Material supply guarantee method and system based on big data material performance monitoring

Abstract

The present application discloses a material supply guarantee method and system based on big data material performance monitoring, belonging to the technical field of material supply. The method includes the following steps: constructing a supply topology map according to the historical transportation data of suppliers; obtaining corresponding historical environmental data based on the supply topology map, and constructing the environmental vulnerability weights of each topological edge in the supply topology map based on the environmental change frequency in the historical environmental data; obtaining the supply association relationship of suppliers according to the supply type based on the supply topology map, and obtaining the supplier risk-bearing coefficient according to the historical supply data of suppliers and the supply association relationship; outputting a material supply guarantee strategy based on future environmental information, environmental vulnerability weights, and supplier risk-bearing coefficients. The present application not only ensures the timeliness of the adjustment of the material supply distribution strategy by considering the risks in the transportation process in advance, but also avoids ignoring high-flexibility suppliers through the supplier risk-bearing ability, ensuring the accuracy of the material supply distribution.

Description

Technical Field

[0001] This application relates to the technical field of material supply, and particularly to a material supply guarantee method and system based on big data material performance monitoring. Background Art

[0002] In today's complex and ever-changing business environment, supply chain management plays a crucial role in the survival and development of enterprises. When dealing with the key link of material supply guarantee, traditional supply chain management technologies have gradually revealed many limitations.

[0003] In the current field of supply chain management, most related technologies use traditional supplier management systems (SMS) combined with basic logistics tracking tools to achieve material supply guarantee. Traditional SMS focuses on aspects such as supplier qualification review, contract management, and product information recording, while logistics tracking tools mainly focus on real-time location monitoring during the transportation of goods.

[0004] However, although the related technologies are aware of the existence of each supplier and their basic supply information, they are only limited to analyzing the supply capacity of a single supplier, which is prone to supply prediction deviations due to the influence of complex supply chains. At the same time, the monitoring of logistics only stays at the level of real-time road condition monitoring. However, if there are problems with real-time transportation, it is already difficult to supply materials according to the required time limit. The related technologies rely on fixed and inflexible solutions, unable to adapt to market changes and the dynamic changes in the actual performance capabilities of suppliers. Real-time monitoring can ensure the accuracy of material supply strategies but cannot meet the timeliness requirements of material supply. Moreover, using historical performance data of suppliers to evaluate the performance of suppliers for real-time adjustment cannot avoid the evaluation errors caused by the instability of other suppliers.

[0005] The patent "Method and Device for Evaluating Electric Power Material Suppliers Based on User Portraits", publication number: CN115907308A, publication date: April 4, 2023, specifically discloses the following: obtaining the basic information of suppliers, cleaning the basic information to obtain the first basic information, performing index processing on the first basic information to generate a derivative index system; constructing a comprehensive supplier evaluation model according to the derivative index system; comprehensively evaluating suppliers through the comprehensive supplier evaluation model to obtain a comprehensive rating, and extracting index labels from the derivative index system; generating a supplier portrait according to the comprehensive rating and index labels, obtaining the auxiliary evaluation of the supplier by the purchasing department according to the supplier portrait, and obtaining the spot-check evaluation of the quality supervision department on the supplier according to the supplier portrait; determining the final evaluation of the supplier according to the auxiliary evaluation, spot-check evaluation, and comprehensive evaluation. This solution constructs portraits for suppliers to evaluate them, but is limited by the static nature of user portraits and cannot adapt to the complex changes in the supply chain network.

[0006] The patent "A supplier classification management method", publication number: CN118644262A, publication date: September 13, 2024, specifically discloses the use of an improved Karajek model to classify procurement content, assigning different scores to the importance and risk of procurement content, that is, at the same level, the score of procurement content importance is higher than the score of procurement content supply risk, and some procurement risks are mitigated through measures taken by the project-level procurement management agency without spending a lot of resources on control at the company level. Although the supply risk is taken into account in this solution, from the perspective of purchasing items, the classification of procurement categories by assigning different scores is relatively isolated and cannot adapt to the complex changes of the supply chain network. Summary of the invention

[0007] In response to the problem in the prior art that material supply and distribution cannot balance accuracy and timeliness, the present application provides a material supply guarantee method and system based on big data material performance monitoring, which utilizes the frequency of environmental changes in the supplier's own supply transportation path to quantify the risk of delay in the transportation process, and at the same time obtains the supplier's risk-bearing capacity based on the supplier's corresponding supply relationship in the overall supply chain, and calculates the supplier's performance possibility based on the supplier's risk-bearing capacity and the probability of environmental changes during transportation. This ensures the timeliness of the adjustment of the material supply and distribution strategy by considering the risks in the transportation process in advance, and avoids neglecting high-flexibility suppliers through the supplier's risk-bearing capacity, thereby ensuring the accuracy of material supply and distribution.

[0008] In order to achieve the above-mentioned technical objectives, a technical solution provided by the present application is a material supply guarantee method based on big data material performance monitoring, including the following steps: S1: Obtain the supplier's historical transportation data, and construct a supply topology map based on the supplier's historical transportation data; S2: Obtain the corresponding historical environmental data based on the supply topology map, and construct the environmental vulnerability weights of each topological edge in the supply topology map based on the frequency of environmental changes in the historical environmental data; S3: Obtain the supply relationship of the supplier according to the supply type based on the supply topology map, and obtain the supplier's risk-bearing coefficient based on the supplier's historical supply data and supply relationship; S4: Obtain the current production plan, obtain the production demand period based on the current production plan, obtain the future environmental information of each supplier within the production demand period, obtain the supplier's performance evaluation results based on the future environmental information, environmental vulnerability weights and supplier risk-bearing coefficients, and output the material supply guarantee strategy based on the supplier's performance evaluation results.

