Signal line insulating material process parameter detection optimization method

Abstract

The invention relates to the technical field of detection, in particular to a signal line insulating material process parameter detection optimization method, which comprises the following steps: determining the data dimension of current input data based on the sampling frequency of each environment parameter and the correlation weight between each environment parameter and the current to-be-processed process parameter; based on the data dimension of the current input data, acquiring corresponding environment parameter data from the initial environment parameter data set as the current input data; performing fuzzification processing on the currently preprocessed input data to obtain an environment feature tag corresponding to the currently preprocessed input data; and comparing the environment feature tag corresponding to the current preprocessed input data with the environment feature tag corresponding to the current to-be-processed process parameter, and adjusting the parameter value of the current to-be-processed process parameter based on a comparison result. According to the invention, the process parameters can be automatically and accurately adjusted as much as possible.

Description

Technical Field

[0001] The present invention relates to the field of detection technology, and in particular to a method for detecting and optimizing process parameters of signal line insulation materials. Background Art

[0002] In the current era of rapid development of the electronic information industry, signal lines, as key carriers of signal transmission, have a performance that directly impacts the stability and reliability of the entire electronic system. Insulation materials, as a crucial component of signal lines, prevent signal leakage and provide electrical isolation. Their performance is closely related to production process parameters. For example, improper temperature control during the production process can cause changes in the internal structure of the insulation material, reducing insulation performance; while unreasonable pressure parameters can lead to uneven insulation thickness, affecting signal transmission stability. Traditionally, process parameter testing and adjustment of signal line insulation materials rely heavily on manual experience and regular spot checks. These methods suffer from low detection efficiency, delayed parameter adjustment, and difficulty adapting to complex and changing production environments. These methods lead to significant fluctuations in product quality and increased production costs. Therefore, developing an efficient and accurate method for optimizing process parameter testing for signal line insulation materials is crucial for improving signal line quality and promoting the development of the electronic information industry. Summary of the Invention

[0003] In view of the above technical problems, the technical solution adopted by the present invention is:

[0004] An embodiment of the present invention provides a method for detecting and optimizing process parameters of a signal line insulation material, the method comprising the following steps:

[0005] S100 , obtaining an environmental parameter dataset of a target spatial region within a current time period to be processed as an initial environmental parameter dataset; the target spatial region is a spatial region used for preparing signal line insulation material.

[0006] S200 , determining the data dimension of current input data based on the sampling frequency of each environmental parameter and the association weight between each environmental parameter and the process parameter to be currently processed.

[0007] S300 , based on the data dimension of the current input data, obtaining corresponding environmental parameter data from the initial environmental parameter data set as the current input data.

[0008] S400 , preprocessing the current input data to obtain the preprocessed input data.

[0009] S500, performing fuzzy processing on the currently preprocessed input data to obtain an environmental feature label corresponding to the currently preprocessed input data; the environmental feature label includes a category label corresponding to each environmental parameter in the currently preprocessed input data.

[0010] S600 , comparing the environmental feature tag corresponding to the currently pre-processed input data with the environmental feature tag corresponding to the currently processed process parameter, and adjusting the parameter value of the currently processed process parameter based on the comparison result.

[0011] The present invention has at least the following beneficial effects:

[0012] An embodiment of the present invention provides a method for optimizing process parameter detection of a signal line insulation material, comprising: obtaining an environmental parameter data set of a target spatial region within a current processing time period as an initial environmental parameter data set; determining the data dimension of current input data based on the sampling frequency of each environmental parameter and the association weight between each environmental parameter and the current process parameter to be processed; obtaining corresponding environmental parameter data from the initial environmental parameter data set as current input data based on the data dimension of the current input data; preprocessing the current input data to obtain current preprocessed input data; performing fuzzy processing on the current preprocessed input data to obtain an environmental feature label corresponding to the current preprocessed input data; comparing the environmental feature label corresponding to the current preprocessed input data with the environmental feature label corresponding to the current process parameter to be processed, and adjusting the parameter value of the current process parameter to be processed based on the comparison result. The present invention can automatically and as accurately as possible adjust process parameters because the data dimension of the input data is determined based on the sampling frequency of each environmental parameter and the association weight between each environmental parameter and the current process parameter to be processed, and the environmental feature label is obtained by fuzzy processing the data.

