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

Abstract

The invention discloses a material insurance supply method and system based on big data material performance monitoring, and belongs to the technical field of material supply, and the method comprises the following steps: constructing a supply topological graph according to the historical transportation data of suppliers; acquiring corresponding historical environment data based on the supply topological graph, and constructing an environment vulnerability weight of each topological side line in the supply topological graph based on an environment change frequency in the historical environment data; based on the supply topological graph, a supply association relationship of suppliers is obtained according to the supply type, and a supplier risk bearing coefficient is obtained according to historical supply data of the suppliers and the supply association relationship; and outputting a material insurance supply strategy based on the future environment information, the environment fragility weight and the supplier risk bearing coefficient. According to the method, the timeliness of adjustment of the material supply distribution strategy is ensured by considering the risk in the transportation process in advance, neglect of high-flexibility suppliers is avoided through the risk bearing capability of the suppliers, and the accuracy of material supply distribution is ensured.

Description

Technical Field

[0001] The present application relates to the field of material supply technology, and in particular 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 vital role in the survival and development of enterprises. Traditional supply chain management technology has gradually exposed many limitations when dealing with the key link of material supply.

[0003] In the current supply chain management field, most of the related technologies use traditional supplier management systems (SMS) combined with basic logistics tracking tools to ensure the supply of materials. Traditional SMS focuses on 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 limited to analyzing the supply capacity of a single supplier, which is easily affected by the complex supply chain and causes supply forecast deviations. At the same time, the monitoring of logistics only stays at the level of real-time road condition monitoring. However, if there is a problem with real-time transportation, it is already difficult to supply materials according to the demand time limit. The related technologies rely on fixed and inflexible solutions, which cannot adapt to market changes and the dynamic changes in the actual performance capabilities of suppliers. Although real-time monitoring can ensure the accuracy of material supply strategies, it cannot meet the timeliness of material supply. Using the supplier's historical performance data to evaluate the supplier's performance for real-time adjustments cannot avoid the evaluation errors caused by the instability of other suppliers.

[0005] The patent "Evaluation method and device for power material suppliers based on user portraits", publication number: CN115907308A, publication date: April 4, 2023, specifically discloses: obtaining the basic information of the supplier, cleaning the basic information to obtain the first basic information, indexing the first basic information, and generating a derivative index system; constructing a comprehensive evaluation model for suppliers based on the derivative index system; comprehensively evaluating suppliers through the supplier comprehensive evaluation model to obtain a comprehensive rating, and extracting indicator labels in the derivative index system; generating supplier portraits based on the comprehensive ratings and indicator labels, obtaining the auxiliary evaluation of the supplier by the procurement department based on the supplier portraits, and obtaining the sampling evaluation of the supplier by the quality supervision department based on the supplier portraits; determining the final evaluation of the supplier based on the auxiliary evaluation, sampling evaluation and comprehensive evaluation. This solution constructs a portrait of the supplier to evaluate the supplier, which is limited to the static nature of the user portrait and cannot adapt to the complex changes of 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] Furthermore, S2 also 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 time series correlation of each transportation path according to the transportation influencing factor and the historical environmental data, and constructing the environmental vulnerability weight of each topological edge in each time series in the supply topology diagram based on the change frequency and time series correlation.

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

[0011] Furthermore, the calculation of the environmental change frequency and time series correlation of each transportation path based on the transportation impact factor and historical environmental data includes: obtaining the supplier's historical transportation delay data, performing feature extraction on the supplier's historical transportation delay data, and obtaining the transportation impact factor; recording the environmental data change records corresponding to the transportation impact factor in the historical environmental data of each transportation path as the environmental change, counting the number of environmental changes in each time series, and constructing the environmental change frequency and time series correlation.

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

[0013] Furthermore, the method of obtaining supply association relationships of suppliers according to supply types based on the supply topology map at least includes: performing topological node clustering analysis based on supply types for each topological level in the supply topology map; obtaining market supply relationships between suppliers at the same topological level based on the clustering results; and constructing supply association relationships based on supply level relationships and market supply relationships.

[0014] Furthermore, the method of obtaining the supplier risk-bearing coefficient based on the supplier's historical supply data and supply association relationship includes: obtaining the fulfillment true value of each time period based on the historical supply data of each supplier in the supplier-related supply chain, and obtaining a first risk-bearing coefficient based on the fulfillment true value ratio; obtaining the fulfillment ratio related to market fluctuations as the second risk-bearing coefficient based on the supplier's market supply relationship; and constructing the supplier risk-bearing coefficient with the first risk-bearing coefficient and the second risk-bearing coefficient.

