Supplier quality evaluation method

Abstract

The invention discloses a supplier quality evaluation method, and relates to the technical field of data quality analysis, and the method comprises the steps: obtaining a data initial quality evaluation label of each supplier; counting an uploaded information quality processing comprehensive data quality evaluation value of each type of suppliers; determining an initial quality detection constraint period of each type of suppliers and performing quality tracking detection; obtaining quality tracking detection data, and analyzing and processing to obtain a quality tracking monitoring result; according to the method, the data quality of the data uploaded by the suppliers is analyzed, the availability of the quality evaluation data is ensured, the suppliers whose initial quality evaluation is unqualified are screened, and the quality of the suppliers is evaluated to be qualified. Quality evaluation resources are concentrated on suppliers whose initial quality evaluation is qualified, customized quality tracking detection is carried out on each type of suppliers, the quality tracking detection efficiency and result are improved, and the quality detection constraint period is dynamically adjusted in time.

Description

Technical Field

[0001] The present invention relates to the technical field of data quality analysis, and in particular to a supplier quality assessment method. Background Art

[0002] Supplier selection is a very important part of an enterprise's supply chain management. Bad suppliers may lead to production delays, product recalls or customer complaints, which will cause serious financial and reputation losses to the enterprise. Effective supplier quality assessment can help enterprises select the most reliable suppliers, thereby improving the overall efficiency of the supply chain and reducing unnecessary inspections and rework.

[0003] The prior art, such as the invention patent with announcement number: CN113537656B, is a method, system, device and medium for evaluating supplier service quality, including: obtaining a data set of historical service quality indicators of multiple suppliers; using at least two different statistical models to independently analyze the data set of historical service quality indicators to obtain preliminary evaluation results of the future service quality of multiple suppliers respectively; and performing weighted averaging on the preliminary evaluation results to obtain a final evaluation result of the future service quality of multiple suppliers.

[0004] Existing technologies, such as the invention patent with announcement number: CN113240270B, are a method and system for channel merchants to select suppliers using big data and prediction algorithms, which belongs to the field of big data communication technology. First, multiple dimensional attributes are set, then the dimensional attributes are combined, and finally each combination is mapped into a virtual channel; the channel merchant's big data platform will collect feedback data from each supplier, and then analyze the quality status of each virtual channel of each supplier from the feedback data according to the set virtual channel; after collecting the indicators of the virtual channels for a period of time, the channel merchant uses prediction technology to predict the quality status of each virtual channel in the future; when the data of the terminal user reaches the channel merchant in the future, the channel merchant first analyzes the virtual channel corresponding to the data sent by the terminal user, and then selects the best supplier in combination with the set objective function; the method improves the efficiency of data transmission in complex and unstable channels based on quality judgment and prediction.

[0005] Based on the above scheme, it can be seen that in the field of quality assessment, the existing technology often relies solely on historical records to evaluate supplier quality. However, in actual applications, due to the large volume of supplier data, the quality assessment data involved are uneven. Relying solely on the evaluation of historical data and conducting quality inspections on all suppliers in a unified manner will result in a waste of quality inspection resources. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention provides a supplier quality assessment method. To achieve the above purpose, the present invention is implemented through the following technical solutions: The supplier quality assessment method includes: The quality assessment data submitted by each supplier at the upload port is obtained, and the initial quality assessment data of each supplier is collected and analyzed to obtain the initial quality evaluation label of each supplier's data.

[0007] According to the initial quality evaluation label of each supplier's data, if the initial quality evaluation label of a supplier's data is unqualified, the quality warning prompt information will be transmitted to the corresponding upload port for reminder. If the initial quality evaluation label of a supplier's data is qualified, the supplier will be recorded as a Class I supplier, and the uploaded information quality parameters of each Class I supplier will be statistically analyzed to obtain the comprehensive data quality evaluation value of each Class I supplier.

[0008] According to the comprehensive data quality evaluation value of each Class A supplier, the initial quality inspection constraint period of each Class A supplier is determined and quality tracking inspection is carried out on each Class A supplier. The quality tracking inspection data is obtained and analyzed to obtain the quality tracking monitoring results of each Class A supplier. Based on the quality tracking monitoring results of each Class A supplier, the initial quality inspection constraint period of each Class A supplier is dynamically adjusted.

[0009] As a preferred technical solution, the initial quality assessment data of each supplier is statistically analyzed, and the specific process is as follows: Based on the quality assessment information submitted by each supplier at the upload port, the initial quality assessment data of each supplier is collected and compiled.

[0010] The initial quality assessment data of each supplier includes image quality assessment data and text quality assessment data.

[0011] The image quality assessment data includes an average peak signal-to-noise ratio, an average resolution, an average edge sharpness, and an average signal-to-noise ratio of each uploaded image.

[0012] The text quality assessment data includes the total number of bytes, text density and the cumulative number of text pages.

[0013] An initial quality assessment data verification value set is extracted from the database, including an image quality assessment data verification value set and a text quality assessment data verification value set.

[0014] A preliminary data quality transmission impact correction coefficient for each supplier is constructed, and the initial quality assessment data of each supplier is compared and analyzed with the initial quality assessment data verification value set to obtain the initial data quality evaluation value of each supplier. The initial data quality evaluation value is used to evaluate the data quality of the quality assessment data submitted by each supplier at the upload port.

