Automatic control method and control system of high-resilience sports ground mat production equipment

Abstract

The invention relates to the technical field of industrial control, in particular to an automatic control method and system for high-resilience sports ground mat production equipment, and the method comprises the steps: obtaining the technological parameter information and observation parameter information of to-be-controlled production equipment; driving preset simulation production equipment to perform simulation production based on the process parameter information and the observation parameter information to obtain a simulation mapping image; judging whether the simulated mapping image contains hidden danger features or not, and if yes, performing hidden danger tracing based on the hidden danger features to obtain a hidden danger tracing result; and optimizing the process parameter information based on the hidden danger traceability result to obtain target execution parameter information, and performing production control based on the target execution parameter information. The probability of phenomena such as bubble merging or bubble collapsing in the floor mat can be reduced conveniently.

Description

Technical Field

[0001] The present application relates to the field of industrial control technology, and in particular to an automatic control method and control system for high-rebound sports mat production equipment. Background Art

[0002] High-resilience sports mats are functional products widely used in venues such as stadiums, gyms, and children's play areas. Their performance and quality are directly related to the safety and experience of users. Early production of high-resilience sports mats relied primarily on manual labor and simple mechanical equipment, resulting in low production efficiency and unstable product quality. In recent years, with the rapid development of technologies such as artificial intelligence, the Internet of Things, and big data, high-resilience sports mat production equipment is moving towards intelligentization. Intelligent equipment not only enables automated control of the production process but also collects production data in real time through sensors. It then processes and analyzes this data using big data analytics and artificial intelligence algorithms, enabling intelligent optimization of the production process and predictive maintenance.

[0003] However, the foaming effect of sports floor mats is a complex multivariable coupling result, which is usually limited by factors such as variable complexity, equipment performance and external interference. Therefore, it is difficult to completely eliminate defects such as combined bubbles or collapsed bubbles. Sports floor mats containing combined bubbles or collapsed bubbles may cause impact force buffering failure, which may endanger the life and health of users. Summary of the Invention

[0004] In order to reduce the probability of bubble formation or bubble collapse in floor mats, thereby improving the production quality of sports floor mats, the present application provides an automatic control method and control system for high-rebound sports floor mat production equipment.

[0005] In a first aspect, the present application provides an automatic control method for a high-resilience sports mat production device, which adopts the following technical solution: An automatic control method for high-resilience sports mat production equipment, comprising: Obtain process parameter information and observation parameter information of the production equipment to be controlled; Based on the process parameter information and the observation parameter information, a preset simulation production device is driven to perform simulation production to obtain a simulation mapping image; Determining whether the simulated mapping image contains hidden danger features, and if so, performing hidden danger tracing based on the hidden danger features to obtain hidden danger tracing results; The process parameter information is optimized based on the hidden danger tracing result to obtain target execution parameter information, and production control is performed based on the target execution parameter information.

[0006] By adopting the above technical solution, after obtaining the process parameter information, the preset simulation model is simulated and driven in combination with the observation parameter information, so as to simulate the actual production process of the production equipment. By identifying and judging whether the simulation mapping image contains hidden danger features, it is convenient to judge whether the process parameter information needs to be optimized. By timely discovering and optimizing the process parameter information in the simulated production process, rather than adjusting the process parameter information in the actual generation process, it is convenient to improve the timeliness and accuracy of discovering hidden danger features. In addition, by optimizing or adjusting the process parameters in time through simulated production, it is convenient to reduce the probability of bubble or bubble collapse in the sports floor mat during the actual production process, thereby improving the production quality of the sports floor mat.

[0007] In one possible implementation, the step of tracing the hidden danger source based on the hidden danger feature to obtain the hidden danger source tracing result includes: Determining a hidden danger surface feature and a hidden danger distribution of the hidden danger feature based on the simulated mapping image, and determining a hidden danger feature distribution map based on the hidden danger surface feature and the hidden danger distribution; Determining a plurality of process parameter combinations based on the process parameter information, and obtaining a process impact distribution map corresponding to each process parameter combination; Performing image matching on the hidden danger feature distribution map and each process impact distribution map, and determining a target process impact distribution map from all process impact distribution maps based on the image matching results, wherein the target process impact distribution map has the highest image matching value with the hidden danger feature distribution map; The hidden danger tracing result is determined according to the process parameter combination corresponding to the target process impact distribution map.

[0008] By adopting the above technical solution, abstract quality defects are converted into visual images by extracting the surface characteristics and spatial distribution patterns of hidden dangers, which is convenient for providing an intuitive analysis benchmark for hidden danger tracing. By combining and analyzing various process parameters, multiple process impact distribution maps are obtained, and then the process parameter combination that is most likely to cause hidden danger characteristics is selected from multiple process parameter combinations through image matching, which is convenient for improving the accuracy of hidden danger tracing.

[0009] In one possible implementation, when the hidden danger surface features include preset target hidden danger features, the method further includes: Determining the target hidden danger distribution and target hidden danger proportion corresponding to the preset target hidden danger feature from the hidden danger feature distribution map; Determining a process impact weight corresponding to the process parameter information based on the target hidden danger distribution and the target hidden danger proportion, and determining a fused process impact diagram based on the process impact weight; The fused process influence map is image matched with the hidden danger characteristic distribution map to obtain a fused matching value. When the fused matching value is higher than a preset matching threshold, the hidden danger tracing result is determined based on the process parameter combination corresponding to the fused process influence map.