[0015] Furthermore, S4 also includes: obtaining the current production plan, and obtaining the production demand period and supply location based on the current production plan; obtaining the transportation forecast path of each supplier based on the supply topology map and the supply location based on similarity calculation; obtaining the future environmental information of the transportation forecast path 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; ranking the suppliers according to the supplier performance evaluation results, and outputting the material supply guarantee strategy based on the supplier production capacity and supplier ranking.

[0016] Furthermore, the transportation prediction path for each supplier is obtained based on the supply topology map and the supply location based on similarity calculation, including: obtaining the transportation overlap probability based on the similarity between the topological nodes on the supply topology map and the supply location; and obtaining the transportation prediction path for each supplier based on the highest transportation overlap probability.

[0017] Furthermore, S4 also includes: obtaining the current production plan, and obtaining the production demand period and supply location based on the current production plan; obtaining the transportation forecast path of each supplier based on the supply topology map and the supply location based on similarity calculation; obtaining 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; retrieving the supplier's risk-bearing coefficient, environmental vulnerability weight and future environmental information according to the transportation demand period, and obtaining the supplier's performance evaluation results according to the supplier's risk-bearing coefficient, environmental vulnerability weight and future environmental information; sorting suppliers according to the supplier's performance evaluation results, and outputting the material supply guarantee strategy based on the supplier's production capacity, supplier sorting and 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 mentioned above, including: a topology construction unit, which is used to construct a supply topology map based on the supplier's historical transportation data, and update the environmental vulnerability weights of each topology edge in the supply topology map based on the frequency of environmental changes in the historical environmental data; a risk analysis unit, which is used to obtain the supplier's risk-bearing coefficient based on the supplier's historical supply data and supply association relationship; a strategy analysis unit, which is used to output a material supply guarantee strategy based on the current production plan for future environmental information, environmental vulnerability weights and supplier risk-bearing coefficients.

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

[0020] The beneficial effects of the present application are as follows: 1. Environmental vulnerability weights between suppliers and manufacturers, and between suppliers are constructed according to the frequency of environmental changes corresponding to the supplier's transportation process, so as to highlight the risk of transportation delays caused by environmental reasons, and then the supply relationship between suppliers is obtained according to the supply topology and supply type, and the supplier's risk-bearing capacity is obtained according to the supply relationship and the supplier's historical supply performance, and then the default risk caused by environmental changes is obtained according to the supplier's future environmental information and environmental vulnerability weights during the production demand period. At the same time, the default risk is corrected with the supplier's risk-bearing coefficient to avoid ignoring the risk-bearing capacity of suppliers with high supply flexibility to environmental vulnerability changes, thereby ensuring the accuracy of the assessment of supplier performance risks, and then signing material supply contracts with suppliers with higher performance possibilities to ensure material supply.

[0021] 2. By calculating the radiation range of the topological edge, factors such as branches and surrounding roads are incorporated into the stability of the transportation path to improve the accuracy of risk assessment of the supplier's transportation process.

[0022] 3. The supplier risk bearing coefficient is obtained by combining the first risk bearing coefficient and the second risk bearing coefficient, so that in the subsequent supplier performance evaluation, the difficulty of overcoming market fluctuations and the performance risks that may be caused by the breach of contract by its upstream and downstream suppliers can be fully considered, thereby improving the accuracy of the supplier performance evaluation.

[0023] 4. Compensate the first transport demand period according to the blank ratio of the transport forecast path, that is, move the last time of the first transport demand period forward according to the blank ratio of the transport forecast path to obtain the transport demand period to compensate for the transport time required for the area that the transport forecast path cannot cover. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a flow chart of the material supply guarantee method based on big data material performance monitoring for this application; Figure 2 A schematic diagram of the supply hierarchy relationship in an embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solutions and advantages of the present application more clear, the present application is further described in detail below in conjunction with the drawings and examples. It should be understood that the specific implementation method described here is only an optimal embodiment of the present application, which is only used to explain the present application and does not limit the scope of protection of the present application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0026] like Figure 1As shown, as the first embodiment of the present application, the material supply guarantee method based on big data material performance monitoring includes the following steps: 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 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 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.

