Product purchasing data management method and system based on digital intelligent supply chain

Abstract

The invention provides a purchased product data management method and system based on a digital intelligent supply chain, and relates to the technical field of intelligent purchasing. According to each purchased commodity, suppliers conforming to a set health range are obtained, a plurality of supply chains to be selected are generated, weather data, geographic data and logistics data of the supply chains to be selected are obtained, and line risks of the supply chains to be selected are analyzed. Analyzing the supply interruption influence risk of the suppliers on the to-be-selected supply chain according to the commodities contained in the to-be-selected supply chain and the health conditions of the corresponding suppliers, and analyzing the stability degree of the supply chain according to the line risk of the to-be-selected supply chain and the supply interruption influence risk of the suppliers on the to-be-selected supply chain. By analyzing the supply stability degree under the influence of the risk suppliers and the risk supply chains, the supply chain with the strongest anti-risk capability is screened out, and the toughness of the whole supply chain can be improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent procurement technology, and in particular to a procurement product data management method and system based on a digital supply chain. Background Art

[0002] Smart procurement is a procurement management solution based on artificial intelligence technology. It provides enterprises with efficient and accurate procurement decision support by analyzing and processing large amounts of procurement data in real time. It uses advanced machine learning, data mining and other technologies to automatically identify and optimize various problems in the procurement process, improve procurement efficiency and reduce procurement costs. Most existing technologies on smart procurement analyze the supply capacity of suppliers by comparing individual suppliers, but lack analysis of whether complex supply chains are stable when involving multiple suppliers.

[0003] For example, a Chinese patent application with publication number CN118569965A discloses a procurement product data management method and system based on a digital supply chain. The invention provides a procurement product data management method and system based on a digital supply chain, the method comprising: S1 constructing a database, including a multi-modular basic data detail layer, a procurement application data layer, and an intelligent procurement review layer; S2 forming differentiated data connection paths between the multi-modular basic data detail layer and the procurement application data layer, realizing data processing of various types of data in the procurement application data layer, dynamic cross-module capture of data to update data processing results, and multi-platform interconnected applications; S3 reorganizing the data map in the intelligent procurement review layer based on the current data processing results in S2 to construct an artificial intelligence algorithm for one-click intelligent rejection of the initial evaluation, and additional one-click precise scoring of the detailed evaluation. The procurement product data management method based on the digital supply chain of the invention realizes horizontal comparison between suppliers and vertical comparison of their respective histories, and gives evaluation results comprehensively, accurately, objectively and quickly.

[0004] In summary, existing technologies lack the ability to analyze the impact of different levels of importance on the stability of the entire supply chain when suppliers interrupt their supply, resulting in inaccurate procurement choices and thus reduced procurement efficiency. Summary of the Invention

[0005] The present invention proposes a procurement product data management method and system based on a digital supply chain, which obtains corresponding supplier supply data according to the purchased goods, analyzes the supplier health status according to the supplier supply data, obtains suppliers that meet the set health range according to each purchased commodity and generates several candidate supply chains, obtains weather data, geographic data and logistics data of the candidate supply chains, analyzes the line risks of the candidate supply chains, analyzes the risks of the candidate supply chains being affected by supplier supply interruptions according to the goods included in the candidate supply chains and the health status of the corresponding suppliers, and analyzes the stability of the supply chain according to the line risks of the candidate supply chains and the risks of the candidate supply chains being affected by supplier supply interruptions. The present invention screens out the supply chain with the strongest risk resistance by analyzing the supply stability under the influence of risky suppliers and risky supply chains, which helps to improve the resilience of the overall supply chain and cope with the changing market environment.

[0006] To solve the above technical problems, the first aspect of the present invention provides a procurement product data management method based on a digital supply chain, comprising the following specific steps:

[0007] Step 1: Obtain the corresponding supplier supply data based on the purchased goods, and analyze the supplier's health status based on the supplier supply data;

[0008] Step 2: Obtain suppliers that meet the set health range for each purchased product and generate several candidate supply chains;

[0009] Step 3: Obtain weather data, geographic data, and logistics data for the candidate supply chain and analyze the route risks of the candidate supply chain;

[0010] Step 4: Analyze the risk of supply disruptions to the candidate supply chain based on the products included in the candidate supply chain and the health status of the corresponding suppliers;

[0011] Step 5: Analyze the stability of the supply chain based on the route risk of the candidate supply chain and the risk of the candidate supply chain being affected by supplier supply interruption.

