Power transaction item matching method based on block chain technology

Abstract

The invention discloses a power transaction item matching method based on a block chain technology, relates to the technical field of transaction item matching, and solves the problem that relatively detailed association matching analysis is not carried out based on the number of specific demand items and the specific demand quantity of a corresponding demand side. According to the invention, related confirmation is carried out on the demand side and the supply side which are matched with the transaction item in the batch, and data analysis of the transaction item is carried out based on the total demand amount of the corresponding demand side for some transaction items and the total supply amount of the corresponding supply side for some transaction items; the method comprises the following steps: identifying a satisfaction category which can be normally matched and an unsatisfaction category which cannot be normally distributed according to the transaction items, determining a plurality of groups of matching modes by adopting a mode of sequentially carrying out association matching aiming at the related transaction items belonging to the unsatisfaction category, and then selecting an optimal matching mode from the plurality of groups of matching modes. Therefore, it is guaranteed that the quantity of the demand side which can be processed by the batch reaches the maximum, and the maximum matching optimization effect is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of transaction item matching, and in particular to a method for matching electricity transaction items based on blockchain technology. Background Art

[0002] Blockchain is a decentralized distributed ledger that is tamper-proof, secure and reliable. It combines distributed storage, peer-to-peer transmission, consensus mechanism, cryptography and other technologies to record transactions and information through a growing data block chain to ensure data security and transparency.

[0003] The application with publication number CN113034227A discloses a method and device for matching electric power users in transactions, which method includes: sending electric power demand information to each sending terminal, and taking the power supplier corresponding to each sending terminal that returns response information according to the electric power demand information as the first power supplier; according to the performance parameters of each first power supplier, determining a target number of the first power suppliers as candidate power suppliers for transactions; wherein the performance parameters include credit value, distance, electric energy attributes and comprehensive attributes. The method and device for matching electric power users in transactions provided by the present invention match the performance parameters of each first power supplier according to the supply and demand relationship, determine a target number of candidate power suppliers for transactions for the consumer, and can match a more suitable power supplier according to the power demand of the consumer, and the matching process is simple and efficient.

[0004] Regarding the matching process of related items in electricity transactions on the blockchain, the correlation matching is generally carried out based on the demand parameters and supply parameters related to the demander and the supplier. However, the original matching method is relatively simple and does not take into account whether the related demanders with multiple sets of demand parameters can be fully satisfied when performing the correlation matching. When there are multiple demanders, a more detailed correlation matching analysis is not carried out based on the specific number of demand items and the specific demand quantity of the corresponding demanders, resulting in the correlation matching effect that cannot reach the optimal state. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides an electricity transaction item matching method based on blockchain technology, which solves the problem of not conducting a more detailed correlation matching analysis based on the specific number of demand items and the specific demand quantity of the corresponding demand party.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a power transaction item matching method based on blockchain technology, comprising the following steps: S1) Confirm the categories of demanders and suppliers of electricity transactions in this batch, confirm the demand categories of demanders and the demand quantities of corresponding demand categories, and then confirm the supply categories of suppliers and the supply quantities of supply categories; S2) Based on the confirmed relevant categories and relevant quantities of the demand parties and suppliers, confirm whether the demand categories associated with different demand parties are of the same category, and based on the total demand quantity determined by the same category and the total supply quantity of the same supply category, mark the corresponding demand category as a satisfied