Digital quality management method and system, equipment and storage medium

Abstract

The invention provides a digital quality management method and system, equipment and a storage medium, and belongs to the technical field of digital quality management, and the method comprises the steps: determining a target quality relationship between target data based on the correlation between multiple pieces of target data, the multiple pieces of target data are multiple pieces of entity information data in the target product supply chain data; constructing a knowledge graph based on the multiple pieces of target data and the target quality relationship between the target data; and analyzing and managing the quality of the target product based on the knowledge graph. According to the digital quality management method and system, the equipment and the storage medium provided by the invention, the root of the problem can be quickly positioned when the quality problem occurs.

Description

Technical Field

[0001] The present disclosure belongs to the technical field of digital quality management, and more specifically, relates to a digital quality management method, system, device, and storage medium. Background Art

[0002] In today's highly competitive market environment, product quality is crucial for the survival and development of enterprises. Traditional quality management methods have many limitations, such as insufficient ability to integrate and analyze data at all links of the target product supply chain, making it difficult to comprehensively and accurately identify quality influencing factors.

[0003] Existing technologies often fail to deeply explore the potential connections between entity information data in the target product supply chain, resulting in difficulty in quickly locating the root cause of quality problems when they occur, thus delaying the solution time. Summary of the Invention

[0004] The purpose of the present disclosure is to provide a digital quality management method, system, device, and storage medium to quickly locate the root cause of quality problems when they occur.

[0005] In the first aspect of the embodiments of the present disclosure, a digital quality management method is provided, including: Determining the target quality relationship between each target data based on the correlation between multiple target data, where the multiple target data are multiple entity information data in the target product supply chain data; Constructing a knowledge graph based on the multiple target data and the target quality relationship between each target data; Analyzing and managing the quality of the target product based on the knowledge graph.

[0006] In the second aspect of the embodiments of the present disclosure, a digital quality management system is provided, including: A quality relationship determination module for determining the target quality relationship between each target data based on the correlation between multiple target data, where the multiple target data are multiple entity information data in the target product supply chain data; A knowledge graph module for constructing a knowledge graph based on the multiple target data and the target quality relationship between each target data; A quality management module for analyzing and managing the quality of the target product based on the knowledge graph.

[0007] In the third aspect of the embodiments of the present disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the above digital quality management method are implemented.

[0008] In a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned digital quality management method are implemented.

[0009] The beneficial effects of the digital quality management method, system, device, and storage medium provided by the embodiments of the present disclosure are as follows: By analyzing the correlation between multiple entity information data in the target product supply chain, the embodiments of the present disclosure determine the target quality relationship, can accurately mine the key factors affecting product quality, and provide a clear direction for quality management. Then, a knowledge graph is constructed based on multiple entity information data and the target quality relationship, and the entities and relationships are presented in an intuitive graphical structure, clearly showing the complex quality associations. Finally, the knowledge graph is used to analyze and manage the quality of the target product. When a quality problem occurs, it is possible to quickly trace and diagnose along the relationship edges, quickly locate the root cause of the problem, and thus take timely measures to solve the problem, improve product quality, and optimize the quality management process of the target product supply chain. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present disclosure. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0011] Figure 1 It is a schematic flowchart of the digital quality management method provided by an embodiment of the present disclosure; Figure 2 It is a structural block diagram of the digital quality management system provided by an embodiment of the present disclosure; Figure 3 It is a schematic block diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0012] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present disclosure. However, those skilled in the art should clearly understand that the present disclosure can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present disclosure.

[0013] To make the objectives, technical solutions, and advantages of the present disclosure clearer, the following will be described through specific embodiments in conjunction with the drawings.

[0014] Please refer to Figure 1 , Figure 1The flowchart of the digital quality management method provided by an embodiment of the present disclosure, the method includes: S101: Determine the target quality relationship between each target data based on the correlation between multiple target data, and the multiple target data are multiple entity information data in the target product supply chain data.

[0015] In this embodiment, the target data is derived from multiple entity information data in the target product supply chain data. The target product supply chain involves links such as raw material procurement, production manufacturing, product transportation, and sales, and a large amount of entity information data is generated in each link, such as: suppliers, production equipment, product batches, etc.

