Intelligent rubber manufacturing optimization control method based on multi-source monitoring data

Abstract

The invention relates to the technical field of rubber manufacturing, and provides an intelligent rubber manufacturing optimization control method based on multi-source monitoring data. The method comprises the following steps: interacting feeding and crushing mechanisms; entering materials are recognized, and charging attribute information is obtained; obtaining multi-source monitoring data, and generating a simulation model; calling the simulation model to train the monitoring model, and identifying the charging attribute information by using the hidden danger monitoring model to obtain a hidden danger index; inputting the hidden danger index into a controller for control optimization, and outputting a control parameter; the controller controls the feeding and smashing mechanism according to the control parameters. The technical problems of element blockage and potential crushing safety hazards caused by splashing and conflicting of crushed particles due to uniform and unchanged processing force when a charging and crushing mechanism treats materials with relatively high hardness in rubber manufacturing and processing are solved, flexible self-adaptive control parameter adjustment by combining with charging types is realized, and the working efficiency is improved. And the working performance and the safety of the feeding and crushing mechanism are improved.

Description

Technical Field

[0001] The present application relates to the field of chemical material manufacturing technology, specifically to the field of rubber manufacturing technology, and in particular to a rubber intelligent manufacturing optimization control method based on multi-source monitoring data. Background Art

[0002] With the rapid development of the rubber manufacturing industry, the requirements for rubber product quality and production efficiency are also constantly increasing. Especially in the feeding and crushing process in rubber manufacturing, due to the diversity of material hardness and the complexity of the processing process, the traditional feeding and crushing control method is often difficult to meet the high efficiency, precision and safety requirements of modern rubber manufacturing. When the hardness of the feed is large, the uniform and unchanging processing force in the traditional feeding and crushing mechanism may cause splashing and collision of crushed particles, which not only affects the stability of the production process, but also may cause jamming of equipment components, thereby causing safety hazards. Summary of the invention

[0003] This application provides a rubber intelligent manufacturing optimization control method based on multi-source monitoring data, aiming to solve the technical problems of component jamming and crushing safety hazards caused by splashing and collision of crushed particles due to uniform and unchanging processing force when the feeding and crushing mechanism in the rubber manufacturing process processes materials with higher hardness.

[0004] In view of the above problems, the present application provides a rubber intelligent manufacturing optimization control method based on multi-source monitoring data.

[0005] The present application provides a rubber intelligent manufacturing optimization control method based on multi-source monitoring data, the method comprising: a feeding and crushing mechanism of an interactive rubber processing equipment; identifying materials entering the feeding and crushing mechanism, and obtaining feeding attribute information, wherein the feeding attribute information includes feeding physical properties, feeding chemical properties and feeding crushing properties; obtaining multi-source monitoring data of the feeding and crushing mechanism, and generating a feeding and crushing simulation model of the feeding and crushing mechanism using the multi-source monitoring data; calling the feeding and crushing simulation model to train a crushing conflict hidden danger monitoring model, and using the crushing conflict hidden danger monitoring model to identify the feeding attribute information and obtain a first conflict hidden danger indicator; inputting the first conflict hidden danger indicator into a crushing optimization controller for control optimization, and outputting a crushing optimization control parameter, wherein the crushing optimization controller includes a preset conflict hidden danger indicator; the crushing optimization controller controls the feeding and crushing mechanism according to the crushing optimization control parameter.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: The above-mentioned rubber intelligent manufacturing optimization control method based on multi-source monitoring data identifies the materials entering the feeding and crushing mechanism and obtains the property information of these materials, including the physical properties, chemical properties and crushing characteristics of the materials. Subsequently, multi-source monitoring data such as temperature, pressure, motor speed, etc. are collected during the operation of the feeding and crushing mechanism. These data are used to build a simulation model of feeding and crushing, which can simulate the actual processing process. After that, a crushing conflict hidden danger monitoring model is trained using this simulation model. The function of this model is to identify which material properties or processing conditions may cause crushing conflicts. Once the potential risk is identified, a conflict hidden danger indicator is generated. Then, this conflict hidden danger indicator is input into the crushing optimization controller. This controller has preset an acceptable risk level, and will perform optimization calculations based on the input indicators to output the best crushing control parameters. Finally, the crushing optimization controller will adjust the operation of the feeding and crushing mechanism according to these optimization parameters to ensure that while maintaining efficient production, the risk of crushing conflicts is reduced, thereby improving the safety and stability of the entire processing process.