[0012] Specifically, the step 1 includes the following specific steps:

[0013] Obtain supplier supply data based on the purchased goods, including supply price, production equipment update time, delivery time, and number of defective goods delivered;

[0014] Analyze abnormal price changes based on suppliers' supply prices;

[0015] Analyze equipment update anomalies based on the supplier's production equipment update schedule;

[0016] Analyze delivery time anomalies based on supplier delivery times;

[0017] Analyze delivery quality anomalies based on the number of defective products delivered by suppliers;

[0018] Analyze supplier health based on abnormal supplier price changes, equipment updates, delivery times, and delivery quality.

[0019] Specifically, the step 2 includes the following specific steps:

[0020] Obtain the corresponding supplier for each purchased commodity, compare the supplier health with the set supplier health threshold, and screen out suppliers whose supplier health exceeds the set supplier health threshold;

[0021] Automatically generate several supply chains to be selected based on the screened suppliers.

[0022] Specifically, the step three includes the following specific steps:

[0023] Obtain weather data, geographic data, and logistics data for the supply chain to be selected, wherein the weather data includes temperature and rainfall, the geographic data includes the supplier's latitude and longitude, and the logistics data includes the number of logistics service providers;

[0024] Analyze the weather vulnerability of candidate supply chains based on temperature and rainfall;

[0025] Analyze the geographic concentration of candidate supply chains based on supplier latitude and longitude;

[0026] Analyze the logistics dependency of the candidate supply chain based on the number of logistics service providers;

[0027] Analyze the route risks of candidate supply chains based on weather vulnerability, geographical concentration, and logistics dependence.

[0028] Specifically, the step 4 includes the following specific steps:

[0029] Analyze the importance of commodities based on the commodities included in the supply chain to be selected;

[0030] Analyze the risk of potential supply chains being affected by supplier disruptions based on the importance of the products and the health of the corresponding suppliers.

[0031] Specifically, the step five includes the following specific steps:

[0032] Analyze the stability of the supply chain based on the route risk of the candidate supply chain and the risk of the candidate supply chain being affected by supplier supply interruption;

[0033] Select suppliers with the most stable supply chain to purchase goods.

[0034] The second aspect of the present invention provides a procurement product data management system based on a digital supply chain, comprising: a supplier health analysis module, a supply chain generation module, a line risk analysis module, a spillover risk analysis module, a supply chain stability analysis module, and a procurement screening module;

[0035] Specifically, the supplier health analysis module is used to obtain corresponding supplier supply data based on the purchased goods, and analyze the supplier health status based on the supplier supply data;

[0036] The supply chain generation module is used to obtain suppliers that meet the set health range according to each purchased commodity and generate several supply chains to be selected;

[0037] The route risk analysis module is used to obtain weather data, geographical data and logistics data of the selected supply chain and analyze the route risk of the selected supply chain;

[0038] The risk analysis module is used to analyze the risk of the selected supply chain being affected by the supply interruption of the supplier based on the commodities included in the selected supply chain and the health status of the corresponding suppliers;

[0039] The supply chain stability analysis module is used to analyze the stability of the supply chain based on the line risk of the selected supply chain and the risk of the selected supply chain being affected by the supplier's supply interruption;

[0040] The procurement screening module is used to screen suppliers corresponding to the supply chain with the best stability for commodity procurement.

[0041] The third aspect of the present invention provides an electronic device, comprising: a memory and a processor, the processor and the memory being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to implement the procurement product data management method based on a digital supply chain as described in the first aspect.

[0042] The fourth aspect of the present invention provides a computer storage medium, which stores computer program instructions. When the computer program instructions are executed, they implement the above-mentioned procurement product data management method based on digital supply chain.