[0016] In this embodiment, if multiple target data belong to numerical data, the Pearson correlation coefficient or Spearman correlation coefficient can be used to calculate the correlation between multiple target data; if multiple target data belong to categorical data, the chi-square test method can be used to calculate the correlation between multiple target data.

[0017] Through correlation analysis, the correlation values between each target data can be obtained, and the correlation values reflect the degree of tightness of the association between the data. For example, if it is found that the purity of raw materials is highly correlated with the stability of the product, it can be determined that the raw material purity is an important factor affecting product quality.

[0018] In this embodiment, according to the calculated correlation value, combined with a preset correlation threshold to determine the target quality relationship. Among them, the correlation threshold can be set empirically according to the requirements of the actual scenario.

[0019] When the correlation value is greater than the threshold, there is a target quality relationship between the corresponding two target data. The target quality relationship can be a causal relationship, an association relationship, etc. For example, there may be a causal relationship between a certain component content of raw materials and a certain performance of the product, and there may be an association relationship between the operating state of production equipment and the qualified rate of the product.

[0020] S102: Construct a knowledge graph based on multiple target data and the target quality relationship between each target data.

[0021] In this embodiment, the knowledge graph represents a semantic network, and the knowledge graph can be used to display entities and their relationships in a graphical structure.

[0022] In this embodiment, the entities corresponding to multiple target data are extracted.

[0023] For example, suppliers, products, production equipment, etc. are used as node entities in the knowledge graph.

[0024] Map the target quality relationship into the knowledge graph as the edge connecting each entity node, thereby constructing the knowledge graph.

[0025] For example, if it is determined that the quality of the raw materials provided by the raw material supplier affects the product quality, an edge representing this quality impact relationship is established between the supplier entity node and the product entity node.

[0026] S103: Analyze and manage the target product quality based on the knowledge graph.

[0027] In this embodiment, when the target product has quality problems, the knowledge graph is used for diagnosis.

[0028] It is possible to start from the target product entity, query and reason along the relationship edges in the knowledge graph, and find out the factors that may cause quality problems.

[0029] For example, if a certain performance index of the product is unqualified, it is possible to trace back to the entities such as the raw materials used, production equipment, and production processes through the knowledge graph, analyze the attributes of these entities and the relationships between them, and find out the possible root causes of the problems.

[0030] It can be concluded from the above that in this embodiment, by analyzing the correlation between multiple entity information data in the target product supply chain to determine the target quality relationship, the key factors affecting product quality can be accurately mined, providing a clear direction for quality management. Then, based on multiple entity information data and the target quality relationship, a knowledge graph is constructed to display entities and relationships in an intuitive graphical structure, clearly presenting complex quality associations. Finally, the knowledge graph is used to analyze and manage the target product quality. When quality problems occur, it is possible to quickly trace and diagnose along the relationship edges, quickly locate the root cause of the problems, and thus take timely measures to solve the problems, improve product quality, and optimize the quality management process of the target product supply chain.

[0031] In an embodiment of the present disclosure, determining the target quality relationship between each target data based on the correlation between multiple target data includes: Calculating the correlation between multiple target data based on the distribution characteristics of the multiple target data to obtain the correlation value between the multiple target data; In response to the correlation value being greater than the preset correlation threshold, determining the two target data corresponding to the correlation value; Determining the corresponding target quality relationship based on the two target data types.

[0032] In this embodiment, the target product supply chain covers multiple links from raw material procurement to sales, and the data types are rich and diverse. There are numerical types such as raw material purity and production equipment temperature, and there are also categorical types such as supplier name and product batch. Directly based on a fixed calculation method will lead to analysis deviations due to abnormal data distribution or special circumstances. Therefore, in this embodiment, for multiple target data, instead of directly applying conventional methods, the correlation is calculated based on their distribution characteristics.

[0033] For example, for numerical data, if its distribution shows obvious non - normal characteristics, the Pearson correlation coefficient cannot be simply used. Instead, a method more suitable for non - normal distribution data, such as the Spearman correlation coefficient, needs to be selected to accurately measure the correlation between variables. For categorical data, similarly, according to its distribution, appropriate statistical methods such as chi - square test should be reasonably selected to ensure that the calculated correlation can truly reflect the internal relationship between the data and obtain the correlation value between multiple target data.

[0034] In this embodiment, the preset correlation threshold is a pre - set correlation threshold, which is used as a standard for judging whether there is a quality relationship between two target data.