[0009] Further, S2 further includes: determining the radiation range of the topological edge based on a preset radiation ratio to obtain corresponding historical environmental data; calculating the environmental change frequency and temporal correlation of each transportation path according to the transportation impact factor and the historical environmental data, and constructing the environmental vulnerability weights of each topological edge in the supply topology graph at each time sequence based on the change frequency and temporal correlation.

[0010] Further, the determining the radiation range of the topological edge based on a preset radiation ratio to obtain corresponding historical environmental data includes: obtaining road data, and determining branch parameters according to the road data and the topological edge; determining a preset radiation ratio based on the branch parameters, and obtaining the radiation range of the topological edge according to the preset radiation ratio; obtaining historical environmental data within the radiation range of the topological edge.

[0011] Further, the calculating the environmental change frequency and temporal correlation of each transportation path according to the transportation impact factor and the historical environmental data includes: obtaining the historical transportation delay data of the supplier, performing feature extraction on the historical transportation delay data of the supplier to obtain the transportation impact factor; using the environmental data change records corresponding to the transportation impact factor in the historical environmental data of each transportation path as environmental changes, counting the number of environmental changes within each time sequence, and constructing the environmental change frequency and temporal correlation.

[0012] Further, S1 further includes: obtaining the historical starting and ending points of the supplier's transportation and the historical transportation path; constructing a topological hierarchy based on the supply hierarchy relationship of the supplier; using the historical starting and ending points of the supplier's operation as topological nodes and the historical transportation path as topological edges, and constructing a supply topology graph based on the topological hierarchy, topological nodes and topological edges.

[0013] Further, the obtaining the supply association relationship of the supplier according to the supply type based on the supply topology graph at least includes: performing topological node clustering analysis on each topological hierarchy in the supply topology graph based on the supply type; obtaining the market supply relationship between each supplier in the same topological hierarchy based on the clustering result; constructing the supply association relationship based on the supply hierarchy relationship and the market supply relationship.

[0014] Further, the obtaining the supplier risk-bearing coefficient according to the historical supply data of the supplier and the supply association relationship includes: obtaining the performance truth value of each period according to the historical supply data of each supplier in the supply chain related to the supplier, and obtaining the first risk-bearing coefficient based on the performance truth value ratio; obtaining the performance ratio related to market fluctuations as the second risk-bearing coefficient according to the market supply relationship of the supplier; constructing the supplier risk-bearing coefficient based on the first risk-bearing coefficient and the second risk-bearing coefficient.

[0015] Further, S4 further includes: obtaining the current production plan, obtaining the production demand period and the supply location based on the current production plan; calculating the transportation prediction paths of each supplier based on the supply topology map and the supply location according to the similarity; obtaining the future environmental information of the transportation prediction paths during the production demand period; outputting the first performance evaluation result based on the future environmental information and the environmental vulnerability weight; correcting the first performance evaluation result based on the supplier risk-bearing coefficient to obtain the supplier performance evaluation result; sorting the suppliers according to the supplier performance evaluation result, and outputting the material supply guarantee strategy based on the supplier production capacity and the supplier sorting.

[0016] Further, calculating the transportation prediction paths of each supplier based on the supply topology map and the supply location according to the similarity includes: obtaining the transportation coincidence probability according to the similarity between the topology nodes on the supply topology map and the supply location; obtaining the transportation prediction paths of each supplier based on the highest transportation coincidence probability.

[0017] Further, S4 further includes: obtaining the current production plan, obtaining the production demand period and the supply location based on the current production plan; calculating the transportation prediction paths of each supplier based on the supply topology map and the supply location according to the similarity; obtaining the transportation demand period based on the production demand period, the blank ratio of the transportation prediction paths, and the supplier historical transportation data; retrieving the supplier risk-bearing coefficient, the environmental vulnerability weight, and the future environmental information according to the transportation demand period, and obtaining the supplier performance evaluation result according to the supplier risk-bearing coefficient, the environmental vulnerability weight, and the future environmental information; sorting the suppliers according to the supplier performance evaluation result, and outputting the material supply guarantee strategy based on the supplier production capacity, the supplier sorting, and the transportation demand period.

[0018] Another technical solution provided by the present application is a material supply guarantee system based on big data material performance monitoring, which is used to implement the method as described above, including: a topology construction unit, which is used to construct a supply topology map according to the supplier historical transportation data and update the environmental vulnerability weight of each topology edge in the supply topology map based on the environmental change frequency in the historical environmental data; a risk analysis unit, which is used to obtain the supplier risk-bearing coefficient according to the supplier historical supply data and the supply association relationship; a strategy analysis unit, which is used to output a material supply guarantee strategy according to the current production plan for the future environmental information, the environmental vulnerability weight, and the supplier risk-bearing coefficient.

[0019] Another technical solution provided by the present application is a computer-readable storage medium, in which a computer program or instruction is stored, and when the computer program or instruction is executed by a processing device, the method as described above is implemented.

