Blockchain-based Commodity Logistics Tracking and Monitoring Method and System

Abstract

The present invention discloses a method and system for tracking and monitoring bulk commodity logistics based on blockchain, which relates to the field of commodity logistics monitoring. By obtaining the logistics node information recorded on the blockchain, the circulation period T of the current logistics node i is extracted. i Including commodity feature information, logistics feature information, and influencing factor information of objective feature information, and determining the influence of each of the influencing factors on the circulation period T i The influence coefficient of the current logistics node i is constructed based on the obtained influence coefficient and influencing factor information. i The prediction model is used to calculate the turnover period T of the current logistics node i. i , analyze the multi-source data that affects the transition period of logistics nodes in the cross-border e-commerce logistics process, and obtain the predicted circulation cycle of the turnover links in the logistics process, so as to effectively prevent the occurrence of logistics blind spots caused by the difficulty in tracking cross-border commodity logistics information data, and realize the effective control and tracking monitoring of cross-border commodity logistics and commodity flow information.

Description

Technical Field

[0001] The present invention relates to the field of commodity logistics monitoring, and particularly to a method and system for tracking and monitoring bulk commodity logistics based on blockchain. Background Art

[0002] In traditional logistics distribution, the tracking of commodity logistics information relies on intermediary agencies or third-party platforms. Logistics data needs to be forwarded and processed multiple times, resulting in increased inaccuracy of information and decreased timeliness of tracking. Due to the isolated storage and non-uniform standards of logistics information, it is difficult to share information among different participants, lacking overall visibility and transparency. Traditional logistics tracking is also vulnerable to the risks of data tampering and illegal access, presenting problems of information security and trustworthiness. With the continuous development of blockchain technology, the use of blockchain technology can provide higher-level logistics tracking visibility and transparency. The decentralized feature of blockchain eliminates the dependence on a single intermediary agency, enabling direct communication and data sharing among participants in each link. By recording logistics data in real time, blockchain can ensure the timeliness and accuracy of data. Participants can update and view logistics data in real time through the blockchain network, realizing real-time monitoring and traceability during the logistics process, and reducing the occurrence of information lag and errors.

[0003] For cross-border e-commerce logistics platforms, although according to the traceability feature of blockchain technology, data information such as the date, location, transportation method, etc. of each link from the outbound of goods, transportation, warehousing to terminal distribution can be completely recorded in chronological order, during the long transportation process of cross-border goods, limited by various influencing factors such as commodity types, weather conditions, and differences in department handover procedures, it is easy to have logistics blind spots during the transfer and transition between adjacent logistics nodes, resulting in a decrease in the timeliness of obtaining real-time information of goods. Therefore, we propose a method and system for tracking and monitoring bulk commodity logistics based on blockchain. Summary of the Invention

[0004] The main object of the present invention is to provide a method and system for tracking and monitoring bulk commodity logistics based on blockchain, which can effectively solve the problems in the background art.

[0005] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] A method for tracking and monitoring bulk commodity logistics based on blockchain, comprising:

[0007] Obtain the logistics node information recorded on the blockchain, and extract the influencing factor information of the current logistics node i's turnover cycle T i for the turnover cycle T iThe influence coefficient, where the influence factor information includes product characteristic information, logistics characteristic information, and objective characteristic information; among them, the product characteristic information includes at least one of product category information, product quantity information, and product quality grade information; the logistics characteristic information includes at least one of transportation mode information, carrying capacity information, and transportation efficiency information; the objective characteristic information includes at least one of environmental delay duration, average turnover duration, and average review cycle;

[0008] Construct the turnover cycle T of the current logistics node i based on the obtained influence coefficient and influence factor information i prediction model, and calculate and obtain the turnover cycle T of the current logistics node i through the prediction model i ;

[0009] The expression of the prediction model is:

[0010]

[0011] In the formula, β 1u represents the influence coefficient of the u-th product characteristic; represents the u-th product characteristic information of the logistics node i; β 2v represents the influence coefficient of the v-th logistics characteristic; represents the v-th logistics characteristic information of the logistics node i; β 3w represents the influence coefficient of the w-th environmental characteristic; represents the w-th environmental characteristic information of the logistics node i; ε is a constant coefficient.

