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

Abstract

The present application relates to an artificial intelligence-based smart supply chain management method, device, system and medium, and relates to the technical field of supply chain management, which includes receiving current cargo order information entered by a client; sending the current cargo order to a supplier information; receiving the shipment entrustment information returned by the supplier; and receiving electronic logistics information of the logistics end based on the current cargo order information and the shipment entrustment information. This application satisfies the company's management requirements for the entire supply chain process, improves the convenience of communication and collaboration between various business nodes in the supply chain, and facilitates personnel to control information related to cargo orders in real time.

Description

Method, device, system and medium for intelligent supply chain management based on artificial intelligence

technical field

The present application relates to the technical field of supply chain management, in particular to an artificial intelligence-based smart supply chain management method, device, system and medium.

Background technique

With the continuous development of economic globalization and informatization, the market competition mode has changed from the traditional competition between enterprises to the confrontation of overall strength between supply chains and supply chains. It is difficult for enterprises to take a place in the market only by relying on their own capabilities. Enterprises must form alliances with upstream and downstream enterprises to form a supply chain system to jointly face complex market competition and achieve rapid response to market demand to achieve the overall supply chain. The effect is maximized, so that the enterprises in the supply chain can achieve a win-win situation.

In order to meet the company's management requirements for the entire supply chain process, it is necessary to build an information system for supply chain business management.

SUMMARY OF THE INVENTION

In order to meet the company's management requirements for the entire supply chain process, the present application provides an artificial intelligence-based smart supply chain management method, device, system and medium.

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

A smart supply chain management method based on artificial intelligence, including:

Receive the current cargo order information entered by the client;

Send the current goods order information to the supplier;

receiving the shipment entrustment information returned by the supplier;

The electronic logistics information of the logistics terminal is received based on the current cargo order information and the shipment consignment information.

By adopting the above technical solutions, the company's management requirements for the entire supply chain process are met, the convenience of communication and collaboration between various business nodes in the supply chain is improved, and it is convenient for personnel to control information related to goods orders in real time.

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

If the client is a client that does not enter the cargo order information for the first time, obtain the historical cargo order information of the client in the first statistical period;

Obtain the level information of the client based on the historical cargo order information;

Obtaining a corresponding analysis strategy based on the level information, where the analysis strategy includes at least one analysis parameter and a prediction model for predicting the quantity of goods requested in the current goods order information based on the at least one analysis parameter;

Based on the analysis strategy, the forecast data of the quantity requested by the client is generated, and the forecast data is compared with the quantity requested in the current cargo order information to obtain the estimated quantity of the requested quantity in the current cargo order information. Feasibility analysis results;

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

By adopting the above technical scheme, the quantity of goods requested in the current order information is analyzed and forecasted through the forecasting model and analysis parameters, and the forecast data is compared with the current quantity of goods requested to obtain a feasibility analysis result. If the difference between the quantity of goods exceeds the unreasonable threshold, the result of the feasibility analysis of the quantity of goods requested in the current order information is unreasonable, indicating that there is a possibility of a statistical error by the buyer, so as to prompt the client of the buyer, It is convenient for the buyer of the client to discover the statistical error in time.

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

If the level information of the client is a first-level customer, a first analysis strategy is generated, and the analysis parameters in the first analysis strategy include the historical cargo order information, company development parameters and industry development parameters;

If the level information of the client is a secondary customer, a second analysis strategy is generated, and the analysis parameters in the second analysis strategy include the historical cargo order information and company development parameters;

If the level information of the client is a third-level customer, a third analysis strategy is generated, and the analysis parameters in the third analysis strategy include the historical cargo order information;

Wherein, the total purchase amount in the historical goods order information corresponding to the first-level customer is located in the first total amount interval, and the total purchase amount in the historical goods order information corresponding to the second-level customer is located in the second total amount interval, and the The total purchase amount in the historical cargo order information corresponding to the third-level customer is located in the third total amount interval, the minimum value of the first total amount interval is greater than the maximum value of the second total amount interval, and the second total amount interval The minimum value of is greater than the maximum value of the third total amount interval.

By adopting the above technical solutions, high-level high-quality buyers can enjoy a better sense of experience, so that high-level high-quality buyers can have more comprehensive analysis parameters, more cost input, and higher prediction data accuracy, thereby It makes the prompt information that the better quality buyers get more reasonable.