[0020] Advantages of the present application: 1. Construct the environmental vulnerability weights between suppliers and manufacturers, and between suppliers and suppliers according to the frequency of environmental changes corresponding to the transportation process of suppliers, so as to highlight the risk of transportation delays caused by environmental reasons for suppliers. Then, obtain the supply association relationship between suppliers according to the supply topology diagram and supply types, and obtain the risk-bearing ability of suppliers based on the supply association relationship and the performance fulfillment of suppliers' historical supplies. Furthermore, obtain the default risk caused by environmental changes according to the future environmental information of suppliers and the environmental vulnerability weights during the production demand period, and at the same time correct the default risk with the supplier risk-bearing coefficient, avoiding ignoring the risk-bearing ability of suppliers with high supply flexibility to environmental vulnerability changes, ensuring the accuracy of the assessment of suppliers' performance fulfillment risks, and then being able to sign material supply contracts with suppliers with higher performance fulfillment probabilities to ensure material supply.

[0021] 2. By calculating the radiation range of the topological side lines, incorporate peripheral road factors such as branch roads into the consideration of the stability of the transportation path, improving the accuracy of the risk assessment of the transportation process of suppliers.

[0022] 3. Obtain the supplier risk-bearing coefficient comprehensively with the first risk-bearing coefficient and the second risk-bearing coefficient, so that when evaluating the performance fulfillment of suppliers subsequently, it is possible to fully consider the insurmountability of market fluctuations and the performance fulfillment risks that may be caused by the default of its upstream and downstream suppliers, improving the accuracy of the assessment of suppliers' performance fulfillment.

[0023] 4. Compensate the first transportation demand period according to the blank ratio of the transportation prediction path, that is, move forward the last time of the first transportation demand period according to the blank ratio of the transportation prediction path to obtain the transportation demand period, so as to compensate for the transportation time required for the area that cannot be covered by the transportation prediction path. Brief Description of the Drawings

[0024] Figure 1 It is a schematic flow diagram of the material supply guarantee method based on big data material performance fulfillment monitoring of the present application;

[0025] Figure 2 It is a schematic diagram of the supply hierarchy relationship in a case of an embodiment of the present application. Detailed Embodiment

[0026] To make the purpose, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiment described here is only the best embodiment of the present application, only used to explain the present application, and does not limit the protection scope of the present application. All other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the protection scope of the present application.

[0027] Such as Figure 1As shown in the following, as the first embodiment of the present application, a material supply guarantee method based on big data material performance monitoring includes the following steps:

[0028] S1: Obtain the historical transportation data of suppliers, and construct a supply topology graph based on the historical transportation data of suppliers;

[0029] S2: Obtain the corresponding historical environmental data based on the supply topology graph, and construct the environmental vulnerability weights of each topological edge in the supply topology graph based on the environmental change frequency in the historical environmental data;

[0030] S3: Obtain the supply association relationships of suppliers according to the supply types based on the supply topology graph, and obtain the supplier risk-bearing coefficients according to the historical supply data of suppliers and the supply association relationships;

[0031] S4: Obtain the current production plan, obtain the production demand period based on the current production plan, obtain the future environmental information of each supplier within the production demand period, obtain the supplier performance evaluation results based on the future environmental information, environmental vulnerability weights, and supplier risk-bearing coefficients, and output the material supply guarantee strategy based on the supplier performance evaluation results.

[0032] In this embodiment, the environmental vulnerability weights between suppliers and manufacturers and between suppliers and suppliers are constructed according to the environmental change frequency corresponding to the transportation process of suppliers, so as to highlight the transportation delay risk caused by environmental reasons for suppliers. Furthermore, the supply association relationships between suppliers are obtained according to the supply topology graph and supply types, and the risk-bearing ability of suppliers is obtained according to the supply association relationships and the performance of suppliers' historical supplies. Furthermore, the default risk caused by environmental changes is obtained according to the future environmental information of suppliers and environmental vulnerability weights within the production demand period, and the default risk is corrected with the supplier risk-bearing coefficient, avoiding ignoring the risk-bearing ability of suppliers with high supply flexibility to environmental vulnerability changes, ensuring the accuracy of the evaluation of suppliers' performance risks, and then being able to sign material supply contracts with suppliers with higher performance probabilities to ensure material supply.

[0033] Among them, step S1 further includes:

[0034] Obtain the historical transportation starting and ending points and historical transportation paths of suppliers;

[0035] Construct a topological hierarchy based on the supply hierarchy relationships of suppliers;

[0036] Use the historical operation starting and ending points of suppliers as topological nodes, use the historical transportation paths as topological edges, and construct a supply topology graph based on the topological hierarchy, topological nodes, and topological edges.

[0037] At this time, according to the historical environmental data corresponding to the historical transportation paths of the suppliers, obtain the environmental vulnerability weights corresponding to each topological edge. Step S2 further includes:

[0038] Based on a preset radiation ratio, determine the radiation range of the topological edge and obtain the corresponding historical environmental data;

[0039] According to the transportation impact factors and the historical environmental data, calculate the environmental change frequency and temporal correlation of each transportation path, and construct the environmental vulnerability weights of each topological edge in the supply topology graph at each time sequence based on the change frequency and temporal correlation.

