High-security component transaction platform based on cloud big data trend analysis

Abstract

The invention discloses a high-security component transaction platform based on cloud big data trend analysis, and relates to the technical field of electronic component transaction. The supplier evaluation module is used for forming competitive power indexes of suppliers; the supplier screening module is used for selecting and obtaining candidate suppliers from suppliers corresponding to the components; the supply chain risk analysis module is used for obtaining supply chain stability coefficients and supply chain maximum supply quantities of the candidate suppliers; the supplier matching module is used for carrying out supplier matching on the components by using the distribution scheme with the maximum stability coefficient; a risk processing module; and a transaction verification module. By arranging the supplier evaluation module, the supplier screening module, the supply chain risk analysis module and the supplier matching module, the obtained transaction scheme can be more reasonable, and the requirements of purchasers can be met.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic component trading, and in particular to a high-security component trading platform based on cloud big data trend analysis. Background Art

[0002] Electronic components are an indispensable part of modern electronic products, and their trading market is growing day by day. In the process of electronic component trading, buyers and sellers need to accurately record and track transactions in order to make reasonable purchasing and sales decisions. With the expansion and complexity of the electronic component market, the trading activities of electronic components are also showing a growing and changing trend. In the transaction process, the quality of the transaction needs to be guaranteed, otherwise, the purchasing needs of the purchaser may not be met.

[0003] However, platform transactions involve a large number of suppliers, and the quality of transactions involves multiple indicators. Existing platforms lack comprehensive consideration of these aspects, resulting in the possibility that the transactions they make may not be the optimal transaction solutions. Summary of the invention

[0004] In order to solve the above technical problems, a high-security component trading platform based on cloud-based big data trend analysis is provided. This technical solution solves the problem that the platform transactions proposed in the above background technology involve a large number of suppliers, and the quality of transactions involves multiple indicators. The existing platform lacks comprehensive consideration of these aspects, resulting in the fact that the transactions it makes may not be the optimal transaction plan.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is: A high-security component trading platform based on cloud-based big data trend analysis, including: A purchasing data acquisition module, wherein the purchasing data acquisition module acquires the types of components to be purchased, the quantity of components, the arrival time, the parameter indicators of components, and the budget of components; A supplier evaluation module, which tracks the cost trend of components to obtain the reasonable cost of components, analyzes the supplier's transaction unit price, delivery quality and delivery time based on the supplier's historical transaction data, and forms a supplier's competitiveness index; A supplier screening module, wherein the supplier screening module forms a screening critical value for suppliers corresponding to the components, and selects a candidate supplier from at least one supplier corresponding to the components based on the screening critical value; A supply chain risk analysis module, which analyzes the supply chain of a candidate supplier to obtain a supply chain stability coefficient and a maximum supply volume of the supply chain of the candidate supplier; A supplier matching module, wherein the supplier matching module calculates a stability coefficient of at least one allocation scheme of components and uses the allocation scheme with the largest stability coefficient to match the suppliers of components; A risk handling module, which obtains unstable factors of suppliers based on cloud big data, generates response plans for unstable factors, and uses the response plans to handle risks when abnormalities occur in the suppliers of components; A transaction verification module, wherein the transaction verification module uploads a verification video of components, wherein the verification video includes the actual use test process of the components and the process of the supplier packaging the components.

[0006] Preferably, the supplier evaluation module tracks the cost trend of components and obtains the reasonable cost of components, including the following steps: The supplier evaluation module obtains the transaction price of at least one characteristic component within a preset time distance from the current time, wherein the type of the characteristic component is consistent with the type of the component to be purchased, and the parameter index of the characteristic component satisfies the parameter index of the component to be purchased; Sort the transaction unit prices of at least one characteristic component in chronological order, and calculate the fluctuation range of adjacent transaction unit prices; When the fluctuation range exceeds the first preset value, the transaction price corresponding to the fluctuation range is deleted; Generating a transaction price range for at least one transaction price that has not been deleted, the center of the transaction price range is the transaction price, and the interval length of the transaction price range is a second preset value; When the number of transaction prices contained in the transaction price range exceeds the preset number, the transaction price corresponding to the transaction price range will be used as the characteristic transaction price; Take the average of at least one characteristic transaction price to obtain the reasonable cost of components.