[0007] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0009] Figure 1 A schematic diagram of a flow chart of a rubber intelligent manufacturing optimization control method based on multi-source monitoring data in one embodiment; Figure 2 It is a schematic diagram of the flow of training a crushing conflict hidden danger monitoring model of a rubber intelligent manufacturing optimization control method based on multi-source monitoring data in one embodiment. DETAILED DESCRIPTION

[0010] The embodiment of the present application provides a rubber intelligent manufacturing optimization control method based on multi-source monitoring data to solve the technical problem of component jamming and crushing safety hazards caused by splashing and collision of crushed particles due to uniform and unchanging processing force when the feeding and crushing mechanism in the rubber manufacturing process processes materials with higher hardness.

[0011] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0012] It should be noted that the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices. Embodiment 1

[0013] like Figure 1 As shown, the present application provides a rubber intelligent manufacturing optimization control method based on multi-source monitoring data, the method comprising: Feeding and crushing mechanism of interactive rubber processing equipment.

[0014] In the rubber manufacturing industry, the feeding and crushing mechanism plays a vital role. It is an indispensable link in the production process of rubber products, responsible for finely crushing and mixing various raw materials to provide uniform raw material supply for subsequent processes. However, with the continuous increase in production demand, the diversity of raw materials has become increasingly prominent, but because a uniform and unchanged processing force is used to process a variety of materials, the processing force is insufficient, resulting in particle splashing and collision during the crushing process, resulting in a waste of raw materials, and may also cause jamming and damage to equipment components, seriously affecting the continuity and safety of production.

[0015] In the embodiment of the present application, in the rubber processing equipment, the feeding and crushing mechanism is a key component. It is responsible for receiving and processing rubber raw materials or other materials, and crushing them into the required particle size through mechanical action. The system terminal is interactively connected with the feeding and crushing mechanism by wireless communication. During the interaction process, the feeding and crushing mechanism will receive the signal from the system terminal and crush the raw materials.

[0016] The material entering the feeding and crushing mechanism is identified, and the feeding property information is obtained, wherein the feeding property information includes the feeding physical properties, the feeding chemical properties and the feeding crushing properties.

[0017] In one embodiment, during the rubber processing, the system terminal identifies the material entering the feeding and crushing mechanism. This identification process not only confirms the type of material, but also requires obtaining detailed attribute information of the material. This information includes the physical properties of the feeding, the chemical properties of the feeding, and the crushing properties of the feeding. Among them, the physical properties of the feeding describe the basic physical properties of the material, such as the hardness, density, shape, etc. of the material. Understanding these physical properties helps the system terminal determine how much force, what speed, and what method the feeding and crushing mechanism should use to crush to ensure the crushing effect and efficiency. The chemical properties of the feeding involve the chemical composition, stability, and possible chemical reactions with other substances of the material. This information is crucial to avoid unwanted chemical changes or the generation of harmful substances during the crushing process. The feeding and crushing characteristics describe the characteristics that the material may exhibit during the crushing process, such as whether it is easy to crush, whether it is easy to form dust, whether it is easy to block the equipment, etc. Understanding these characteristics helps the system terminal optimize the operating parameters to reduce blockage, improve crushing efficiency, and reduce dust generation. By identifying these characteristics, the crushing process can be controlled more accurately to ensure the safety and efficiency of rubber processing.

[0018] Multi-source monitoring data of the material feeding and crushing mechanism is acquired, and a material feeding and crushing simulation model of the material feeding and crushing mechanism is generated by using the multi-source monitoring data.

