Intermodal freight weight management method

Abstract

This invention relates to a method for managing the weight of goods in multimodal transport, comprising the following steps: (1) a courier company branch collects express packages and transports them to the courier company's city transit station; (2) the courier company's city transit station selects express packages to be delivered for multimodal transport, weighs the packages using intelligent weighing equipment, checks whether the weight and cost are consistent while scanning the package tracking number, and then records it to the blockchain; and divides the packages according to the transport city, recording the tracking number of each package to the blockchain; (3) the courier company transports the multimodal transport goods to the multimodal transport consignment point and hands them over to the multimodal transport carrier; (4) the multimodal transport entity delivers the goods to the courier company in the target city; (5) the weight of the transported goods is recorded; (6) offline error analysis is performed, and records with an absolute error value greater than 5% are analyzed monthly. The advantage of this invention is that it proposes a weight management method based on a game-theoretic mechanism in multimodal transport to strengthen the fair and trustworthy mechanism outside the system.

Description

Technical Field

[0001] This invention relates to a method for managing the weight of multimodal transport goods, belonging to the field of logistics management. Background Technology

[0002] Multimodal transport is a method of transporting goods using multiple modes of transportation, including trains, airplanes, and ships. It typically involves logistics companies and carriers such as railway and air transport entities, making the process relatively complex and more difficult to regulate, involving multiple parties. Unlike logistics companies that transport goods using their own vehicles, multimodal transport is sensitive to cargo weight. Therefore, if employees of courier companies smuggle personal goods, it will increase the company's transportation costs. Conversely, if carrier company employees underreport the weight of goods, it will reduce the carrier company's revenue. Managing cargo weight is a shared need for both logistics companies and carrier companies. Weighing instruments may have various human and non-human errors, leading to discrepancies between the total weight recorded by the courier company and the weight measured during multimodal transport, making it difficult to detect violations.

[0003] A consortium blockchain lies between a public blockchain and a private blockchain, possessing some decentralized characteristics. It is initiated by several organizations and maintained jointly by allies. It is only open to members of a specific group and a limited number of third parties. Participants in a consortium blockchain are pre-selected or directly designated. Multiple pre-selected nodes are designated within the consortium blockchain as ledger keepers. The generation of each block is jointly determined by these designated nodes. Other nodes can participate in transactions but do not participate in the ledger recording process.

[0004] In multimodal transport scenarios, existing technologies primarily ensure traceability and tamper-proofing of each goods handover. However, their coverage is limited to recording the responsible party, time, and location after data is uploaded to the system. Current management methods struggle to detect and manage issues such as individual courier company personnel smuggling contraband or carrier weighing equipment parameters being tampered with. The purpose of this invention is to propose a multimodal transport goods weight error management method based on blockchain and smart terminal devices. This method uses a game-theoretic approach to increase the difficulty of violations for each participant and helps identify non-human-caused malfunctions. Summary of the Invention

[0005] To overcome the shortcomings of existing technologies, this invention provides a method for managing the weight of multimodal transport goods. The technical solution of this invention is as follows:

[0006] A method for managing the weight of multimodal transport cargo includes the following steps:

[0007] (1) The courier company's store picks up the courier and transports it to the courier company's city transit station;

[0008] (2) The city transit station of the express company selects the express shipments that need to be delivered for multimodal transport, uses intelligent weighing equipment to weigh the express shipments, and checks whether the express weight and fee are consistent while scanning the express shipment tracking number, and then records it to the blockchain; and according to the city of transport, the express shipments are divided into boxes, and the tracking number of each box is recorded to the blockchain.

[0009] (3) The express delivery company will transport the intermodal goods to the multimodal transport consignment point and hand them over to the intermodal carrier;

[0010] (4) Delivery between the multimodal transport entity and the express delivery company in the target city;

[0011] (5) Use game theory to record the weight of transported goods;

[0012] (6) Offline error analysis: records with an absolute error value greater than 5% will be analyzed monthly.

