An ultra-high voltage transformer production and manufacturing quality control evaluation system and method

Abstract

The application belongs to the field of transformer quality evaluation, and particularly relates to an ultra-high voltage transformer production and manufacturing quality control evaluation system and method, which comprises the following steps: a data acquisition module acquires actual execution values of core control points related to the intrinsic quality of a transformer in real time through a sensor group; a deviation matrix construction module compares the collected values with preset values to calculate deviation values and construct a product evaluation matrix; a data processing module is used for forward processing and standardization processing of the matrix, and based on the standardized data, calculates the Euclidean distance from each product to the optimal solution and the worst solution, thereby providing a quantitative basis for comprehensive scoring; a scoring and sorting module calculates the comprehensive score of each product, and generates a product quality ranking after normalization processing; and a quality improvement guidance module is used for counting the quality distribution proportion of different batches of products according to the ranking results, generating a quality control capability report, and outputting a customized installation process scheme for products with poor ranking in the same batch.

Description

Technical Field

[0001] This invention belongs to the field of transformer quality assessment, specifically a quality control and assessment system and method for the production and manufacturing of ultra-high voltage transformers. Background Technology

[0002] Transformers are customized products with low levels of standardization and automation in production, typically manufactured in large batches of small quantities. Due to significant technical differences between products, it is difficult to assess transformer manufacturing quality control capabilities using conventional methods. Simply statistically analyzing parameters and evaluating their compliance one by one yields only a "compliant" or "non-compliant" result, failing to provide a holistic evaluation of the entire transformer. This hinders the assessment of the factory's overall manufacturing quality control capabilities and the degree of variation between and within batches. Existing compliance assessment methods are no longer sufficient for evaluating transformer manufacturing quality control capabilities, thus limiting the improvement of these capabilities. Summary of the Invention

[0003] To address the fact that existing conformity assessments are no longer sufficient for evaluating the quality control capabilities of transformer manufacturing, this invention aims to provide a quality control assessment system and method for ultra-high voltage transformer manufacturing. The system addresses the problem of assessing the overall quality control capabilities of transformer manufacturing. Through the assessment results: 1) assessments between different batches to determine whether the overall manufacturing quality control capabilities of the factory have improved; 2) within the same batch, forming an assessment ranking of individual transformer components, facilitating focused attention on the installation and processing of lower-ranked products during on-site installation, thus providing precise support.

[0004] The technical solution adopted by this invention to achieve the above objectives is: a quality control and evaluation system for the production and manufacturing of ultra-high voltage transformers, comprising:

[0005] The data acquisition module is used to collect real-time actual execution values ​​of core control points related to the intrinsic quality of the transformer during the production and manufacturing process of ultra-high voltage transformers through a sensor array.

[0006] The deviation matrix construction module is used to compare the collected actual execution values ​​with the preset technical requirements values, calculate the deviation value, and construct a product evaluation matrix for products in the same or different batches. The rows of the matrix correspond to the core control point parameters, and the columns correspond to the products to be evaluated.

[0007] The data processing module is used to perform positive processing on the product evaluation matrix to unify the direction of indicators, standardize the data to eliminate differences in dimensions, and calculate the Euclidean distance of each product to the optimal and worst solutions based on the standardized data, so as to provide a quantitative basis for the comprehensive score.

[0008] The scoring and ranking module is used to calculate the comprehensive score of each product and generate a product quality ranking after normalization.

[0009] The quality improvement guidance module is used to calculate the quality distribution ratio of different batches of products based on the ranking results, generate a quality control capability report, and output customized installation process solutions for products that rank lower in the same batch.

[0010] The core control points include: necessary items among coil support bar verticality, core diameter, silicon steel sheet shearing burrs, drying process temperature, and oil injection vacuum; and also include at least one of the following: coil inner and outer diameter, core stacking gap size, and lead wire to box wall distance; and all parameters are transmitted and stored in real time through an industrial Internet of Things platform.

[0011] The sensor group includes:

[0012] The verticality detection sensor is a laser sensor, which is located on the reference surface of the winding machine or coil support structure and aligned with the vertical direction of the support bar, and is used to monitor the angular deviation between the support bar and the reference surface in real time.

[0013] The core diameter measuring sensor is a laser rangefinder, which is fixed on the ring bracket at the core stacking station and arranged around the outer diameter of the core. It is used to measure the outer diameter of the core after the core stacking is completed.

