CN114819820A

CN114819820A - Intelligent supply chain management method, device, system and medium based on artificial intelligence

Info

Publication number: CN114819820A
Application number: CN202210413010.2A
Authority: CN
Inventors: 刘艳军; 伍锡兴; 周子勋
Original assignee: Beijing Longteng Weishidai Technology Information Co ltd
Current assignee: Beijing Longteng Weishidai Technology Information Co ltd
Priority date: 2022-04-20
Filing date: 2022-04-20
Publication date: 2022-07-29

Abstract

The application relates to an intelligent supply chain management method, device, system and medium based on artificial intelligence, relating to the technical field of supply chain management and comprising the steps of receiving current goods order information input by a client; sending the current goods order information to a supply terminal; receiving shipment entrusting information returned by the supply end; and receiving electronic logistics information of a logistics end based on the current goods order information and the shipment entrusting information. The method and the system meet the management requirements of a company on the whole process of the supply chain, improve the convenience of communication and cooperation among all service nodes of the supply chain, and facilitate the personnel to master the information related to the goods orders in real time.

Description

Intelligent supply chain management method, device, system and medium based on artificial intelligence

Technical Field

The present application relates to the field of supply chain management technologies, and in particular, to a method, an apparatus, a system, and a medium for intelligent supply chain management based on artificial intelligence.

Background

With the development of economic globalization and informatization, the market competition mode is converted from traditional competition between enterprises into overall strength opposition between supply chains. Enterprises only rely on self abilities and are difficult to occupy a place in the market, the enterprises and upstream and downstream enterprises form an alliance to form a supply chain system to face complex market competition and achieve quick response to market demands, and the overall effect of the supply chain is maximized, so that the enterprises of the supply chain achieve a win-win situation.

In order to meet the management requirements of companies on the whole flow of the supply chain, an informatization system for supply chain business management needs to be constructed.

Disclosure of Invention

In order to meet the management requirements of companies on the whole process of the supply chain, the application provides an intelligent supply chain management method, device, system and medium based on artificial intelligence.

In a first aspect, the present application provides an intelligent supply chain management method based on artificial intelligence, which adopts the following technical scheme:

an intelligent supply chain management method based on artificial intelligence comprises the following steps:

receiving current goods order information input by a client;

sending the current goods order information to a supply terminal;

receiving shipment entrusting information returned by the supply end;

and receiving electronic logistics information of a logistics end based on the current goods order information and the shipment entrusting information.

By adopting the technical scheme, the management requirements of a company on the whole process of the supply chain are met, the convenience of communication and cooperation among all service nodes of the supply chain is improved, and personnel can conveniently master the information related to the goods orders in real time.

Optionally, before the sending the current goods order information to the supply terminal, the method further includes:

if the client is a client which does not input the goods order information for the first time, acquiring historical goods order information of the client in a first statistical period;

acquiring grade information of the client based on the historical goods order information;

acquiring a corresponding analysis strategy based on the grade information, wherein the analysis strategy comprises at least one analysis parameter and a prediction model for predicting the quantity of required goods in the current goods order information based on the at least one analysis parameter;

generating forecast data of the quantity of the required goods of the client based on the analysis strategy, and comparing the forecast data with the quantity of the required goods in the current goods order information to obtain a feasibility analysis result of the quantity of the required goods in the current goods order information;

and if the feasibility analysis result is unreasonable, sending prompt information to the client.

By adopting the technical scheme, the over-prediction model and the analysis parameters analyze and predict the quantity of the required goods in the current goods order information, the predicted data is compared with the current quantity of the required goods to obtain a feasibility analysis result, if the difference value between the predicted data and the quantity of the required goods exceeds an unreasonable threshold value, the feasibility analysis result of the quantity of the required goods of the current goods order information is unreasonable, the possibility of the statistical error of the purchasing party is indicated, the client side of the purchasing party is prompted, and the purchasing party of the client side can find the situation of the statistical error in time.

