Method and system for optimizing wear resistance of rubber transmission belt

Abstract

The invention discloses a method and a system for optimizing wear resistance of a rubber conveying belt, and relates to the technical field of conveying belts. The method comprises the following steps: collecting a material historical transmission log meeting a life threshold; configuring a covering rubber wear thickness prediction version model; a material conveying scheme is obtained; based on the transmission belt cover rubber failure wear thickness, calling a cover rubber wear thickness prediction version model, and carrying out wear resistance frequency analysis to obtain a transmission prediction frequency; and when the predicted number of times of transmission is smaller than or equal to the threshold value of the number of times of transmission, the material set temperature, the blanking set height and the material set weight are adjusted, and an optimized material transmission scheme is obtained for transmission control. The technical problem that in the prior art, it is difficult to quantitatively analyze the abrasion degree of the material to the conveying belt, and consequently the specification of the material capable of being borne cannot be accurately determined is solved, the material conveying scheme is optimized through data analysis and model prediction, too fast abrasion of the conveying belt is avoided, and the technical effect of prolonging the service life of the conveying belt is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of conveyor belts, and in particular to a method and system for optimizing the wear resistance of a rubber conveyor belt. Background Art

[0002] In the field of industrial production and logistics transportation, rubber conveyor belts are the "blood vessels" connecting various links, and their importance is self-evident. Rubber conveyors carry all kinds of materials and shuttle efficiently between production lines and warehouses. They are key equipment to ensure production continuity and smooth logistics. Their performance and life directly affect production efficiency and operating costs. However, with the continuous expansion of industrial production scale and the growing demand for logistics, rubber conveyor belts are facing unprecedented challenges. In practical applications, conveyor belts often face problems of rapid wear and premature failure due to the influence of material characteristics. This wear not only increases maintenance costs, but may also cause production interruptions due to frequent replacement of conveyor belts, affecting overall production plans and economic benefits. Summary of the invention

[0003] The present application provides a method and system for optimizing the wear resistance of a rubber conveyor belt, which solves the technical problem in the prior art that it is difficult to quantify and analyze the degree of wear caused by materials on the conveyor belt, resulting in an inability to accurately determine the specifications of the materials that can be carried.

[0004] In view of the above problems, the present application provides a method and system for optimizing the wear resistance of a rubber transmission belt.

[0005] In a first aspect of the present application, a method for optimizing the wear resistance of a rubber transmission belt is provided, the method comprising: Taking the conveyor belt model and the conveyor equipment model as constraints, collect historical material transmission logs that meet the life threshold, wherein any material historical transmission log includes material temperature characteristic values, drop height characteristic values, material weight characteristic values ​​and material flow characteristic values; based on the material historical transmission log, perform four-element joint frequency sorting on the material temperature characteristic values, the drop height characteristic values, the material weight characteristic values ​​and the material flow characteristic values ​​to obtain material temperature thresholds, drop height thresholds, material weight thresholds and material flow thresholds; based on the material temperature thresholds, the drop height thresholds, the material weight thresholds and the material flow thresholds, perform training sample sorting on the conveyor belt model and the conveyor equipment model to obtain material temperature record data sets, drop height record data sets, material weight record data sets, material flow record data sets, transmission times record data sets and covering rubber wear thickness record data sets; according to the material temperature record data sets, the drop height record data sets, the material weight record data sets, the The material flow record data set, the transmission number record data set and the covering rubber wear thickness record data set are used to configure a covering rubber wear thickness prediction version model; a material transmission plan is obtained, wherein the material transmission plan includes a material set temperature, a blanking set height, a material set weight and a material set flow rate; when the material set temperature is greater than the material temperature threshold, or / and the blanking set height is greater than the blanking height threshold, or / and the material set weight is greater than the material weight threshold, or / and the material set flow rate is greater than the material flow rate threshold, based on the failure wear thickness of the conveyor belt covering rubber, the covering rubber wear thickness prediction version model is retrieved, the material set temperature, the blanking set height, the material set weight and the material set flow rate are analyzed for the wear resistance times, and the transmission prediction times are obtained; when the transmission prediction times are less than or equal to the transmission times threshold, the material set temperature, the blanking set height and the material set weight are adjusted to obtain an optimized material transmission plan for transmission control.

