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

Abstract

The present application discloses a workshop management method, system, equipment and medium based on the industrial Internet of Things, and relates to the technical field of workshop management. The method includes: obtaining process information of each production area in the workshop, and determining the processing position corresponding to the work-in-progress of each production area and the surface parameter information corresponding to the processing position according to the process information; determining the optimal lighting parameters and optimal type of the light source in each production area according to the surface parameter information; determining the optimal number of light sources in each production area according to the optimal lighting parameters; obtaining the first current position information of the work-in-progress and the second current position information of the target production personnel, and determining the optimal position of the light source according to the first current position information, the second current position information and the surface parameter information; generating a light source control instruction according to the optimal type, the optimal number and the optimal position to control the light sources in each production area in the workshop. The present application has the effect of improving the intelligent level of the workshop.

Description

Technical Field

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

[0002] Workshop management is a crucial link in manufacturing and production enterprises. It involves production organization, resource allocation, process optimization and personnel management. With the intensification of global competition and changes in market demand, enterprises are facing multiple challenges such as improving production efficiency, reducing costs, improving product quality and shortening delivery cycles. Therefore, the effectiveness of workshop management directly affects the overall operational performance of the enterprise.

[0003] The light in the workshop has a significant impact on production. It significantly improves employees' attention to details, which helps to identify product defects in a timely manner. Sufficient and uniform light enables workers to more clearly observe surface flaws, dimensional deviations or process errors on products, avoiding missed inspections due to unclear vision. This timely defect identification can not only reduce rework and scrap rates and improve production efficiency, but also ensure that the quality of the products finally delivered to customers meets standards, thereby enhancing the company's reputation and market competitiveness.

[0004] At present, the traditional lighting method in the workshop is to use fixed lighting for lighting, but this mode is becoming more and more limited when facing different processes and diversified products. Especially for work-in-progress with high precision, it is often difficult for inspectors to identify product defects in time according to the current lighting conditions. Fixed lighting cannot adapt to different work tasks and visual requirements, resulting in insufficient or excessive light in some cases, which affects employees' observation ability and judgment accuracy. Therefore, traditional lighting methods are difficult to adapt to intelligent, complex and diversified production needs. Summary of the invention

[0005] In order to improve the intelligence level of workshops, this application provides workshop management methods, systems, equipment and media based on industrial Internet of Things.

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

[0007] The workshop 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 process information of each production area in the workshop, and determine the processing position corresponding to the work-in-progress of each production area and the surface parameter information corresponding to the processing position according to the process information;

[0009] Determine the optimal illumination parameters and the optimal type of the light source in each of the production areas according to the surface parameter information;

[0010] Determining the optimal number of the light sources in each of the production areas according to the optimal lighting parameters;

[0011] Acquire the first current position information of the work-in-progress in each of the production areas and the second current position information of the target production personnel, and determine the optimal position of the light source in each of the production areas according to the first current position information, the second current position information and the surface parameter information;

[0012] A light source control instruction is generated according to the optimal type, the optimal quantity and the optimal position to control the light sources in each of the production areas in the workshop.

[0013] By adopting the above technical solution, the process information of each production area in the workshop is first obtained, and the processing position corresponding to the work-in-produced product in each production area and the surface parameter information corresponding to the processing position are determined according to the process information, and then the optimal lighting parameters and optimal types of the light sources in each production area are determined according to the surface parameter information, and then the optimal number of light sources in each production area is determined according to the optimal lighting parameters, and then the first current position information of the work-in-produced product in each production area and the second current position information of the target production personnel are obtained, and the optimal position of the light source in each production area is determined according to the first current position information, the second current position information and the surface parameter information, and finally the light source control instruction is generated according to the optimal type, the optimal number and the optimal position to control the light source in each production area in the workshop. In the above method, the light sources in each production area in the workshop are adjusted and controlled respectively according to the processing position, surface parameter information, first current position information of the work-in-produced product in each production area and the second current position information of the target production personnel, so as to facilitate the target production personnel to timely discover the defects of the work-in-produced product, the intelligent level is higher, and the lighting requirements in the production process can be better met, and it is suitable for intelligent, complex and diversified production needs.

[0014] Optionally, the step of determining, according to the process information, processing positions corresponding to the work-in-progress in each of the production areas and surface parameter information corresponding to the processing positions includes:

[0015] For each production area, determining, according to the process information, a processing position corresponding to the work-in-progress in the production area and a processing operation and processing parameters corresponding to the processing position;

[0016] Acquire material structure information corresponding to the work-in-progress in the production area, and determine material information of the processing position according to the material structure information;

[0017] Surface parameter information corresponding to the processing position is determined according to the processing operation, the processing parameters and the material information; the surface parameter information includes surface roughness and surface finish.