[0010] By adopting the above technical solution and presetting the target hidden danger feature screening mechanism, it is convenient to automatically focus on key defect types, and by analyzing the hidden danger distribution and hidden danger proportion of the target hidden danger features, it is convenient to accurately trace the process parameters that may cause hidden danger phenomena. After determining the process influence weight corresponding to the process parameter information, it is verified through image matching. That is, only when the fusion matching value obtained after image matching of the fused process influence map and the hidden danger feature distribution map meets the conditions, the hidden danger tracing result will be determined according to the fused process influence map, thereby facilitating further improving the accuracy of determining the hidden danger tracing result.

[0011] In one possible implementation, performing production control based on the target execution parameter information includes: Based on the target execution parameter information, the preset simulation production equipment is driven to perform optimized simulation production to obtain an optimized simulation mapping image; determining an optimized layer according to the simulated mapping image before optimization and the optimized simulated mapping image after optimization; Determining the hidden danger optimization coverage rate and the hidden danger optimization rate based on the optimized layer; When the hidden danger optimization coverage rate is higher than a preset coverage rate threshold, and the hidden danger optimization rate is higher than a preset optimization rate threshold, production control is performed based on the target execution parameter information.

[0012] By adopting the above technical solution, a closed-loop process of parameter optimization-simulation verification-effect quantification is implemented to achieve two-way verification of process improvement, which facilitates early inspection and analysis of production effects after process parameter information adjustment, thereby avoiding the passive mode of trial and error-rework and improving optimization speed and accuracy.

[0013] In one possible implementation, when no optimization operation is detected within the first preset time period, the method further includes: Obtaining historical production log information corresponding to a second preset time period, and determining a first intelligent control adjustment parameter interval corresponding to the process parameter information based on the historical production log information; Acquire actual observation parameter information corresponding to the current moment, generate feedback information based on the actual observation parameter information, and feed back the feedback information to relevant staff members to remind the relevant staff members to select the observation parameter of interest from the actual observation parameter information; After receiving the observation parameters of interest selected by the relevant staff, determining the second intelligent control adjustment parameter interval corresponding to the process parameter information based on the observation parameters of interest; Based on the first intelligent control adjustment parameter interval and the second intelligent control adjustment parameter interval, a target intelligent control adjustment parameter interval is determined, and intelligent auxiliary control is started based on the target intelligent control adjustment parameter interval.

[0014] By adopting the above technical solution, the first intelligent control adjustment parameter range is obtained through historical production log information, and then the second intelligent control adjustment parameter range is determined based on expert advice. The target intelligent control adjustment parameter range is jointly determined through the dual methods of intelligent calculation and manual intervention, which facilitates the improvement of the accuracy of the intelligent assisted control stage, thereby facilitating the reduction of the probability of bubble merging or bubble collapse in the intelligent assisted control stage. By starting intelligent assisted control, the processing pressure of the system in the process of automatic control of production equipment can be reduced.

[0015] In one possible implementation, after the intelligent assisted regulation is initiated based on the target intelligent control adjustment parameter range, the method further includes: Obtaining a control log and an actual production image corresponding to a third preset time period, and determining a control intervention value based on the control log; Determining a control area based on the control log, and determining a cell parameter change rate corresponding to the control area from the actual production image; The maintenance time is determined based on the regulation intervention value and the cell parameter change rate, and the intelligent assistance exit time is determined based on the maintenance time.

[0016] By adopting the above technical solution, by analyzing the control log after starting the intelligent assisted control, it is convenient to evaluate whether the production status of the production equipment is stable. In addition, by identifying and analyzing the bubble parameter change rate corresponding to the control area from the actual production image, it is convenient to monitor the quality of the finished sports floor mats during the intelligent assisted control process. By coupling the production status and finished product quality of the production equipment during the intelligent assisted control stage, the maintenance time of the intelligent assisted control operation is analyzed and determined to avoid exiting the intelligent assisted control operation too early or too late.

[0017] In a second aspect, the present application provides a control system that adopts the following technical solution: A control system, comprising: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the automatic control method of the above-mentioned high-rebound sports mat production equipment.

[0018] In a third aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium includes: a computer program that can be loaded by a processor and executed by the automatic control method of the high-resilience sports mat production equipment.

[0019] In a fourth aspect, the present application provides a computer program product that adopts the following technical solution: A computer program product includes a computer program, which, when executed by a processor, implements the automatic control method of the high-rebound sports mat production equipment.

[0020] In summary, this application includes at least one of the following beneficial technical effects: After obtaining the process parameter information, the preset simulation model is simulated and driven in combination with the observation parameter information to simulate the actual production process of the production equipment. By identifying and judging whether the simulation mapping image contains hidden danger features, it is convenient to judge whether the process parameter information needs to be optimized. By timely discovering and optimizing the process parameter information during the simulated production process, rather than adjusting the process parameter information during the actual generation process, it is convenient to improve the timeliness and accuracy of discovering hidden danger features. In addition, by optimizing or adjusting the process parameters in time through simulated production, it is convenient to reduce the probability of bubble formation or bubble collapse in the sports floor mat during the actual production process, thereby improving the production quality of the sports floor mat.