[0035] Compare the calculated correlation value with the preset correlation threshold. When the correlation value is greater than the preset correlation threshold, determine the two target data corresponding to the correlation value. This means that the degree of association between these two data has reached a certain level.

[0036] After determining that there is a quality relationship between two target data, determine the specific target quality relationship according to the types of these two target data.

[0037] For example, if two target data are the content of a certain component of raw materials and a certain performance of the product, and their correlation value is greater than the threshold, it indicates that there is a causal relationship between them, that is, the change in the raw material component content can cause the change of the product performance. If two target data are the operating state of production equipment and the qualified rate of products, and the correlation is significant, it indicates that there is an association relationship between them, that is, there is a certain connection between different operating states of production equipment and the product qualified rate.

[0038] It can be concluded from the above that in this embodiment, by dynamically analyzing the data distribution characteristics and selecting an appropriate correlation calculation method, the analysis deviation of the fixed algorithm in the abnormal data scenario is effectively avoided, and the accuracy of quality relationship recognition is improved. Combining the threshold determination mechanism, key influencing factors with high correlation degrees can be quickly screened out, providing a clear direction for quality management.

[0039] In an embodiment of the present disclosure, calculating the correlation between multiple target data based on the distribution characteristics of the multiple target data to obtain the correlation value between the multiple target data includes: Calculating the correlation between multiple target data based on the first formula; The first formula is:

[0040] Among them, represents the correlation value between the target data and the target data, represents the mean value in the target data, represents the mean value in the target data, represents the business weight factor corresponding to the target data at the same moment, represents the distribution feature adjustment factor corresponding to the target data at the same moment, represents the time series dynamic factor corresponding to the target data at the same moment, represents the quantity of the target data.

[0041] In this embodiment, it is assumed that we have two target data sequences and , and their time series is .

[0042] Consider the influence of the distribution feature of the data on the correlation. Different distribution features may lead to inaccurate results of the traditional correlation calculation method, so it is necessary to adjust the data.

[0043] First, calculate the skewness coefficient , and the kurtosis coefficient , .

[0044] For each data point i, the distribution feature adjustment factor is expressed as:

[0045] The distribution feature adjustment factor can adjust the correlation deviation caused by the distribution difference, making the correlation calculation under different distribution features more reasonable. Therefore, in this embodiment, only one calculation formula can be used to achieve the correlation calculation under different distribution features.

[0046] The target product supply chain data has the characteristics of time series, and the recent data is more valuable for reference to the current quality status and correlation. Therefore, it is necessary to assign different weights to the data at different time points. The time series dynamic factor can be expressed as:

[0047] Among them, is the maximum timestamp in the time series T, is the decay coefficient, which can be adjusted according to the actual situation to control the speed of time decay.

[0048] In the target product supply chain scenario, different data points have different degrees of influence on product quality. The business weight factor is used to reflect the relative importance of the i-th data point in the entire business process. For example, in automobile manufacturing, the weight of engine-related data is higher than that of interior color data because the engine has a more critical impact on vehicle quality. By assigning corresponding weights to different data points, the contribution of important data to the correlation can be highlighted, making the calculation results more in line with the actual business logic.

[0049] It can be concluded from the above that in this embodiment, by introducing the business weight factor, the distribution feature adjustment factor, and the time series dynamic factor, the accuracy and adaptability of the correlation calculation are improved. The business weight factor highlights the impact of key data, the distribution feature adjustment factor eliminates the deviation caused by data distribution differences, and the time series dynamic factor strengthens the timeliness of recent data. The combination of the three makes the correlation value more suitable for the application of the target product supply chain quality management scenario.

[0050] In an embodiment of the present disclosure, it further includes: Constructing a distributed storage network based on supply chain data, where the network nodes in the distributed storage network are the data in the supply chain data; Encrypting the corresponding data on the network nodes to obtain supply chain ciphertext data.

[0051] In this embodiment, each data in the supply chain data is regarded as a network node of the distributed storage network. In the target product supply chain scenario, the supply chain data covers all aspects from raw material procurement information, equipment parameters during the production process, quality inspection data to product sales records. Each data becomes a node in the network, constituting the basic architecture of the distributed storage network.