[0018] By adopting the above technical scheme, in order to determine the processing position and the surface parameter information corresponding to the processing position, for each production area, the processing position corresponding to the work-in-progress in the production area and the processing operation and processing parameters corresponding to the processing position are determined according to the process information, and then the material structure information corresponding to the work-in-progress in the production area is obtained, and the material information of the processing position is determined according to the material structure information, and finally the surface parameter information corresponding to the processing position is determined according to the processing operation, processing parameters and material information.

[0019] Optionally, the material information includes material properties, and the step of determining the surface parameter information corresponding to the processing position according to the processing operation, the processing parameters and the material information includes:

[0020] Acquire historical processing data, the historical processing data including historical processing operations, historical processing parameters and historical material properties, and divide the historical processing data according to a preset ratio to form a training set, a validation set and a test set;

[0021] Training a pre-established surface parameter prediction model according to the training set to obtain a trained surface parameter prediction model;

[0022] Optimizing the trained surface parameter prediction model according to the validation set to obtain an optimized surface parameter prediction model;

[0023] Testing the optimized surface parameter prediction model according to the test set to obtain a tested surface parameter prediction model;

[0024] The processing operation, the processing parameters and the material properties are input into a tested surface parameter prediction model to obtain surface parameter information corresponding to the processing position.

[0025] By adopting the above technical scheme, in order to obtain the surface parameter information corresponding to the processing position, historical processing data is obtained, and the historical processing data includes historical processing operations, historical processing parameters and historical material properties, and the historical processing data is divided according to a preset ratio to form a training set, a verification set and a test set, and then a pre-established surface parameter prediction model is trained according to the training set to obtain a trained surface parameter prediction model, and then the trained surface parameter prediction model is optimized according to the verification set to obtain an optimized surface parameter prediction model, and then the optimized surface parameter prediction model is tested according to the test set to obtain a tested surface parameter prediction model, and finally the processing operation, processing parameters and material properties are input into the tested surface parameter prediction model to obtain the surface parameter information corresponding to the processing position.

[0026] Optionally, the step of determining the optimal illumination parameters and the optimal type of the light sources in each of the production areas according to the surface parameter information includes:

[0027] For each production area, determine the standard lighting parameters and the optimal type of light source of the processing position corresponding to the work-in-progress in the production area according to the surface parameter information, and use the standard lighting parameters of the processing position as the standard lighting parameters of the production area;

[0028] Acquire the area of ​​the daylighting windows of the production area, the light transmittance of the daylighting windows and the natural light parameters outside the workshop, and determine the natural light parameters of the production area based on the area of ​​the daylighting windows, the light transmittance of the daylighting windows and the natural light parameters outside the workshop;

[0029] The optimal lighting parameters of the production area are determined according to the standard lighting parameters of the production area and the natural light parameters of the production area; the optimal lighting parameters include optimal lighting intensity.

[0030] By adopting the above technical scheme, in order to determine the optimal lighting parameters and the optimal type of the light source, for each production area, the standard lighting parameters of the processing positions corresponding to the work-in-progress in the production area and the optimal type of the light source are determined according to the surface parameter information, and the standard lighting parameters of the processing positions are used as the standard lighting parameters of the production area. Then, the area of ​​the skylights in the production area, the light transmittance of the skylights and the natural light parameters outside the workshop are obtained, and the natural light parameters of the production area are determined based on the area of ​​the skylights, the light transmittance of the skylights and the natural light parameters outside the workshop. Finally, the optimal lighting parameters of the production area are determined based on the standard lighting parameters of the production area and the natural light parameters of the production area.

[0031] Optionally, the step of determining the optimal number of the light sources in each of the production areas according to the optimal illumination parameters includes:

[0032] For each production area, obtaining the area of ​​the production area, and determining the total luminous flux of the production area according to the area and the optimal light intensity;

[0033] Determine a single luminous flux of the light source according to the optimal type, and determine a theoretical number of light sources in the production area according to the total luminous flux and the single luminous flux;

[0034] A corresponding light source quantity adjustment factor is determined according to the optimal type, and an optimal quantity of the light sources in the production area is determined according to the theoretical quantity and the light source quantity adjustment factor.