[0019] In one embodiment, in order to optimize the working efficiency and safety of the feeding and crushing mechanism during rubber processing, the system terminal obtains multi-source monitoring data of the mechanism. These data come from different sensors and monitoring devices, including mechanical motion parameters, temperature, pressure, vibration, etc. These data provide the system terminal with comprehensive information on the real-time working status of the feeding and crushing mechanism. Subsequently, key features related to the feeding and crushing performance are extracted from the multi-source monitoring data. Then, according to the physical structure and working principle of the feeding and crushing mechanism, the physical framework of the simulation model is designed. For example, if the feeding and crushing mechanism uses a rotating blade for crushing, the simulation model needs to simulate the rotational motion of the blade, the interaction between the material and the blade, etc. After the physical framework design is completed, the system terminal establishes the corresponding mathematical framework based on the physical framework. This includes mathematical equations and formulas that describe processes such as material flow, blade cutting, and energy transfer. After that, the system terminal sets the parameters of the simulation model based on the extracted key features. These parameters include blade speed, feed speed, material hardness, etc. Then, the boundary conditions of the simulation model are determined, such as the location, shape and size of the material inlet and outlet, and the simulation time, etc. Then, according to the complexity of the simulation model and the computing resources, a suitable algorithm is selected for simulation calculation. For example, the discrete element method (DEM) is used to simulate the movement and interaction of material particles, or the computational fluid dynamics (CFD) method is used to simulate the flow state of materials. After the parameters and algorithms are set, the system terminal uses the historical working data of the feeding and crushing mechanism to verify and calibrate the simulation model. This includes comparing the simulation results with the actual production data to check the prediction accuracy and reliability of the model. If errors or deficiencies are found in the model, the model is adjusted and optimized to improve its prediction performance. The adjustment process includes modifying the model structure, checking whether the model parameters are set reasonably, etc. Conversely, the system terminal outputs the current simulation model to generate a feeding and crushing simulation model. This model is a simulation of the actual working process of the feeding and crushing mechanism, which can reflect the influence of different operating parameters and material properties on the crushing effect. By adjusting the parameters in the model, the crushing effect under different conditions can be predicted, thereby providing guidance for actual production.

[0020] Furthermore, the present application provides that after obtaining the feeding and crushing simulation model, the method further includes: According to the feeding and crushing simulation model, the internal space of the crushing chamber is identified; the dangerous area of ​​the internal space of the crushing chamber is marked, and the marked area is output; the feeding and crushing simulation model is called to simulate with the feeding attribute sample data set, the crushing damage data of the marked area is monitored, and the crushing monitoring sample data set is generated.

[0021] Optionally, in the process of feeding and crushing, in order to ensure the safety and efficiency of production, the system terminal first identifies the key area in the feeding and crushing equipment in the feeding and crushing simulation model, that is, the built-in space of the crushing chamber. This space is the main place where the material is crushed and is also the potential dangerous area. Subsequently, the system terminal marks the dangerous area of ​​the built-in space of the crushing chamber. This is based on the physical structure, working principle and past experience of the equipment to identify those areas that are prone to damage or safety accidents due to material impact, blade wear or equipment failure. These areas will be specially marked to form identification areas. Once the identification area is determined, the system terminal uses the established feeding and crushing simulation model for simulation. In this simulation process, the feeding attribute sample data set is used as input. This data set contains various possible feeding conditions, such as material type, particle size, humidity, temperature and feeding speed. These attributes are all key factors affecting the crushing process. Through simulation, the system terminal can monitor the damage data of these identification areas during the crushing process, such as wear degree, temperature change, pressure distribution, etc. Afterwards, the system terminal organizes the monitored data into a crushing monitoring sample data set. This data set not only records various data in the simulation process, but also reflects the actual working status and potential risks of the marked area. By analyzing this data set, we can further understand the working performance of the built-in space of the crushing chamber, realize comprehensive monitoring and optimization of the feeding and crushing process, and improve the safety and efficiency of production.

[0022] The feed crushing simulation model is called to train a crushing conflict hidden danger monitoring model, and the feed attribute information is identified using the crushing conflict hidden danger monitoring model to obtain a first conflict hidden danger indicator.

[0023] In one embodiment, in the production process of feeding and crushing, in order to prevent and reduce possible conflict hazards, the system terminal calls the established feeding and crushing simulation model to train a crushing conflict hazard monitoring model. Specifically, the system terminal uses the feeding and crushing simulation model to simulate various feeding scenarios and collects the crushing monitoring sample data sets generated in these scenarios. Subsequently, the degree of conflict hazard is identified for this crushing monitoring sample data set, and this identification is determined based on expert advice and historical experience. After that, the system terminal uses these data to train the crushing conflict hazard monitoring model. After the training is completed, the system terminal obtains a crushing conflict hazard monitoring model that can identify potential conflict hazards in the feeding attribute information. Then, the system terminal inputs the feeding attribute information into this model, and the model will analyze and judge this information based on the learned knowledge, and output a first conflict hazard indicator. These indicators can intuitively reflect the degree of conflict hazards that may exist under the current feeding conditions, thereby helping the system terminal to take timely measures to avoid or reduce the occurrence of conflict hazards, so as to improve the safety and stability of the production process.