[0027] In this embodiment, environmental vulnerability weights between suppliers and manufacturers and between suppliers are constructed according to the frequency of environmental changes corresponding to the supplier's transportation process to highlight the risk of transportation delays caused by environmental reasons. Then, the supply association relationship between suppliers is obtained according to the supply topology and supply type. The supplier's risk-bearing capacity is obtained according to the supply association relationship and the supplier's historical supply performance. Then, the default risk caused by environmental changes is obtained according to the supplier's future environmental information and environmental vulnerability weights during the production demand period. At the same time, the default risk is corrected with the supplier's risk-bearing coefficient to avoid ignoring the risk-bearing capacity of suppliers with high supply flexibility to environmental vulnerability changes, thereby ensuring the accuracy of the assessment of supplier performance risks, and then signing material supply contracts with suppliers with higher performance possibilities to ensure material supply.

[0028] Wherein, step S1 also includes: 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.

[0029] At this time, the environmental vulnerability weights corresponding to each topological edge are obtained according to the historical environmental data corresponding to the supplier's historical transportation path. Step S2 also 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 according to the transportation influencing factors and historical environmental data. 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.

[0030] According to the preset radiation ratio, the topological edge inclusion range is radiated to expand the reference range of environmental impact and improve the accuracy of environmental judgment on the transportation path. Therefore, the environmental factors that affect transportation are used as transportation influencing factors to calculate the frequency of environmental changes in the transportation path and quantify the unstable factors of supplier transportation. Road maintenance and road closures caused by bad weather on the transportation path are used as environmental changes. 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 on 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. The frequency of environmental changes is calculated based on the preset time period. It is understandable that ordinary weather changes such as sunny days turning into rainy days will not affect transportation, but relatively bad weather such as typhoon weather and rainstorm weather are prone to cause transportation delays. Therefore, the number of warnings that are not recommended to go out in the transportation path can be used as the number of environmental changes caused by weather factors, thereby showing the impact of bad weather.

[0031] In this embodiment, the transportation influencing factors include at least weather factors and road factors. For example, coastal areas are susceptible to typhoons in summer. At this time, the environmental stability of the transportation routes in coastal areas in summer is poor, that is, the environmental fragility weight is high. In some areas, due to serious road overload, excessive road maintenance, and frequent road changes, the environmental stability of the transportation routes in the area is poor and the environmental fragility weight is high.

[0032] Wherein, determining the radiation range of the topological edge based on the preset radiation ratio to obtain the corresponding historical environment data includes: 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.

[0033] Obtain road data through big data, such as using geographic information system platforms, traffic management published data resources, etc., obtain other roads connected to the road segment corresponding to the topological edge line according to the corresponding position of the topological edge line in the actual geographic space and the road data, use other roads as branches, and obtain branch parameters.

[0034] The branch parameters include at least the number of branches and the level of branches. The number of branches reflects the complexity and scalability of the road network around the road segment corresponding to the topological edge. The more branches there are, the more potential branch routes there are to choose from during transportation. The level of branches reflects the carrying capacity, communication speed and road condition stability of the branches. For example, a high-level highway can quickly transport a large amount of materials, but there are many access restrictions. The level of rural roads is low, and although it is flexible, the transportation efficiency may be limited. The preset radiation ratio is determined based on the number of branches and the level of branches. A higher preset radiation ratio is given to topological edges with more branches and higher branch levels. According to the determined preset radiation ratio, combined with the actual geographical location of the topological edge, the radiation range is calculated using spatial calculation methods.

[0035] The historical environmental data includes at least weather data and road data. The weather data includes information such as temperature, precipitation, wind speed, wind direction, and sunshine duration. The road data includes at least road change data, road maintenance data, and road congestion data. In this embodiment, by calculating the radiation range of the topological edge, factors such as branch roads and other surrounding roads are incorporated into the consideration of transportation path stability, thereby improving the accuracy of risk assessment of the supplier's transportation process.

[0036] The frequency and temporal correlation of environmental changes on each transport route are calculated based on transport influencing factors and historical environmental data, including: 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 change records of the corresponding transportation influencing factors in the historical environmental data are taken 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.

[0037] By extracting the characteristics of the causes of transportation delays in the supplier's historical transportation delay data and screening out accidental factors, we obtain the transportation influencing factors. For each transportation route, we count the number of times the environmental status changes at different time sequences. Based on the correlation between the number of environmental changes and the time sequence, we build the correlation between the frequency of environmental changes and the time sequence.

