Engineering plastic scheme intelligent analysis method based on deep learning

Abstract

The invention relates to the technical field of data processing, in particular to an engineering plastic scheme intelligent analysis method based on deep learning, which comprises the following steps: transmitting engineering plastic data collected in an engineering plastic scheme to a to-be-processed position, and dividing the optimized data into a training set, a verification set and a test set; training an initial model by using the training set to output a deep learning model; analyzing the engineering plastic scheme by using the deep learning model to output an analysis result, and optimizing the engineering plastic scheme according to the analysis result; determining the analysis stability of the engineering plastic scheme based on the variance of the lost data volume ratio of the engineering plastic data; if the analysis stability does not meet the requirement, adjusting the maximum number of concurrent transmission connections; and if the training effectiveness does not meet the requirement, adjusting the learning rate of the deep learning model. The analysis stability of the engineering plastic scheme is improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to an intelligent analysis method for engineering plastic solutions based on deep learning. Background Art

[0002] The intelligent analysis method for engineering plastic solutions based on deep learning is an advanced system that uses deep learning technology to conduct efficient and accurate analysis and solution recommendations for the complexity of engineering plastic materials and their application fields. This method can predict the performance of engineering plastic products, optimize formula design, and guide production process adjustments by processing a large amount of material performance data, molding process parameters, application environment conditions and other information, thereby improving product quality and development efficiency and reducing R&D costs.

[0003] Chinese Patent Publication No.: CN109558896A discloses a disease intelligent analysis method and system based on ultrasound omics and deep learning, the method comprising at least the following steps: obtaining a number of ultrasound data of the lesion site to obtain multimodal ultrasound omics data; inputting the multimodal ultrasound omics data into a trained deep learning neural network, and adjusting the connection weights, proportion convolution and pooling layers of neurons according to the multimodal ultrasound omics data to obtain adjusted multimodal ultrasound omics data; using classifiers under different modalities to classify each data in the adjusted multimodal ultrasound omics data to obtain the classification probability of all modalities including each classification; according to the confusion scores between the modalities given by the discriminator, the classification probabilities of all modalities are weighted averaged to obtain the score of each classification; based on clinical outcome indicators and genomics data, according to the score of each classification, the high-risk index is calculated by the logistic regression method, the classification model is established by the decision tree or Adaboost method, and the t-test and Pearson / Spearman correlation analysis are used to obtain the prognosis judgment result, the efficacy evaluation result and the auxiliary diagnosis result. It can be seen that the intelligent disease analysis method and system based on ultrasound genomics and deep learning have the problem that due to the overly strict firewall rules, some normal engineering plastic solution data transmission may be mistaken as external attacks, thereby interrupting the data transmission line, resulting in partial data loss and thus causing a decrease in the analysis stability of the engineering plastic solution. Summary of the invention

[0004] To this end, the present invention provides an intelligent analysis method for engineering plastic solutions based on deep learning, so as to overcome the problem in the prior art that due to the overly strict setting of firewall rules, some normal engineering plastic solution data transmission may be mistaken as external attacks, thereby interrupting the data transmission line, resulting in partial data loss and thus causing the analysis stability of the engineering plastic solution to decrease.

[0005] To achieve the above-mentioned purpose, the present invention provides an intelligent analysis method for engineering plastic solutions based on deep learning, comprising: transmitting the engineering plastic data collected in the engineering plastic solution to a position to be processed, and performing cleaning, denoising, conversion and feature extraction operations in sequence to output optimized data, and dividing the optimized data into a training set, a validation set and a test set; using the training set to train an initial model to output a deep learning model, and using the validation set and the test set to perform validation and test operations on the deep learning model respectively; using the deep learning model to analyze the engineering plastic solution to output an analysis result, and according to the analysis The engineering plastics scheme is optimized according to the results; the amount of lost data of engineering plastics data and the total amount of engineering plastics data in several acquisition cycles are obtained respectively; the analytical stability of the engineering plastics scheme is determined based on the variance of the proportion of the lost data amount of engineering plastics data; if the analytical stability does not meet the requirements, the maximum number of concurrent transmission connections is adjusted, or the training effectiveness of the deep learning model is determined based on the reuse rate of the optimized data; if the training effectiveness does not meet the requirements, the learning rate of the deep learning model is adjusted, or the acquisition frequency of the engineering plastics data is adjusted based on the byte difference between the acquisition value of the engineering plastics data and the actual value.