[0024] Further, if Figure 2 As shown, the present application provides calling the feeding and crushing simulation model to train the crushing conflict hidden danger monitoring model, and the method also includes: A feeding attribute sample data set is set, and the feeding crushing simulation model is called to simulate with the feeding attribute sample data set, and a crushing monitoring sample data set corresponding to the feeding attribute sample data set is obtained; the feeding attribute sample data set and the crushing monitoring sample data set as well as identification information identifying the degree of conflict hidden danger are used for training, and a crushing conflict hidden danger monitoring model is output; according to the crushing conflict hidden danger monitoring model, a first conflict hidden danger indicator is output.

[0025] Preferably, in order to build a model capable of monitoring the potential conflict hazards during the feeding and crushing process, the system terminal first obtains a feeding attribute sample data set, and uses the same method as above to simulate the operation using the feeding and crushing simulation model to generate a crushing monitoring sample data set. Subsequently, the system terminal combines the feeding attribute sample data set, the crushing monitoring sample data set, and the identification information of the degree of conflict potential hazards to form a complete training data set. The training data set is then divided into a training set, a validation set, and a test set. After that, the number of nodes in the input layer, the hidden layer, and the output layer of the multilayer perceptron (MLP) is determined according to the complexity of the problem. The number of nodes in the input layer should match the number of features of the feeding attributes and the crushing monitoring data, and the number of nodes in the output layer should match the classification of the degree of conflict potential hazards. Then, the system terminal randomly initializes the weights and biases of the neural network and uses the training set for iterative training. During the training process, the system terminal randomly extracts a batch of sample data from the training set each time, inputs the sample data into the neural network, and calculates the output through forward propagation. The loss function is then used to calculate the difference between the output and the identification information of the degree of conflict potential hazards. Further, the gradient is calculated by the back propagation algorithm, and the network parameters are updated using the optimizer. Repeat the above steps until the preset number of training rounds is reached. After each training iteration, the system terminal uses the validation set to evaluate the performance of the model, which is done by calculating the accuracy on the validation set. If the accuracy on the validation set begins to decrease, it means that overfitting has occurred. At this time, training should be stopped to prevent overfitting. After all training is completed, the system terminal uses the test set to evaluate the final performance of the model. If the performance meets the requirements, the current model is output to generate a crushing conflict hidden danger monitoring model. Otherwise, the weights and biases are reinitialized, or the structure of the multi-layer perceptron is adjusted, and retraining is performed until the performance meets the requirements. When there is feed attribute data input, the crushing conflict hidden danger monitoring model can quickly analyze this data and output a first conflict hidden danger indicator to avoid or reduce the occurrence of conflict hidden dangers.

[0026] Furthermore, the present application provides an output crushing conflict hidden danger monitoring model, and the method also includes: A plurality of crushing granularity samples are set; a first feedback network layer is generated according to the plurality of crushing granularity samples, and feedback training is performed on the crushing conflict hidden danger monitoring model according to the first feedback network layer to obtain an optimized crushing conflict hidden danger monitoring model.

[0027] Optionally, in the process of constructing the crushing conflict hidden danger monitoring model, the system terminal first sets a plurality of different crushing particle size samples. These samples represent different crushing results, that is, the different particle sizes reached by the material during the crushing process. The size of the particle size directly affects the crushing effect and the operating status of the equipment, and is therefore one of the important factors in evaluating the crushing conflict hidden danger. Subsequently, the system terminal selects the Elman neural network to design a feedback network layer, because the Elman neural network can process time-dependent data and capture the impact of particle size changes on the equipment state. The function of this feedback network layer is to generate a corresponding feedback signal based on the input crushing particle size sample. Then initialize the parameters of this feedback network layer. These parameters include weights, biases, etc., which will be adjusted during the training process to optimize the performance of the network layer. Afterwards, the system terminal uses multiple crushing particle size samples as input data to train the feedback network layer. The output is calculated by forward propagation, and the difference between the predicted result and the actual label is calculated using the loss function. Then, the optimizer is used to update the network parameters to reduce the prediction error. The training process is repeated until the preset number of training rounds is reached. Then, the system terminal connects the trained feedback network layer to the crushing conflict hidden danger monitoring model. Ensure that the crushing conflict hidden danger monitoring model can receive input from the feedback network layer and adjust its output accordingly. Further, the system terminal determines the way in which the feedback network layer provides feedback to the crushing conflict hidden danger monitoring model, that is, the output of the feedback network layer is used as one of the inputs of the monitoring model. For each crushing granularity sample, the system terminal inputs it into the feedback network layer. The feedback network layer calculates and outputs a feedback signal based on the input granularity sample and internal parameters. These signals reflect the conflict hidden dangers or changes in the equipment status that may exist at the current granularity. Subsequently, the feedback signal generated by the feedback network layer is used to correct and optimize the output of the monitoring model. By adjusting the parameters and structure of the monitoring model, it can better use the feedback signal to predict and identify conflict hidden dangers. Repeat the feedback training process to gradually optimize the performance of the monitoring model. In each iteration, the system terminal evaluates the performance of the model based on the feedback signal and the prediction results of the monitoring model, and adjusts the model parameters to improve the prediction effect. Afterwards, the optimized model is verified using an independent verification data set to evaluate its performance in actual applications. If the model performance meets the requirements, the optimized crushing conflict hidden danger monitoring model is output. This model not only considers the impact of feed properties on the crushing process, but also combines the impact of particle size changes on conflict risks, so it can more comprehensively and accurately evaluate the conflict risks in the crushing process. The optimized model can more accurately predict and identify potential conflict risks at different particle sizes, improving the safety and stability of the crushing process.