[0006] A second aspect of the present application provides a rubber transmission belt wear resistance optimization system, the system comprising: A data acquisition module, the data acquisition module is used to collect historical material transmission logs that meet the life threshold based on the conveyor belt model and the transmission equipment model as constraints, wherein any material historical transmission log includes material temperature characteristic values, drop height characteristic values, material weight characteristic values ​​and material flow characteristic values; a frequency sorting module, the frequency sorting module is used to perform four-element joint frequency sorting on the material temperature characteristic values, the drop height characteristic values, the material weight characteristic values ​​and the material flow characteristic values ​​based on the material historical transmission log, and obtain material temperature threshold, drop height threshold, material weight threshold and material flow threshold; a sample sorting module, the sample sorting module is used to perform training sample sorting on the conveyor belt model and the transmission equipment model based on the material temperature threshold, the drop height threshold, the material weight threshold and the material flow threshold, and obtain material temperature record data set, drop height record data set, material weight record data set, material flow record data set, transmission number record data set and covering rubber wear thickness record data set; a model configuration module, the model configuration module is used to select the material temperature record data set, the drop height record data set, The material weight record data set, the material flow record data set, the transmission number record data set and the cover rubber wear thickness record data set are used to configure the cover rubber wear thickness prediction version model; a solution acquisition module, the solution acquisition module is used to obtain the material transmission plan, wherein the material transmission plan includes the material setting temperature, the blanking setting height, the material setting weight and the material setting flow rate; the analysis module, the analysis module is used to retrieve the cover rubber wear thickness prediction version model based on the failure wear thickness of the conveyor belt cover rubber when the material setting temperature is greater than the material temperature threshold, or / and the blanking setting height is greater than the blanking height threshold, or / and the material setting weight is greater than the material weight threshold, or / and the material setting flow rate is greater than the material flow rate threshold, and perform wear resistance analysis on the material setting temperature, the blanking setting height, the material setting weight and the material setting flow rate to obtain the transmission prediction number; the control module, the control module is used to adjust the material setting temperature, the blanking setting height and the material setting weight when the transmission prediction number is less than or equal to the transmission number threshold, and obtain the optimized material transmission plan for transmission control.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: First, with the conveyor belt model and the transmission equipment model as constraints, the historical material transmission logs that meet the life threshold are collected, wherein any material historical transmission log includes material temperature characteristic values, material drop height characteristic values, material weight characteristic values ​​and material flow characteristic values. Then, based on the material historical transmission logs, the material temperature characteristic values, the drop height characteristic values, the material weight characteristic values ​​and the material flow characteristic values ​​are subjected to four-element joint frequency sorting to obtain the material temperature threshold, the drop height threshold, the material weight threshold and the material flow threshold. Further, based on the material temperature threshold, the drop height threshold, the material weight threshold and the material flow threshold, the conveyor belt model and the transmission equipment model are trained and sorted to obtain the material temperature record data set, the drop height record data set, the material weight record data set, the material flow record data set, the transmission number record data set and the cover glue wear thickness record data set; and according to the material temperature record data set, the drop height record data set, the material weight record data set, the material flow record data set, the transmission number record data set and the cover glue wear thickness record data set, the cover glue wear thickness prediction version model is configured. Next, a material transmission scheme is obtained, wherein the material transmission scheme includes a material setting temperature, a material setting height, a material setting weight, and a material setting flow rate; when the material setting temperature is greater than the material temperature threshold, or / and the material setting height is greater than the material height threshold, or / and the material setting weight is greater than the material weight threshold, or / and the material setting flow rate is greater than the material flow rate threshold, based on the wear thickness of the conveyor belt cover rubber failure, the cover rubber wear thickness prediction version model is retrieved, and the wear times of the material setting temperature, the material setting height, the material setting weight, and the material setting flow rate are analyzed to obtain the transmission prediction times. Finally, when the transmission prediction times are less than or equal to the transmission times threshold, the material setting temperature, the material setting height, and the material setting weight are adjusted to obtain an optimized material transmission scheme for transmission control. The technical problem that it is difficult to quantify and analyze the degree of wear caused by the material on the conveyor belt in the prior art, resulting in the inability to accurately determine the material specifications that can be carried, is solved. The material transmission scheme is optimized through data analysis and model prediction, and the excessive wear of the conveyor belt is avoided, achieving the technical effect of extending the service life of the conveyor belt. 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 flow chart of a method for optimizing the wear resistance of a rubber transmission belt provided in an embodiment of the present application; Figure 2A schematic diagram of the structure of a rubber transmission belt wear resistance optimization system provided in an embodiment of the present application.

[0010] Explanation of the reference numerals: data acquisition module 11 , frequency sorting module 12 , sample sorting module 13 , model configuration module 14 , solution acquisition module 15 , analysis module 16 , control module 17 . DETAILED DESCRIPTION

[0011] The present application provides a method and system for optimizing the wear resistance of a rubber conveyor belt, thereby solving the technical problem in the prior art that it is difficult to quantify and analyze the degree of wear caused by materials on the conveyor belt, resulting in an inability to accurately determine the specifications of the materials that can be carried.

[0012] 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.

[0013] 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 are inherent to these processes, methods, products or devices.

[0014] Embodiment 1, as Figure 1 As shown, the present application provides a method for optimizing the wear resistance of a rubber transmission belt, wherein the method comprises: With the conveyor belt model and the conveyor equipment model as constraints, historical material transmission logs that meet the life threshold are collected, where any material historical transmission log includes material temperature characteristic values, material drop height characteristic values, material weight characteristic values, and material flow characteristic values.

[0015] By obtaining the conveyor belt model and conveyor equipment model of the target conveyor belt, and using the conveyor belt model and conveyor equipment model as constraints, the material transfer logs that meet the preset life threshold are screened out from the historical database. The preset life threshold is used to screen out the material transfer logs corresponding to the conveyor belts that have reached or exceeded the life in actual use. These logs record in detail the key parameters of each material transfer process, including material temperature characteristic values, drop height characteristic values, material weight characteristic values, and material flow characteristic values.

[0016] Based on the material history transmission log, the material temperature characteristic value, the material drop height characteristic value, the material weight characteristic value and the material flow characteristic value are subjected to four-element joint frequency sorting to obtain the material temperature threshold, the drop height threshold, the material weight threshold and the material flow threshold.