[0015] As a preferred technical solution, the preliminary construction of the data quality transmission impact correction coefficient of each supplier specifically includes: During the transmission of quality assessment data from each supplier, the average network speed, average network bandwidth, average delay and average network packet loss rate are collected synchronously.

[0016] The data transmission quality assessment value of each supplier is obtained through analysis and processing. Based on the data transmission quality assessment value of each supplier, the data quality transmission impact correction coefficient corresponding to each data transmission quality assessment value interval pre-stored in the database is matched to obtain the data quality transmission impact correction coefficient of each supplier.

[0017] The data quality transmission impact correction coefficient is used to correct the impact on the data during the data transmission process.

[0018] As a preferred technical solution, obtaining the initial quality evaluation label of the data of each supplier specifically includes: An initial quality evaluation threshold is extracted from the database. If the initial quality evaluation value of a supplier's data is greater than or equal to the initial quality evaluation threshold, the initial quality evaluation label of the supplier's data is determined to be qualified.

[0019] If the initial quality evaluation value of a supplier's data is less than the initial quality evaluation threshold, the initial quality evaluation label of the supplier's data is judged to be unqualified.

[0020] As a preferred technical solution, the quality parameters of uploaded information of each Class I supplier are analyzed and processed to obtain a comprehensive data quality evaluation value of each Class I supplier, specifically including: The quality parameters of the uploaded information of each first-class supplier include the field coverage rate, the number of matching fields and the field standardization rate of the information filled in by each first-class supplier; Extracting a minimum value set of uploaded information quality parameters from the database, including a minimum field coverage rate, a minimum matching field number, and a minimum field standardization rate; Comprehensively process and analyze the uploaded information quality parameters and the lowest value set of uploaded information quality parameters of each Class I supplier to obtain the uploaded information quality assessment value of each Class I supplier; Based on the uploaded information quality evaluation value and the initial data quality evaluation value of each Class A supplier, comprehensive processing and analysis are performed to obtain the comprehensive data quality evaluation value of each Class A supplier, and the comprehensive data quality evaluation value of each Class A supplier is used to evaluate the comprehensive supply quality of each Class A supplier.

[0021] As a preferred technical solution, the initial quality inspection constraint period of each Class I supplier is determined according to the comprehensive data quality evaluation value of each Class I supplier, specifically including: Based on the comprehensive data quality evaluation value of each Class A supplier, a mapping match is performed with the sampling period corresponding to each comprehensive data quality evaluation value interval pre-stored in the database to obtain the sampling period of each Class A supplier and record it as the initial quality inspection constraint period of each Class A supplier.

[0022] As a preferred technical solution, the quality tracking test is carried out on each Class I supplier to obtain the quality tracking test data, which specifically includes: Based on the initial quality inspection constraint period of each Class A supplier, a mapping and matching is performed with the initial quality inspection sample quantity corresponding to each initial quality inspection constraint period pre-stored in the database, the initial quality inspection sample quantity of each Class A supplier is obtained, an initial quality inspection reminder is given, and the quality tracking inspection data of each Class A supplier is tracked and obtained.

[0023] The quality tracking and testing data include the sample failure rate, sample specification average error value and sample average defect rate of each Class A supplier.

[0024] As a preferred technical solution, the quality tracking test data is obtained and analyzed to obtain the quality tracking monitoring results of each Class I supplier, specifically including: The standard value set of quality tracking test data extracted from the database includes the critical value of sample failure rate, the critical value of sample specification average error and the critical value of sample average defect rate.

[0025] The quality tracking and inspection data of each first-class supplier is compared with the quality tracking and inspection data verification value set to obtain the quality tracking monitoring value of each first-class supplier.

[0026] The quality tracking and monitoring value of each Class A supplier is compared with the quality tracking and monitoring threshold pre-stored in the database. If the quality tracking and monitoring value of a class of suppliers is greater than or equal to the quality tracking and monitoring threshold, the quality tracking and monitoring result of the class of suppliers is determined to be qualified.

[0027] If the quality tracking and monitoring value of a certain type of supplier is less than the quality tracking and monitoring threshold, the quality tracking and monitoring result of this type of supplier is judged to be unqualified.

[0028] As a preferred technical solution, the obtaining of the quality tracking and monitoring values ​​of each Class I supplier specifically includes: ; in, is the quality tracking monitoring value of the t-th Class A supplier, is the sample failure rate of the t-th class 1 supplier, is the average error value of sample specifications of the t-th category 1 supplier, is the sample average defect rate of the t-th class I supplier, is the critical value of sample failure rate, is the critical value of the sample specification average error, is the critical value of the sample average defect rate, is the supply quality influencing factor of the t-th Class I supplier, t is the Class I supplier number, , x is the total number of suppliers of a type, Mish function is the activation function, .

[0029] As a preferred technical solution, the method of dynamically adjusting the initial quality inspection constraint period of each Class A supplier based on the quality tracking and monitoring results of each Class A supplier includes: If the quality tracking and monitoring results of a certain type of supplier are qualified, the initial quality inspection constraint period of this type of supplier will continue to be used as the quality inspection constraint period.

