Digital intelligent supply chain power equipment supplier quality evaluation method and system

Abstract

The invention discloses a digital intelligent supply chain power equipment supplier quality evaluation method and system, and relates to the technical field of digital intelligent supply chain management, and the method comprises the steps: collecting the data of each link of a supply chain, and carrying out the preprocessing of the collected data; an anomaly detection model is constructed, and data verification is carried out; supplier quality analysis is carried out based on the verified supply chain data; generating a supplier intelligent portrait in combination with a credible label technology; comprehensive evaluation is carried out, and a supplier quality evaluation report is generated; according to the method, a complete and rigorous supplier quality evaluation system is constructed, the accuracy and safety of collected data are ensured, dynamic monitoring of supplier quality data is realized through an intelligent algorithm and comprehensive data analysis, potential quality problems are found in time, and the supplier quality evaluation efficiency is improved. And finally, powerful support and guarantee are provided for supply chain management of enterprises.

Description

Technical Field

[0001] The present invention relates to the field of digital supply chain management technology, and in particular to a digital supply chain power equipment supplier quality assessment method and system. Background Art

[0002] With the continuous deepening of global economic integration, supply chain management has gradually become one of the key factors for enterprises to enhance their competitiveness. In recent years, with the vigorous development of digital and intelligent technologies, digital supply chain has gradually become an important way for enterprises to improve management level and operational efficiency. By integrating technologies such as the Internet of Things, big data, and artificial intelligence, digital supply chain has realized real-time data collection and intelligent analysis of all links in the supply chain, providing a scientific basis for corporate decision-making. Against this background, the supplier quality assessment of power equipment has gradually become an important link in ensuring the stability and reliability of the supply chain. The quality of power equipment suppliers directly affects the safety and stability of the power system. Therefore, how to effectively evaluate supplier quality has become the focus of industry attention.

[0003] Traditional supply chain management mainly relies on manual records and empirical judgments. The data is scattered and lacks real-time performance, which leads to inefficient supply chains and difficulty in responding to rapidly changing market demands. Existing supplier quality assessment technologies have problems such as insufficient real-time assessment and a single assessment dimension. Existing technologies generally lack a full-dimensional portrait of suppliers and fail to fully utilize the depth and breadth of data, resulting in one-sidedness and lag in assessment results. Summary of the invention

[0004] In view of the problems existing in the above-mentioned existing digital supply chain power equipment supplier quality assessment method and system, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is the lack of real-time evaluation and the single evaluation dimension.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a digital supply chain power equipment supplier quality assessment method, which includes collecting data from various links of the supply chain and preprocessing the collected data; building an anomaly detection model to perform data verification; performing supplier quality analysis based on the verified supply chain data; generating supplier intelligent portraits in combination with trusted label technology; and performing comprehensive evaluation to generate a supplier quality assessment report.

[0007] As a preferred solution of the digital supply chain power equipment supplier quality assessment method of the present invention, wherein: the data collected from each link of the supply chain and preprocessed the collected data refers to, Deploy data collection nodes at all stages of the supply chain, including production, warehousing, transportation, and delivery. Use sensors and high-definition cameras at data collection nodes to collect supply chain data in real time, including product production records, quality inspection results, transportation time, customer feedback, and product return quantities. Establish a green database of supplier's full life cycle quality data, adopt distributed off-chain point-to-point data transmission technology, independently transmit the data of each data collection node to the green database of supplier's full life cycle quality data, and share the data with other data collection nodes. During the data transmission process, use a combination of symmetric encryption and asymmetric encryption algorithms to encrypt the data; Preprocess the collected supply chain data, including data cleaning, deduplication, missing value processing and data standardization.

[0008] As a preferred solution of the digital supply chain power equipment supplier quality assessment method of the present invention, wherein: the construction of anomaly detection model refers to, Based on the preprocessed supply chain data, features are extracted and a feature vector F is generated, wherein the feature vector F includes production date, product batch number, quality inspection result, shipping time, customer feedback and product return quantity; Use the Isolation Forest algorithm to build an anomaly detection model, use historical data to train the anomaly detection model, and use the Genetic Algorithm to optimize the anomaly detection model; The feature vector F extracted based on real-time supply chain data is input into the trained anomaly detection model to calculate the anomaly score R of each feature vector.