[0012] The process for obtaining the influence coefficient includes the following steps:

[0013] Obtain the historical data of the turnover cycle T of the current logistics node i and the influence factor information in the logistics node information recorded on the blockchain i and the historical data of the influence factor information;

[0014] Construct a data sequence E = {T i} with the obtained historical data of the turnover cycle T, and construct a data sequence F = {F i}, F 1}, F 2},..., F n} with the historical data of the influence factor information, where F n represents the data set of the n-th influence factor; n is the number of influence factors;

[0015] Calculate the correlation coefficient ξ jk between each data point in the data sequence F and the data sequence E, ξ jkThe correlation coefficient between the k-th data point representing the j-th influencing factor and the data sequence E, where j ∈ n, is ξ calculated according to the following formula jk Determine the correlation degree R between the j-th influencing factor and the turnover cycle j , and the calculation formula is where Q is the total amount of data

[0016] Based on the calculated correlation degree R j Determine the influence coefficient of the j-th influencing factor on the turnover cycle T i The calculation formula is

[0017] The calculation formula of the correlation coefficient ξ jk is

[0018]

[0019] In the formula, Δmin = min[min(|T ik '-F jk |)], where min[min(|T ik '-F jk |)] is to take the minimum value of the calculation result of |T ik '-F jk |, and then take the minimum value of all calculated minimum values; Δmax = max[max(|T ik '-F jk |)], where max[max(|T ik '-F jk |)] is to take the maximum value of the calculation result of |T ik '-F jk |, and then take the maximum value of all calculated maximum values; T ik ' is the standardized value of the k-th turnover cycle data point in the data sequence E, and T ik is the k-th turnover cycle data value in the data sequence E is the average value of the turnover cycle data in the data sequence E F jk is the standardized value of the k-th data point of the j-th influencing factor, and where f jk is the k-th data value of the j-th influencing factor is the average value of the data of the j-th influencing factor ρ is the resolution coefficient, and its value range is (0, 1].

[0020] ​A blockchain-based bulk commodity logistics tracking and monitoring system, including a logistics node information acquisition module, an influence coefficient acquisition module, and a prediction model construction module;

[0021] The logistics node information acquisition module is used to acquire the logistics node information recorded on the blockchain and extract the turnover cycle T of the current logistics node i i and influence factor information, where the influence factor information includes commodity characteristic information, logistics characteristic information, and objective characteristic information;

[0022] The influence coefficient acquisition module is used to determine the influence coefficients of each of the influence factors on the turnover cycle T according to the acquired influence factor information of the current logistics node i and its turnover cycle T i ; i The influence coefficient of;

[0023] The prediction model construction module is used to construct a prediction model for the turnover cycle T of the current logistics node i according to the acquired influence coefficients and influence factor information, and calculate and obtain the turnover cycle T of the current logistics node i through the prediction model i ; i ;

[0024] The system further includes a memory, a processor, and an electronic program stored in the memory and capable of running on the processor.

[0025] The present invention has the following beneficial effects

[0026] Compared with the prior art, by acquiring the logistics node information recorded on the blockchain, extracting the turnover cycle T of the current logistics node i i influence factor information including commodity characteristic information, logistics characteristic information, and objective characteristic information, and determining the influence coefficients of each of the influence factors on the turnover cycle T i , constructing a prediction model for the turnover cycle T of the current logistics node i according to the acquired influence coefficients and influence factor information, and calculating and obtaining the turnover cycle T of the current logistics node i through the prediction model i , analyzing multi-source data during the turnover transition period of logistics nodes in the cross-border e-commerce logistics process, obtaining the predicted turnover cycle of the turnover link in the logistics process, thereby effectively preventing the occurrence of logistics blind spots caused by the difficulty of tracking and tracing cross-border commodity logistics information data, and realizing the effective control and tracking and monitoring of the logistics and commodity flow information of cross-border commodities. i ; BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 is a flowchart of the method for tracking and monitoring bulk commodity logistics based on blockchain of the present invention;

[0028] Figure 2This is the structural block diagram of the commodity logistics tracking and monitoring system based on blockchain of the present invention. Specific embodiments

[0029] The present invention will be further described below in conjunction with specific embodiments. Among them, the attached drawings are only for illustrative purposes, showing only schematic diagrams, not physical diagrams, and should not be construed as limiting the present invention. In order to better illustrate the specific embodiments of the present invention, some components in the attached drawings will be omitted, enlarged or reduced, which does not represent the size of the actual product.