[0043] The fifth aspect of the present invention provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the above-mentioned procurement product data management method based on digital supply chain.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] The present invention obtains the supply data of corresponding suppliers based on the purchased goods, analyzes the health status of the suppliers based on the supply data of the suppliers, obtains suppliers that meet the set health range based on each purchased goods and generates several supply chains to be selected, obtains the weather data, geographical data and logistics data of the supply chains to be selected, analyzes the line risks of the supply chains to be selected, analyzes the risks of the supply chains to be affected by the supply interruption of suppliers based on the goods included in the supply chains to be selected and the health status of the corresponding suppliers, and analyzes the stability of the supply chains based on the line risks of the supply chains to be selected and the risks of the supply chains to be affected by the supply interruption of suppliers. The present invention screens out the supply chains with the strongest risk resistance by analyzing the supply stability under the influence of risky suppliers and risky supply chains, which helps to improve the resilience of the overall supply chain and cope with the changing market environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0047] Figure 1 A flowchart of the procurement product data management method based on the digital supply chain provided by the present invention;

[0048] Figure 2 This is a flowchart of step 1 of the procurement product data management method based on digital supply chain provided by the present invention;

[0049] Figure 3 This is a flowchart of step three of the procurement product data management method based on digital supply chain provided by the present invention;

[0050] Figure 4 This is a schematic diagram of the structure of the procurement product data management system based on the digital supply chain provided by the present invention;

[0051] Figure 5 This is a schematic structural diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0052] In order to enable people skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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 making creative work should fall within the scope of protection of the present invention.

[0053] Based on the above analysis, existing technologies lack the ability to analyze the impact of commodities of different importance on the stability of the entire supply chain when suppliers interrupt supply, resulting in inaccurate procurement selection and thus reduced procurement efficiency. Therefore, it is necessary to analyze the supply stability under the influence of risky suppliers and risky supply chains, and screen out the supply chain with the strongest risk resistance, so as to improve the resilience of the overall supply chain and cope with the changing market environment.

[0054] In order to solve the above technical problems, Figure 1 As shown, the present invention provides a procurement product data management method based on a digital supply chain, comprising the following specific steps:

[0055] Step 1: Obtain the corresponding supplier supply data based on the purchased goods, and analyze the supplier's health status based on the supplier supply data;

[0056] In this embodiment, if Figure 2 As shown, step one includes the following specific steps:

[0057] Obtain supplier supply data based on purchased goods, including supply price, production equipment update time, delivery time, and number of defective products delivered;

[0058] Analyze abnormal price changes based on suppliers' supply prices;

[0059] In this embodiment, the price change anomaly can be obtained by using a price change anomaly value calculation formula, which can be expressed as:

[0060]

[0061] In the formula, T1 represents the abnormal value of price change, I represents the number of times the supplier's supply price is obtained, and P i represents the supplier price obtained for the i-th time, P i-1 Indicates the supplier supply price obtained for the i-1th time, It represents the average supply price of competitive suppliers obtained for the i-1th time. The formula for calculating the abnormal value of price change is to analyze the amplitude of supplier price change and the difference with the average supply price of competitive suppliers. It can be used to exclude suppliers whose prices change frequently and whose price adjustments are unreasonable or whose supply prices are obviously more than a reasonable gap with those of competitive suppliers. In this step, for example, the supply prices are five times, namely 10, 12, 13, 14, and 13, and the corresponding average supply prices are 11, 12, 14, 16, and 16, respectively; the abnormal value of price change is calculated to be: (2 / 10+1 / 11+1 / 12+1 / 11+1 / 13+1 / 14+1 / 14+2 / 16+3 / 16+1 / 13)=1.074.

[0062] Analyze equipment update anomalies based on the supplier's production equipment update schedule;

[0063] In this embodiment, the device update anomaly can be obtained by using a device update anomaly value calculation formula, which can be expressed as:

[0064]

[0065] Where T2 represents the abnormal value of device update, J represents the number of devices of the supplier, and D j represents the update time of the supplier's j-th device, that is, the time from the beginning of use of the device to the replacement of the new device, represents the average update time of the jth device of a competing supplier, when hour, The value is 0. The formula for calculating the equipment update abnormal value is to compare the supplier's equipment update time with the average update time of competitive suppliers. It can be used to exclude suppliers with outdated equipment. In this step, for example, the number of suppliers' equipment is 3, and the update time is 5 days, 80 days and 13 days respectively. The average update time of competitive suppliers is 6 days, 75 days and 14 days. The equipment update abnormal value is: (5 / 75) = 0.0667.