[0013] The specific steps (3) are as follows: 3-1, the staff of the express delivery company and the staff of the intermodal transport carrier jointly scan the QR code on the package to confirm the package information;

[0014] 3-2. Use smart sealing equipment to seal the box. After sealing, upload the sealing information, including time and latitude and longitude, to the blockchain through the smart sealing equipment.

[0015] 3-3. After the employees of the courier company and the carrier company confirm the box information, the handover is confirmed. The courier company weighs each package. The transport box contains several packages. The sum of the weights of each package plus the weight of the standard packaging box is the weight w1. At the handover point, the packages are already packed in the standard packaging box, and the carrier company can weigh them directly to obtain the weight w2. The weight w1 comes from the courier company's weighing of each item plus the weight of the standard packaging box, and w2 comes from the weight weighed by the carrier company. The weight data w1 and w2 are uploaded to the blockchain.

[0016] The specific steps (4) are as follows:

[0017] 4-1. Courier company employees inspect the intermodal transport container for damage and the seal for damage;

[0018] 4-2. Staff from both the multimodal transport carrier and the courier company scan the QR code on the transfer box, confirm the detailed information, place the transfer box on the weighing device to obtain the weight w3, and upload it to the blockchain.

[0019] The specific steps (5) are as follows:

[0020] 5-1. Obtain the weighing error between the courier company and the carrier.

[0021] If the error |m| ≤ 5%, then the courier company's weighing method is considered reliable. As the accounting weight; in this case, the courier and the carrier agree on the weight of the goods weighed, and the minimum weight value is used as the basis for billing; if someone from the courier company smuggles goods without scanning, it is difficult to guarantee that the error is within 5%;

[0022] If the error |m| > 5%, calculate its average value. Through formula

[0023] |wE|, where w is w1, w2, or w3; the deviations of w1, w2, and w3 are dw1, dw2, and dw3, respectively; if dw1 is the largest, then according to In this billing scenario, if the weighing deviation of the two smart weighing devices in the two cities of the carrier is less than the weighing deviation of the weight reported by the courier, it is considered that the courier's weighing error is larger, and the carrier's weighing result is used. This algorithm allows the courier to manually adjust the smart weighing equipment, and the malfunction error of the courier's smart weighing equipment does not have a negative impact on the billing result.

[0024] If the error |m| > 5%, calculate its average value. Using the formula |wE|, where w is w1, w2, or w3, the deviations of w1, w2, and w3 are calculated as dw1, dw2, and dw3, respectively. If dw2 or dw3 is the largest, the billing will still be based on w1. In this case, it is considered that the carrier's smart weighing device has a large error, and the courier company's weighing will be used as the standard. All records with an error |m|>5% will be uploaded to the offline analysis center.

[0025] The specific steps (6) are as follows:

[0026] Collecting all data from a month with intermodal transport errors greater than 5% will produce the following data:

[0027] The Record_ID field is used to record the shipping record ID. w1 is used to identify the courier company's weighing, and w2 and w3 are used to represent the two weighings by the shipping company. The w1_scale, w2_scale, and w3_scale fields are used to represent the device IDs used by the smart weighing system for w1, w2, and w3 weighing, respectively.

[0028] To identify weighing equipment with significantly more than twice the average number of errors: Use an SQL filter statement to obtain the source of the error equipment: `select X,count(X)from ABNORMAL_RECORD group by X;` where `X` is replaced with "w1_scale", "w2_scale", and "w3_scale". This will result in three sets of data for the weighing equipment IDs and the number of errors. Merge these three sets by adding the number of errors for the same ID, resulting in a single set of equipment IDs and error data: `vt = {id1:vt1,id2:vt2,id3:vt3,...idn:vtn}`, where there are no duplicates from `id1` to `idn`. Then, query the total number of weighings for `id1`, `id2`, ..., `idn` to obtain the set `vat = {id1:vat1,id2:vat2,...,idn:vatn}`.