[0035] By adopting the above technical scheme, in order to determine the optimal number of light sources, for each production area, the area of ​​the production area is obtained, and the total luminous flux of the production area is determined based on the area and the optimal light intensity, and then the single luminous flux of the light source is determined according to the optimal type, and the theoretical number of light sources in the production area is determined based on the total luminous flux and the single luminous flux, and finally, the corresponding light source quantity adjustment factor is determined according to the optimal type, and the optimal number of light sources in the production area is determined based on the theoretical number and the light source quantity adjustment factor.

[0036] Optionally, the step of determining the optimal position of the light source in each of the production areas according to the first current position information, the second current position information and the surface parameter information includes:

[0037] Acquire workshop scanning data, and perform feature extraction on the workshop scanning data to obtain corresponding point cloud feature data;

[0038] Establishing a lighting model according to the point cloud feature data;

[0039] The optimal position of the light source in each of the production areas is determined according to the first current position information, the second current position information, the surface parameter information and the illumination model.

[0040] By adopting the above technical solution, in order to determine the optimal position of the light source in each production area, the workshop scanning data is first obtained, and the features of the workshop scanning data are extracted to obtain the corresponding point cloud feature data, and then the lighting model is established according to the point cloud feature data. Finally, the optimal position of the light source in each production area is determined according to the first current position information, the second current position information, the surface parameter information and the lighting model.

[0041] Optionally, the method further includes:

[0042] Obtaining the suspended matter concentration value in the workshop, and matching the suspended matter concentration value in a preset table to obtain a corresponding target ventilation rate; the preset table includes a suspended matter concentration range and a ventilation rate, and the preset table is used to represent a mapping relationship between the suspended matter concentration range and the ventilation rate;

[0043] A ventilation control instruction is generated according to the target ventilation rate to control the ventilation system to perform ventilation.

[0044] By adopting the above technical solution, in order to control the suspended matter concentration in the workshop to ensure the lighting quality, the suspended matter concentration value in the workshop is first obtained, and the suspended matter concentration value is matched in the preset table to obtain the corresponding target ventilation rate. The preset table includes the suspended matter concentration range and the ventilation rate. The preset table is used to represent the mapping relationship between the suspended matter concentration range and the ventilation rate, and then a ventilation control instruction is generated according to the target ventilation rate to control the ventilation system for ventilation.

[0045] In the second aspect, the present application also provides an Internet of Things system, which adopts the following technical solutions:

[0046] The Internet of Things system includes a management platform, a sensor network platform and an object platform which are sequentially connected in communication, wherein the management platform is configured with:

[0047] A surface parameter information generating module, used to obtain process information of each production area in the workshop, and determine the processing position corresponding to the work-in-progress of each production area and the surface parameter information corresponding to the processing position according to the process information;

[0048] An optimal type determination module, used to determine the optimal illumination parameters and the optimal type of the light source in each of the production areas according to the surface parameter information;

[0049] An optimal quantity determination module, used to determine the optimal quantity of the light sources in each of the production areas according to the optimal lighting parameters;

[0050] an optimal position determination module, used to obtain the first current position information of the work-in-progress in each of the production areas and the second current position information of the target production personnel, and determine the optimal position of the light source in each of the production areas according to the first current position information, the second current position information and the surface parameter information;

[0051] A light source control module is used to generate a light source control instruction according to the optimal type, the optimal quantity and the optimal position, so as to control the light sources in each of the production areas in the workshop.

[0052] In a third aspect, the present application also provides a computer device, which adopts the following technical solution:

[0053] 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.

[0054] In a fourth aspect, the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0055] 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.

[0056] In summary, the present application includes at least the following beneficial technical effects: firstly, the process information of each production area in the workshop is obtained, and the processing position corresponding to the work-in-produced product in each production area and the surface parameter information corresponding to the processing position are determined according to the process information, and then the optimal lighting parameters and the optimal type of the light source in each production area are determined according to the surface parameter information, and then the optimal number of light sources in each production area is determined according to the optimal lighting parameters, and then the first current position information of the work-in-produced product in each production area and the second current position information of the target production personnel are obtained, and the optimal position of the light source in each production area is determined according to the first current position information, the second current position information and the surface parameter information, and finally the light source control instruction is generated according to the optimal type, the optimal number and the optimal position to control the light source in each production area in the workshop. In the above method, the light sources in each production area in the workshop are adjusted and controlled respectively according to the processing position, the surface parameter information, the first current position information of the work-in-produced product in each production area and the second current position information of the target production personnel, so as to facilitate the target production personnel to timely discover the defects of the work-in-produced product, the intelligent level is higher, and the lighting requirements in the production process can be better met, and it is suitable for intelligent, complex and diversified production needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a schematic diagram of the overall process of an embodiment of the present application.

