A tobacco-based big data management method

Abstract

The present invention discloses a tobacco-based big data management method, which relates to the technical field of intelligent management, including matching seller information according to the seller name, establishing a consumer database according to customer tags, calculating the seller's confidence, building a monitoring model, and testing the monitoring model, calculating the distribution ratio of the producer's information, generating a planting distribution strategy, matching the optimal transportation scheduling route and the number of transport vehicles, tracking the path of the transport vehicle, and issuing a transportation warning signal. The beneficial effects of the present invention are: by dividing the management area and the numbered sub-area, the overall efficiency and productivity are improved, a consumer database is established, and customer satisfaction and loyalty are improved. Through the monitoring model, it is beneficial to the security and stability of transactions, the planting distribution strategy is formulated, the waste of resources is reduced, the path of the transport vehicle is tracked in real time, the distribution efficiency is improved, and the safety and timeliness of the goods are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent management, and in particular to a tobacco-based big data management method. Background Art

[0002] In recent years, the tobacco industry has used big data technology to integrate and analyze various types of data, including market demand, supply chain information and sales data, consumer preferences, etc., to help companies conduct market forecasting, product optimization and cost control, thereby improving the company's management efficiency and market competitiveness, and bringing more innovation and development opportunities to the industry.

[0003] At present, the Chinese invention patent with publication number CN 116776290 A discloses a method for constructing a tobacco big data model. The invention uses Internet technology to build a tobacco planting big data warehouse through multiple applications oriented to data value, combined with Internet of Things collection technology, and uses artificial intelligence algorithms to build various models of tobacco production, so as to realize various growth warnings, pest and disease predictions, plant health evaluation, etc. of tobacco planting, and realize the sustainable and healthy development of the industry. However, in the related technologies, tobacco big data is not analyzed and managed from multiple dimensions, lacking comprehensive analysis and comprehensive management, and transportation is not managed based on big data, which is easy to cause waste of human resources and lack of economic benefits. Summary of the invention

[0004] The technical problem solved by the present invention is: in the related technology, tobacco big data is not analyzed and managed from multiple dimensions, lacks comprehensive analysis and comprehensive management, and transportation is not managed based on big data, which easily causes waste of human resources and lacks economic benefits.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a tobacco-based big data management method, comprising the following steps:

[0006] Step S1, dividing the management area into several sub-areas, numbering the sub-areas, wherein the sub-areas include sellers, buyers and producers, obtaining the seller names in the sub-areas, matching the seller information according to the seller names, obtaining the buyer information in the sub-areas and the producer information in the sub-areas;

[0007] Step S2, according to the buyer information in the sub-area, set a customer tag for the buyer, establish a consumer database according to the customer tag, calculate the seller's confidence according to the seller information, build a monitoring model according to the seller's confidence, and test the monitoring model until the accuracy of the monitoring model reaches a first threshold and then stops the test;

[0008] Step S3, calculating the distribution ratio of the producer information according to the buyer information, and generating a planting distribution strategy according to the distribution ratio;

[0009] Step S4, matching the optimal transport scheduling route and the number of transport vehicles according to the seller information, tracking the path of the transport vehicle, and issuing a transport warning signal.

[0010] As a preferred solution of the tobacco-based big data management method described in the present invention, step S1 includes the following sub-steps:

[0011] Step S11, dividing the management area into a number of sub-areas, and numbering the sub-areas, wherein the numbers are natural numbers;

[0012] Step S12, the sub-area includes a seller, a buyer and a manufacturer respectively, the seller name in the sub-area is obtained, the merchant database is retrieved, and the seller information is matched according to the seller name, the seller information includes illegal case information, sales quantity information and first location information, the merchant database includes the seller's name, illegal case information, sales quantity information and first location information, the seller name is input into the merchant database, and the illegal case information, sales quantity information and first location information matching the seller name are obtained;

[0013] Step S31, obtaining buyer information in the sub-area, the buyer information including occupation information, gender information, age information, and information on the type of tobacco consumed;

[0014] Step S32, obtaining the producer information in the sub-area, wherein the producer information includes the producer's second location information and production area information.