[0007] Preferably, the analysis obtains the supplier's transaction unit price, delivery quality and delivery time, and forms the supplier's competitiveness index, including the following steps: Divide the component budget by the number of components to get the component budget unit price; Use AHP to set the weights of transaction price, delivery quality and delivery time; Based on historical data, the proportion of components actually delivered by suppliers that meet component parameter indicators is counted as delivery quality; Use the competitiveness index formula to calculate the supplier's competitiveness index; The competitiveness index formula is as follows: , Among them, A is the competitiveness index, is the weight of the transaction price, is the weight of delivery quality, is the weight of delivery time, B is the budget unit price of components, b is the transaction unit price, C is the delivery quality, D is the arrival time, and d is the delivery time.

[0008] Preferably, the supplier screening module forms a screening critical value for suppliers corresponding to components, including the following steps: Sort the suppliers corresponding to the components from large to small according to their competitiveness index to obtain a supplier sequence , n is the total number of suppliers corresponding to the components; Get Supplier Component inventory , where i is a subscript; Get Feature Supplier , feature suppliers are satisfied with More than 2 times the number of components, and No more than 2 times the number of components; Feature Supplier The competitiveness index is used as the screening threshold.

[0009] Preferably, the selecting a candidate supplier from at least one supplier corresponding to the component based on the screening critical value comprises the following steps: From at least one supplier corresponding to the component, a supplier whose competitiveness index is greater than the screening critical value is selected as a candidate supplier.

[0010] Preferably, the supply chain risk analysis module analyzes the supply chain of the candidate supplier to obtain the supply chain stability coefficient and the maximum supply volume of the supply chain of the candidate supplier, including the following steps: Based on cloud big data, obtain the probability that the supply chain of candidate suppliers will be blocked by natural disasters; Obtain the profit and loss situation of the source factory of the supply chain. When the source factory of the supply chain is profitable, the closure probability of the source factory is 0. When the source factory of the supply chain is loss-making, obtain the loss amount of the source factory of the supply chain, and based on the cloud big data, obtain the maximum value of the loss amount as the loss limit. The loss amount is divided by the loss limit to obtain the closure probability of the source factory. Calculate the first probability of the break in the funding source of the supply chain's funding chain; The second probability of insufficient raw material supply at the source factory is calculated; Use AHP to determine the weights of natural disasters, profit and loss, capital chain and raw material supply; Use the supply chain formula to calculate the supply chain stability coefficient; Count the average supply of raw materials from the source factory, obtain the raw material consumption of each component to be purchased, and divide the average supply of raw materials by the raw material consumption to obtain the maximum supply of the supply chain; The supply chain formula is as follows: , Among them, E is the supply chain stability coefficient, is the weight of natural disasters, is the weight of profit and loss situation, is the weight of the capital chain, is the weight of raw material supply, F is the probability that the supply chain of the candidate supplier is blocked by natural disasters, G is the closure probability of the source factory, H is the first probability, and I is the second probability.

[0011] Preferably, the supplier matching module calculates the stability coefficient of at least one allocation scheme of components including the following steps: The number of components to be purchased is randomly decomposed into at least one to-be-completed number, and the to-be-completed number is allocated to candidate suppliers, and the to-be-completed number does not exceed the maximum supply of the supply chain of the candidate supplier, and each decomposition and allocation method of the number of components to be purchased forms an allocation plan; Use the stability coefficient formula to calculate the stability coefficient of the allocation plan; The stability factor formula is as follows: , Among them, J is the stability coefficient of the allocation scheme, q is the number of candidate suppliers allocated to the quantity to be completed, and m is the subscript. is the supply chain stability coefficient of the candidate supplier, The quantity to be completed that is allocated to the candidate supplier.

[0012] Preferably, the risk processing module obtains unstable factors of suppliers based on cloud big data, including the following steps: In the cloud big data, supplier anomaly statistics are performed, and the probability of supplier anomalies is obtained by statistics; Abnormal situations in suppliers with a probability greater than the preset probability are regarded as unstable factors in the existence of the supplier.

[0013] Preferably, the response plan to unstable factors includes the following steps: Make a judgment on the solution of unstable factors. When the unstable factors can be eliminated, obtain the corresponding solutions to the unstable factors based on the cloud big data as a response plan; When unstable factors cannot be eliminated, the response plan is to replace suppliers with unstable factors with candidate suppliers who are not responsible for procurement tasks.