[0021] Through the closed-loop process of parameter optimization-simulation verification-effect quantification, two-way verification of process improvement is achieved, which facilitates early inspection and analysis of the production effect after the process parameter information is adjusted, thereby avoiding the passive mode of trial and error-rework and improving the optimization rate and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a flow chart of an automatic control method for a high-resilience sports mat production device according to an embodiment of the present application; Figure 2 This is a schematic diagram of a production control process in an embodiment of the present application; Figure 3 It is a structural diagram of a control system in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The following is combined with Figures 1 to 3 This application is described in further detail.

[0024] After reading this specification, those skilled in the art may make non-creative modifications to this embodiment as needed, but such modifications are protected by patent law as long as they fall within the scope of the claims of this application.

[0025] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0026] It should be noted that in the optional embodiments of the present application, the object information and other related data involved, when the embodiments in the present application are applied to specific products or technologies, need to obtain the permission or consent of the object, and the collection, use and processing of the relevant data need to comply with the relevant laws, regulations and standards of the relevant countries and regions. In other words, if the embodiments of the present application involve data related to the object, it needs to be obtained through the authorization and consent of the object, the authorization and consent of the relevant departments, and in compliance with the relevant laws, regulations and standards of the country and region. If personal information is involved in the embodiments, the acquisition of all personal information requires the consent of the individual. If sensitive information is involved, the separate consent of the information subject needs to be obtained. The embodiments also need to be implemented with the authorization and consent of the object.

[0027] Specifically, embodiments of the present application provide an automatic control method for high-rebound sports mat production equipment, which is executed by a control system, which can be a server or a terminal device. The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, tablet computer, laptop computer, desktop computer, etc., but is not limited to these. The terminal device and server can be directly or indirectly connected via wired or wireless communication, which is not limited in the embodiments of the present application.

[0028] refer to Figure 1 , Figure 1 : is a flow chart of an automatic control method for a high-resilience sports mat production device according to an embodiment of the present application, the method comprising steps S110 to S140, wherein: Step S110: Acquire process parameter information and observation parameter information of the production equipment to be controlled.

[0029] Specifically, the production equipment to be controlled is high-rebound sports mat production equipment that needs to be automatically controlled. The production equipment to be controlled can cover the entire process of foaming, mixing, extrusion and molding. Among them, the foaming equipment is the core equipment for producing high-rebound sports mats. It is mainly used to generate chemically cross-linked polyethylene foam materials through chemical reactions. The foaming equipment can be a high-pressure foaming machine or PU foaming equipment. The specific foaming equipment can be set by relevant staff according to actual production conditions; the mixing equipment is mainly used to pre-mix the raw materials before foaming to ensure the uniformity of the formula, which directly affects the physical properties of the final product; the extrusion equipment is mainly used to evenly mix the foamed material with other additives such as antioxidants and flame retardants, and form continuous particles or sheets through the extrusion process. The extrusion equipment can be a twin-screw extruder; the molding equipment is mainly used to heat and plasticize the particles or sheets output by the twin-screw extruder, and then extrude them into the final shape of the mat through a mold.

[0030] The process parameter information of the production equipment to be controlled includes but is not limited to the temperature, pressure, cooling rate and raw material ratio involved in the processing process. Among them: Regarding temperature, since chemical cross-linked polyethylene foaming is a chemical reaction, temperature will affect the reaction speed and foam quality. If the temperature is too high, it may cause the reaction to be too fast, and the foam structure will be unstable. If the temperature is too low, it may cause incomplete reaction, which may affect the rebound performance. Therefore, when automatically controlling the production equipment to be controlled, it is necessary to consider and analyze the temperature of the foaming, mixing and extrusion stages; Regarding pressure, since pressure will directly affect the formation and distribution of bubbles in the sports air cushion during the foaming process, insufficient pressure may cause the bubbles in the sports air cushion to be too large or unevenly distributed. , bubble collapse or bubble merging occurs. When the pressure is too high, it may inhibit the generation of bubbles in the sports air cushion, which may affect the elasticity. Therefore, when automatically controlling the production equipment to be controlled, it is necessary to consider and analyze the pressure situation; Regarding the cooling rate, since the cooling rate will affect the solidification speed and structural stability of the foam, a cooling rate that is too fast may cause internal stress, which may easily cause deformation of the sports mat. A cooling rate that is too slow may lead to low production efficiency and may even cause overheating and decomposition. Therefore, when automatically controlling the production equipment to be controlled, it is necessary to consider and analyze the cooling rate to ensure that the product size is stable and the internal structure is uniform; Regarding the raw material ratio, the raw material ratio directly affects the physical properties of the sports mat, such as rebound rate, density and hardness. The process parameter information of the production equipment to be controlled can be collected by the temperature sensor, pressure sensor, flow meter and weighing sensor set at the production equipment to be controlled, and uploaded to the control system. The specific process parameter information is not specifically limited in the embodiment of this application and can be set by relevant technical personnel according to actual needs.

[0031] Observation parameter information for the production equipment to be controlled includes, but is not limited to, equipment status and environmental parameters. Equipment status includes, but is not limited to, operating status, fault status, and maintenance status, while environmental parameters include, but are not limited to, temperature and humidity. Specific information can be set by relevant technical personnel based on actual needs. Observation parameter information can be collected by sensors installed at the production equipment to be controlled and recorded by relevant personnel before being uploaded to the control system.

[0032] Step S120: driving a preset simulation production device to perform simulation production based on the process parameter information and the observation parameter information to obtain a simulation mapping image.