[0058] Figure 2 It is a structural diagram of one application scenario of the system of an embodiment of the present application.

[0059] Figure 3 It is a structural diagram of another application scenario of the system of an embodiment of the present application.

[0060] Figure 4 It is a structural block diagram of the computer device of the present application. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solutions and advantages of this application more clear, the following Figure 1-4It 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.

[0062] The embodiment of the present application discloses a workshop management method based on industrial Internet of Things.

[0063] Reference Figure 1 The workshop management 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 which are sequentially connected in communication. The method is executed by the management platform and includes:

[0064] Step S11, obtaining process information of each production area in the workshop, and determining the processing position corresponding to the work-in-progress in each production area and the surface parameter information corresponding to the processing position according to the process information.

[0065] It should be noted that different production areas usually carry out different types of processes, such as cleaning, deburring, milling, cutting, grinding and other processes; surface parameter information includes surface roughness, surface hardness, surface finish, surface color and other parameters.

[0066] Step S12, determining the optimal illumination parameters and the optimal type of the light source in each production area according to the surface parameter information.

[0067] It is understandable that the optimal lighting parameter information includes parameters such as light intensity, color temperature, color rendering index, etc., and the types of light sources include LED lamps, fluorescent lamps, high-intensity discharge lamps, halogen lamps, etc.

[0068] Step S13, determining the optimal number of light sources in each production area according to the optimal lighting parameters.

[0069] It should be noted that the optimal number of light sources refers to the optimal number corresponding to the optimal type of light sources in step S12.

[0070] Step S14, obtaining the first current position information of the work-in-progress in each production area and the second current position information of the target production personnel, and determining the optimal position of the light source in each production area based on the first current position information, the second current position information and the surface parameter information.

[0071] It can be understood that the second current position information corresponding to the work-in-progress in the production area can be obtained through RFID technology or sensors, and the target production personnel can be patrol personnel or equipment operators in the production area. The target production personnel carry a positioning device and can obtain the corresponding second current position information in real time; in actual production, the positions of the work-in-progress and the target production personnel usually change, so the position of the light source usually needs to be adjusted synchronously.

[0072] Step S15, generating a light source control instruction according to the optimal type, optimal quantity and optimal position to control the light sources in each production area in the workshop.

[0073] In the above implementation, the process information of each production area in the workshop is first obtained, and the processing position corresponding to the work-in-produced product in each production area and the surface parameter information corresponding to the processing position are determined according to the process information, and then the optimal lighting parameters and the optimal type of the light source in each production area are determined according to the surface parameter information, and then the optimal number of light sources in each production area is determined according to the optimal lighting parameters, and then the first current position information of the work-in-produced product in each production area and the second current position information of the target production personnel are obtained, and the optimal position of the light source in each production area is determined according to the first current position information, the second current position information and the surface parameter information, and finally the light source control instruction is generated according to the optimal type, the optimal number and the optimal position to control the light source in each production area in the workshop. In the above method, the light sources in each production area in the workshop are adjusted and controlled respectively according to the processing position, surface parameter information, first current position information of the work-in-produced product in each production area and the second current position information of the target production personnel, so as to facilitate the target production personnel to timely discover the defects of the work-in-produced product, the intelligent level is higher, and the lighting requirements in the production process can be better met, which is suitable for intelligent, complex and diversified production needs.

[0074] As a further implementation of the workshop management method, the step of determining the processing position corresponding to the work-in-progress in each production area and the surface parameter information corresponding to the processing position according to the process information includes:

[0075] Step S21, for each production area, determining the processing position corresponding to the work-in-progress in the production area and the processing operation and processing parameters corresponding to the processing position according to the process information.

[0076] Step S22, obtaining material structure information corresponding to the work-in-progress in the production area, and determining material information of the processing position according to the material structure information.

[0077] Step S23, determining the surface parameter information corresponding to the processing position according to the processing operation, processing parameters and material information.

[0078] The surface parameter information includes surface roughness and surface finish.