[0015] As a preferred solution of the tobacco-based big data management method described in the present invention, step S2 includes the following sub-steps:

[0016] Step S21, numbering the buyer, the number being a combination of the sub-region number and a natural number, obtaining occupational information, gender information, age information, and tobacco consumption type information corresponding to the buyer number, setting a customer tag for the buyer, the customer tag including the buyer number, tobacco consumption type information corresponding to the buyer number, gender information corresponding to the buyer number, age information corresponding to the buyer number, and occupational information corresponding to the buyer number, and establishing a consumer database based on the customer tag;

[0017] Step S22, obtaining the seller name, matching the corresponding illegal case information, sales quantity information and first location information according to the seller name, calculating the confidence of the seller according to the illegal case information, and constructing a monitoring model according to the confidence of the seller;

[0018] Step S23, testing the monitoring model until the accuracy of the monitoring model reaches a first threshold, then stopping the testing.

[0019] As a preferred solution of the tobacco-based big data management method described in the present invention, the calculation logic of the seller's confidence includes:

[0020] Obtain illegal case information and sales quantity information, calculate the ratio of illegal case information to sales quantity information, set the ratio of illegal case information to sales quantity information as the illegal proportion, and the calculation expression of the seller's confidence is:

[0021] Seller’s confidence = 1-illegal proportion.

[0022] As a preferred solution of the tobacco-based big data management method described in the present invention, the construction logic of the monitoring model includes:

[0023] Calculate the confidence of each seller in the sub-region where the seller is located, calculate the average of the confidences of the sellers, record it as a first average, calculate the deviation of the confidence of the sellers in the sub-region according to the first average, record it as a first deviation, perform weighted calculation on the first deviation and the first average, and obtain a first result;

[0024] Obtain the confidence of the seller, calculate the difference between the seller's confidence and the first result, and take the absolute value of the difference, recorded as the first absolute value; when the first absolute value is greater than the first threshold, set the number of monitoring times to the first number; when the first absolute value is less than or equal to the first threshold and greater than the second threshold, set the number of monitoring times to the second number; when the first absolute value is less than or equal to the second threshold and greater than the third threshold, set the number of monitoring times to the third number; when the first absolute value is less than or equal to the fourth threshold, set the number of monitoring times to the fourth number; the number of monitoring times refers to the number of times included in one monitoring cycle, and one monitoring cycle is one quarter.

[0025] As a preferred solution of the tobacco-based big data management method described in the present invention, step S3 includes the following sub-steps:

[0026] Step S31, obtaining tobacco consumption information and production area information;

[0027] Step S32, calculating the proportion of tobacco types in the production area information, the calculation logic of the proportion includes:

[0028] Obtaining consumption tobacco information, retrieving a production database, inputting a tobacco model, obtaining the amount of raw materials required to produce a box of tobacco, calculating the product of the consumption tobacco information and the amount of raw materials, recording it as a first product, calculating a ratio of the first product to the production area information, setting the ratio of the first product to the production area information as the proportion of tobacco types in the production area information, traversing each consumption tobacco information in the sub-area, and calculating the proportion of each tobacco type in the production area information;

[0029] Step S33, generating a planting distribution strategy according to the proportion of tobacco type information in the production area information.

[0030] As a preferred solution of the tobacco-based big data management method described in the present invention, the planting distribution strategy includes:

[0031] Obtain the proportion of each tobacco type in the production area information, convert the proportion of each tobacco type in the production area information into a fraction with the same denominator, use the denominator as the number of divisions, divide the production area information into several parts according to the number of divisions, and the planting distribution of each tobacco type is the number of shares corresponding to the numerator.

[0032] As a preferred solution of the tobacco-based big data management method described in the present invention, step S4 includes the following sub-steps:

[0033] Step S41, obtaining the second location information, sales quantity information and first location information of the manufacturer;

[0034] Step S42, matching the optimal transportation scheduling route and the number of transport vehicles;

[0035] Step S43, tracking the path of the transport vehicle, when the path of the transport vehicle deviates from the optimal transport scheduling route, issuing a transport warning signal, until the path of the transport vehicle does not deviate from the optimal transport scheduling route, stop issuing the transport warning signal.

[0036] As a preferred solution of the tobacco-based big data management method described in the present invention, the scheduling strategy of the optimal transportation scheduling route includes:

[0037] Acquire the first location information and the second location information, traverse the paths of the first location information and the second location information on the map, compare the paths, select the path with the shortest distance, and set the path with the shortest distance as the optimal transportation scheduling route;

[0038] The selection logic of the number of transport vehicles includes:

[0039] The sales quantity and the maximum carrying quantity of a transport vehicle are obtained, a ratio of the sales quantity to the maximum carrying quantity of a transport vehicle is calculated, and the ratio of the sales quantity to the maximum carrying quantity of a transport vehicle is set as the number of transport vehicles.