[0033] Specifically, the process parameter information and observation parameter information of the production equipment to be controlled are collected during the trial operation phase after the production equipment to be controlled is started. The production and processing conditions of the production equipment to be controlled in the future period are simulated and predicted based on the process parameter information and observation parameter information collected during the trial operation phase.

[0034] The simulation prediction process may include: establishing a preset simulated production equipment corresponding to the production equipment to be controlled, wherein, based on the production process of high-rebound sports mats, a mathematical model or physical model can be established to describe the foaming, mixing, extrusion and molding processes of the actual production equipment to be controlled. The mathematical model may include differential equations, difference equations or statistical models to describe the interaction and influence between process parameters. The physical model may be based on finite element analysis or computational fluid dynamics to more accurately simulate the physical phenomena in the production process, such as the formation and curing process of foam. The process of constructing a preset simulated production equipment is not specifically limited in the embodiment of this application, as long as it can simulate the production and processing process of the production equipment to be controlled. After constructing the preset simulation production equipment, the process parameter information and observation parameter information can be input into the preset simulation production equipment so that the preset simulation production equipment can simulate the production and processing tasks that may be faced in the future according to the current state of the actual production equipment to be controlled. A large amount of simulation data will be generated during the simulated production process. The simulation data includes but is not limited to key simulation indicator data such as simulated temperature distribution, simulated pressure changes, and simulated foam structure. The key simulation indicator data can be converted into an intuitive image, that is, a simulated mapping image, using the preset data visualization technology. Among them, the preset data visualization technology can be tools such as Matplotlib, OpenGL or Unity. The specific preset data visualization technology is not specifically limited in the embodiments of this application.

[0035] Step S130: determining whether the simulated mapping image contains hidden danger features. If so, performing hidden danger tracing based on the hidden danger features to obtain hidden danger tracing results.

[0036] Specifically, potential danger features can be coalescence or collapse. These features can be identified from the simulated mapping image using a preset feature recognition algorithm. When a potential danger feature is included in the simulated mapping image, it indicates that coalescence or collapse may occur during the simulated production process. In this case, the cause of the potential danger feature needs to be traced. Coalescing refers to the phenomenon in which two or more bubbles merge into a single large bubble during the foaming process, typically due to a pressure difference between the bubbles or insufficient melt strength. Collapse refers to the collapse of bubbles after formation, likely due to the diffusion of gas from the pores into the melt, causing the pores to lose support and collapse. Abnormal temperatures can reduce the melt strength of the polymer, making it easier for cells to rupture, merge, or collapse during growth. Abnormal pressure can cause insufficient pressure to cause the pressure inside the bubbles to fall below ambient pressure, prompting gas to escape from the bubbles and causing collapse. Abnormal cooling rates can cause the surface of the bubbles to solidify while the interior remains hot due to excessive cooling. Heat dissipation from the bubbles can lead to bubble rupture or collapse. Abnormal raw material ratios can affect the physical properties of the foam, such as strength and toughness, due to excessive or insufficient proportions of cross-linkers or additives, increasing the risk of collapse. Therefore, when hidden danger features are included in the simulation mapping image, abnormalities may exist in the process parameter information. In this case, it is necessary to identify the abnormal process parameter information through hidden danger tracing.

[0037] Furthermore, in order to improve the accuracy of hidden danger tracing, hidden danger tracing is performed based on hidden danger characteristics to obtain hidden danger tracing results, which may specifically include: Determine the hidden danger surface characteristics and hidden danger distribution of the hidden danger characteristics based on the simulated mapping image, and determine the hidden danger characteristic distribution map based on the hidden danger surface characteristics and hidden danger distribution; determine multiple process parameter combinations based on the process parameter information, and obtain the process influence distribution map corresponding to each process parameter combination; perform image matching on the hidden danger characteristic distribution map and each process influence distribution map, and determine the target process influence distribution map from all the process influence distribution maps based on the image matching results, and the image matching value between the target process influence distribution map and the hidden danger characteristic distribution map is the highest; determine the hidden danger tracing result according to the process parameter combination corresponding to the target process influence distribution map.

[0038] Specifically, the hidden danger surface features and hidden danger distribution corresponding to the hidden danger features can be identified from the simulated mapping image based on a preset feature recognition algorithm. Hidden danger surface features can include bubble morphology, collapse depth, bubble color, and bubble brightness. Bubbles with merged or collapsed bubbles may lose their original round or elliptical shapes and become flat or concave. Furthermore, merged or collapsed bubbles may cause changes in their internal structure, resulting in decreased bubble brightness or color changes. Therefore, the hidden dangers present in the simulated mapping image can be further analyzed by observing the hidden danger surface features. The hidden danger distribution represents the distribution or aggregation of hidden danger features in the simulated mapping image. The hidden danger feature distribution map, determined based on the hidden danger surface features and hidden danger distribution, only includes areas in the simulated mapping image where hidden danger features exist, as well as the hidden danger surface features corresponding to each hidden danger feature. That is, the hidden danger feature distribution map represents a partial image of the simulated mapping image. Based on the surface features and distribution of hidden dangers, when determining the hidden danger feature distribution map, the specific position of each hidden danger feature in the simulated mapping image can be determined first. This can be achieved by recording the center coordinates, boundary coordinates or pixel positions of each hidden danger feature, and then mapping the extracted hidden danger surface features and the specific positions corresponding to each hidden danger feature into a two-dimensional or three-dimensional space to generate a hidden danger feature distribution map. The specific generation method is not specifically limited in the embodiments of this application.