[0066] Analyze delivery time anomalies based on supplier delivery times;

[0067] In this embodiment, the delivery time anomaly can be obtained by using the delivery time anomaly value calculation formula, which can be expressed as:

[0068]

[0069] Where T3 represents the abnormal value of delivery time, N represents the number of supplier deliveries, and ΔE n It represents the delivery cycle of the supplier for the nth delivery, that is, the time from the customer placing an order to the supplier delivering the goods, E bn Indicates the supplier's agreed delivery period for the nth time, which can be obtained from the purchase contract. It represents the average delivery cycle of competitive suppliers. The formula for calculating delivery time outliers can be used to exclude suppliers who deliver late or whose delivery is below the industry supply level by comparing the delivery cycle with the agreed cycle and the competitive average delivery cycle. In this embodiment, for example, an example of delivery time outliers is: the supplier delivers 6 times, and the delivery cycles are 3 days, 3 days, 4 days, 4 days, 5 days and 4 days respectively. The agreed delivery cycle is 5 days, and the average delivery cycle is 4 days. Therefore, the calculated delivery time outlier is: (0.6*0.75)+(0.6*0.75)+0.8*1*3+1*1.25=4.55.

[0070] Analyze delivery quality anomalies based on the number of defective products delivered by suppliers;

[0071] In this embodiment, the delivery quality abnormality can be obtained by using a delivery quality abnormality value calculation formula, which can be expressed as:

[0072]

[0073] Where, T4 represents the abnormal value of delivery quality, L n Indicates the number of defective products delivered by the supplier for the nth time, L mn Indicates the number of goods delivered by the supplier for the nth time, represents the defect rate of goods delivered by the supplier for the nth time, The delivery quality outlier calculation formula compares the product defect rate with the average product defect rate of competing suppliers. It can be used to exclude suppliers with excessively high product defect rates and product quality below the industry production level.

[0074] Analyze supplier health based on abnormal supplier price changes, equipment updates, delivery times, and delivery quality.

[0075] In this embodiment, the supplier health can be obtained by using the supplier health calculation formula, which can be expressed as:

[0076] T m =(1 / exp(T1+T2+T3+T4))×100%;

[0077] Where, T m Represents the supplier health, and exp() represents the exponential function with the real number e as the base.

[0078] Step 2: Obtain suppliers that meet the set health range for each purchased product and generate several candidate supply chains;

[0079] In this embodiment, step 2 includes the following specific steps:

[0080] Obtain the corresponding supplier for each purchased commodity, compare the supplier health with the set supplier health threshold, and screen out suppliers whose supplier health exceeds the set supplier health threshold;

[0081] In this embodiment, suppliers that have been eliminated by customers due to failure in the comprehensive strength evaluation of the merchants or dissatisfaction with the cooperation are obtained, and the supply data of the eliminated suppliers are obtained. The health of the eliminated suppliers is analyzed based on abnormal price changes, abnormal equipment updates, abnormal delivery times, and abnormal delivery quality of the eliminated suppliers, and the minimum health value of the eliminated suppliers is used as the set supplier health threshold.

[0082] Automatically generate several supply chains to be selected based on the screened suppliers.

[0083] In this embodiment, for example, a customer needs goods A, B, and C. After screening, suppliers A1, A2, and A3 can produce goods A, suppliers B1 and B2 can produce goods B, and suppliers C1 and C2 can produce goods C. Then the customer has 12 supply chains to choose from, namely A1-B1-C1, A1-B2-C1, A1-B1-C2, A1-B2-C2, A2-B1-C1, A2-B2-C1, A2-B1-C2, A2-B2-C2, A3-B1-C1, A3-B2-C1, A3-B1-C2, and A3-B2-C2.

[0084] Step 3: Obtain weather data, geographic data, and logistics data for the candidate supply chain and analyze the route risks of the candidate supply chain;

[0085] In this embodiment, if Figure 3 As shown, step three includes the following specific steps:

[0086] Obtain weather data, geographic data, and logistics data for the candidate supply chain. Weather data includes temperature and rainfall, geographic data includes supplier latitude and longitude, and logistics data includes the number of logistics service providers.