[0013] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0015] Figure 1 A flowchart of a method for detecting and optimizing process parameters of signal line insulation materials provided by an embodiment of the present invention;

[0016] Figure 2 Schematic diagram for obtaining category labels corresponding to environmental parameters. DETAILED DESCRIPTION

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. The terms used herein in the specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0019] It should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the steps as sequential processes, many of the steps can be performed in parallel, concurrently, or simultaneously. In addition, the order of the steps can be rearranged. A process can be terminated when its operation is completed, but can also have additional steps not included in the accompanying drawings. A process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0020] The embodiment of the present invention provides a method for detecting and optimizing process parameters of signal line insulation materials, such as Figure 1 As shown, the method includes the following steps:

[0021] S100: Obtain an environmental parameter dataset of a target spatial area within a current processing time period as an initial environmental parameter dataset.

[0022] In an embodiment of the present invention, the current pending time period may be a time period before the current processing time, and the duration of the current pending time period may be set based on actual needs. In one exemplary embodiment, the duration of the current pending time period may be equal to the longest adjustment time interval of the process parameter in history. The adjustment time interval refers to the time interval for adjusting the process parameter. The adjustment time interval of a process parameter may be the average adjustment time interval of the process parameter obtained based on historical data.

[0023] In the embodiment of the present invention, the target space area is a space area used to prepare signal line insulation materials, specifically a production workshop for signal line insulation materials.

[0024] In the embodiment of the present invention, the environmental parameters may be environmental parameters within the target space area, and may include temperature, humidity, dust content, etc. Each environmental parameter may be acquired by a corresponding sensor.

[0025] S200 , determining the data dimension of current input data based on the sampling frequency of each environmental parameter and the association weight between each environmental parameter and the process parameter to be currently processed.

[0026] In the embodiment of the present invention, since the sampling frequency of the sensor of each environmental parameter is different, the amount of data of each environmental parameter in the same time period is different. Therefore, it is necessary to unify the data dimension to make the processing result as accurate as possible.

[0027] Furthermore, in an embodiment of the present invention, the data dimension of the current input data satisfies the following conditions:

[0028] DS=(∑ n i＝1 (d i ×k i )) / ∑ n i＝1 k i ;

[0029] Among them, DS is the data dimension of the current input data, that is, the number of data required for each environmental parameter, d i is the data window size of the i-th environmental parameter, i ranges from 1 to n, n is the number of environmental parameters, k i is the association weight between the i-th environmental parameter and the current process parameter to be processed.

[0030] In the embodiment of the present invention, d i =f i / f i nosie ; Among them, f i is the sampling frequency of the i-th environmental parameter, f i nosie is the main noise frequency of the sensor corresponding to the i-th environmental parameter.

[0031] In the embodiment of the present invention, k i The following conditions must be met:

[0032] k i =∑ m j＝1 ((X ij -AvgX)(Y ij -AvgY)) / (∑ m j＝1 (X ij -AvgX) 2 ∑ m j＝1 (Y ij -AvgY) 2 ) 1 / 2 ;

[0033] Among them, X ij The jth data value among the m i-th environmental parameter data values collected in the historical time period is set. The value of j ranges from 1 to m. AvgX is the average value of the m i-th environmental parameter data values. Y ij For X ij The data value of the current process parameter to be processed corresponding to the corresponding collection moment, AvgY is the average value of the m data values of the current process parameter to be processed corresponding to the m data values of the i-th environmental parameter.

