area is a concentration gradient area where the processing equipment needs to be transferred out, and the transfer-in area is a concentration gradient area where the processing equipment can be transferred in.

[0074] Step S14: obtaining processing rate data corresponding to at least one call-out area, and determining a target call-out device corresponding to the at least one call-out area and a target call-in area corresponding to the target call-out device according to the processing rate data, dust concentration and preset concentration.

[0075] The processing rate data includes at least one processing rate of each device in the call-out area.

[0076] Step S15 , obtaining a production scheduling instruction according to the target transfer-out equipment and the target transfer-in area, so as to transfer the target transfer-out equipment corresponding to at least one transfer-out area into the target transfer-in area.

[0077] In the above embodiment, image data of the target area is obtained, and dust spatial distribution data of the target area is obtained based on the image data. The target area is then divided according to the dust spatial distribution data to obtain at least one concentration gradient area. The dust concentration of the at least one concentration gradient area is then obtained, and at least one transfer-out area and at least one transfer-in area are determined based on the dust concentration and a preset concentration. The concentration gradient area includes at least one transfer-out area and at least one transfer-in area. The processing rate data corresponding to the at least one transfer-out area is then obtained, and the target transfer-out device corresponding to the at least one transfer-out area and the target transfer-in area corresponding to the target transfer-out device are determined based on the processing rate data, the dust concentration, and the preset concentration. Finally, a production scheduling instruction is obtained based on the target transfer-out device and the target transfer-in area to transfer the target transfer-out device corresponding to the at least one transfer-out area to the target transfer-in area. In this way, during the processing process, each processing equipment can be moved and scheduled according to the dust concentration at each location in the target area to ensure that the dust concentration at each location in the target area is at a safe level, thereby improving the safety of the production environment, and improving the intelligent and automated level of production, and meeting the dust management and control requirements under specific production environments.

[0078] As a further embodiment of the production scheduling method, the step of obtaining dust concentration data of the target area based on the image data includes:

[0079] Step S21 , performing a preprocessing operation on the image data according to the image data to obtain a corresponding preprocessed image.

[0080] It should be noted that preprocessing operation is an image processing technology. Image preprocessing includes image scaling, image normalization, image enhancement, edge detection, and denoising. Through preprocessing operation, the original image can be effectively preprocessed to obtain a processed image that is more suitable for subsequent analysis or training. Different application scenarios usually require different preprocessing combinations. The specific selection and parameter settings should be adjusted according to actual needs.

[0081] Step S22 : extracting features of the dust particles based on the preprocessed image to obtain corresponding dust particle feature data.

[0082] Step S23: Acquire historical image data, and perform data annotation on the historical image data to obtain corresponding model training data.

[0083] It should be noted that the historical image data is an image containing dust in the air, the data annotation object is the dust in the image, and the annotation type is the attribute of the dust, such as the attribute of the dust particles (size, density, etc. of the dust particles).

[0084] Step S24: divide the model training data into a training set and a test set according to a preset ratio, and train the pre-selected deep learning model based on the model training data to obtain a trained deep learning model.

[0085] It should be noted that the preset ratio is usually set according to actual needs, for example, the preset ratio is set to 7:3 or 8:2.

[0086] Step S25: Testing and verifying the trained deep learning model based on the test set to obtain a dust particle recognition model.

[0087] Step S26: input the dust particle characteristic data into the dust particle recognition model to obtain the dust spatial distribution data of the target area.

[0088] In the above embodiment, in order to obtain the dust spatial distribution data of the target area, the image data is first preprocessed according to the image data to obtain the corresponding preprocessed image, and then the dust particles are feature extracted according to the preprocessed image to obtain the corresponding dust particle feature data, and then the historical image data is obtained, and the historical image data is data labeled to obtain the corresponding model training data, and then the model training data is divided into a training set and a test set according to a preset ratio, and a pre-selected deep learning model is trained according to the model training data to obtain a trained deep learning model, and then the trained deep learning model is tested and verified according to the test set to obtain a dust particle recognition model, and finally the dust particle feature data is input into the dust particle recognition model to obtain the dust spatial distribution data of the target area.