[0017] The material temperature characteristic values, material drop height characteristic values, material weight characteristic values ​​and material flow characteristic values ​​are extracted from the material historical transmission log, and the material temperature characteristic values, material drop height characteristic values, material weight characteristic values ​​and material flow characteristic values ​​are subjected to four-element joint frequency sorting to identify the key factors leading to rapid wear of the conveyor belt and their critical values, and the material temperature threshold value, drop height threshold value, material weight threshold value and material flow threshold value are obtained.

[0018] Furthermore, based on the material history transmission log, the material temperature characteristic value, the material drop height characteristic value, the material weight characteristic value and the material flow characteristic value are subjected to four-element joint frequency sorting to obtain the material temperature threshold, the drop height threshold, the material weight threshold and the material flow threshold, including: Construct a normalized axis of material temperature, a normalized axis of blanking height, a normalized axis of material weight and a normalized axis of material flow; construct a transmission log distribution space according to the normalized axis of material temperature, the normalized axis of blanking height, the normalized axis of material weight and the normalized axis of material flow; distribute the historical transmission logs of the material in the transmission log distribution space based on the characteristic value of the material temperature, the characteristic value of the blanking height, the characteristic value of the material weight and the characteristic value of the material flow, and obtain a set of particle distribution coordinates; perform centralized particle sorting according to the set of particle distribution coordinates to obtain the material temperature threshold, the blanking height threshold, the material weight threshold and the material flow threshold.

[0019] Specifically, the material temperature characteristic value is converted into a unified range (usually 0 to 1) for comparison and analysis; similar to the material temperature, the blanking height characteristic value, the material weight characteristic value and the material flow characteristic value are also normalized to ensure that all characteristic values ​​are on the same scale; according to the material temperature normalization axis, the blanking height normalization axis, the material weight normalization axis and the material flow normalization axis, a four-dimensional transmission log distribution space is constructed, in which each dimension represents a characteristic value (material temperature, blanking height, material weight, material flow), and the point on each dimension represents the normalized value of the corresponding characteristic value; each record in the material history transmission log is mapped to the four-dimensional transmission log distribution space according to its material temperature characteristic value, blanking height characteristic value, material weight characteristic value and material flow characteristic value, so that each record becomes a point (or particle) in a four-dimensional space and forms a particle distribution coordinate set; the particle distribution coordinate set is sorted in the four-dimensional space to find the material temperature threshold, blanking height threshold, material weight threshold and material flow threshold.

[0020] Furthermore, centralized particle sorting is performed according to the particle distribution coordinate set to obtain the material temperature threshold, the material drop height threshold, the material weight threshold and the material flow threshold, including: The particle Euclidean distance set of the particle distribution coordinate set is counted; the particle distribution coordinate set is unsupervisedly clustered according to the Euclidean distance threshold and combined with the particle Euclidean distance set to obtain a particle coordinate clustering result; the particle clusters whose coordinate quantity within the class is less than or equal to the coordinate quantity threshold are deleted to obtain a high-frequency particle set; the high-frequency particle set is analyzed by LOF algorithm to obtain the particle with the maximum local reachable density, and the material temperature threshold, the blanking height threshold, the material weight threshold and the material flow threshold are output.

[0021] Specifically, the Euclidean distance between each pair of particles in the particle distribution coordinate set is calculated to obtain the particle Euclidean distance set; the particle distribution coordinate set is clustered using an unsupervised clustering algorithm (such as K-means, DBSCAN, etc.), and in the clustering process, a Euclidean distance threshold is set to determine which particles should be classified into the same cluster; after clustering, multiple particle clusters are obtained, and the particle clusters whose number of coordinates within the class is less than or equal to the coordinate number threshold are deleted to obtain a high-frequency particle set; for the high-frequency particle set, the local outlier factor (LOF) algorithm is used for analysis. The LOF algorithm is an algorithm for identifying outliers in a data set. It works by comparing the local density of each point with its neighbors. If the local density of a point is significantly lower than the local density of its neighbors, the point is regarded as an outlier; the LOF algorithm is used in reverse to select those particles with the highest local reachable density as representatives, and output their corresponding material temperature, drop height, material weight and material flow values ​​as thresholds.

[0022] Based on the material temperature threshold, the blanking height threshold, the material weight threshold and the material flow threshold, the conveyor belt model and the conveying equipment model are trained and sorted to obtain a material temperature record data set, a blanking height record data set, a material weight record data set, a material flow record data set, a transmission number record data set and a covering rubber wear thickness record data set.

[0023] Based on the material temperature threshold, material drop height threshold, material weight threshold and material flow threshold, the transmission records corresponding to the conveyor belt model and the transmission equipment model are further screened to construct data sets for model training. These data sets include material temperature record data sets, drop height record data sets, material weight record data sets, material flow record data sets, transmission number record data sets and cover rubber wear thickness record data sets.

[0024] Furthermore, based on the material temperature threshold, the material drop height threshold, the material weight threshold and the material flow threshold, the conveyor belt model and the conveying equipment model are sorted for training samples to obtain a material temperature record data set, a drop height record data set, a material weight record data set, a material flow record data set, a transmission number record data set and a cover rubber wear thickness record data set, including: Collect the first material transmission sample constrained by the conveyor belt model and the conveying equipment model, wherein the first material transmission sample includes first material temperature recording data, first material drop height recording data, first material weight recording data, and first material flow recording data; when the first material temperature recording data is greater than the material temperature threshold, or / and the first material drop height recording data is greater than the drop height threshold, or / and the first material weight recording data is greater than the material weight threshold, or / and the first material flow recording data is greater than the material flow threshold, add the first material transmission sample into the training sample.