[0029] Through formula Calculate the proportion of weighing error generated by each device to obtain the data set of weighing error generation proportions for each device, vtr = {id1:vtr1,id2:vtr2,id3:vtr3,...idn:vtrn}; calculate the average error proportion. Investigate equipment with an error ratio greater than 2 times vtravg;

[0030] We fit the weighing errors of courier companies and carriers by grouping them into w1_scale and w2_scale, and w1_scale and w3_scale respectively; the steps are as follows:

[0031] The SQL statement `select distinct(X) from ABNORMAL_RECORD;` replaces `X` with `w1_scale`, `w2_scale`, and `w3_scale`, resulting in three sets of error weighing devices: `vtd1 = {vtd11, vtd12, vtd13 ...}`, `vtd2 = {vtd21, vtd22, vtd23 ...}`, and `vtd3 = {vtd31, vtd32, vtd33 ...}`. Here, `vtd1` represents the courier company's list of error weighing devices, while `vtd2` and `vtd3` represent the carrier's list.

[0032] The following SQL statement retrieves the comparison groups of courier company and carrier equipment: select record_id,w1,w2 from ABNORMAL_RECORD where w1_scale=vtd1x and w2_scale=vtd2x; where vtd1x and vtd2x are elements in vtd1 and vtd2 respectively. Execute the above SQL statement to iterate through all elements of vtd1 and vtd2, resulting in a data analysis set analysis_a = {a1, a2, a3...}, where each element is the corresponding element of vtd1 and vtd2, and the data set containing Record_ID, w1, w2, w1_scale, and w2_scale fields is retrieved. For vtd1 and vtd3, obtain a data analysis set analysis_b = {b1, b2, b3...}, where each element is the corresponding element of vtd1 and vtd3, and the data set containing record_id, w1, and w3 is retrieved. Filter out samples from analysis_a and analysis_b that have fewer than 20 samples or do not meet the criteria. The elements are defined, where n is the number of elements in analysis_a or analysis_b, wa corresponds to w1, and wb corresponds to w2 in analysis_a and w3 in analysis_b; the filtered sets analysis_final_a and analysis_final_b are obtained, and a first-order linear fit and a second-order linear fit are performed on all elements of the two groups respectively; for each group of samples, Y = aX + b and Y = aX are fitted respectively. 2 The parameters a, b, and c of Y + bX + c are obtained. For each set of fitted parameters, w1 is used as X and substituted into the fitting function. w1 is substituted into X, and w2 is substituted into Y. The MATLAB polyfit function is used to perform first-order and second-order fitting to obtain the values ​​of parameters a, b, and c. Substituting a, b, c, and w1 into the equations Y = aX + b and Y = aX² + bX + c respectively, the corresponding Y values ​​are obtained. Taking the sample analysis_final_a as an example, a dataset containing Record_ID, w1, w2, w1_scale, w2_scale, and Y is obtained. Assuming that the number of records in this sample is n, the formula for calculating the average fitting error rate is as follows:

[0033] Among them, w2 k Y represents the value of w2 for the k-th sample in the sample. k This represents the value of Y obtained by substituting w1 and the parameter into the k-th sample in the sample;

[0034] Find the group with an average fitting error rate δ < 5%, and investigate the problem of the weighing equipment corresponding to this group. Since the weighing deviation rate of each sample is greater than 10%, and the fitting error rate is less than 5% after fitting, it can be determined that the weighing equipment has a regular deviation.

[0035] The advantages of this invention are:

[0036] 1. In multimodal transport, a weight management method based on game theory mechanism is proposed to strengthen the fair and trustworthy mechanism outside the system.

[0037] 2. In the game mechanism, the two parties weigh the equipment three times. Based on the error deviation, the system makes it extremely difficult to manipulate the accounting results through methods such as deviation elimination and priority control, thereby effectively reducing private cheating. In the event of a large error, the system uses statistical analysis and error fitting to find the faulty weighing equipment, which greatly increases the complexity of tampering with the weighing equipment and protects the fairness mechanism.