[0052] The supply chain data is dispersed and stored on multiple nodes, which can improve the storage reliability of the data and avoid the risk of data loss caused by the failure of a single storage center. At the same time, distributed storage is conducive to improving the data access efficiency. Different nodes can process data requests in parallel, reducing the burden on a single node. Especially when dealing with a large amount of supply chain data, it can effectively improve the overall performance of the system.

[0053] For the data on each network node, an encryption algorithm is used for encryption operations. The encryption algorithm can convert it into ciphertext form. The encryption algorithms include symmetric encryption algorithms (such as AES) or asymmetric encryption algorithms (such as RSA), and appropriate algorithms can be selected according to the sensitivity of the data and actual requirements.

[0054] The encrypted data is stored on the network nodes in ciphertext form. Even if the data is stolen during transmission or the storage nodes are attacked, without the corresponding decryption key, the attacker cannot obtain the true content of the data. This enhances the security of supply chain data and protects the sensitive information of enterprises, such as raw material procurement prices, production process parameters, etc., preventing economic losses or competitive disadvantages caused by data leakage to enterprises.

[0055] It can be concluded from the above that in this embodiment, by constructing a distributed storage network and encrypting node data, a higher level of security guarantee is provided for supply chain data from both the storage architecture and data protection aspects, ensuring the security and reliability of data in the target product supply chain management.

[0056] In an embodiment of the present disclosure, the quality of the target product is analyzed and managed based on a knowledge graph, including: Determining the detection result of the target product quality based on the change rate of the target product quality data, and the detection result includes qualified and unqualified; In response to the detection result of the target product quality being unqualified, determining a target path related to the target product quality from the knowledge graph; Determining the root cause affecting the target product quality based on the target path.

[0057] In this embodiment, during the entire life cycle of the product, such as production, transportation, and sales, the quality data of the target product is continuously collected. These data can be the physical properties of the product (such as strength, hardness), chemical indicators (such as component content), appearance characteristics (such as dimensional accuracy), etc. By comparing the quality data at different time points, the change rate of the quality data is calculated to reflect the dynamic change of the product quality over time.

[0058] The qualified threshold of the quality data change rate can be set in advance according to the quality standards of the product, historical data, and industry experience. The calculated change rate of the target product quality data is compared with the qualified threshold. If the change rate is less than the threshold, it indicates that the product quality is relatively stable, and the detection result is determined to be qualified; conversely, if the change rate is greater than or equal to the threshold, it indicates that the product quality may be abnormal, and the detection result is determined to be unqualified.

[0059] In this embodiment, when the detection result of the target product quality is unqualified, the target product is used as the starting node in the knowledge graph. The knowledge graph contains various entities in the target product supply chain and the quality relationships between them. Searching along the relationship chain in the knowledge graph, a path composed of a series of entities and relationships related to the target product quality is found. These paths record various factors affecting the product quality and their interactions during the entire process from raw material procurement to final production and shaping of the product.

[0060] For example, starting from the target product, the raw materials used can be found through the "usage" relationship, and then the suppliers can be found through the "supply" relationship between the raw materials and the suppliers. The relevant production equipment can also be found through the "production" relationship between the product and the production equipment, etc.

[0061] In this embodiment, a detailed analysis is performed on the found target path, and a comprehensive investigation is carried out on each entity and relationship on the path. Analyze the attributes and status of each entity, as well as the impact of their interactions on product quality.

[0062] For example, check whether the quality indicators of the raw materials meet the requirements, whether the operating parameters of the production equipment are normal, whether the operations of the personnel are standardized, whether the production process is reasonable, etc.

[0063] Through the comprehensive evaluation and analysis of various factors on the target path, the root causes affecting the quality of the target product are determined. The root causes can include raw material quality problems, production equipment failures, personnel operation errors, unreasonable production processes, etc. After determining the root causes, the enterprise can take targeted improvement measures, such as replacing raw material suppliers, repairing or upgrading production equipment, strengthening personnel training, optimizing production processes, etc., to improve product quality.

[0064] It can be concluded from the above that by combining the change rate of product quality data and the associated information of the knowledge graph, a complete product quality analysis and management system is formed, which can quickly and accurately locate the root causes of quality problems and provide strong support for the enterprise to improve product quality and production efficiency.