[0025] Specifically, a first material transmission sample is collected under the constraints of a conveyor belt model and a conveying equipment model, wherein the first material transmission sample includes first material temperature recording data, first material drop height recording data, first material weight recording data, and first material flow recording data; it is determined whether the first material temperature recording data, the first material drop height recording data, the first material weight recording data, and the first material flow recording data of the first material transmission sample are greater than corresponding threshold conditions; if at least one characteristic value in the first material transmission sample exceeds the threshold, that is, the first material temperature recording data is greater than the material temperature threshold, or / and the first material drop height recording data is greater than the drop height threshold, or / and the first material weight recording data is greater than the material weight threshold, or / and the first material flow recording data is greater than the material flow threshold, then the first material transmission sample is added to the training sample.

[0026] A covering rubber wear thickness prediction version model is configured according to the material temperature record data set, the falling material height record data set, the material weight record data set, the material flow record data set, the transmission number record data set and the covering rubber wear thickness record data set.

[0027] The material temperature record data set, the falling height record data set, the material weight record data set, the material flow record data set, the transmission number record data set and the covering rubber wear thickness record data set are used as training data; the training data is used to train the machine learning model, and the model will learn the complex relationship between the material properties and the wear thickness of the conveyor belt covering rubber. After the training is completed, the covering rubber wear thickness prediction version model is obtained. The covering rubber wear thickness prediction version model can predict the wear times or covering rubber wear thickness of the conveyor belt under specific working conditions based on the input material property parameters.

[0028] Furthermore, according to the material temperature record data set, the drop height record data set, the material weight record data set, the material flow record data set, the transmission times record data set and the cover rubber wear thickness record data set, a cover rubber wear thickness prediction version model is configured, including: The material temperature record data set, the falling height record data set, the material weight record data set, the material flow record data set, the transmission number record data set and the covering rubber wear thickness record data set are divided into a training data set and a verification data set; according to the training data set, a plurality of covering rubber wear thickness prediction candidate version models are configured; according to the verification data set, the plurality of covering rubber wear thickness prediction candidate version models are sorted by the maximum accuracy value to obtain the covering rubber wear thickness prediction version model.

[0029] Preferably, the material temperature record data set, the blanking height record data set, the material weight record data set, the material flow record data set, the transmission number record data set and the covering rubber wear thickness record data set are merged to form a comprehensive data set; the comprehensive data set is divided into a training data set and a verification data set according to a certain ratio (such as 70% training set, 30% verification set) to ensure that there is no overlap between the training set and the verification set, and the data of the covering rubber wear thickness remain corresponding during the division; the training data is divided into multiple groups, and based on the machine learning algorithm, multiple version models that conform to the sample input space are trained. During the training process, the model will learn the mapping relationship from input features (such as material temperature, blanking height, material weight, material flow, transmission number) to output targets (covering rubber wear thickness), and then obtain multiple covering rubber wear thickness prediction candidate version models; the verification data set is used to sort the multiple covering rubber wear thickness prediction candidate version models with the maximum accuracy, that is, the model with the strongest generalization ability is used as the covering rubber wear thickness prediction version model.

[0030] A material transmission scheme is obtained, wherein the material transmission scheme includes a set material temperature, a set material drop height, a set material weight, and a set material flow rate.

[0031] Develop a material transmission plan based on actual needs, where the material transmission plan includes material set temperature, material drop set height, material set weight and material set flow rate.

[0032] When the material set temperature is greater than the material temperature threshold, or / and the drop setting height is greater than the drop height threshold, or / and the material set weight is greater than the material weight threshold, or / and the material set flow rate is greater than the material flow rate threshold, based on the failure wear thickness of the conveyor belt covering rubber, the covering rubber wear thickness prediction version model is retrieved, and the wear resistance times of the material set temperature, the drop setting height, the material set weight and the material set flow rate are analyzed to obtain the transmission prediction times.

[0033] The material set temperature, material set height, material set weight and material set flow rate in the material transmission plan are compared with the corresponding material temperature threshold, material drop height threshold, material weight threshold and material flow rate threshold; when at least one of the material set temperature, material set height, material set weight and material set flow rate in the material transmission plan is greater than the corresponding threshold, based on the failure wear thickness of the conveyor belt covering rubber, the covering rubber wear thickness prediction version model is retrieved to perform wear resistance number analysis, and the expected service life of the conveyor belt under the material transmission plan, that is, the predicted number of transmissions, is obtained through model prediction.

[0034] Further, when the material setting temperature is greater than the material temperature threshold, or / and the drop setting height is greater than the drop height threshold, or / and the material setting weight is greater than the material weight threshold, or / and the material setting flow rate is greater than the material flow rate threshold, based on the failure wear thickness of the conveyor belt cover rubber, the cover rubber wear thickness prediction version model is retrieved, and the wear times of the material setting temperature, the drop setting height, the material setting weight and the material setting flow rate are analyzed to obtain the transmission prediction times, including: An initial value of the transmission prediction number is randomly set to generate a first transmission setting number; a prediction is performed based on the covering rubber wear thickness prediction version model according to the first transmission setting number, the material setting temperature, the blanking setting height, the material setting weight and the material setting flow rate to obtain a covering rubber wear thickness prediction value; when a thickness deviation between the covering rubber wear thickness prediction value and the failure wear thickness of the conveyor belt covering rubber is less than or equal to a thickness deviation threshold, the first transmission setting number is set to the transmission prediction number; when a thickness deviation between the covering rubber wear thickness prediction value and the failure wear thickness of the conveyor belt covering rubber is greater than the thickness deviation threshold, and the covering rubber wear thickness prediction value is greater than the failure wear thickness of the conveyor belt covering rubber, the first transmission setting number is reduced by one; when a thickness deviation between the covering rubber wear thickness prediction value and the failure wear thickness of the conveyor belt covering rubber is greater than the thickness deviation threshold, and the covering rubber wear thickness prediction value is less than the failure wear thickness of the conveyor belt covering rubber, the first transmission setting number is increased by one.