[0039] Because process parameter information contains multiple process parameters, multiple process parameter combinations corresponding to the process parameter information can be obtained through feature combination. Each process parameter combination contains at least one process parameter. For example, if the process parameter information contains three process parameters, namely A, B, and C, the corresponding process parameter combinations may be A, B, C, AB, AC, BC, and ABC. Different process parameter anomalies may result in different anomalies. For example, temperature anomalies primarily affect the chemical reaction rate and foam quality, potentially leading to overly rapid or slow reactions and unstable foam structures. Pressure anomalies directly affect bubble formation and distribution. Temperature anomalies may manifest on the simulated mapping image as irregular bubble morphology, changes in color, or brightness. Pressure anomalies may manifest on the simulated mapping image as bubbles of varying sizes and uneven distribution. Different process parameter combinations correspond to different process impact distribution maps. The process impact distribution map corresponding to each process parameter combination can be determined by relevant personnel based on historical experimental data and uploaded to the control system in advance.

[0040] The process impact distribution map is the surface characteristics and distribution of bubbles that may be caused by abnormalities in the process parameters included in the corresponding process parameter combination. The hidden danger characteristic distribution map is matched with the process impact distribution map corresponding to each process parameter combination, and the target process impact distribution map with the highest similarity to the hidden danger characteristic distribution map is determined from multiple process impact distribution maps. That is, when an abnormality occurs in the process parameter combination corresponding to the target process impact distribution map, the situation in the hidden danger characteristic distribution map is likely to occur. At this time, the final hidden danger tracing result is determined based on the process parameter combination corresponding to the target process impact distribution map, which facilitates improving the pertinence and accuracy of determining the hidden danger tracing result.

[0041] Furthermore, when the hidden danger surface features include preset target hidden danger features, the method provided in the embodiment of the present application further includes: Determine the target hidden danger distribution and target hidden danger proportion corresponding to the preset target hidden danger characteristics from the hidden danger characteristic distribution map; determine the process influence weight corresponding to the process parameter information based on the target hidden danger distribution and target hidden danger proportion, and determine the fused process influence map based on the process influence weight; perform image matching on the fused process influence map and the hidden danger characteristic distribution map to obtain a fused matching value. When the fused matching value is higher than the preset matching threshold, determine the hidden danger tracing result based on the process parameter combination corresponding to the fused process influence map.

[0042] Specifically, the preset target hidden danger feature can be a preset bubble shape, a bubble collapse depth higher than a preset depth threshold, a preset bubble color, and a bubble brightness higher than a preset brightness. The specific content can be determined by relevant staff based on historical experimental data and uploaded to the control system. When the hidden danger surface feature does not contain the preset target hidden danger feature, the process impact distribution map determined by the relevant staff based on historical experimental data can be directly used. However, when the hidden danger surface feature contains the preset target hidden danger feature, the hidden danger situation is more serious. At this time, the corresponding fusion process impact map can be personalized according to the specific performance of the hidden danger feature in the hidden danger feature distribution map. Specifically, the target hidden danger distribution and target hidden danger proportion corresponding to the preset target hidden danger feature can be identified from the hidden danger feature distribution map according to the preset feature recognition algorithm. Different target hidden danger distributions and target hidden danger proportions have different corresponding process impact weights. Among them, the target hidden danger distribution can be the aggregation score of the target hidden danger feature, which can be determined by calculating the adjacent distance between each target hidden danger feature and its nearest neighbor target hidden danger feature in the hidden danger feature distribution map, and then analyzing the distribution of these adjacent distances according to the preset spatial statistical algorithm. The preset spatial statistical algorithm can be Ripley's K function. The specific algorithm is not specifically limited in the embodiment of this application.

[0043] The process influence weights corresponding to the target hidden danger distribution and the target hidden danger proportion can be determined based on the preset influence weight mapping relationship. For example, the process parameter information corresponding to the hidden danger characteristic distribution diagram includes three process parameters, namely A, B, and C. The aggregation score corresponding to the target hidden danger distribution is 3, and when the target hidden danger proportion is 40%, the corresponding process influence weights are A:B:C=2:3:5. The specific content of the preset influence weight mapping relationship is not specifically limited in the embodiment of this application and can be determined by relevant staff based on historical experimental data and uploaded to the control system. According to the determined process influence weights, the corresponding fusion process influence diagram can be determined. For example, when the process influence weights are A:B:C=2:3:5, the corresponding fusion process influence diagram is the impact caused by 20% of the process parameter A being abnormal, the impact caused by 30% of the process parameter B being abnormal, and the fusion phenomenon that may be caused by 50% of the process parameter C being abnormal.

[0044] After determining the fusion process influence map, it is necessary to perform image matching on the fusion process influence map and the hidden danger feature distribution map to obtain a fusion matching value. Only when the fusion matching value is higher than the preset matching threshold value can the hidden danger tracing result be determined based on the process parameter combination corresponding to the fusion process influence map. If the fusion matching value is not higher than the preset matching threshold value, it is still necessary to use the method of determining the hidden danger tracing result when the hidden danger surface feature does not contain the preset target hidden danger feature in the above embodiment.

[0045] Step S140: Optimize process parameter information based on the hidden danger tracing results, obtain target execution parameter information, and perform production control based on the target execution parameter information.

