CN112613722A - Supply chain system service distribution method based on block chain by means of reinforcement learning - Google Patents
Supply chain system service distribution method based on block chain by means of reinforcement learning Download PDFInfo
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Abstract
The invention relates to a block chain-based supply chain system service distribution method by means of reinforcement learning, and belongs to the technical field of supply chain system service process management. The method comprises the following steps: step 1, a user submits a service request, a distribution server receives the request, a service server set is obtained based on the category of the service request, and estimated time and estimated cost of the service server are collected; step 2, based on the estimated execution time and cost of the collected service provider, calculating an approximately optimal solution and then distributing services to the supply chain nodes based on the solution, specifically: step A, selecting a service party as a current distribution strategy; step B, calculating the rewards obtained by executing the current distribution strategy in the current state; step C, calculating a value function, and obtaining an approximate optimal solution after iteration; step D, distributing the service to the supply chain nodes through an intelligent contract by the approximately optimal distribution solution; and step 3, data uplink. The method has the advantages of good reliability, adaptability, low time overhead and higher efficiency.
Description
Technical Field
The invention relates to a block chain-based supply chain system service distribution method by means of reinforcement learning, and belongs to the technical field of supply chain system service process management.
Background
The supply chain system has been developed from the logistics management stage, the value-added stage to the current network chain stage, into a system comprising a plurality of entities such as material suppliers, manufacturers, warehousers, carriers, distributors, retailers, and end customers. Supply chain management refers to the activities and processes of planning, coordinating, operating, controlling and optimizing the entire supply chain system, with the goal of being able to bring the correct products, required by the customer, to the correct location at the correct time, in the correct quantities, quality and status, and to minimize the total cost of this process. The supply chain is composed of a plurality of participating main bodies, a large amount of interaction and cooperation must exist among different main bodies, various information generated in the whole supply chain operation process is discretely stored in respective systems of all links, and information flow lacks transparency. This can cause two serious problems: firstly, the situation and the existing problems of related matters are difficult to accurately know by all participating bodies on the chain due to the fact that information is not transparent and smooth, and therefore the efficiency of a supply chain is affected; secondly, when disputes occur among the main bodies of the supply chain, the demonstration and the accountability are time-consuming and labor-consuming, and even become infeasible under some conditions. The block chain technology can make data public and transparent among transaction parties, so that a complete and smooth information flow is formed on the whole supply chain, the participating parties can be ensured to find problems existing in the operation process of the supply chain system in time, a problem solving method is pertinently found, and the overall efficiency of supply chain management is improved. In addition, the characteristics of data non-falsification and timestamp existence certification of the blockchain can be well applied to solving disputes among all participating main bodies in a supply chain system, and easy demonstration and accountability are realized. The fundamental goal of enterprises is to seek to maximize their own profit. This is accomplished by satisfying the needs of downstream enterprises well and relying on the supply of upstream enterprises. In order to consider the competitiveness of product operation from the overall and whole aspects, business allocation in a supply chain system becomes a future important development direction for improving the operation efficiency of social resources.
Therefore, in order to meet the requirement of overall cooperation efficiency in the supply chain management system, an efficient method for service distribution must be designed.
For this case, the service allocation of the supply chain system can be generally realized by the following schemes:
the first scheme is as follows: and a service distribution method based on label similarity distribution. Under the method, the business capability of different business service parties is modeled, the business is formed by combining different elements including time, economic cost and the like, and the business distribution server distributes activities according to information such as activity labels and the like to model the business into tuples of different elements. When receiving the service to be distributed, the similarity measurement is performed, and the service distribution is generally divided into two steps, wherein the first step is to determine all service parties capable of providing the current service, and the step is called to establish mapping. And secondly, calculating the feature similarity after the mapping relation is established. In the process of calculating the similarity measurement, relevant attributes involved in the measurement process are defined, and similarity coefficients of the attributes in different business service parties are calculated by means of analysis of the attributes. And finally selecting the service party with the highest similarity.
Scheme II: a service distribution method of a continuous double auction mechanism based on game theory. Firstly, traders give respective product requirement information and trading quotations for trading, wherein the traders comprise a business requester and a business server; secondly, according to quotation rules in the market, the supply chain system judges whether the quotation of the trader is reasonable, if the quotation is reasonable, the trader is accepted by the market, and if the quotation is not reasonable, the trader is informed to quotate again; thirdly, allocating buyers and sellers in the market according to the transaction rules, and determining transaction products, transaction fees, delivery time and the like; fourthly, displaying quoted prices of buyers and sellers in the market, quoted orders, historical trading results and the like according to the information publishing rule; and finally, judging whether the transaction is terminated according to transaction rules, if not, returning to the first step again to start a new transaction, and if the end conditions are met, closing the bidirectional auction market to finish service distribution. Under the scheme, the service requester and the service server both make autonomous scheduling decisions, and can ensure that the service requester and the service server in the supply chain system have enough motivation to remain in the supply chain system and play a role.