[0030] The specific implementation process of the technical solution of the present invention includes the following steps:

[0031] Step 1: Obtain the logistics node information recorded on the blockchain;

[0032] Generally speaking, the logistics process of cross-border e-commerce imports includes outbound customs clearance abroad, international logistics, inbound customs clearance, domestic logistics, etc. Each process can be further divided into links such as goods allocation, warehousing, declaration, customs clearance, and distribution. These turnover links may make the cross-border e-commerce logistics span longer and have more logistics node data. Any problem or delay in any of these links or steps will affect the subsequent process. From the above cross-border e-commerce logistics process, the information collection scattered among different cross-border e-commerce participants and various links is numerous and complex, involving consumers, sellers, cross-border e-commerce platforms, logistics parties, payment intermediaries, tax departments, etc. And these cross-border e-commerce participants are often not in the same region, making it difficult to collect, track, and audit information data. By applying blockchain technology to cross-border e-commerce supervision, all participants such as cross-border enterprises, consumers, payment institutions, customs, and tax departments are effectively connected as members on the chain; in the blockchain system, each party stores the commodity information on the chain, and the input information is open and transparent. Members can share data information with each other and cross-verify to ensure that the information is equal to the transaction facts. Applying blockchain technology to the cross-border e-commerce logistics platform can make the logistics information of commodities match the physical flow direction in real time, realizing the full traceability of the logistics process. According to the traceability feature of blockchain technology, for each commodity from outbound, transportation, warehousing to terminal distribution, data information such as the date, location, and transportation method of each link is completely recorded in the blockchain in chronological order. The information of each link is seamlessly connected and interconnected to ensure that cross-border commodities are in a tracked state during the long transportation process. Any party in the logistics process can obtain the logistics node information recorded on the blockchain through a legally authenticated identity on the chain.

[0033] Step 2: Extract the influencing factor information of the current logistics node i's turnover cycle T i ;

[0034] Among them, the influencing factor information includes product characteristic information, logistics characteristic information, and objective characteristic information; among them, the product characteristic information includes at least one of product category information, product quantity information, and product quality grade information; the logistics characteristic information includes at least one of transportation mode information, carrying capacity information, and transportation efficiency information; the objective characteristic information includes at least one of environmental delay duration, average turnover duration, and average review cycle;

[0035] It should be noted that for qualitative data, such as product category information data, after extraction, it can be quantified by the subjective assignment method. By formulating an assignment standard to score the product category information data. In addition, at different logistics nodes, the formulated assignment standards are also different. For example, for the loading node, the loading difficulty can be used as the assignment standard to process the product category. The easier the product category is to load, the smaller the impact on the turnover cycle of the node, and the lower the quantified score after assignment. On the contrary, the more difficult the product category is to load, the greater the impact on the turnover cycle of the node, and the higher the quantified score after assignment; for the warehousing node, the storage difficulty can be used as the assignment standard to process the product category. The easier the product category is to store, the smaller the impact on the turnover cycle of the node, and the lower the quantified score after assignment. On the contrary, the more difficult the product category is to store, the greater the impact on the turnover cycle of the node, and the higher the quantified score after assignment;

[0036] Step 3: Determine the influence coefficient of each influencing factor on the turnover cycle T i ;

[0037] The specific process is as follows:

[0038] Step 31: Obtain the historical data of the turnover cycle T of the current logistics node i and the influencing factor information in the logistics node information recorded on the blockchain; i ;

[0039] Step 32: Construct a data sequence E = {T i} with the obtained historical data of the turnover cycle T, and construct a data sequence F = {F i}, F 1},..., F 2} with the historical data of the influencing factor information, where F n represents the data set of the nth influencing factor; n is the number of influencing factor items; n ;

[0040] Step 33: Calculate the correlation coefficient ξ jk between each data point in the data sequence F and the data sequence E, ξ jk represents the correlation coefficient between the kth data point of the jth influencing factor and the data sequence E, j ∈ n. According to the calculated correlation coefficient ξ jkDetermine the correlation degree R between the j-th influencing factor and the turnover cycle j , and the calculation formula is as follows: where Q is the total amount of data;

