CN114925934B - A prediction model validation method for architectural ceramics production - Google Patents
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Abstract
本发明涉及模型验证技术领域，尤其涉及一种用于建筑陶瓷生产的预测模型验证方法，包括以下步骤：步骤S1：对待验证的预测模型进行离线验证，得到离线验证结果；步骤S2：判断离线验证结果是否符合离线生产要求，如果符合，则进行步骤S3，如果不符合，调整预测模型，再重复步骤S1；步骤S3：对通过离线验证的预测模型进行在线验证，得到在线验证结果；步骤S4：判断在线验证结果是否符合在线生产要求，如果符合，则完成验证，如果不符合，调整预测模型，再重复步骤S1。本发明能验证建筑陶瓷生产的预测模型的可行性和准确性，同时保障验证过程中预测模型的有效性且降低试错成本。
The present invention relates to the field of model verification technology, and in particular to a prediction model verification method for building ceramic production, comprising the following steps: step S1: offline verification of a prediction model to be verified to obtain an offline verification result; step S2: judging whether the offline verification result meets the offline production requirements, if yes, proceeding to step S3, if not, adjusting the prediction model, and repeating step S1; step S3: online verification of the prediction model that has passed the offline verification to obtain an online verification result; step S4: judging whether the online verification result meets the online production requirements, if yes, completing the verification, if not, adjusting the prediction model, and repeating step S1. The present invention can verify the feasibility and accuracy of the prediction model for building ceramic production, while ensuring the effectiveness of the prediction model in the verification process and reducing the trial and error cost.
Description
Technical Field
The invention relates to the technical field of model verification, in particular to a prediction model verification method for building ceramic production.
Background
Along with the continuous improvement of national economy level and the continuous progress of social productivity in China, ceramic furniture selected for home decoration is more and more, and people pay more attention to the variety and quality of the ceramic furniture when selecting a plurality of different brands and factories. The ceramic tile is used as a product which is necessary for product decoration, and has good quality. How to accurately detect the quality condition of ceramic tiles has become an important issue of concern for construction engineering.
In order to realize the assessment of ceramic tile quality at present and to better perfect ceramic tile quality analysis, a building ceramic big data prediction model is generated, which mainly collects building ceramic production raw material data, process data and equipment data, and carries out advanced estimation on ceramic quality indexes by adjusting big data modeling analysis production states and prediction production parameters, but the prediction model depends on rigid generation of existing data, is not suitable for actual production, and a method is needed for verifying feasibility and accuracy.
Disclosure of Invention
The invention aims to provide a prediction model verification method for building ceramic production, which can verify the feasibility and accuracy of a prediction model for building ceramic production, and simultaneously ensure the validity of the prediction model in the verification process and reduce the trial-and-error cost.
To achieve the purpose, the invention adopts the following technical scheme:
a predictive model verification method for use in the production of architectural ceramics, comprising the steps of:
step S1, performing off-line verification on a prediction model to be verified to obtain an off-line verification result;
Step S2, judging whether an offline verification result meets the offline production requirement, if so, carrying out step S3, and if not, adjusting a prediction model and repeating step S1;
Step S3, performing online verification on the prediction model passing offline verification to obtain an online verification result;
and S4, judging whether the online verification result meets the online production requirement, if so, completing verification, and if not, adjusting the prediction model, and repeating the step S1.
Preferably, in step S1, the offline verification is performed on the prediction model to be verified, to obtain an offline verification result, which includes the following steps:
S11, inputting N pieces of history data of actual production into a prediction model to be verified, obtaining N pieces of offline prediction production parameters, and drawing the N pieces of prediction production parameters into an offline prediction production curve;
Step S12, drawing actual production history parameters corresponding to N actual production history data into an actual production history curve;
Step S13, drawing a predicted production upper limit curve and a predicted production lower limit curve according to the upper limit and the lower limit of a predicted standard of a predicted model, and defining a region between the predicted production upper limit curve and the predicted production lower limit curve as a predicted region;
step S14, drawing a process standard upper limit curve and a process standard lower limit curve according to the upper limit and the lower limit of the production process standard, wherein a region between the process standard upper limit curve and the process standard lower limit curve is defined as a qualified region;
And S15, drawing an offline predicted production curve, an actual production history curve, a predicted production upper limit curve, a predicted production lower limit curve, a process standard upper limit curve and a process standard lower limit curve into a curve graph together to obtain an offline verification result.