[0028] The required crushing granularity is obtained, and the optimized crushing conflict hidden danger monitoring model performs feedback optimization on the first conflict hidden danger indicator according to the required crushing granularity, and outputs a second conflict hidden danger indicator; the second conflict hidden danger indicator is input into the crushing optimization controller for control optimization.

[0029] Optionally, during the crushing production process, the system terminal specifies the required product crushing particle size, which is a key parameter in the production process. In order to ensure the safety and efficiency of the production process, the system terminal uses an optimized crushing conflict hidden danger monitoring model to monitor potential conflict hidden dangers. Specifically, the system terminal inputs the required crushing particle size into the first feedback network layer to generate a feedback signal. Subsequently, this feedback signal is combined with the first conflict hidden danger indicator and input into the crushing conflict hidden danger monitoring model. The crushing conflict hidden danger monitoring model uses the input feedback signal to feedback optimize the first conflict hidden danger indicator based on the knowledge learned during the feedback training process, so as to obtain a more accurate and second conflict hidden danger indicator that meets the current production needs. After that, the system terminal inputs this second conflict hidden danger indicator into the crushing optimization controller. Based on this indicator, the controller combines the current production conditions and equipment status to perform control optimization, that is, adjust and optimize production parameters, such as feeding speed, crusher speed, etc., to minimize conflict hidden dangers and improve production safety and efficiency.

[0030] Furthermore, the present application provides that after training the crushing conflict hidden danger monitoring model, the method further includes: A temperature monitoring module is provided, and temperature monitoring data is obtained according to the temperature monitoring module; a temperature impact analysis is performed according to the temperature monitoring data and the added material attribute information to obtain a temperature impact index; a second feedback network layer is established according to the temperature impact index, and feedback optimization is performed on the crushing conflict hidden danger monitoring model according to the second feedback network layer.

[0031] Preferably, after the training of the crushing conflict hidden danger monitoring model is completed, the system terminal sets the temperature monitoring module, which is an important step to ensure the stability and safety of the crushing process. Through the temperature monitoring module, the system terminal obtains the temperature data in the crushing process in real time, which is crucial for analyzing the impact of temperature on the crushing process. After obtaining the temperature monitoring data, the system terminal organizes the temperature monitoring data and the feeding attribute information to ensure that their timestamps match. Subsequently, the temperature data and the feeding attribute data are preliminarily analyzed to understand their changing trends and ranges. Among them, the changes in temperature under different feeding attributes are particularly analyzed, and whether the temperature fluctuates significantly when certain attributes change. Afterwards, based on the preliminary analysis results, the system terminal calculates the average value and volatility of the temperature at different feeding attribute levels as a temperature impact indicator. Then, the second feedback network layer is constructed according to the temperature impact indicator using the same method as the aforementioned construction of the first feedback network layer. The role of this network layer is to feedback and optimize the crushing conflict hidden danger monitoring model according to the temperature impact indicator. Finally, the system terminal performs the same feedback optimization operation and further optimizes the crushing conflict hazard monitoring model through continuous learning and adjustment to ensure that the production process is carried out within a controllable temperature range and reduce the conflict risks caused by temperature changes.

[0032] The first conflict potential risk indicator is input into a crushing optimization controller for control optimization, and a crushing optimization control parameter is output, wherein the crushing optimization controller includes a preset conflict potential risk indicator.