[0006] Further, determining the analytical stability of the engineering plastic solution includes:

[0007] Compare the variance of the missing data percentage of engineering plastics data with the preset first variance;

[0008] If the variance of the proportion of the amount of missing data of the engineering plastic data is greater than the preset first variance, it is determined that the analysis stability of the engineering plastic solution does not meet the requirements.

[0009] Furthermore, the training effectiveness of the deep learning model is verified, including:

[0010] Comparing the variance of the missing data ratio of the engineering plastic data with the preset first variance and the preset second variance respectively;

[0011] If the variance of the proportion of lost data in the engineering plastic data is greater than the preset first variance and less than or equal to the preset second variance, it is preliminarily determined that the training effectiveness of the deep learning model does not meet the requirements, and whether the training effectiveness of the deep learning model meets the requirements is determined based on the reuse rate of the optimized data.

[0012] Furthermore, adjusting the maximum number of concurrent transmission connections includes:

[0013] Comparing the variance of the missing data ratio of the engineering plastic data with the preset second variance;

[0014] If the variance of the proportion of lost data of the engineering plastic data is greater than a preset second variance, the maximum number of concurrent transmission connections is increased.

[0015] Furthermore, the increase range of the maximum number of concurrent transmission connections is determined by the difference between the variance of the proportion of lost data volume of engineering plastics data and a preset second variance.

[0016] Furthermore, adjusting the learning rate of the deep learning model includes:

[0017] Comparing the reuse rate of the optimized data with a preset first reuse rate and a preset second reuse rate respectively;

[0018] If the reuse rate of the optimized data is greater than the preset first reuse rate, it is determined that the training effectiveness of the deep learning model does not meet the requirements;

[0019] If the reuse rate of the optimized data is greater than the preset first reuse rate and less than or equal to the preset second reuse rate, reducing the learning rate of the deep learning model;

[0020] If the reuse rate of the optimized data is greater than the preset second reuse rate, it is preliminarily determined that the quality of the engineering plastics data does not meet the requirements, and whether the quality of the engineering plastics data meets the requirements is determined based on the byte difference between the engineering plastics data collection value and the actual value.

[0021] Furthermore, the reduction range of the learning rate of the deep learning model is determined by optimizing the difference between the reuse rate of the data and a preset first reuse rate.

[0022] Furthermore, the acquisition frequency of the engineering plastic data is adjusted, including:

[0023] Comparing the byte difference between the engineering plastic data collection value and the actual value with a preset difference;

[0024] If the byte difference between the engineering plastic data collection value and the actual value is greater than the preset difference, it is determined that the quality of the engineering plastic data does not meet the requirements, and the collection frequency of the engineering plastic data is reduced.

[0025] Furthermore, the byte difference between the collected value and the actual value of the engineering plastic data is the difference between the byte amount of the collected engineering plastic data and the byte amount of the actual engineering plastic data.

[0026] Furthermore, the reduction range of the frequency of collecting the engineering plastic data is determined by the difference between the byte difference between the collected value of the engineering plastic data and the actual value and a preset difference.