Optionally, the obtaining of the corresponding analysis policy based on the level information includes:

if the grade information of the client is a first-grade client, generating a first analysis strategy, wherein analysis parameters in the first analysis strategy comprise historical goods order information, company development parameters and industry development parameters;

if the grade information of the client is a secondary client, generating a second analysis strategy, wherein analysis parameters in the second analysis strategy comprise the historical goods order information and company development parameters;

if the grade information of the client is a third-grade client, generating a third analysis strategy, wherein analysis parameters in the third analysis strategy comprise the historical goods order information;

the total purchase amount in the historical goods order information corresponding to the primary customer is located in a first total amount interval, the total purchase amount in the historical goods order information corresponding to the secondary customer is located in a second total amount interval, the total purchase amount in the historical goods order information corresponding to the tertiary customer is located in a third total amount interval, the minimum value of the first total amount interval is larger than the maximum value of the second total amount interval, and the minimum value of the second total amount interval is larger than the maximum value of the third total amount interval.

By adopting the technical scheme, the high-grade high-quality purchaser can enjoy better experience, the analysis parameters corresponding to the high-grade high-quality purchaser are more comprehensive, the cost investment is more, the accuracy of the predicted data is higher, and the more reasonable prompt information is obtained by the high-grade purchaser.

Optionally, before sending the prompt information to the client, the method further includes:

and acquiring a corresponding prompt strategy based on the grade information of the client.

By adopting the technical scheme, the experience of high-quality buyers is improved.

Optionally, after sending the prompt message to the client, the method further includes:

receiving confirmation information of the feasibility analysis result returned by the client;

and if the confirmation information comprises correction information of the analysis parameters, adjusting the analysis parameters based on the correction information.

By adopting the technical scheme, the ordering habit parameter in the analysis parameters is adjusted according to the self requirement of the purchasing company, and if the required quantity is predicted continuously according to the ordering habit parameter before adjustment, the error is larger, the reference value is lower, so that the analysis parameters need to be adjusted according to the correction information.

Optionally, after the receiving the electronic logistics information of the logistics end based on the current goods order information and the shipment entrusting information, the method further includes:

receiving a refund request of the client, judging whether the current goods order information is signed or not based on the electronic logistics information, and judging whether the current goods order information has the remaining refund operation times or not;

and if the current goods order information is not signed and the remaining refundable operation times exist, executing refund operation based on the refund request.

By adopting the technical scheme, in order to reduce the condition that a purchaser frequently and maliciously sends refund requests through the client, the current goods order information of each client corresponds to refundable operation times with limited times.

Optionally, after the refund operation is performed based on the refund request, the method further includes:

acquiring the total number of the goods orders for executing the refund operation received in a second statistical period;

and if the total number is not less than the preset threshold value, reducing the grabbing frequency of all the order states.

By adopting the technical scheme, the refreshing times of all the order states are increased, so that the order states can be updated in time, the timeliness of knowing that the goods order subjected to the refund execution operation is lost is improved, and the related remedial measures can be taken in time when the goods order is lost.

In a second aspect, the present application provides an intelligent supply chain management device based on artificial intelligence, which adopts the following technical solution:

an intelligent supply chain management device based on artificial intelligence, comprising,

the first receiving module is used for receiving the current goods order information input by the client;

the sending module is used for sending the current goods order information to a supply terminal;

the second receiving module is used for receiving shipment entrusting information returned by the supply end;

and the third receiving module is used for receiving the electronic logistics information of the logistics end based on the current goods order information and the shipment entrusting information.

In a third aspect, the present application provides an intelligent supply chain management system based on artificial intelligence, which adopts the following technical scheme:

an intelligent supply chain management system based on artificial intelligence comprises a memory, a server, a client, a supply end and a logistics end;

the memory having stored thereon a computer program that can be loaded by the server and that performs the method of any of claims 1 to 7;

the client is used for inputting the current goods order information;

the supply end is used for generating shipment entrusting information after completing stock preparation based on the current goods order information and sending the shipment entrusting information to the server;

and the logistics terminal is used for generating electronic logistics information according to the transportation state of the goods of the current goods order information and sending the electronic logistics information to the server.

In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solutions:

a computer-readable storage medium storing a computer program capable of being loaded by a processor and executing the artificial intelligence based intelligent supply chain management method of any one of the first aspect.

Drawings

Fig. 1 is a schematic flowchart of a management method according to an embodiment of the present application.