[0014] Preferably, when an abnormality occurs in the supplier of components, using a response plan to handle the risk includes the following steps: Use unstable factors to identify abnormalities that occur at component suppliers and obtain the characteristic unstable factors that actually occur; Use response plans corresponding to characteristic unstable factors to handle risks.

[0015] Compared with the prior art, the present invention has the following beneficial effects: By setting up supplier evaluation module, supplier screening module, supply chain risk analysis module and supplier matching module, all suppliers related to the components to be purchased are evaluated, and a comprehensive evaluation is conducted from multiple angles such as cost, component quality, delivery time and supply chain stability. As a result, the transaction plan obtained can be more reasonable and at the same time, it can meet the needs of the purchaser. In addition, in the subsequent component delivery process, the quality and delivery time of the components can be guaranteed, thereby ensuring the completion of the entire transaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a schematic diagram of the structure of a high-security component trading platform based on cloud big data trend analysis of the present invention; Figure 2 A schematic diagram of a flow chart of a supplier evaluation module of the present invention tracking the cost trend of components and obtaining the reasonable cost of components; Figure 3 A schematic diagram of a process for obtaining the supplier's transaction unit price, delivery quality and delivery time through analysis of the present invention to form a supplier's competitiveness index; Figure 4 A schematic diagram of a process for forming a screening critical value of suppliers corresponding to components by a supplier screening module of the present invention; Figure 5 A schematic diagram of a flow chart of analyzing the supply chain of a candidate supplier by the supply chain risk analysis module of the present invention to obtain the supply chain stability coefficient and the maximum supply quantity of the supply chain of the candidate supplier; Figure 6 A schematic diagram of a flow chart for calculating a stability coefficient of at least one allocation scheme of components for a supplier matching module of the present invention; Figure 7 A schematic diagram of a process for obtaining unstable factors existing in suppliers based on cloud big data by the risk processing module of the present invention; Figure 8 It is a flow chart of the solution for generating a response to unstable factors of the present invention; Fig. 9 It is a flowchart of using a response plan to handle risks when an abnormality occurs in a component supplier of the present invention. DETAILED DESCRIPTION

[0017] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0018] Reference Figure 1 As shown, a high-security component trading platform based on cloud big data trend analysis includes: A purchasing data acquisition module, wherein the purchasing data acquisition module acquires the types of components to be purchased, the quantity of components, the arrival time, the parameter indicators of components, and the budget of components; A supplier evaluation module, which tracks the cost trend of components to obtain the reasonable cost of components, analyzes the supplier's transaction unit price, delivery quality and delivery time based on the supplier's historical transaction data, and forms a supplier's competitiveness index; A supplier screening module, wherein the supplier screening module forms a screening critical value for suppliers corresponding to the components, and selects a candidate supplier from at least one supplier corresponding to the components based on the screening critical value; A supply chain risk analysis module, which analyzes the supply chain of a candidate supplier to obtain a supply chain stability coefficient and a maximum supply volume of the supply chain of the candidate supplier; A supplier matching module, wherein the supplier matching module calculates a stability coefficient of at least one allocation scheme of components and uses the allocation scheme with the largest stability coefficient to match the suppliers of components; A risk handling module, which obtains unstable factors of suppliers based on cloud big data, generates response plans for unstable factors, and uses the response plans to handle risks when abnormalities occur in the suppliers of components; A transaction verification module, wherein the transaction verification module uploads a verification video of components, wherein the verification video includes the actual use test process of the components and the process of the supplier packaging the components.

[0019] In this scheme, a preliminary evaluation is conducted on suppliers who can provide the components to be purchased from three aspects: transaction unit price, delivery quality and delivery time, to form a supplier competitiveness index. Therefore, candidate suppliers can be selected based on the supplier competitiveness index, and then the stability coefficients of different allocation schemes are calculated among the candidate suppliers based on the supply chain stability coefficient. Thus, the allocation scheme with the largest stability coefficient is selected. This scheme can ensure the quality of the delivered components. At the same time, the delivery time can also be guaranteed, and the probability of delay is small. Therefore, it can meet the needs of the purchaser and ensure the completion of the transaction.