category or an unsatisfied category. The specific sub-steps are as follows: S21. Based on different demand categories determined by different demand parties, identify whether there are similar categories among several different demand categories, sum up several demand quantities of the similar categories, and determine the total demand quantity of the corresponding similar categories. If there are no similar categories, mark the demand quantity of the corresponding demand category as the total demand quantity of such categories. S22, confirming the supply category corresponding to the same category from the different supply categories determined by the supplier, and then determining the total supply quantity of the same category based on the same supply categories provided by several suppliers; S23, identifying the total demand and total supply belonging to the same category, if the total demand ≤ the total supply, marking the demand category associated with the corresponding category as a satisfied category; if the total demand > the total supply, marking the demand category associated with the corresponding category as an unsatisfied category; S3) Based on the determined satisfaction categories, initially match the demand categories belonging to the satisfaction categories with the supply categories related to the suppliers, give priority to matching the demand categories that meet the single satisfaction, and then redistribute the demand categories that do not meet the single satisfaction, including: S31. The demand quantity of the demand category belonging to the satisfied category is calibrated as XQ i , where i represents different demand categories, and then determines the supply quantity GY of the corresponding supplier for the same supply category k , where k represents different supply categories; S32, based on the determined demand XQ i , will satisfy GY k >XQ i The supply category is marked as the category to be matched, and the category to be matched here is consistent with the demand quantity XQ i For the same category, select its GY from several groups of matching categories k With XQ i The category to be matched with the smallest difference is taken as the matching category, and the residual difference CZ in this matching process is determined, and the residual difference CZ=GY k -XQ i , after confirming such demand categories one by one and completing the matching process, the remaining quantity difference CZ is confirmed synchronously; S33. Based on unmatched demand categories and unmatched supply categories: Prioritize the unmatched supply categories belonging to the same category as the matching items of the corresponding demand category, and then select the remaining surplus differences from the remaining supply categories of other categories in the same category, using the maximum to minimum selection method to select the surplus differences. When the total value of all selected surplus differences meets this related demand category, stop selecting and complete the matching process of this demand category. S4), based on the determined unsatisfied categories, determine the demand side to which the unsatisfied categories belong, and then determine whether the corresponding demand side has a single unsatisfied category or multiple unsatisfied categories, and preferentially match the relevant demand side with a single unsatisfied category, and then re-match the relevant demand side with multiple unsatisfied categories. The specific sub-steps are: S41. Prioritize the identification of demand parties with single unsatisfied categories, identify the unsatisfied categories of such demand parties, prioritize numerical matching from supply categories of the same category, and complete the matching process of such demand parties; S42. For relevant demand parties with multiple unsatisfied categories, identify supply categories of the same category based on the unsatisfied categories, and then match the multiple unsatisfied categories in sequence based on the total supply of the supply categories, determine multiple different matching methods in this matching process, and then use the same method to process other unsatisfied categories, and confirm several different matching methods; From several different matching methods: first determine the total number of relevant demand parties with multiple unsatisfied categories G1, then identify the corresponding matching method to complete the relevant matching, determine the total number of relevant demand parties with unsatisfied categories G2, determine the change difference Gz, and Gz=G1-G2, then determine the total remaining supply volume Zh of different supply categories after the corresponding matching method completes the relevant matching, and use PD=Gz×C1+Zh×C2 to confirm the evaluation value PD determined by this matching method, where C1 and C2 are preset fixed coefficient factors; S43, based on different evaluation values ​​PD determined by a number of different matching methods, a minimum value is selected, and the matching method corresponding to the minimum value is used as the final matching method and executed.