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

A corresponding prompt policy is obtained based on the level information of the client.

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

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

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

If the confirmation information includes correction information of the analysis parameter, the analysis parameter is adjusted based on the correction information.

By adopting the above technical solution, the order habit parameters in the analysis parameters are adjusted according to the needs of the buyer's company. At this time, if the ordering habit parameters before the adjustment are continued to predict the quantity of goods to be ordered, the error will be large, and the reference value will be higher. Therefore, the analysis parameters need to be adjusted according to the correction information.

Optionally, after receiving the electronic logistics information of the logistics terminal based on the current cargo order information and the shipment consignment information, the method further includes:

Receive a refund request from the client, determine whether the current cargo order information has been signed for based on the electronic logistics information, and determine whether the current cargo order information has the remaining number of refundable operations;

If the current goods order information is not received and there is the remaining number of refundable operations, a refund operation is performed based on the refund request.

By adopting the above technical solution, in order to reduce the frequent malicious sending of refund requests by the buyer through the client, the current goods order information of each client corresponds to a limited number of refundable operations.

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

Obtain the total number of goods orders for which the refund operation is received in the second statistical period;

If the total number is not less than the preset threshold, the fetching frequency of all order states is reduced.

By adopting the above technical solution, the number of refreshes of all order statuses is increased, so that the order status can be updated in time, the timeliness of knowing that the goods order is lost after the refund operation is performed is improved, and it is convenient to take relevant remedial measures in a timely manner when the loss occurs.

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

A smart supply chain management device based on artificial intelligence, comprising,

The first receiving module is used to receive the current cargo order information entered by the client;

a sending module, configured to send the current goods order information to the supplier;

a second receiving module, configured to receive the shipment entrustment information returned by the supplier;

The third receiving module is configured to receive the electronic logistics information of the logistics terminal based on the current cargo order information and the shipment entrustment information.

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

A smart supply chain management system based on artificial intelligence, including storage, server and client, supply side, logistics side;

A computer program capable of being loaded by the server and executing the method according to any one of claims 1 to 7 is stored on the memory;

The client is used to input current cargo order information;

The supplier is configured to generate shipment entrustment information after completing stocking based on the current goods order information, and send the shipment entrustment information to the server;

The logistics terminal is configured to generate electronic logistics information according to the transportation status of the goods in the current goods order information, and send 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 smart supply chain management method described in any one of the first aspects.

Description of drawings

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

FIG. 2 is a structural 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 ways

The present application will be further described in detail below with reference to the accompanying drawings.

The embodiment of the present application provides an artificial intelligence-based smart supply chain management method. The method takes the server as the execution body. The following will describe the processing flow shown in FIG. 1 in detail with reference to the specific implementation manner. The content may be as follows, including :

Step 101, receiving the current cargo order information entered by the client;

The client is a terminal such as a mobile phone and a computer of the buyer used to enter the purchase information. The current order information includes the current purchase time, the type of the requested item, the quantity of the requested item, and the delivery address.

Step 102, sending current goods order information to the supplier;

The supply end is a terminal such as a mobile phone and a computer of the supplier for supplying goods. The supply end obtains stocking information after receiving the current goods order information, and the stocking information is the stocking quantity based on the current goods order information.

Step 103, receiving the shipment entrustment information returned by the supplier;

The shipment consignment information includes the available time for shipment and the shipping address, etc. When the quantity in stock in the stocking information is consistent with the quantity requested in the current order, the operation of transporting the goods can be performed.

Step 104: Receive the electronic logistics information of the logistics terminal based on the current cargo order information and the shipment consignment information.

The logistics terminal is the terminal such as the mobile phone and computer of the logistics provider used to transport the goods. The electronic logistics information includes the transportation route, the transportation vehicle and the cargo location information. The electronic logistics information updates the transportation status of the cargo through the cargo location information.

This satisfies the company's management requirements for the entire supply chain process, improves the convenience of communication and collaboration between various business nodes in the supply chain, and facilitates real-time control of information related to cargo orders.

Before displaying the supply chain interface of the client, supply side and logistics side, in order to ensure the confidentiality and security of the supply chain information, the user's identity needs to be verified through the login operation. The login methods include account password login, pre-stored mobile phone number Verify login, etc.