[0065] In an embodiment of the present disclosure, based on the change rate of the target product quality data, the detection result of the target product quality is determined. The detection result includes qualified and unqualified, and includes: In response to the change rate of the target product quality data being less than the change rate threshold, mark the target product quality as qualified; In response to the change rate of the target product quality data being greater than or equal to the change rate threshold, mark the target product quality as unqualified.

[0066] In this embodiment, the target product quality data at different time points can be collected, and the change rate of the quality data between adjacent time points can be calculated. Assume that at time a certain quality index value of the product is , and at time this index value becomes , then the change rate r of this quality index within the time period can be expressed as:

[0067] The change rate reflects the degree of change of the product quality index within a certain period of time.

[0068] In this embodiment, the change rate threshold can be determined based on product characteristics, historical data, etc. The change rate threshold is the key criterion for judging whether the product quality is qualified.

[0069] When the change rate of the calculated target product quality data is less than the preset change rate threshold, it indicates that the product quality index fluctuates within the normal range and the product quality is relatively stable. At this time, the target product quality is marked as qualified, which means that during the current production or use process of the product, its quality performance meets the expected requirements and can meet the relevant quality standards and usage needs.

[0070] If the change rate of the target product quality data is greater than or equal to the change rate threshold, it means that the change of the product quality index exceeds the normal range and there are quality instability or potential quality problems. The target product quality is marked as unqualified. The enterprise needs to conduct further inspections and analyses on the product, find out the reasons for the abnormal change rate of the quality data, and take corresponding improvement measures.

[0071] It can be concluded from the above that this embodiment provides an objective judgment criterion for product quality detection based on the method of comparing the quality data change rate and the threshold, which helps enterprises to timely discover product quality problems and ensure the stability and reliability of product quality.

[0072] In an embodiment of the present disclosure, it further includes: Determining the change rate threshold based on the second formula; The second formula is:

[0073] Wherein, represents the change rate threshold, represents the production equipment status coefficient, represents the production batch coefficient, represents the time decay coefficient, represents the historical average change rate.

[0074] In this embodiment, the historical average change rate is the average change rate calculated based on a large amount of historical product quality data, which reflects the typical fluctuation of the quality index of the product under normal production and use conditions. Collect the quality index data of n historical samples. For each sample i, calculate the change rate of its quality index within a specific time period, and then calculate the average value:

[0075] In this embodiment, the operating status and performance of the production equipment will directly affect the product quality. Situations such as equipment aging, faults, or improper maintenance may lead to an increase in the change rate of product quality indicators. Therefore, the production equipment status coefficient is considered in the second formula . Based on the operating parameters of the equipment (such as temperature, pressure, rotational speed, etc.) and maintenance records, the health status score of the equipment is predicted through a machine learning model (such as a neural network) .

[0076] For example: various parameters during the operation of the equipment are collected in real time, such as temperature, pressure, rotational speed, vibration frequency, etc.; the maintenance records of the equipment are collected, including information such as maintenance time, maintenance content, and replaced parts. The above data is cleaned and data standardized, and the data is normalized to the range of 0 - 1. The processed data is input into a trained convolutional neural network model to predict the health status score of the equipment , with a value range of 0 - 1, and the lower the score, the worse the equipment status. Then:

[0077] Among them, is an adjustable coefficient used to control the influence degree of the equipment status on the threshold

[0078] In this embodiment, factors such as process adjustment and personnel operation differences may exist between different production batches, which can lead to different change rates of product quality indicators. Therefore, the production batch coefficient is considered in the second formula . Analyze the product quality data of each production batch, calculate the dispersion degree of the product quality indicators within the batch (such as variance ), and compare it with the average dispersion degree of all batches :

[0079] Among them, is an adjustable coefficient used to control the influence degree of production batch differences on the threshold

[0080] In this embodiment, over time, the reference value of early historical data for the current product quality change gradually decreases. An exponential decay function or other methods can be used to calculate the time decay coefficient according to the time interval between data collection time and the current time, and its value decreases as the time interval increases

[0081] In this embodiment, the historical average change rate is calculated based on historical product quality data. Then, the current production equipment status is evaluated respectively to obtain , and the quality dispersion of the current production batch is analyzed to obtain , and determining based on the timeliness of the data . Finally, substitute these four parameters into the second formula to calculate the change rate threshold. Compare the actual change rate of the target product quality data with this threshold. If it is less than the threshold, mark the product quality as qualified; if it is greater than or equal to the threshold, mark it as unqualified, thereby realizing the effective detection and management of product quality.