Fig. 2 is a block diagram of a management apparatus according to an embodiment of the present application.

Fig. 3 is a block diagram of a program structure of a management system according to an embodiment of the present application.

Detailed Description

The present application is described in further detail below with reference to the attached drawings.

The embodiment of the present application provides an intelligent supply chain management method based on artificial intelligence, the method uses a server as an execution main body, and the following detailed description is made on a processing flow shown in fig. 1 with reference to specific embodiments, and the contents may be as follows:

step 101, receiving current goods order information input by a client;

the client is a terminal such as a mobile phone and a computer of a purchaser for inputting purchasing information, and the current goods order information comprises current purchasing time, a type of the required goods, the quantity of the required goods, a receiving address and the like.

102, sending current goods order information to a supply terminal;

the supply end is a terminal such as a mobile phone and a computer of a supplier for supplying goods, and the supply end acquires stock information after receiving the current goods order information, wherein the stock information is the quantity of stock according to the current goods order information.

103, receiving shipment entrusting information returned by a supply end;

the shipment order information includes a shipment available time, a shipment address, and the like, and when the stock quantity in the stock information coincides with the stock quantity in the current cargo order, an operation of transporting the cargo can be performed.

And 104, receiving electronic logistics information of the logistics end based on the current goods order information and the shipment entrusting information.

The logistics end is a terminal such as a mobile phone and a computer of a logistics provider for transporting goods, the electronic logistics information comprises a transportation route, a transportation vehicle and goods position information, and the electronic logistics information updates the transportation state of the goods through the goods position information.

Therefore, the management requirements of a company on the whole process of the supply chain are met, the convenience of communication and cooperation among all service nodes of the supply chain is improved, and personnel can conveniently master the information related to the goods orders in real time.

Before the supply chain interfaces of the client, the supply end and the logistics end are displayed, in order to ensure the confidentiality and the safety of supply chain information, the identity of a user needs to be verified through login operation, and the login mode comprises account password login, prestored mobile phone number verification login and the like.

The method comprises the steps that a supply chain display interface of a client is a purchasing center, the purchasing center comprises services such as purchase ordering, purchase approval and historical goods order inquiry related to current goods order information, the purchase ordering interface comprises a shortcut ordering and a purchase template, the supply chain display interface of a supply end which improves convenience of operation of purchaser personnel corresponding to the client is a supply center, the supply center comprises services such as sales contract, sales order, sales report, goods shipment creation of current goods order information and goods shipment entrustment information creation, the sales contract interface comprises contract approval related to current goods order information, the sales order interface comprises order creation and order approval related to current goods order information, and the sales report comprises a sales contract report and a sales order report; the supply chain display interface of the logistics end comprises the creating, auditing and transporting states of the electronic logistics information of the current goods order information goods, and also comprises a reverse center, wherein the reverse center comprises reverse processing services such as purchasing goods returning, rejecting, after-sale management, after-sale orders and the like.

In one embodiment, the current goods order information is created and ordered by the buyer through the client according to the current quantity of requested goods, and since the current quantity of requested goods is manually counted by the buyer, in order to reduce the influence caused by the counting error of the buyer, step 102 further includes the following processing: if the client is the client which does not input the goods order information for the first time, acquiring historical goods order information of the client in a first statistical period; acquiring grade information of a client based on historical goods order information; acquiring a corresponding analysis strategy based on the grade information, wherein the analysis strategy comprises at least one analysis parameter and a prediction model for predicting the quantity of the required goods in the current goods order information based on the at least one analysis parameter; generating forecast data of the quantity of the required goods of the client based on the analysis strategy, and comparing the forecast data with the quantity of the required goods in the current goods order information to obtain a feasibility analysis result of the quantity of the required goods in the current goods order information; and if the feasibility analysis result is unreasonable, sending prompt information to the client.

The method comprises the steps of analyzing and predicting the quantity of the required goods in current goods order information through a prediction model and analysis parameters, comparing prediction data with the current quantity of the required goods to obtain a feasibility analysis result, and if the difference value between the prediction data and the quantity of the required goods exceeds an unreasonable threshold value, judging that the possibility of errors in statistics of a buyer exists, prompting the client of the buyer, and facilitating the buyer of the client to find the situation of errors in statistics in time.