[0014] The shearing burr detection sensor is located above the conveyor belt at the exit of the shearing equipment. It is used to acquire real-time images of the edges of the sheared silicon steel sheets, analyze the burr height, and trigger the sorting mechanism.

[0015] Temperature sensors are evenly distributed on the inner wall of the drying oven and in the material loading area to monitor the temperature distribution inside the drying oven;

[0016] The oil filling vacuum sensor is a vacuum pressure sensor installed at the end of the vacuum pipeline of the oil filling equipment and close to the transformer oil tank interface. It is used to monitor the vacuum fluctuation during the oil filling process in real time.

[0017] The inner and outer diameter monitoring sensor is a non-contact laser scanner, which is installed at the end of the robotic arm or on a fixed measuring frame at the coil winding station. It is used to dynamically measure the inner and outer diameter dimensions during the coil winding process to prevent dimensional deviations.

[0018] The core stack gap detection sensor is a capacitive displacement sensor, which is embedded in the clamping surface of the core stacking fixture and is used to detect the size of the gap after the silicon steel sheets of the core are stacked in real time.

[0019] The lead-to-box wall distance monitoring sensor is an ultrasonic ranging sensor, which is installed in the lead installation area inside the transformer box and close to the inner side of the box wall. It is used to measure the safe distance between the lead and the box wall to prevent insulation failure or discharge risk.

[0020] The data processing module includes: a forwarding unit, a standardization unit, an optimal solution and worst solution calculation unit, and a distance calculation unit;

[0021] The positive transformation unit is used to perform absolute value transformation on the negative values ​​in the deviation matrix and to transform the minimum value index into the maximum value index.

[0022] The standardized unit is used to perform dimensionless standardization on the normalized data to obtain the standardized matrix.

[0023] The optimal and worst solution calculation unit is used to extract the maximum and minimum values ​​of the parameters of each core control point based on the standardized matrix, and generate the optimal solution vector and the worst solution vector respectively.

[0024] The distance calculation unit is used to calculate the distance of each product to the optimal and worst solutions using the Euclidean distance formula.

[0025] The quality improvement guidance module includes: an inter-batch comparison unit and a same-batch early warning unit;

[0026] The batch comparison unit is used to analyze the changing trends of the proportion of the top 10%, middle 80%, and bottom 10% of products in different batches, and to generate suggestions for improving the factory's manufacturing capabilities.

[0027] The batch warning unit is used to automatically trigger the process parameter review process for the bottom 5% of products and retrieve historical defect data from the production database to optimize the installation plan.

[0028] An evaluation method for a quality control assessment system for ultra-high voltage transformer manufacturing includes the following steps:

[0029] S1: Collect the actual parameters of the core control points during the production process through the sensor group of the data acquisition module, and obtain the deviation between the actual parameters of the core control points and the standard values.

[0030] S2: Construct a product evaluation matrix and perform positiveization and standardization processes in sequence;

[0031] S3: Calculate the optimal and worst solutions, and generate a comprehensive score for each product based on the Euclidean distance formula;

[0032] S4: Rank the products by quality based on the scoring results and output a batch quality analysis report and targeted improvement measures.

[0033] In step S1, obtaining the deviation between the actual parameters of the collected core control points and the standard values ​​specifically involves:

[0034] The actual parameter a of each core control point i Its corresponding standard value astd The deviation is as follows:

[0035] Δa i =|a i -a std |

[0036] For the minimum value of burr height, the deviation value is directly taken as the difference between the actual value and the standard value; for the maximum value of vacuum degree, the deviation value is Δa. i =max(a std -a i ,0);

[0037] All deviation data are transmitted in real time to the data processing module through the industrial IoT platform, and step S2 is executed to construct the product evaluation matrix.

[0038] Step S2 specifically includes:

[0039] S2-1: Establish matrix A for product evaluation, namely:

[0040] A = (a ij ), i, j≥1

[0041] Where i represents the evaluation index for each row, and j represents the object to be evaluated for each column;

[0042] S2-2: Forwarding process:

[0043] The positive transformation unit performs absolute value transformation on the negative values ​​in the deviation matrix and converts them using the formula x′=max(a i -x converts the minimum index into a maximum index;

[0044] S2-3: The range standardization method is used to remove dimensions from the normalized data. The standardization formula is:

[0045]

[0046] Among them, a ij For the original data after forward transformation, a i Given the dataset of evaluation metrics across all products, max(a i ) and min(a i ) represent the maximum and minimum values ​​of the i-th core control point, respectively, z ij The standardized data is then used to generate the final standardized matrix Z = (z ij ).