[0033] In one embodiment, in order to ensure the safety and efficiency of production, after the potential conflict hidden danger is monitored and the first conflict hidden danger index is calculated, the system terminal inputs the first conflict hidden danger index into the crushing optimization controller for control optimization. The crushing optimization controller includes a simulation component, which includes simulation models of key equipment such as a crushing roller group. The simulation component can simulate various situations in the actual production process, including key indicators such as crushing effect, energy consumption, and temperature under different production parameters. When the first conflict hidden danger index is input into the crushing optimization controller, the crushing optimization controller will generate crushing control parameters based on this index by adjusting production parameters such as the rotation speed of the crushing roller and the feeding speed. Subsequently, the crushing optimization controller performs multiple simulation runs through the internal simulation component. These simulation runs will evaluate the degree of conflict hidden dangers under different parameter combinations, as well as other key indicators such as production efficiency and energy consumption. Through simulation evaluation, the crushing optimization controller can find a set of control parameters that meet the requirements of the preset conflict hidden danger index. This set of parameters can optimize production efficiency while ensuring production safety. Once this control parameter is found, the crushing optimization controller outputs this set of parameters to the system terminal to generate the crushing optimization control parameter.

[0034] Furthermore, the present application provides inputting the first conflict potential indicator into a crushing optimization controller to perform control optimization and output crushing optimization control parameters, and the method further includes: The crushing optimization controller includes a crushing roller group, and the crushing roller group includes a first crushing roller and a second crushing roller; the first conflict hidden danger index is input into the crushing optimization controller, and the first crushing roller and the second crushing roller are controlled and optimized with the preset conflict hidden danger index as the adaptation target, and the crushing optimization control parameters are output; wherein the parameter indicators of the crushing optimization control parameters include feed speed, feed amount and roller group rotation speed.

[0035] Optionally, in order to ensure the safety and efficiency of the operation, the system terminal uses a crushing optimization controller to manage the operation of the optimized crushing roller group. The crushing roller group inside the crushing optimization controller also includes a first crushing roller and a second crushing roller. When the system terminal inputs the first conflict hidden danger index into the crushing optimization controller, it compares the input first conflict hidden danger index with the preset conflict hidden danger index inside the crushing optimization controller to determine whether the current conflict hidden danger is within an acceptable range. If the first conflict hidden danger index exceeds the preset range, the controller will start the control optimization process. First, the controller will adjust the parameters related to the operation of the crushing roller group, such as the feed speed, feed amount, and the rotation speed of the first crushing roller and the second crushing roller. After each parameter adjustment, the controller will use the internal simulation component to simulate and evaluate the operation of the first crushing roller and the second crushing roller. This simulation process will take into account various factors such as material properties and mechanical efficiency to predict the impact of the adjusted parameter combination on the conflict hidden danger index, and use the crushing conflict hidden danger monitoring model to calculate the new conflict hidden danger index. Based on the results of the simulation evaluation, the controller will determine whether the current parameter combination meets the requirements of the preset conflict hidden danger index. If not, the controller will adjust the parameters again and repeat the simulation evaluation process until a set of parameter combinations that meet the requirements is found. When the controller finds a set of parameter combinations that meet the preset conflict hidden danger index requirements, the system terminal outputs this set of parameters as the crushing optimization control parameters. This set of parameters will be directly used to guide the operation of the first crushing roller and the second crushing roller in actual production.

[0036] The pulverizing optimization controller controls the feeding pulverizing mechanism according to the pulverizing optimization control parameters.

[0037] In one embodiment, after the crushing optimization controller generates the crushing optimization control parameters, it will directly apply these parameters to control the operation of the feeding crushing mechanism. Specifically, the crushing optimization controller will directly adjust the operation of the feeding crushing mechanism according to the output crushing optimization control parameters, such as the feed speed, feed amount, and the rotation speed of the crushing roller group. For example, if the control parameters indicate that the feed speed needs to be reduced or the feed amount needs to be reduced, the controller will send corresponding instructions to the feeding crushing mechanism to ensure that the actual feed amount matches the preset value. Similarly, if the control parameters require adjusting the rotation speed of the crushing roller group, the controller will also adjust the output of the feeding crushing mechanism accordingly to achieve precise control of the rotation speed.