[0030] If the quality tracking and monitoring results of a certain type of supplier are unqualified, the quality warning prompt information will be transmitted to the corresponding upload port for reminder, and the quality tracking and monitoring value of this type of supplier will be subtracted from the quality tracking and monitoring threshold to obtain the quality inspection constraint cycle adjustment value of this type of supplier, and the quality inspection constraint cycle adjustment days corresponding to each quality inspection constraint cycle adjustment value interval pre-stored in the database will be mapped and matched to obtain the quality inspection constraint cycle adjustment days of this type of supplier, and the quality inspection constraint cycle of this type of supplier will be shortened.

[0031] Compared with the prior art, the embodiments of the present invention have at least the following beneficial effects: (1) The present invention provides a supplier quality assessment method, which ensures the availability of quality assessment data by analyzing the data quality of the information uploaded by the supplier, reduces the additional analysis costs caused by data quality abnormalities, improves the supplier quality assessment efficiency, screens out suppliers that are initially evaluated as unqualified, and concentrates quality assessment resources on suppliers that are initially evaluated as qualified, thereby reducing data processing costs and improving assessment efficiency.

[0032] (2) The present invention can effectively implement more scientific and reasonable quality tracking and detection for suppliers with low comprehensive data quality evaluation values ​​by performing customized quality tracking and detection on each Class I supplier, thereby reducing the cost of quality tracking and detection, optimizing resource allocation, avoiding over-detection or under-detection, improving the efficiency and results of quality tracking and detection, and timely and dynamically adjusting the quality detection constraint cycle.

[0033] (3) The present invention helps correct the errors generated during data transmission by increasing the data quality transmission impact correction coefficient, improves the reliability of data analysis and processing results, and effectively enhances the overall performance of data transmission.

[0034] Of course, any product implementing the present invention does not necessarily need to achieve all of the above advantages at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of the system method flow of the present invention. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] In the description of the present invention, it should be understood that the terms "opening", "upper", "lower", "thickness", "top", "middle", "length", "inside", "all around" and the like indicating orientation or positional relationship are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the components or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0038] See also Figure 1 As shown, the embodiment of the present invention provides a supplier quality assessment method, which specifically includes: The quality assessment data submitted by each supplier at the upload port is obtained, and the initial quality assessment data of each supplier is collected and analyzed to obtain the initial quality evaluation label of each supplier's data.

[0039] In an embodiment of the present invention, each supplier uploads quality assessment information to a quality assessment platform to apply for admission. The quality assessment platform is used to conduct quality assessment on each supplier and bring suppliers with qualified quality assessment results online to the platform.

[0040] The initial quality evaluation data of each supplier is collected and analyzed to obtain the initial quality evaluation label of each supplier's data. The specific process is as follows: The quality assessment information submitted by each supplier at the upload port is used to collect statistics on the initial quality assessment data of each supplier.

[0041] The initial quality assessment data of each supplier includes image quality assessment data and text quality assessment data.

[0042] The image quality assessment data includes the average peak signal-to-noise ratio, average resolution, average edge sharpness and average signal-to-noise ratio of each uploaded image, which is obtained using MATLAB tool.

[0043] Peak Signal-to-Noise Ratio (PSNR) is an objective measure of image distortion, used to compare the quality difference between the original image and the image after processing (such as compression or transmission). The higher the PSNR value, the smaller the distortion and the better the image quality. It is usually used to quantify the performance of compression algorithms and to evaluate the impact of algorithms on image quality in image processing tasks.

[0044] Edge sharpness refers to the degree of enhancing the edge contrast of an image during image processing, which is usually achieved through edge detection and contrast enhancement algorithms. Edge sharpening can make the outlines of objects in an image clearer and improve the visual quality of the image. In image quality assessment, edge sharpness is used to measure the impact of image processing algorithms on image detail and clarity.

[0045] The text quality assessment data includes the total number of bytes, text density and cumulative number of text pages, and is obtained using a natural language processing tool.

[0046] The total number of bytes refers to the number of bytes contained in a text file. An English character usually occupies 1 byte, while a Chinese character may occupy 2 or more bytes. The total number of bytes can be used to measure the length or size of the text. It provides information about the magnitude of the text and determines whether the text meets a specific length requirement.

[0047] Text density refers to the amount of information per unit length (such as per page or paragraph), which can be measured by counting the number of words or characters per unit length. Text density can assess the information richness of a text. High text density may mean that the text contains a lot of information. Low text density means that the text is concise but lacks sufficient information.

[0048] The cumulative number of text pages refers to the total number of pages occupied by the text content. The cumulative number of text pages can provide information about the physical length of the text, and the number of pages is an important indicator for measuring the length of the text.

[0049] An initial quality assessment data verification value set is extracted from the database, including an image quality assessment data verification value set and a text quality assessment data verification value set.

[0050] The analysis and processing obtains the initial quality evaluation value of each supplier's data, and also includes the preliminary construction of the data quality transmission impact correction coefficient of each supplier, including: During the transmission of quality assessment data from each supplier, the average network speed, average network bandwidth, average delay and average network packet loss rate are collected synchronously.

[0051] The average network speed check value, the average network bandwidth check value, the average delay check value and the average network packet loss rate check value are extracted from the database.