[0035] Specifically, an initial value of the transmission prediction number is randomly set as the first transmission setting number; the first transmission setting number and the material setting temperature, the material setting height, the material setting weight and the material setting flow rate are used to predict the covering rubber wear thickness version model to obtain the wear thickness of the conveyor belt covering rubber under the first transmission setting number, that is, the covering rubber wear thickness prediction value; the covering rubber wear thickness prediction value is compared with the failure wear thickness of the conveyor belt covering rubber. If the thickness deviation between the predicted wear thickness and the failure wear thickness is less than or equal to the preset thickness deviation threshold, then the first transmission setting number can be considered to be a reasonable predicted transmission number, and it is set as the final transmission prediction number; if the predicted wear thickness is greater than the failure wear thickness, then the predicted wear thickness is greater than the failure wear thickness. degree, and the deviation exceeds the thickness deviation threshold, then it means that the set number of transmissions may be too many. In order to find a predicted number closer to the true value, it is necessary to subtract one from the first transmission setting number and repeat the above prediction and comparison process; on the contrary, if the predicted wear thickness is less than the failure wear thickness, and the deviation exceeds the thickness deviation threshold, then it means that the set number of transmissions may be too few. In order to find a predicted number closer to the true value, it is necessary to add one to the first transmission setting number and repeat the above prediction and comparison process; the above process will be repeated until the deviation between the predicted wear thickness and the failure wear thickness is less than or equal to the thickness deviation threshold, and the first transmission setting number obtained at this time is the final transmission prediction number.

[0036] Furthermore, it also includes: When the material set temperature is less than or equal to the material temperature threshold, and the material set height is less than or equal to the material height threshold, and the material set weight is less than or equal to the material weight threshold, and the material set flow rate is less than or equal to the material flow rate threshold, transmission control is performed according to the material transmission plan.

[0037] When all material setting parameters (material setting temperature, material setting height, material setting weight and material setting flow rate) meet or are below their corresponding thresholds, it means that under the current material handling conditions, the conveyor belt and the entire system are expected to operate safely and efficiently without additional parameter adjustment or optimization, and transmission control can be performed according to the current material transmission plan.

[0038] When the predicted number of transmissions is less than or equal to a transmission number threshold, the material set temperature, the material drop set height, and the material set weight are adjusted to obtain an optimized material transmission plan for transmission control.

[0039] Compare the predicted number of transmissions with the set transmission threshold. If the predicted number of transmissions is less than or equal to the transmission threshold, it means that the current transmission scheme may cause premature wear of the conveyor belt. Therefore, it is necessary to properly adjust parameters such as the material set temperature, the material set height, and the material set weight to extend the service life of the conveyor belt. The optimized material transmission scheme will be used in the actual transmission control process to optimize and improve the wear resistance of the conveyor belt.

[0040] In summary, the embodiments of the present application have at least the following technical effects: First, with the conveyor belt model and the transmission equipment model as constraints, the historical material transmission logs that meet the life threshold are collected, wherein any material historical transmission log includes material temperature characteristic values, material drop height characteristic values, material weight characteristic values ​​and material flow characteristic values. Then, based on the material historical transmission logs, the material temperature characteristic values, the drop height characteristic values, the material weight characteristic values ​​and the material flow characteristic values ​​are subjected to four-element joint frequency sorting to obtain the material temperature threshold, the drop height threshold, the material weight threshold and the material flow threshold. Further, based on the material temperature threshold, the drop height threshold, the material weight threshold and the material flow threshold, the conveyor belt model and the transmission equipment model are trained and sorted to obtain the material temperature record data set, the drop height record data set, the material weight record data set, the material flow record data set, the transmission number record data set and the cover glue wear thickness record data set; and according to the material temperature record data set, the drop height record data set, the material weight record data set, the material flow record data set, the transmission number record data set and the cover glue wear thickness record data set, the cover glue wear thickness prediction version model is configured. Next, a material transmission scheme is obtained, wherein the material transmission scheme includes a material setting temperature, a material setting height, a material setting weight, and a material setting flow rate; when the material setting temperature is greater than the material temperature threshold, or / and the material setting height is greater than the material height threshold, or / and the material setting weight is greater than the material weight threshold, or / and the material setting flow rate is greater than the material flow rate threshold, based on the wear thickness of the conveyor belt cover rubber failure, the cover rubber wear thickness prediction version model is retrieved, and the wear times of the material setting temperature, the material setting height, the material setting weight, and the material setting flow rate are analyzed to obtain the transmission prediction times. Finally, when the transmission prediction times are less than or equal to the transmission times threshold, the material setting temperature, the material setting height, and the material setting weight are adjusted to obtain an optimized material transmission scheme for transmission control. The technical problem that it is difficult to quantify and analyze the degree of wear caused by the material on the conveyor belt in the prior art, resulting in the inability to accurately determine the material specifications that can be carried, is solved. The material transmission scheme is optimized through data analysis and model prediction, and the excessive wear of the conveyor belt is avoided, achieving the technical effect of extending the service life of the conveyor belt.

