CN112257978A - Method and device for intelligently scheduling agricultural product resources - Google Patents
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Abstract
The invention provides a method and a device for intelligently scheduling agricultural product resources, wherein the method comprises the following steps: a delivery destination information acquisition step of acquiring delivery destination information; an agricultural product information acquisition step of respectively acquiring a purchase price S of an agricultural product at a producing place, a daily price P of the agricultural product at a delivery destination, and an amount R of the agricultural product from the producing place to the delivery destination; a transportation route and transportation cost calculation step, wherein an optimal transportation route is calculated according to the address of the origin and the position information of the delivery destination, so that the transportation distance d from the origin to the delivery destination and the transportation cost F are obtained; a loss information acquisition step of acquiring a sales loss ratio LT of agricultural products at a delivery destination; and an optimum delivery destination calculation step of determining an optimum delivery destination of the agricultural product according to an objective function based on delivery destination information, purchase price S, daily price P, delivery amount R, distance d, transportation cost F, and sales loss ratio LT.
Description
Technical Field
The invention relates to the field of big data analysis, in particular to a method and a device for intelligently scheduling agricultural product resources by using big data.
Background
In the process of agricultural product circulation, as the agricultural products are easy to rot and damage, large in size, scattered in production regions and the like, and the wholesale market expenses of all sizes distributed in the country are different, the market quotation of the agricultural products is greatly influenced by factors such as weather and seasons, and the like. After the agricultural products are on the market, how to reasonably schedule the distribution of the agricultural products to the market and how to adjust the production and marketing contradictions to submit economic benefits and reduce the loss of the agricultural products are an important ring in the circulation of the agricultural products.
In the prior art, some agricultural product resource scheduling methods have been proposed, but have many defects. For example, there is known a method of guiding a price index of agricultural product circulation, which removes the highest value and the lowest value in the obtained price data of agricultural products and averages the remaining data after obtaining trading information of agricultural products. Further, threshold percentage of upper and lower limits is set, and data outside the threshold is cleared, and the price data after the above processing is used to calculate the price index.
In the above-described method of guiding the price index, only the price factor is considered, and many factors affecting the price change, such as logistics, market expenses, loss of goods, etc., are not considered. In addition, in the calculation process of the price index, a threshold value is set to filter data, however, in practical application, the percentage change is large even if the price fluctuation is small for the categories like Chinese cabbage, so that the price after threshold value filtering cannot reflect the real situation. In addition, in the guiding method, a base price which directly influences the price index needs to be manually set, and an unreasonable base price has a large influence on the result, but a method for evaluating whether the base price is reasonable is lacked in the method.
In addition, an early warning management system for agricultural product market price is also known, which predicts the price of an agricultural product and performs risk detection by using a big data analysis model using a time, region, agricultural product, risk monitoring linear regression model (logistic regression model) and Apriori algorithm after collecting price data of the agricultural product.
However, in the early warning management system, the logistic model can only predict whether the price of the agricultural product is rising or falling, and cannot predict the rising or falling amplitude of the price of the agricultural product. Furthermore, since the price change of agricultural products is periodic, the accuracy of the result of prediction by using a logistic model alone is not high.
Disclosure of Invention
In order to solve the above technical problems, such as the technical problems of low data processing precision, unreasonable parameter setting, low result accuracy and the like in the prior art described above, the present invention provides a method and an apparatus for scheduling agricultural product resources based on agricultural big data and machine learning, so as to intelligently schedule agricultural products to an optimal delivery destination (delivery market) under the comprehensive consideration of various factors.
According to the method and the device for intelligently scheduling agricultural product resources, disclosed by the invention, data of multiple dimensions can be integrated, and the maximum value of an objective function for determining a delivery destination market is calculated by using agricultural big data and machine learning so as to determine the optimal delivery market of agricultural products on the market and realize intelligent scheduling of agricultural products. Therefore, the accuracy of the calculation of the objective function can be improved, and the calculated result is more in line with the actual situation, so that the agricultural product scheduling is optimized, the contradiction between production and sales is adjusted, the agricultural product loss is reduced, and the economic benefit is improved.
According to a first aspect of the invention, a method for intelligently scheduling agricultural product resources is provided, which is characterized by comprising the following steps:
a delivery destination information acquisition step of acquiring delivery destination information including position information of the delivery destination and cost information of the delivery destination;
an agricultural product information acquisition step of acquiring a purchase price S of the agricultural product at a producing place and a daily price P of the agricultural product at the delivery destination, and an amount R of shipment of the agricultural product from the producing place to the delivery destination, respectively;
a transportation route and transportation cost calculation step of calculating an optimal transportation route according to the address of the origin and the position information of the delivery destination, thereby obtaining a transportation distance d and a transportation cost F from the origin to the delivery destination;
a loss information acquisition step of acquiring a sales loss ratio LT of the agricultural product at the delivery destination; and
an optimum delivery destination calculation step of determining a destination of an optimum delivery of the agricultural product according to an objective function based on the delivery destination information, the purchase price S, the daily price P, the delivery amount R, the distance d, the transportation cost F, and the sales loss ratio LT.