[0052] The data transmission quality evaluation value of each supplier is obtained through analysis and processing. The specific processing conditions are as follows: ; in, P is the data transmission quality assessment value of the quality assessment platform, is the average network speed of the ith provider, is the average network bandwidth of the ith provider, is the average delay of the ith supplier, is the average network packet loss rate of the i-th provider, is the average network speed check value, is the average network bandwidth check value, is the average delay check value, is the average network packet loss rate check value, i is the supplier number, , n is the total number of suppliers, softplus function is a built-in function in Python, .

[0053] Based on the data transmission quality assessment value of each supplier, the data quality transmission impact correction coefficient corresponding to each data transmission quality assessment value interval pre-stored in the database is matched to obtain the data quality transmission impact correction coefficient of each supplier.

[0054] The data quality transmission impact correction coefficient is used to correct the impact on the data during the data transmission process.

[0055] The initial quality assessment data of each supplier is compared and analyzed with the initial quality assessment data verification value set to obtain the initial quality evaluation value of each supplier's data.

[0056] The specific processing conditions of the initial quality evaluation value of the data of each supplier are as follows: , in, is the comprehensive data quality evaluation value of the i-th supplier, is the average peak signal-to-noise ratio of each image uploaded by the i-th supplier, is the average resolution of images uploaded by the i-th supplier, is the average edge sharpness of each image uploaded by the i-th supplier, is the average signal-to-noise ratio of each image uploaded by the i-th supplier, is the total number of bytes of text data uploaded by the i-th supplier, is the text density of the text data uploaded by the i-th supplier, is the cumulative number of text pages uploaded by the i-th supplier, is the average peak signal-to-noise ratio calibration value, is the average resolution calibration value, is the average edge sharpness verification value, is the average signal-to-noise ratio verification value, is the total number of bytes check value, is the text density check value, is the cumulative text page check value, is the correction coefficient for data quality transmission impact, i is the supplier number, , n is the total number of suppliers.

[0057] It should be noted that the image quality assessment data includes the average peak signal-to-noise ratio, average resolution, average edge sharpness and average signal-to-noise ratio of each uploaded image. There is a certain correlation between these parameters. High-resolution images usually contain more details. If the degree of distortion is the same, the peak signal-to-noise ratio of high-resolution images may be lower than that of low-resolution images because the loss of details is more obvious in high-resolution images. Edge sharpening itself is an image enhancement technology that changes the pixel value of the image, thereby affecting the calculation result of the peak signal-to-noise ratio. High-resolution images can better show the effect of edge sharpening. Edge sharpening will increase the visual quality of the image.

[0058] It should also be noted that there is a certain correlation between the parameters of text quality assessment data, including total bytes, text density and cumulative text pages. The total bytes are a direct measure of the amount of text, while the text density is a measure of the amount of information based on a certain amount of text. There is a direct positive correlation between the total bytes and the text density. That is, the higher the total bytes of the text, the higher the text density is usually, and vice versa. The text density can be calculated by dividing the total bytes by the number of pages. Under the same layout and format, the higher the total bytes, the more cumulative text pages are usually. The relationship between text density and cumulative text pages reflects the amount of information in the text on each page. Under the same layout, if the text density is high, then the cumulative number of text pages will be less at the same total number of bytes, because each page contains more information. If the text density is low, it means that the amount of information per page is less, resulting in a larger number of cumulative text pages.

[0059] The obtaining of the initial quality evaluation labels of the data of each supplier specifically includes: An initial quality evaluation threshold is extracted from the database. If the initial quality evaluation value of a supplier's data is greater than or equal to the initial quality evaluation threshold, the initial quality evaluation label of the supplier's data is determined to be qualified.

[0060] If the initial quality evaluation value of a supplier's data is less than the initial quality evaluation threshold, the supplier's data initial quality evaluation label is determined to be unqualified, and the quality warning prompt information is transmitted to the corresponding upload port for reminder.

[0061] The processing method of the initial quality evaluation threshold is as follows: in a specific embodiment, the initial quality evaluation threshold is set directly in the database during the development of the quality assessment platform involved in the embodiment of the present invention. There are many methods for setting the initial quality evaluation threshold, such as obtaining it through statistical analysis, including counting the initial quality evaluation values ​​of data under historical low data quality (for example, the average image resolution is less than or equal to 640x480 pixels), and averaging the initial quality evaluation values ​​of the data obtained multiple times to obtain the initial quality evaluation threshold, or experts judge the boundary between high quality and low quality through corresponding historical data to set it, thereby adjusting the threshold.

[0062] According to the initial quality evaluation label of each supplier's data, if the initial quality evaluation label of a supplier's data is unqualified, the quality warning prompt information will be transmitted to the corresponding upload port for reminder. If the initial quality evaluation label of a supplier's data is qualified, the supplier will be recorded as a Class I supplier, and the uploaded information quality parameters of each Class I supplier will be statistically analyzed to obtain the comprehensive data quality evaluation value of each Class I supplier.

[0063] The statistics of the quality parameters of the uploaded information of each Class I supplier are analyzed and processed to obtain the comprehensive data quality evaluation value of each Class I supplier, specifically including: The uploaded information quality parameters of each Class A supplier include the field coverage, number of matching fields and field standardization rate of the supplier basic related information manually filled in and uploaded by each Class A supplier on the quality assessment platform, the supplier basic related information, for example, the supplier's geographical location, main business and business scale, etc.