[0009] As a preferred solution of the digital supply chain power equipment supplier quality assessment method of the present invention, wherein: the data verification refers to, Set an anomaly score threshold Rl and compare it with the anomaly score R; If R ≥ Rl, the data is marked as abnormal. For the data marked as abnormal, data filling and correction are performed based on historical data, and the verified and corrected data are fed back to suppliers, logistics and customers; If R<Rl, the data is not marked.

[0010] As a preferred solution of the digital supply chain power equipment supplier quality assessment method of the present invention, wherein: the supplier quality analysis based on the verified supply chain data refers to, Calculate product qualification rate Q:

[0011] Among them, N q is the number of qualified products, N p is the total product quantity; Calculate customer feedback score D:

[0012] Among them, R i is the feedback score of the i-th customer, and n is the total number of customers who provide feedback; Calculate product return rate H:

[0013] Where C is the number of returned products and m is the total number of sold products.

[0014] As a preferred solution of the digital supply chain power equipment supplier quality assessment method of the present invention, wherein: the supplier portrait generated based on the preprocessed supply chain data refers to, Design supplier smart profile templates, including basic information, quality indicators, risk assessments, and compliance records of suppliers; Map the features extracted from the supply chain data after data verification to the supplier intelligent portrait template to generate the supplier intelligent portrait; Use trusted labeling technology to define different types of labels based on various features in the portrait, including quality labels, risk labels, and compliance labels; The labeled supplier intelligent portrait is stored in the green database of supplier’s full life cycle quality data.

[0015] As a preferred solution of the digital supply chain power equipment supplier quality assessment method of the present invention, wherein: the comprehensive assessment and generation of the supplier quality assessment report refers to: Calculate the supplier quality comprehensive score Y based on product qualification rate Q, customer feedback score D and product return rate H:

[0016] Among them, w 1 、w 2 and w 3 They are the weights of product qualification rate Q, customer feedback score D and product return rate H; Generate a supplier quality assessment report based on the supplier quality comprehensive score Y and the trusted labeled supplier intelligent portrait.

[0017] As a preferred solution of the digital supply chain power equipment supplier quality assessment system of the present invention, it includes: Data collection and preprocessing module, used to collect supply chain data and preprocess the collected data; Data verification module, used to build anomaly detection models and verify preprocessed data; Data analysis module, used to analyze the verified data; Intelligent portrait generation module, used to generate supplier intelligent portraits in combination with trusted label technology; Comprehensive evaluation module, used to evaluate supplier quality and generate evaluation reports.

[0018] A computer device comprises: a memory and a processor; the memory stores a computer program, and the processor implements the steps of a digital supply chain power equipment supplier quality assessment method when executing the computer program.

[0019] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for quality assessment of power equipment suppliers in a digital supply chain.

[0020] The beneficial effects of the present invention are as follows: the present invention ensures the accuracy and security of the collected data by constructing a complete and rigorous supplier quality evaluation system, and realizes dynamic monitoring of supplier quality data through intelligent algorithms and comprehensive data analysis, so as to timely discover potential quality problems, and ultimately provides strong support and guarantee for the supply chain management of the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0022] Figure 1 This is a flow chart of the quality assessment method for power equipment suppliers in the digital supply chain.

[0023] Figure 2 The figure is a flowchart of data verification.

[0024] Figure 3 This is the structural diagram of the digital supply chain power equipment supplier quality assessment system. DETAILED DESCRIPTION

[0025] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0026] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0027] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0028] Example 1 Reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and which provides a method for evaluating the quality of power equipment suppliers in a digital supply chain. The method for evaluating the quality of power equipment suppliers in a digital supply chain includes: S1. Collect data from all links of the supply chain and pre-process the collected data; Furthermore, collecting data from all links of the supply chain and preprocessing the collected data means, Deploy data collection nodes at all stages of the supply chain, including production, warehousing, transportation, and delivery. Use sensors and high-definition cameras at data collection nodes to collect supply chain data in real time, including product production records, quality inspection results, transportation time, customer feedback, and product return quantities. Establish a green database of supplier's full life cycle quality data, adopt distributed off-chain point-to-point data transmission technology, independently transmit the data of each data collection node to the green database of supplier's full life cycle quality data, and share the data with other data collection nodes. During the data transmission process, use a combination of symmetric encryption and asymmetric encryption algorithms to encrypt the data; Pre-process the collected supply chain data, including data cleaning, deduplication, missing value processing and data standardization, to ensure data quality and consistency.