The first solution has the following technical disadvantages:
the adaptability is poor. The accuracy of the modeling directly affects the quality of the traffic distribution. Under the scene that the supply and demand relationship is complex and the related factors are changed, the scheme cannot meet the requirements of a dynamic supply chain.
The second solution has the following technical disadvantages:
the efficiency is low. There are situations where it is necessary to wait for multiple rounds of negotiation between the service requester and the service server, and the successful execution rate of the scheme is low within a limited time.
Disclosure of Invention
The invention aims to provide a block chain-based supply chain system service allocation method by means of reinforcement learning, aiming at the technical defects of poor applicability and low efficiency in the service allocation aspect of the existing supply chain system.
In order to achieve the above purpose, the present invention adopts the following technical scheme.
The service distribution method of the supply chain system relies on the following definitions:
definition 1: the supply chain system is a network chain structure formed by upstream and downstream enterprises which are involved in the production and circulation process and provide products or services for end users;
definition 2: the block chain network refers to an infrastructure network for supply chain management, and nodes of the infrastructure network comprise supply chain nodes and distribution servers;
definition 3: the supply chain node refers to a user in a supply chain system based on a block chain, and comprises a supplier, a manufacturer, a distribution enterprise, a retail enterprise and a consumer. Each supply chain node processes transaction information through a blockchain;
definition 4: the distribution server refers to a node which calculates and executes a service distribution strategy in the block chain network;
definition 5: smart contracts, a computer protocol intended to propagate, verify, or execute contracts in an informational manner; the intelligent contract receives the service request of the user, transfers the service to the distribution server, and links the data information of each service in the supply chain management system;
the service allocation method of the supply chain system comprises the steps of requesting a service, calculating an approximately optimal solution, allocating the service and allocating a strategy data uplink, and specifically comprises the following steps:
step 1, a user submits a service request to a distribution server, the distribution server receives the request, a service server set is obtained based on the category of the service request, and estimated time and estimated cost of the service server are collected, and the method specifically comprises the following substeps:
step 1.1, a user submits a service request to a block chain network through an intelligent contract;
step 1.2, after receiving a service request, an allocation server in the block chain network sends a service time and cost estimation request to a supply chain node capable of providing corresponding service based on the category of the service request;
step 1.3, after receiving the request of the distribution server, the service server side estimates the service execution time and cost and sends the service execution time and cost to the distribution server;
step 2, the distribution server calculates an approximate optimal solution based on the estimated execution time and cost of the collected service server, and distributes services to the supply chain nodes based on the solution, and the method specifically comprises the following substeps:
step 2.1, the distribution server in the block chain network receives the estimated service execution time and cost of the service party, and selects one service party as the current distribution strategy based on the greedy method;
step 2.2, the distribution server calculates the reward obtained by executing the current distribution strategy in the current state, wherein the reward is composed of an estimated service execution time and an estimated service execution cost, and the estimated service execution time and the estimated service execution cost have different proportions according to the service category;
step 2.3, the distribution server calculates a value function, and an approximate optimal solution is obtained after multiple iterations;
wherein the cost function represents the accumulated reward which can be brought by using the current strategy in the current state; the distribution strategy of which the reward function is minimum is satisfied by the approximate optimal solution;
step 2.1 to step 2.3 increase the usability for the constantly changing application scenarios by multiple iterations;
step 2.4, the distribution server distributes services to the supply chain nodes through an intelligent contract according to the obtained approximately optimal distribution solution;
step 3, data uplink, namely the uplink of the service allocation strategy to be determined to be executed, specifically comprises the following steps:
step 3.1, the distribution server sends the determined and executed service distribution strategy to the block chain network through the intelligent contract;
step 3.2, each node of the block chain network identifies the transaction;
wherein, the transaction refers to the executed service distribution strategy;
and 3.3, after the nodes of the block chain network successfully identify, adding the transaction to the block chain.