Further, in the delivery destination information acquiring step, the cost information of the delivery destination includes a daily cost M of the agricultural commodity at the delivery destination and a market cost C of the agricultural commodity at the delivery destination.
Further, the objective function is:
in the objective function, i represents the origin of the agricultural commodity, j represents the delivery destination, k represents a sale date of the agricultural commodity at the delivery destination, and t represents a category of the agricultural commodity.
Further, in the objective function,
Further, the delivery destination corresponding to the maximum value of the objective function is calculated by using a genetic algorithm to be used as an optimal delivery destination.
According to a second aspect of the present invention, there is provided an apparatus for intelligently scheduling agricultural product resources, the apparatus comprising:
a delivery destination information acquisition module for acquiring delivery destination information including location information of the delivery destination and cost information of the delivery destination;
an agricultural product information acquisition module for respectively acquiring a purchase price S of the agricultural product at a producing place and a daily price P of the agricultural product at the delivery destination, and an amount R of shipment of the agricultural product from the producing place to the delivery destination;
a transportation route and transportation cost calculation module for calculating an optimal transportation route from the address of the origin and the location information of the delivery destination, thereby obtaining a transportation distance d and a transportation cost F from the origin to the delivery destination;
a loss information acquisition module for acquiring a sales loss ratio LT of the agricultural product at the delivery destination; and
an optimal delivery destination calculation module for determining an optimal delivery destination of the agricultural commodity according to an objective function based on the delivery destination information, the purchase price S, the daily price P, the delivery amount R, the distance d, the transportation cost F, and the sales loss ratio LT.
Further, the cost information of the delivery destination includes a daily cost M of the agricultural commodity at the delivery destination and a market cost C of the agricultural commodity at the delivery destination.
Further, the objective function is:
in the objective function, i represents the origin of the agricultural commodity, j represents the delivery destination, k represents a sale date of the agricultural commodity at the delivery destination, and t represents a category of the agricultural commodity.
Further, in the objective function,
Further, the optimal delivery destination calculation module calculates the delivery destination corresponding to the maximum value of the objective function by using a genetic algorithm, and the optimal delivery destination is used as the optimal delivery destination.
According to a third aspect of the present invention, there is provided an apparatus for intelligently scheduling agricultural product resources, the apparatus comprising: one or more processors; storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to carry out the method described in the first aspect.
According to a fourth aspect of the present invention, there is provided a computer readable medium, characterized in that, wherein said medium has stored thereon a program which is executed to implement the method as described in the first aspect above.
The technical solutions of the present invention will be described in further detail below with reference to the drawings and preferred embodiments of the present invention, and the advantageous effects of the present invention will be further apparent.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention.
FIG. 1 is a diagram illustrating the steps of a method for intelligently scheduling agricultural commodity resources in accordance with a preferred embodiment of the present invention.
Fig. 2 is a schematic block diagram of an apparatus for intelligently scheduling agricultural commodity resources according to a preferred embodiment of the present invention.
FIG. 3 is a schematic block diagram of a computer system of an apparatus for intelligently scheduling agricultural commodity resources in accordance with the present invention.
Detailed Description
The technical solution of the present invention will be clearly and completely described below with reference to the specific embodiments of the present invention and the accompanying drawings. It is to be understood that the described embodiments are only some of the presently preferred embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The steps of the method for intelligently scheduling agricultural commodity resources according to the invention are described below with reference to fig. 1.
FIG. 1 is a diagram illustrating the steps of a method for intelligently scheduling agricultural commodity resources in accordance with a preferred embodiment of the present invention. As shown in fig. 1, the method for intelligently scheduling agricultural product resources according to the invention comprises the following steps: a shipment destination information acquisition step S1, a produce information acquisition step S2, a transportation route and transportation cost calculation step S3, a loss information acquisition step S4, and an optimal shipment destination place calculation step S5. The above steps of the method for intelligently scheduling agricultural resources of the invention will be described in detail below.
S1: delivery destination information acquisition step
Acquiring information of a delivery destination, wherein the delivery destination is a delivery market, the information of the delivery destination comprises position information of the delivery destination and various cost information of the delivery destination, and the various cost information comprises daily cost M of agricultural products (of various types) at the delivery destination and market cost C (yuan/ton) of the agricultural products at the delivery destination.
It should be noted that, in particular, the daily fee M of the agricultural product at the delivery destination includes a labor fee for sale, a packaging fee, an unloading fee, a miscellaneous fee, and the like, which are generated during sale in the market, and may be different every day. The market fee C includes a fixed fee charged by the market such as a market management fee, a booth fee, a parking space fee, and the like.