[0047] Step S3 specifically includes:

[0048] S3-1: Calculate the optimal and worst solutions, i.e.:

[0049] Optimal solution Z + =(max{a 11 ,a 21 ,…,a n1},max{a 12 ,a 22 ,…,a n2},…,max{a 1m ,a 2m ,…,a nm}); 1≤i≤n、1≤j≤m;

[0050] worst solution Z - =(min{a 11 ,a 21 ,…,a n1},min{a 12 ,a 22 ,…,a n2},…,min{a 1m ,a 2m ,…,a nm}); 1≤i≤n、1≤j≤m;

[0051] S3-2: Calculate the distances from each evaluated object to the optimal and worst solutions, respectively:

[0052] Distance from the evaluated object to the optimal solution:

[0053]

[0054] Distance from the evaluated object to the optimal solution:

[0055]

[0056] S3-3: Generate a comprehensive score for each product based on the Euclidean distance formula from each evaluated object to the optimal and worst solutions:

[0057] S i =D i - / (D i + +D i - ), 0≤S i ≤1

[0058] The calculation results are normalized and ranked. The final comprehensive score for each product is as follows:

[0059] In step S4, the improvement measures include:

[0060] For the bottom 5% of products in the same batch, adjust the process parameters in their installation process and add online monitoring nodes;

[0061] For factory production lines whose rankings have declined for two consecutive batches, a process for calibrating production equipment and retraining operators will be initiated.

[0062] The present invention has the following beneficial effects and advantages:

[0063] 1. This invention innovatively applies the TOPSIS algorithm to the evaluation of transformer manufacturing quality control capabilities, pioneering a new approach to overall transformer quality evaluation. This facilitates the implementation of overall transformer quality evaluation work and breaks away from the traditional conformity evaluation model.

[0064] 2. The TOPSIS algorithm in this invention can be implemented by programming, which facilitates the calculation of results in complex situations involving multiple dimensions and multiple entities. It has a fast calculation speed, accurate calculation results, and is convenient and practical.

[0065] 3. The overall evaluation results of transformer manufacturing quality control capabilities help the quality management system to carry out quality improvement and enhancement, and help guide technical personnel or quality management staff in their work. Attached Figure Description

[0066] Figure 1 The flowchart of the quality control and evaluation method for ultra-high voltage transformer manufacturing of the present invention. Detailed Implementation

[0067] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments.

[0068] This invention discloses a method for evaluating the quality control capability of ultra-high voltage transformer manufacturing based on the TOPSIS algorithm. The method involves setting a series of core control points closely related to the inherent quality of the transformer throughout the entire manufacturing process. By collecting the deviation values ​​between the actual execution values ​​and technical requirements of these core control points during production, a deviation value matrix is ​​established for products in the same batch or different batches. The deviation value data is then normalized and standardized, and the ideal optimal and worst-case solutions are calculated. The distance of each product to the optimal and worst-case solutions is calculated, the calculation results are normalized, and the products are ranked according to the final results.

[0069] This invention discloses a quality control and evaluation system for the production and manufacturing of ultra-high voltage transformers, comprising:

[0070] (I) Data Acquisition Module

[0071] The data acquisition module is used to collect real-time actual execution values ​​of core control points related to the intrinsic quality of the transformer during the production and manufacturing process of ultra-high voltage transformers through a sensor array.

[0072] The core control points include: necessary items among coil support bar verticality, core diameter, silicon steel sheet shearing burrs, drying process temperature, and oil injection vacuum; and also include at least one of the following: coil inner and outer diameter, core stacking gap size, and lead wire to box wall distance; and all parameters are transmitted and stored in real time through an industrial Internet of Things platform.

[0073] The sensor group includes:

[0074] The verticality detection sensor is a laser sensor, which is located on the reference surface of the winding machine or coil support structure and aligned with the vertical direction of the support bar, and is used to monitor the angular deviation between the support bar and the reference surface in real time.