[0029] By deploying data collection nodes in various links of the supply chain, such as production, warehousing, transportation and delivery, real-time monitoring and acquisition of data throughout the entire life cycle of the supply chain is achieved. This all-round data collection can ensure that every link from raw materials to finished product delivery is accurately recorded, avoiding information omissions or delays, thereby improving the transparency and traceability of the supply chain. In addition, the use of technical means such as sensors, RFID tags and high-definition cameras not only broadens the dimensions of data collection to include environmental conditions, product status and logistics paths, but also improves the accuracy and real-time nature of the data. This fusion of multi-source data provides more comprehensive and reliable data support for subsequent quality assessment, reduces the deviations and misjudgments that may be caused by a single data source, and through distributed off-chain point-to-point data transmission technology, efficient data transmission and processing are achieved. Unlike traditional centralized data storage, distributed transmission technology can effectively disperse the load of data storage, improve the speed and stability of data transmission, ensure that there is no delay or data loss during large-scale data processing, and use symmetric encryption and asymmetric encryption algorithms to encrypt data to ensure data security and privacy.

[0030] S2. Build an anomaly detection model and perform data verification; Furthermore, building an anomaly detection model means, Based on the preprocessed supply chain data, features are extracted and a feature vector F is generated, wherein the feature vector F includes production date, product batch number, quality inspection result, shipping time, customer feedback and product return quantity; Use the Isolation Forest algorithm to build an anomaly detection model, use historical data to train the anomaly detection model, and use the Genetic Algorithm to optimize the anomaly detection model; The feature vector F extracted based on real-time supply chain data is input into the trained anomaly detection model to calculate the anomaly score R of each feature vector.

[0031] Through multi-dimensional feature extraction and comprehensive analysis, the depth of data analysis is improved, which can reveal potential problems that are difficult to find in a single dimension, realize all-round monitoring of supplier quality, and prevent the one-sided evaluation caused by a single indicator. The use of intelligent anomaly detection mechanism realizes automatic identification and early warning of potential risks, which can help enterprises take countermeasures in advance and reduce the risk of supply chain disruptions or quality accidents.

[0032] Furthermore, data verification means, Anomaly score threshold Rl is set up and compared with anomaly score R. The setting of anomaly score threshold Rl is determined by experts based on customer requirements and relevant system specifications; If R ≥ Rl, the data is marked as abnormal. For the data marked as abnormal, data filling and correction are performed based on historical data, and the verified and corrected data are fed back to suppliers, logistics and customers; If R<Rl, the data is not marked.

[0033] By establishing an abnormal score threshold Rl and having experts set this threshold based on customer requirements and relevant system specifications, the data verification process is highly flexible and adaptable, and can dynamically adjust the verification standards according to the specific requirements of different supply chain links and product categories, thereby ensuring the accuracy of data verification and achieving precise adaptation to different application scenarios. By marking abnormal data and sending it to suppliers, logistics and customers, potential risks caused by supply chain data can be reduced, and a more efficient collaboration mechanism can be established between suppliers, logistics and customers. In addition, data verification can improve data quality, provide a solid data foundation for supplier quality analysis, intelligent portrait generation and comprehensive evaluation, and ensure the scientificity and effectiveness of the entire quality assessment process.

[0034] S3. Conduct supplier quality analysis based on verified supply chain data; Furthermore, supplier quality analysis based on verified supply chain data refers to: Calculate product qualification rate Q:

[0035] Among them, N q is the number of qualified products, N p is the total product quantity; Calculate customer feedback score D:

[0036] Among them, R i is the feedback score of the i-th customer, and n is the total number of customers who provide feedback; Calculate product return rate H:

[0037] Where C is the number of returned products and m is the total number of sold products.