[0064] Field coverage refers to the ratio of the number of fields actually filled with data to the number of all fields in the data template when filling in information. It is used to measure the completeness of the data set and ensure that key information is not omitted.

[0065] The number of matching fields refers to the number of fields that can match each other when comparing two or more information data sets. This is often used in data integration or data alignment scenarios to evaluate the consistency and compatibility between different data sources.

[0066] The field standardization rate refers to the ratio of the number of fields filled in accordance with the predetermined standards or format specifications to the total number of fields in the information data set. It is used to measure the consistency and standardization of data and improve the quality and availability of data.

[0067] A minimum value set of uploaded information is extracted from the database, including a minimum field coverage rate, a minimum matching field number, a minimum value, and a minimum field standardization rate.

[0068] The uploaded information of each Class I supplier and the lowest value set of uploaded information are processed and analyzed comprehensively to obtain the uploaded information quality evaluation value of each Class I supplier. The specific processing conditions are: ; in, is the quality evaluation value of the uploaded information of the t-th Class I supplier, is the field coverage of the t-th class 1 supplier, is the number of matching fields for the t-th class 1 supplier, is the field standardization rate of the t-th class 1 supplier, is the minimum value of field coverage, is the minimum number of matching fields. is the minimum value of the field standardization rate, t is the supplier number of a category, , x is the total number of suppliers of a category, softplus function is a built-in function in Python, .

[0069] It should be noted that the quality parameters of the uploaded information of each Class I supplier, including the field coverage rate, the number of matching fields, and the field standardization rate of each Class I supplier, have a certain correlation. If the field coverage rate is low, the number of matching fields may also be low, because the missing fields cannot participate in the matching. On the contrary, a high field coverage rate helps to increase the number of matching fields. The field coverage rate focuses on the filling of the fields, while the field standardization rate focuses on whether the format and standards of these fields meet the requirements. Even if the field coverage rate is high, if the field standardization rate is low, the quality of the data may still not meet the requirements, because the non-standardization of the data format will affect the readability and processability of the data. A high field standardization rate usually means a high field coverage rate, because standardized data usually means that the data collection process is relatively complete and rigorous. If the number of matching fields is high, but the field standardization rate is low, then in the actual data processing, you may encounter data integration problems, because non-standardized data requires additional cleaning and conversion work. These three parameters are interdependent and jointly affect the quality of the data. A high field coverage rate, the number of matching fields, and the field standardization rate usually mean higher data quality, which helps improve data processing efficiency, reduce errors, and support better business decisions. If one parameter performs poorly, the overall data quality may be affected.

[0070] Based on the uploaded information quality evaluation value and the initial data quality evaluation value of each Class A supplier, a comprehensive data quality evaluation value of each Class A supplier is obtained through comprehensive processing and analysis. The comprehensive data quality evaluation value of each Class A supplier is used to evaluate the comprehensive quality of the data uploaded by each Class A supplier. The specific processing methods include: ; in, is the comprehensive data quality evaluation value of the t-th first-class supplier, is the initial quality evaluation value of the data of the t-th class 1 supplier, is the quality evaluation value of the uploaded information of the t-th Class I supplier, t is the Class I supplier number, , x is the total number of suppliers of a category, is the activation function, .

[0071] According to the comprehensive data quality evaluation value of each Class A supplier, the initial quality inspection constraint period of each Class A supplier is determined and quality tracking inspection is carried out on each Class A supplier. The quality tracking inspection data is obtained and analyzed to obtain the quality tracking monitoring results of each Class A supplier. Based on the quality tracking monitoring results of each Class A supplier, the initial quality inspection constraint period of each Class A supplier is dynamically adjusted.

[0072] According to the comprehensive data quality evaluation value of each Class A supplier, the initial quality inspection constraint cycle of each Class A supplier is determined, including: Based on the comprehensive data quality evaluation value of each Class A supplier, a mapping and matching is performed with the sampling cycles corresponding to the comprehensive data quality evaluation value intervals pre-stored in the quality assessment platform to obtain the sampling cycle of each Class A supplier and record it as the initial quality inspection constraint cycle of each Class A supplier.

[0073] Conduct quality tracking tests on each Class I supplier and obtain quality tracking test data, including: Based on the initial quality inspection constraint period of each Class A supplier, a mapping and matching is performed with the initial quality inspection sample quantity corresponding to each initial quality inspection constraint period pre-stored in the database, the initial quality inspection sample quantity of each Class A supplier is obtained, an initial quality inspection reminder is given, and the quality tracking inspection data of each Class A supplier is tracked and obtained.

[0074] The quality tracking and testing data include the sample failure rate, sample specification average error value and sample average defect rate of each Class A supplier.

[0075] The sample failure rate refers to the percentage of the number of failed samples in the total number of all tested samples during the testing process. It reflects the proportion of batches of products provided by suppliers that do not meet the predetermined quality standards. It is used to measure the overall quality level of suppliers' products.

