Building material product quality detection intelligent management and control method and system

Abstract

The invention discloses an intelligent management and control method and system for building material product quality detection, and relates to the field of product quality detection. A virtual production workshop of a to-be-detected building material product is constructed, production information of a physical production workshop is collected by adopting interactive fusion of physical information and data, real-time interaction with production information of the virtual production workshop is carried out, and a production process flow of the to-be-detected building material product is determined. The method comprises the following steps: collecting production information parameters of each sub-process in a qualified production process, taking a to-be-detected building material product simulation model of a virtual production workshop as an inspection object, obtaining a jth production information parameter value of an ith sub-process in a (k + 1) th production process of the inspection object, and calculating and determining a state level of the parameter value, and determining a risk factor of the ith sub-process in the (k + 1) th production process flow according to the state level of the obtained production information parameter value, and calculating a qualified rate risk characterization value of the to-be-detected building material product in the (k + 1) th production process flow.

Description

Technical Field

[0001] The present invention relates to the field of product quality testing, and in particular to an intelligent management and control method and system for building material product quality testing. Background Art

[0002] Product quality inspection is an important part of quality management in production. According to the use requirements of the product, the quality characteristics of the product are converted into specific quality requirements, which are clearly stipulated in the product technical standards (national standards, industry standards, enterprise standards) and other related product design drawings and process manufacturing technical documents, as the basis for quality inspection and the reference benchmark for comparing the inspection results after inspection. In order to ensure product quality, quality inspection must be carried out on raw materials, purchased parts, outsourced parts, roughcasts, semi-finished products, and finished products in the production process, so that unqualified raw materials will not be put into production, unqualified semi-finished products will not be transferred, unqualified parts will not be assembled, and unqualified products will not leave the factory, so as to ensure product quality.

[0003] For the quality inspection of plywood of building materials products, the current inspection process is to take samples of the finished board and then conduct performance tests to obtain relevant performance index parameters for evaluating its performance in use. This is a result-based inspection of the finished product, while the performance of the board depends on the production control in the processing process. The existing inspection process cannot achieve synchronous control of product quality through real-time control of the production dynamics of the production link, which makes the inspection process lag behind the production process, which easily leads to a lag in inspection efficiency and waste of production materials. To this end, we propose an intelligent control method and system for quality inspection of building materials products. Summary of the invention

[0004] The main purpose of the present invention is to provide a method and system for intelligent management and control of building material product quality inspection, which can effectively solve the problems in the background technology.

[0005] To achieve the above object, the technical solution adopted by the present invention is: An intelligent control method for building material product quality detection, comprising: The digital twin technology is used to build a virtual production workshop for the building materials to be tested. The production information of the physical production workshop is collected through the interactive fusion of physical information and data, and the production information of the physical production workshop and the virtual production workshop are interacted in real time; the production information includes personnel parameters, equipment parameters, material parameters, and environmental parameters in the production process of the building materials to be tested; Determine the production process of the building materials product to be tested, including the first sub-process, the second sub-process to the nth sub-process, and collect the production information parameters of each sub-process in the qualified production process , It is represented by the j-th production information parameter value of the i-th sub-process in the k-th production process, where k=1,2,...K, and K is the total amount of data; Take the simulation model of the building material product to be inspected in the virtual production workshop as the inspection object, and obtain the j-th production information parameter value of the i-th sub-process in the k+1-th production process of the inspection object , calculate the interquartile range IQR of the K production information parameter values ​​obtained, the calculation formula is: IQR=Q3-Q1, where Q3 is the third quartile of the K production information parameter values, Q1 is the first quartile of the K production information parameter values, and the parameter value is determined according to the calculation result Status level , the principles for determination are: when or hour, =R; when hour, =0; in, is a constant coefficient, and >1; R is the status level value; The value range of R is [1, R max ), where R max is the state level threshold, and the value of R is as follows: when or When R=1; R∝ ; when ≥0, R∝ ; when When <0, R∝ ; Determine the risk factor of the i-th sub-process in the k+1-th production process according to the status level of the obtained production information parameter value , the calculation formula is: , where is represented as the state level weight, and ; m is the type of parameter whose status level is not 0, and the qualified rate risk characterization value of the building materials product to be tested in the k+1th production process is calculated , the calculation formula is:

[0006] Where a is a constant coefficient, and a∈(0,1); is represented by the risk factor weight of the ith sub-process in the production process, and ; The qualified rate risk characterization value is used to characterize the possibility that the building material product to be tested in the k+1th production process is a qualified product, wherein the larger the qualified rate risk characterization value is, the lower the possibility that the building material product to be tested is a qualified product.