The analysis parameters comprise the ordering habit parameters of the buyer for purchasing through the client, and the ordering habit parameters comprise the ordering time, the ordering frequency and the like, for example, the ordering time is 15 per month, and the ordering frequency is once per month.

As an alternative to this embodiment, the prediction model may be a gray prediction model. It is easy to understand that the more comprehensive the analysis parameters, the more accurate the prediction data in the analysis result, but the higher the calculation cost such as the calculation amount and the labor in the corresponding analysis calculation process; therefore, for different levels of the client, the analysis parameters and the generated cost in the adopted analysis strategy are different, and correspondingly, the accuracy of the predicted data is also different, so that in order to enable high-level high-quality buyers to enjoy better experience, the analysis parameters corresponding to the high-level high-quality buyers are more comprehensive, the cost investment is more, the accuracy of the predicted data is higher, and the prompt information obtained by the higher-quality buyers is more reasonable.

Specifically, the step of obtaining the corresponding analysis strategy based on the grade information includes the following steps: if the grade information of the client is a first-grade client, generating a first analysis strategy, wherein analysis parameters in the first analysis strategy comprise historical goods order information, company development parameters and industry development parameters; if the grade information of the client is a secondary client, generating a second analysis strategy, wherein analysis parameters in the second analysis strategy comprise historical goods order information and company development parameters; if the grade information of the client is a third-grade client, generating a third analysis strategy, wherein analysis parameters in the third analysis strategy comprise historical goods order information; the total purchase amount in the historical goods order information corresponding to the primary customer is located in a first total amount interval, the total purchase amount in the historical goods order information corresponding to the secondary customer is located in a second total amount interval, the total purchase amount in the historical goods order information corresponding to the tertiary customer is located in a third total amount interval, the minimum value of the first total amount interval is larger than the maximum value of the second total amount interval, and the minimum value of the second total amount interval is larger than the maximum value of the third total amount interval.

In this embodiment, the first statistical period may be three years, and the ranking rule of the client is as follows: classifying the customer grades according to the total purchase amount of three years in the historical goods order information; for example, the total purchasing amount is a first total amount interval of one million to two million, a second total amount interval of five hundred thousand to one million, and a third total amount interval of less than fifty thousand, and the total purchasing amount in the classification may be set according to the industry situation and the unit price situation of the goods.

The company development parameters comprise business income increase rate, gross profit increase rate, net profit increase rate, re-investment cash rate, fixed asset income rate, total asset increase rate, net asset increase rate and the like acquired based on big data, and the industry development parameters comprise industry total sales amount, industry total income, industry total profit, seasonal data influencing total business amount of clothing industry and the like, currency expansion rate, bank interest rate, tax system and tax rate, national income level, purchasing power and the like acquired based on big data.

In one embodiment, since the prompting cost of the client is limited, in order to ensure the experience of the good buyer, the following processing is correspondingly included before the step of sending the prompting information to the client: and acquiring a corresponding prompt strategy based on the grade information of the client.

If the grade information of the client is a third-grade client, sending a warning short message to the client in a pop-up window mode, where the warning short message may be preset warning content, for example, the warning content is: "the quantity of the current order for goods is at risk"; if the grade information of the client is a secondary client, sending an analysis report to the client in a popup mode, wherein the analysis report comprises prediction data of the quantity of the required goods of the client and a feasibility analysis result; and each first-level client corresponds to a binding worker for carrying out service, if the grade information of the client is the first-level client, the binding worker information corresponding to the client is obtained, the prediction data, the analysis parameters and the prediction model of the requested goods quantity of the client are sent to the binding workers, and the binding workers check the binding workers and then carry out telephone communication with a client-side responsible person.

In one embodiment, the ordering habit parameter in the analysis parameters is adjusted according to the needs of the purchasing company, and at this time, if the requested quantity is predicted according to the ordering habit parameter before adjustment, the accuracy of the prediction data is reduced, so correspondingly, the following processing is further included after the step of sending the prompt information to the client: receiving confirmation information of feasibility analysis results returned by the client; and if the confirmation information comprises correction information of the analysis parameters, adjusting the analysis parameters based on the correction information.