[0079] In the above-mentioned embodiment, in order to determine the processing position and the surface parameter information corresponding to the processing position, for each production area, the processing position corresponding to the work-in-progress in the production area and the processing operation and processing parameters corresponding to the processing position are determined according to the process information, and then the material structure information corresponding to the work-in-progress in the production area is obtained, and the material information of the processing position is determined according to the material structure information, and finally, the surface parameter information corresponding to the processing position is determined according to the processing operation, processing parameters and material information.

[0080] As a further implementation of the workshop management method, the material information includes material properties, and the step of determining the surface parameter information corresponding to the processing position according to the processing operation, the processing parameters and the material information includes:

[0081] Step S31, obtaining historical processing data, and dividing the historical processing data according to a preset ratio to form a training set, a verification set and a test set.

[0082] Among them, historical processing data includes historical processing operations, historical processing parameters and historical material properties.

[0083] Step S32: training the pre-established surface parameter prediction model according to the training set to obtain a trained surface parameter prediction model.

[0084] Step S33, optimizing the trained surface parameter prediction model according to the verification set to obtain an optimized surface parameter prediction model.

[0085] Step S34, testing the optimized surface parameter prediction model according to the test set to obtain a tested surface parameter prediction model.

[0086] Step S35 , inputting the processing operation, processing parameters and material properties into the tested surface parameter prediction model to obtain surface parameter information corresponding to the processing position.

[0087] In the above-mentioned embodiment, in order to obtain the surface parameter information corresponding to the processing position, historical processing data is acquired, and the historical processing data includes historical processing operations, historical processing parameters and historical material properties, and the historical processing data is divided according to a preset ratio to form a training set, a verification set and a test set, and then a pre-established surface parameter prediction model is trained according to the training set to obtain a trained surface parameter prediction model, and then the trained surface parameter prediction model is optimized according to the verification set to obtain an optimized surface parameter prediction model, and then the optimized surface parameter prediction model is tested according to the test set to obtain a tested surface parameter prediction model, and finally the processing operation, processing parameters and material properties are input into the tested surface parameter prediction model to obtain the surface parameter information corresponding to the processing position.

[0088] As a further implementation of the workshop management method, the step of determining the optimal illumination parameters and the optimal type of the light source in each production area according to the surface parameter information includes:

[0089] Step S41, for each production area, determine the standard lighting parameters and the optimal type of light source of the processing position corresponding to the work-in-progress in the production area according to the surface parameter information, and use the standard lighting parameters of the processing position as the standard lighting parameters of the production area.

[0090] It is understandable that standard lighting parameters include parameters such as light intensity, color temperature, and color rendering index.

[0091] Step S42, obtaining the area of ​​the skylights in the production area, the light transmittance of the skylights and the natural light parameters outside the workshop, and determining the natural light parameters of the production area based on the area of ​​the skylights, the light transmittance of the skylights and the natural light parameters outside the workshop.

[0092] Step S43, determining the optimal lighting parameters of the production area according to the standard lighting parameters of the production area and the natural light parameters of the production area.

[0093] Among them, the optimal lighting parameters include optimal lighting intensity.

[0094] In the above embodiment, in order to determine the optimal lighting parameters and the optimal type of the light source, for each production area, the standard lighting parameters of the processing positions corresponding to the work-in-progress in the production area and the optimal type of the light source are determined based on the surface parameter information, and the standard lighting parameters of the processing positions are used as the standard lighting parameters of the production area. Then, the area of ​​the skylights in the production area, the light transmittance of the skylights and the natural light parameters outside the workshop are obtained, and the natural light parameters of the production area are determined based on the area of ​​the skylights, the light transmittance of the skylights and the natural light parameters outside the workshop. Finally, the optimal lighting parameters of the production area are determined based on the standard lighting parameters of the production area and the natural light parameters of the production area.

[0095] As a further implementation of the workshop management method, the step of determining the optimal number of light sources in each production area according to the optimal lighting parameters includes:

[0096] Step S51, for each production area, the area of ​​the production area is obtained, and the total luminous flux of the production area is determined according to the area and the optimal light intensity.

[0097] It should be noted that the total luminous flux (Φ) = area (A) × optimal light intensity (E).

[0098] Step S52, determining the single luminous flux of the light source according to the optimal type, and determining the theoretical number of light sources in the production area according to the total luminous flux and the single luminous flux.

[0099] It should be noted that the database stores single luminous fluxes corresponding to different types of light sources. In step S52, the optimal type is matched and searched in the database to obtain the single luminous flux of the light source.

[0100] Step S53, determining the corresponding light source quantity adjustment factor according to the optimal type, and determining the optimal number of light sources in the production area according to the theoretical number and the light source quantity adjustment factor.