[0007] An intelligent management and control system for building material product quality inspection, including a production information collection module, a data interaction module, a twin workshop construction module, a process flow acquisition module, a data analysis module, and a qualified rate risk characterization module; The production information collection module is used to collect production information of the physical production workshop including personnel parameters, equipment parameters, material parameters, and environmental parameters in the production process of the building materials product to be tested through the interactive fusion of physical information and data; The twin workshop construction module is used to construct a virtual production workshop for the building materials products to be tested using digital twin technology; The data interaction module is used to interact the production information of the physical production workshop with the virtual production workshop in real time; The process flow acquisition module is used to determine the production process flow of the building material product to be tested; The data analysis module is used to calculate the K production information parameter values ​​of each sub-process in the production qualified process. The interquartile range IQR in the virtual production workshop is used as the inspection object, and the j-th production information parameter value of the i-th sub-process in the k+1-th production process of the inspection object is determined according to the calculated interquartile range IQR result. Status level ; The qualified rate risk characterization module is used to determine the risk factor of the i-th sub-process in the k+1-th production process according to the state level of the obtained production information parameter value. , and calculate the qualified rate risk characterization value of the building materials product to be tested in the k+1th production process to characterize the possibility of qualified products ; Among them, the relationship between the qualified rate risk characterization value and the possibility of qualified products is: when the qualified rate risk characterization value is larger, the possibility that the building material product to be tested is a qualified product is lower; The system also includes a memory, a processor, and a computer program stored on the memory and executable on the processor.

[0008] The present invention has the following beneficial effects: Compared with the existing technology, the digital twin technology is used to build a virtual production workshop for the building materials to be tested, and the production information of the physical production workshop is collected through the interactive fusion of physical information and data. The production information of the physical production workshop and the virtual production workshop are interacted in real time to determine the production process of the building materials to be tested, and the production information parameters of each sub-process in the qualified production process are collected. , taking the simulation model of the building material product to be inspected in the virtual production workshop as the inspection object, obtaining the j-th production information parameter value of the i-th sub-process in the k+1-th production process of the inspection object , calculate the interquartile range IQR of the K production information parameter values ​​obtained, and determine the parameter value based on the calculation results Status level , determine the risk factor of the i-th sub-process in the k+1-th production process according to the status level of the obtained production information parameter value , and calculate the qualified rate risk characterization value of the building materials products to be tested in the k+1th production process , it can conduct real-time control of the production dynamics of the production process, thereby achieving synchronous control of product quality, improving the timeliness and efficiency of the testing process, and reducing the waste of production materials to a certain extent.

[0009] Compared with the prior art, the technical solution of the present invention adopts digital twin technology to construct a virtual production workshop for the building materials products to be tested. Based on digital twin technology, it can realize all-round real-time monitoring and information collection of the workshop product status, accurately capture product quality information and process it in time. After production is completed, panoramic backtracking can be performed, thereby providing technical support for determining the location and causes of waste and defective products, and facilitating the optimization of material ratios in the production process according to the analysis results, thereby improving product quality and reducing the generation rate of waste and defective products.

[0010] Compared with the prior art, the technical solution of the present invention can conduct risk assessment on whether a product is qualified during the production process. In the production link, a risk alarm threshold can be set. When the risk assessment result exceeds the threshold, production information that may lead to unqualified products can be discovered in time, thereby locating the process position of the processing technology, facilitating timely adjustment of the production information of the production sub-process. In addition, when it is found that the risk of a product being a qualified product is relatively high, it can be presented in the virtual production workshop in time and automatically classified as waste or defective products for recycling and processing, thereby realizing intelligent quality inspection and control, improving production efficiency, and avoiding the complicated process of manual retrieval and the risks caused by human operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1A schematic diagram of a process of an intelligent control method for quality inspection of building materials products according to the present invention; Figure 2 This is a structural block diagram of an intelligent management and control system for building material product quality inspection according to the present invention; Figure 3 The overall structural block diagram of the virtual production workshop constructed according to the technical solution of the present invention. DETAILED DESCRIPTION

[0012] The present invention will be further described below in conjunction with specific implementation methods, wherein the accompanying drawings are only used for exemplary descriptions and represent only schematic diagrams rather than actual drawings, and should not be understood as limiting the present invention. In order to better illustrate the specific implementation methods of the present invention, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product.