[0027] Compared with the prior art, the beneficial effect of the present invention lies in that the method of the present invention adjusts the maximum number of concurrent transmission connections according to the variance of the proportion of lost data volume of engineering plastics data. Since the firewall rules are set too strictly, some normal engineering plastics solution data transmission may be mistaken for external attacks, thereby interrupting the data transmission line and causing partial data loss. By increasing the maximum number of concurrent transmission connections, more of these concurrent data transmission tasks can be allowed to proceed smoothly, more normal data transmission connections can be accepted, and data loss caused by connection number restrictions can be reduced. The learning rate of the deep learning model is adjusted according to the reuse rate of the optimized data. Since it is difficult to obtain plastic sample data in some rare engineering plastics and special scenarios, it leads to errors in model training. The amount of data is small, and the number of training rounds is large during training, resulting in training mismatch, which in turn leads to over-training of the model. By reducing the learning rate of the deep learning model, the update of the model parameters can be smoother, allowing the model to more carefully find the optimal solution on limited data. The collection frequency of the engineering plastics data is adjusted according to the byte difference between the collected value and the actual value of the engineering plastics data. Due to the long use time of the collection equipment, it ages, resulting in a decrease in performance when collecting data, and deviations in the collected data, which leads to a decrease in the quality of the engineering plastics data. By reducing the collection frequency of engineering plastics data, the amount of unstable data can be reduced, so that subsequent data processing and analysis can more effectively utilize the relatively stable data portion, thereby improving the analysis stability of the engineering plastics solution.

[0028] Furthermore, the method of the present invention adjusts the maximum number of concurrent transmission connections by setting a preset first variance and a preset second variance. Since the firewall rules are set too strictly, some normal engineering plastic solution data transmissions may be mistaken for external attacks, thereby interrupting the data transmission line and causing partial data loss. By increasing the maximum number of concurrent transmission connections, more of these concurrent data transmission tasks can be allowed to proceed smoothly, more normal data transmission connections can be accepted, and data loss caused by connection number restrictions can be reduced, further improving the analysis stability of the engineering plastic solution.

[0029] Furthermore, the method of the present invention adjusts the learning rate of the deep learning model by setting a preset first reuse rate and a preset second reuse rate. Since it is difficult to obtain plastic sample data for certain rare engineering plastics and special scenarios, the amount of data used to train the model is small and the number of training rounds is large, resulting in training mismatch and thus excessive model training. By reducing the learning rate of the deep learning model, the update of the model parameters can be smoother, allowing the model to more carefully find the optimal solution based on limited data, further improving the analytical stability of the engineering plastic solution.

[0030] Furthermore, the method of the present invention adjusts the frequency of collecting engineering plastics data by setting a preset difference amount. Since the collection equipment is used for a long time and becomes aged, its performance in collecting data is reduced and the collected data is biased, which leads to a decrease in the quality of the engineering plastics data. By reducing the frequency of collecting engineering plastics data, the amount of unstable data can be reduced, so that subsequent data processing and analysis can more effectively utilize the relatively stable data portion, further improving the analytical stability of the engineering plastics solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is an overall flow chart of an intelligent analysis method for engineering plastic solutions based on deep learning according to an embodiment of the present invention;

[0032] Figure 2 This is a logic flow chart of an intelligent analysis method for engineering plastic solutions based on deep learning according to an embodiment of the present invention;

[0033] Figure 3 A specific flow chart of the process of adjusting the maximum number of concurrent transmission connections in the intelligent analysis method for engineering plastic solutions based on deep learning in an embodiment of the present invention;

[0034] Figure 4 This is a specific flow chart of the process of adjusting the learning rate of a deep learning model in the intelligent analysis method for engineering plastic solutions based on deep learning in an embodiment of the present invention. DETAILED DESCRIPTION

[0035] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0036] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0037] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0038] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0039] See also Figure 1 , Figure 2 , Figure 3 as well as Figure 4 As shown, they are respectively an overall flow chart of the intelligent analysis method for engineering plastic solutions based on deep learning, a logical flow chart, a specific flow chart of the process of adjusting the maximum number of concurrent transmission connections, and a specific flow chart of the process of adjusting the learning rate of the deep learning model. The intelligent analysis method for engineering plastic solutions based on deep learning of the present invention comprises:

[0040] Step S1, transmitting the engineering plastic data collected in the engineering plastic scheme to the position to be processed, and performing cleaning, denoising, conversion and feature extraction operations in sequence to output optimized data, and dividing the optimized data into a training set, a validation set and a test set;