The supply chain display interface of the client is the procurement center. The procurement center includes the purchase order, purchase approval and historical goods order inquiry about the current goods order information. The purchase order interface includes the quick order and the purchase template, which improves the The convenience of the operation of the buyer's personnel corresponding to the client The supply chain display interface of the supply side is the supply center, and the supply center includes the sales contract, sales order, sales report, the creation of the goods shipment of the current goods order information, and the shipment of the goods. Businesses such as the creation of entrustment information, among which, the sales contract interface includes contract approval for current goods order information, the sales order interface includes order creation and order approval for current goods order information, and sales reports include sales contract reports and sales order reports; logistics; The supply chain display interface on the side includes the creation, review, and transportation status of the electronic logistics information of the current goods order information. The supply chain display interface on the logistics side also includes a reverse center, which includes purchase returns, rejections, after-sales management and after-sales orders. Handle business.

In one embodiment, the current goods order information is created and placed by the buyer through the client according to the current quantity of goods requested. Since the current quantity of goods requested is manually counted by the buyer, in order to reduce the impact of errors in the buyer's statistics , before step 102, it also includes the following processing: if the client is a client that does not enter the cargo order information for the first time, obtain the historical cargo order information of the client in the first statistical period; obtain the level information of the client based on the historical cargo order information; A corresponding analysis strategy is obtained based on the level information, and the analysis strategy includes at least one analysis parameter and a prediction model for predicting the quantity of goods requested in the current cargo order information based on the at least one analysis parameter; Forecast data, compare the forecast data with the quantity of goods requested in the current order information, and obtain the feasibility analysis result of the quantity of goods requested in the current goods order information; if the result of the feasibility analysis is unreasonable, a prompt will be sent to the client information.

Analyze and forecast the quantity of goods in the current order information through the forecasting model and analysis parameters, and compare the forecast data with the current quantity of goods required to obtain a feasibility analysis result. If the difference between the forecast data and the quantity of goods required exceeds If the threshold is unreasonable, the result of the feasibility analysis of the quantity of goods requested in the current order information is unreasonable, indicating that there is a possibility of a buyer's statistical error, so as to prompt the client of the buyer, which is convenient for the buyer of the client. Timely detection of statistical errors.

The analysis parameters include the ordering habit parameters of the buyer when purchasing through the client. The ordering habit parameters include the ordering time and ordering frequency. For example, the ordering time is the 15th of each month, and the ordering frequency is once a month.

As an optional implementation manner of this embodiment, the prediction model may be a gray prediction model. It is easy to understand that the more comprehensive the analysis parameters are, the more accurate the predicted data in the analysis results, but the higher the computational cost and labor in the corresponding analysis and calculation process; therefore, for different levels of clients, the analysis strategies used The analysis parameters and the resulting costs are different, and accordingly, the accuracy of the forecast data is also different. In order to make the high-level high-quality buyers enjoy a better sense of experience, the analysis parameters corresponding to the high-level high-quality buyers are more comprehensive. , more cost input, and higher accuracy of forecast data, so that the better the buyer gets, the more reasonable the prompt information is.

Specifically, the step of obtaining the corresponding analysis strategy based on the level information includes the following processing: if the level information of the client is a first-level customer, a first analysis strategy is generated, and the analysis parameters in the first analysis strategy include historical cargo order information and company development parameters. and industry development parameters; if the client's level information is a second-level customer, a second analysis strategy is generated, and the analysis parameters in the second analysis strategy include historical cargo order information and company development parameters; if the client's level information is a third-level customer, Then a third analysis strategy is generated, and the analysis parameters in the third analysis strategy include historical cargo order information; wherein, the total purchase amount in the historical cargo order information corresponding to the first-level customer is located in the first total amount range, and the historical goods corresponding to the second-level customer The total purchase amount in the cargo order information is located in the second total amount interval, the total purchase amount in the historical cargo order information corresponding to the third-level customer is located in the third total amount interval, and the minimum value of the first total amount interval is greater than the second total amount interval. The maximum value of the second total amount interval is greater than the maximum value of the third total amount interval.

In this embodiment, the first statistical period may be three years, and the classification rule of the client terminal is: classify the customer grade according to the total purchase amount of three years in the historical cargo order information; for example, the total purchase amount is one million to two hundred 10,000 is the first total amount range, the total purchase amount of 500,000 to 1 million is the second total amount range, and the total purchase amount below 500,000 is the third total amount range. The unit price of the goods is set.