[0089] As a further implementation of the production scheduling method, performing a preprocessing operation on the image data according to the image data to obtain a corresponding preprocessed image includes:

[0090] Step S31, performing denoising processing on the image data according to the image data to obtain image A;

[0091] Step S32, performing image enhancement on image A according to image A to obtain image B;

[0092] Step S33 , performing image segmentation on image B according to image B to obtain a corresponding pre-processed image.

[0093] In the above embodiment, in order to obtain appropriate preprocessing, the image data is first denoised according to the image data to obtain image A, and then image A is enhanced according to image A to obtain image B, and finally image B is segmented according to image B to obtain the corresponding preprocessed image.

[0094] As a further embodiment of the production scheduling method, the step of dividing the target area according to the dust spatial distribution data to obtain at least one concentration gradient area includes:

[0095] In step S41 , corresponding image features are extracted according to the image data, the image features are converted into point cloud data according to the image features, and a three-dimensional model of the target area is constructed according to the point cloud data.

[0096] Among them, image features include color features, brightness features and texture features.

[0097] Step S42: obtaining a preset grid division accuracy, and performing grid division on the target area according to the three-dimensional model and the grid division accuracy to obtain grid data corresponding to the three-dimensional model.

[0098] It is understandable that meshing accuracy usually depends on the detail requirements of the 3D model and the computing resources.

[0099] Step S43: determining dust particle data of each grid according to the dust spatial distribution data and the grid data.

[0100] The dust particle data includes the number of dust particles and the size of dust particles.

[0101] Step S44 : determining the dust particle density of each grid according to the dust particle data and the grid division accuracy, and performing cluster analysis on each grid according to the dust particle density to obtain a grid clustering result for each grid.

[0102] It should be noted that in step S44, when performing cluster analysis, it is necessary to select a suitable clustering algorithm based on the characteristics of the data (i.e., dust particle density). Common clustering methods include K-means clustering, hierarchical clustering, DBSCAN, etc. K-means clustering is suitable for data with relatively uniform distribution and approximately spherical shape, hierarchical clustering is suitable for cluster analysis that requires a hierarchical structure, and DBSCAN is suitable for data with more noise or irregular shape; then the dust particle density data is standardized. If the density value ranges of different grids vary greatly, the clustering effect can be improved by standardization (such as Z-score standardization or Min-Max scaling), and then the number of clusters is determined (if necessary). For K-means clustering, the number of clusters (K) needs to be determined in advance. The K value can be assisted by methods such as the Elbow Method or Silhouette Score, and then cluster analysis is performed, that is, the dust particle density data is input into the selected clustering algorithm to perform cluster analysis. The clustering algorithm will automatically classify grids with the same or similar dust particle density into the same category, and then evaluate the clustering results by analyzing the clustering results, such as the center of each cluster, the number of grids in each category, the distribution, etc. At the same time, the clustering effect is evaluated using evaluation indicators (such as silhouette coefficient), and then the clustering results are visualized. For example, visualization tools (such as scatter plots, cluster diagrams, etc.) are used to display the clustering results to help understand the distribution of dust particle density in different grids. Finally, the clustering results are output, that is, the cluster category to which each grid belongs is recorded, and the clustering result file is compiled for subsequent grid merging in step S35.

[0103] Step S45 , merging the grids according to the grid clustering result to obtain at least one concentration gradient region.

[0104] It can be understood that from step S44, the clustering result can be used to obtain the cluster category to which each grid belongs, and each category represents a group of grids with similar or similar dust particle density; then the grids are merged according to the preset merging criteria, for example: grids of the same cluster category are selected for merging, or a merging threshold is set, such as the difference in average dust particle density between adjacent cluster grids is less than a certain value, thereby obtaining at least one concentration gradient region. After the merging is completed, these regions can be described by the average dust particle density of each region or the spatial range and shape of the region.