[0041] Step S2, using the training set to train the initial model to output a deep learning model, and using the validation set and the test set to perform validation and test operations on the deep learning model respectively;

[0042] Step S3, using the deep learning model to analyze the engineering plastic solution to output an analysis result, and optimizing the engineering plastic solution according to the analysis result;

[0043] Step S4, respectively obtaining the amount of lost data of the engineering plastics data and the total amount of data of the engineering plastics data in a number of acquisition cycles;

[0044] Step S5, determining the analytical stability of the engineering plastics solution based on the variance of the missing data ratio of the engineering plastics data;

[0045] Step S6, if the analysis stability does not meet the requirements, adjusting the maximum number of concurrent transmission connections, or determining the training effectiveness of the deep learning model based on the multiplexing rate of the optimized data;

[0046] Step S7: if the training effectiveness does not meet the requirements, the learning rate of the deep learning model is adjusted, or the collection frequency of the engineering plastics data is adjusted based on the byte difference between the collected value of the engineering plastics data and the actual value.

[0047] Specifically, the engineering plastics solution includes determining the performance selection of engineering plastics, determining the cost of engineering plastics, and determining the processing technology of engineering plastics.

[0048] Specifically, the engineering plastics data include the processing temperature of engineering plastics, the extrusion rate of engineering plastics, and the impurity content of engineering plastics.

[0049] Specifically, the optimized data include complete engineering plastic tensile strength data, engineering plastic injection pressure data after unified unit system, and engineering plastic processing temperature data with erroneous values ​​removed.

[0050] Specifically, deep learning models include convolutional neural networks, recurrent neural networks, and deep neural networks.

[0051] Specifically, the analysis results include the prediction of heat deformation temperature of engineering plastics, electrical conductivity of engineering plastics, and injection molding temperature of engineering plastics.

[0052] Specifically, the maximum number of concurrent transmission connections is the maximum number of engineering plastic data transmission connections that the system can support at the same time.

[0053] Specifically, the reuse rate of optimized data is the ratio of the amount of optimized data reused to the total data usage.

[0054] Specifically, the learning rate of a deep learning model is a hyperparameter used to control the step size of adjusting model parameters according to the gradient of the loss function during the training process of the deep learning model.

[0055] In implementation, the method of the present invention adjusts the maximum number of concurrent transmission connections according to the variance of the proportion of lost data volume of engineering plastics data. Since the firewall rules are set too strictly, some normal engineering plastics solution data transmission may be mistaken for external attacks, thereby interrupting the data transmission line and causing partial data loss. By increasing the maximum number of concurrent transmission connections, more of these concurrent data transmission tasks can be allowed to proceed smoothly, more normal data transmission connections can be accepted, and data loss caused by connection number restrictions can be reduced. The learning rate of the deep learning model is adjusted according to the reuse rate of the optimized data. Since it is difficult to obtain plastic sample data in some rare engineering plastics and special scenarios, the amount of data is small when training the model. A large number of training rounds during training leads to training mismatch, which in turn leads to over-training of the model. By reducing the learning rate of the deep learning model, the update of the model parameters can be smoother, allowing the model to more carefully find the optimal solution on limited data. The collection frequency of the engineering plastics data is adjusted according to the byte difference between the collected value and the actual value of the engineering plastics data. Due to the long use time of the collection equipment, it ages, resulting in a decrease in performance when collecting data, and deviations in the collected data, which leads to a decrease in the quality of the engineering plastics data. By reducing the collection frequency of engineering plastics data, the amount of unstable data can be reduced, so that subsequent data processing and analysis can more effectively utilize the relatively stable data portion, thereby improving the analytical stability of the engineering plastics solution.

[0056] Specifically, the analytical robustness of the engineering plastics solution was determined, including:

[0057] The amount of missing data of engineering plastics data and the total amount of engineering plastics data in several collection cycles are obtained respectively, and the variance of the proportion of the amount of missing data of engineering plastics data is calculated;

[0058] Comparing the variance of the missing data ratio of the engineering plastic data with a preset first variance;

[0059] If the variance of the proportion of the amount of missing data of the engineering plastic data is greater than the preset first variance, it is determined that the analysis stability of the engineering plastic solution does not meet the requirements.