[0087] Analyze the weather vulnerability of candidate supply chains based on temperature and rainfall;

[0088] In this embodiment, the weather vulnerability can be obtained by using a weather vulnerability calculation formula, which can be expressed as:

[0089]

[0090] In the formula, H1 represents weather vulnerability, K represents the number of suppliers in the supply chain to be selected, M represents the number of weather data collection times, and t km represents the temperature collected at the location of the k-th supplier for the mth time, represents the median value of the commodity storage temperature range at the kth supplier’s location, s km represents the mth rainfall collected at the location of the kth supplier, s kbrepresents the recent average rainfall at the location of the k-th supplier, which can be the past week, the past month, etc. The weather vulnerability calculation formula analyzes the degree of weather anomaly through temperature deviation and rainfall deviation;

[0091] For example, weather vulnerability poses multiple risks to cargo transportation, potentially leading to shipping delays, cargo damage, increased costs, and even supply chain disruptions. Severe weather (such as heavy rain, snow, and typhoons) can lead to road closures, flight cancellations, or ship suspensions, causing shipping delays. Extreme weather (such as high temperatures, low temperatures, and humidity) can damage cargo during transportation, leading to lower cargo quality, increased returns and customer complaints, and increased insurance and compensation costs. Severe weather can also lead to changes in shipping routes, increased fuel consumption, or the need for additional protective measures, increasing transportation costs, reducing corporate profit margins, and increasing supply chain uncertainty.

[0092] Analyze the geographic concentration of candidate supply chains based on supplier latitude and longitude;

[0093] In this embodiment, the geographical concentration can be obtained by using a geographical concentration calculation formula, which can be expressed as:

[0094]

[0095] Where H2 represents the geographical concentration, d k Represents the distance between the kth supplier and its closest supplier, obtained by longitude and latitude, It represents the average distance between k suppliers in the candidate supply chain. For example, the average distance of the candidate supply chain A1-B1-C1 is the sum of the distance between A1 and B1 plus the distance between A1 and C1 plus the distance between B1 and C1 divided by 3. The formula for calculating geographic concentration analyzes the degree of clustering of supplier distribution by the average nearest zero distance of suppliers.

[0096] Analyze the logistics dependency of the candidate supply chain based on the number of logistics service providers;

[0097] In this embodiment, the logistics dependency can be obtained through a logistics dependency calculation formula, which can be expressed as:

[0098]

[0099] In the formula, H3 represents logistics dependence, R represents the number of logistics service providers, and R kr represents the cooperation volume of the kth supplier’s rth logistics service provider, R b It represents the total business volume of the k-th supplier. The logistics dependence calculation formula analyzes the concentration of logistics business through the proportion of cooperative business volume of logistics service providers.

[0100] The benefits are as follows: The logistics dependence calculation formula can help companies quantify their reliance on specific logistics service providers by analyzing the proportion of cooperative business volume of logistics service providers. By calculating logistics dependence, companies can clearly understand whether their logistics business is overly concentrated in a few logistics service providers;

[0101] Analyze the route risks of candidate supply chains based on weather vulnerability, geographical concentration, and logistics dependence.

[0102] In this embodiment, the line risk can be obtained by using a line risk calculation formula, which can be expressed as:

[0103] H m =exp(H1+H2+H3);

[0104] Where H m Indicates the risk level of the line.

[0105] Step 4: Analyze the risk of supply disruptions to the candidate supply chain based on the products included in the candidate supply chain and the health status of the corresponding suppliers;

[0106] In this embodiment, step 4 includes the following specific steps:

[0107] Analyze the importance of commodities based on the commodities included in the supply chain to be selected;

[0108] In this embodiment, the importance of a product can be obtained by using a product importance calculation formula, which can be expressed as:

[0109]

[0110] In the formula, U represents the importance of the product, u represents the sales volume of the product, and u b represents the total sales of the candidate supply chain, v represents the commodity profit, and v b It represents the total profit of the candidate supply chain. The product importance calculation formula analyzes the importance of the product to the supply chain through sales share and profit contribution rate.

[0111] Analyze the risk of potential supply chains being affected by supplier disruptions based on the importance of the products and the health of the corresponding suppliers.

[0112] In this embodiment, the risk of the selected supply chain being affected by the supplier's supply interruption can be obtained through the risk calculation formula. The risk calculation formula can be expressed as:

[0113] Z=U×(1-T m );

[0114] Where Z represents the contagion risk.