[0075] The core diameter measuring sensor is a laser rangefinder, which is fixed on the ring support at the core stacking station and arranged around the outer diameter of the core. It is used to measure the outer diameter of the core after the core stacking is completed.

[0076] The shearing burr detection sensor is located above the conveyor belt at the exit of the shearing equipment. It is used to acquire real-time images of the edges of the sheared silicon steel sheets, analyze the burr height, and trigger the sorting mechanism.

[0077] Temperature sensors are evenly distributed on the inner wall of the drying oven and in the material loading area to monitor the temperature distribution inside the drying oven;

[0078] The oil filling vacuum sensor is a vacuum pressure sensor installed at the end of the vacuum pipeline of the oil filling equipment and close to the transformer oil tank interface. It is used to monitor the vacuum fluctuation during the oil filling process in real time.

[0079] The inner and outer diameter monitoring sensor is a non-contact laser scanner, which is installed at the end of the robotic arm or on a fixed measuring frame at the coil winding station. It is used to dynamically measure the inner and outer diameter dimensions during the coil winding process to prevent dimensional deviations.

[0080] The core stack gap detection sensor is a capacitive displacement sensor, which is embedded in the clamping surface of the core stacking fixture and is used to detect the size of the gap after the silicon steel sheets of the core are stacked in real time.

[0081] The lead-to-box wall distance monitoring sensor is an ultrasonic ranging sensor, which is installed in the lead installation area inside the transformer box and close to the inner side of the box wall. It is used to measure the safe distance between the lead and the box wall to prevent insulation failure or discharge risk.

[0082] (II) Deviation Matrix Construction Module

[0083] The deviation matrix construction module is used to compare the collected actual execution values ​​with the preset technical requirements values, calculate the deviation value, and construct a product evaluation matrix for products in the same or different batches. The rows of the matrix correspond to the core control point parameters, and the columns correspond to the products to be evaluated.

[0084] (III) Data Processing Module

[0085] The data processing module is used to perform positive processing on the product evaluation matrix to unify the direction of indicators, standardize the data to eliminate differences in dimensions, and calculate the Euclidean distance of each product to the optimal and worst solutions based on the standardized data, so as to provide a quantitative basis for the comprehensive score.

[0086] The data processing module includes: a forwarding unit, a standardization unit, an optimal and worst solution calculation unit, and a distance calculation unit;

[0087] The positive transformation unit is used to perform absolute value transformation on the negative values ​​in the deviation matrix and to transform the minimum value index into the maximum value index.

[0088] The standardized unit is used to perform dimensionless standardization on the normalized data to obtain the standardized matrix.

[0089] The optimal and worst solution calculation unit is used to extract the maximum and minimum values ​​of the parameters of each core control point based on the standardized matrix, and generate the optimal solution vector and the worst solution vector respectively.

[0090] The distance calculation unit is used to calculate the distance of each product to the optimal and worst solutions using the Euclidean distance formula.

[0091] (iv) Other modules

[0092] The scoring and ranking module is used to calculate the comprehensive score of each product and generate a product quality ranking after normalization.

[0093] The quality improvement guidance module is used to calculate the quality distribution ratio of different batches of products based on the ranking results, generate a quality control capability report, and output customized installation process solutions for products that rank lower in the same batch.

[0094] The quality improvement guidance module includes: an inter-batch comparison unit and a same-batch early warning unit;

[0095] The batch comparison unit is used to analyze the changing trends of the proportion of the top 10%, middle 80%, and bottom 10% of products in different batches, and to generate suggestions for improving the factory's manufacturing capabilities.

[0096] The batch warning unit is used to automatically trigger the process parameter review process for the bottom 5% of products and retrieve historical defect data from the production database to optimize the installation plan.

[0097] Example 1:

[0098] like Figure 1The diagram shown is a flowchart of the quality control and evaluation method for ultra-high voltage transformer manufacturing according to the present invention. The evaluation method of the quality control and evaluation system for ultra-high voltage transformer manufacturing according to the present invention includes the following steps:

[0099] S1: Collect the actual parameters of the core control points during the production process through the sensor group of the data acquisition module, and obtain the deviation between the actual parameters of the core control points and the standard values.