[0034] In an embodiment of the present invention, the length of the historical time period is set to be longer than the length of the current time period to be processed. The length of the historical time period can be determined based on actual needs, as long as it includes the time period in which the current process parameters to be processed change.

[0035] In the embodiment of the present invention, since the data dimension of the current input data takes into account the sampling frequency of each environmental parameter and the association weight between each environmental parameter and the current process parameter to be processed, the obtained data dimension can be as reasonable and accurate as possible.

[0036] S300 , based on the data dimension of the current input data, obtaining corresponding environmental parameter data from the initial environmental parameter data set as the current input data.

[0037] In an embodiment of the present invention, for the data dimension of the current input data, the data corresponding to the environmental parameters with a low sampling frequency can be first obtained from the initial environmental parameter data set as reference data. Then, the collection time of the reference data is used as the reference time to obtain the data of other environmental parameters corresponding to each reference time, and then the current input data is obtained.

[0038] In an embodiment of the present invention, the data of other environmental parameters corresponding to each reference time may be the data of other environmental parameters corresponding to the same time as or closest to the reference time. For example, for reference time a, if time a is not present in all the collection times of another environmental parameter, the data value of the time closest to time a is selected as the data value of the environmental parameter.

[0039] S400 , preprocessing the current input data to obtain the preprocessed input data.

[0040] In the embodiment of the present invention, in S400, the rth data value X corresponding to each environmental parameter in the current pre-processed input data is r after The following conditions must be met:

[0041] X r after =α×(X rcon )+(1-α)X r-1 after ;

[0042] X r con The current input data and X r after The corresponding data value is α, which is a preset coefficient and can be an empirical value. In an exemplary embodiment, it can be a number between 0.1 and 0.3. The value of r ranges from 1 to DS.

[0043] In the embodiment of the present invention, the above preprocessing can make the data close to the current processing time play a greater role, that is, the closer the data to the current processing time is, the greater the impact on the process parameters.

[0044] S500, performing fuzzy processing on the currently preprocessed input data to obtain an environmental feature label corresponding to the currently preprocessed input data; the environmental feature label includes a category label corresponding to each environmental parameter in the currently preprocessed input data.

[0045] Furthermore, in S500, the category label corresponding to each environmental parameter is obtained by Figure 2 The steps shown are obtained:

[0046] S501: Determine a fuzzy set corresponding to each environmental parameter based on a value range corresponding to each environmental parameter.

[0047] In embodiments of the present invention, the value range corresponding to each environmental parameter can be determined based on actual conditions, for example, based on historical environmental parameter data within the target spatial region. In embodiments of the present invention, the number of fuzzy sets can be set based on actual needs, for example, based on the operating environmental parameters corresponding to the process parameters. For example, for temperature, the corresponding fuzzy sets may include fuzzy sets representing high temperature, medium temperature, and low temperature.

[0048] S502 : Based on a preset membership function, obtain the membership degree of each data value corresponding to the environmental parameter in the currently preprocessed input data to each fuzzy set.

[0049] In an embodiment of the present invention, the preset membership function may be a Gaussian function.

[0050] Those skilled in the art know that any method for obtaining the membership degree of each data value corresponding to the environmental parameter in the current preprocessed input data to each fuzzy set based on a preset membership function falls within the scope of protection of the present invention.

[0051] S503, based on the membership degree of each data value to each fuzzy set, determine the fuzzy set to which the data set corresponding to the environmental parameter in the currently preprocessed input data belongs, and use the category corresponding to the fuzzy set to which the data set corresponding to the environmental parameter in the currently preprocessed input data belongs as the category label corresponding to the environmental parameter.

[0052] In one embodiment of the present invention, the fuzzy set to which the data set corresponding to the environmental parameter in the current preprocessed input data belongs can be obtained by the following steps:

[0053] S10, get the g-th fuzzy set A g The average membership degree AvgA of the data set corresponding to the environmental parameter g =(1 / DS)∑ DS r＝1 β gr ; Among them, β gr The rth data value in the data set corresponding to the environmental parameter belongs to the fuzzy set A g The membership degree of G ranges from 1 to Q, where Q is the number of fuzzy sets.