[0041] Embodiment 2 is based on the same inventive concept as the method for optimizing the wear resistance of a rubber transmission belt in the above embodiment. Figure 2 As shown, the present application provides a rubber transmission belt wear resistance optimization system, wherein the system includes: The data acquisition module 11 is used to collect the historical transmission logs of materials that meet the life threshold based on the transmission belt model and the transmission equipment model, wherein any one of the historical transmission logs of materials includes the material temperature characteristic value, the material drop height characteristic value, the material weight characteristic value and the material flow characteristic value; the frequency sorting module 12 is used to perform four-element joint frequency sorting on the material temperature characteristic value, the material drop height characteristic value, the material weight characteristic value and the material flow characteristic value based on the material history transmission log, and obtain the material temperature threshold, the drop height threshold, the material weight characteristic value and the material flow characteristic value. material weight threshold and material flow threshold; a sample sorting module 13, the sample sorting module 13 is used to sort the training samples of the conveyor belt model and the transmission equipment model based on the material temperature threshold, the blanking height threshold, the material weight threshold and the material flow threshold, and obtain the material temperature record data set, the blanking height record data set, the material weight record data set, the material flow record data set, the transmission number record data set and the covering rubber wear thickness record data set; a model configuration module 14, the model configuration module 14 is used to configure the covering rubber wear thickness prediction version model according to the material temperature record data set, the blanking height record data set, the material weight record data set, the material flow record data set, the transmission number record data set and the covering rubber wear thickness record data set; a solution acquisition module 15, the solution acquisition module 15 is used to obtain a material transmission solution, wherein the material transmission solution includes a material set temperature, a blanking height, a material set weight and a material set flow; an analysis module 16, the analysis module 16 is used when the material set temperature is greater than the material temperature threshold, or / and the blanking height is greater than the blanking height threshold, or / and the material set weight When the weight of the material is greater than the material weight threshold, or / and the set material flow rate is greater than the material flow rate threshold, based on the wear thickness of the conveyor belt covering rubber failure, the cover rubber wear thickness prediction version model is retrieved, and the wear times of the material set temperature, the drop setting height, the material set weight and the material set flow rate are analyzed to obtain the transmission prediction times; the control module 17, the control module 17 is used to adjust the material set temperature, the drop setting height and the material set weight when the transmission prediction times is less than or equal to the transmission times threshold, so as to obtain the optimized material transmission plan for transmission control.

[0042] Furthermore, the control module 17 is used to execute the following method: When the material set temperature is less than or equal to the material temperature threshold, and the material set height is less than or equal to the material height threshold, and the material set weight is less than or equal to the material weight threshold, and the material set flow rate is less than or equal to the material flow rate threshold, transmission control is performed according to the material transmission plan.

[0043] Furthermore, the frequency sorting module 12 is used to perform the following method: Construct a normalized axis of material temperature, a normalized axis of blanking height, a normalized axis of material weight and a normalized axis of material flow; construct a transmission log distribution space according to the normalized axis of material temperature, the normalized axis of blanking height, the normalized axis of material weight and the normalized axis of material flow; distribute the historical transmission logs of the material in the transmission log distribution space based on the characteristic value of the material temperature, the characteristic value of the blanking height, the characteristic value of the material weight and the characteristic value of the material flow, and obtain a set of particle distribution coordinates; perform centralized particle sorting according to the set of particle distribution coordinates to obtain the material temperature threshold, the blanking height threshold, the material weight threshold and the material flow threshold.

[0044] Furthermore, the frequency sorting module 12 is used to perform the following method: The particle Euclidean distance set of the particle distribution coordinate set is counted; the particle distribution coordinate set is unsupervisedly clustered according to the Euclidean distance threshold and combined with the particle Euclidean distance set to obtain a particle coordinate clustering result; the particle clusters whose coordinate quantity within the class is less than or equal to the coordinate quantity threshold are deleted to obtain a high-frequency particle set; the high-frequency particle set is analyzed by LOF algorithm to obtain the particle with the maximum local reachable density, and the material temperature threshold, the blanking height threshold, the material weight threshold and the material flow threshold are output.

[0045] Furthermore, the sample sorting module 13 is used to perform the following method: Collect the first material transmission sample constrained by the conveyor belt model and the conveying equipment model, wherein the first material transmission sample includes first material temperature recording data, first material drop height recording data, first material weight recording data, and first material flow recording data; when the first material temperature recording data is greater than the material temperature threshold, or / and the first material drop height recording data is greater than the drop height threshold, or / and the first material weight recording data is greater than the material weight threshold, or / and the first material flow recording data is greater than the material flow threshold, add the first material transmission sample into the training sample.

[0046] Furthermore, the model configuration module 14 is used to execute the following method: The material temperature record data set, the falling height record data set, the material weight record data set, the material flow record data set, the transmission number record data set and the covering rubber wear thickness record data set are divided into a training data set and a verification data set; according to the training data set, a plurality of covering rubber wear thickness prediction candidate version models are configured; according to the verification data set, the plurality of covering rubber wear thickness prediction candidate version models are sorted by the maximum accuracy value to obtain the covering rubber wear thickness prediction version model.