[0007] Preferably, in step S2, the relevant categories include demand categories or supply categories, and the relevant data include demand quantities or supply quantities.

[0008] Preferably, in step S33, when preferentially selecting from unmatched supply categories belonging to the same category, if there are no unmatched supply categories, matching selection is directly performed from the remaining residual differences of other supply categories of the same category.

[0009] Preferably, the method further comprises the following steps: S5. Display the demand categories that have not completed relevant matching in this matching process, and simultaneously send a waiting signal to the demand party associated with the corresponding demand category.

[0010] The present invention provides a method for matching power trading items based on blockchain technology. Compared with the prior art, it has the following beneficial effects: The present invention performs relevant confirmation on the demand side and the supply side of the batch of transaction items that have matching, and performs data analysis on the transaction items based on the total demand amount of certain transaction items corresponding to the demand policy and the total supply amount of certain transaction items corresponding to the supply policy, so as to identify the satisfied categories that can be matched normally and the unsatisfied categories that cannot be allocated normally; For the relevant transaction items that meet the category, the normal matching method is used for related matching, and the related party with the closest demand and supply is selected as the matching party, and then the remaining demand parties are matched. This matching method can fully reduce the number of batch supply of the corresponding supplier, so that the overall matching method is optimal; For related transaction items belonging to unmet categories, a method of sequential association matching is used to determine multiple groups of matching methods, and then the best matching method is selected from the multiple groups of matching methods, so as to ensure that the number of demand parties that can be processed in this batch is maximized, thereby ensuring the maximum matching optimization effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0012] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0013] Example 1 See also Figure 1 , this application provides a power transaction item matching method based on blockchain technology, comprising the following steps: S1) Confirm the categories of the demanders and suppliers of electricity transactions in this batch, confirm the demand categories of the demanders and the demand quantities of the corresponding demand categories, and then confirm the supply categories of the suppliers and the supply quantities of the supply categories. Specifically, in the corresponding blockchain trading platform, the demanders will have corresponding demand electricity trading items, and the specific trading item categories will be input by the corresponding demanders. The input parameters also include the corresponding demand data. Similarly, the suppliers also have corresponding trading item supplies, corresponding specific supply categories and corresponding supply quantities. S2) Based on the confirmed relevant categories (demand categories or supply categories) and relevant quantities (demand quantity or supply quantity) of the demand parties and suppliers, confirm whether the demand categories associated with different demand parties are of the same category, and based on the total demand quantity determined by the same category and the total supply quantity of the same supply category, mark the corresponding demand category as a satisfied category or an unsatisfied category, wherein the specific sub-steps for the relevant marking are: S21. Based on different demand categories determined by different demand parties, identify whether several different demand categories have the same category (that is, belong to the same category item), sum up several demand quantities of the same category, and determine the total demand quantity of the corresponding category. If there is no similar category: mark the demand quantity of the corresponding demand category as the total demand quantity of the category; S22, confirming the supply category corresponding to the same category from the different supply categories determined by the supplier, and then determining the total supply quantity of the same category based on the same supply categories provided by several suppliers; S23, identify the total demand and total supply of the same category. If the total demand is less than or equal to the total supply, mark the demand category associated with the corresponding category as a satisfied category. If the total demand is greater than the total supply, mark the demand category associated with the corresponding category as an unsatisfied category. For example, there are two demanders A and B, where the three categories of demand of demander A are A1, A2 and A3, and the three categories of demand of demander B are A1, B1 and B2. If the two demanders are in the same category, then their total demand can be determined. Then, from the category data provided by the supplier, identify the total supply of the category that the corresponding supplier can provide in this batch. Then, based on the total demand and the total supply, determine whether the demand category is a satisfied category or an unsatisfied category. S3) Based on the determined satisfaction category, the demand category belonging to the satisfaction category is initially matched with the supply category related to the supplier, and the demand category that meets the single satisfaction is preferentially matched, and then the demand category that does not meet the single satisfaction is redistributed, so that the satisfaction category achieves the best matching effect in the initial allocation. The single satisfaction means that this demand category only needs to match one group of supply categories to determine that the matching quantity meets the standard, and the non-single satisfaction means that this demand category needs to match multiple groups of supply categories to determine that the matching quantity meets the standard. The specific sub-steps of the initial matching include: S31. The demand quantity of the demand category belonging to the satisfied category is calibrated as XQ i , where i represents different demand categories, and then determines the supply quantity GY of the corresponding supplier for the same supply category k , where k represents different supply categories. Specifically, the determined supply category is the same category as the demand category; S32, based on the determined demand XQ i , will satisfy GY k >XQ i The supply category is marked as the category to be matched, and the category to be matched here is consistent with the demand quantity XQ i For the same category, select its GY from several groups of matching categories k With XQ i The category to be matched with the smallest difference is taken as the matching category, and the residual difference CZ in this matching process is determined, and the residual difference CZ=GY k -XQ i , after confirming such demand categories one by one and completing the matching process, the remaining quantity difference CZ is confirmed synchronously; S33, based on the unmatched demand categories and unmatched supply categories, the unmatched supply categories belonging to the same category are preferentially selected as the matching items of the corresponding demand categories, and then the residual differences of the remaining supply categories of other categories are selected in a selection method from the maximum value to the minimum value of the residual differences. When the total value of all the selected residual differences meets the relevant demand category, the selection is stopped and the matching process of this demand category is completed. If there is no unmatched supply category, the matching selection is directly performed from the residual differences of the supply categories of other categories. Specifically, in the category matching process of steps S31-S32, The relevant demand categories that have a single group of matches have been matched. The matching is based on the demand of this demand category and the selection of categories from the supply category that is closest to and greater than this demand. The remaining demand categories are the matching categories that cannot be met by a single group of supply categories. For such demand categories, category selection is prioritized from other supply categories. The same category must be selected during the selection process. For example, if the demander requires transaction item A, only the supplier with supply transaction item B can be selected. Priority is given to the relevant selection of the same category that has not been matched, and then the relevant selection is made from the residual difference to complete the relevant matching selection process of the entire demand category.