[0054] S11, obtain max{AvgA1, ..., AvgA g ,……,AvgA Q}The corresponding fuzzy set is used as the fuzzy set to which the data set corresponding to the environmental parameter in the current preprocessed input data belongs.

[0055] In another embodiment of the present invention, the fuzzy set to which the data set corresponding to the environmental parameter in the current preprocessed input data belongs can be obtained by the following steps:

[0056] S20, get the g-th fuzzy set A g The weighted average membership of the data set corresponding to the environmental parameter WAvgA g =(1 / DS)∑ DS r＝1 W r ×β gr / ∑ DS r＝1 W r ;W r is the weight of the rth data value in the data set corresponding to the environmental parameter, where the data value closer to the current processing time has a greater weight, that is, 0<W1<W2<……<W r <……<W DS <1.

[0057] S21, obtain max{WAvgA1, ..., WAvgA g ,……,WAvgA Q}The corresponding fuzzy set is used as the fuzzy set to which the data set corresponding to the environmental parameter in the current preprocessed input data belongs.

[0058] Since the weight of each data value is taken into consideration in this embodiment, the fuzzy set to which each environmental parameter belongs is determined more accurately.

[0059] In an exemplary embodiment, the environmental feature labels corresponding to the currently pre-processed input data may be (high temperature, high humidity, low dust).

[0060] S600 , comparing the environmental feature tag corresponding to the currently pre-processed input data with the environmental feature tag corresponding to the currently processed process parameter, and adjusting the parameter value of the currently processed process parameter based on the comparison result.

[0061] Further, in S600, if there is at least one difference between the environmental feature label corresponding to the current preprocessed input data and the environmental feature label corresponding to the current process parameter to be processed, the parameter value of the current process parameter to be processed is adjusted based on the environmental feature label corresponding to the current preprocessed input data.

[0062] In an embodiment of the present invention, the process parameter to be processed may be any process parameter involved in the production process of the signal line insulation material. The process parameters involved in the production process of the signal line insulation material may include raw material melting temperature, raw material extrusion temperature, raw material solidification temperature, extrusion pressure, die pressure, cooling pressure, raw material ratio, etc.

[0063] In an embodiment of the present invention, if there is at least one difference between the environmental feature label corresponding to the currently preprocessed input data and the environmental feature label corresponding to the currently processed process parameter, it means that the environmental feature label corresponding to the currently preprocessed input data is different from the environmental feature label corresponding to the currently processed process parameter, and the currently processed process parameter may not be suitable for the environmental feature label corresponding to the currently preprocessed input data. If no adjustment is made, the insulation performance of the prepared signal line may be unreasonable. Therefore, it is necessary to adjust the environmental feature label corresponding to the currently processed process parameter. The specific adjustment can be made based on actual needs. For example, the parameter value of the currently processed process parameter corresponding to the environmental feature label corresponding to the currently preprocessed input data can be obtained from the preset environmental feature label and process parameter correspondence table as the parameter value of the currently processed process parameter.

[0064] In an embodiment of the present invention, each row of data in the preset table of correspondences between environmental characteristic tags and process parameters includes a corresponding environmental characteristic tag and a parameter value of the process parameter. Those skilled in the art will appreciate that each process parameter has an appropriate environmental characteristic tag, and that the appropriate environmental characteristic tag for each process parameter can be obtained based on historical data.

[0065] Those skilled in the art will appreciate that all process parameters can be adjusted by repeating steps S200 to S600 .

[0066] An embodiment of the present invention also provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are configured to execute the method described in the embodiment of the present invention.

[0067] An embodiment of the present invention further provides a computer-readable storage medium storing computer-executable instructions, wherein the computer instructions are used to execute the method described in the embodiment of the present invention.