[0040] Radiate the range included in the topological edge according to the preset radiation ratio, expand the environmental impact reference range, and improve the accuracy of the environmental evaluation of the transportation path. Thus, taking the environmental factors that affect transportation as the transportation impact factors, calculate the environmental change frequency of the transportation path, and quantify the unstable factors of the supplier's transportation. Taking the road maintenance of the transportation path, road closures caused by bad weather, etc. as the environmental change situations, for example, the number of road closures caused by road maintenance in the transportation path is recorded as the number of environmental changes caused by road maintenance of the transportation path, and the number of road closures caused by bad weather in the transportation path is recorded as the number of environmental changes caused by bad weather. Based on this, calculate the environmental change frequency based on a preset time period. It can be understood that since ordinary weather changes such as changing from sunny to rainy do not affect transportation, but relatively bad weather such as typhoon weather and heavy rain weather are likely to cause transportation delays, the number of warning times not recommended to go out in the transportation path can be used as the number of environmental changes caused by weather factors, so as to show the impact of bad weather.

[0041] In this embodiment, the transportation impact factors at least include weather factors and road factors. For example, coastal areas are vulnerable to typhoon impacts in summer. At this time, the environmental stability of the transportation paths in coastal areas in summer is poor, that is, the environmental vulnerability weight is high. And in some areas, due to serious overloading of roads, excessive road repairs, and frequent road changes, the environmental stability of the transportation paths in this area is poor, and the environmental vulnerability weight is high.

[0042] Among them, based on a preset radiation ratio, determining the radiation range of the topological edge and obtaining the corresponding historical environmental data includes:

[0043] Obtain road data, and determine the branch parameters according to the road data and the topological edge;

[0044] Based on the branch parameters, determine the preset radiation ratio, and obtain the radiation range of the topological edge according to the preset radiation ratio;

[0045] Obtain the historical environmental data within the radiation range according to the radiation range of the topological edge.

[0046] Obtain road data through big data, such as obtaining road data by using a geographic information system platform, traffic management published data resources, etc. According to the corresponding position of the topological edge line in the actual geographical space and the road data, obtain other roads connected to the road segment corresponding to the topological edge line, and use the other roads as branch roads to obtain branch road parameters.

[0047] The branch road parameters at least include the number of branch roads and the branch road grade. The number of branch roads reflects the complexity and expandability of the road network around the road segment corresponding to the topological edge line. The more the number of branch roads, the more potential branch routes are available for selection during transportation. The branch road grade reflects the bearing capacity, communication speed, and road condition stability of the branch road. For example, the highway has a high grade and can transport a large amount of materials quickly, but there are many access restrictions. The rural road has a low grade. Although it is flexible, the transportation efficiency may be limited. Determine the preset radiation ratio according to the number of branch roads and the branch road grade, and give a higher preset radiation ratio to the topological edge lines with more branch roads and a higher branch road grade. According to the determined preset radiation ratio, combined with the actual geographical location where the topological edge line is located, use the spatial calculation method to calculate the radiation range.

[0048] The historical environmental data at least includes weather data and road data. The weather data includes information such as temperature, precipitation, wind speed, wind direction, and sunshine duration. The road data at least includes road change data, road maintenance data, and road congestion data. In this embodiment, by calculating the radiation range of the topological edge line, surrounding road factors such as branch roads are taken into account in the consideration of the stability of the transportation path, improving the accuracy of the risk assessment of the supplier's transportation process.

[0049] Calculating the environmental change frequency and temporal correlation of each transportation path according to the transportation impact factor and historical environmental data includes:

[0050] Obtain the supplier's historical transportation delay data, perform feature extraction on the supplier's historical transportation delay data, and obtain the transportation impact factor;

[0051] Take the environmental data change record corresponding to the transportation impact factor in the historical environmental data as the environmental change, count the number of environmental changes in each time series, and construct the environmental change frequency and temporal correlation.

[0052] By performing feature extraction on the transportation delay reason features in the supplier's historical transportation delay data and screening out occasional factors, obtain the transportation impact factor, and for each transportation path, count the number of times the environmental state changes in different time series, and construct the environmental change frequency and temporal correlation according to the correlation between the number of environmental changes and the time series.

[0053] At this time, according to the environmental change frequency and temporal correlation corresponding to each transportation path, construct the topological edge environmental vulnerability weights corresponding to the time series. For example, if there are multiple road maintenance operations on the branch or transportation path in the historical environmental data obtained according to the radiation range, it indicates that the possibility of maintenance of this transportation path and its branch is relatively high, the delay during transportation may be greater, and the environmental vulnerability weight is higher.

[0054] Based on the supply topology map, obtaining the supply association relationships of suppliers according to the supply type includes at least:

[0055] Conduct topological node clustering analysis for each topological level in the supply topology map based on the supply type;

[0056] Based on the clustering results, obtain the market supply relationships among suppliers at the same topological level;

[0057] According to the supply level relationship in the supply topology map, obtain the supply chains related to each supplier;

[0058] According to the market supply relationship and the supply chain, obtain the supply association relationships of suppliers.

[0059] In this embodiment, by constructing the topological levels of the supply topology map through the supply level relationships among suppliers, and then conducting clustering analysis on suppliers of the same level according to the supply type, the supply association relationships among suppliers can be clearly and intuitively obtained, including the supply level relationship and the market supply relationship, which is beneficial to improving the accuracy of subsequent performance evaluation of suppliers.