[0038] Based on the traceability and immutability of blockchain, this invention proposes a weight management method. By using blockchain technology to achieve mutual trust in data within the information system, technical means are used in actual weighing to ensure the standardization of the weighing process. An error game algorithm is adopted, which raises the threshold and cost for human cheating while providing automatic conclusion analysis for non-human faults, so that both parties can protect their own interests on the basis of fairness and impartiality. Attached Figure Description

[0039] Figure 1 This is a schematic diagram of the courier company and the courier provider in this invention.

[0040] Figure 2 This is a flowchart illustrating the present invention. Detailed Implementation

[0041] The present invention will be further described below with reference to specific embodiments, and the advantages and features of the present invention will become clearer as a result. However, these embodiments are merely exemplary and do not constitute any limitation on the scope of the present invention. Those skilled in the art should understand that modifications or substitutions can be made to the details and form of the technical solutions of the present invention without departing from the spirit and scope of the present invention, but all such modifications and substitutions fall within the protection scope of the present invention.

[0042] See Figure 1 This invention relates to a method for managing the weight of multimodal transport cargo, which includes the following steps:

[0043] (1) The courier company's store picks up the courier and transports it to the courier company's city transit station;

[0044] (2) The city transit station of the express company selects the express shipments that need to be delivered for multimodal transport, uses intelligent weighing equipment to weigh the express shipments, and checks whether the express weight and fee are consistent while scanning the express shipment tracking number, and then records it to the blockchain; and according to the city of transport, the express shipments are divided into boxes, and the tracking number of each box is recorded to the blockchain.

[0045] (3) The express delivery company will transport the intermodal goods to the multimodal transport consignment point and hand them over to the intermodal carrier;

[0046] (4) Delivery between the multimodal transport entity and the express delivery company in the target city;

[0047] (5) Use game theory to record the weight of transported goods;

[0048] (6) Offline error analysis: records with an absolute error value greater than 5% will be analyzed monthly.

[0049] The specific steps (3) are as follows: 3-1, the staff of the express delivery company and the staff of the intermodal transport carrier jointly scan the QR code on the package to confirm the package information;

[0050] 3-2. Use smart sealing equipment to seal the box. After sealing, upload the sealing information, including time and latitude and longitude, to the blockchain through the smart sealing equipment.

[0051] 3-3. After the employees of the courier company and the carrier company confirm the box information, the handover is confirmed. The courier company weighs each package. The transport box contains several packages. The sum of the weights of each package plus the weight of the standard packaging box is the weight w1. At the handover point, the packages are already packed in the standard packaging box, and the carrier company can weigh them directly to obtain the weight w2. The weight w1 comes from the courier company's weighing of each item plus the weight of the standard packaging box, and w2 comes from the weight weighed by the carrier company. The weight data w1 and w2 are uploaded to the blockchain.

[0052] The specific steps (4) are as follows:

[0053] 4-1. Courier company employees inspect the intermodal transport container for damage and the seal for damage;

[0054] 4-2. Staff from both the multimodal transport carrier and the courier company scan the QR code on the transfer box, confirm the detailed information, place the transfer box on the weighing device to obtain the weight w3, and upload it to the blockchain.

[0055] The specific steps (5) are as follows:

[0056] 5-1. Obtain the weighing error between the courier company and the carrier.

[0057] If the error |m| ≤ 5%, then the courier company's weighing method is considered reliable. As the accounting weight; in this case, the courier and the carrier agree on the weight of the goods weighed, and the minimum weight value is used as the basis for billing; if someone from the courier company smuggles goods without scanning, it is difficult to guarantee that the error is within 5%;

[0058] If the error |m| > 5%, calculate its average value. Using the formula |wE|, where w is w1, w2, or w3, the deviations of w1, w2, and w3 are dw1, dw2, and dw3, respectively. If dw1 is the largest, then according to... In this billing scenario, if the weighing deviation of the two smart weighing devices in the two cities of the carrier is less than the weighing deviation of the weight reported by the courier, it is considered that the courier's weighing error is larger, and the carrier's weighing result is used. This algorithm allows the courier to manually adjust the smart weighing equipment, and the malfunction error of the courier's smart weighing equipment does not have a negative impact on the billing result.