[0068] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in the present invention can be achieved. This is not limited herein.

[0069] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for detecting and optimizing process parameters of signal line insulation materials, characterized in that: The method comprises the following steps: S100, obtaining an environmental parameter dataset of a target spatial region within a current processing time period as an initial environmental parameter dataset; the target spatial region is a spatial region used for preparing signal line insulation material; S200 , determining a data dimension of current input data based on a sampling frequency of each environmental parameter and an association weight between each environmental parameter and a current process parameter to be processed; S300, based on the data dimension of the current input data, obtaining corresponding environmental parameter data from the initial environmental parameter data set as the current input data; S400, preprocessing the current input data to obtain the current preprocessed input data; S500, performing fuzzy processing on the currently preprocessed input data to obtain an environmental feature label corresponding to the currently preprocessed input data; the environmental feature label includes a category label corresponding to each environmental parameter in the currently preprocessed input data; S600 , comparing the environmental feature tag corresponding to the currently pre-processed input data with the environmental feature tag corresponding to the currently processed process parameter, and adjusting the parameter value of the currently processed process parameter based on the comparison result.

2. The method according to claim 1, characterized in that The data dimensions of the current input data meet the following conditions: DS=(∑ n i＝1 (d i ×k i )) / ∑ n i＝1 k i ; Among them, DS is the data dimension of the current input data, d i is the data window size of the i-th environmental parameter, i ranges from 1 to n, n is the number of environmental parameters, k i is the association weight between the i-th environmental parameter and the current process parameter to be processed, where d i =f i / f i nosie ; Among them, f i is the sampling frequency of the i-th environmental parameter, f i nosie is the main noise frequency of the sensor corresponding to the i-th environmental parameter.

3. The method according to claim 2, characterized in that k i The following conditions must be met: k i =∑ m j＝1 ((X ij -AvgX)(Y ij -AvgY)) / (∑ m j＝1 (X ij -AvgX) 2 ∑ m j＝1 (Y ij -AvgY) 2 ) 1 / 2 ; Among them, X ij The jth data value among the m i-th environmental parameter data values collected in the historical time period is set. The value of j ranges from 1 to m. AvgX is the average value of the m i-th environmental parameter data values. Y ij For X ij The data value of the current process parameter to be processed corresponding to the corresponding collection moment, AvgY is the average value of the m data values of the current process parameter to be processed corresponding to the m data values of the i-th environmental parameter.

4. The method according to claim 1, wherein The environmental parameters include temperature, humidity and dust content.

5. The method according to claim 2, characterized in that In S400, the rth data value X corresponding to each environmental parameter in the current pre-processed input data is r after The following conditions must be met: X r after =α×(X r con )+(1-α)X r-1 after ; X r con The current input data is the same as X r after The corresponding data value, α is the preset coefficient, and the value of r is 1 to DS.

6. The method according to claim 1, characterized in that In S500, the category label corresponding to each environmental parameter is obtained through the following steps: S501, determining a fuzzy set corresponding to each environmental parameter based on a value range corresponding to each environmental parameter; S502, based on a preset membership function, obtaining the membership degree of each data value corresponding to the environmental parameter in the current preprocessed input data to each fuzzy set; S503, based on the membership degree of each data value to each fuzzy set, determine the fuzzy set to which the data set corresponding to the environmental parameter in the currently preprocessed input data belongs, and use the category corresponding to the fuzzy set to which the data set corresponding to the environmental parameter in the currently preprocessed input data belongs as the category label corresponding to the environmental parameter.

7. The method according to claim 6, characterized in that The preset membership function is a Gaussian function.

8. The method according to claim 1, characterized in that In S600, if there is at least one difference between the environmental feature label corresponding to the current preprocessed input data and the environmental feature label corresponding to the current process parameter to be processed, the parameter value of the current process parameter to be processed is adjusted based on the environmental feature label corresponding to the current preprocessed input data.

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