[0060] Specifically, the supply level relationship is the position and role of the supplier in the supply chain. Through the supply level relationship, suppliers are divided into different topological levels. For example, Figure 2 As shown, raw material suppliers are located in the first layer, component suppliers are located in the second layer, and manufacturers are located in the third layer. Then, clustering analysis is conducted on suppliers based on their supply types such as bolt supply, nut supply, steel plate supply, etc. At this time, the supply level relationship among suppliers is demonstrated based on the topological levels, and the same market supply relationship is obtained based on the clustering results. The supply association relationships among suppliers are constructed based on the supply level relationship and the same market supply relationship.

[0061] Obtaining the supplier risk-bearing coefficients according to the historical supply data and supply association relationships of suppliers includes:

[0062] Obtain the performance true values for each period based on the historical supply data of each supplier in the supply chain related to the supplier, and obtain the first risk-bearing coefficient based on the performance true value ratio;

[0063] Obtain the performance ratio related to market fluctuations as the second risk-bearing coefficient according to the market supply relationship of the supplier;

[0064] Construct the supplier risk - bearing coefficient with the first risk - bearing coefficient and the second risk - bearing coefficient.

[0065] In this embodiment, the possibility of a supplier's performance in case of default of the other suppliers in the supply chain is obtained by the supplier's response to the performance of the other suppliers in the supply chain and its own performance. The first risk - bearing coefficient quantifies the risk - bearing ability of the supplier in a loaded supply - chain environment. At the same time, the second risk - bearing coefficient quantifies the risk - bearing ability of the supplier compared with suppliers of the same type under market fluctuations. The supplier risk - bearing coefficient is comprehensively obtained from the first risk - bearing coefficient and the second risk - bearing coefficient, so that when evaluating the supplier's performance subsequently, the insurmountability of market fluctuations and the performance risks caused by the default of its upstream and downstream suppliers can be fully considered, improving the accuracy of the supplier performance evaluation.

[0066] For example, there are six suppliers A, B, C, D, E, and F. In the supply topology diagram, there is a supply chain among suppliers A, B, C, and E. Supplier E is the first - layer supply, supplier C is the second - layer supply, supplier B is the third - layer supply, and supplier A is the fourth - layer supply. While suppliers D, F, and A belong to the same - level and same - type suppliers. At this time, calculate the supplier risk - bearing coefficient of supplier A: Obtain the performance truth values of each period in the historical supply data of suppliers A, B, C, and E. Taking one year as an example, construct the performance truth - value table of each month for suppliers A, B, C, and E.

[0067] Table 1 Performance truth - value table of each month for suppliers A, B, C, and E

[0068]

[0069] Furthermore, calculate the performance truth - value ratio of each period of supplier A:

[0070] ;

[0071] where, is the performance truth - value ratio of supplier A, is the performance truth - value of supplier A, is the performance truth - value of supplier B, is the performance truth - value of supplier C, is the performance truth - value of supplier E.

[0072] According to the performance truth - value table of each month for suppliers A, B, C, and E, calculate the performance truth - value ratio table of supplier A as shown in Table 2:

[0073] Table 2 Performance truth - value ratio table of supplier A

[0074]

[0075] As shown in Table 3, the performance truth tables of Supplier A, Supplier D, and Supplier F within one year are as follows:

[0076] Table 3 Performance Truth Tables of Supplier A, Supplier D, and Supplier F within One Year

[0077]

[0078] As can be seen from Table 3, Supplier A, Supplier D, and Supplier F of the same type all had issues of breach of contract in July and August, and Supplier D and Supplier F still had issues of breach of contract in September. Thus, the performance fulfillment ratio related to the market fluctuation of Supplier A is 33.3%.

[0079] Thus, by obtaining the performance truth ratio table of Supplier A within one year and the performance fulfillment ratio related to the market fluctuation, the supply chain stability of Supplier A and the risk resistance ability against market fluctuations can be obtained, the accuracy of the supplier performance risk assessment can be improved, and materials can be supplied by suppliers with higher performance stability to ensure the material supply.

[0080] Step S4 further includes:

[0081] Obtain the current production plan, and based on the current production plan, obtain the production demand period and the supply location;

[0082] Based on the supply topology map and the supply location, calculate the transportation prediction paths of each supplier through similarity calculation;

[0083] Obtain the future environmental information of the transportation prediction paths during the production demand period;

[0084] Output the first performance evaluation result based on the future environmental information and the environmental vulnerability weight;

[0085] Revise the first performance evaluation result based on the supplier risk-bearing coefficient to obtain the supplier performance evaluation result;

[0086] Rank the suppliers according to the supplier performance evaluation result, and output the material supply guarantee strategy based on the supplier production capacity and the supplier ranking.

[0087] In this embodiment, the production demand period and the supply location are obtained according to the requirements of the production plan. The transportation prediction paths of each supplier are calculated based on the similarity between the supply location and the historical supply locations of the suppliers. The first performance evaluation result is output according to the future environmental information and the corresponding environmental vulnerability weights during the production demand period of the transportation prediction paths, and the first performance evaluation result is corrected according to the supplier risk-bearing coefficient, so as to comprehensively evaluate the supplier's performance ability based on the supplier's risk-bearing ability and transportation risk, improve the accuracy of the evaluation of the supplier's performance risk, and ensure the efficiency and reliability of material supply.