[0100] This involves identifying the core control points in the manufacturing process of each transformer component. These core control points must be closely related to the inherent quality of the product. Examples include: coil support bar verticality, coil inner and outer diameters, coil height, deviation of the lead-out position, core diameter, burrs from silicon steel sheet cutting, gap size during core stacking, core column verticality, distance between lead wires and clamps or sharp corners, distance between lead wires and tank walls, number of voltage reductions during drying, temperature, heating time, product evacuation time, vacuum degree of the degassing cylinder during oil injection, oil temperature, and oil flow rate. These parameters are used to establish a dimensional evaluation system for transformer manufacturing quality control capabilities.

[0101] The actual parameter a of each core control point i Its corresponding standard value a std The deviation is as follows:

[0102] Δa i =|a i -a std |

[0103] For the minimum value of burr height, the deviation value is directly taken as the difference between the actual value and the standard value; for the maximum value of vacuum degree, the deviation value is Δa. i =max(a std -a i ,0);

[0104] All deviation data are transmitted in real time to the data processing module through the industrial IoT platform, and step S2 is executed to construct the product evaluation matrix.

[0105] S2: Construct a product evaluation matrix and perform positiveization and standardization processes in sequence;

[0106] S2-1: Establish matrix A for product evaluation, namely:

[0107] A = (a ij ), i, j≥1

[0108] Where i represents the evaluation index for each row, and j represents the object to be evaluated for each column;

[0109] S2-2: Forwarding process:

[0110] The positive transformation unit performs absolute value transformation on the negative values ​​in the deviation matrix and then converts them using the formula x′=max(a i -x converts the minimum index into the maximum index;

[0111] S2-3: The range standardization method is used to remove dimensions from the normalized data. The standardization formula is:

[0112]

[0113] Among them, a ij For the original data after forward transformation, a i Given the dataset of evaluation metrics across all products, max(a i ) and min(a i ) represent the maximum and minimum values ​​of the i-th core control point, respectively, z ij The standardized data is then used to generate the final standardized matrix Z = (z ij ).

[0114] S3: Calculate the optimal and worst solutions, and generate a comprehensive score for each product based on the Euclidean distance formula;

[0115] S3-1: Calculate the optimal and worst solutions, i.e.:

[0116] Optimal solution Z + =(max{a 11 ,a 21 ,…,a n1},max{a 12 ,a 22 ,…,a n2},…,max{a 1m ,a 2m ,…,a nm}); 1≤i≤n、1≤j≤m;

[0117] worst solution Z - =(min{a 11 ,a 21 ,…,a n1},min{a 12 ,a 22 ,…,a n2},…,min{a 1m ,a 2m ,…,a nm}); 1≤i≤n、1≤j≤m;

[0118] S3-2: Calculate the distances from each evaluated object to the optimal and worst solutions, respectively:

[0119] Distance from the evaluated object to the optimal solution:

[0120]

[0121] Distance from the evaluated object to the optimal solution:

[0122]

[0123] S3-3: Generate a comprehensive score for each product based on the Euclidean distance formula from each evaluated object to the optimal and worst solutions:

[0124] S i =D i - / (D i + +D i - ), 0≤S i ≤1

[0125] The calculation results are normalized and ranked. The final comprehensive score for each product is as follows:

[0126] S4: Regarding the ranking, for different batches of products, calculate the percentage of products ranking at the top, middle, and bottom, and guide the factory's quality management department to implement improvement measures for quality assurance capabilities. For products within the same batch, classify products within the batch according to their quality based on the ranking, and guide the factory's technical department to develop enhanced on-site installation plans for products ranking lower.

[0127] The improvements in this embodiment include:

[0128] For the bottom 5% of products in the same batch, adjust the process parameters in their installation process and add online monitoring nodes;

[0129] For factory production lines whose rankings have declined for two consecutive batches, a process for calibrating production equipment and retraining operators will be initiated.

[0130] In summary, this invention applies the TOPSIS algorithm to the evaluation of transformer manufacturing quality control capabilities, innovatively realizing a holistic evaluation of transformer manufacturing quality. It breaks through the traditional conformity evaluation, which is conducive to the improvement of transformer manufacturing plants and transformer manufacturing quality. It provides more tools and applications for transformer manufacturing quality assessment, and guides quality management personnel to develop quality improvement systems or technical personnel to develop on-site installation enhancement plans based on the ranking.