[0041] Step 34: According to the calculated correlation degree R j Determine the influence coefficient of the j-th influencing factor on the turnover cycle T i ; The calculation formula is as follows:

[0042] where the correlation coefficient ξ jk The calculation formula is as follows:

[0043]

[0044] In the formula, Δmin = min[min(|T ik '- F jk |)], where min[min(|T ik '- F jk |)] is to take the minimum value of the calculation result of |T ik '- F jk |, and then take the minimum value among all the calculated minimum values; Δmax = max[max(|T ik '- F jk |)], where max[max(|T ik '- F jk |)] is to take the maximum value of the calculation result of |T ik '- F jk |, and then take the maximum value among all the calculated maximum values; T ik ' is the standardized value of the k-th turnover cycle data point in the data sequence E, and T ik is the k-th turnover cycle data value in the data sequence E, is the average value of the turnover cycle data in the data sequence E, F jk is the standardized value of the k-th data point of the j-th influencing factor, and where f jk is the k-th data value of the j-th influencing factor, is the average value of the data of the j-th influencing factor, ρ is the discrimination coefficient, and the value range is (0, 1].

[0045] Step 4: Construct a prediction model for the turnover cycle T i of the current logistics node i based on the obtained influence coefficient and influencing factor information, and calculate the turnover cycle T i of the current logistics node i through the prediction model;

[0046] The expression of the prediction model is as follows:

[0047]

[0048] In the formula, β 1u represents the influence coefficient of the u-th commodity feature; represents the u-th commodity feature information of the logistics node i; β 2v represents the influence coefficient of the v-th logistics feature; represents the v-th logistics feature information of the logistics node i; β 3w represents the influence coefficient of the w-th environmental feature; represents the w-th environmental feature information of the logistics node i; ε is a constant coefficient.

[0049] 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 by the above embodiments. What is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for tracking and monitoring bulk commodity logistics based on blockchain, characterized in that: include: Obtain the logistics node information recorded on the blockchain and extract the circulation cycle T of the current logistics node i i The influencing factor information is used to determine the influence of each influencing factor on the circulation period T i The influencing factor information includes commodity feature information, logistics feature information, and objective feature information; According to the acquired influence coefficient and influencing factor information, the circulation cycle T of the current logistics node i is constructed. i The prediction model is used to calculate the turnover period T of the current logistics node i. i ; The expression of the prediction model is: In the formula, β 1u Expressed as the influence coefficient of the u-th commodity feature; Represented as the characteristic information of the u-th item of goods at logistics node i; β 2v It is expressed as the influence coefficient of the vth logistics characteristic; It is represented as the vth item of logistics characteristic information of logistics node i; β 3w Expressed as the influence coefficient of the wth environmental characteristic; It is represented as the w-th environmental feature information of logistics node i; ε is a constant coefficient; The process of obtaining the influence coefficient includes the following steps: Get the circulation cycle T of the current logistics node i in the logistics node information recorded on the blockchain i and historical data of information on influencing factors; To obtain the turnover period T i The historical data constructs the data sequence E = {T i }, construct a data sequence F = {F1, F2, ..., F n }, where F n It is represented as the data set of the nth influencing factor; n is the number of influencing factors; Calculate the correlation coefficient ξ between each data point in data sequence F and data sequence E jk ,ξ jk It is expressed as the correlation coefficient between the kth data point of the jth influencing factor and the data sequence E, j∈n, according to the calculated correlation coefficient ξ jk Determine the correlation R between the jth influencing factor and the circulation cycle j , the calculation formula is: Among them, Q is the total amount of data; According to the calculated correlation R j Determine the jth influencing factor for the turnover period T i The influence coefficient The calculation formula is:

2. The method for tracking and monitoring bulk commodity logistics based on blockchain according to claim 1 is characterized in that: The commodity characteristic information includes at least one of commodity category information, commodity quantity information, and commodity quality grade information; the logistics characteristic information includes at least one of transportation mode information, carrying capacity information, and transportation efficiency information; the objective characteristic information includes at least one of environmental delay duration, average circulation time, and average review cycle.