[0115] Step 5: Analyze the stability of the supply chain based on the route risk of the candidate supply chain and the risk of the candidate supply chain being affected by supplier supply interruption.

[0116] In this embodiment, step five includes the following specific steps:

[0117] Analyze the stability of the supply chain based on the route risk of the candidate supply chain and the risk of the candidate supply chain being affected by supplier supply interruption;

[0118] In this embodiment, the supply chain stability can be obtained through a supply chain stability calculation formula, which can be expressed as:

[0119] W=1 / exp(Z+H m );

[0120] Where W represents the stability of the supply chain.

[0121] Select suppliers with the most stable supply chain to purchase goods.

[0122] like Figure 4 As shown, a procurement product data management system based on digital supply chain includes: supplier health analysis module, supply chain generation module, line risk analysis module, impact risk analysis module, supply chain stability analysis module and procurement screening module;

[0123] Specifically, the supplier health analysis module is used to obtain the corresponding supplier supply data based on the purchased goods, and analyze the supplier health status based on the supplier supply data;

[0124] The supply chain generation module is used to obtain suppliers that meet the set health range for each purchased commodity and generate several supply chains to be selected;

[0125] The route risk analysis module is used to obtain weather data, geographic data, and logistics data of the candidate supply chain and analyze the route risk of the candidate supply chain;

[0126] The impact risk analysis module is used to analyze the risk of supply disruptions to the candidate supply chain based on the commodities included in the candidate supply chain and the health status of the corresponding suppliers;

[0127] The supply chain stability analysis module is used to analyze the stability of the supply chain based on the route risk of the candidate supply chain and the risk of the candidate supply chain being affected by supplier supply interruption;

[0128] The procurement screening module is used to select suppliers with the most stable supply chain for commodity procurement.

[0129] like Figure 5As shown, an embodiment of the present invention further provides an electronic device, which may include a processor and a memory, wherein the processor and the memory may be connected via a bus or other means.

[0130] The processor may be a central processing unit (CPU). The processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above chips.

[0131] The processor executes various functional applications and data processing of the processor by running software programs, instructions, and modules stored in the memory, thereby implementing a procurement product data management method based on a digital supply chain in an embodiment of the present invention. The specific steps are as follows:

[0132] Obtain the corresponding supplier supply data based on the purchased goods, analyze the supplier health status based on the supplier supply data, obtain suppliers that meet the set health range based on each purchased commodity and generate several candidate supply chains, obtain the weather data, geographic data and logistics data of the candidate supply chains, analyze the route risks of the candidate supply chains, analyze the risks of the candidate supply chains being affected by supplier supply interruptions based on the goods included in the candidate supply chains and the health status of the corresponding suppliers, and analyze the stability of the supply chain based on the route risks of the candidate supply chains and the risks of the candidate supply chains being affected by supplier supply interruptions.

[0133] As a computer-readable storage medium, the memory can be used to store software programs, computer executable programs and modules, such as a module corresponding to a procurement product data management system based on a digital supply chain in an embodiment of the present invention.

[0134] One or more modules are stored in the memory, and when executed by the processor, they execute a procurement product data management method based on a digital supply chain in the embodiment.

[0135] The specific details of the above electronic device can be understood by referring to the corresponding descriptions and effects in the method embodiment, and will not be repeated here.

[0136] The present invention also provides a computer storage medium, which stores computer program instructions. When the computer program instructions are executed, the specific steps of the above-mentioned procurement product data management method based on digital supply chain are implemented.

[0137] The present invention also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the specific steps of the above-mentioned procurement product data management method based on digital supply chain.

[0138] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods, wherein the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD), etc. The storage medium can also include a combination of the above-mentioned types of memory.

[0139] The various embodiments of the present invention are described in a progressive manner. The same and similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0140] The systems, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions.

[0141] From the description of the above embodiments, it can be seen that those skilled in the art can clearly understand that the present invention can be implemented by means of software plus a necessary general-purpose hardware platform. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product. The computer software product can be stored in a storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute certain parts of the methods of various embodiments of the present invention.

[0142] The present invention may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communications network. In distributed computing environments, program modules may be located in local and remote computer storage media, including storage devices.