[0131] Those skilled in the art will understand that the above description is merely a preferred embodiment of the present invention, and the features described in the various embodiments and / or claims of this disclosure can be combined or combined in various ways, even if such combinations or combinations are not explicitly described in this disclosure. This is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0132] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its spirit and scope. Thus, if these modifications and modifications of the invention fall within the scope of the claims and their equivalents, the invention is also intended to include these modifications and modifications.

Claims

1. A quality control and evaluation system for the production and manufacturing of ultra-high voltage transformers, characterized in that, include: The data acquisition module is used to collect real-time actual execution values ​​of core control points related to the intrinsic quality of the transformer during the production and manufacturing process of ultra-high voltage transformers through a sensor array. The deviation matrix construction module is used to compare the collected actual execution values ​​with the preset technical requirements values, calculate the deviation value, and construct a product evaluation matrix for products in the same or different batches. The rows of the matrix correspond to the core control point parameters, and the columns correspond to the products to be evaluated. The data processing module is used to perform positive processing on the product evaluation matrix to unify the direction of indicators, standardize the data to eliminate differences in dimensions, and calculate the Euclidean distance of each product to the optimal and worst solutions based on the standardized data, so as to provide a quantitative basis for the comprehensive score. The scoring and ranking module is used to calculate the comprehensive score of each product and generate a product quality ranking after normalization. The quality improvement guidance module is used to calculate the quality distribution ratio of different batches of products based on the ranking results, generate a quality control capability report, and output customized installation process solutions for products that rank lower in the same batch.

2. The quality control and evaluation system for the production and manufacturing of ultra-high voltage transformers according to claim 1, characterized in that, The core control points include: necessary items among coil support bar verticality, core diameter, silicon steel sheet shearing burrs, drying process temperature, and oil injection vacuum; and also include at least one of the following: coil inner and outer diameter, core stacking gap size, and lead wire to box wall distance; and all parameters are transmitted and stored in real time through an industrial Internet of Things platform.

3. The quality control and evaluation system for the production and manufacturing of ultra-high voltage transformers according to claim 1, characterized in that, The sensor group includes: The verticality detection sensor is a laser sensor, which is located on the reference surface of the winding machine or coil support structure and aligned with the vertical direction of the support bar, and is used to monitor the angular deviation between the support bar and the reference surface in real time. The core diameter measuring sensor is a laser rangefinder, which is fixed on the ring support at the core stacking station and arranged around the outer diameter of the core. It is used to measure the outer diameter of the core after the core stacking is completed. The shearing burr detection sensor is located above the conveyor belt at the exit of the shearing equipment. It is used to acquire real-time images of the edges of the sheared silicon steel sheets, analyze the burr height, and trigger the sorting mechanism. Temperature sensors are evenly distributed on the inner wall of the drying oven and in the material loading area to monitor the temperature distribution inside the drying oven; The oil filling vacuum sensor is a vacuum pressure sensor installed at the end of the vacuum pipeline of the oil filling equipment and close to the transformer oil tank interface. It is used to monitor the vacuum fluctuation during the oil filling process in real time. The inner and outer diameter monitoring sensor is a non-contact laser scanner, which is installed at the end of the robotic arm or on a fixed measuring frame at the coil winding station. It is used to dynamically measure the inner and outer diameter dimensions during the coil winding process to prevent dimensional deviations. The core stack gap detection sensor is a capacitive displacement sensor, which is embedded in the clamping surface of the core stacking fixture and is used to detect the size of the gap after the silicon steel sheets of the core are stacked in real time. The lead-to-box wall distance monitoring sensor is an ultrasonic ranging sensor, which is installed in the lead installation area inside the transformer box and close to the inner side of the box wall. It is used to measure the safe distance between the lead and the box wall to prevent insulation failure or discharge risk.

4. The quality control and evaluation system for the production and manufacturing of ultra-high voltage transformers according to claim 1, characterized in that, The data processing module includes: a forwarding unit, a standardization unit, an optimal solution and worst solution calculation unit, and a distance calculation unit; The positive transformation unit is used to perform absolute value transformation on the negative values ​​in the deviation matrix and to transform the minimum value index into the maximum value index. The standardized unit is used to perform dimensionless standardization on the normalized data to obtain the standardized matrix. The optimal and worst solution calculation unit is used to extract the maximum and minimum values ​​of the parameters of each core control point based on the standardized matrix, and generate the optimal solution vector and the worst solution vector respectively. The distance calculation unit is used to calculate the distance of each product to the optimal and worst solutions using the Euclidean distance formula.