[0038] Furthermore, the present application provides that the feeding and crushing mechanism also includes a material drying device, and the method also includes: According to the feed attribute information, the material viscosity index is identified; if the material viscosity index is greater than a preset viscosity index, a crushing blockage hazard monitoring model is trained according to the feed crushing simulation model; Preferably, the system terminal identifies the viscosity index of the material based on the feeding attribute information. This viscosity index describes the degree of viscosity of the material under specific conditions, and is of great significance for predicting whether the material is prone to clogging during the crushing process. If the identified material viscosity index is greater than the preset viscosity index, this means that the material has a higher risk of clogging during the crushing process. In order to discover and prevent this clogging hazard in advance, the system terminal uses the feeding and crushing simulation model to train a crushing clogging hazard monitoring model. This training process is similar to the aforementioned training of the crushing conflict hazard monitoring model. A crushing clogging hazard monitoring model is trained using a feeding attribute sample data set, a crushing monitoring sample data set, and identification information that identifies the degree of clogging hazard. This crushing clogging hazard monitoring model can learn the mapping relationship between the material viscosity index and the clogging hazard. Once the training is completed, the crushing clogging hazard monitoring model can monitor the material's viscosity index in real time and predict whether there is a clogging hazard.

[0039] The feed attribute information is identified by using the crushing blockage hazard monitoring model to obtain a first blockage hazard index; if the first blockage hazard index is greater than a preset blockage hazard index, the material drying equipment is activated to dry the material.

[0040] Preferably, the system terminal inputs the material feeding attribute information into the crushing blockage hazard monitoring model, and the crushing blockage hazard monitoring model identifies and analyzes this information based on the learned knowledge, and outputs a first blockage hazard index. This indicator directly reflects the possibility of the material being blocked during the crushing process. Subsequently, this first blockage hazard index is compared with the preset blockage hazard index. If the first blockage hazard index is greater than the preset blockage hazard index, it means that the material has a high risk of blockage, and immediate measures need to be taken to reduce this risk. At this time, the system terminal activates the material drying equipment to dry the material. The drying process can reduce the viscosity of the material, thereby reducing the possibility of blockage during the crushing process.

[0041] Furthermore, the present application provides activating the material drying device to dry the material, and the method further includes: A safety viscosity index is generated with the difference between the first clogging hazard index and the preset clogging hazard index as a target; the safety viscosity index is converted into a moisture content, and a preset moisture content is output; the preset moisture content is used as the drying target of the material drying equipment for drying, and after drying, the material falls into the crushing chamber of the feeding and crushing mechanism.

[0042] Optionally, in order to prevent the potential blockage during the crushing process, the system terminal first calculates the difference between the first potential blockage indicator and the preset potential blockage indicator, and this difference is used to generate a safe viscosity indicator. This safe viscosity indicator is an intermediate value, which represents the viscosity of the material under which the crushing operation can be safely carried out to avoid the occurrence of blockage. Subsequently, the system converts the safe viscosity indicator into a moisture content. This is because the viscosity of the material is often closely related to its moisture content. Through the moisture content conversion, the system terminal can derive a specific preset moisture content that can be used to guide the drying operation. This conversion process is based on a viscosity indicator-moisture content association database, which is established based on experimental data and historical data and records the viscosity indicator and the corresponding moisture content. Afterwards, the system terminal uses this preset moisture content as the drying target of the material drying equipment to dry the material. When the material reaches the preset moisture content, the drying process is completed, and the material is fed into the crushing chamber of the feeding crushing mechanism for crushing.

[0043] In summary, the embodiments of the present application have at least the following technical effects: The embodiment of the present application collects and analyzes the feeding attribute information, combines the multi-source monitoring data to establish the feeding and crushing simulation model, and then trains the crushing conflict hidden danger monitoring model. The crushing conflict hidden danger monitoring model is used to identify the conflict hidden danger index of the material, and based on the conflict hidden danger index, the control optimization is performed in the crushing optimization controller, and the optimized crushing control parameters are output to achieve precise control of the feeding and crushing mechanism. In addition, when the viscosity of the material exceeds the preset index, the material drying equipment is activated, and the material is dried by calculating the safe viscosity index and converting it into a preset moisture content to reduce the risk of blockage. When optimizing the monitoring model, not only the feeding attribute sample data set is used, but also different crushing particle size samples are set, and the feedback network layer is constructed to feedback train and optimize the model to improve the monitoring accuracy. These technical effects jointly solve the technical problems of the splashing and conflict of crushed particles caused by the uniform and unchanging processing force when the feeding and crushing mechanism in the rubber manufacturing process processes materials with higher hardness, and then cause the component jamming and crushing safety hazards, and realize the effect of adjusting the flexible adaptive control parameters by combining the type of feeding to improve the working performance and safety of the feeding and crushing mechanism.