[0040] As a preferred solution of the tobacco-based big data management method described in the present invention, the logic of tracking the path of the transport vehicle includes:

[0041] A label is attached to the transport vehicle, the label includes the transport vehicle number and a locator, the locator wirelessly communicates with the terminal, obtains the optimal transport scheduling route corresponding to the transport vehicle number, tracks the path of the transport vehicle, and displays the transport time and transport warning signal on the terminal.

[0042] The beneficial effects of the present invention are as follows: by dividing management areas and numbered sub-areas, it is helpful to accurately obtain and manage the information of sellers, buyers and producers, which helps to optimize the allocation and utilization of resources, improve overall efficiency and productivity, set customer tags according to buyer information, and establish a consumer database, which is helpful to understand and predict customer needs, help enterprises to carry out precise marketing and services, and improve customer satisfaction and loyalty. Through the monitoring model, it is helpful to timely discover and deal with abnormal situations, ensure market order, and facilitate the security and stability of transactions. The distribution ratio of producer information is calculated according to buyer information, so as to formulate planting distribution strategies, facilitate dynamic adjustment of market demand, improve production efficiency and quality, and reduce resource waste. The optimal transportation scheduling route and the number of transport vehicles are matched according to seller information, the path of the transport vehicle is tracked in real time, and a transportation warning signal is issued, which helps to reduce transportation costs, improve distribution efficiency, and ensure the safety and timeliness of goods. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A basic flow chart of a tobacco-based big data management method provided for one embodiment of the present invention. DETAILED DESCRIPTION

[0044] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0045] Example 1, reference Figure 1 , as an embodiment of the present invention, provides a tobacco-based big data management method, comprising the following steps:

[0046] Step S1, dividing the management area into several sub-areas, numbering the sub-areas, wherein the sub-areas include sellers, buyers and producers, obtaining the seller names in the sub-areas, matching the seller information according to the seller names, obtaining the buyer information in the sub-areas and the producer information in the sub-areas;

[0047] Step S2, according to the buyer information in the sub-area, set a customer tag for the buyer, establish a consumer database according to the customer tag, calculate the seller's confidence according to the seller information, build a monitoring model according to the seller's confidence, and test the monitoring model until the accuracy of the monitoring model reaches a first threshold and then stops the test;

[0048] Step S3, calculating the distribution ratio of the producer information according to the buyer information, and generating a planting distribution strategy according to the distribution ratio;

[0049] Step S4, matching the optimal transport scheduling route and the number of transport vehicles according to the seller information, tracking the path of the transport vehicle, and issuing a transport warning signal.

[0050] Preferably, in this embodiment, by dividing the management area and the numbered sub-area, it is helpful to accurately obtain and manage the information of the seller, buyer and producer, help to optimize the allocation and utilization of resources, improve overall efficiency and productivity, set customer tags according to the buyer's information, and establish a consumer database, which is helpful to understand and predict customer needs, help enterprises to carry out precise marketing and services, and improve customer satisfaction and loyalty. Through the monitoring model, it is helpful to timely discover and deal with abnormal situations, ensure market order, and facilitate the security and stability of transactions. The distribution ratio of the producer's information is calculated according to the buyer's information, so as to formulate a planting distribution strategy, facilitate dynamic adjustment of market demand, improve production efficiency and quality, and reduce resource waste. The optimal transportation scheduling route and the number of transport vehicles are matched according to the seller's information, the path of the transport vehicle is tracked in real time, and a transportation warning signal is issued, which helps to reduce transportation costs, improve distribution efficiency, and ensure the safety and timeliness of goods.

[0051] The step S1 comprises the following sub-steps:

[0052] Step S11, dividing the management area into a number of sub-areas, and numbering the sub-areas, wherein the numbers are natural numbers;

[0053] Step S12, the sub-area includes a seller, a buyer and a manufacturer respectively, the seller name in the sub-area is obtained, the merchant database is retrieved, and the seller information is matched according to the seller name, the seller information includes illegal case information, sales quantity information and first location information, the merchant database includes the seller's name, illegal case information, sales quantity information and first location information, the seller name is input into the merchant database, and the illegal case information, sales quantity information and first location information matching the seller name are obtained;

[0054] Step S31, obtaining buyer information in the sub-area, the buyer information including occupation information, gender information, age information, and information on the type of tobacco consumed;

[0055] Step S32, obtaining the producer information in the sub-area, wherein the producer information includes the producer's second location information and production area information.