[0101] It should be noted that, according to the actual situation, it is necessary to consider the factors affecting the illumination, such as the efficiency loss of the light source, light attenuation, etc. These factors can be determined by past experience or experimental data; usually, the value range of the light source quantity adjustment factor is between 1.1 and 1.3, and the specific value depends on the actual situation.

[0102] In the above embodiment, in order to determine the optimal number of light sources, for each production area, the area of ​​the production area is obtained, and the total luminous flux of the production area is determined based on the area and the optimal light intensity. Then, the single luminous flux of the light source is determined according to the optimal type, and the theoretical number of light sources in the production area is determined based on the total luminous flux and the single luminous flux. Finally, the corresponding light source quantity adjustment factor is determined according to the optimal type, and the optimal number of light sources in the production area is determined based on the theoretical number and the light source quantity adjustment factor.

[0103] As a further implementation of the workshop management method, the step of determining the optimal position of the light source in each production area according to the first current position information, the second current position information and the surface parameter information includes:

[0104] Step S61, acquiring workshop scanning data, and performing feature extraction on the workshop scanning data to obtain corresponding point cloud feature data.

[0105] Step S62: establishing a lighting model according to the point cloud feature data.

[0106] Step S63, determining the optimal position of the light source in each production area according to the first current position information, the second current position information, the surface parameter information and the lighting model.

[0107] In the above embodiment, in order to determine the optimal position of the light source in each production area, the workshop scanning data is first obtained, and features are extracted from the workshop scanning data to obtain corresponding point cloud feature data. Then, a lighting model is established based on the point cloud feature data. Finally, the optimal position of the light source in each production area is determined based on the first current position information, the second current position information, the surface parameter information and the lighting model.

[0108] As a further implementation of the workshop management method, the method further includes:

[0109] Step S71, obtain the suspended matter concentration value in the workshop, and match the suspended matter concentration value in the preset table to obtain the corresponding target ventilation rate. The preset table includes the suspended matter concentration range and the ventilation rate, and the preset table is used to represent the mapping relationship between the suspended matter concentration range and the ventilation rate.

[0110] Step S72: generating a ventilation control instruction according to the target ventilation rate to control the ventilation system to perform ventilation.

[0111] In the above embodiment, in order to control the suspended matter concentration in the workshop to ensure the lighting quality, the suspended matter concentration value in the workshop is first obtained, and the suspended matter concentration value is matched in the preset table to obtain the corresponding target ventilation rate. The preset table includes the suspended matter concentration range and the ventilation rate. The preset table is used to represent the mapping relationship between the suspended matter concentration range and the ventilation rate. Then, a ventilation control instruction is generated according to the target ventilation rate to control the ventilation system for ventilation.

[0112] The embodiment of the present application also discloses an Internet of Things system.

[0113] refer to Figure 2 The IoT system includes a management platform, a sensor network platform and an object platform which are sequentially connected in communication. The management platform is configured with:

[0114] The surface parameter information generation module is used to obtain the process information of each production area in the workshop, and determine the processing position corresponding to the work-in-progress in each production area and the surface parameter information corresponding to the processing position according to the process information;

[0115] An optimal type determination module is used to determine the optimal lighting parameters and optimal type of the light source in each production area according to the surface parameter information;

[0116] An optimal quantity determination module is used to determine the optimal quantity of light sources in each production area according to the optimal lighting parameters;

[0117] An optimal position determination module is used to obtain the first current position information of the work-in-progress in each production area and the second current position information of the target production personnel, and determine the optimal position of the light source in each production area according to the first current position information, the second current position information and the surface parameter information;

[0118] The light source control module is used to generate light source control instructions according to the optimal type, optimal quantity and optimal position to control the light sources in various production areas in the workshop.

[0119] The overall framework of another application scenario of the industrial Internet of Things system of this application is as follows Figure 3As 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 service platform is composed of a service master database, multiple service sub-platforms and multiple service sub-databases; the management platform includes a surface parameter information generation module, an optimal type determination module, an optimal quantity determination module, an optimal position determination module, and a light source control module, and the management platform can interact with the sensor network platform and the service platform; the sensor network platform may include a sensor master database, multiple sensor sub-platforms and multiple sensor sub-databases, and the sensor network platform can interact with the object platform.

[0120] Through the interaction between the various functional platforms of the industrial Internet of Things 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 management of equipment.