[0014] Example 2 In the specific implementation process of this embodiment, compared with the above embodiment, this embodiment mainly focuses on the relevant selected matching of unsatisfied categories. In order to achieve a better matching process, the following steps are also included: S4), based on the determined unsatisfied categories, determine the demand side to which the unsatisfied categories belong, and then determine whether the corresponding demand side has a single unsatisfied category or multiple unsatisfied categories, and prioritize matching the relevant demand side with a single unsatisfied category, and then re-match the relevant demand side with multiple unsatisfied categories. Specifically, why can re-matching be performed here? Because the determination of the unsatisfied categories is based on the total amount. For example: there are three demand sides who all demand the same transaction item in quantities of 50, 80, and 100, but the total amount that several suppliers can supply for this type of transaction item is 200. Then, when performing relevant processing and supply, this transaction item will be marked as an unsatisfied category in advance. When matching again, 50 and 80 can both meet the relevant supply matching process, and 50, 100, 80 and 100 can all meet the relevant supply matching process. How to achieve the optimal matching process is the specific way processed and executed in this step; Among them, the specific sub-steps for detailed matching of unsatisfied categories are: S41. Prioritize the confirmation of the demand parties with single unsatisfied categories, identify the unsatisfied categories of such demand parties, prioritize the numerical matching from the supply categories of the same category, and complete the matching process of such demand parties (the unsatisfied categories have relevant demand quantities. Once the demand quantities are determined, relevant selections can be made from the total supply quantities in the corresponding supply categories, and a single supply category or multiple supply categories that meet such demand quantities are used as matching items for this unsatisfied category); S42. For relevant demand parties with multiple unsatisfied categories, identify supply categories of the same category based on the unsatisfied categories, and then match the multiple unsatisfied categories in sequence based on the total supply of the supply categories, determine multiple different matching methods in this matching process, and then use the same method to process other unsatisfied categories, and confirm several different matching methods; From several different matching methods: first determine the total number G1 of related demand parties with multiple unsatisfied categories, then identify the corresponding matching method to complete the relevant matching, determine the total number G2 of related demand parties with unsatisfied categories, determine the change difference Gz, and Gz=G1-G2, then determine the total remaining supply Zh of different supply categories after the corresponding matching method completes the relevant matching, and use PD=Gz×C1+Zh×C2 to confirm the evaluation value PD determined by this matching method, where C1 and C2 are preset fixed coefficient factors, and the specific values ​​are prepared in advance by the operator based on experience. For example: suppose there are three groups of related demand parties with unsatisfied categories, and the demand parties are A, B and C. A has two groups of unsatisfied categories A1 (200) and A2 (150), and B has three groups of unsatisfied categories A1 (100), A2 (50), and B2 (350). The values ​​in brackets belong to the corresponding demand parties. Quantity, C has three groups of unsatisfied categories A1 (200), B2 (150) and D2 (200), and its suppliers have supply quantities of related categories, namely: A1 {400}, A2 {170}, B2 {450}. Among them, C's unsatisfied category D2 (300) can be satisfied first and can be matched first, but in the subsequent processing, the supply quantity A1 {400} is allocated to demand parties A and C, and then demand party B cannot be satisfied. Among them, A2 is supplied to A, then demand parties B and C cannot be satisfied. When A2 is supplied to B, demand parties A and C cannot be satisfied. After multiple different related matching, if it can be guaranteed that demand parties A and C are satisfied, there is no unsatisfied item, and there is only demand party B, then this matching method is the best matching method, and there is no situation where the demand party waits. Therefore, the above-mentioned related processing is to handle this process to ensure that the corresponding transaction item matching can achieve the best; S43, based on different evaluation values ​​PD determined by a number of different matching methods, a minimum value is selected, and the matching method corresponding to the minimum value is used as the final matching method and executed.

[0015] S5. Display the demand categories that have not completed the relevant matching in this matching process, and simultaneously send a waiting signal to the demand party associated with the corresponding demand category. In the category matching process of the next batch, this type of demand category will be matched first.

[0016] Example 3 The specific implementation process of this embodiment includes the entire implementation process of the above two groups of embodiments.

[0017] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0018] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A power transaction item matching method based on blockchain technology, characterized in that: The following steps are involved: S1) Confirm the categories of demanders and suppliers of electricity transactions in this batch, confirm the demand categories of demanders and the demand quantities corresponding to the demand categories, and then confirm the supply categories of suppliers and the supply quantities of the supply categories; S2) Based on the confirmed relevant categories and relevant quantities of the demanders and suppliers, confirm whether the demand categories associated with different demanders are of the same category, and based on the total demand quantity determined by the same category and the total supply quantity of the same supply category, mark the corresponding demand category as a satisfied category or an unsatisfied category; S3) Based on the determined satisfaction categories, initially matching the demand categories belonging to the satisfaction categories with the supply categories related to the suppliers, giving priority to matching the demand categories that meet the single satisfaction, and then reallocating the demand categories that do not meet the single satisfaction; S4), perform detailed matching on the determined unsatisfied categories, determine the demand party to which the unsatisfied categories belong, and then determine whether the corresponding demand party has a single unsatisfied category or multiple unsatisfied categories, and give priority to matching the relevant demand parties with a single unsatisfied category, and then re-match the relevant demand parties with multiple unsatisfied categories.