[0088] Specifically, calculating the transportation prediction paths of each supplier based on the similarity of the supply topology map and the supply location includes:

[0089] Obtaining the transportation coincidence probability according to the similarity between the topological nodes on the supply topology map and the supply location;

[0090] Obtaining the transportation prediction paths of each supplier based on the highest transportation coincidence probability.

[0091] Due to the instability of freight transportation, suppliers tend to prefer the routes that have been transported during the transportation process to avoid supply delays caused by unfamiliar routes. At this time, according to the similarity between the historical supply destinations of the suppliers and the current supply location, the most likely transportation routes of the suppliers are predicted, and the transportation risks of the suppliers are evaluated based on this.

[0092] Obtaining the transportation coincidence probability according to the similarity between the topological nodes on the supply topology map and the supply location includes:

[0093] Calculating the distance similarity and the regional feature similarity between the topological nodes corresponding to each supplier and the supply location;

[0094] Constructing a preset coincidence weight according to the historical transportation tools of the suppliers, and obtaining the transportation coincidence probability based on the preset coincidence weight, the distance similarity, and the regional feature similarity.

[0095] According to the actual geographical location distance between the historical supply location and the current supply location, in this embodiment, the great circle distance algorithm is used to solve it to take into account the influence of the earth's curvature. The closer the distance, the higher the distance similarity between the historical supply location and the current supply location. At the same time, considering the regional feature similarity between the region where the historical supply location is located and the region where the current supply location is located, such as road grade, traffic flow, and distribution of logistics hubs, etc. If both the region where the historical supply location is located and the region where the current supply location is located are in industrial parks with developed logistics, and there are highways and railway hubs nearby, then the regional feature similarity is higher. If the historical supply location is in a remote mountainous area with inconvenient transportation, and the current supply location is in an urban logistics center, then the regional feature similarity is lower.

[0096] Construct a preset coincidence weight through the supplier's historical transportation tools. For example, for suppliers using trucks for transportation, it is necessary to consider whether they can meet the truck access conditions if the current supply location is in the urban central area. Obtain the preset coincidence weight based on the adaptability between the supplier's historical transportation tools and the current supply location. For example, if the adaptability between the supplier's historical transportation tools and the current supply location is high, the preset coincidence weight of distance similarity is higher; if the adaptability between the supplier's historical transportation tools and the current supply location is low, the preset coincidence weight of regional feature similarity is higher, that is:

[0097] ;

[0098] Among them, is the transportation coincidence probability, is the number of times the transportation tool suitable for the current supply location appears in the supplier's historical transportation process, is the total number of the supplier's historical transportation, is the distance similarity, is the regional feature similarity. It can be understood that here the transportation tool suitable for the current supply location refers to the transportation tool that meets the restrictive features. For example, in some areas, there are restrictions on the height of vehicles. If the transportation tool meets this height restriction, it is considered that the transportation tool is the suitable transportation tool for this area.

[0099] Take the set of topological edges corresponding to the topological nodes with the highest coincidence probability as the transportation prediction path. Obtain the environmental vulnerability weight of the transportation prediction path according to the future environmental information, and perform weighted calculation to obtain the first performance evaluation result. In this embodiment, take the total probability of all possible environmental changes of the transportation prediction path as the first performance evaluation result of the supplier, and perform comprehensive calculation based on the supplier's risk-bearing coefficient and the first performance evaluation result to obtain the supplier's performance evaluation result, which not only considers the transportation risk of the supplier, but also quantifies the supplier's risk-bearing ability for performance. That is, if the supplier's risk-bearing ability is strong, even if its transportation path is greatly affected by environmental changes, the supplier is more capable of compensating to avoid default; while if the supplier's risk-bearing ability is weak, even if the transportation path is less affected by environmental changes, the supplier is more likely to default due to accidental problems. At this time, the supplier's performance evaluation result is:

[0100] ;

[0101] is the supplier's performance probability, is the first performance evaluation result, is the supplier's risk-bearing coefficient.

[0102] Call the corresponding supplier risk - bearing coefficient of the supplier according to the current production demand period, calculate the supplier risk - bearing coefficient and the first performance evaluation result of the current production demand period to obtain the supplier performance probability.

[0103] In this embodiment, the supplier performance probability of the overall period is used as the supplier performance evaluation result, that is:

[0104] ;

[0105] where T is the current production demand period, is the first performance evaluation result at time t, is the supplier risk - bearing coefficient at time t.

[0106] It can be understood that since the actual transportation time of the supplier cannot be determined, the overall evaluation is carried out for the entire production demand period, and the production demand period is the previous period when the materials must arrive according to the production plan. In some cases, the evaluation period of each supplier can be calculated according to the estimated transportation time, that is, only the performance of the supplier when shipping before the latest period is evaluated, reducing the evaluation calculation amount and improving the evaluation efficiency.

[0107] Furthermore, according to the supplier performance evaluation result, the suppliers are sorted. Based on the supplier production capacity constraint and the supplier sorting, select the suppliers that can meet the production demand and have better performance evaluation results for material supply to ensure the reliability of material supply.