[0013] The specific implementation process of the technical solution of the inventor includes the following steps: Step 1: Determine the production process of the building material product to be tested, including the first sub-process, the second sub-process to the nth sub-process; In this embodiment, a three-layer plywood is taken as an example, and its structure includes a surface layer and a sandwich layer. The production process of the three-layer plywood can be summarized as follows: it includes four steps: a gluing process, a pressing process, a heating process, and a cooling process. The specific processing process is: firstly, glue is applied between the surface layer and the sandwich layer, and the surface layer and the sandwich layer are pressed together by a hydraulic press. During the pressing process, heating and heat preservation operations are required to ensure the pressing effect. After pressing, cooling is performed to solidify the glue, thereby completing the processing of the plywood; Step 2: Use digital twin technology to build a virtual production workshop for the building materials to be tested, collect production information from the physical production workshop through the interactive fusion of physical information and data, and interact with the production information of the physical production workshop and the virtual production workshop in real time; the production information includes personnel parameters, equipment parameters, material parameters, and environmental parameters in the production process of the building materials to be tested; like Figure 3 As shown, it is an overall structural block diagram of a virtual production workshop constructed by the technical solution of the present invention using digital twin technology. Based on digital twin technology, it is possible to realize all-round real-time monitoring and information collection of the status of workshop products, accurately capture product quality information and process it in a timely manner, and perform panoramic backtracking after production is completed, thereby providing technical support for determining the location and cause of waste and defective products, and facilitating the optimization of material ratios in the production process according to the analysis results, thereby improving product quality and reducing the generation rate of waste and defective products; In this embodiment, for the first sub-process, its production information mainly includes the amount of glue applied, glue characteristic parameters, etc.; for the second sub-process, its production information mainly includes the pressing force, pressing time, etc.; for the third sub-process, its production information mainly includes the heating time, heating temperature, etc.; for the fourth sub-process, its production information mainly includes the processing characteristic parameters such as cooling time and cooling temperature, wherein the changes of the above parameters will have a certain impact on the performance of the final plywood product; Step 3: Collect production information parameters of each sub-process in the qualified production process , It is represented by the j-th production information parameter value of the i-th sub-process in the k-th production process, where k=1,2,...K, and K is the total amount of data; Step 4: Calculate the interquartile range IQR of the obtained K production information parameter values, and the calculation formula is: IQR=Q3-Q1, where Q3 is the third quartile of the K production information parameter values, and Q1 is the first quartile of the K production information parameter values; Step 5: Take the simulation model of the building material product to be inspected in the virtual production workshop as the inspection object, and obtain the j-th production information parameter value of the i-th sub-process in the k+1-th production process of the inspection object , determine the parameter value based on the calculation results Status level , where the status level of the parameter value changes with the degree of data abnormality, and the determination principle is: when or hour, =R; when hour, =0; in, is a constant coefficient, and >1; R is the status level value; The value range of R is [1, R max ), where R max is the state level threshold, and the value of R is as follows: when or When R=1; R∝ ; when ≥0, R∝ ; when When <0, R∝ ; Step 6: Determine the risk factor of the i-th sub-process in the k+1-th production process according to the status level of the obtained production information parameter value , the calculation formula is: , where is represented as the state level weight, and ; m is the type of parameter whose status level is not 0, and the qualified rate risk characterization value of the building materials product to be tested in the k+1th production process is calculated , the calculation formula is:

[0014] Where a is a constant coefficient, and a∈(0,1); is represented by the risk factor weight of the ith sub-process in the production process, and ; The qualified rate risk characterization value is used to characterize the possibility that the building materials product to be tested is a qualified product in the k+1th production process. The larger the qualified rate risk characterization value is, the lower the possibility that the building materials product to be tested is a qualified product. In the production process, the risk alarm threshold can be set, including the risk alarm threshold of the sub-process and the qualified rate risk alarm threshold of the product. In the production process, when the risk factor in the i-th sub-process is When it is greater than the set sub-process risk alarm threshold, it means that the processing flow of this sub-process cannot meet the product requirements, and equipment failure may occur, resulting in serious deviations in the production information of this link. The over-threshold alarm is used to prompt equipment maintenance or production parameter adjustment; when the qualified rate risk representation value of the building materials product to be tested in the k+1th production process is greater than the set qualified rate risk alarm threshold, it means that the produced product is likely to be unqualified; It should be noted that the pass rate risk alarm threshold set at this time can be the overall risk value after the entire process processing steps, or it can be the cumulative risk value of the previous several processing steps, that is, in this embodiment, the n value in the pass rate risk characterization value calculation formula can be an integer less than 4. When the production process proceeds to any of the pressing process, heating process, and cooling process, if the cumulative risk value is greater than the set pass rate risk alarm threshold, it means that the defective rate of the produced products is likely to be high. Therefore, the subsequent processes do not need to be implemented. Therefore, when the risk assessment result exceeds the threshold, the production information that may lead to unqualified products can be discovered in time, thereby locating the process position of the processing technology, which is convenient for timely adjustment of the production information of the production sub-process. In addition, when it is found that the risk of the product being a qualified product is likely to be high, it can be presented in the virtual production workshop in a timely manner and automatically classified as waste or defective products for recycling and processing, thereby realizing intelligent quality inspection and control, improving production efficiency, and avoiding the complicated process of manual retrieval and the risks caused by human operation.