[0020] Reference Figure 2As shown in the figure, the supplier evaluation module tracks the cost trend of components and obtains the reasonable cost of components, including the following steps: The supplier evaluation module obtains the transaction price of at least one characteristic component within a preset time distance from the current time, wherein the type of the characteristic component is consistent with the type of the component to be purchased, and the parameter index of the characteristic component satisfies the parameter index of the component to be purchased; Sort the transaction unit prices of at least one characteristic component in chronological order, and calculate the fluctuation range of adjacent transaction unit prices; When the fluctuation range exceeds the first preset value, the transaction price corresponding to the fluctuation range is deleted; Generating a transaction price range for at least one transaction price that has not been deleted, the center of the transaction price range is the transaction price, and the interval length of the transaction price range is a second preset value; When the number of transaction prices contained in the transaction price range exceeds the preset number, the transaction price corresponding to the transaction price range will be used as the characteristic transaction price; Take the average of at least one characteristic transaction price to obtain the reasonable cost of components.

[0021] There is a certain fluctuation in the prices of components, and some prices are outliers. When calculating reasonable costs, these prices need to be eliminated. Therefore, it is necessary to calculate the fluctuation range and obtain the characteristic transaction unit price. The characteristic transaction unit price is the more important price within a preset time in the past. Therefore, it needs to be reflected in the reasonable cost of components.

[0022] Reference Figure 3 As shown in the figure, the supplier's transaction unit price, delivery quality and delivery time are analyzed to form the supplier's competitiveness index, which includes the following steps: Divide the component budget by the number of components to get the component budget unit price; Use AHP to set the weights of transaction price, delivery quality and delivery time; Based on historical data, the proportion of components actually delivered by suppliers that meet component parameter indicators is counted as delivery quality; Use the competitiveness index formula to calculate the supplier's competitiveness index; The competitiveness index formula is as follows: , Among them, A is the competitiveness index, is the weight of the transaction price, is the weight of delivery quality, is the weight of delivery time, B is the budget unit price of components, b is the transaction unit price, C is the delivery quality, D is the arrival time, and d is the delivery time.

[0023] The transaction unit price, delivery quality and delivery time are different parameters. When the results are integrated, weights need to be set, otherwise they cannot be superimposed. In this solution, a proportional relationship is used to reflect the parameters of the transaction unit price and delivery time. For the delivery quality, it is set according to the situation that the components actually delivered meet the component parameter indicators. Bb is the difference between the budget unit price of the component and the transaction unit price. The larger the result, the lower the cost. Dd is the difference between the arrival time and the delivery time. The arrival time is the demand of the purchaser, and the delivery time is the delivery time of the supplier. The result only needs to be a non-negative number, and its proportion in the competitiveness index is small. Therefore, A can reflect the comprehensive situation of the transaction unit price, delivery quality and delivery time, and then select the supplier who meets the demand based on this.

[0024] Reference Figure 4 As shown, the supplier screening module forms the screening critical value of the supplier corresponding to the component, including the following steps: Sort the suppliers corresponding to the components from large to small according to their competitiveness index to obtain a supplier sequence , n is the total number of suppliers corresponding to the components; Get Supplier Component inventory , where i is a subscript; Get Feature Supplier , feature suppliers are satisfied with More than 2 times the number of components, and No more than 2 times the number of components; Feature Supplier The competitiveness index is used as the screening threshold.

[0025] The purpose of using 2 times the number of components for comparison and screening is that the number of candidate suppliers obtained exceeds the actual demand. This is to ensure that when a candidate supplier who undertakes the procurement task has an abnormality and its abnormality cannot be eliminated, a candidate supplier who does not undertake the procurement task will be used as a supplementary replacement. If 1 times the number of components is used for comparison and screening, when an abnormality occurs and the abnormality cannot be eliminated, it is necessary to reselect the supplier, which will take extra time. In addition, candidate suppliers are also affected by other factors, such as the stability of the supplier's supply chain. More candidate suppliers are needed to select solutions, so that more stable solutions can be selected in subsequent selections, ensuring the completion of the transaction.

[0026] Based on the screening threshold, selecting a candidate supplier from at least one supplier corresponding to the component includes the following steps: From at least one supplier corresponding to the component, a supplier whose competitiveness index is greater than the screening critical value is selected as a candidate supplier.