Preferably, in step S2, the determining whether the offline verification result meets the offline production requirement includes the following steps:
s21, judging whether an actual production history curve is in a prediction area or not;
And S22, judging whether the predicted production upper limit curve and the predicted production lower limit curve are in the qualified area.
Preferably, in step S21, the determining whether the actual production history curve is within the prediction area includes the following steps:
Step S211, calculating the predicted duty ratio of the actual production history curve not in the predicted area;
Step S212, judging whether the predicted duty ratio is smaller than the predicted error judgment point, if the predicted duty ratio is smaller than the predicted error judgment point, executing step S22, and if the predicted duty ratio is larger than or equal to the predicted error judgment point, outputting information that the predicted model is wrong, modifying the predicted model, and repeatedly executing step S1.
Preferably, in step S22, the determining whether the predicted upper production limit curve and the predicted lower production limit curve are within the qualified area includes the steps of:
step S221, calculating the qualified duty ratio which is not in the qualified area;
Step S222, judging whether the qualified duty ratio is smaller than the qualified error judgment point, if the qualified duty ratio is smaller than the qualified error judgment point, outputting the prediction model to pass through the offline verification information, executing step S3, and if the qualified duty ratio is larger than or equal to the qualified error judgment point, outputting the information that the prediction model is in error, and repeating step S1.
Preferably, in step S3, online verification is performed on the prediction model that passes the offline verification, to obtain an online verification result, including the following steps:
S31, inputting N real-time production data into a prediction model which passes offline verification to obtain N online prediction production parameters;
And S32, drawing N online prediction production parameters into an online production curve to obtain an online verification result.
Preferably, in step S4, the determining whether the online verification result meets the online production requirement includes the following steps:
step S41, sending the online verification result to an expert module, and receiving expert judgment information;
Step S42, if the expert judges that the information is in accordance, the production is carried out according to the on-line prediction production parameters, the actual real parameters during the production are recorded, and the steps S43-S44 are executed;
If the expert judges that the information is not in conformity, the prediction model is adjusted, and the step S1 is repeatedly executed;
step S43, transmitting the actual real parameters and the online predicted production parameters to an expert module to obtain difference analysis information;
and S44, optimizing the prediction model according to the difference analysis result, and repeatedly executing the step S3 until the difference between the actual real parameters and the online prediction production parameters is within the set allowable range, and judging that the prediction model has effectiveness.
Preferably, the difference analysis information comprises offline test difference analysis information and online test difference analysis information;
the off-line test difference analysis information comprises a parameter error rate of an actual real parameter and an on-line predicted production parameter, wherein the parameter error rate is (on-line predicted production parameter-actual real parameter)/actual real parameter 100%;
The online test difference analysis information comprises a production efficiency difference result and a quality difference result;
The production efficiency difference result is a production efficiency difference result of an actual production efficiency obtained from an actual real parameter and a predicted production efficiency obtained from an online predicted production parameter, the production efficiency difference result being (predicted production efficiency-actual production efficiency)/actual production efficiency 100%;
The quality difference result is a quality difference result of an actual quality obtained from an actual real parameter and a predicted quality obtained from an on-line predicted production parameter, the quality difference result being (predicted quality-actual quality)/actual quality 100%.
The technical scheme has the advantages that after the offline production requirements are met through offline verification, the prediction model is verified on line based on the established online production requirements, and through multiple times of adjustment and judgment of whether the prediction data of the prediction model meet the process production requirements and the prediction production requirements designed in the production requirements, the verification operability and the rationality of the prediction model are met, meanwhile, the prediction data can gradually reach the production requirements, and the quality of the ceramic bricks actually produced is better estimated and analyzed.
Drawings
FIG. 1 is a flow diagram of a predictive model verification method for use in the production of architectural ceramics of the present invention;
FIG. 2 is a schematic diagram of a predictive model verification method for use in the production of architectural ceramics in accordance with the present invention;
FIG. 3 is a schematic diagram of an offline verification result of a first embodiment of a predictive model verification method for architectural ceramic production of the present invention;
FIG. 4 is a schematic diagram of an offline verification result of a second embodiment of a predictive model verification method for architectural ceramic production of the present invention;
FIG. 5 is a schematic diagram of the results of an on-line verification of a third embodiment of a predictive model verification method for architectural ceramic production of the present invention;
FIG. 6 is a schematic diagram of an on-line verification result of a fourth embodiment of a predictive model verification method for architectural ceramic production of the present invention;
Detailed Description
The technical scheme of the invention is further described below by the specific embodiments with reference to the accompanying drawings.