[0076] The average error value of sample specifications refers to the average deviation between the actual specifications of the test sample and the specified specifications. This indicator is usually used to measure the accuracy of product specifications such as size, weight, performance, etc. It is used to evaluate the accuracy of product processing or manufacturing processes. It helps determine whether the product meets the requirements.

[0077] The average defect rate of samples refers to the ratio of the number of defects found in the test samples to the total number of test samples. Defects may include appearance defects, material defects, etc. It reflects the quality of product appearance and function.

[0078] Analyze and process to obtain the quality tracking and monitoring results of each Class I supplier, including: The quality tracking test data standard value set includes a sample failure rate critical value, a sample specification average error critical value, and a sample average defect rate critical value.

[0079] The quality tracking and testing data of each Class A supplier is compared with the quality tracking and testing data verification value set to obtain the quality tracking and monitoring value of each Class A supplier, including: ; in, is the quality tracking monitoring value of the t-th Class A supplier, is the sample failure rate of the t-th class 1 supplier, is the average error value of sample specifications of the t-th category 1 supplier, is the sample average defect rate of the t-th class I supplier, is the critical value of sample failure rate, is the critical value of the sample specification average error, is the critical value of the sample average defect rate, is the supply quality influencing factor of the t-th Class I supplier, t is the Class I supplier number, , x is the total number of suppliers of a type, Mish function is the activation function, .

[0080] It should be noted that the quality tracking test data includes the sample failure rate, sample specification average error value and sample average defect rate of each Class I supplier. There is a certain correlation between these parameters. The sample specification average error value and sample average defect rate are often the direct factors leading to the increase in sample failure rate. If the sample specification error is large or there are many defects, the number of unqualified samples is likely to increase. On the contrary, if the sample specification average error value and defect rate can be controlled, the failure rate will usually decrease. The sample specification average error value usually reflects the accuracy of technical specifications such as product size and performance, which is the basic level of product quality. The sample average defect rate focuses more on the appearance and surface quality of the product, which is a higher level of quality. The sample failure rate combines factors such as specification error and defects, and is an overall evaluation indicator of product quality. Even if the sample specification average error value is small, if the defect rate is high, the overall quality of the product is not up to standard. Similarly, even if the product has few appearance defects, if the specification error is large, the product may not meet the use requirements.

[0081] The quality tracking and monitoring value of each Class A supplier is compared with the quality tracking and monitoring threshold pre-stored in the database. If the quality tracking and monitoring value of a class of suppliers is greater than or equal to the quality tracking and monitoring threshold, the quality tracking and monitoring result of the class of suppliers is determined to be qualified.

[0082] If the quality tracking and monitoring value of a certain type of supplier is less than the quality tracking and monitoring threshold, the quality tracking and monitoring result of this type of supplier is judged to be unqualified.

[0083] The quality tracking and monitoring threshold is processed as follows: in a specific embodiment, the quality tracking and monitoring threshold is set directly in the database during the development of the quality assessment platform involved in the embodiment of the present invention. There are many methods for setting the quality tracking and monitoring threshold, such as obtaining it through statistical analysis, including statistically analyzing the quality tracking and monitoring values ​​under historical low sample quality (for example, the sample failure rate is greater than or equal to 10%), and averaging the quality tracking and monitoring values ​​obtained multiple times to obtain the quality tracking and monitoring threshold, or experts judge the boundary between high sample quality and high sample quality through corresponding historical data to set it, thereby adjusting the threshold.

[0084] Based on the quality tracking and monitoring results of each Class A supplier, the initial quality inspection constraint cycle of each Class A supplier is dynamically adjusted, including: If the quality tracking and monitoring results of a certain type of supplier are qualified, the initial quality inspection constraint period of this type of supplier will continue to be used as the quality inspection constraint period.

[0085] If the quality tracking and monitoring results of a certain type of supplier are unqualified, the quality warning prompt information will be transmitted to the corresponding upload port for reminder, and the quality tracking and monitoring value of this type of supplier will be subtracted from the quality tracking and monitoring threshold to obtain the quality inspection constraint cycle adjustment value of this type of supplier, and the quality inspection constraint cycle adjustment days corresponding to each quality inspection constraint cycle adjustment value interval pre-stored in the database will be mapped and matched to obtain the quality inspection constraint cycle adjustment days of this type of supplier, and the quality inspection constraint cycle of this type of supplier will be shortened.

[0086] The processing of the supply quality influencing factors of each category of suppliers specifically includes: Statistics on the supply data of each Class A supplier within the initial quality inspection constraint period include the average daily supply delivery volume, average supply delivery speed and platform negative review rate.

[0087] The average daily supply delivery volume refers to the average number of products delivered by a supplier within a certain period of time (usually one day). It is used to measure the production capacity and supply stability of suppliers. It assesses whether suppliers can meet market demand and order volume. It ensures the continuity of supply.

[0088] Average supply delivery speed refers to the average time from the time each order is placed to the time the product is delivered. It is used to reflect the supplier's response speed and logistics efficiency. It assesses whether the supplier can deliver the product on time.

[0089] The platform negative review rate refers to the proportion of negative reviews given by users who are dissatisfied with the supplier's products or services to the total reviews in the quality evaluation platform involved in the embodiment of the present invention. It is used to measure the market acceptance of the supplier's product quality and service level.