Advantageous effects
The invention provides a block chain-based supply chain system service allocation method by utilizing reinforcement learning, which has the following beneficial effects compared with the prior art:
1. the method has good reliability, and specifically comprises the following steps: the supply chain system is established based on a non-centralized block chain network, data is stored and processed in a non-centralized manner and is difficult to tamper, and the conditions of single-point failure and attack on a central node can be effectively prevented;
2. the method has good adaptability, compared with the first scheme: the method is based on the reinforcement learning technology of Q learning, can meet more complex business requirements, and is effectively applied to the conditions of dynamic joining of supply chain system participants and capability change of each party;
3. the method is higher in efficiency, compared with the scheme two: the method has the advantages of low time overhead, capability of reducing the response time of the service, shortening the service construction period and higher efficiency.
Drawings
Fig. 1 is a schematic flow chart of a block chain-based service distribution method of a supply chain system using reinforcement learning according to the present invention.
Detailed Description
The following drawings and detailed description are used to explain the embodiments of the block chain and differential privacy-based method for protecting related information according to the present invention.
Example 1
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. The described embodiments are only some embodiments of the invention, not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present invention provides a technical solution: the embodiment describes in detail service allocation in a supply chain scenario enabled by a block chain. For example, in the scenario of an automobile production supply chain, a company A of a certain assembly core enterprise has business requirements for production procurement, warehouse stock, logistics transportation and the like. Aiming at the logistics transportation business request of the company A, the distribution server in the block chain network distributes business according to the business capability of the logistics transportation company, and the business capability is reflected by the time and the economic cost for executing business service.
This embodiment describes that the method of the present invention is used to allocate services in the supply chain system, and includes the following processes:
1. the company A submits a business request to a distribution server, the distribution server receives the request, a business server set is obtained based on the category of the business request, and the estimated time and the estimated cost of the business server are collected. This stage corresponds to step 1 in the summary of the invention, and the specific implementation includes the following substeps:
step 1.1 company A submits the service request to the block chain network through an intelligent contract, which is illustrated by the delivery service in the embodiment;
step 1.2 after receiving the service request, the distribution server in the blockchain network sends a service time and cost estimation request to a supply chain node capable of providing corresponding service based on the category of the service request. (in practice, it may be a delivery company such as east, Feng, Tong, Da, and administration);
step 1.3, after receiving the request of the distribution server, the service server side estimates the service execution time and cost required by the service server side and sends the service execution time and cost to the distribution server;
step 2, the distribution server calculates an approximate optimal distribution strategy based on the collected estimated execution time and cost of the service server side, and then distributes services to the supply chain node based on the approximate optimal distribution strategy;
step 2.2 to step 2.3 are specifically implemented based on reinforcement learning of Q learning, and compared with the first scheme, the method has good adaptability, and specifically comprises the following steps: based on the reinforcement learning technology, the dynamic situation that an enterprise side continuously joins and withdraws in a supply chain system can be dealt with, and the usability is increased in a continuously changing application scene; in addition, when the distribution server distributes the service to the supply chain node, the more complex service requirement can be met, and the method is effectively applied to the conditions of dynamic joining of the block chain network participants and capability change of the participants.
Step 2, specifically:
step 2.1, after receiving the estimated service execution time and cost of the service party, the distribution server in the block chain network selects a certain company to execute the service as the current service distribution strategy based on the greedy method;
compared with the second scheme, the step 2.1 needs less time and expense, can reduce the response time of the service, shortens the construction period of the service and has higher efficiency.
Step 2.2 the allocation server calculates the rewards obtained under the current traffic allocation policy. The reward is composed of estimated service execution time and estimated service execution cost, and the estimated service execution time and the estimated service execution cost have different proportions according to the service category;
specifically, the scheme is based on the reinforcement learning Q learning technology calculation value and is used for measuring the quality of the strategy. The reward function is
Alpha is a discount factor, Q(s), which measures satisfactiont,at)＝Q(st,at)+α(r+γ(Q(st+1,at+1)-Q(st,at) ) is a cost function, γ is a decay factor for the accumulated reward, assuming that the value is 0.2; and measuring the quality of the current service distribution according to the value of the current service distribution. If the existing knowledge indicates that the values of a general company, a Dada company and a Feng company are 0.8, 0.5 and 0.6 respectively, selecting the Feng company with the optimal value to execute the business;
step 2.3, constructing an approximately optimal solution, and after executing a plurality of service distribution strategies, iterating the local solution distributed each time for a plurality of times to obtain the approximately optimal solution, so that the distribution server distributes services according to the approximately optimal distribution strategy; if the value of the optimal company is a certain company after the iteration of the system setting times is carried out, the business is distributed to the certain company;
the estimated execution time and cost in the step 2 and the supply chain node distribution service based on the approximate optimal distribution strategy belong to data non-centralized processing; the processing depends on a block chain energized supply chain system, and the supply chain system is established by a non-centralized block chain network, so that the conditions of single-point failure and attack on a central node can be effectively prevented; this indicates that the method has good reliability.