[0027] Reference Figure 5 As shown, the supply chain risk analysis module analyzes the supply chain of the candidate supplier, and obtaining the supply chain stability coefficient and the maximum supply volume of the candidate supplier includes the following steps: Based on cloud big data, obtain the probability that the supply chain of candidate suppliers will be blocked by natural disasters; Obtain the profit and loss situation of the source factory of the supply chain. When the source factory of the supply chain is profitable, the closure probability of the source factory is 0. When the source factory of the supply chain is loss-making, obtain the loss amount of the source factory of the supply chain, and based on the cloud big data, obtain the maximum value of the loss amount as the loss limit. The loss amount is divided by the loss limit to obtain the closure probability of the source factory. Calculate the first probability of the break in the funding source of the supply chain's funding chain; The second probability of insufficient raw material supply at the source factory is calculated; Use AHP to determine the weights of natural disasters, profit and loss, capital chain and raw material supply; Use the supply chain formula to calculate the supply chain stability coefficient; Count the average supply of raw materials from the source factory, obtain the raw material consumption of each component to be purchased, and divide the average supply of raw materials by the raw material consumption to obtain the maximum supply of the supply chain; The supply chain formula is as follows: , Among them, E is the supply chain stability coefficient, is the weight of natural disasters, is the weight of profit and loss situation, is the weight of the capital chain, is the weight of raw material supply, F is the probability that the supply chain of the candidate supplier is blocked by natural disasters, G is the closure probability of the source factory, H is the first probability, and I is the second probability.

[0028] The supplier's supply chain is affected by many factors, so it is necessary to generate corresponding weights, integrate the impact of multiple factors, and then make a corresponding assessment of the stability of the supply chain.

[0029] Reference Figure 6 As shown, the supplier matching module calculates the stability coefficient of at least one allocation scheme of components, including the following steps: The number of components to be purchased is randomly decomposed into at least one to-be-completed number, and the to-be-completed number is allocated to candidate suppliers, and the to-be-completed number does not exceed the maximum supply of the supply chain of the candidate supplier, and each decomposition and allocation method of the number of components to be purchased forms an allocation plan; Use the stability coefficient formula to calculate the stability coefficient of the allocation plan; The stability factor formula is as follows: , Among them, J is the stability coefficient of the allocation scheme, q is the number of candidate suppliers allocated to the quantity to be completed, and m is the subscript. is the supply chain stability coefficient of the candidate supplier, The quantity to be completed that is allocated to the candidate supplier.

[0030] When allocating, there are many ways to allocate the components to be purchased. For each allocation method, the stability coefficient of the allocation plan is calculated. According to the definition of J, when the number of components provided by candidate suppliers with a small supply chain stability coefficient increases, J will decrease. When the number of components provided by candidate suppliers with a large supply chain stability coefficient increases, J will increase. Therefore, J can reflect the stability of the allocation plan, and the most reliable allocation plan can be selected according to J, and transactions can be conducted according to the plan. In this way, the risks of the transaction can be dispersed, thereby ensuring that the probability of abnormal transactions is low and the security of the transaction is guaranteed.

[0031] Reference Figure 7 As shown, the risk processing module is based on cloud big data, and obtaining the unstable factors of suppliers includes the following steps: In the cloud big data, supplier anomaly statistics are performed, and the probability of supplier anomalies is obtained by statistics; Abnormal situations in suppliers with a probability greater than the preset probability are regarded as unstable factors in the existence of the supplier.

[0032] Reference Figure 8 As shown, the response plan to unstable factors includes the following steps: Make a judgment on the solution of unstable factors. When the unstable factors can be eliminated, obtain the corresponding solutions to the unstable factors based on the cloud big data as a response plan; When unstable factors cannot be eliminated, the response plan is to replace suppliers with unstable factors with candidate suppliers who are not responsible for procurement tasks.

[0033] Reference Fig. 9 As shown in the figure, when an abnormality occurs in the supplier of components, the risk management using the response plan includes the following steps: Use unstable factors to identify abnormalities that occur at component suppliers and obtain the characteristic unstable factors that actually occur; Use response plans corresponding to characteristic unstable factors to handle risks.