[0046] Specifically, when optimizing process parameter information, the process parameters to be optimized can be identified from the hidden danger tracing results first, and then targeted adjustments can be made based on the process parameters to be optimized. The specific adjustment value is not specifically limited in the embodiment of this application, as long as it can be ensured that the abnormal probability of bubble formation or bubble collapse during actual processing based on the optimized process parameter information is lower than the preset probability threshold. The specific preset probability threshold is not specifically limited in the embodiment of this application, and can be determined by relevant staff based on historical experimental data and uploaded to the control system.

[0047] For the embodiments of the present application, after obtaining the process parameter information, the preset simulation model is simulated and driven in combination with the observation parameter information, so as to simulate the actual production process of the production equipment. By identifying and judging whether the simulation mapping image contains hidden danger features, it is convenient to judge whether the process parameter information needs to be optimized. By timely discovering and optimizing the process parameter information during the simulated production process, rather than adjusting the process parameter information during the actual generation process, it is convenient to improve the timeliness and accuracy of discovering hidden danger features. In addition, by optimizing or adjusting the process parameters in time through simulated production, it is convenient to reduce the probability of bubbles or bubble collapse in the sports floor mats during the actual production process, thereby improving the production quality of the sports floor mats.

[0048] Furthermore, in order to improve the optimization rate and accuracy, when performing production control based on target execution parameter information, steps S210 to S240 may be specifically included, such as Figure 2 As shown, where: Step S210: driving the preset simulation production equipment to perform optimized simulation production based on the target execution parameter information to obtain an optimized simulation mapping image.

[0049] Step S220: determining an optimized layer according to the simulated mapping image before optimization and the optimized simulated mapping image after optimization.

[0050] Specifically, after determining the target execution parameter information, actual production is not carried out directly based on the target execution parameter information. Instead, an optimization simulation is first performed based on the target execution parameter information to obtain an optimized simulation mapping image. The optimization results are evaluated by analyzing the optimized simulation mapping image. When the optimization effect meets the preset conditions, actual production control of the production equipment to be controlled can be carried out based on the target execution parameter information.

[0051] The target execution parameter information can be input into a preset simulation production device for optimized simulation production. After obtaining the optimized simulation mapping image, the pre-optimized simulation mapping image and the optimized simulation mapping image are subjected to image preprocessing to improve the image quality of the simulated mapping image and the optimized simulation mapping image, thereby facilitating subsequent operations such as feature extraction and analysis. Image preprocessing includes but is not limited to denoising, contrast enhancement, and image correction. Preset image processing techniques can be used to extract hidden danger features from the optimized simulation mapping image and the simulated mapping image, such as bubble morphology, collapse depth, color change, brightness anomalies, etc. Preset feature matching algorithms are then used to identify changes in hidden danger features between the two images, and the optimized layer is determined based on these changes.

[0052] Step S230: Determine the hidden danger optimization coverage rate and the hidden danger optimization rate based on the optimized layer.

[0053] Step S240: When the hidden danger optimization coverage rate is higher than the preset coverage rate threshold, and the hidden danger optimization rate is higher than the preset optimization rate threshold, production control is performed based on the target execution parameter information.

[0054] Specifically, the hidden danger optimization coverage rate is used to reflect the degree of coverage of the hidden danger area containing hidden danger features in the simulated mapping image by the optimization measures. It can be obtained by calculating the ratio of the area of ​​the hidden danger area after optimization to the area of ​​the hidden danger area before optimization in the optimization layer. The formula can be expressed as: hidden danger optimization coverage rate = (area of ​​the hidden danger area before optimization - area of ​​the hidden danger area after optimization) / area of ​​the hidden danger area before optimization × 100%. The hidden danger optimization rate is used to reflect the degree of reduction of hidden dangers containing hidden danger features in the simulated mapping image by the optimization measures. It can be obtained by calculating the change in the number of hidden danger features before and after optimization. The formula can be expressed as: hidden danger optimization rate = (number of hidden danger features before optimization - number of hidden danger features after optimization) / number of hidden danger features before optimization × 100%. When the hidden danger optimization coverage rate is higher than the preset coverage rate threshold, and the hidden danger optimization rate is higher than the preset optimization rate threshold, it indicates that the optimization effect meets the preset conditions. At this time, actual production control can be performed on the production equipment to be controlled based on the target execution parameter information. Among them, the specific preset coverage rate threshold and the preset optimization rate threshold are not specifically limited in the embodiment of the present application. They can be determined by relevant staff based on historical experimental data and uploaded to the control system.

[0055] Through the closed-loop process of parameter optimization-simulation verification-effect quantification, two-way verification of process improvement is achieved, which facilitates early inspection and analysis of the production effect after the process parameter information is adjusted, thereby avoiding the passive mode of trial and error-rework and improving the optimization rate and accuracy.

[0056] Furthermore, when no optimization operation is detected within the first preset time period, the method provided in the embodiment of the present application further includes: Obtain historical production log information corresponding to a second preset time period, and determine a first intelligent control adjustment parameter interval corresponding to the process parameter information based on the historical production log information; obtain actual observation parameter information corresponding to the current moment, generate feedback information based on the actual observation parameter information, and feed back the feedback information to relevant staff to remind relevant staff to select the focus observation parameters from the actual observation parameter information; after receiving the focus observation parameters selected by the relevant staff, determine the second intelligent control adjustment parameter interval corresponding to the process parameter information based on the focus observation parameters; determine the target intelligent control adjustment parameter interval based on the first intelligent control adjustment parameter interval and the second intelligent control adjustment parameter interval, and start intelligent assisted regulation based on the target intelligent control adjustment parameter interval.