[0108] As the second embodiment of the present application, step S4 further includes:

[0109] Obtain the current production plan, and based on the current production plan, obtain the production demand period and the supply location;

[0110] Based on the supply topology map and the supply location, calculate the transportation prediction paths of each supplier according to the similarity;

[0111] Based on the production demand period, the blank ratio of the transportation prediction path, and the supplier historical transportation data, obtain the transportation demand period;

[0112] According to the transportation demand period, retrieve the supplier risk - bearing coefficient, the environmental vulnerability weight, and the future environmental information, and obtain the supplier performance evaluation result according to the supplier risk - bearing coefficient, the environmental vulnerability weight, and the future environmental information;

[0113] According to the supplier performance evaluation result, sort the suppliers, and output the material supply guarantee strategy based on the supplier production capacity, the supplier sorting, and the transportation demand period.

[0114] In this embodiment, considering that the current supply location may be inconsistent with the historical supply location, resulting in deviations in the transportation prediction of suppliers, temporal compensation is performed according to the blank ratio of the transportation prediction path, that is, the ratio of the different sections between the current supply location and the historical supply location to the overall section. The first transportation demand period is calculated based on the latest time when the materials needed for production arrive and the historical transportation demand time of the supplier. This period is the period between the current time and (the latest time when the materials needed for production arrive - the historical transportation demand time of the supplier). At this time, the first transportation demand period is compensated according to the blank ratio of the transportation prediction path, that is, the latest time of the first transportation demand period is moved forward according to the blank ratio of the transportation prediction path to obtain the transportation demand period, so as to compensate for the transportation time required for the area that cannot be covered by the transportation prediction path.

[0115] Among them, the material supply guarantee strategy output based on the supplier production capacity, supplier ranking, and transportation demand period includes:

[0116] An optimization objective function is constructed based on the minimum inventory cost, and the supplier selection strategy and order time sequence are output according to the optimization objective function based on the supplier production capacity, supplier ranking, and transportation demand period.

[0117] Under the condition of ensuring the stability of supplier performance, the supplier selection strategy and order time sequence that meet the transportation demand period are obtained through iterative calculation of the minimum inventory cost, and the supply stability is ensured while reducing the inventory cost.

[0118] As the third embodiment of this application, a material supply guarantee system based on big data material performance monitoring includes:

[0119] A topology construction unit for constructing a supply topology map according to the historical transportation data of suppliers and updating the environmental vulnerability weights of each topology edge in the supply topology map based on the environmental change frequency in the historical environmental data;

[0120] A risk analysis unit for obtaining the supplier risk-bearing coefficient according to the historical supply data of suppliers and the supply association relationship;

[0121] A strategy analysis unit for outputting a material supply guarantee strategy according to the current production plan for future environmental information, environmental vulnerability weights, and supplier risk-bearing coefficients.

[0122] In this embodiment, the topology construction unit realizes the construction of the supply topology map and the dynamic update of the environmental vulnerability weight, facilitating the user to intuitively obtain the supply chain relationship among various suppliers and facilitating the subsequent accurate analysis of the supply chain. In the topology supply unit, drawing tools such as Graphviz and Cacoo can be used to take the supply starting point and supply focus of the supplier as the topology nodes in the topology map, and construct the actual transportation paths that have occurred between suppliers as the topology edges, forming a supply topology map that intuitively presents the transportation association among suppliers.

[0123] After obtaining the supply topology map output by the topology construction unit, the risk analysis unit calculates the supplier risk bearing coefficient in combination with the supplier's historical supply data and supply association relationship. When formulating the material supply guarantee strategy, the strategy analysis unit considers the needs of the current production plan, uses the supply topology map to obtain the corresponding future environmental information, environmental vulnerability weight, and the supplier risk bearing coefficient output by the risk analysis unit to conduct the sorting of supplier performance evaluation. The suppliers with higher performance probabilities are ranked higher. The suppliers are selected in sequence according to the sorting based on the production capacity of the suppliers, and the suppliers with higher performance probabilities are used for material supply to ensure the stability of material supply.

[0124] As the fourth embodiment of this application, a computer-readable storage medium is provided for storing computer programs or instructions. When the computer programs or instructions are executed by a processing device, the above-mentioned material supply guarantee method based on big data material performance monitoring is realized. The computer-readable storage medium can be any available medium that a computing device can store or a data storage device such as a data center containing one or more available media. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid-state drive), etc.

[0125] The above-mentioned specific implementation manners are the preferred implementation manners of the material supply guarantee method and system based on big data material performance monitoring of this application, and do not limit the specific implementation scope of this application. The scope of this application includes but is not limited to this specific implementation manner. Any equivalent changes made according to the shape and structure of this application are within the protection scope of this application.

Claims

1. A material supply guarantee method based on big data material performance monitoring, characterized by: The steps include: S1: Obtain the supplier's historical transportation data and build a supply topology map based on the supplier's historical transportation data; S2: Obtain the corresponding historical environmental data based on the supply topology graph, and construct the environmental vulnerability weight of each topological edge in the supply topology graph based on the frequency of environmental changes in the historical environmental data; S3: Based on the supply topology, the supply association relationship of suppliers is obtained according to the supply type, and the supplier risk bearing coefficient is obtained according to the supplier's historical supply data and supply association relationship; S4: Obtain the current production plan, obtain the production demand period based on the current production plan, obtain the future environmental information of each supplier during the production demand period, obtain the supplier performance evaluation results based on the future environmental information, environmental vulnerability weights and supplier risk bearing coefficients, and output the material supply guarantee strategy based on the supplier performance evaluation results; The S2 further includes: Determine the radiation range of the topological edge based on the preset radiation ratio to obtain the corresponding historical environmental data; The environmental change frequency and time series correlation of each transportation path are calculated based on transportation influencing factors and historical environmental data, and the environmental vulnerability weights of each topological edge at each time series in the supply topology graph are constructed based on the change frequency and time series correlation. The obtaining of the supplier risk bearing coefficient according to the supplier's historical supply data and supply association relationship includes: Obtain the true value of contract performance in each period based on the historical supply data of each supplier in the supplier-related supply chain, and obtain the first risk bearing coefficient based on the proportion of the true value of contract performance; According to the supplier's market supply relationship, the performance ratio related to market fluctuations is obtained as the second risk bearing coefficient; The supplier risk bearing coefficient is constructed by the first risk bearing coefficient and the second risk bearing coefficient.