[0059] If the error |m| > 5%, calculate its average value. Using the formula |wE|, where w is w1, w2, or w3, the deviations of w1, w2, and w3 are calculated as dw1, dw2, and dw3, respectively. If dw2 or dw3 is the largest, the billing will still be based on w1. In this case, it is considered that the carrier's smart weighing device has a large error, and the courier company's weighing will be used as the standard. All records with an error |m|>5% will be uploaded to the offline analysis center.

[0060] The specific steps (6) are as follows:

[0061] Collecting all data from a month with intermodal transport errors greater than 5% will produce the following data:

[0062] The Record_ID field is used to record the shipping record ID. w1 is used to identify the courier company's weighing, and w2 and w3 are used to represent the two weighings by the shipping company. The w1_scale, w2_scale, and w3_scale fields are used to represent the device IDs used by the smart weighing system for w1, w2, and w3 weighing, respectively.

[0063] To identify weighing equipment with significantly more than twice the average number of errors: Use an SQL filter statement to obtain the source of the error equipment: `select X,count(X)from ABNORMAL_RECORD group by X;` where `X` is replaced with "w1_scale", "w2_scale", and "w3_scale". This will result in three sets of data for the weighing equipment IDs and the number of errors. Merge these three sets by adding the number of errors for the same ID, resulting in a single set of equipment IDs and error data: `vt = {id1:vt1,id2:vt2,id3:vt3,...idn:vtn}`, where there are no duplicates from `id1` to `idn`. Then, query the total number of weighings for `id1`, `id2`, ..., `idn` to obtain the set `vat = {id1:vat1,id2:vat2,...,idn:vatn}`.

[0064] Through formula Calculate the proportion of weighing error generated by each device to obtain the data set of weighing error generation proportions for each device, vtr = {id1:vtr1,id2:vtr2,id3:vtr3,...idn:vtrn}; calculate the average error proportion. Investigate equipment with an error ratio greater than 2 times vtravg;

[0065] We fit the weighing errors of courier companies and carriers by grouping them into w1_scale and w2_scale, and w1_scale and w3_scale respectively; the steps are as follows:

[0066] The SQL statement `select distinct(X) from ABNORMAL_RECORD;` replaces `X` with `w1_scale`, `w2_scale`, and `w3_scale`, resulting in three sets of error weighing devices: `vtd1 = {vtd11, vtd12, vtd13 ...}`, `vtd2 = {vtd21, vtd22, vtd23 ...}`, and `vtd3 = {vtd31, vtd32, vtd33 ...}`. Here, `vtd1` represents the courier company's list of error weighing devices, while `vtd2` and `vtd3` represent the carrier's list.

[0067] The following SQL statement retrieves the comparison groups of courier company and carrier equipment: select record_id,w1,w2 from ABNORMAL_RECORD where w1_scale=vtd1x and w2_scale=vtd2x; where vtd1x and vtd2x are elements in vtd1 and vtd2 respectively. Execute the above SQL statement to iterate through all elements of vtd1 and vtd2, resulting in a data analysis set analysis_a = {a1, a2, a3...}, where each element is the corresponding element of vtd1 and vtd2, and the data set containing Record_ID, w1, w2, w1_scale, and w2_scale fields is retrieved. For vtd1 and vtd3, obtain a data analysis set analysis_b = {b1, b2, b3...}, where each element is the corresponding element of vtd1 and vtd3, and the data set containing record_id, w1, and w3 is retrieved. Filter out samples from analysis_a and analysis_b that have fewer than 20 samples or do not meet the criteria. The elements are defined, where n is the number of elements in analysis_a or analysis_b, wa corresponds to w1, and wb corresponds to w2 in analysis_a and w3 in analysis_b; the filtered sets analysis_final_a and analysis_final_b are obtained, and a first-order linear fit and a second-order linear fit are performed on all elements of the two groups respectively; for each group of samples, Y = aX + b and Y = aX are fitted respectively. 2 The parameters a, b, and c of Y + bX + c are obtained. For each set of fitted parameters, w1 is used as X and substituted into the fitting function. w1 is substituted into X, and w2 is substituted into Y. The MATLAB polyfit function is used to perform first-order and second-order fitting to obtain the values ​​of parameters a, b, and c. Substituting a, b, c, and w1 into the equations Y = aX + b and Y = aX² + bX + c respectively, the corresponding Y values ​​are obtained. Taking the sample analysis_final_a as an example, a dataset containing Record_ID, w1, w2, w1_scale, w2_scale, and Y is obtained. Assuming that the number of records in this sample is n, the formula for calculating the average fitting error rate is as follows:

[0068] Among them, w2 k Y represents the value of w2 for the k-th sample in the sample. k This represents the value of Y obtained by substituting w1 and the parameter into the k-th sample in the sample;

[0069] Find the group with an average fitting error rate δ < 5%, and investigate the problem of the weighing equipment corresponding to this group. Since the weighing deviation rate of each sample is greater than 10%, and the fitting error rate is less than 5% after fitting, it can be determined that the weighing equipment has a regular deviation.

[0070] For example, courier company A transports 100 packages from city c1 to city c2. At courier company A's transit station in city c1, a smart weighing device is used to weigh the packages, amc1 = {m1, m2, ..., m100}, and the weight is recorded in the blockchain.

[0071] Courier company A transports 100 packages to the multimodal transport transfer station in city C1. Together with the multimodal transport staff, they seal the boxes, scan the QR code on the sealed boxes to confirm the box information (including the box weight bm and the tracking number tran0001), and use a smart sealing device to seal the boxes. After sealing, the smart sealing device uploads sealing information including time and latitude / longitude to the blockchain. At this point, it is known that the courier company weighs the multimodal transport goods as w1, and the multimodal transport party uses the smart device to weigh the total weight as w2, and uploads this information to the blockchain. The transport process then begins.

[0072] After the goods are transported to city C2, at the C2 multimodal transport transfer station, the intermodal transport company uses smart equipment to weigh the entire container to obtain W3, and uploads the result to the blockchain.

[0073] The back-end system begins pre-billing. When the weighing deviation is less than 5%, it indicates that the weighing errors of the courier company and the shipping company are small, and the courier company's weighing value is used for pre-billing to protect their rights. When the weighing deviation is greater than 5%, it indicates a discrepancy between the courier company's and the shipping company's weighing values, requiring handling on a case-by-case basis. When w1 deviates from the average value the most, the largest deviation w1 is discarded, and the shipping company's weighing value is used as the standard. This would easily expose any smuggling activities by the courier company. When w2 or w3 deviates from the average value the most, the largest deviation is discarded. In this case, the shipping company's two weighing deviations are large, reducing their trust in the courier company's weighing results. To protect the courier company, the courier company's weighing value is chosen for pre-billing.

[0074] At the end of the month, weighings with deviations greater than 5% will be analyzed. First, equipment with deviations exceeding twice the average deviation will be investigated to rule out equipment malfunctions or tampering. Next, error records will be fitted, primarily targeting the carrier company. If a set of equipment has undergone uniform adjustments, a fitting pattern will emerge, and this will be sent to relevant parties for verification. The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in this invention, based on the technical solution and inventive concept of the present invention, should be included within the scope of protection of this invention.