[0060] Specifically, the training effectiveness of the deep learning model is verified, including:

[0061] Comparing the variance of the missing data ratio of the engineering plastic data with the preset first variance and the preset second variance respectively;

[0062] If the variance of the proportion of lost data in the engineering plastic data is greater than the preset first variance and less than or equal to the preset second variance, it is preliminarily determined that the training effectiveness of the deep learning model does not meet the requirements, and whether the training effectiveness of the deep learning model meets the requirements is determined based on the reuse rate of the optimized data.

[0063] It can be understood that the three intervals corresponding to the preset first variance and the preset second variance correspond to three situations respectively:

[0064] The first interval is when the variance of the missing data percentage of the engineering plastics data is less than or equal to the preset first variance, corresponding to the situation where the analytical stability of the engineering plastics solution meets the requirements;

[0065] The second interval is the variance of the missing data ratio of engineering plastics data that is greater than the preset first variance and less than or equal to the preset second variance. It corresponds to the difficulty in obtaining sample data of some rare engineering plastics and plastics in special scenarios, resulting in less data when training the model and more training rounds, resulting in training mismatch and thus over-training of the model:

[0066] The third interval is that the variance of the proportion of lost data of engineering plastics data is greater than the preset second variance. The corresponding firewall rules are set too strictly, which may mistake some normal engineering plastic solution data transmission as external attacks, thereby interrupting the data transmission line and causing some data loss.

[0067] In practice, the preset first variance is generally selected in the range of [0.02, 0.04], and the preset second variance is generally selected in the range of [0.05, 0.07].

[0068] Preferably, the preferred embodiment of the preset first variance is 0.03, and the preferred embodiment of the preset second variance is 0.06.

[0069] Specifically, the variance of the percentage of lost data of engineering plastics data is the variance of the percentage of lost data of engineering plastics data within several collection cycles. The calculation method of the variance of the percentage of lost data of engineering plastics data is a conventional technical means well known to technical personnel in this field. Therefore, the calculation process of the variance of the percentage of lost data of engineering plastics data will not be repeated here.

[0070] Specifically, the proportion of lost data of engineering plastics data is the ratio of the lost data of engineering plastics data in several collection cycles to the total data volume of engineering plastics data.

[0071] In implementation, the method of the present invention determines the analytical stability of the engineering plastic solution by setting a preset first variance and a preset second variance, thereby reducing the impact of the decreased analytical accuracy of the engineering plastic solution due to inaccurate determination of the analytical stability of the engineering plastic solution, and further improving the analytical stability of the engineering plastic solution.

[0072] Specifically, adjusting the maximum number of concurrent transmission connections includes:

[0073] Comparing the variance of the missing data ratio of the engineering plastic data with the preset second variance;

[0074] If the variance of the proportion of lost data of the engineering plastic data is greater than a preset second variance, the maximum number of concurrent transmission connections is increased.

[0075] Specifically, the increase range of the maximum number of concurrent transmission connections is determined by the difference between the variance of the proportion of lost data volume of engineering plastics data and a preset second variance.

[0076] Specifically, when the difference between the variance of the percentage of lost data volume of engineering plastics data and the preset second variance is within 0.02, the maximum number of concurrent transmission connections increases to 1.2 times the original number; when the difference between the variance of the percentage of lost data volume of engineering plastics data and the preset second variance exceeds 0.02, the maximum number of concurrent transmission connections increases by 60 for every 0.01 increase. For example, the difference between the variance of the percentage of lost data volume of engineering plastics data and the preset second variance is 0.04, the current maximum number of concurrent transmission connections is 500, and the increased maximum number of concurrent transmission connections is 500×1.2+60×2=720.