[0038] At this time, the topological edge environmental vulnerability weight of the corresponding time series is constructed according to the frequency of environmental changes and time series correlation corresponding to each transportation path. For example, if there are multiple road maintenance on branches or transportation paths according to the historical environmental data obtained from the radiation range, it means that the transportation path and its branches are likely to be maintained, the delay during transportation may be greater, and the environmental vulnerability weight is higher.

[0039] Acquiring the supply association relationship of suppliers according to the supply type based on the supply topology diagram 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; Obtain the supply chain related to each supplier based on the supply hierarchy relationship in the supply topology diagram; Obtain supplier supply relationships based on market supply relationships and supply chains.

[0040] In this embodiment, the topological hierarchy of the supply topology map is constructed through the supply hierarchy relationship between suppliers, and suppliers at the same level are clustered according to supply type. This enables a clear and intuitive understanding of the supply association relationships between suppliers, including supply hierarchy relationships and market supply relationships, which is conducive to improving the accuracy of subsequent supplier performance evaluations.

[0041] Specifically, the supply hierarchy relationship refers to the position and role of suppliers in the supply chain. Through the supply hierarchy relationship, suppliers are divided into different topological levels, such as Figure 2 As shown in the figure, raw material suppliers are located in the first layer, parts suppliers are located in the second layer, and manufacturers are located in the third layer. Then, suppliers are clustered based on their supply types, such as bolt supply, nut supply, and steel plate supply. At this time, the supply hierarchy relationship between suppliers is highlighted based on the topological hierarchy, and the supply relationship in the same market is obtained based on the clustering results. The supply association relationship between each supplier is constructed based on the supply hierarchy relationship and the supply relationship in the same market.

[0042] The supplier risk coefficient obtained based on the supplier's historical supply data and supply 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.

[0043] In this embodiment, the supplier's performance possibility in the event of default of other suppliers in the supply chain is obtained by the supplier's response to the performance of other suppliers in the supply chain and the supplier's own performance, and the supplier's risk-bearing capacity in a load supply chain environment is quantified by the first risk-bearing coefficient, while the supplier's risk-bearing capacity compared to suppliers of the same type under market fluctuations is quantified by the second risk-bearing coefficient. The supplier's risk-bearing coefficient is obtained by combining the first risk-bearing coefficient and the second risk-bearing coefficient, so that in the subsequent evaluation of supplier performance, the difficulty of overcoming market fluctuations and the performance risks that may be caused by the default of its upstream and downstream suppliers can be fully considered, thereby improving the accuracy of the supplier performance evaluation.

[0044] 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-tier supplier, supplier C is the second-tier supplier, supplier B is the third-tier supplier, and supplier A is the fourth-tier supplier. Suppliers D, F, and A belong to the same level and type of suppliers. At this time, calculate the supplier risk bearing coefficient of supplier A: obtain the fulfillment truth values ​​of each period in the historical supply data of suppliers A, B, C, and E, as shown in Table 1. Taking one year as an example, construct the fulfillment truth table of suppliers A, B, C, and E in each month.

[0045] Table 1 The performance truth table of suppliers A, B, C and E in each month

[0046] Then the true value ratio of supplier A’s performance in each period is calculated: ; in, is the true value proportion of supplier A’s performance, is the true value of supplier A's performance, is the true value of supplier B’s performance, is the true value of supplier C's performance, is the true value of supplier E’s performance.

[0047] According to the performance truth tables of suppliers A, B, C, and E in each month, we can calculate the performance truth ratio table of supplier A as shown in Table 2: Table 2 A supplier's performance truth value ratio table

[0048] As shown in Table 3, this is the performance truth table of Supplier A, Supplier D, and Supplier F within one year: Table 3 The performance truth table of suppliers A, D and F within one year

[0049] It can be seen from Table 3 that suppliers A, D and F of the same type all had breach of contract problems in July and August, while suppliers D and F still had breach of contract problems in September. Therefore, the fulfillment ratio of supplier A related to market fluctuations is 33.3%.

[0050] Therefore, by obtaining the true value ratio table of supplier A's performance within one year and the performance ratio related to market fluctuations, we can obtain supplier A's supply chain stability and risk resistance to market fluctuations, improve the accuracy of supplier performance risk assessment, and supply materials with suppliers with higher performance stability to ensure material supply.

[0051] Step S4 also includes: 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.