[0034] During actual supply, various anomalies may occur, so they need to be handled. To facilitate handling, anomalies with a higher probability of occurrence are counted as unstable factors, and response plans for unstable factors are formed in advance. Then, when anomalies occur during actual supply, corresponding handling can be carried out according to the situation.

[0035] It is understandable that the storage medium may be a magnetic medium, such as a floppy disk, a hard disk, or a magnetic tape; an optical medium, such as a DVD; or a semiconductor medium, such as a solid state drive (SSD).

[0036] In summary, the advantages of the present invention are: by setting up a supplier evaluation module, a supplier screening module, a supply chain risk analysis module and a supplier matching module, all suppliers related to the components to be purchased are evaluated, and a comprehensive evaluation is performed from multiple angles such as cost, component quality, delivery time and supply chain stability. As a result, the resulting transaction plan can be more reasonable and at the same time, can meet the needs of the purchaser. In addition, in the subsequent component delivery process, the quality and delivery time of the components can be guaranteed, thereby ensuring the completion of the entire transaction.

[0037] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A high-security component trading platform based on cloud big data trend analysis, characterized in that: include: A purchasing data acquisition module, wherein the purchasing data acquisition module acquires the types of components to be purchased, the quantity of components, the arrival time, the parameter indicators of components, and the budget of components; A supplier evaluation module, which tracks the cost trend of components to obtain the reasonable cost of components, analyzes the supplier's transaction unit price, delivery quality and delivery time based on the supplier's historical transaction data, and forms a supplier's competitiveness index; A supplier screening module, wherein the supplier screening module forms a screening critical value for suppliers corresponding to the components, and selects a candidate supplier from at least one supplier corresponding to the components based on the screening critical value; A supply chain risk analysis module, which analyzes the supply chain of a candidate supplier to obtain a supply chain stability coefficient and a maximum supply volume of the supply chain of the candidate supplier; A supplier matching module, wherein the supplier matching module calculates a stability coefficient of at least one allocation scheme of components and uses the allocation scheme with the largest stability coefficient to match the suppliers of components; A risk handling module, which obtains unstable factors of suppliers based on cloud big data, generates response plans for unstable factors, and uses the response plans to handle risks when abnormalities occur in the suppliers of components; A transaction verification module, wherein the transaction verification module uploads a verification video of components, wherein the verification video includes the actual use test process of the components and the process of the supplier packaging the components.

2. A high-security component trading platform based on cloud big data trend analysis according to claim 1, characterized in that: The supplier evaluation module tracks the cost trend of components and obtains the reasonable cost of components, including the following steps: The supplier evaluation module obtains the transaction price of at least one characteristic component within a preset time distance from the current time, wherein the type of the characteristic component is consistent with the type of the component to be purchased, and the parameter index of the characteristic component satisfies the parameter index of the component to be purchased; Sort the transaction unit prices of at least one characteristic component in chronological order, and calculate the fluctuation range of adjacent transaction unit prices; When the fluctuation range exceeds the first preset value, the transaction price corresponding to the fluctuation range is deleted; Generating a transaction price range for at least one transaction price that has not been deleted, the center of the transaction price range is the transaction price, and the interval length of the transaction price range is a second preset value; When the number of transaction prices contained in the transaction price range exceeds the preset number, the transaction price corresponding to the transaction price range will be used as the characteristic transaction price; Take the average of at least one characteristic transaction price to obtain the reasonable cost of components.

3. A high-security component trading platform based on cloud big data trend analysis according to claim 2, characterized in that: The analysis obtains the supplier's transaction unit price, delivery quality and delivery time, and forms the supplier's competitiveness index, including the following steps: Divide the component budget by the number of components to get the component budget unit price; Use AHP to set the weights of transaction price, delivery quality and delivery time; Based on historical data, the proportion of components actually delivered by suppliers that meet component parameter indicators is counted as delivery quality; Use the competitiveness index formula to calculate the supplier's competitiveness index; The competitiveness index formula is as follows: , Among them, A is the competitiveness index, is the weight of the transaction price, is the weight of delivery quality, is the weight of delivery time, B is the budget unit price of components, b is the transaction unit price, C is the delivery quality, D is the arrival time, and d is the delivery time.