The characters involved in the method in this embodiment are as in table 1:
TABLE 1 the method of the present embodiment relates to character description
The Q learning based service allocation method of steps 2.1 to 2.3 is as follows:
method 1 service distribution method based on Q learning
Inputting: w [ m ], SPs (N (k)), gamma, alpha
And (3) outputting: pi*(s)
(1) OpS initial Q (s, a) ═ 0, pi(s), s ═ s 0; // distribution Server initialization
(2)for t＝1,2,...50do
(3) OpS SPsures state st; // the distribution server determines its status
(4)OpS chooses an SP Ni to process a task WjRecording to pi(s) and E-greedy algorithm; v/distribution server selects a node to execute the service according to the current state
(5)Ni produces
and
// estimated time and cost of a node after receiving a service
(6)OpS computes reward function
// the distribution Server calculates the Current reward function
(7)OpS computes Q(st,at)＝Q(st,at)+α(r+γQ(st+1,at+1)-Q(st,at) ); // distribution server computing cost function
(8)OpS computes strategyπ(s)＝ar gminQ(s,at+1)and sets s＝st(ii) a V/the distribution Server updates the traffic distribution policy
(9)End for
(10)π*(s) ═ pi(s); // get an approximately optimal traffic distribution strategy
(11)returnπ*(s)
In method 1, input W [ m ]]SPs (n (k)), γ, α refer to the balance factors in the traffic set, the node set, the timely reward attenuation factor, and the reward function. The transport service of company A is denoted as W m]First, the allocation server initializes the allocation policy s0That is, the distribution server selects a certain company to perform the delivery service. Secondly, the distribution server enters a loop iteration stage, and in the process, the distribution server selects a new delivery company to process the business according to a distribution strategy of the current iteration round and an e-greedy method; the distribution server selects a strategy with a smaller value function for updating, namely, a delivery company with lower value is preferentially selected to execute the business; when the circulation is finished, the distribution server calculates to obtain an approximately optimal distribution strategy pi*(s), selecting a certain company with the highest value to execute the logistics transportation business in the subsequent delivery business;
step 2.4, the distribution server distributes the service to the supply chain nodes through an intelligent contract based on the obtained approximately optimal distribution strategy;
step 3, data uplink, namely the uplink of the service allocation strategy to be determined to be executed, specifically comprises the following steps:
step 3.1, the distribution server sends the determined and executed service distribution strategy and service data to the block chain network through an intelligent contract, namely the distribution server sends the logistics transportation service transaction of a certain company A and a certain company to the block chain network;
step 3.2, each node of the block chain network identifies the transaction;
the transaction refers to an executed service distribution strategy, such as that the logistics transportation service of company A is executed by a certain company;
and 3.3, after the supply chain link points of the block chain network are successfully identified, adding the transaction to the block chain.
While the foregoing is directed to the preferred embodiment of the present invention, it is not intended that the invention be limited to the embodiment and the drawings disclosed herein. Equivalents and modifications may be made without departing from the spirit of the disclosure, which is to be considered as within the scope of the invention.
Claims (7)
1. A supply chain system service distribution method based on block chains by utilizing reinforcement learning is characterized in that: by the following definitions:
definition 1: the supply chain system is a network chain structure formed by upstream and downstream enterprises which are involved in the production and circulation process and provide products or services for end users;
definition 2: the block chain network refers to an infrastructure network for supply chain management, and nodes of the infrastructure network comprise supply chain nodes and distribution servers;
definition 3: the supply chain node refers to a user in a supply chain system based on a block chain, and comprises a supplier, a manufacturer, a distribution enterprise, a retail enterprise and a consumer; each supply chain node processes transaction information through a blockchain;
definition 4: the distribution server refers to a node which calculates and executes a service distribution strategy in the block chain network;
definition 5: smart contracts, a computer protocol intended to propagate, verify, or execute contracts in an informational manner; the intelligent contract receives the service request of the user, transfers the service to the distribution server, and links the data information of each service in the supply chain management system;
the service allocation method of the supply chain system comprises the steps of requesting a service, calculating an approximately optimal solution, allocating the service and allocating a strategy data uplink, and specifically comprises the following steps:
step 1, a user submits a service request to a distribution server, the distribution server receives the request, a service server set is obtained based on the category of the service request, and estimated time and estimated cost of the service server are collected;
step 2, the distribution server calculates an approximate optimal solution based on the estimated execution time and cost of the collected service server, and distributes services to the supply chain nodes based on the solution, and the method specifically comprises the following substeps:
step 2.1, the distribution server in the block chain network receives the estimated service execution time and cost of the service party and selects the current distribution strategy;
step 2.2, the distribution server calculates the rewards obtained by executing the current distribution strategy in the current state;
step 2.3, the distribution server calculates a value function, and an approximate optimal solution is obtained after multiple iterations;
step 2.1 to step 2.3 increase the usability for the constantly changing application scenarios by multiple iterations;
step 2.4, the distribution server distributes services to the supply chain nodes through an intelligent contract according to the obtained approximately optimal distribution solution;
and 3, data uplink, namely uplink of the service allocation strategy to be determined to be executed.