[0047] Furthermore, the analysis module 16 is used to perform the following method: An initial value of the transmission prediction number is randomly set to generate a first transmission setting number; a prediction is performed based on the covering rubber wear thickness prediction version model according to the first transmission setting number, the material setting temperature, the blanking setting height, the material setting weight and the material setting flow rate to obtain a covering rubber wear thickness prediction value; when a thickness deviation between the covering rubber wear thickness prediction value and the failure wear thickness of the conveyor belt covering rubber is less than or equal to a thickness deviation threshold, the first transmission setting number is set to the transmission prediction number; when a thickness deviation between the covering rubber wear thickness prediction value and the failure wear thickness of the conveyor belt covering rubber is greater than the thickness deviation threshold, and the covering rubber wear thickness prediction value is greater than the failure wear thickness of the conveyor belt covering rubber, the first transmission setting number is reduced by one; when a thickness deviation between the covering rubber wear thickness prediction value and the failure wear thickness of the conveyor belt covering rubber is greater than the thickness deviation threshold, and the covering rubber wear thickness prediction value is less than the failure wear thickness of the conveyor belt covering rubber, the first transmission setting number is increased by one.

[0048] 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 or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0049] 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.

[0050] 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 method for optimizing the wear resistance of a rubber transmission belt, characterized in that: The method comprises: With the conveyor belt model and the conveyor equipment model as constraints, collect the historical material transmission logs that meet the life threshold, where any historical material transmission log includes the material temperature characteristic value, the material drop height characteristic value, the material weight characteristic value and the material flow characteristic value; Based on the material history transmission log, the material temperature characteristic value, the material drop height characteristic value, the material weight characteristic value and the material flow characteristic value are subjected to four-element joint frequency sorting to obtain a material temperature threshold value, a drop height threshold value, a material weight threshold value and a material flow threshold value; Based on the material temperature threshold, the material drop height threshold, the material weight threshold and the material flow threshold, the conveyor belt model and the conveying equipment model are sorted for training samples to obtain a material temperature record data set, a drop height record data set, a material weight record data set, a material flow record data set, a transmission number record data set and a cover rubber wear thickness record data set; According to the material temperature record data set, the material drop height record data set, the material weight record data set, the material flow record data set, the transmission times record data set and the cover rubber wear thickness record data set, a cover rubber wear thickness prediction version model is configured; Obtaining a material transmission plan, wherein the material transmission plan includes a material set temperature, a material drop set height, a material set weight, and a material set flow rate; When the material setting temperature is greater than the material temperature threshold, or / and the drop setting height is greater than the drop height threshold, or / and the material setting weight is greater than the material weight threshold, or / and the material setting flow rate is greater than the material flow rate threshold, based on the failure wear thickness of the conveyor belt cover rubber, the cover rubber wear thickness prediction version model is retrieved, and the wear times of the material setting temperature, the drop setting height, the material setting weight and the material setting flow rate are analyzed to obtain the transmission prediction times; When the predicted number of transmissions is less than or equal to a transmission number threshold, the material set temperature, the material drop set height, and the material set weight are adjusted to obtain an optimized material transmission plan for transmission control.

2. The method according to claim 1, characterized in that Also includes: When the material set temperature is less than or equal to the material temperature threshold, and the material set height is less than or equal to the material height threshold, and the material set weight is less than or equal to the material weight threshold, and the material set flow rate is less than or equal to the material flow rate threshold, transmission control is performed according to the material transmission plan.

3. The method according to claim 2, characterized in that Based on the material history transmission log, the material temperature characteristic value, the material drop height characteristic value, the material weight characteristic value and the material flow characteristic value are subjected to four-element joint frequency sorting to obtain a material temperature threshold, a drop height threshold, a material weight threshold and a material flow threshold, including: Construct the normalized axis of material temperature, the normalized axis of material height, the normalized axis of material weight and the normalized axis of material flow; Constructing a transmission log distribution space according to the material temperature normalized axis, the material drop height normalized axis, the material weight normalized axis, and the material flow normalized axis; Based on the material temperature characteristic value, the material drop height characteristic value, the material weight characteristic value and the material flow characteristic value, the material history transmission log is distributed in the transmission log distribution space to obtain a particle distribution coordinate set; Centralized particle sorting is performed according to the particle distribution coordinate set to obtain the material temperature threshold, the material drop height threshold, the material weight threshold and the material flow threshold.

4. The method according to claim 3, characterized in that Centralized particle sorting is performed according to the particle distribution coordinate set to obtain the material temperature threshold, the material drop height threshold, the material weight threshold and the material flow threshold, including: Counting the particle Euclidean distance set of the particle distribution coordinate set; According to the Euclidean distance threshold, unsupervised clustering is performed on the particle distribution coordinate set in combination with the particle Euclidean distance set to obtain a particle coordinate clustering result; Delete the particle clusters whose coordinate quantity in the class is less than or equal to the coordinate quantity threshold to obtain the high-frequency particle set; The high-frequency particle set is subjected to LOF algorithm analysis to obtain particles with the local maximum reachable density, and the material temperature threshold, the material drop height threshold, the material weight threshold and the material flow threshold are output.