[0052] In this embodiment, the production demand period and the supply location are obtained according to the needs of the production plan, and the transportation forecast path of each supplier is calculated according to the similarity between the supply location and the supplier's historical supply location. The first performance evaluation result is output according to the future environmental information of the transportation forecast path in the production demand period and the corresponding environmental vulnerability weight, and the first performance evaluation result is corrected according to the supplier's risk-bearing coefficient. The supplier's performance capacity is comprehensively evaluated based on the supplier's risk-bearing capacity and transportation risk, so as to improve the accuracy of the assessment of the supplier's performance risk and ensure the efficiency and reliability of material supply.

[0053] Specifically, the transportation forecast paths of each supplier are obtained based on the supply topology and supply location based on similarity calculation, 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.

[0054] Due to the instability of freight, suppliers prefer routes that have already been used during the transportation process to avoid supply delays due to unfamiliarity with the routes. At this time, the most likely transportation route of the supplier is predicted based on the similarity between the supplier's historical supply destination and the current supply location, and the supplier's transportation risk is evaluated based on this.

[0055] The transportation overlap probability is obtained based on the similarity between the topological nodes on the supply topology graph and the supply location, including: Calculate the distance similarity between the topological node corresponding to each supplier and the supply location, as well as the regional feature similarity; The preset coincidence weights are constructed according to the supplier's historical transportation tools, and the transportation coincidence probability is obtained based on the preset coincidence weights, distance similarity, and regional feature similarity.

[0056] According to the actual geographical location distance between the historical supply location and the current supply location, the great circle distance algorithm is used in this embodiment to solve the problem, so as 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, the similarity of regional characteristics between the area where the historical supply location is located and the area where the current supply location is located is considered, such as road grade, traffic flow, and distribution of logistics hubs. If the area where the historical supply location and the area where the current supply location are located are both located in industrial parks with developed logistics and are surrounded by highways and railway hubs, the similarity of regional characteristics is relatively high. 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, the similarity of regional characteristics is relatively low.

[0057] The preset overlap weights are constructed through the supplier's historical transportation tools. For example, suppliers who use truck transportation need to consider whether the truck access conditions can be met if the current supply location is located in the city center. The preset overlap weights are obtained based on the compatibility between the supplier's historical transportation tools and the current supply location. If the supplier's historical transportation tools have a high degree of compatibility with the current supply location, the preset overlap weight of distance similarity is higher. If the supplier's historical transportation tools have a low degree of compatibility with the current supply location, the preset overlap weight of regional feature similarity is higher, that is: ; in, is the transport overlap probability, The number of times the transportation tool that is adapted to the current supply location appears in the supplier's historical transportation process, is the total number of historical shipments of the supplier, is the distance similarity, It is understandable that the currently available transportation means here refers to the transportation means that meet the restrictive characteristics, such as the height restrictions on vehicles in some areas. If the transportation means meets the height restrictions, it is considered that the transportation means is an adapted transportation means for the area.

[0058] The topological edge set corresponding to the topological node with the highest probability of overlap is used as the transportation prediction path, and the environmental vulnerability weight of the transportation prediction path is obtained according to the future environmental information, and a weighted calculation is performed to obtain the first performance evaluation result. In this embodiment, the possible total probability of all environmental changes in the transportation prediction path is used as the first performance evaluation result of the supplier, and a comprehensive calculation is performed 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 supplier's transportation risk, but also quantifies the supplier's risk-bearing capacity for performance. That is, if the supplier's risk-bearing capacity is strong, then even if the transportation path is greatly affected by environmental changes, the supplier is more capable of compensating to avoid breach of contract, while the supplier's risk-bearing capacity is weak, even if the transportation path is less affected by environmental changes, the supplier is more likely to be breached by occasional problems. At this time, the supplier's performance evaluation result is: ; is the probability of supplier fulfillment, The first performance evaluation result is The supplier risk factor.

[0059] The supplier risk coefficient corresponding to the supplier is called according to the current production demand period, and the supplier risk coefficient of the current production demand period and the first performance evaluation result are calculated to obtain the supplier performance probability.

[0060] In this embodiment, the supplier performance probability of the entire period is used as the supplier performance evaluation result, that is: ; Among them, 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.