[0121] Specifically, the industrial Internet of Things system of this embodiment includes a management platform, which is configured with: a surface parameter information generation module, which is used to obtain the process information of each production area in the workshop, and determine the processing position corresponding to the work-in-progress in each production area and the surface parameter information corresponding to the processing position based on the process information; an optimal type determination module, which is used to determine the optimal lighting parameters and the optimal type of the light source in each production area based on the surface parameter information; an optimal quantity determination module, which is used to determine the optimal number of light sources in each production area based on the optimal lighting parameters; an optimal position determination module, which is used to obtain the first current position information of the work-in-progress in each production area and the second current position information of the target production personnel, and determine the optimal position of the light source in each production area based on the first current position information, the second current position information and the surface parameter information; a light source control module, which is used to generate light source control instructions based on the optimal type, optimal quantity and optimal position to control the light sources in each production area in the workshop.

[0122] The Internet of Things system of the present invention can implement any one of the workshop management methods of the Industrial Internet of Things, and the specific working process of the Internet of Things system of the present invention can refer to the corresponding process in the above-mentioned workshop management method of the Industrial Internet of Things.

[0123] The embodiment of the present application also discloses a computer device.

[0124] 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 one of the above-mentioned industrial Internet of Things workshop management methods is implemented.

[0125] The embodiment of the present application also discloses a computer-readable storage medium.

[0126] A computer-readable storage medium stores a computer program that can be loaded by a processor and execute any one of the above-mentioned workshop management methods for industrial Internet of Things.

[0127] 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.

[0128] 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. The workshop management method based on industrial Internet of Things is characterized by: 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 connected in communication, and the method is executed by the management platform, including: Acquire process information of each production area in the workshop, and determine the processing position corresponding to the work-in-progress of each production area and the surface parameter information corresponding to the processing position according to the process information; Determine the optimal lighting parameters and optimal type of the light source in each of the production areas according to the surface parameter information; the optimal lighting parameters include optimal lighting intensity, optimal color temperature, and optimal color rendering index, and the optimal type is one of the preset light source types, and the preset light source types include LED lamps, fluorescent lamps, high-intensity discharge lamps, and halogen lamps; Determining the optimal number of the light sources in each of the production areas according to the optimal lighting parameters; Acquire the first current position information of the work-in-progress in each of the production areas and the second current position information of the target production personnel, and determine the optimal position of the light source in each of the production areas according to the first current position information, the second current position information and the surface parameter information; the target production personnel include patrol personnel; Generate a light source control instruction according to the optimal type, the optimal quantity and the optimal position to control the light sources in each of the production areas in the workshop; The step of determining the processing position corresponding to the work-in-progress in each of the production areas and the surface parameter information corresponding to the processing position according to the process information comprises: For each production area, determining, according to the process information, a processing position corresponding to the work-in-progress in the production area and a processing operation and processing parameters corresponding to the processing position; Acquire material structure information corresponding to the work-in-progress in the production area, and determine material information of the processing position according to the material structure information; Determining surface parameter information corresponding to the processing position according to the processing operation, the processing parameters and the material information; the surface parameter information includes surface roughness and surface finish; The step of determining the optimal illumination parameters and the optimal type of the light source in each of the production areas according to the surface parameter information comprises: For each production area, determine the standard lighting parameters and the optimal type of light source of the processing position corresponding to the work-in-progress in the production area according to the surface parameter information, and use the standard lighting parameters of the processing position as the standard lighting parameters of the production area; Acquire the area of ​​the daylighting windows of the production area, the light transmittance of the daylighting windows and the natural light parameters outside the workshop, and determine the natural light parameters of the production area based on the area of ​​the daylighting windows, the light transmittance of the daylighting windows and the natural light parameters outside the workshop; Determining optimal lighting parameters of the production area according to standard lighting parameters of the production area and natural light parameters of the production area; the optimal lighting parameters include optimal lighting intensity; The step of determining the optimal position of the light source in each of the production areas according to the first current position information, the second current position information and the surface parameter information comprises: Acquire workshop scanning data, and perform feature extraction on the workshop scanning data to obtain corresponding point cloud feature data; Establishing a lighting model according to the point cloud feature data; The optimal position of the light source in each of the production areas is determined according to the first current position information, the second current position information, the surface parameter information and the lighting model.