[0044] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0045] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0046] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A rubber intelligent manufacturing optimization control method based on multi-source monitoring data, characterized in that: The method comprises: Feeding and crushing mechanism of interactive rubber processing equipment; Identify the material entering the feeding and crushing mechanism, and obtain the feeding property information, wherein the feeding property information includes the feeding physical property, the feeding chemical property and the feeding crushing property; Acquire multi-source monitoring data of the feeding and crushing mechanism, and generate a feeding and crushing simulation model of the feeding and crushing mechanism using the multi-source monitoring data; Calling the feed crushing simulation model to train a crushing conflict hidden danger monitoring model, using the crushing conflict hidden danger monitoring model to identify the feed attribute information, and obtaining a first conflict hidden danger indicator; Inputting the first conflict hidden danger index into a crushing optimization controller for control optimization, and outputting crushing optimization control parameters, wherein the crushing optimization controller includes a preset conflict hidden danger index; The pulverizing optimization controller controls the feeding pulverizing mechanism according to the pulverizing optimization control parameters.

2. The method according to claim 1, characterized in that The feeding and crushing simulation model is called to train a crushing conflict hidden danger monitoring model, and the method includes: Setting a feed attribute sample data set, calling the feed pulverization simulation model to simulate the feed attribute sample data set, and obtaining a pulverization monitoring sample data set corresponding to the feed attribute sample data set; Using the feed attribute sample data set and the crushing monitoring sample data set as well as identification information identifying the degree of potential conflict risks for training, outputting a crushing conflict potential risk monitoring model; According to the crushing conflict hidden danger monitoring model, a first conflict hidden danger indicator is output.

3. The method according to claim 2, characterized in that After obtaining the feeding and crushing simulation model, the method further includes: According to the feeding and pulverizing simulation model, identifying the internal space of the pulverizing chamber; Marking the dangerous area of ​​the internal space of the crushing chamber and outputting the marked area; The feed crushing simulation model is called to simulate the feed property sample data set, the crushing damage data of the marked area is monitored, and a crushing monitoring sample data set is generated.

4. The method according to claim 1, characterized in that The feeding and crushing mechanism also includes a material drying device, including: According to the material property information, identifying the material viscosity index; If the material viscosity index is greater than a preset viscosity index, a crushing blockage hidden danger monitoring model is trained according to the material adding crushing simulation model; Using the crushing blockage hazard monitoring model to identify the feed attribute information, and obtain a first blockage hazard indicator; If the first blocking potential risk index is greater than a preset blocking potential risk index, the material drying equipment is activated to dry the material.

5. The method according to claim 4, characterized in that Activating the material drying device to dry the material, the method comprising: Taking the difference between the first blocking potential risk indicator and the preset blocking potential risk indicator as a target, generating a safety viscosity indicator; The safety viscosity index is converted into a moisture content, and a preset moisture content is output. The preset moisture content is used as the drying target of the material drying equipment for drying. After drying, the material falls into the crushing chamber of the feeding crushing mechanism.

6. The method according to claim 2, characterized in that Outputting a crushing conflict hidden danger monitoring model, the method also includes: Set up multiple crushing particle size samples; generating a first feedback network layer according to the plurality of crushing granularity samples, and performing feedback training on the crushing conflict hidden danger monitoring model according to the first feedback network layer to obtain an optimized crushing conflict hidden danger monitoring model; Obtaining the demand crushing granularity, the optimized crushing conflict hidden danger monitoring model performs feedback optimization on the first conflict hidden danger indicator according to the demand crushing granularity, and outputs a second conflict hidden danger indicator; The second conflict potential indicator is input into the pulverizing optimization controller to perform control optimization.

7. The method according to claim 6, characterized in that The first conflict hidden danger index is input into a crushing optimization controller for control optimization, and a crushing optimization control parameter is output. The method includes: The pulverizing optimization controller includes a crushing roller group, and the crushing roller group includes a first crushing roller and a second crushing roller; The first conflict hidden danger index is input into the crushing optimization controller, the first crushing roller and the second crushing roller are controlled and optimized with the preset conflict hidden danger index as the adaptation target, and the crushing optimization control parameter is output; The parameter indicators of the crushing optimization control parameters include feed speed, feed amount and roller group rotation speed.

8. The method according to claim 1, characterized in that After training the crushing conflict hidden danger monitoring model, the method further includes: Setting a temperature monitoring module, and obtaining temperature monitoring data according to the temperature monitoring module; Perform temperature impact analysis based on the temperature monitoring data and the added material attribute information to obtain a temperature impact index; A second feedback network layer is established according to the temperature impact index, and feedback optimization is performed on the crushing conflict hidden danger monitoring model according to the second feedback network layer.

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