[0057] Specifically, the first preset time period is a period of time after the last optimization operation was detected. The duration corresponding to the first preset time period can be 30 minutes or 40 minutes. The specific duration is not specifically limited in the embodiments of this application. If no optimization operation is detected within the first preset time period, it can be indicated that the operating state of the production equipment to be controlled is relatively stable in the first preset time period. The second preset time period is a period of time before the current moment. The duration corresponding to the second preset time period can be 24 hours or 36 hours. The specific duration is not specifically limited in the embodiments of this application.

[0058] The historical production log information includes key data such as the historical set values ​​of various process parameters, historical actual processing values, abnormal events in the production process, and product quality inspection results in the historical production stage. After obtaining the historical production log information corresponding to the second preset time period, the historical production log information can be cleaned and preprocessed to remove invalid data in the historical production log information. The first intelligent control adjustment parameter range corresponding to the process parameter information is determined based on the historical production log information to reflect the stable working range of the production equipment to be controlled in the historical production process. That is, in the historical production stage, when the production equipment to be controlled performs processing operations according to the first intelligent control adjustment parameter range, the probability of abnormality or failure of the production equipment to be controlled is low.

[0059] The actual observation parameter information is the actual equipment status and actual environmental parameters actually collected at the current moment. Different actual observation parameter information may face different hidden risks. Feedback information containing the actual observation parameter information is fed back to relevant staff through the user interface, alarm system or mobile application, etc., to remind relevant staff to select the focus observation parameters based on personal experience. The focus observation parameters are the process parameters that the relevant staff believe require special attention or adjustment in the current production process. After receiving the focus observation parameters fed back by the relevant staff, the safety range of the focus observation parameters can be predicted based on the process knowledge base or expert system contained in the control system, that is, the second intelligent control adjustment parameter range. Among them, the process knowledge base or expert system can be determined by the relevant staff based on historical experimental data and uploaded to the control system in advance.

[0060] The first intelligent control adjustment parameter interval and the second intelligent control adjustment parameter interval are intersected and fused to obtain a target intelligent control adjustment parameter interval that comprehensively considers historical stability and real-time attention needs. The various process parameters in the process parameter information are automatically adjusted according to the target intelligent control adjustment parameter interval to keep them within the target interval. The target intelligent control adjustment parameter interval is jointly determined by intelligent calculation and manual intervention, which facilitates improving the accuracy of the intelligent auxiliary control stage, thereby facilitating reducing the probability of bubble merging or bubble collapse in the intelligent auxiliary control stage. By starting intelligent auxiliary control, the processing pressure of the system in the process of automatic control of production equipment can be reduced.

[0061] Furthermore, in order to avoid exiting the intelligent assisted control operation too early or too late, after starting the intelligent assisted control based on the target intelligent control adjustment parameter range, the method provided in the embodiment of the present application further includes: Obtain the control log and actual production image corresponding to the third preset time period, and determine the control intervention value based on the control log; determine the control area based on the control log, and determine the bubble parameter change rate corresponding to the control area from the actual production image; determine the maintenance time based on the control intervention value and the bubble parameter change rate, and determine the intelligent assistance exit time based on the maintenance time.

[0062] Specifically, the third preset time period is a period of time after the intelligent assisted control operation is turned on. After the intelligent assisted control operation is turned on, it is not necessary to control the production equipment to be controlled according to the real-time situation. It is only necessary to ensure that each process parameter is within the corresponding intelligent control adjustment parameter range. However, the production equipment to be controlled is not always suitable for the intelligent assisted control state. The control log within the third preset time period can be extracted from the control system. The control log should contain detailed records of the intelligent control operation, such as the number of controls, control amplitude, control time and other key information. The corresponding control interference value can be obtained by quantifying the control log. Among them, the more the number of controls, the larger the control amplitude, and the higher the control frequency, the higher the corresponding control interference value.

[0063] At the same time, actual production images within the third preset time period can also be obtained from the monitoring system of the production site. According to the feature recognition algorithm, the changes in the pore parameters corresponding to the pore structure within the third preset time period can be determined from the actual production images. The pore parameters can be pore density, pore size uniformity, wall thickness ratio, etc. The pore parameter change rate can be determined by calculating the difference between each pore parameter at different time points and dividing it by the time interval. The pore parameter change rate is the mean change rate of all pore parameters.

[0064] Different control intervention values ​​and cell parameter change rates correspond to different maintenance durations. Based on a preset duration mapping relationship, the maintenance duration of the control intervention value and cell parameter change rate corresponding to the actual production image can be determined. Starting from the current moment, the moment reached after the maintenance duration elapses is the intelligent assistance exit moment. At the intelligent assistance exit moment, the exit operation is executed, stopping the intervention of intelligent assisted control, and re-regulating the controlled production equipment in real time based on the real-time situation. By coupling the production status of production equipment and the quality of the finished product during the intelligent assisted control phase, the maintenance duration of the intelligent assisted control operation is analyzed and determined to avoid premature or late exit from the intelligent assisted control operation.

[0065] In the embodiment of the present application, a control system is provided, such as Figure 3 As shown, Figure 3 The control system 300 shown includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, via a bus 302. Optionally, the control system 300 may further include a transceiver 304. It should be noted that in practical applications, the number of transceivers 304 is not limited to one, and the structure of the control system 300 does not constitute a limitation on the embodiments of the present application.