2. According to the workshop management method based on industrial Internet of Things in claim 1, the material information includes material properties, and the step of determining the surface parameter information corresponding to the processing position according to the processing operation, the processing parameters and the material information comprises: Acquire historical processing data, the historical processing data including historical processing operations, historical processing parameters and historical material properties, and divide the historical processing data according to a preset ratio to form a training set, a validation set and a test set; Training a pre-established surface parameter prediction model according to the training set to obtain a trained surface parameter prediction model; Optimizing the trained surface parameter prediction model according to the validation set to obtain an optimized surface parameter prediction model; Testing the optimized surface parameter prediction model according to the test set to obtain a tested surface parameter prediction model; The processing operation, the processing parameters and the material properties are input into a tested surface parameter prediction model to obtain surface parameter information corresponding to the processing position.

3. The workshop management method based on industrial Internet of Things according to claim 1 is characterized in that: The step of determining the optimal number of the light sources in each of the production areas according to the optimal illumination parameters comprises: For each production area, obtaining the area of ​​the production area, and determining the total luminous flux of the production area according to the area and the optimal light intensity; Determine a single luminous flux of the light source according to the optimal type, and determine a theoretical number of light sources in the production area according to the total luminous flux and the single luminous flux; A corresponding light source quantity adjustment factor is determined according to the optimal type, and an optimal quantity of the light sources in the production area is determined according to the theoretical quantity and the light source quantity adjustment factor.

4. The workshop management method based on industrial Internet of Things according to claim 1 is characterized in that: The method further comprises: Obtaining the suspended matter concentration value in the workshop, and matching the suspended matter concentration value in a preset table to obtain a corresponding target ventilation rate; the preset table includes a suspended matter concentration range and a ventilation rate, and the preset table is used to represent a mapping relationship between the suspended matter concentration range and the ventilation rate; A ventilation control instruction is generated according to the target ventilation rate to control the ventilation system to perform ventilation.

5. Internet of Things system, characterized in that, 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 surface parameter information generation module is used to obtain process information of each production area in the workshop, and determine the processing position corresponding to the work-in-progress of each production area and the surface parameter information corresponding to the processing position according to the process information; An optimal type determination module, used to determine the optimal lighting parameters and optimal type of the light source in each of the production areas according to the surface parameter information; the optimal lighting parameters include optimal lighting intensity, optimal color temperature, and optimal color rendering index, and the optimal type is one of the preset light source types, and the preset light source types include LED lamps, fluorescent lamps, high-intensity discharge lamps, and halogen lamps; An optimal quantity determination module, used to determine the optimal quantity of the light sources in each of the production areas according to the optimal lighting parameters; an optimal position determination module, for obtaining the first current position information of the work-in-progress in each of the production areas and the second current position information of the target production personnel, and determining the optimal position of the light source in each of the production areas according to the first current position information, the second current position information and the surface parameter information; the target production personnel include patrol personnel; A light source control module, used to generate a light source control instruction according to the optimal type, the optimal number and the optimal position, so as to control the light sources in each of the production areas in the workshop; The step of determining the processing position corresponding to the work-in-progress in each of the production areas and the surface parameter information corresponding to the processing position according to the process information comprises: For each production area, determining, according to the process information, a processing position corresponding to the work-in-progress in the production area and a processing operation and processing parameters corresponding to the processing position; Acquire material structure information corresponding to the work-in-progress in the production area, and determine material information of the processing position according to the material structure information; Determining surface parameter information corresponding to the processing position according to the processing operation, the processing parameters and the material information; the surface parameter information includes surface roughness and surface finish; The step of determining the optimal illumination parameters and the optimal type of the light source in each of the production areas according to the surface parameter information comprises: For each production area, determine the standard lighting parameters and the optimal type of light source of the processing position corresponding to the work-in-progress in the production area according to the surface parameter information, and use the standard lighting parameters of the processing position as the standard lighting parameters of the production area; Acquire the area of ​​the daylighting windows of the production area, the light transmittance of the daylighting windows and the natural light parameters outside the workshop, and determine the natural light parameters of the production area based on the area of ​​the daylighting windows, the light transmittance of the daylighting windows and the natural light parameters outside the workshop; Determining optimal lighting parameters of the production area according to standard lighting parameters of the production area and natural light parameters of the production area; the optimal lighting parameters include optimal lighting intensity; The step of determining the optimal position of the light source in each of the production areas according to the first current position information, the second current position information and the surface parameter information comprises: Acquire workshop scanning data, and perform feature extraction on the workshop scanning data to obtain corresponding point cloud feature data; Establishing a lighting model according to the point cloud feature data; The optimal position of the light source in each of the production areas is determined according to the first current position information, the second current position information, the surface parameter information and the lighting model.

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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