[0061] It is understandable that since the actual transportation time of the supplier cannot be determined, the entire production demand period is used for overall evaluation, which is the pre-order period during which the materials must arrive according to the production plan. In some cases, the evaluation period of each supplier can be calculated based on the estimated transportation time, that is, only the performance of the supplier in shipping before the latest period is evaluated, which reduces the evaluation calculation amount and improves the evaluation efficiency.

[0062] Then, suppliers are ranked according to the results of supplier performance evaluation. Based on supplier production capacity constraints and supplier ranking, suppliers who can meet production needs and have good performance evaluation results are selected for material supply to ensure the reliability of material supply.

[0063] As a second embodiment of the present application, step S4 further includes: 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.

[0064] In this embodiment, taking into account that the current supply location and the historical supply location may be inconsistent, resulting in deviations in the supplier's transportation forecast, timing compensation is performed based on the blank ratio of the transportation forecast path, that is, the ratio of the difference section between the current supply location and the historical supply location to the overall section. The first transportation demand time period is calculated based on the last time of arrival of materials needed for production and the supplier's historical transportation demand time. This time period is the period from the current time to (the last time of arrival of materials needed for production - the supplier's historical transportation demand time). At this time, the first transportation demand time period is compensated based on the blank ratio of the transportation forecast path, that is, the last time of the first transportation demand time period is moved forward according to the blank ratio of the transportation forecast path to obtain the transportation demand time period, so as to compensate for the transportation time required for the areas that cannot be covered by the transportation forecast path.

[0065] Among them, the strategies for ensuring the supply of output materials based on supplier production capacity, supplier ranking and transportation demand period include: An optimization objective function is constructed based on the minimum inventory cost to optimize the objective function and output the supplier selection strategy and order timing according to the supplier's production capacity, supplier sorting and transportation demand period.

[0066] While ensuring the stability of supplier performance, the supplier selection strategy and order timing that meet the transportation demand period are obtained through iterative calculation of the minimum inventory cost, ensuring supply stability while reducing inventory costs.

[0067] As a third embodiment of the present application, a material supply guarantee system based on big data material performance monitoring includes: 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.

[0068] In this embodiment, the construction of the supply topology map and the dynamic update of the environmental vulnerability weights are realized through the topology construction unit, which is convenient for users to intuitively obtain the supply chain relationship between various suppliers and facilitate 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 supplier's supply starting point and supply focus as the topological nodes in the topology map, and the actual transportation paths between suppliers are constructed as topological edges, forming a supply topology map that intuitively presents the transportation relationship between suppliers.

[0069] 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 and uses the supply topology map to obtain the corresponding future environmental information, environmental vulnerability weights, and the supplier risk-bearing coefficient output by the risk analysis unit to rank the supplier performance evaluation. Suppliers with a higher probability of performance are ranked higher. Suppliers are selected in order according to their production capacity, and materials are supplied by suppliers with a higher probability of performance to ensure the stability of material supply.

[0070] As a fourth embodiment of the present application, a computer-readable storage medium is provided for storing a computer program or instruction. When the computer program or instruction is executed by a processing device, the above-mentioned material supply guarantee method based on big data material performance monitoring is implemented. The computer-readable storage medium can be any available medium that can be stored by a computing device 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 tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid-state hard disk), etc.

[0071] The specific implementation method described above is a preferred implementation method of the material supply method and system based on big data material fulfillment monitoring of the present application, and is not intended to limit the specific implementation scope of the present application. The scope of the present application includes but is not limited to the specific implementation method. All equivalent changes made in accordance with the shape and structure of the present application are within the protection scope of the present 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.

2. The material supply guarantee method based on big data material performance monitoring as claimed in claim 1 is characterized by: 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 according to the transportation influencing factors and historical environmental data. The environmental vulnerability weights of each topological edge in each time series in the supply topology graph are constructed based on the change frequency and time series correlation.

3. The material supply guarantee method based on big data material performance monitoring as claimed in claim 2 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.

4. The material supply guarantee method based on big data material performance monitoring as claimed in claim 2 is characterized by: 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.

5. The material supply guarantee method based on big data material performance monitoring as claimed in claim 1, characterized in that: 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.

6. The material supply guarantee method based on big data material performance monitoring as claimed in claim 5 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.

7. The material supply guarantee method based on big data material performance monitoring as claimed in claim 1 is characterized by: 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.

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 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.

9. The material supply guarantee method based on big data material performance monitoring as claimed in claim 1, characterized in that: 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.

10. 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.

11. 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 10, 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.

12. 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 10 is implemented.

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