[0066] Processor 301 can be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic device, transistor logic device, hardware component, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 301 can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0067] Bus 302 may include a path for transmitting information between the above components. Bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. Bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, Figure 3 The fact that only one line is used does not mean that there is only one bus or one type of bus.

[0068] The memory 303 may be a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory) or other type of dynamic storage device that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0069] The memory 303 is used to store application code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the above method embodiment.

[0070] The control system includes, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Servers are also possible. Figure 3 The control system shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0071] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding contents of the aforementioned method embodiment.

[0072] An embodiment of the present application provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the method in any of the above embodiments is implemented.

[0073] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the flowcharts of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0074] The above description is only part of the implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. An automatic control method for high-resilience sports mat production equipment, characterized in that: include: Obtain process parameter information and observation parameter information of the production equipment to be controlled; Based on the process parameter information and the observation parameter information, a preset simulation production device is driven to perform simulation production to obtain a simulation mapping image; Determining whether the simulated mapping image contains hidden danger features, and if so, performing hidden danger tracing based on the hidden danger features to obtain hidden danger tracing results; The process parameter information is optimized based on the hidden danger tracing result to obtain target execution parameter information, and production control is performed based on the target execution parameter information.

2. The automatic control method for high-resilience sports mat production equipment according to claim 1, characterized in that: The hidden danger tracing based on the hidden danger characteristics to obtain the hidden danger tracing result includes: Determining a hidden danger surface feature and a hidden danger distribution of the hidden danger feature based on the simulated mapping image, and determining a hidden danger feature distribution map based on the hidden danger surface feature and the hidden danger distribution; Determining a plurality of process parameter combinations based on the process parameter information, and obtaining a process impact distribution map corresponding to each process parameter combination; Performing image matching on the hidden danger feature distribution map and each process impact distribution map, and determining a target process impact distribution map from all process impact distribution maps based on the image matching results, wherein the target process impact distribution map has the highest image matching value with the hidden danger feature distribution map; The hidden danger tracing result is determined according to the process parameter combination corresponding to the target process impact distribution map.

3. The automatic control method for high-resilience sports mat production equipment according to claim 2, characterized in that: When the hidden danger surface features include preset target hidden danger features, it also includes: Determining the target hidden danger distribution and target hidden danger proportion corresponding to the preset target hidden danger feature from the hidden danger feature distribution map; Determining a process impact weight corresponding to the process parameter information based on the target hidden danger distribution and the target hidden danger proportion, and determining a fused process impact diagram based on the process impact weight; The fused process influence map is image matched with the hidden danger characteristic distribution map to obtain a fused matching value. When the fused matching value is higher than a preset matching threshold, the hidden danger tracing result is determined based on the process parameter combination corresponding to the fused process influence map.

4. The automatic control method for high-resilience sports mat production equipment according to claim 1, characterized in that: The performing of production control based on the target execution parameter information includes: Based on the target execution parameter information, the preset simulation production equipment is driven to perform optimized simulation production to obtain an optimized simulation mapping image; determining an optimized layer according to the simulated mapping image before optimization and the optimized simulated mapping image after optimization; Determining the hidden danger optimization coverage rate and the hidden danger optimization rate based on the optimized layer; When the hidden danger optimization coverage rate is higher than a preset coverage rate threshold, and the hidden danger optimization rate is higher than a preset optimization rate threshold, production control is performed based on the target execution parameter information.

5. The automatic control method for high-resilience sports mat production equipment according to claim 1, characterized in that: When no optimization operation is detected within the first preset time period, the method further includes: Obtaining historical production log information corresponding to a second preset time period, and determining a first intelligent control adjustment parameter interval corresponding to the process parameter information based on the historical production log information; Acquire actual observation parameter information corresponding to the current moment, generate feedback information based on the actual observation parameter information, and feed back the feedback information to relevant staff members to remind the relevant staff members to select the observation parameter of interest from the actual observation parameter information; After receiving the observation parameters of interest selected by the relevant staff, determining the second intelligent control adjustment parameter interval corresponding to the process parameter information based on the observation parameters of interest; Based on the first intelligent control adjustment parameter interval and the second intelligent control adjustment parameter interval, a target intelligent control adjustment parameter interval is determined, and intelligent auxiliary control is started based on the target intelligent control adjustment parameter interval.

6. The automatic control method for high-resilience sports mat production equipment according to claim 1, characterized in that: After the intelligent auxiliary control is started based on the target intelligent control adjustment parameter range, the method further includes: Obtaining a control log and an actual production image corresponding to a third preset time period, and determining a control intervention value based on the control log; Determining a control area based on the control log, and determining a cell parameter change rate corresponding to the control area from the actual production image; The maintenance time is determined based on the regulation intervention value and the cell parameter change rate, and the intelligent assistance exit time is determined based on the maintenance time.

7. A control system, characterized in that: The control system includes: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute an automatic control method for a high-rebound sports mat production equipment according to any one of claims 1-6.

8. A computer-readable storage medium, characterized in that include: The invention stores a computer program which can be loaded by a processor and executes an automatic control method for a high-resilience sports mat production device as claimed in any one of claims 1 to 6.

9. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the steps of the automatic control method of a high-rebound sports mat production equipment according to any one of claims 1 to 6.

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