4. A high-security component trading platform based on cloud big data trend analysis according to claim 3, characterized in that: The supplier screening module forms a screening critical value of a supplier corresponding to a component, including the following steps: Sort the suppliers corresponding to the components from large to small according to their competitiveness index to obtain a supplier sequence , n is the total number of suppliers corresponding to the components; Get Supplier Component inventory , where i is a subscript; Get Feature Supplier , feature suppliers are satisfied with More than 2 times the number of components, and No more than 2 times the number of components; Feature Supplier The competitiveness index is used as the screening threshold.

5. A high-security component trading platform based on cloud big data trend analysis according to claim 4, characterized in that: The step of selecting a candidate supplier from at least one supplier corresponding to the component based on the screening critical value comprises the following steps: From at least one supplier corresponding to the component, a supplier whose competitiveness index is greater than the screening critical value is selected as a candidate supplier.

6. A high-security component trading platform based on cloud big data trend analysis according to claim 5, characterized in that: The supply chain risk analysis module analyzes the supply chain of the candidate supplier to obtain the supply chain stability coefficient and the maximum supply volume of the supply chain of the candidate supplier, including the following steps: Based on cloud big data, obtain the probability that the supply chain of candidate suppliers will be blocked by natural disasters; Obtain the profit and loss situation of the source factory of the supply chain. When the source factory of the supply chain is profitable, the closure probability of the source factory is 0. When the source factory of the supply chain is loss-making, obtain the loss amount of the source factory of the supply chain, and based on the cloud big data, obtain the maximum value of the loss amount as the loss limit. The loss amount is divided by the loss limit to obtain the closure probability of the source factory. Calculate the first probability of the break in the funding source of the supply chain's funding chain; The second probability of insufficient raw material supply at the source factory is calculated; Use AHP to determine the weights of natural disasters, profit and loss, capital chain and raw material supply; Use the supply chain formula to calculate the supply chain stability coefficient; Count the average supply of raw materials from the source factory, obtain the raw material consumption of each component to be purchased, and divide the average supply of raw materials by the raw material consumption to obtain the maximum supply of the supply chain; The supply chain formula is as follows: , Among them, E is the supply chain stability coefficient, is the weight of natural disasters, is the weight of profit and loss situation, is the weight of the capital chain, is the weight of raw material supply, F is the probability that the supply chain of the candidate supplier is blocked by natural disasters, G is the closure probability of the source factory, H is the first probability, and I is the second probability.

7. A high-security component trading platform based on cloud big data trend analysis according to claim 6, characterized in that: The supplier matching module calculates the stability coefficient of at least one allocation scheme of components, including the following steps: The number of components to be purchased is randomly decomposed into at least one to-be-completed number, and the to-be-completed number is allocated to candidate suppliers, and the to-be-completed number does not exceed the maximum supply of the supply chain of the candidate supplier, and each decomposition and allocation method of the number of components to be purchased forms an allocation plan; Use the stability coefficient formula to calculate the stability coefficient of the allocation plan; The stability factor formula is as follows: , Among them, J is the stability coefficient of the allocation scheme, q is the number of candidate suppliers allocated to the quantity to be completed, and m is the subscript. is the supply chain stability coefficient of the candidate supplier, The quantity to be completed that is allocated to the candidate supplier.

8. A high-security component trading platform based on cloud big data trend analysis according to claim 7, characterized in that: The risk processing module is based on cloud big data, and obtaining the unstable factors of suppliers includes the following steps: In the cloud big data, supplier anomaly statistics are performed, and the probability of supplier anomalies is obtained by statistics; Abnormal situations in suppliers with a probability greater than the preset probability are regarded as unstable factors in the existence of the supplier.

9. A high-security component trading platform based on cloud big data trend analysis according to claim 8, characterized in that: The solution to the unstable factors includes the following steps: Make a judgment on the solution of unstable factors. When the unstable factors can be eliminated, obtain the corresponding solutions to the unstable factors based on the cloud big data as a response plan; When unstable factors cannot be eliminated, the response plan is to replace suppliers with unstable factors with candidate suppliers who are not responsible for procurement tasks.

10. A high-security component trading platform based on cloud big data trend analysis according to claim 9, characterized in that: When an abnormality occurs in the supplier of components, the risk management using the response plan includes the following steps: Use unstable factors to identify abnormalities that occur at component suppliers and obtain the characteristic unstable factors that actually occur; Use response plans corresponding to characteristic unstable factors to handle risks.

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