The confirmation information includes judgment information of the prediction data or correction information of the analysis parameter, the judgment information includes a check result of the quantity of the required goods in the current goods order information, and the check result is a result after manual confirmation by a purchaser of the client. If the required goods quantity in the current goods order information is correct, the confirmation information comprises correction information, the reason that the feasibility analysis result is unreasonable is that the ordering habit parameters are adjusted, and therefore the correction information comprises the adjusted ordering habit parameters.

For example, the order placing time in the order placing habit parameters is adjusted to 10 and 20 per month, the order placing frequency is adjusted to twice per month, that is, the required quantity of each month is input into the goods order twice, if the analysis parameters before adjustment are continuously applied for prediction, the error is large, the reference value is low, and therefore the analysis parameters need to be adjusted according to the correction information.

In one embodiment, when the buyer at the client finds that the order placing error such as the requested goods quantity or the requested goods type requires the order refunding, the method further comprises the following steps after receiving the electronic logistics information of the logistics terminal based on the current goods order information and the shipment order information: receiving a refund request of a client, judging whether the current goods order information is signed up or not based on the electronic logistics information, and judging whether the current goods order information has the remaining refund operation times or not; and if the current goods order information is not signed in and the remaining refundable operation times exist, executing the refund operation based on the refund request.

In order to reduce the situation that a purchaser frequently and maliciously sends refund requests through clients, the current goods order information of each client corresponds to refundable operation times with limited times, and if the current goods order information signs for and/or does not have the remaining refundable operation times, the purchaser can send the refund requests to a server through the clients but cannot carry out refund operations and send notifications to the corresponding clients.

In one embodiment, in order to reduce the possibility of losing the goods order after the refund operation is performed, the step of performing the refund operation based on the refund request further comprises the following steps: acquiring the total number of the received goods orders for executing refund operation in the second statistical period; and if the sum is not less than the preset threshold value, reducing the grabbing frequency of all the order states.

In this embodiment, the second statistical period may be one week, the preset threshold is ten orders, and when the total number of the goods orders for which the refund operation is performed in one week is not less than ten orders, the capturing frequency of all the order states is reduced, that is, the refresh times of all the order states are increased, so that the order states can be updated in time, the timeliness of knowing that the goods orders for which the refund operation is performed are lost is improved, and it is convenient to take relevant measures in time when the goods orders are lost.

Referring to fig. 2, based on the same technical concept, the embodiment of the present application further discloses an intelligent supply chain management device 200 based on artificial intelligence, where the management device 200 includes:

a first receiving module 201, configured to receive current goods order information entered by a client;

a sending module 202, configured to send current goods order information to a supply end;

the second receiving module 203 is used for receiving shipment entrusting information returned by the supply end;

and a third receiving module 204, configured to receive electronic logistics information of the logistics end based on the current goods order information and the shipment commission information.

Optionally, the management apparatus 200 further includes:

the first acquisition module is used for acquiring historical goods order information of the client in a statistical period when the client is a client which does not input goods order information for the first time;

the second acquisition module is used for acquiring the grade information of the client based on the historical goods order information;

the third acquisition module is used for acquiring a corresponding analysis strategy based on the grade information, wherein the analysis strategy comprises at least one analysis parameter and a prediction model for predicting the quantity of required goods in the current goods order information based on the at least one analysis parameter;

the generation module is used for generating forecast data of the quantity of the required goods of the client based on the analysis strategy, comparing the forecast data with the quantity of the required goods in the current goods order information and obtaining a feasibility analysis result of the quantity of the required goods in the current goods order information;

and the sending prompt module is used for sending prompt information to the client when the feasibility analysis result is unreasonable.

Optionally, the sending prompting module includes:

and the obtaining submodule is used for obtaining the corresponding prompt strategy based on the grade information of the client.

Optionally, the management apparatus 200 further includes:

the fourth receiving module is used for receiving confirmation information of the feasibility analysis result returned by the client;

and the adjusting module is used for adjusting the analysis parameters based on the correction information when the confirmation information comprises the correction information of the analysis parameters.