As shown in fig. 1, a prediction model verification method for building ceramic production includes the following steps:
step S1, performing off-line verification on a prediction model to be verified to obtain an off-line verification result;
Step S2, judging whether an offline verification result meets the offline production requirement, if so, carrying out step S3, and if not, adjusting a prediction model and repeating step S1;
Step S3, performing online verification on the prediction model passing offline verification to obtain an online verification result;
and S4, judging whether the online verification result meets the online production requirement, if so, completing verification, and if not, adjusting the prediction model, and repeating the step S1.
The method for verifying the prediction model for building ceramic production comprises the steps of firstly verifying offline to meet offline production requirements, then verifying the prediction model on line based on established online production requirements, and judging whether prediction data of the prediction model meet process production requirements and prediction production requirements designed in production requirements or not through multiple times of adjustment, so that verification operability and rationality of the prediction model are met, meanwhile, the prediction data gradually reach production requirements, and quality of ceramic bricks actually produced is better estimated and analyzed.
To explain further, in step S1, the offline verification is performed on the prediction model to be verified to obtain an offline verification result, which includes the following steps:
S11, inputting N pieces of history data of actual production into a prediction model to be verified, obtaining N pieces of offline prediction production parameters, and drawing the N pieces of prediction production parameters into an offline prediction production curve;
Step S12, drawing actual production history parameters corresponding to N actual production history data into an actual production history curve;
Step S13, drawing a predicted production upper limit curve and a predicted production lower limit curve according to the upper limit and the lower limit of a predicted standard of a predicted model, and defining a region between the predicted production upper limit curve and the predicted production lower limit curve as a predicted region;
step S14, drawing a process standard upper limit curve and a process standard lower limit curve according to the upper limit and the lower limit of the production process standard, wherein a region between the process standard upper limit curve and the process standard lower limit curve is defined as a qualified region;
And S15, drawing an offline predicted production curve, an actual production history curve, a predicted production upper limit curve, a predicted production lower limit curve, a process standard upper limit curve and a process standard lower limit curve into a curve graph together to obtain an offline verification result.
As shown in fig. 2, in this embodiment, the offline verification result is drawn on the graph through a plurality of parameter data, so that the change situation of the statistical data can be intuitively fed back.
Further describing, in step S2, the determining whether the offline verification result meets the offline production requirement includes the following steps:
s21, judging whether an actual production history curve is in a prediction area or not;
And S22, judging whether the predicted production upper limit curve and the predicted production lower limit curve are in the qualified area.
In the embodiment, the verification quality of the offline verification is improved through continuous judgment in the offline verification.
Further describing, in step S21, the step of determining whether the actual production history curve is within the predicted area includes the steps of:
Step S211, calculating the predicted duty ratio of the actual production history curve not in the predicted area;
Step S212, judging whether the predicted duty ratio is smaller than the predicted error judgment point, if the predicted duty ratio is smaller than the predicted error judgment point, executing step S22, and if the predicted duty ratio is larger than or equal to the predicted error judgment point, outputting information that the predicted model is wrong, modifying the predicted model, and repeatedly executing step S1.
Further describing, in step S22, the determining whether the predicted upper production limit curve and the predicted lower production limit curve are within the qualified area includes the steps of:
step S221, calculating the qualified duty ratio which is not in the qualified area;
Step S222, judging whether the qualified duty ratio is smaller than the qualified error judgment point, if the qualified duty ratio is smaller than the qualified error judgment point, outputting the prediction model to pass through the offline verification information, executing step S3, and if the qualified duty ratio is larger than or equal to the qualified error judgment point, outputting the information that the prediction model is in error, and repeating step S1.
In the embodiment, the offline prediction production parameters are combined with the production process standards and the actual production history parameters for comparison and analysis, so that the verification quality of offline verification is further improved. The process comprises the steps of drawing N off-line predicted production parameters output by a prediction model into a predicted production upper limit curve and a predicted production lower limit curve, judging that all the N off-line predicted production parameters fall within a process standard upper limit curve and a process standard lower limit curve required by a production process standard, and comparing the N off-line predicted production parameters with an actual production history curve of actual production history parameters, wherein the N off-line predicted production parameters have no statistical difference, namely 95% points of the actual production history parameters fall within a predicted parameter interval output by the model, so that the model is available, the experience of workers can be learned, and the off-line verification result meets the actual production requirement.