[0082] Corresponding to the digital quality management method in the above embodiment, Figure 2 is a structural block diagram of a digital quality management system provided by an embodiment of the present disclosure. For the sake of convenience of description, only the parts related to the embodiments of the present disclosure are shown. Refer to Figure 2 , the digital quality management system 20 includes: a quality relationship determination module 21, a knowledge graph module 22, and a quality management module 23.

[0083] Among them, the quality relationship determination module 21 is used to determine the target quality relationship between each target data based on the correlation between multiple target data, and the multiple target data are multiple entity information data in the target product supply chain data; The knowledge graph module 22 is used to construct a knowledge graph based on multiple target data and the target quality relationship between each target data; The quality management module 23 is used to analyze and manage the target product quality based on the knowledge graph.

[0084] In an embodiment of the present disclosure, the quality relationship determination module 21 is specifically used for: Calculate the correlation between multiple target data based on the distribution characteristics of the multiple target data to obtain the correlation value between the multiple target data; In response to the correlation value being greater than a preset correlation threshold, determine the two target data corresponding to the correlation value; Determine the corresponding target quality relationship based on the two target data types.

[0085] In an embodiment of the present disclosure, the quality relationship determination module 21 is specifically further used for: Calculate the correlation between multiple target data based on the first formula; The first formula is:

[0086] Among them, represents the correlation value between the target data and the target data , represents the mean value in the target data , represents the mean value in the target data . Represents the business weight factor corresponding to the target data at the same moment, Represents the distribution feature adjustment factor corresponding to the target data at the same moment, Represents the time series dynamic factor corresponding to the target data at the same moment, Represents the quantity of the target data.

[0087] In an embodiment of the present disclosure, the digital quality management system 20 further includes: an encryption module, specifically used for: Construct a distributed storage network based on the supply chain data, where the network nodes in the distributed storage network are the data in the supply chain data; Encrypt the corresponding data on the network nodes to obtain the supply chain ciphertext data.

[0088] In an embodiment of the present disclosure, the quality management module 23 is specifically used for: Determine the detection result of the target product quality based on the change rate of the target product quality data, and the detection result includes qualified and unqualified; In response to the detection result of the target product quality being unqualified, determine the target path related to the target product quality from the knowledge graph; Determine the root cause affecting the target product quality based on the target path.

[0089] In an embodiment of the present disclosure, the quality management module 23 is specifically further used for: In response to the change rate of the target product quality data being less than the change rate threshold, mark the target product quality as qualified; In response to the change rate of the target product quality data being greater than or equal to the change rate threshold, mark the target product quality as unqualified.

[0090] In an embodiment of the present disclosure, the quality management module 23 is specifically further used for: Determine the change rate threshold based on the second formula; The second formula is:

[0091] Wherein, Represents the change rate threshold, Represents the production equipment status coefficient, Represents the production batch coefficient, Represents the time decay coefficient, Represents the historical average change rate.

[0092] See Figure 3 , Figure 3 Is the schematic block diagram of the electronic device provided by an embodiment of the present disclosure. As Figure 3The electronic device 300 in the present embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The above-mentioned processors 301, input devices 302, output devices 303, and memories 304 communicate with each other through a communication bus 305. The memory 304 is used to store a computer program, and the computer program includes program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. Among them, the processor 301 is configured to call the program instructions to execute the functions of each module / unit in the above system embodiments, for example Figure 2 the functions of the modules 21 to 23 shown.

[0093] It should be understood that in the embodiments of the present disclosure, the so-called processor 301 may be a central processing unit (CPU), and this processor may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or this processor may also be any conventional processor, etc.

[0094] The input device 302 may include a touchpad, a fingerprint acquisition sensor (for acquiring the fingerprint information and the direction information of the fingerprint of the user), a microphone, etc., and the output device 303 may include a display (such as an LCD), a speaker, etc.

[0095] The memory 304 may include a read-only memory and a random access memory, and provide instructions and data to the processor 301. A part of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information about the device type.

[0096] In specific implementation, the processors 301, input devices 302, and output devices 303 described in the embodiments of the present disclosure may implement the implementation manners described in the first embodiment and the second embodiment of the digital quality management method provided by the embodiments of the present disclosure, and may also implement the implementation manner of the electronic device described in the embodiments of the present disclosure, which will not be elaborated here.