[0090] A supply data verification value set is extracted from the database, including a daily average supply delivery volume verification value, an average supply delivery speed verification value, and a platform negative review rate verification value.

[0091] The supply data of each first-class supplier within the initial quality inspection constraint period is compared and analyzed with the supply data verification value set to obtain the supply quality influencing factor of each first-class supplier.

[0092] The supply quality influencing factors of each Class A supplier are used for comprehensive analysis to obtain the quality tracking performance of each Class A supplier. The specific processing method is as follows: ; in, is the supply quality influencing factor of the t-th first-class supplier, is the average daily supply delivery volume of the t-th first-class supplier, is the average supply delivery speed of the t-th class 1 supplier, is the platform negative review rate of the t-th first-class supplier, is the daily average supply delivery verification value, is the average supply delivery speed check value, is the verification value of the platform's negative review rate, t is the number of a type of supplier, , x is the total number of suppliers of a type, Swish function is the activation function, , Mish function is the activation function, .

[0093] It should be noted that the supply data of each Class I supplier within the initial quality inspection constraint period, including the average daily supply delivery volume, average supply delivery speed and platform negative review rate, have a certain correlation. If the average daily supply delivery volume increases, while the supply chain resources and capabilities remain unchanged, the average supply delivery speed may decrease because suppliers need more time to handle larger orders. Large fluctuations in the average daily supply delivery volume may affect product quality and delivery reliability, which may lead to an increase in the platform negative review rate. Faster average supply delivery speeds are generally associated with better customer satisfaction, which may reduce the platform negative review rate. If the average supply delivery speed is slow, it may lead to customer dissatisfaction, thereby increasing the platform negative review rate. The average daily supply delivery volume and average supply delivery speed are key indicators for measuring supply chain efficiency. An efficient supply chain can respond to demand quickly and maintain a stable delivery volume, which helps to reduce the platform negative review rate. The platform negative review rate directly reflects customer satisfaction with the supplier. If the supplier can maintain a high average daily supply delivery volume and a faster average supply delivery speed while ensuring product quality, customer satisfaction will usually increase and the platform negative review rate will decrease accordingly. There is a mutual influence between these parameters. For example, if a supplier sacrifices product quality in order to increase delivery speed, it may lead to an increase in the platform's negative review rate, even if the average daily supply delivery volume and average supply delivery speed data are good.

[0094] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0095] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can understand and use the present invention well. As long as they do not deviate from the structure of the present invention or exceed the scope defined by the present invention, they should all belong to the protection scope of the present invention.

Claims

1. Supplier quality assessment method, characterized by: include: Obtain the quality assessment data submitted by each supplier at the upload port, and collect and analyze the initial quality assessment data of each supplier to obtain the initial quality evaluation label of each supplier's data; According to the initial quality evaluation label of each supplier's data, if the initial quality evaluation label of a supplier's data is unqualified, the quality warning prompt information is transmitted to the corresponding upload port for reminder; if the initial quality evaluation label of a supplier's data is qualified, the supplier is recorded as a Class I supplier, and the quality parameters of the uploaded information of each Class I supplier are counted and analyzed, and the comprehensive data quality evaluation value of each Class I supplier is obtained through processing; According to the comprehensive data quality evaluation value of each Class A supplier, the initial quality inspection constraint period of each Class A supplier is determined and quality tracking inspection is carried out on each Class A supplier. The quality tracking inspection data is obtained and analyzed to obtain the quality tracking monitoring results of each Class A supplier. Based on the quality tracking monitoring results of each Class A supplier, the initial quality inspection constraint period of each Class A supplier is dynamically adjusted.

2. The supplier quality assessment method according to claim 1, characterized in that: The initial quality assessment data of each supplier is statistically analyzed, and the specific process is as follows: Based on the quality assessment information submitted by each supplier at the upload port, the initial quality assessment data of each supplier is collected; The initial quality assessment data of each supplier includes image quality assessment data and text quality assessment data; The image quality assessment data includes the average peak signal-to-noise ratio, average resolution, average edge sharpness and average signal-to-noise ratio of each uploaded image; The text quality assessment data includes the total number of bytes, text density and cumulative number of text pages; Extracting an initial quality assessment data verification value set from a database, including an image quality assessment data verification value set and a text quality assessment data verification value set; A preliminary data quality transmission impact correction coefficient for each supplier is constructed, and the initial quality assessment data of each supplier is compared and analyzed with the initial quality assessment data verification value set to obtain the initial data quality evaluation value of each supplier. The initial data quality evaluation value is used to evaluate the data quality of the quality assessment data submitted by each supplier at the upload port.

3. The supplier quality assessment method according to claim 2, characterized in that: The preliminary construction of the data quality transmission impact correction coefficient of each supplier specifically includes: During the transmission of quality assessment data from each supplier, the average network speed, average network bandwidth, average delay and average network packet loss rate are collected simultaneously; Analyze and process to obtain the data transmission quality assessment value of each supplier, and match the data transmission quality assessment value of each supplier with the data quality transmission impact correction coefficient corresponding to each data transmission quality assessment value interval pre-stored in the database to obtain the data quality transmission impact correction coefficient of each supplier; The data quality transmission impact correction coefficient is used to correct the impact on the data during the data transmission process.