Among them, the company's development parameters include the operating income growth rate, gross profit growth rate, net profit growth rate, reinvested cash ratio, fixed asset new rate, total asset growth rate and net asset growth rate based on big data. The parameters include total industry sales, industry total revenue, industry total profit, seasonal data affecting the total turnover of the clothing industry, inflation rate, bank interest rate, tax system and tax rate, national income level and purchasing power based on big data acquisition.

In one embodiment, since the prompt cost of the client is limited, in order to ensure the high-quality purchaser's sense of experience, correspondingly, before sending the prompt information to the client, the step further includes the following processing: obtaining the corresponding prompt policy based on the customer's level information.

If the client's level information is a third-level customer, a warning message will be sent to the client in the form of a pop-up window. The warning message can be the preset warning content, for example, the warning content is: "The quantity of the current order is at risk"; if The client's level information is a second-level customer, and an analysis report is sent to the client in the form of a pop-up window. In this embodiment, the analysis report includes the forecast data and feasibility analysis results of the client's required quantity of goods; Corresponding to the bound staff who provide services, if the client's level information is a first-level customer, obtain the bound staff information corresponding to the client, and send the forecast data of the quantity of goods requested by the client, the analysis parameters, and the forecast model. To the binding staff, the binding staff will check and communicate with the person in charge of the client by telephone.

In one embodiment, the ordering habit parameters in the analysis parameters are adjusted according to the needs of the buyer company. At this time, if you continue to predict the quantity of goods requested according to the ordering habitual parameters before adjustment, the accuracy of the predicted data will be reduced. , so correspondingly, after sending the prompt information to the client, the step also includes the following processing: receiving the confirmation information of the feasibility analysis result returned by the client; if the confirmation information includes the correction information of the analysis parameters, then adjust the analysis parameters based on the correction information .

The confirmation information includes the judgment information of the forecast data or the correction information of the analysis parameters, and the judgment information includes the check result of the quantity requested in the current order information, and the check result is the result of manual confirmation by the buyer of the client. If the quantity of goods requested in the current order information is correct, the confirmation information includes correction information. The reason why the feasibility analysis result is unreasonable is that the order habit parameters have been adjusted. Therefore, the correction information includes the adjusted order habit. parameter.

For example, the order time in the order habit parameter is adjusted to the 10th and 20th of each month, and the order frequency is adjusted to twice a month, that is, the monthly quantity of goods requested is entered into the goods order twice. Predicting the analysis parameters before adjustment has a large error and a low reference value, so it is necessary to adjust the analysis parameters according to the correction information.

In one embodiment, when the buyer of the client finds that the order is incorrectly placed, such as the quantity or type of goods to be ordered, and the order needs to be refunded, a refund operation needs to be performed. Accordingly, the steps are based on the current order information of the goods and the shipment consignment. After receiving the electronic logistics information of the logistics terminal, the information also includes the following processing: receiving the refund request from the client, judging whether the current cargo order information has been signed based on the electronic logistics information, and judging whether the current cargo order information has the remaining number of refundable operations; The current goods order information has not been signed and there are the remaining number of refundable operations, the refund operation will be performed based on the refund request.

In order to reduce the frequent malicious sending of refund requests by the buyer through the client, the current cargo order information of each client corresponds to a limited number of refundable operations. The number of refund operations. Although the buyer can send a refund request to the server through the client, it cannot perform the refund operation and send a notification to the corresponding client.

In one embodiment, in order to reduce the possibility of loss of the goods order after the refund operation is performed, accordingly, the step further includes the following processing after performing the refund operation based on the refund request: obtaining the execution received in the second statistical period The total number of goods orders for refund operations; if the total number is not less than the preset threshold, the fetching frequency of all order statuses will be reduced.

In this embodiment, the second statistical period may be one week, and the preset threshold is ten orders. When the total number of goods orders for which the refund operation is performed within one week is not less than ten orders, the frequency of grabbing all order statuses at that time The reduction operation is performed, that is, the number of refreshes of all order statuses is increased, so that the order status can be updated in time, which improves the timeliness of knowing that the goods order has been lost after the refund operation is performed, and facilitates the timely taking of relevant remedial measures when the loss occurs.

2 , based on the same technical concept, an embodiment of the present application further discloses an artificial intelligence-based smart supply chain management device 200, the management device 200 includes:

The first receiving module 201 is used to receive the current cargo order information entered by the client;

The sending module 202 is used for sending the current goods order information to the supplier;

The second receiving module 203 is configured to receive the shipment entrustment information returned by the supplier;

The third receiving module 204 is configured to receive the electronic logistics information of the logistics terminal based on the current cargo order information and the shipment entrustment information.