[0105] In the above embodiment, to obtain at least one concentration gradient region, corresponding image features are first extracted from the image data. The image features include color features, brightness features, and texture features. The image features are then converted into point cloud data based on the image features. A three-dimensional model of the target region is then constructed based on the point cloud data. A pre-set meshing accuracy is then obtained, and the target region is meshed based on the three-dimensional model and the meshing accuracy to obtain mesh data corresponding to the three-dimensional model. Dust particle data for each mesh is then determined based on the dust spatial distribution data and the mesh data. The dust particle data includes the number and size of dust particles. The dust particle density for each mesh is then determined based on the dust particle data and the meshing accuracy. Cluster analysis is then performed on each mesh based on the dust particle density to obtain mesh clustering results for each mesh. Finally, the meshes are merged based on the mesh clustering results to obtain at least one concentration gradient region.

[0106] As a further embodiment of the production scheduling method, the step of determining at least one transfer-out area and at least one transfer-in area according to the dust concentration and the preset concentration includes:

[0107] Step S51 : for each concentration gradient area in at least one concentration gradient area, obtaining a target dust concentration of the concentration gradient area according to the dust concentration, and determining whether the target dust concentration is greater than the preset concentration according to the target dust concentration and the preset concentration.

[0108] Step S52: If yes, the concentration gradient region is used as the call-out region.

[0109] Specifically, if the target dust concentration is greater than a preset concentration, the concentration gradient area is used as an outgoing area.

[0110] Step S53: If not, the concentration gradient region is used as the input region.

[0111] Specifically, if the target dust concentration is not greater than the preset concentration, the concentration gradient area is used as the adjustment-out area.

[0112] It should be noted that, in this embodiment, when the target dust concentration is equal to the preset concentration, it is conceivable that the target dust concentration in a certain concentration gradient area may exceed the preset concentration at the next moment, and therefore also needs to be included in the scope of the adjustment area.

[0113] In the above embodiment, in order to determine the transfer-out area and the transfer-in area, for each concentration gradient area in at least one concentration gradient area, the target dust concentration of the concentration gradient area is obtained according to the dust concentration, and it is judged whether the target dust concentration is greater than the preset concentration according to the target dust concentration and the preset concentration. If the target dust concentration is greater than the preset concentration, the concentration gradient area is used as the transfer-out area; if the target dust concentration is not greater than the preset concentration, the concentration gradient area is used as the transfer-out area.

[0114] As a further embodiment of the production scheduling method, the step of determining a target outgoing device corresponding to at least one outgoing area and a target incoming area corresponding to the target outgoing device based on processing rate data, dust concentration, and a preset concentration includes:

[0115] Step S61 : for each of the at least one call-out area, determining a processing rate ratio corresponding to each processing device in the call-out area according to the processing rate data.

[0116] Step S62: determining a single dust concentration corresponding to each processing equipment in the transfer area according to the processing rate ratio and the dust concentration.

[0117] Step S63 , obtaining the dust concentration of each adjusted-out area corresponding to each adjusted-out area according to the dust concentration, and determining the dust concentration difference of each adjusted-out area corresponding to each adjusted-out area according to the dust concentration of the adjusted-out area and the preset concentration.

[0118] Step S64 , screening each processing equipment in each transfer area according to the difference between the single dust concentration and the dust concentration of the transfer area, and obtaining the target transfer equipment corresponding to each transfer area.

[0119] According to the difference between the single dust concentration and the dust concentration of the transfer area, each processing equipment in each transfer area is screened to obtain the target transfer equipment corresponding to each transfer area.