4. The supplier quality assessment method according to claim 1, characterized in that: The obtaining of the initial quality evaluation labels of the data of each supplier specifically includes: Extracting an initial quality evaluation threshold from the database, if the initial quality evaluation value of a supplier's data is greater than or equal to the initial quality evaluation threshold, the initial quality evaluation label of the supplier's data is determined to be qualified; If the initial quality evaluation value of a supplier's data is less than the initial quality evaluation threshold, the initial quality evaluation label of the supplier's data is judged to be unqualified.

5. The supplier quality assessment method according to claim 1, characterized in that: The statistics of the quality parameters of the uploaded information of each Class I supplier are analyzed and processed to obtain the comprehensive data quality evaluation value of each Class I supplier, specifically including: The quality parameters of the uploaded information of each first-class supplier include the field coverage rate, the number of matching fields and the field standardization rate of the information filled in by each first-class supplier; Extracting a minimum value set of uploaded information quality parameters from the database, including a minimum field coverage rate, a minimum matching field number, and a minimum field standardization rate; Comprehensively process and analyze the uploaded information quality parameters and the lowest value set of uploaded information quality parameters of each Class I supplier to obtain the uploaded information quality assessment value of each Class I supplier; Based on the uploaded information quality evaluation value and the initial data quality evaluation value of each Class A supplier, comprehensive processing and analysis are performed to obtain the comprehensive data quality evaluation value of each Class A supplier, and the comprehensive data quality evaluation value of each Class A supplier is used to evaluate the comprehensive supply quality of each Class A supplier.

6. The supplier quality assessment method according to claim 5, characterized in that: Determining the initial quality inspection constraint period of each Class A supplier based on the comprehensive data quality evaluation value of each Class A supplier specifically includes: Based on the comprehensive data quality evaluation value of each Class A supplier, a mapping match is performed with the sampling period corresponding to each comprehensive data quality evaluation value interval pre-stored in the database to obtain the sampling period of each Class A supplier and record it as the initial quality inspection constraint period of each Class A supplier.

7. The supplier quality assessment method according to claim 1, characterized in that: The quality tracking and testing of each Class I supplier is carried out to obtain quality tracking and testing data, specifically including: Based on the initial quality inspection constraint period of each Class A supplier, the initial quality inspection sample quantity corresponding to each initial quality inspection constraint period stored in the database is mapped and matched, the initial quality inspection sample quantity of each Class A supplier is obtained, and an initial quality inspection reminder is given, and the quality tracking inspection data of each Class A supplier is tracked and obtained; The quality tracking and testing data include the sample failure rate, sample specification average error value and sample average defect rate of each first-class supplier.

8. The supplier quality assessment method according to claim 7, characterized in that: The quality tracking test data is obtained and analyzed to obtain the quality tracking monitoring results of each Class A supplier, specifically including: Extracting a set of standard values ​​of quality tracking test data from the database includes a critical value of sample non-conforming rate, a critical value of sample specification average error, and a critical value of sample average defect rate; Compare the quality tracking and testing data of each first-class supplier with the quality tracking and testing data verification value set to obtain the quality tracking and monitoring value of each first-class supplier; The quality tracking and monitoring value of each first-class supplier is compared with the quality tracking and monitoring threshold value stored in the database. If the quality tracking and monitoring value of a certain class of suppliers is greater than or equal to the quality tracking and monitoring threshold value, the quality tracking and monitoring result of the first-class supplier is determined to be qualified; If the quality tracking and monitoring value of a certain type of supplier is less than the quality tracking and monitoring threshold, the quality tracking and monitoring result of this type of supplier is judged to be unqualified.

9. The supplier quality assessment method according to claim 8, characterized in that: The obtaining of the quality tracking and monitoring values ​​of each Class I supplier specifically includes: ； in, is the quality tracking monitoring value of the t-th Class A supplier, is the sample failure rate of the t-th class 1 supplier, is the average error value of sample specifications of the t-th category 1 supplier, is the sample average defect rate of the t-th class I supplier, is the critical value of sample failure rate, is the critical value of the sample specification average error, is the critical value of the sample average defect rate, is the supply quality influencing factor of the t-th Class I supplier, t is the Class I supplier number, , x is the total number of suppliers of a type, Mish function is the activation function, .

10. The supplier quality assessment method according to claim 1, characterized in that: The method of dynamically adjusting the initial quality inspection constraint period of each Class A supplier based on the quality tracking and monitoring results of each Class A supplier includes: If the quality tracking and monitoring results of a certain type of supplier are qualified, the initial quality inspection constraint period of this type of supplier will continue to be the quality inspection constraint period; If the quality tracking and monitoring results of a certain type of supplier are unqualified, the quality warning prompt information will be transmitted to the corresponding upload port for reminder, and the quality tracking and monitoring value of this type of supplier will be subtracted from the quality tracking and monitoring threshold to obtain the quality inspection constraint cycle adjustment value of this type of supplier, and the quality inspection constraint cycle adjustment days corresponding to each quality inspection constraint cycle adjustment value interval pre-stored in the database will be mapped and matched to obtain the quality inspection constraint cycle adjustment days of this type of supplier, and the quality inspection constraint cycle of this type of supplier will be shortened.

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