[0056] In this embodiment, preferably, by dividing the management area and the numbering sub-areas, it is helpful to accurately obtain and manage the information of sellers, buyers and producers, help optimize the allocation and utilization of resources, and improve overall efficiency and productivity.

[0057] The step S2 includes the following sub-steps:

[0058] Step S21, numbering the buyer, the number being a combination of the sub-region number and a natural number, obtaining occupational information, gender information, age information, and tobacco consumption type information corresponding to the buyer number, setting a customer tag for the buyer, the customer tag including the buyer number, tobacco consumption type information corresponding to the buyer number, gender information corresponding to the buyer number, age information corresponding to the buyer number, and occupational information corresponding to the buyer number, and establishing a consumer database based on the customer tag;

[0059] In this embodiment, it is preferred to set customer tags according to buyer information and establish a consumer database, which is conducive to understanding and predicting customer needs, helping enterprises to conduct precise marketing and services, and improving customer satisfaction and loyalty.

[0060] Step S22, obtaining the seller name, matching the corresponding illegal case information, sales quantity information and first location information according to the seller name, calculating the confidence of the seller according to the illegal case information, and constructing a monitoring model according to the confidence of the seller;

[0061] Step S23, testing the monitoring model until the accuracy of the monitoring model reaches a first threshold, then stopping the testing.

[0062] The calculation logic of the seller's confidence includes:

[0063] Obtain illegal case information and sales quantity information, calculate the ratio of illegal case information to sales quantity information, set the ratio of illegal case information to sales quantity information as the illegal proportion, and the calculation expression of the seller's confidence is:

[0064] Seller’s confidence = 1-illegal proportion.

[0065] The construction logic of the monitoring model includes:

[0066] Calculate the confidence of each seller in the sub-region where the seller is located, calculate the average of the confidences of the sellers, record it as a first average, calculate the deviation of the confidence of the sellers in the sub-region according to the first average, record it as a first deviation, perform weighted calculation on the first deviation and the first average, and obtain a first result;

[0067] Obtain the confidence of the seller, calculate the difference between the seller's confidence and the first result, and take the absolute value of the difference, recorded as the first absolute value; when the first absolute value is greater than the first threshold, set the number of monitoring times to the first number; when the first absolute value is less than or equal to the first threshold and greater than the second threshold, set the number of monitoring times to the second number; when the first absolute value is less than or equal to the second threshold and greater than the third threshold, set the number of monitoring times to the third number; when the first absolute value is less than or equal to the fourth threshold, set the number of monitoring times to the fourth number; the number of monitoring times refers to the number of times included in one monitoring cycle, and one monitoring cycle is one quarter.

[0068] In this embodiment, the monitoring model is preferably used to timely discover and handle abnormal situations, ensure market order, and facilitate the security and stability of transactions.

[0069] The step S3 includes the following sub-steps:

[0070] Step S31, obtaining tobacco consumption information and production area information;

[0071] Step S32, calculating the proportion of tobacco types in the production area information, the calculation logic of the proportion includes:

[0072] Obtaining consumption tobacco information, retrieving a production database, inputting a tobacco model, obtaining the amount of raw materials required to produce a box of tobacco, calculating the product of the consumption tobacco information and the amount of raw materials, recording it as a first product, calculating a ratio of the first product to the production area information, setting the ratio of the first product to the production area information as the proportion of tobacco types in the production area information, traversing each consumption tobacco information in the sub-area, and calculating the proportion of each tobacco type in the production area information;

[0073] Step S33, generating a planting distribution strategy according to the proportion of tobacco type information in the production area information.

[0074] The planting distribution strategy includes:

[0075] Obtain the proportion of each tobacco type in the production area information, convert the proportion of each tobacco type in the production area information into a fraction with the same denominator, use the denominator as the number of divisions, divide the production area information into several parts according to the number of divisions, and the planting distribution of each tobacco type is the number of shares corresponding to the numerator.

[0076] In this embodiment, it is preferred that the distribution ratio of the producer information is calculated based on the buyer information, so as to formulate a planting distribution strategy, facilitate dynamic adjustment of market demand, improve production efficiency and quality, and reduce resource waste.

[0077] The step S4 includes the following sub-steps:

[0078] Step S41, obtaining the second location information, sales quantity information and first location information of the manufacturer;

[0079] Step S42, matching the optimal transportation scheduling route and the number of transport vehicles;

[0080] Step S43, tracking the path of the transport vehicle, when the path of the transport vehicle deviates from the optimal transport scheduling route, issuing a transport warning signal, until the path of the transport vehicle does not deviate from the optimal transport scheduling route, stop issuing the transport warning signal.