[0120] It should be noted that in step S54, screening is required according to pre-set screening criteria. For example, the outbound area includes areas A and B, and the inbound area includes area C. The equipment in area A that is outbound includes a1 (corresponding to a single dust concentration of 20) and a2 (corresponding to a single dust concentration of 20). The equipment in area B that is inbound includes b1 (corresponding to a single dust concentration of 10), b2 (corresponding to a single dust concentration of 20), and b3 (corresponding to a single dust concentration of 35). The equipment in area C that is inbound includes c1 (corresponding to a single dust concentration of 35), c2 (corresponding to a single dust concentration of 25), and c3 (corresponding to a single dust concentration of 25). The dust concentration difference in the outbound areas is 30. Then when filtering, for calling out area A, any device between a1 and a2 can be used as the target calling-out device; for calling out area B, only b3 can be used as the target calling-out device; for calling into area C, c1 and c2 can be used as the target calling-out devices, or c1 and c3 can be used as the target calling-out devices. For calling into area C, the specific method to be selected can be set according to the actual situation.

[0121] Step S55 , determining the target incoming area corresponding to the target outgoing device according to the target outgoing device, the dust concentration, and the preset concentration.

[0122] In the above embodiment, in order to determine the target incoming area corresponding to the target outgoing equipment, for each outgoing area in at least one outgoing area, the processing rate ratio corresponding to each processing equipment in the outgoing area is determined according to the processing rate data, and then the single dust concentration corresponding to each processing equipment in the outgoing area is determined according to the processing rate ratio and the dust concentration, and then the outgoing area dust concentration corresponding to each outgoing area is obtained according to the dust concentration, and the outgoing area dust concentration difference corresponding to each outgoing area is determined according to the outgoing area dust concentration and the preset concentration, and then the processing equipment in each outgoing area is screened according to the single dust concentration and the outgoing area dust concentration difference to obtain the target outgoing equipment corresponding to each outgoing area, and finally the target incoming area corresponding to the target outgoing equipment is determined according to the target outgoing equipment, dust concentration and preset concentration.

[0123] As a further implementation of the production scheduling method, the step of determining the target incoming area corresponding to the target outgoing equipment according to the target outgoing equipment, the dust concentration, and the preset concentration includes:

[0124] Step S71: for each of the at least one transferred-in area, obtain the transferred-in area dust concentration corresponding to the transferred-in area according to the dust concentration, and determine the transferred-in area dust concentration difference corresponding to the transferred-in area according to the transferred-in area dust concentration and the preset concentration.

[0125] Step S72: acquiring a target dust concentration corresponding to each target outgoing device according to the single dust concentration, and determining a target incoming area corresponding to each target outgoing device from at least one to-be-introduced area according to the difference between the target dust concentration and the dust concentration in the incoming area.

[0126] It should be noted that, assuming the incoming areas include E and F, the dust concentration differences between the outgoing areas corresponding to A and B are 30 and 50, respectively, and the target outgoing devices include x1, x2, and x3, with target dust concentrations of 10, 17, and 35, respectively. Therefore, in Plan 1, the target incoming areas for x1 and x2 are E, and the target incoming area for x3 is F. That is, x1 and x2 are moved into Area E, and x3 is moved into Area F. In Plan 2, the target incoming areas for x1 and x3 are F, and the target incoming area for x2 is E. That is, x1 and x3 are moved into Area F, and x2 is moved into Area E. The specific plan to be used is typically determined based on actual needs, such as randomly selecting a plan or choosing the one that minimizes call time.

[0127] In the above embodiment, in order to determine the target call-in area corresponding to the target call-out device, for each call-in area in at least one call-in area, the call-in area dust concentration corresponding to the call-in area is obtained according to the dust concentration, and the call-in area dust concentration difference corresponding to the call-in area is determined according to the call-in area dust concentration and the preset concentration. Then, the target dust concentration corresponding to each target call-out device is obtained according to the single dust concentration, and the target call-in area corresponding to each target call-out device is determined from at least one area to be called in according to the target dust concentration and the call-in area dust concentration difference.

[0128] The embodiments of the present application also disclose a production scheduling system based on the Industrial Internet of Things.