Optionally, the management apparatus 200 further includes:

the fifth receiving module is used for receiving a refund request of the client, judging whether the current goods order information is signed based on the electronic logistics information and judging whether the current goods order information has the remaining refundable operation times;

and the execution module is used for executing refund operation based on the refund request when the current goods order information is not signed for and the remaining refund operation times exist.

Optionally, the management apparatus 200 further includes:

the fourth acquisition module is used for acquiring the total number of the goods orders which are received in the counting period and execute the refund operation;

and the frequency reducing module is used for reducing the grabbing frequency of all order states when the total number is not less than a preset threshold value.

Referring to fig. 3, based on the same technical concept, the embodiment of the present application further discloses an artificial intelligence based intelligent supply chain management system 300, where the management system 300 includes: a storage 301, a server 302, a client 303, a supply end 304 and a logistics end 305;

the memory 301 is connected to the server 302 via a communication bus, and the memory 301 stores thereon a computer program that can be loaded by the server 302 and execute an artificial intelligence based intelligent supply chain management method.

The memory 301 may be used to store instructions, programs, code, sets of codes, or sets of instructions. The memory 301 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing an artificial intelligence based intelligent supply chain management method, etc.; the data storage area can store data and the like related to the intelligent supply chain management method based on artificial intelligence.

The server 302 may include one or more processing cores. The server 302 performs various functions of the present application and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 301 to invoke data stored in the memory 301. It is understood that, for different devices, electronic devices for implementing the functions of the server 302 may be other devices, and the embodiments of the present application are not limited in particular.

The client 303, the supply terminal 304 and the logistics terminal 305 are respectively connected with the server 302, and the client 303 is used for inputting current goods order information and sending the current goods order information to the server 302; the supply terminal 304 is configured to generate shipment delegation information after completing stock based on the current goods order information, and send the shipment delegation information to the server 302; the logistics terminal 305 is configured to generate electronic logistics information according to the transportation state of the goods of the current goods order information, and send the electronic logistics information to the server 302.

Based on the same technical concept, embodiments of the present invention further provide a computer-readable storage medium storing a computer program capable of being loaded by a processor and executing the artificial intelligence based intelligent supply chain management method provided by the above embodiments.

In this embodiment, the computer readable storage medium may be a tangible device that retains and stores instructions for use by an instruction execution device. The computer readable storage medium may be, but is not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any combination of the foregoing. In particular, the computer readable storage medium may be a portable computer diskette, a hard disk, a U-disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a podium random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, an optical disk, a magnetic disk, a mechanical coding device, and any combination thereof.

The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.

The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the application referred to in the present application is not limited to the embodiments with a particular combination of the above-mentioned features, but also encompasses other embodiments with any combination of the above-mentioned features or their equivalents without departing from the spirit of the application. For example, the above features may be replaced with (but not limited to) features having similar functions as those described in this application.

Claims

1. An intelligent supply chain management method based on artificial intelligence is characterized by comprising the following steps:

receiving current goods order information input by a client;

sending the current goods order information to a supply terminal;

receiving shipment entrusting information returned by the supply end;

2. The method of claim 1, further comprising, prior to said sending said current goods order information to a supplier:

3. The method of claim 2, wherein the obtaining the corresponding analysis policy based on the rank information comprises:

4. The method according to claim 2 or 3, wherein before said sending the prompt message to the client, further comprising:

5. The method of claim 4, further comprising, after the sending the prompt message to the client:

6. The method of claim 1, further comprising, after the receiving electronic logistics information of a logistics end based on the current goods order information and shipment order information:

7. The method of claim 6, further comprising, after said performing a refund operation based on the refund request:

8. An intelligent supply chain management device based on artificial intelligence is characterized in that the device comprises,

9. An intelligent supply chain management system based on artificial intelligence is characterized by comprising a memory, a server, a client, a supply end and an logistics end;

the client is used for inputting the current goods order information;

the supply end is used for generating shipment entrusting information after completing stock preparation based on the current cargo order information and sending the shipment entrusting information to the server;

10. A computer-readable storage medium, in which a computer program is stored which can be loaded by a processor and which executes the method of any one of claims 1 to 7.