Further describing, in step S3, online verification is performed on the prediction model that passes the offline verification, to obtain an online verification result, including the following steps:
S31, inputting N real-time production data into a prediction model which passes offline verification to obtain N online prediction production parameters;
And S32, drawing N online prediction production parameters into an online production curve to obtain an online verification result.
Further describing, in step S4, the step of determining whether the online verification result meets the online production requirement includes the following steps:
step S41, sending the online verification result to an expert module, and receiving expert judgment information;
Step S42, if the expert judges that the information is in accordance, the production is carried out according to the on-line prediction production parameters, the actual real parameters during the production are recorded, and the steps S43-S44 are executed;
If the expert judges that the information is not in conformity, the prediction model is adjusted, and the step S1 is repeatedly executed;
step S43, transmitting the actual real parameters and the online predicted production parameters to an expert module to obtain difference analysis information;
and S44, optimizing the prediction model according to the difference analysis result, and repeatedly executing the step S3 until the difference between the actual real parameters and the online prediction production parameters is within the set allowable range, and judging that the prediction model has effectiveness.
Because the use of model prediction parameters can possibly lead to unstable production and even production quality accidents, whether the production requirements are met or not is verified off-line again through multiple times before on-line verification, and meanwhile, the identity of production conditions is ensured. Secondly, the online prediction production parameters predicted by the prediction model need to be discussed and verified with a process expert and a firing expert, and if the online prediction production parameters accord with the production process, the online prediction production parameters are adjusted and produced. Meanwhile, big data model expert, process expert, firing expert and quality control expert track on line, and production accidents are prevented. Meanwhile, the change condition of production, particularly the final product quality condition, is recorded at any time, and the product quality defect irrelevant to the kiln is screened out to judge the accuracy of the on-line prediction production parameters predicted by the prediction model.
Preferably, the difference analysis information comprises offline test difference analysis information and online test difference analysis information;
the off-line test difference analysis information comprises a parameter error rate of an actual real parameter and an on-line predicted production parameter, wherein the parameter error rate is (on-line predicted production parameter-actual real parameter)/actual real parameter 100%;
The online test difference analysis information comprises a production efficiency difference result and a quality difference result;
The production efficiency difference result is a production efficiency difference result of an actual production efficiency obtained from an actual real parameter and a predicted production efficiency obtained from an online predicted production parameter, the production efficiency difference result being (predicted production efficiency-actual production efficiency)/actual production efficiency 100%;
The quality difference result is a quality difference result of an actual quality obtained from an actual real parameter and a predicted quality obtained from an on-line predicted production parameter, the quality difference result being (predicted quality-actual quality)/actual quality 100%.
To further illustrate the validated operability and rationality of the present invention, four examples are provided below. Example one off-line validation of ball milling prediction model
Table 1 offline verification of predicted mud fineness for ball milling prediction model
The outline is that the off-line verification qualification rate of the ball milling model predicted slurry quality is 90%, and the normal production product index is 90%, so that the off-line predicted production parameter of the prediction model accords with the off-line production requirement, and the ball milling prediction model accords with the production requirement.
Embodiment two off-line verification of kiln prediction model
Table 2 offline verification of kiln prediction model predicted temperature results
The summary is that the temperature predicted by the kiln model is within the set range of the process standard temperature, and the actual production trend is basically consistent, which indicates that the kiln model is predicted to meet the production process requirement.
Example III proof of ball milling prediction model
Table 3 on-line verification of the predicted mud fineness of ball milling prediction model
The result shows that the predicted slurry fineness is basically consistent with the actual slurry fineness, the maximum absolute value of the error rate is 4 percent, the error rate is less than or equal to 5 percent, and the model effect meets the requirement, so that the ball milling fineness prediction model meets the production process requirement, and the prediction accuracy is high.
Fourth embodiment on-line verification of kiln prediction model
Table 4 kiln model prediction parameters on-line validation results for production of superior rates
And (3) finding that the error value is within +/-5 according to the error value of the high-class rate of each day in the normal production and verification process, and indicating that the kiln prediction model is available.
The technical principle of the present application is described above in connection with the specific embodiments. The description is made for the purpose of illustrating the general principles of the application and should not be taken in any way as limiting the scope of the application. Other embodiments of the application will occur to those skilled in the art from consideration of this specification without the exercise of inventive faculty, and such equivalent modifications and alternatives are intended to be included within the scope of the application as defined in the claims.
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