3. The method for tracking and monitoring bulk commodity logistics based on blockchain according to claim 1 is characterized in that: Correlation coefficient ξ jk The calculation formula is: Where, Δmin=min[min(|T ik '-F jk |)], where min[min(|T ik '-F jk |)] is to take |T ik '-F jk |Calculate the minimum value of the result, and then take the minimum value of all calculated minimum values; Δmax=max[max(|T ik '-F jk |)], where max[max(|T ik '-F jk |)] is to take |T ik '-F jk |Calculate the maximum value of the result, and then take the maximum value of all calculated maximum values; T ik ' is the standardized value of the kth circulation cycle data point in the data sequence E, and T ik is the kth circulation cycle data value in the data sequence E, is the mean value of the turnover cycle data in the data sequence E, F jk is the normalized value of the kth data point of the jth influencing factor, and Among them, f jk is the kth data value of the jth influencing factor, is the data mean of the jth influencing factor, ρ is the resolution coefficient, and its value range is (0,1].

4. The commodity logistics tracking and monitoring system based on blockchain is characterized by: It includes logistics node information acquisition module, influence coefficient acquisition module and prediction model construction module; The logistics node information acquisition module is used to obtain the logistics node information recorded on the blockchain and extract the circulation period T of the current logistics node i. i The influencing factor information includes commodity feature information, logistics feature information, and objective feature information; the commodity feature information includes at least one of commodity category information, commodity quantity information, and commodity quality grade information; the logistics feature information includes at least one of transportation mode information, transportation capacity information, and transportation efficiency information; the objective feature information includes at least one of environmental delay duration, average circulation duration, and average review cycle; The influence coefficient acquisition module is used to obtain the current logistics node i according to the circulation period T i The influencing factor information is used to determine the influence of each influencing factor on the circulation period T i The influence coefficient of The influence coefficient acquisition process includes the following steps: Get the circulation cycle T of the current logistics node i in the logistics node information recorded on the blockchain i and historical data of information on influencing factors; To obtain the turnover period T i The historical data constructs the data sequence E = {T i }, construct a data sequence F = {F1, F2, ..., F n }, where F n It is represented as the data set of the nth influencing factor; n is the number of influencing factors; Calculate the correlation coefficient ξ between each data point in data sequence F and data sequence E jk ,ξ jk It is expressed as the correlation coefficient between the kth data point of the jth influencing factor and the data sequence E, j∈n, according to the calculated correlation coefficient ξ jk Determine the correlation R between the jth influencing factor and the circulation cycle j , the calculation formula is: Among them, Q is the total amount of data; According to the calculated correlation R j Determine the jth influencing factor for the turnover period T i The influence coefficient The calculation formula is: Among them, the correlation coefficient ξ jk The calculation formula is: Where, Δmin=min[min(|T ik '-F jk |)], where min[min(|T ik '-F jk |)] is to take |T ik '-F jk |Calculate the minimum value of the result, and then take the minimum value of all calculated minimum values; Δmax=max[max(|T ik '-F jk |)], where max[max(|T ik '-F jk |)] is to take |T ik '-F jk |Calculate the maximum value of the result, and then take the maximum value of all calculated maximum values; T ik ' is the standardized value of the kth circulation cycle data point in the data sequence E, and T ik is the kth circulation cycle data value in the data sequence E, is the mean value of the turnover cycle data in the data sequence E, F jk is the normalized value of the kth data point of the jth influencing factor, and Among them, f jk is the kth data value of the jth influencing factor, is the data mean of the jth influencing factor, ρ is the resolution coefficient, and its value range is (0,1]; The prediction model building module is used to build the circulation cycle T of the current logistics node i according to the acquired influence coefficient and influence factor information. i The prediction model is used to calculate the turnover period T of the current logistics node i. i ; The expression of the prediction model is: In the formula, β 1u Expressed as the influence coefficient of the u-th commodity feature; Represented as the characteristic information of the u-th commodity at logistics node i; β 2v It is expressed as the influence coefficient of the vth logistics characteristic; Represented as the vth item of logistics characteristic information of logistics node i; β 3w Expressed as the influence coefficient of the wth environmental characteristic; It is represented as the w-th environmental characteristic information of logistics node i; ε is a constant coefficient.

5. The blockchain-based commodity logistics tracking and monitoring system according to claim 4 is characterized in that: The system also includes a memory, a processor, and an electronic program stored in the memory and capable of running on the processor, wherein the processor can implement the steps of the method according to any one of claims 1 to 3 when running the electronic program.

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