[0038] Through quantitative analysis of product qualification rate Q, customer feedback score D and product return rate H, the production quality data and market feedback data are effectively integrated, and a comprehensive evaluation of supplier quality is achieved, enabling enterprises to have a more comprehensive understanding of the overall quality performance of suppliers, thereby making more accurate supplier selection decisions, improving supplier management efficiency and optimizing supply chain quality control, helping enterprises gain an advantage in a highly competitive market environment.

[0039] S4. Generate intelligent supplier portraits by combining trusted label technology; Furthermore, the supplier profile is generated based on the preprocessed supply chain data. Design supplier smart profile templates, including basic information, quality indicators, risk assessments, and compliance records of suppliers; Map the features extracted from the supply chain data after data verification to the supplier intelligent portrait template to generate the supplier intelligent portrait; Use trusted labeling technology to define different types of labels based on various features in the portrait, including quality labels, risk labels, and compliance labels. The quality label indicates the quality level of the product, and the risk label indicates the risk level of the supplier and batch of products. The risk level is obtained based on the anomaly detection results summarized from historical data. The labeled supplier intelligent portrait is stored in the green database of supplier’s full life cycle quality data.

[0040] By designing a supplier smart portrait template, the data of each link in the supply chain after data verification and processing is mapped to the supplier smart portrait, generating a comprehensive portrait that includes the supplier's basic information, quality indicators, risk assessment and compliance records. Enterprises can view the supplier's quality and risk status through the supplier smart portrait, which greatly improves management efficiency. In combination with trusted labeling technology, the historical performance characteristics, real-time status characteristics and potential risk characteristics in the supplier smart portrait are labeled and managed, and the different performances of suppliers can be identified and classified in the form of labels. This labeling management method not only facilitates the refined management of suppliers, but also enables early warning and classified control of supplier risks, helping enterprises to better grasp the overall quality status of suppliers and enable enterprises to formulate response strategies more accurately.

[0041] S5. Conduct comprehensive assessment and generate supplier quality assessment report; Furthermore, a comprehensive assessment is conducted to generate a supplier quality assessment report. Calculate the supplier quality comprehensive score Y based on product qualification rate Q, customer feedback score D and product return rate H:

[0042] Among them, w 1 、w 2 and w 3 They are the weights of product qualification rate Q, customer feedback score D and product return rate H. The weights are established by relevant industry experts based on customer needs combined with historical data and relevant specifications. The higher the Y value, the better the supplier quality and service. A supplier quality assessment report is generated based on the supplier's comprehensive quality score Y and the trusted and labeled supplier's intelligent portrait, which serves as a reference for customers to select suppliers.

[0043] By combining multi-dimensional data and comprehensive scoring to achieve a comprehensive assessment, companies can quickly and intuitively understand the overall quality and service level of suppliers, thereby achieving more scientific supplier selection and management. Accurate analysis through supplier intelligent portraits combined with trusted label technology provides a basis for accurate classification and evaluation of suppliers, and ultimately generates a supplier quality assessment report, which can provide data-driven support for the company's procurement decisions, supplier management and risk control. Through this report, companies can better grasp quality risks and supplier performance in supply chain management, achieve supply chain optimization and risk minimization, and significantly enhance the company's overall competitiveness.

[0044] Example 2 Reference Figure 3 , which is the second embodiment of the present invention, which is different from the previous embodiment and provides a digital supply chain power equipment supplier quality assessment system, including: Data collection and preprocessing module, used to collect supply chain data and preprocess the collected data; Data verification module, used to build anomaly detection models and verify preprocessed data; Data analysis module, used to analyze the verified data; Intelligent portrait generation module, used to generate supplier intelligent portraits in combination with trusted label technology; Comprehensive evaluation module, used to evaluate supplier quality and generate evaluation reports.

[0045] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0046] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0047] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0048] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit with a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit with a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0049] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A digital supply chain power equipment supplier quality assessment method, characterized by: include, Collect data from all links of the supply chain and pre-process the collected data; Build anomaly detection models and perform data verification; Conduct supplier quality analysis based on verified supply chain data; Combine trusted label technology to generate intelligent supplier portraits; Conduct comprehensive assessment and generate supplier quality assessment report.