Claims

1. A method for managing the weight of multimodal transport goods, characterized in that, Includes the following steps: (1) The express delivery company's stores collect the express delivery and transport it to the express delivery company's city transit station; (2) The city transit station of the express company selects the express shipments that need to be delivered for multimodal transport, uses intelligent weighing equipment to weigh the express shipments, and checks whether the express weight and fee are consistent while scanning the express shipment tracking number, and then records it to the blockchain; and according to the city of transport, the express shipments are divided into boxes, and the tracking number of each box is recorded to the blockchain. (3) The express delivery company will transport the intermodal goods to the multimodal transport consignment point and hand them over to the intermodal carrier; (4) Delivery between the multimodal transport carrier and the express delivery company in the target city; (5) Use a game theory approach to record the weight of transported goods; if the error |m|≤5%, then the courier company's weighing is considered reliable, and the weight is used... As accounting weight; If the error |m| > 5%, calculate the average weight. Through formula Where w is w1, w2 or w3, the deviations of w1, w2, and w3 are dw1, dw2, and dw3, respectively. If dw1 is the largest, then according to Billing; if dw2 or dw3 has the largest value, it will still be billed according to w1; The weight of w1 comes from the weight of each item weighed by the courier company plus the weight of the standard packaging box; w2 comes from the weight weighed by the shipping company; w3 is the weight obtained by weighing the transshipment box on the weighing machine. (6) Offline error analysis: records with an absolute error value greater than 5% will be analyzed monthly. First, the equipment with a deviation ratio greater than twice the average deviation ratio of the weighing equipment will be investigated to rule out equipment failure or equipment cheating. The error records will be fitted. For the carrier company, if some equipment has been uniformly adjusted, a fitting pattern will appear.

2. The multimodal transport cargo weight management method according to claim 1, characterized in that, The specific steps (3) are as follows: 3-1. The courier company staff and the intermodal transport carrier staff jointly scan the QR code on the package to confirm the package information; 3-2. Use smart sealing equipment to seal the box. After sealing, upload the sealing information, including time and latitude and longitude, to the blockchain through the smart sealing equipment. 3-3. After the employees of the courier company and the carrier company confirm the box information, the handover is confirmed. The courier company weighs each package. The transport box contains several packages. The sum of the weights of each package plus the weight of the standard packaging box is the weight w1. At the handover point, the packages are already packed in the standard packaging box. The shipping company can weigh them directly to obtain the weight w2. The data of weights w1 and w2 are then uploaded to the blockchain.

3. The multimodal transport cargo weight management method according to claim 2, characterized in that, The specific steps (4) are as follows: 4-1. Courier company employees inspect the intermodal transport container for damage and the seal for damage; 4-2. Staff from both the multimodal transport carrier and the courier company scan the QR code on the transfer box, confirm the detailed information, and upload it to the blockchain.

4. The multimodal transport cargo weight management method according to claim 3, characterized in that, The specific steps (5) are as follows: 5-1. Obtain the weighing error between the courier company and the carrier. , If the error |m| ≤ 5%, the courier company's weighing method is considered reliable. As the accounting weight; in this case, the courier and the carrier agree on the weight of the goods weighed, and the minimum weight value is used as the basis for billing. If the error |m| > 5%, calculate the average weight. Through formula Where w is w1, w2 or w3, the deviations of w1, w2, and w3 are dw1, dw2, and dw3, respectively. If dw1 is the largest, then according to In this billing scenario, if the weighing deviation of the two smart weighing devices in the two cities of the carrier is less than the weighing deviation of the weight reported by the courier, it is considered that the courier's weighing error is larger, and the carrier's weighing result is used. This algorithm allows the courier to manually adjust the smart weighing equipment, and the malfunction error of the courier's smart weighing equipment does not have a negative impact on the billing result. If the error |m| > 5%, calculate its average value. Through formula Where w is w1, w2 or w3, the deviations of w1, w2, w3 are dw1, dw2, dw3 respectively. If dw2 or dw3 is the largest, it is still charged according to w1. In this case, it is considered that the carrier's smart weighing device has a large error, and the express company's weighing is the standard. All records with error |m|>5% are uploaded to the offline analysis center.