2. The power transaction item matching method based on blockchain technology according to claim 1 is characterized in that: In step S2, the specific sub-steps of marking the corresponding demand category as a satisfied category or an unsatisfied category are: S21. Based on different demand categories determined by different demand parties, identify whether there are similar categories among several different demand categories, sum up several demand quantities of the similar categories, and determine the total demand quantity of the corresponding similar categories. If there are no similar categories, mark the demand quantity of the corresponding demand category as the total demand quantity of such categories. S22. From the different supply categories determined by the suppliers, identify the supply categories corresponding to the same category, and then determine the total supply quantity of the same category based on the same supply categories provided by several suppliers; S23. Identify the total demand and total supply belonging to the same category. If the total demand ≤ the total supply, mark the demand category associated with the corresponding same category as the satisfied category.

3. The power transaction item matching method based on blockchain technology according to claim 2 is characterized in that: In the step S23, if the total demand quantity is greater than the total supply quantity, the demand category associated with the corresponding same category is marked as an unsatisfied category.

4. The power transaction item matching method based on blockchain technology according to claim 2 is characterized in that: In step S3, the specific sub-steps of initially matching the demand categories belonging to the satisfied categories with the supply categories related to the suppliers include: S31. The demand quantity of the demand category belonging to the satisfied category is calibrated as XQ i , where i represents different demand categories, and then determines the supply quantity GY of the corresponding supplier for the same supply category k , where k represents different supply categories; S32, based on the determined demand XQ i , will satisfy GY k >XQ i The supply category is marked as the category to be matched, and the category to be matched here is consistent with the demand quantity XQ i For the same category, select its GY from several groups of matching categories k With XQ i The category to be matched with the smallest difference is taken as the matching category, and the residual difference CZ in this matching process is determined, and the residual difference CZ=GY k -XQ i , after confirming such demand categories one by one, the remaining quantity difference CZ is confirmed synchronously based on the matching process; S33. Based on unmatched demand categories and unmatched supply categories: Prioritize unmatched supply categories belonging to the same category as matching items for the corresponding demand categories, and then select the remaining margin differences of other supply categories in the same category from the maximum to the minimum margin difference. When the total value of all selected margin differences meets the relevant demand category, stop selecting and complete the matching process of this demand category.

5. The power transaction item matching method based on blockchain technology according to claim 4 is characterized in that: In the step S33, when preferentially selecting from unmatched supply categories belonging to the same category, if there are no unmatched supply categories, matching selection is directly performed from the remaining residual differences of other supply categories of the same category.

6. The power transaction item matching method based on blockchain technology according to claim 1 is characterized in that: In step S4, the specific sub-steps for detailed matching of unsatisfied categories are: S41. Prioritize the identification of demand parties with single unsatisfied categories, identify the unsatisfied categories of such demand parties, prioritize numerical matching from supply categories of the same category, and complete the matching process of such demand parties; S42. For relevant demand parties with multiple unsatisfied categories, identify supply categories of the same category based on the unsatisfied categories, and then match the multiple unsatisfied categories in sequence based on the total supply of the supply categories, determine multiple different matching methods in this matching process, and then use the same method to process other unsatisfied categories, and confirm several different matching methods; From several different matching methods: first determine the total number of relevant demand parties with multiple unsatisfied categories G1, then identify the corresponding matching method to complete the relevant matching, determine the total number of relevant demand parties with unsatisfied categories G2, determine the change difference Gz, and Gz=G1-G2, then determine the total remaining supply volume Zh of different supply categories after the corresponding matching method completes the relevant matching, and use PD=Gz×C1+Zh×C2 to confirm the evaluation value PD determined by this matching method, where C1 and C2 are preset fixed coefficient factors; S43, based on different evaluation values ​​PD determined by a number of different matching methods, a minimum value is selected, and the matching method corresponding to the minimum value is used as the final matching method and executed.

7. The power transaction item matching method based on blockchain technology according to claim 1 is characterized in that: The following steps are also included: S5. Display the demand categories that have not completed relevant matching in this matching process, and simultaneously send a waiting signal to the demand party associated with the corresponding demand category.

8. The power transaction item matching method based on blockchain technology according to claim 1 is characterized in that: In step S2, the relevant categories include demand categories or supply categories, and the relevant quantities include demand quantities or supply quantities.

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