5. The method according to claim 1, characterized in that Based on the material temperature threshold, the material drop height threshold, the material weight threshold and the material flow threshold, the conveyor belt model and the conveying equipment model are sorted for training samples to obtain a material temperature record data set, a drop height record data set, a material weight record data set, a material flow record data set, a transmission number record data set and a cover rubber wear thickness record data set, including: Collecting a first material transmission sample constrained by the conveyor belt model and the conveying equipment model, wherein the first material transmission sample includes first material temperature recording data, first material drop height recording data, first material weight recording data, and first material flow recording data; When the first material temperature record data is greater than the material temperature threshold, or / and the first material drop height record data is greater than the drop height threshold, or / and the first material weight record data is greater than the material weight threshold, or / and the first material flow record data is greater than the material flow threshold, the first material transfer sample is added to the training sample.

6. The method according to claim 1, characterized in that According to the material temperature record data set, the drop height record data set, the material weight record data set, the material flow record data set, the transmission times record data set and the cover rubber wear thickness record data set, a cover rubber wear thickness prediction version model is configured, including: The material temperature record data set, the drop height record data set, the material weight record data set, the material flow record data set, the transmission times record data set and the cover rubber wear thickness record data set are divided into a training data set and a verification data set; According to the training data set, multiple models for predicting the wear thickness of the cover rubber are configured; According to the validation data set, the multiple cover rubber wear thickness prediction candidate version models are sorted with the maximum accuracy to obtain the cover rubber wear thickness prediction version model.

7. The method according to claim 1, characterized in that When the material setting temperature is greater than the material temperature threshold, or / and the drop setting height is greater than the drop height threshold, or / and the material setting weight is greater than the material weight threshold, or / and the material setting flow rate is greater than the material flow rate threshold, based on the wear thickness of the conveyor belt cover rubber failure, the cover rubber wear thickness prediction version model is retrieved, and the wear times of the material setting temperature, the drop setting height, the material setting weight and the material setting flow rate are analyzed to obtain the transmission prediction times, including: Randomly setting an initial value of the transmission prediction number to generate a first transmission setting number; According to the first transmission setting number, the material setting temperature, the material drop setting height, the material setting weight and the material setting flow rate, prediction is performed based on the cover rubber wear thickness prediction version model to obtain a cover rubber wear thickness prediction value; When the thickness deviation between the predicted value of the cover rubber wear thickness and the failed wear thickness of the transmission belt cover rubber is less than or equal to a thickness deviation threshold, setting the first transmission setting number of times to the predicted transmission number of times; When the thickness deviation between the predicted value of the cover rubber wear thickness and the failed wear thickness of the transmission belt cover rubber is greater than the thickness deviation threshold, and the predicted value of the cover rubber wear thickness is greater than the failed wear thickness of the transmission belt cover rubber, the first transmission setting number is reduced by one; When the thickness deviation between the predicted value of the covering rubber wear thickness and the failure wear thickness of the transmission belt covering rubber is greater than the thickness deviation threshold, and the predicted value of the covering rubber wear thickness is less than the failure wear thickness of the transmission belt covering rubber, the first transmission setting number is increased by one.

8. A rubber transmission belt wear resistance optimization system, characterized in that: A method for optimizing the wear resistance of a rubber transmission belt according to any one of claims 1 to 7, the system comprising: A data collection module, wherein the data collection module is used to collect historical material transmission logs that meet the life threshold based on the conveyor belt model and the transmission equipment model, wherein any historical material transmission log includes a material temperature characteristic value, a material drop height characteristic value, a material weight characteristic value, and a material flow characteristic value; A frequency sorting module, wherein the frequency sorting module is used to perform four-element joint frequency sorting on the material temperature characteristic value, the material drop height characteristic value, the material weight characteristic value and the material flow characteristic value based on the material history transmission log, to obtain a material temperature threshold, a drop height threshold, a material weight threshold and a material flow threshold; A sample sorting module, the sample sorting module is used to sort the training samples of the conveyor belt model and the conveying equipment model based on the material temperature threshold, the blanking height threshold, the material weight threshold and the material flow threshold, and obtain a material temperature record data set, a blanking height record data set, a material weight record data set, a material flow record data set, a transmission number record data set and a cover rubber wear thickness record data set; A model configuration module, the model configuration module is used to configure a cover rubber wear thickness prediction version model according to the material temperature record data set, the drop height record data set, the material weight record data set, the material flow record data set, the transmission number record data set and the cover rubber wear thickness record data set; A scheme acquisition module, the scheme acquisition module is used to obtain a material transmission scheme, wherein the material transmission scheme includes a material set temperature, a material drop set height, a material set weight and a material set flow rate; An analysis module, wherein the analysis module is used to retrieve a wear thickness prediction version model of the covering rubber based on the failure wear thickness of the conveyor belt covering rubber, perform wear resistance times analysis on the material setting temperature, the material setting height, the material setting weight and the material setting flow rate, and obtain a transmission prediction times when the material setting temperature is greater than the material temperature threshold, or / and the material setting height is greater than the material height threshold, or / and the material setting weight is greater than the material weight threshold, or / and the material setting flow rate is greater than the material flow rate threshold; A control module is used to adjust the material set temperature, the material drop set height and the material set weight when the predicted number of transmissions is less than or equal to a transmission number threshold, so as to obtain an optimized material transmission plan for transmission control.

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