2. The material supply guarantee method based on big data material performance monitoring as claimed in claim 1 is characterized by: The step of determining the radiation range of the topological edge based on the preset radiation ratio and obtaining the corresponding historical environment data comprises: Obtain road data, and determine branch parameters based on the road data and topological edges; Determine a preset radiation ratio based on branch parameters, and obtain a radiation range of the topology edge according to the preset radiation ratio; According to the radiation range of the topological edge, the historical environmental data within the radiation range is obtained.

3. The material supply guarantee method based on big data material performance monitoring as claimed in claim 1, characterized in that: The calculation of the environmental change frequency and time series correlation of each transportation path according to the transportation influencing factors and historical environmental data includes: Obtain the supplier's historical transportation delay data, perform feature extraction on the supplier's historical transportation delay data, and obtain transportation influencing factors; The environmental data changes of the corresponding transportation influencing factors in the historical environmental data of each transportation route are recorded as environmental changes. The number of environmental changes in each time series is counted, and the frequency of environmental changes and time series correlation are constructed.

4. The material supply guarantee method based on big data material performance monitoring as claimed in claim 1 is characterized by: The S1 further comprises: Obtain the supplier's historical transportation start and end points and historical transportation routes; Building a topological hierarchy based on the supplier supply hierarchy relationships; The supplier's historical operation start and end points are used as topological nodes, and the historical transportation paths are used as topological edges. A supply topology graph is constructed based on topological levels, topological nodes, and topological edges.

5. The material supply guarantee method based on big data material performance monitoring as claimed in claim 4 is characterized by: The acquiring of the supply association relationship of suppliers according to the supply type based on the supply topology graph at least includes: Perform topological node clustering analysis based on supply type for each topological level in the supply topology diagram; Based on the clustering results, the market supply relationship between suppliers at the same topological level is obtained; Build supply association relationships based on supply hierarchy relationships and market supply relationships.

6. The material supply guarantee method based on big data material performance monitoring as claimed in claim 1, characterized in that: The S4 further comprises: Obtain the current production plan, and obtain the production demand period and supply location based on the current production plan; The transportation forecast path of each supplier is obtained based on the supply topology map and the supply location based on similarity calculation; Obtain future environmental information of transportation forecast routes during production demand periods; Output the first compliance assessment results based on future environmental information and environmental vulnerability weights; The first performance evaluation result is modified based on the supplier's risk bearing coefficient to obtain the supplier's performance evaluation result; Suppliers are ranked according to the results of supplier performance evaluation, and material supply guarantee strategies are output based on supplier production capacity and supplier ranking.

7. The material supply guarantee method based on big data material performance monitoring as claimed in claim 6 is characterized by: The transportation prediction path of each supplier is obtained by calculating the similarity based on the supply topology and the supply location, including: Obtain the probability of transportation overlap based on the similarity between the topological nodes on the supply topology graph and the supply locations; Get the predicted shipping routes for each supplier based on the highest shipping coincidence probability.

8. The material supply guarantee method based on big data material performance monitoring as claimed in claim 1, characterized in that: The S4 further comprises: Obtain the current production plan, and obtain the production demand period and supply location based on the current production plan; The transportation forecast path of each supplier is obtained based on the supply topology map and the supply location based on similarity calculation; Obtain the transportation demand period based on the production demand period, the blank ratio of the transportation forecast path and the supplier's historical transportation data; Retrieve the supplier's risk-bearing coefficient, environmental vulnerability weight, and future environmental information according to the transportation demand period, and obtain the supplier's performance evaluation results based on the supplier's risk-bearing coefficient, environmental vulnerability weight, and future environmental information; Suppliers are ranked according to the results of supplier performance evaluation, and material supply guarantee strategies are output based on supplier production capacity, supplier ranking and transportation demand period.

9. A material supply guarantee system based on big data material performance monitoring, used to implement the method according to any one of claims 1 to 8, characterized in that: include: A topology construction unit is used to construct a supply topology map based on the supplier's historical transportation data, and to update the environmental vulnerability weight of each topology edge in the supply topology map based on the frequency of environmental changes in the historical environmental data; The risk analysis unit is used to obtain the supplier risk bearing coefficient based on the supplier's historical supply data and supply association relationship; The strategy analysis unit is used to output material supply security strategies based on the current production plan, future environmental information, environmental vulnerability weights, and supplier risk-bearing coefficients.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program or instruction, and when the computer program or instruction is executed by a processing device, the method according to any one of claims 1 to 8 is implemented.

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