[0097] In another embodiment of the present disclosure, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and the computer program includes program instructions. When the program instructions are executed by a processor, all or part of the processes in the method of the above embodiment are implemented. It can also be completed by instructing relevant hardware through the computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or system that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0098] The computer-readable storage medium can be the internal storage unit of the electronic device in any of the foregoing embodiments, such as the hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device. Further, the computer-readable storage medium can also include both the internal storage unit and the external storage device of the electronic device. The computer-readable storage medium is used to store the computer program and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store the data that has been output or will be output.

[0099] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described according to their functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present disclosure.

[0100] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described electronic devices and units can refer to the corresponding processes in the foregoing method embodiments and will not be repeated here.

[0101] In several embodiments provided in the present application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed coupling or direct coupling or communication connection to each other can be an indirect coupling or communication connection through some interfaces or units, or can also be an electrical, mechanical or other form of connection.

[0102] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or can also be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiments of the present disclosure.

[0103] In addition, each functional unit in various embodiments of the present disclosure can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0104] The above are only the specific implementation manners of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present disclosure can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should be covered within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be subject to the protection scope of the claims.

Claims

1. A digital quality management method, characterized in that: include: Determining a target quality relationship between each target data based on the correlation between multiple target data, wherein the multiple target data are multiple entity information data in the target product supply chain data; Building a knowledge graph based on the multiple target data and target quality relationships between the target data; The target product quality is analyzed and managed based on the knowledge graph.

2. The digital quality management method according to claim 1, characterized in that: The determining of the target quality relationship between each target data based on the correlation between the multiple target data includes: Calculate the correlation between the multiple target data based on the distribution characteristics of the multiple target data to obtain the correlation value between the multiple target data; In response to the correlation value being greater than a preset correlation threshold, determining two target data corresponding to the correlation value; A corresponding target quality relationship is determined based on the two target data types.

3. The digital quality management method according to claim 2, characterized in that: The step of calculating the correlation between the plurality of target data based on the distribution characteristics of the plurality of target data to obtain the correlation value between the plurality of target data includes: Calculate the correlation between the plurality of target data based on the first formula; The first formula is: in, Represents target data and target data The correlation value between Represents target data The mean value in Represents target data The mean value in Indicates the business weight factor corresponding to the target data at the same time, Indicates the distribution characteristic adjustment factor corresponding to the target data at the same time, Represents the time series dynamic factor corresponding to the target data at the same time, Indicates the amount of target data.

4. The digital quality management method according to claim 1, characterized in that: Also includes: Building a distributed storage network based on the supply chain data, wherein the network nodes in the distributed storage network are data in the supply chain data; The corresponding data on the network nodes is encrypted to obtain the supply chain ciphertext data.

5. The digital quality management method according to claim 1, characterized in that: The analyzing and managing the target product quality based on the knowledge graph includes: Determining a test result of the target product quality based on a change rate of the target product quality data, wherein the test result includes qualified and unqualified; In response to a detection result of the target product quality being unqualified, determining a target path related to the target product quality from the knowledge graph; The root causes affecting the quality of the target product are determined based on the target path.

6. The digital quality management method according to claim 5, characterized in that: The test result of the target product quality is determined based on the change rate of the target product quality data, and the test result includes qualified and unqualified, including: In response to a change rate of the target product quality data being less than a change rate threshold, marking the target product quality as qualified; In response to a change rate of the target product quality data being greater than or equal to a change rate threshold, the target product quality is marked as unqualified.

7. The digital quality management method according to claim 6, characterized in that: Also includes: Determining the change rate threshold based on a second formula; The second formula is: in, represents the change rate threshold, Represents the production equipment status coefficient, represents the production batch coefficient, represents the time attenuation coefficient, Represents the historical average rate of change.

8. A digital quality management system, characterized in that: include: A quality relationship determination module, used to determine a target quality relationship between each target data based on the correlation between multiple target data, wherein the multiple target data are multiple entity information data in the target product supply chain data; A knowledge graph module, used for constructing a knowledge graph based on the multiple target data and the target quality relationship between each target data; The quality management module is used to analyze and manage the quality of the target product based on the knowledge graph.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images