[0077] In implementation, the method of the present invention adjusts the maximum number of concurrent transmission connections by setting a preset first variance and a preset second variance. Since the firewall rules are set too strictly, some normal engineering plastic solution data transmissions may be mistaken for external attacks, thereby interrupting the data transmission line and causing some data loss. By increasing the maximum number of concurrent transmission connections, more of these concurrent data transmission tasks can be allowed to proceed smoothly, more normal data transmission connections can be accepted, and data loss caused by connection number restrictions can be reduced, further improving the analysis stability of the engineering plastic solution.

[0078] Specifically, adjusting the learning rate of the deep learning model includes:

[0079] The amount of data reused for the optimized data and the total amount of data used are respectively obtained, and the reuse rate of the optimized data is calculated;

[0080] Comparing the reuse rate of the optimized data with a preset first reuse rate and a preset second reuse rate respectively;

[0081] If the reuse rate of the optimized data is greater than the preset first reuse rate, it is determined that the training effectiveness of the deep learning model does not meet the requirements;

[0082] If the reuse rate of the optimized data is greater than the preset first reuse rate and less than or equal to the preset second reuse rate, reducing the learning rate of the deep learning model;

[0083] If the reuse rate of the optimized data is greater than the preset second reuse rate, it is preliminarily determined that the quality of the engineering plastics data does not meet the requirements, and whether the quality of the engineering plastics data meets the requirements is determined based on the byte difference between the engineering plastics data collection value and the actual value.

[0084] It can be understood that the three intervals corresponding to the preset first multiplexing rate and the preset second multiplexing rate correspond to three situations respectively:

[0085] The first interval is when the reuse rate of the optimized data is less than or equal to the preset first reuse rate, corresponding to the situation where the training effectiveness of the deep learning model meets the requirements;

[0086] The second interval is when the reuse rate of the optimized data is greater than the preset first reuse rate and less than or equal to the preset second reuse rate. This corresponds to the difficulty in obtaining sample data of some rare engineering plastics and plastics in special scenarios, resulting in a small amount of data when training the model and a large number of training rounds, which leads to training mismatch and thus over-training of the model.

[0087] The third interval is when the reuse rate of the optimized data is greater than the preset second reuse rate. The corresponding collection equipment has been used for a long time and has aged, resulting in a decrease in performance when collecting data and deviations in the collected data.

[0088] In practice, the preset first multiplexing rate is generally selected in the range of [43%, 47%], and the preset second multiplexing rate is generally selected in the range of [48%, 52%].

[0089] Preferably, the preferred embodiment of the preset first multiplexing rate is 45%, and the preferred embodiment of the preset second multiplexing rate is 50%.

[0090] In implementation, the method of the present invention determines the training effectiveness of the deep learning model by setting a preset first reuse rate and a preset second reuse rate, thereby reducing the impact of the decreased analysis stability of the engineering plastic solution due to inaccurate determination of the training effectiveness of the deep learning model, and further improving the analysis stability of the engineering plastic solution.

[0091] Specifically, the reduction range of the learning rate of the deep learning model is determined by optimizing the difference between the reuse rate of the data and the preset first reuse rate.

[0092] Specifically, when the difference between the reuse rate of the optimized data and the preset first reuse rate is within 5%, the learning rate of the deep learning model is reduced to 0.95 times the original value; when the difference between the reuse rate of the optimized data and the preset first reuse rate exceeds 5%, the learning rate of the deep learning model is reduced by 0.002 for every 3% exceeding it. For example, the difference between the reuse rate of the optimized data and the preset first reuse rate is 11%, the current learning rate of the deep learning model is 0.04, and the reduced learning rate of the deep learning model is 0.04×0.95-0.002×2=0.034.

[0093] In implementation, the method of the present invention adjusts the learning rate of the deep learning model by setting a preset first reuse rate and a preset second reuse rate. Since it is difficult to obtain plastic sample data for certain rare engineering plastics and special scenarios, the amount of data used to train the model is small and the number of training rounds is large, resulting in training mismatch and thus excessive model training. By reducing the learning rate of the deep learning model, the update of the model parameters can be smoother, allowing the model to more carefully find the optimal solution on limited data, further improving the analytical stability of the engineering plastic solution.