[0015] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for intelligent control of building material product quality detection, the method is used for building material product quality detection and control, characterized in that: include: Use digital twin technology to build a virtual production workshop for the building materials to be tested, collect production information from the physical production workshop through the interactive fusion of physical information and data, and interact with the production information of the physical production workshop and the virtual production workshop in real time; Determine the production process of the building materials product to be tested, including the first sub-process, the second sub-process to the nth sub-process, and collect the production information parameters of each sub-process in the qualified production process , It is represented by the j-th production information parameter value of the i-th sub-process in the k-th production process, where k=1,2,...K, and K is the total amount of data; Take the simulation model of the building material product to be inspected in the virtual production workshop as the inspection object, and obtain the j-th production information parameter value of the i-th sub-process in the k+1-th production process of the inspection object , calculate the interquartile range IQR of the K production information parameter values ​​obtained, and determine the parameter value based on the calculation results Status level ; Determine the risk factor of the i-th sub-process in the k+1-th production process according to the status level of the obtained production information parameter value , and calculate the qualified rate risk characterization value of the building materials products to be tested in the k+1th production process , the calculation formula is: Where a is a constant coefficient, and a∈(0,1); is represented by the risk factor weight of the ith sub-process in the production process, and .

2. According to claim 1, a method for intelligent management and control of building material product quality detection is characterized in that: The production information includes personnel parameters, equipment parameters, material parameters, and environmental parameters in the production process of the building material product to be tested.

3. The intelligent control method for building material product quality detection according to claim 1 is characterized in that: The calculation formula for the interquartile range IQR among the K production information parameter values ​​is: IQR=Q3-Q1, where Q3 is the third quartile among the K production information parameter values, and Q1 is the first quartile among the K production information parameter values.

4. The intelligent control method for building material product quality detection according to claim 1 is characterized in that: Status Level The determination principle is: when or hour, =R; when hour, =0; in, is a constant coefficient, and >1; R is the status level value; IQR is the interquartile range among the K production information parameter values; Q3 is the third quartile among the K production information parameter values; Q1 is the first quartile among the K production information parameter values; For parameter value status level.

5. The intelligent control method for building material product quality detection according to claim 1 is characterized in that: Risk Factors The calculation formula is: In the formula, is represented as the state level weight, and ; m is a parameter type whose status level is not 0.

6. The intelligent control method for building material product quality detection according to claim 1 is characterized by: The qualified rate risk characterization value is used to characterize the possibility that the building material product to be tested in the k+1th production process is a qualified product; Among them, when the qualified rate risk characterization value is larger, the possibility that the building material product to be tested is a qualified product is lower.

7. The intelligent control method for building material product quality detection according to claim 4 is characterized by: The value range of R is [1, R max ), where R max is the state level threshold, and the value of R is as follows: when or When R=1; R∝ ； when ≥0, R∝ ; when When <0, R∝ .

8. An intelligent management and control system for building material product quality detection, characterized in that: The system is used to implement the steps of a method for intelligent control of building material product quality inspection as described in any one of claims 1 to 7, including a production information collection module, a data interaction module, a twin workshop construction module, a process flow acquisition module, a data analysis module, and a qualified rate risk characterization module; The production information acquisition module is used to collect production information of the physical production workshop through the interactive fusion of physical information and data, wherein the production information includes personnel parameters, equipment parameters, material parameters, and environmental parameters in the production process of the building material product to be tested; The twin workshop construction module is used to construct a virtual production workshop for the building materials products to be tested using digital twin technology; The data interaction module is used to interact the production information of the physical production workshop with the virtual production workshop in real time; The process flow acquisition module is used to determine the production process flow of the building material product to be tested, including the first sub-flow, the second sub-flow to the nth sub-flow; The data analysis module is used to calculate the K production information parameter values ​​of each sub-process in the production qualified process. The interquartile range IQR in the virtual production workshop is used as the inspection object, and the j-th production information parameter value of the i-th sub-process in the k+1-th production process of the inspection object is determined according to the calculated interquartile range IQR result. Status level ; The qualified rate risk characterization module is used to determine the risk factor of the i-th sub-process in the k+1-th production process according to the state level of the obtained production information parameter value. , and calculate the qualified rate risk characterization value of the building materials products to be tested in the k+1th production process .

9. The intelligent management and control system for building material product quality detection according to claim 8 is characterized by: The system further comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor can implement the steps of any one of the methods of claims 1-7 when executing the program.

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