[0143] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the contents 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 those skilled in the art can better understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A procurement product data management method based on digital supply chain, characterized by: The specific steps include: Step 1: Obtain the corresponding supplier supply data based on the purchased goods, and analyze the supplier's health status based on the supplier supply data; Step 2: Obtain suppliers that meet the set health range for each purchased product and generate several candidate supply chains; Step 3: Obtain weather data, geographic data, and logistics data for the candidate supply chain and analyze the route risks of the candidate supply chain; Step 4: Analyze the risk of supply disruptions to the candidate supply chain based on the products included in the candidate supply chain and the health status of the corresponding suppliers; Step 5: Analyze the stability of the supply chain based on the route risk of the candidate supply chain and the risk of the candidate supply chain being affected by supplier supply interruption.

2. The method for managing procurement product data based on a digital supply chain according to claim 1, wherein: The step 1 includes the following specific steps: Obtain supplier supply data based on the purchased goods, including supply price, production equipment update time, delivery time, and number of defective goods delivered; Analyze abnormal price changes based on suppliers' supply prices; Analyze equipment update anomalies based on the supplier's production equipment update schedule; Analyze delivery time anomalies based on supplier delivery times; Analyze delivery quality anomalies based on the number of defective products delivered by suppliers; Analyze supplier health based on abnormal supplier price changes, equipment updates, delivery times, and delivery quality.

3. The method for managing procurement product data based on a digital supply chain according to claim 2, wherein: The step 2 includes the following specific steps: Obtain the corresponding supplier for each purchased commodity, compare the supplier health with the set supplier health threshold, and screen out suppliers whose supplier health exceeds the set supplier health threshold; Automatically generate several supply chains to be selected based on the screened suppliers.

4. The method for managing procurement product data based on a digital supply chain according to claim 3, wherein: The step three includes the following specific steps: Obtain weather data, geographic data, and logistics data for the supply chain to be selected, wherein the weather data includes temperature and rainfall, the geographic data includes the supplier's latitude and longitude, and the logistics data includes the number of logistics service providers; Analyze the weather vulnerability of candidate supply chains based on temperature and rainfall; Analyze the geographic concentration of candidate supply chains based on supplier latitude and longitude; Analyze the logistics dependency of the candidate supply chain based on the number of logistics service providers; Analyze the route risks of candidate supply chains based on weather vulnerability, geographical concentration, and logistics dependence.

5. The method for managing procurement product data based on a digital supply chain according to claim 4, characterized in that: The step 4 includes the following specific steps: Analyze the importance of commodities based on the commodities included in the supply chain to be selected; Analyze the risk of potential supply chains being affected by supplier disruptions based on the importance of the products and the health of the corresponding suppliers.

6. The method for managing procurement product data based on a digital supply chain according to claim 5, characterized in that: The step five includes the following specific steps: Analyze the stability of the supply chain based on the route risk of the candidate supply chain and the risk of the candidate supply chain being affected by supplier supply interruption; Select suppliers with the most stable supply chain to purchase goods.

7. A procurement product data management system based on a digital supply chain, used to implement the procurement product data management method based on a digital supply chain as described in any one of claims 1 to 6, characterized in that: include: Supplier health analysis module, supply chain generation module, line risk analysis module, impact risk analysis module, supply chain stability analysis module and procurement screening module; Specifically, the supplier health analysis module is used to obtain corresponding supplier supply data based on the purchased goods, and analyze the supplier health status based on the supplier supply data; The supply chain generation module is used to obtain suppliers that meet the set health range according to each purchased commodity and generate several supply chains to be selected; The route risk analysis module is used to obtain weather data, geographical data and logistics data of the selected supply chain and analyze the route risk of the selected supply chain; The risk analysis module is used to analyze the risk of the selected supply chain being affected by the supply interruption of the supplier based on the commodities included in the selected supply chain and the health status of the corresponding suppliers; The supply chain stability analysis module is used to analyze the stability of the supply chain based on the line risk of the selected supply chain and the risk of the selected supply chain being affected by the supplier's supply interruption; The procurement screening module is used to screen suppliers corresponding to the supply chain with the best stability for commodity procurement.

8. An electronic device, characterized in that: include: A memory and a processor, wherein the processor and the memory are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to execute a procurement product data management method based on a digital supply chain as described in any one of claims 1 to 6.

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