2. The method of claim 1, wherein the method comprises: step 1, specifically comprising the following substeps:
step 1.1, a user submits a service request to a block chain network through an intelligent contract;
step 1.2, after receiving a service request, an allocation server in the block chain network sends a service time and cost estimation request to a supply chain node capable of providing corresponding service based on the category of the service request;
step 1.3, after receiving the request of the distribution server, the service server side estimates the service execution time and cost and sends the service execution time and cost to the distribution server.
3. The method of claim 2, wherein the method comprises: in step 2.1, based on the greedy method, a service party is selected as the current allocation strategy.
4. The method of claim 3, wherein the method comprises: in step 2.2, the reward is composed of two parts of estimated service execution time and estimated service execution cost, and the estimated service execution time and the estimated service execution cost have different proportions according to the service category.
5. The method of claim 4, wherein the method comprises: in step 2.3, the cost function represents the accumulated reward which can be brought by using the current strategy in the current state; the approximately optimal solution is the allocation strategy that satisfies the minimum reward function.
6. The method of claim 5, wherein the method comprises: in step 3.2, the transaction refers to the implemented traffic distribution policy.
7. The method of claim 6, wherein the method comprises: step 3, specifically:
step 3.1, the distribution server sends the determined and executed service distribution strategy to the block chain network through the intelligent contract;
step 3.2, each node of the block chain network identifies the transaction;
and 3.3, after the nodes of the block chain network successfully identify, adding the transaction to the block chain.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114612242A (en) * | 2022-03-17 | 2022-06-10 | 四川大学 | Data management method and device based on multi-value chain collaborative mapping block chain |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060167741A1 (en) * | 2005-01-25 | 2006-07-27 | Cisco Technology, Inc. | System and method for designing a supply chain |
| CN109754104A (en) * | 2017-11-03 | 2019-05-14 | 飞榴(上海)信息科技有限公司 | Using the method for artificial intelligence optimization's enterprise supply chain, system, equipment and medium |
| CN110298623A (en) * | 2019-04-23 | 2019-10-01 | 上海能链众合科技有限公司 | A kind of supply chain business data controlling system based on block chain |
| US20200012984A1 (en) * | 2018-07-09 | 2020-01-09 | Société Enkidoo Technologies | System and method for supply chain optimization |
-
2020
- 2020-12-18 CN CN202011499786.8A patent/CN112613722B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060167741A1 (en) * | 2005-01-25 | 2006-07-27 | Cisco Technology, Inc. | System and method for designing a supply chain |
| CN109754104A (en) * | 2017-11-03 | 2019-05-14 | 飞榴(上海)信息科技有限公司 | Using the method for artificial intelligence optimization's enterprise supply chain, system, equipment and medium |
| US20200012984A1 (en) * | 2018-07-09 | 2020-01-09 | Société Enkidoo Technologies | System and method for supply chain optimization |
| CN110298623A (en) * | 2019-04-23 | 2019-10-01 | 上海能链众合科技有限公司 | A kind of supply chain business data controlling system based on block chain |
Non-Patent Citations (1)
| Title |
|---|
| 杜伟佳: "区块链环境下供应链节点企业博弈研究", 《中国优秀硕士学位论文全文数据库》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114612242A (en) * | 2022-03-17 | 2022-06-10 | 四川大学 | Data management method and device based on multi-value chain collaborative mapping block chain |
| CN114612242B (en) * | 2022-03-17 | 2023-06-16 | 四川大学 | Data management method and device based on multi-value chain collaborative mapping block chain |
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