[0094] Specifically, adjusting the collection frequency of the engineering plastics data includes:

[0095] Respectively obtaining the byte amount of the collected engineering plastic data and the byte amount of the actual engineering plastic data, and calculating the byte difference between the collected value of the engineering plastic data and the actual value;

[0096] Comparing the byte difference between the engineering plastic data collection value and the actual value with a preset difference;

[0097] If the byte difference between the engineering plastic data collection value and the actual value is greater than the preset difference, it is determined that the quality of the engineering plastic data does not meet the requirements, and the collection frequency of the engineering plastic data is reduced.

[0098] It can be understood that the two intervals corresponding to the preset difference amount correspond to two situations:

[0099] The first interval is when the byte difference between the engineering plastic data collection value and the actual value is less than or equal to the preset difference, corresponding to the situation where the quality of the engineering plastic data meets the requirements;

[0100] The second interval is when the byte difference between the engineering plastic data collection value and the actual value is greater than the preset difference. The corresponding collection equipment has been used for a long time and has aged, resulting in a decrease in performance when collecting data and deviations in the collected data.

[0101] In practice, the preset difference amount is generally selected in the range of [700Byte, 800Byte].

[0102] Preferably, the preset difference amount is 750 Byte.

[0103] In implementation, the method of the present invention determines the quality of engineering plastic data by setting a preset difference amount, thereby reducing the impact of the decreased analytical stability of the engineering plastic solution due to inaccurate determination of the quality of the engineering plastic data, and further improving the analytical stability of the engineering plastic solution.

[0104] Specifically, the byte difference between the collected value and the actual value of the engineering plastic data is the difference between the byte amount of the collected engineering plastic data and the byte amount of the actual engineering plastic data.

[0105] Specifically, the reduction range of the acquisition frequency of the engineering plastic data is determined by the difference between the byte difference between the acquired value of the engineering plastic data and the actual value and a preset difference.

[0106] Specifically, when the difference between the byte difference between the engineering plastic data collection value and the actual value and the preset difference is within 100Byte, the collection frequency of the engineering plastic data is reduced to 0.9 times the original; when the difference between the byte difference between the engineering plastic data collection value and the actual value and the preset difference exceeds 100Byte, the collection frequency of the engineering plastic data is reduced by 1 time / minute for every 50Byte exceeding. For example, the difference between the byte difference between the engineering plastic data collection value and the actual value and the preset difference is 200Byte, the current collection frequency of the engineering plastic data is 10 times / minute, and the reduced collection frequency of the engineering plastic data is 10×0.9-1×2=7 times / minute.

[0107] In implementation, the method of the present invention adjusts the frequency of collecting engineering plastic data by setting a preset difference amount. Since the collection equipment is used for a long time and becomes aged, the performance of collecting data is reduced and the collected data is biased, which leads to a decrease in the quality of the engineering plastic data. By reducing the frequency of collecting engineering plastic data, the amount of unstable data can be reduced, so that subsequent data processing and analysis can more effectively utilize the relatively stable data portion, further improving the analytical stability of the engineering plastic solution.

[0108] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. An intelligent analysis method for engineering plastic solutions based on deep learning, characterized in that: include: The engineering plastic data collected in the engineering plastic solution is transmitted to the position to be processed, and cleaning, denoising, conversion and feature extraction operations are performed in sequence to output optimized data, and the optimized data is divided into a training set, a validation set and a test set; Using the training set to train the initial model to output a deep learning model, and using the validation set and the test set to perform validation and test operations on the deep learning model respectively; Analyzing the engineering plastic solution using the deep learning model to output analysis results, and optimizing the engineering plastic solution according to the analysis results; The amount of lost data of engineering plastics data and the total amount of data of engineering plastics data in several acquisition cycles are obtained respectively; Determine the analytical stability of the engineering plastics solution based on the variance of the missing data percentage of the engineering plastics data; If the analysis stability does not meet the requirements, the maximum number of concurrent transmission connections is adjusted, or the training effectiveness of the deep learning model is determined based on the multiplexing rate of the optimized data; If the training effectiveness does not meet the requirements, the learning rate of the deep learning model is adjusted, or the collection frequency of the engineering plastics data is adjusted based on the byte difference between the collected value of the engineering plastics data and the actual value.