5. The quality control and evaluation system for the production and manufacturing of ultra-high voltage transformers according to claim 1, characterized in that, The quality improvement guidance module includes: an inter-batch comparison unit and a same-batch early warning unit; The batch comparison unit is used to analyze the changing trends of the proportion of the top 10%, middle 80%, and bottom 10% of products in different batches, and to generate suggestions for improving the factory's manufacturing capabilities. The batch warning unit is used to automatically trigger the process parameter review process for the bottom 5% of products and retrieve historical defect data from the production database to optimize the installation plan.

6. An evaluation method based on the quality control evaluation system for ultra-high voltage transformer manufacturing as described in any one of claims 1 to 5, characterized in that, Includes the following steps: S1: Collect the actual parameters of the core control points during the production process through the sensor group of the data acquisition module, and obtain the deviation between the actual parameters of the core control points and the standard values. S2: Construct a product evaluation matrix and perform positiveization and standardization processes in sequence; S3: Calculate the optimal and worst solutions, and generate a comprehensive score for each product based on the Euclidean distance formula; S4: Rank the products by quality based on the scoring results and output a batch quality analysis report and targeted improvement measures.

7. The evaluation method for a quality control evaluation system for ultra-high voltage transformer manufacturing according to claim 6, characterized in that, In step S1, obtaining the deviation between the actual parameters of the collected core control points and the standard values ​​specifically involves: The actual parameter a of each core control point i Its corresponding standard value a std The deviation is as follows: Yes i =|a i -in std | For the minimum value of burr height, the deviation value is directly taken as the difference between the actual value and the standard value; for the maximum value of vacuum degree, the deviation value is Δa. i =max(a std -a i ,0); All deviation data are transmitted in real time to the data processing module through the industrial IoT platform, and step S2 is executed to construct the product evaluation matrix.

8. The evaluation method for a quality control evaluation system for ultra-high voltage transformer manufacturing according to claim 6, characterized in that, Step S2 specifically includes: S2-1: Establish matrix A for product evaluation, namely: A=(a ij ),i、j≥1 Where i represents the evaluation index for each row, and j represents the object to be evaluated for each column; S2-2: Forwarding process: The positive transformation unit performs absolute value transformation on the negative values ​​in the deviation matrix and converts them using the formula x′=max(a i -x converts the minimum index into a maximum index; S2-3: The range standardization method is used to remove dimensions from the normalized data. The standardization formula is: Among them, a ij For the original data after forward transformation, a i Given the dataset of evaluation metrics across all products, max(a i ) and min(a i ) represent the maximum and minimum values ​​of the i-th core control point, respectively, z ij The standardized data is then used to generate the final standardized matrix Z = (z ij ).

9. The evaluation method for a quality control evaluation system for ultra-high voltage transformer manufacturing according to claim 6, characterized in that, Step S3 specifically includes: S3-1: Calculate the optimal and worst solutions, i.e.: Best solution Z + = (max{a 11 ,a 21 ,…,a n1 },max{a 12 ,a 22 ,…,a n2 },…,max{a 1m ,a 2m ,…,a nm }); 1 ≤ i ≤ n, 1 ≤ j ≤ m; Worst solution Z － = (min{a 11 ,a 21 ,…,a n1 },min{a 12 ,a 22 ,…,a n2 },…,min{a 1m ,a 2m ,…,a nm }); 1 ≤ i ≤ n, 1 ≤ j ≤ m; S3-2: Calculate the distances from each evaluated object to the optimal and worst solutions, respectively: Distance from the evaluated object to the optimal solution: Distance from the evaluated object to the optimal solution: S3-3: Generate a comprehensive score for each product based on the Euclidean distance formula from each evaluated object to the optimal and worst solutions: S i =D i － / (D i + +D i － ),0≤S i ≤1 The calculation results are normalized and ranked. The final comprehensive score for each product is as follows:

10. The evaluation method of the quality control evaluation system for ultra-high voltage transformer manufacturing according to claim 6, characterized in that, In step S4, the improvement measures include: For the bottom 5% of products in the same batch, adjust the process parameters in their installation process and add online monitoring nodes; For factory production lines whose rankings have declined for two consecutive batches, a process for calibrating production equipment and retraining operators will be initiated.

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