[0129] refer to Figure 2 The production scheduling system based on the industrial Internet of Things includes a management platform, a sensor network platform and an object platform that are sequentially connected in communication. The management platform is configured with:

[0130] An image data processing module is used to obtain image data of a target area and obtain dust spatial distribution data of the target area based on the image data;

[0131] A target area division module is used to divide the target area according to the dust spatial distribution data to obtain at least one concentration gradient area;

[0132] a scheduling area determination module, configured to obtain the dust concentration of at least one concentration gradient area and determine at least one dispatch-out area and at least one dispatch-in area based on the dust concentration and a preset concentration; a concentration gradient area includes at least one dispatch-out area and at least one dispatch-in area;

[0133] a call-out device determination module, configured to obtain processing rate data corresponding to at least one call-out area, and determine a target call-out device corresponding to the at least one call-out area and a target call-in area corresponding to the target call-out device based on the processing rate data, the dust concentration, and a preset concentration;

[0134] The production scheduling module is used to obtain a production scheduling instruction according to a target outgoing device and a target incoming area, so as to transfer the target outgoing device corresponding to at least one outgoing area into the target incoming area.

[0135] The overall framework of another application scenario of the production scheduling system based on industrial Internet of Things of this application is as follows Figure 3 As shown, the system may include a user platform, a service platform, a management platform, a sensor network platform, and an object platform, which interact in sequence, forming a five-platform architecture based on the Industrial Internet of Things. The management platform includes an image data processing module, a target area division module, a scheduling area determination module, a dispatched equipment determination module, and a production scheduling module. The service platform includes a central service database, n service sub-platforms, and n service sub-databases. Each service sub-platform can communicate with its corresponding service sub-database, and each service sub-database can communicate with the central service database. The sensor network platform includes a central sensor database and n sensor network sub-platforms. Each sensor network sub-platform is equipped with a sensor sub-database, and each sensor network sub-platform can communicate with the central sensor database.

[0136] Specifically, in another application scenario mentioned above, the production scheduling system based on the industrial Internet of Things includes a management platform, which is configured to: obtain image data of the target area, and obtain dust space distribution data of the target area based on the image data; divide the target area according to the dust space distribution data to obtain at least one concentration gradient area; obtain the dust concentration of the at least one concentration gradient area, and determine at least one transfer-out area and at least one transfer-in area based on the dust concentration and a preset concentration; the concentration gradient area includes the at least one transfer-out area and the at least one transfer-in area; obtain processing rate data corresponding to the at least one transfer-out area, and determine the target transfer-out device corresponding to the at least one transfer-out area and the target transfer-in area corresponding to the target transfer-out device based on the processing rate data, the dust concentration and the preset concentration; obtain production scheduling instructions based on the target transfer-out device and the target transfer-in area to transfer the target transfer-out device corresponding to the at least one transfer-out area to the target transfer-in area.

[0137] Through the interaction between the various functional platforms of the industrial Internet of Things-based production scheduling system based on the above three 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.

[0138] The production scheduling system based on industrial Internet of Things of the present invention can implement any one of the production scheduling methods based on industrial Internet of Things, and the specific working process of the production scheduling system based on industrial Internet of Things of the present invention can refer to the corresponding process in the above-mentioned production scheduling method based on industrial Internet of Things.

[0139] The embodiment of the present application also discloses a computer device.

[0140] refer to Figure 4 A 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 of the above-mentioned production scheduling methods based on the industrial Internet of Things is implemented.

[0141] The embodiment of the present application also discloses a computer-readable storage medium.

[0142] A computer-readable storage medium stores a computer program that can be loaded by a processor and execute any one of the above-mentioned production scheduling methods based on the industrial Internet of Things.

[0143] Among them, computer-readable storage media can be any tangible medium that contains or stores a program that can be used by or in combination 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.

[0144] The above are all preferred embodiments of the present application and are not intended to limit the scope of protection of this application. Unless otherwise stated, any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features. In other words, unless otherwise stated, each feature is merely an example of a series of equivalent or similar features.