[0081] The scheduling strategy of the optimal transportation scheduling route includes:

[0082] Acquire the first location information and the second location information, traverse the paths of the first location information and the second location information on the map, compare the paths, select the path with the shortest distance, and set the path with the shortest distance as the optimal transportation scheduling route;

[0083] The selection logic of the number of transport vehicles includes:

[0084] The sales quantity and the maximum carrying quantity of a transport vehicle are obtained, a ratio of the sales quantity to the maximum carrying quantity of a transport vehicle is calculated, and the ratio of the sales quantity to the maximum carrying quantity of a transport vehicle is set as the number of transport vehicles.

[0085] Preferably, in this embodiment, the optimal transportation scheduling route and the number of transport vehicles are matched according to the seller's information, the routes of the transport vehicles are tracked in real time, and transport warning signals are issued, which helps to reduce transportation costs, improve distribution efficiency, and ensure the safety and timeliness of goods.

[0086] The logic for tracking the path of the transporter includes:

[0087] A label is attached to the transport vehicle, the label includes the transport vehicle number and a locator, the locator wirelessly communicates with the terminal, obtains the optimal transport scheduling route corresponding to the transport vehicle number, tracks the path of the transport vehicle, and displays the transport time and transport warning signal on the terminal.

[0088] The present invention divides management areas and numbered sub-areas, which is conducive to accurately obtaining and managing the information of sellers, buyers and producers, helping to optimize the allocation and utilization of resources, and improving overall efficiency and productivity. It sets customer tags according to buyer information and establishes a consumer database, which is conducive to understanding and predicting customer needs, helping enterprises to conduct precise marketing and services, and improving customer satisfaction and loyalty. Through the monitoring model, it is conducive to timely discovering and handling abnormal situations, ensuring market order, and facilitating the security and stability of transactions. The distribution ratio of producer information is calculated according to buyer information, so as to formulate planting distribution strategies, facilitate dynamic adjustment of market demand, improve production efficiency and quality, and reduce resource waste. The optimal transportation scheduling route and the number of transport vehicles are matched according to seller information, the path of the transport vehicle is tracked in real time, and a transportation warning signal is issued, which helps to reduce transportation costs, improve distribution efficiency, and ensure the safety and timeliness of goods.

[0089] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program codes. Among them, the storage medium may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, magnetic disk or optical disk. These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0090] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A tobacco-based big data management method, characterized in that: The following steps are involved: Step S1, dividing the management area into several sub-areas, numbering the sub-areas, wherein the sub-areas include sellers, buyers and producers, obtaining the seller names in the sub-areas, matching the seller information according to the seller names, obtaining the buyer information in the sub-areas and the producer information in the sub-areas; Step S2, according to the buyer information in the sub-area, set a customer tag for the buyer, establish a consumer database according to the customer tag, calculate the seller's confidence according to the seller information, build a monitoring model according to the seller's confidence, and test the monitoring model until the accuracy of the monitoring model reaches a first threshold and then stops the test; Step S3, calculating the distribution ratio of the producer information according to the buyer information, and generating a planting distribution strategy according to the distribution ratio; Step S4, matching the optimal transport scheduling route and the number of transport vehicles according to the seller information, tracking the path of the transport vehicle, and issuing a transport warning signal; The step S1 comprises the following sub-steps: Step S11, dividing the management area into a number of sub-areas, and numbering the sub-areas, wherein the numbers are natural numbers; Step S12, the sub-area includes a seller, a buyer and a manufacturer respectively, the seller name in the sub-area is obtained, the merchant database is retrieved, and the seller information is matched according to the seller name, the seller information includes illegal case information, sales quantity information and first location information, the merchant database includes the seller's name, illegal case information, sales quantity information and first location information, the seller name is input into the merchant database, and the illegal case information, sales quantity information and first location information matching the seller name are obtained; Step S31, obtaining buyer information in the sub-area, the buyer information including occupation information, gender information, age information, and information on the type of tobacco consumed; Step S32, obtaining producer information in the sub-area, wherein the producer information includes the producer's second location information and production area information; The step S2 includes the following sub-steps: Step S21, numbering the buyer, the number being a combination of the sub-region number and a natural number, obtaining occupational information, gender information, age information, and tobacco consumption type information corresponding to the buyer number, setting a customer tag for the buyer, the customer tag including the buyer number, tobacco consumption type information corresponding to the buyer number, gender information corresponding to the buyer number, age information corresponding to the buyer number, and occupational information corresponding to the buyer number, and establishing a consumer database based on the customer tag; Step S22, obtaining the seller name, matching the corresponding illegal case information, sales quantity information and first location information according to the seller name, calculating the confidence of the seller according to the illegal case information, and constructing a monitoring model according to the confidence of the seller; Step S23, testing the monitoring model until the accuracy of the monitoring model reaches a first threshold and then stopping the testing; The calculation logic of the seller's confidence includes: Obtain illegal case information and sales quantity information, calculate the ratio of illegal case information to sales quantity information, set the ratio of illegal case information to sales quantity information as the illegal proportion, and the calculation expression of the seller's confidence is: Seller’s confidence = 1-illegal proportion.