5. The method for managing the weight of multimodal transport goods according to claim 1 or 2, characterized in that, The specific step (6) is as follows: Collect all data with intermodal transport errors greater than 5% for one month, and the following data will be generated: The Record_ID field is used to record the shipping record ID. w1 is used to identify the courier company's weighing, and w2 and w3 are used to represent the two weighings by the shipping company. The w1_scale, w2_scale, and w3_scale fields are used to represent the device IDs used by the smart weighing system for w1, w2, and w3 weighing, respectively. To identify weighing equipment with significantly more than twice the average number of errors: Use an SQL filter statement to obtain the source of the error equipment: `select X, count(X) from ABNORMAL_RECORD group by X;` where `X` is replaced with "w1_scale", "w2_scale", and "w3_scale". This will result in three sets of data for the weighing equipment IDs and the number of errors. Merge these three sets by adding the number of errors for the same ID, resulting in a single set of equipment IDs and error data: `vt={id1:vt1,id2:vt2, id3:vt3,...idn:vtn}`, where there are no duplicates from `id1` to `idn`. Then, query the total number of weighings for `id1`, `id2`, ..., `idn`, resulting in the set `vat={id1:vat1, id2:vat2, ..., idn:vatn}`. Through formula Calculate the proportion of weighing error generated by each device to obtain a data set of weighing error generation proportions for each device, vtr={id1:vtr1, id2:vtr2, id3:vtr3,...idn:vtrn}; calculate the average error proportion. Investigate and check equipment with an error ratio greater than 2 times vtravg; We fit the weighing errors of courier companies and carriers by grouping them into w1_scale and w2_scale, and w1_scale and w3_scale respectively; the steps are as follows: The SQL statement `select distinct(X) from ABNORMAL_RECORD;` replaces `X` with `w1_scale`, `w2_scale`, and `w3_scale`, resulting in three sets of error weighing devices: `vtd1={vtd11, vtd12, vtd13……}`, `vtd2={vtd21, vtd22, vtd23……}`, and `vtd3={vtd31, vtd32, vtd33...}`. Here, `vtd1` represents the courier company's list of error weighing devices, while `vtd2` and `vtd3` represent the carrier's list. The following SQL statement is used to obtain the comparison groups of express delivery company and carrier equipment: `select record_id, w1, w2from ABNORMAL_RECORD where w1_scale=vtd1x and w2_scale=vtd2x;` where `vtd1x` and `vtd2x` are elements in `vtd1` and `vtd2`, respectively. The SQL statement is executed to iterate through all elements in `vtd1` and `vtd2`, resulting in a data analysis set `analysis_a={a1, a2, a3…….}`, where each element is the corresponding element of `vtd1` and `vtd2`. This query retrieves a data set containing the fields `Record_ID`, `w1`, `w2`, `w1_scale`, and `w2_scale`. For `vtd1` and `vtd3`, a data analysis set `analysis_b={b1, b2, b3……}` is obtained, where each element is the corresponding element of `vtd1` and `vtd3`. For analysis_a and analysis_b, filter out samples with fewer than 20 samples, or samples that do not meet the requirements. Elements >10%, where n is the number of elements in analysis_a or analysis_b, wa corresponds to w1, wb corresponds to w2 in analysis_a and w3 in analysis_b; obtain the filtered sets analysis_final_a and analysis_final_b, and perform a first-order linear fit and a second-order linear fit on all elements of the two groups respectively; fit Y=aX+b and Y=aX to each group of samples respectively. 2 The parameters a, b, and c of the equation +b+c are obtained; for each set of fitting parameters, w1 is used as X and substituted into the fitting function, w1 is substituted into X, and w2 is substituted into Y. The MATLAB polyfit function is used to perform first-order and second-order fitting to obtain the values ​​of parameters a, b, and c; then a, b, c, and w1 are substituted into the equations Y=aX+b and Y=aX, respectively. 2 The values ​​of Y are obtained by adding b and c. Taking the sample from analysis_final_a as an example, a dataset containing Record_ID, w1, w2, w1_scale, w2_scale, and Y is obtained. Assuming that the number of records in this sample is n, the formula for calculating the average fitting error rate is as follows: ,in, This represents the value of w2 for the k-th sample in the sample. This represents the value of Y obtained by substituting w1 and the parameter into the k-th sample in the sample; Find the group with an average fitting error rate δ < 5%, and investigate the problem of the weighing equipment corresponding to this group. Since the weighing deviation rate of each sample is greater than 10%, and the fitting error rate is less than 5% after fitting, it can be determined that the weighing equipment has a regular deviation.

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