Claims

1. A production scheduling 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 communicatively connected in sequence. The method is executed by the management platform and includes: Acquire image data of a target area, and acquire dust spatial distribution data of the target area based on the image data; Dividing the target area according to the dust spatial distribution data to obtain at least one concentration gradient area; Obtaining the dust concentration of the at least one concentration gradient region, and determining at least one adjustment-out region and at least one adjustment-in region based on the dust concentration and a preset concentration; the concentration gradient region includes the at least one adjustment-out region and the at least one adjustment-in region; Acquiring processing rate data corresponding to the at least one adjusted-out area, the processing rate data including processing rates of each processing device in the at least one adjusted-out area, and determining a target adjusted-out device corresponding to the at least one adjusted-out area and a target adjusted-in area corresponding to the target adjusted-out device based on the processing rate data, the dust concentration, and a preset concentration; Acquire a production scheduling instruction according to the target transfer-out equipment and the target transfer-in area, so as to transfer the target transfer-out equipment corresponding to the at least one transfer-out area into the target transfer-in area; The step of determining the target call-out device corresponding to the at least one call-out area and the target call-in area corresponding to the target call-out device according to the processing rate data, the dust concentration and the preset concentration includes: For each of the at least one call-out area, determining a processing rate ratio corresponding to each processing device in the call-out area according to the processing rate data; determining a single dust concentration corresponding to each of the processing equipment in the transfer-out area according to the processing rate ratio and the dust concentration; Obtaining the dust concentration of the adjusted-out area corresponding to each adjusted-out area according to the dust concentration, and determining the dust concentration difference of the adjusted-out area corresponding to each adjusted-out area according to the dust concentration of the adjusted-out area and the preset concentration; Screening each of the processing equipment in each of the transfer-out areas according to the difference between the single dust concentration and the dust concentration in the transfer-out area, and obtaining target transfer-out equipment corresponding to each of the transfer-out areas; Determining a target incoming area corresponding to the target outgoing device according to the target outgoing device, the dust concentration, and a preset concentration; The step of determining the target incoming area corresponding to the target outgoing device according to the target outgoing device, the dust concentration, and a preset concentration includes: For each of the at least one transferred-in area, obtaining a corresponding transferred-in area dust concentration according to the dust concentration, and determining a corresponding transferred-in area dust concentration difference according to the transferred-in area dust concentration and the preset concentration; The target dust concentration corresponding to each target outgoing device is obtained according to the single dust concentration, and the target incoming area corresponding to each target outgoing device is determined from the at least one area to be incoming according to the difference between the target dust concentration and the dust concentration of the incoming area.

2. The production scheduling method based on industrial Internet of Things according to claim 1 is characterized in that: The step of acquiring dust concentration data of the target area according to the image data comprises: performing a preprocessing operation on the image data according to the image data to obtain a corresponding preprocessed image; Extracting features of dust particles based on the preprocessed image to obtain corresponding dust particle feature data; Acquire historical image data, and perform data annotation on the historical image data to obtain corresponding model training data; Dividing the model training data into a training set and a test set according to a preset ratio, and training a pre-selected deep learning model based on the model training data to obtain a trained deep learning model; Testing and verifying the trained deep learning model based on the test set to obtain a dust particle recognition model; The dust particle characteristic data is input into the dust particle recognition model to obtain the dust spatial distribution data of the target area.

3. The production scheduling method based on industrial Internet of Things according to claim 2 is characterized in that: Performing a preprocessing operation on the image data according to the image data to obtain a corresponding preprocessed image includes: Performing denoising processing on the image data according to the image data to obtain image A; Performing image enhancement on the image A according to the image A to obtain image B; Image segmentation is performed on the image B according to the image B to obtain a corresponding preprocessed image.