Optionally, the management apparatus 200 further includes:

The first obtaining module is used to obtain the historical cargo order information of the client within the statistical period when the client is not the client that entered the cargo order information for the first time;

The second obtaining module is used to obtain the level information of the client based on the historical cargo order information;

A third obtaining module, configured to obtain a corresponding analysis strategy based on the level information, where the analysis strategy includes at least one analysis parameter and a prediction model for predicting the quantity of goods requested in the current order information based on the at least one analysis parameter;

The generating module is used to generate the forecast data of the quantity of goods requested by the client based on the analysis strategy, compare the forecast data with the quantity of goods requested in the current order information, and obtain the feasibility analysis result of the quantity of goods requested in the information of the current goods order;

The sending prompt module is used to send prompt information to the client when the feasibility analysis result is unreasonable.

Optionally, the sending prompt module includes:

The obtaining submodule is used to obtain the corresponding prompt policy based on the client's level information.

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;

The adjustment module is configured to adjust the analysis parameters based on the correction information when the correction information of the analysis parameters is included in the confirmation information.

Optionally, the management apparatus 200 further includes:

The fifth receiving module is used for receiving the refund request from the client, and based on the electronic logistics information, judging whether the current cargo order information has been signed for, and judging whether the current cargo order information has the remaining number of refundable operations;

The execution module is used to execute the refund operation based on the refund request when the current cargo order information has not been signed and there are remaining refundable operation times.

Optionally, the management apparatus 200 further includes:

The fourth obtaining module is used to obtain the total number of goods orders received in the statistical period for performing refund operations;

The frequency reduction module is used to reduce the capture frequency of all order statuses when the total number is not less than the preset threshold.

3, based on the same technical concept, an embodiment of the present application also discloses an artificial intelligence-based smart supply chain management system 300, the management system 300 includes: a memory 301, a server 302, a client 303, a supply end 304, Logistics end 305;

The memory 301 is connected to the server 302 through a communication bus, and a computer program capable of being loaded by the server 302 and executing the intelligent supply chain management method based on artificial intelligence is stored in the memory 301 .

Memory 301 may be used to store instructions, programs, codes, 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 smart supply chain management method, etc. ; The storage data area can store the data involved in the intelligent supply chain management method based on artificial intelligence.

Server 302 may include one or more processing cores. The server 302 invokes the data stored in the memory 301 by running or executing the instructions, programs, code sets or instruction sets stored in the memory 301 to execute various functions of the present application and process data. It can be understood that, for different devices, the electronic device used to implement the function of the server 302 may also be other, which is not specifically limited in this embodiment of the present application.

The client 303, the supply end 304 and the logistics end 305 are all connected to the server 302 respectively. The client 303 is used to input the current cargo order information and send the current cargo order information to the server 302; the supply end 304 is used to input the current cargo order information based on the current cargo order After the information is stocked, the shipment entrustment information is generated, and the shipment entrustment information is sent to the server 302; the logistics terminal 305 is used to generate electronic logistics information according to the transportation status of the goods in the current 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 smart supply chain management method provided by the foregoing embodiments.

In this embodiment, the computer-readable storage medium may be a tangible device that holds and stores instructions for use by the instruction execution device. The computer-readable storage medium can be, but is not limited to, electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any combination of the above. Specifically, the computer-readable storage medium may be a portable computer disk, 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 random access memory Access memory (SRAM), portable compact disk read only memory (CD-ROM), digital versatile disk (DVD), memory sticks, floppy disks, optical disks, magnetic disks, mechanical coding devices, and any combination of the above.

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

The above description is only a preferred embodiment of the present application and an illustration of the applied technical principles. Those skilled in the art should understand that the scope of the application involved in this application is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, and should also cover, without departing from the concept of the above-mentioned application, the technical solutions made of the above-mentioned technical features or Other technical solutions formed by any combination of its equivalent features. For example, a technical solution is formed by replacing the above-mentioned features with the technical features applied in this application (but not limited to) with similar functions.

Claims (10)

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;

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

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

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.

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

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.

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

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

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

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.

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:

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.

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

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.

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

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.

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 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 cargo 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.

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.

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