2. The method for intelligent analysis of engineering plastic solutions based on deep learning according to claim 1, characterized in that: Determine the analytical robustness of the engineered plastics solution, including: Compare the variance of the missing data percentage of engineering plastics data with the preset first variance; If the variance of the proportion of the amount of missing data of the engineering plastic data is greater than the preset first variance, it is determined that the analysis stability of the engineering plastic solution does not meet the requirements.

3. The method for intelligent analysis of engineering plastic solutions based on deep learning according to claim 2, characterized in that: Verify the training effectiveness of the deep learning model, including: Comparing the variance of the missing data ratio of the engineering plastic data with the preset first variance and the preset second variance respectively; If the variance of the proportion of lost data in the engineering plastic data is greater than the preset first variance and less than or equal to the preset second variance, it is preliminarily determined that the training effectiveness of the deep learning model does not meet the requirements, and whether the training effectiveness of the deep learning model meets the requirements is determined based on the reuse rate of the optimized data.

4. The method for intelligent analysis of engineering plastic solutions based on deep learning according to claim 3 is characterized in that: The maximum number of concurrent transmission connections is adjusted, including: Comparing the variance of the missing data ratio of the engineering plastic data with the preset second variance; If the variance of the proportion of lost data of the engineering plastic data is greater than a preset second variance, the maximum number of concurrent transmission connections is increased.

5. The method for intelligent analysis of engineering plastic solutions based on deep learning according to claim 4 is characterized in that: The increase range of the maximum number of concurrent transmission connections is determined by the difference between the variance of the proportion of lost data volume of the engineering plastics data and a preset second variance.

6. The method for intelligent analysis of engineering plastic solutions based on deep learning according to claim 3, characterized in that: Adjusting the learning rate of the deep learning model includes: Comparing the reuse rate of the optimized data with a preset first reuse rate and a preset second reuse rate respectively; If the reuse rate of the optimized data is greater than the preset first reuse rate, it is determined that the training effectiveness of the deep learning model does not meet the requirements; If the reuse rate of the optimized data is greater than the preset first reuse rate and less than or equal to the preset second reuse rate, reducing the learning rate of the deep learning model; If the reuse rate of the optimized data is greater than the preset second reuse rate, it is preliminarily determined that the quality of the engineering plastics data does not meet the requirements, and whether the quality of the engineering plastics data meets the requirements is determined based on the byte difference between the engineering plastics data collection value and the actual value.

7. The method for intelligent analysis of engineering plastic solutions based on deep learning according to claim 6, characterized in that: The reduction range of the learning rate of the deep learning model is determined by optimizing the difference between the reuse rate of the data and the preset first reuse rate.

8. The method for intelligent analysis of engineering plastic solutions based on deep learning according to claim 7, characterized in that: The collection frequency of the engineering plastic data is adjusted, including: Comparing the byte difference between the engineering plastic data collection value and the actual value with a preset difference; If the byte difference between the engineering plastic data collection value and the actual value is greater than the preset difference, it is determined that the quality of the engineering plastic data does not meet the requirements, and the collection frequency of the engineering plastic data is reduced.

9. The method for intelligent analysis of engineering plastic solutions based on deep learning according to claim 8, characterized in that: The byte difference between the engineering plastic data collection value and the actual value is the difference between the byte amount of the collected engineering plastic data and the byte amount of the actual engineering plastic data.

10. The method for intelligent analysis of engineering plastic solutions based on deep learning according to claim 9, characterized in that: The reduction range of the acquisition frequency of the engineering plastic data is determined by the difference between the byte difference between the acquired value of the engineering plastic data and the actual value and the preset difference.

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