4. The production scheduling method based on industrial Internet of Things according to claim 1 is characterized in that: The step of dividing the target area according to the dust spatial distribution data to obtain at least one concentration gradient area includes: Extracting corresponding image features from the image data, the image features including color features, brightness features, and texture features, converting the image features into point cloud data based on the image features, and constructing a three-dimensional model of the target area based on the point cloud data; Obtaining a preset grid division accuracy, and performing grid division on the target area according to the three-dimensional model and the grid division accuracy to obtain grid data corresponding to the three-dimensional model; Determining dust particle data of each grid according to the dust spatial distribution data and the grid data; the dust particle data includes the number of dust particles and the size of dust particles; Determining the dust particle density of each grid according to the dust particle data and the grid division accuracy, and performing cluster analysis on each grid according to the dust particle density to obtain a grid clustering result for each grid; The grids are merged according to the grid clustering result to obtain at least one concentration gradient region.

5. The production scheduling method based on industrial Internet of Things according to claim 1 is characterized in that: The step of determining at least one transfer-out area and at least one transfer-in area according to the dust concentration and a preset concentration includes: For each of the at least one concentration gradient region, obtaining a target dust concentration of the concentration gradient region according to the dust concentration, and determining whether the target dust concentration is greater than the preset concentration according to the target dust concentration and a preset concentration; If so, the concentration gradient region is used as the call-out region; If not, the concentration gradient region is used as the input region.

6. The production scheduling system based on industrial Internet of Things is characterized by: The system comprises a management platform, a sensor network platform and an object platform which are communicatively connected in sequence, wherein the management platform is configured with: An image data processing module is used to obtain image data of a target area and obtain dust spatial distribution data of the target area based on the image data; a target area division module, configured to divide the target area according to the dust spatial distribution data to obtain at least one concentration gradient area; a scheduling area determination module, configured to obtain the dust concentration of the at least one concentration gradient area, and determine at least one dispatch-out area and at least one dispatch-in area based on the dust concentration and a preset concentration; the concentration gradient area includes the at least one dispatch-out area and the at least one dispatch-in area; a call-out device determination module, configured to obtain processing rate data corresponding to the at least one call-out area, and determine a target call-out device corresponding to the at least one call-out area and a target call-in area corresponding to the target call-out device based on the processing rate data, the dust concentration, and a preset concentration; a production scheduling module, configured to obtain a production scheduling instruction according to the target outgoing device and the target incoming area, so as to transfer the at least one outgoing area into the target incoming area corresponding to the target outgoing device; The step of determining the target call-out device corresponding to the at least one call-out area and the target call-in area corresponding to the target call-out device according to the processing rate data, the dust concentration and the preset concentration includes: For each of the at least one call-out area, determining a processing rate ratio corresponding to each processing device in the call-out area according to the processing rate data; determining a single dust concentration corresponding to each of the processing equipment in the transfer-out area according to the processing rate ratio and the dust concentration; Obtaining the dust concentration of the adjusted-out area corresponding to each adjusted-out area according to the dust concentration, and determining the dust concentration difference of the adjusted-out area corresponding to each adjusted-out area according to the dust concentration of the adjusted-out area and the preset concentration; Screening each of the processing equipment in each of the transfer-out areas according to the difference between the single dust concentration and the dust concentration in the transfer-out area, and obtaining target transfer-out equipment corresponding to each of the transfer-out areas; Determining a target incoming area corresponding to the target outgoing device according to the target outgoing device, the dust concentration, and a preset concentration; The step of determining the target incoming area corresponding to the target outgoing device according to the target outgoing device, the dust concentration, and a preset concentration includes: For each of the at least one transferred-in area, obtaining a corresponding transferred-in area dust concentration according to the dust concentration, and determining a corresponding transferred-in area dust concentration difference according to the transferred-in area dust concentration and the preset concentration; The target dust concentration corresponding to each target outgoing device is obtained according to the single dust concentration, and the target incoming area corresponding to each target outgoing device is determined from the at least one area to be incoming according to the difference between the target dust concentration and the dust concentration of the incoming area.

7. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and the processor implements the method according to any one of claims 1 to 5 when executing the computer program.

8. 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 5.

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