2. The digital supply chain power equipment supplier quality assessment method according to claim 1 is characterized by: The collecting of data from each link of the supply chain and preprocessing of the collected data refers to: Deploy data collection nodes at all stages of the supply chain, including production, warehousing, transportation, and delivery. Use sensors and high-definition cameras at data collection nodes to collect supply chain data in real time, including product production records, quality inspection results, transportation time, customer feedback, and product return quantities. Establish a green database of supplier's full life cycle quality data, adopt distributed off-chain point-to-point data transmission technology, independently transmit the data of each data collection node to the green database of supplier's full life cycle quality data, and share the data with other data collection nodes. During the data transmission process, use a combination of symmetric encryption and asymmetric encryption algorithms to encrypt the data; Preprocess the collected supply chain data, including data cleaning, deduplication, missing value processing and data standardization.

3. The digital supply chain power equipment supplier quality assessment method according to claim 2 is characterized by: The constructing of anomaly detection model refers to: Based on the preprocessed supply chain data, features are extracted and a feature vector F is generated, wherein the feature vector F includes production date, product batch number, quality inspection result, shipping time, customer feedback and product return quantity; Use the Isolation Forest algorithm to build an anomaly detection model, use historical data to train the anomaly detection model, and use the Genetic Algorithm to optimize the anomaly detection model; The feature vector F extracted based on real-time supply chain data is input into the trained anomaly detection model to calculate the anomaly score R of each feature vector.

4. The digital supply chain power equipment supplier quality assessment method according to claim 3 is characterized by: The data verification refers to: Set an anomaly score threshold Rl and compare it with the anomaly score R; If R ≥ Rl, the data is marked as abnormal. For the data marked as abnormal, data filling and correction are performed based on historical data, and the verified and corrected data are fed back to suppliers, logistics and customers; If R<Rl, the data is not marked.

5. The digital supply chain power equipment supplier quality assessment method according to claim 4 is characterized by: The supplier quality analysis based on verified supply chain data refers to: Calculate product qualification rate Q: Among them, N q is the number of qualified products, N p is the total product quantity; Calculate customer feedback score D: Among them, R i is the feedback score of the i-th customer, and n is the total number of customers who provide feedback; Calculate product return rate H: Where C is the number of returned products and m is the total number of sold products.

6. The digital supply chain power equipment supplier quality assessment method according to claim 5 is characterized by: Generating a supplier profile based on pre-processed supply chain data refers to: Design supplier smart profile templates, including basic information, quality indicators, risk assessments, and compliance records of suppliers; Map the features extracted from the supply chain data after data verification to the supplier intelligent portrait template to generate the supplier intelligent portrait; Use trusted labeling technology to define different types of labels based on various features in the portrait, including quality labels, risk labels, and compliance labels; The labeled supplier intelligent portrait is stored in the green database of supplier’s full life cycle quality data.

7. The digital supply chain power equipment supplier quality assessment method according to claim 6 is characterized by: The said comprehensive assessment and generation of supplier quality assessment report refers to: Calculate the supplier quality comprehensive score Y based on product qualification rate Q, customer feedback score D and product return rate H: Among them, w1, w2 and w3 are the weights of product qualification rate Q, customer feedback score D and product return rate H respectively; Generate a supplier quality assessment report based on the supplier quality comprehensive score Y and the trusted labeled supplier intelligent portrait.

8. A digital supply chain power equipment supplier quality assessment system according to any one of claims 1 to 7, characterized in that: include, Data collection and preprocessing module, used to collect supply chain data and preprocess the collected data; Data verification module, used to build anomaly detection models and verify preprocessed data; Data analysis module, used to analyze the verified data; Intelligent portrait generation module, used to generate supplier intelligent portraits in combination with trusted label technology; Comprehensive evaluation module, used to evaluate supplier quality and generate evaluation reports.

9. A computer device comprising: A memory and a processor; the memory stores a computer program, characterized in that: when the processor executes the computer program, the steps of the digital supply chain power equipment supplier quality assessment method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the digital supply chain power equipment supplier quality assessment method described in any one of claims 1 to 7 are implemented.

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