2. A tobacco-based big data management method according to claim 1, characterized in that: The construction logic of the monitoring model includes: Calculate the confidence of each seller in the sub-region where the seller is located, calculate the average of the confidences of the sellers, record it as a first average, calculate the deviation of the confidence of the sellers in the sub-region according to the first average, record it as a first deviation, perform weighted calculation on the first deviation and the first average, and obtain a first result; Obtain the confidence of the seller, calculate the difference between the seller's confidence and the first result, and take the absolute value of the difference, recorded as the first absolute value; when the first absolute value is greater than the first threshold, set the number of monitoring times to the first number; when the first absolute value is less than or equal to the first threshold and greater than the second threshold, set the number of monitoring times to the second number; when the first absolute value is less than or equal to the second threshold and greater than the third threshold, set the number of monitoring times to the third number; when the first absolute value is less than or equal to the fourth threshold, set the number of monitoring times to the fourth number; the number of monitoring times is the number of times included in a monitoring cycle, and the monitoring cycle is one quarter.

3. A tobacco-based big data management method according to claim 1, characterized in that: The step S3 includes the following sub-steps: Step S31, obtaining tobacco consumption information and production area information; Step S32, calculating the proportion of tobacco types in the production area information, the calculation logic of the proportion includes: Obtaining consumption tobacco information, retrieving a production database, inputting a tobacco model, obtaining the amount of raw materials required to produce a box of tobacco, calculating the product of the consumption tobacco information and the amount of raw materials, recording it as a first product, calculating a ratio of the first product to the production area information, setting the ratio of the first product to the production area information as the proportion of tobacco types in the production area information, traversing each consumption tobacco information in the sub-area, and calculating the proportion of each tobacco type in the production area information; Step S33, generating a planting distribution strategy according to the proportion of tobacco type information in the production area information.

4. A tobacco-based big data management method as claimed in claim 3, characterized in that: The planting distribution strategy includes: Obtain the proportion of each tobacco type in the production area information, convert the proportion of each tobacco type in the production area information into a fraction with the same denominator, use the denominator as the number of divisions, divide the production area information into several parts according to the number of divisions, and the planting distribution of each tobacco type is the number of shares corresponding to the numerator.

5. The tobacco-based big data management method according to claim 1, characterized in that: The step S4 includes the following sub-steps: Step S41, obtaining the second location information, sales quantity information and first location information of the manufacturer; Step S42, matching the optimal transportation scheduling route and the number of transport vehicles; Step S43, tracking the path of the transport vehicle, when the path of the transport vehicle deviates from the optimal transport scheduling route, issuing a transport warning signal, until the path of the transport vehicle does not deviate from the optimal transport scheduling route, stop issuing the transport warning signal.

6. A tobacco-based big data management method according to claim 5, characterized in that: The scheduling strategy of the optimal transportation scheduling route includes: Acquire the first location information and the second location information, traverse the paths of the first location information and the second location information on the map, compare the paths, select the path with the shortest distance, and set the path with the shortest distance as the optimal transportation scheduling route; The selection logic of the number of transport vehicles includes: The sales quantity and the maximum carrying quantity of a transport vehicle are obtained, a ratio of the sales quantity to the maximum carrying quantity of a transport vehicle is calculated, and the ratio of the sales quantity to the maximum carrying quantity of a transport vehicle is set as the number of transport vehicles.

7. A tobacco-based big data management method according to claim 5, characterized in that: The logic for tracking the path of the transporter includes: A label is attached to the transport vehicle, the label includes the transport vehicle number and a locator, the locator wirelessly communicates with the terminal, obtains the optimal transport scheduling route corresponding to the transport vehicle number, tracks the path of the transport vehicle, and displays the transport time and transport warning signal on the terminal.

Images