S2: agricultural product information acquisition step
A purchase price S of the agricultural product at a producing place and a daily price P of the agricultural product at a delivery destination and an amount R of the agricultural product from the producing place to the delivery destination are respectively obtained.
S3: transportation route and transportation cost calculation step
An optimum transport route from the place of origin to the delivery destination is calculated from the address of the place of origin and the location information of the delivery destination acquired in step S1, thereby obtaining a transport distance d from the place of origin to the delivery destination, and based on the transport distance d, a transport cost F (ton/yuan/km) is further calculated and acquired.
S4: loss information acquisition step
The sales loss ratio LT of the agricultural product at the delivery destination is acquired.
It should be noted that the sequence of the above steps S1-S4 is not fixed, but may be changed, that is, the sequence of the above steps S1 to S4 may be changed appropriately without affecting the implementation of the present invention. For example, it may be performed in the order of step S4, step S2, step S1, step S3, and the like.
And the above-described steps S1 to S4 of the present invention are only exemplary embodiments of the present invention, and may be combined into, for example, one step, e.g., a parameter acquisition step. In this parameter acquisition step, the respective parameters described in the above-described steps S1 to S4 are acquired.
S5: optimal delivery destination calculation step
The optimum delivery destination of the agricultural product is determined according to an objective function based on the delivery destination information acquired in step S1, the purchase price S, the daily price P, and the delivery volume R acquired in step S2, the transportation distance d and the transportation cost F acquired in step S3, and the sales loss ratio LT acquired in step S4. The objective function (i.e., the objective function representing the revenue) is:
in the above objective function, i denotes a producing area of the agricultural produce, j denotes a delivery destination, k denotes a sale date of the agricultural produce at the delivery destination, and t denotes a category of the agricultural produce.
And more specifically, in conjunction with the respective parameter information, S, obtained in steps S1-S4itRepresenting the purchasing cost of the t categories at the i producing area; mjtkRepresents the cost of the t item on the kth day of j delivery destination (market); pjtkShowing the daily price of the agricultural products of the t class in the j market in the market; cjtRepresents the cost (Yuan/ton) of t categories in j market; LT represents the k-th market sales loss ratio of t types; rijtThe delivery quantity of t products from the i producing area to the j market is represented; fitjRepresenting the transportation cost of t categories from i origin to j market; dijRepresenting the transport distance from the i origin to the j market.
Based on the above definitions for the respective parameters, in the above objective function,
Therefore, the delivery destination corresponding to the maximum value of the objective function is calculated by using a genetic algorithm (ga-VRPTW algorithm) to be used as the optimal delivery destination, so that the intelligent scheduling of the agricultural products is realized.
By utilizing the method for intelligently scheduling agricultural product resources, the technical problems of low data processing precision, unreasonable parameter setting, low result accuracy and the like in the prior art are solved. The method for intelligently scheduling agricultural product resources can integrate data of multiple dimensions, and the maximum value of the target function for determining the delivery destination market is calculated by using agricultural big data and machine learning, so that the optimal delivery market of agricultural products on the market is determined, and the intelligent scheduling of the agricultural products is realized.
According to the method for intelligently scheduling agricultural product resources, disclosed by the invention, data of parameters of multiple dimensions are integrated, the accuracy of objective function calculation is improved, and the calculated result is more in line with the actual situation, so that agricultural product scheduling is optimized, the production and marketing contradiction is adjusted, the agricultural product loss is reduced, and the economic benefit is improved.
The method for intelligently scheduling agricultural product resources according to the invention is described above, and another embodiment of the invention describes a device for intelligently scheduling agricultural product resources. An apparatus 100 for intelligently scheduling agricultural resources according to the present invention will be described with reference to fig. 2.
Fig. 2 is a schematic block diagram of an apparatus for intelligently scheduling agricultural commodity resources according to a preferred embodiment of the present invention. As shown in fig. 2, the apparatus 100 for intelligently scheduling agricultural resources according to the present invention includes a delivery destination information acquisition module 110, an agricultural information acquisition module 120, a transportation route and transportation cost calculation module 130, a loss information acquisition module 140, and an optimal delivery destination calculation module 150. The above-described respective modules will be described in detail below.
The delivery destination information acquiring module 110 is configured to acquire delivery destination information, where the delivery destination is a delivery market, and the delivery destination information includes location information of the delivery destination and various cost information of the delivery destination, where the various cost information includes a daily cost M of the agricultural products (of various categories) at the delivery destination and a market cost C (dollars per ton) of the agricultural products at the delivery destination.
It should be noted that, in particular, the daily fee M of the agricultural product at the delivery destination includes a labor fee for sale, a packaging fee, an unloading fee, a miscellaneous fee, and the like, which are generated during sale in the market, and may be different every day. The market fee C includes a fixed fee charged by the market such as a market management fee, a booth fee, a parking space fee, and the like.
The agricultural product information obtaining module 120 is used for obtaining a purchase price S of an agricultural product at a producing place, a daily price P of the agricultural product at a delivery destination, and a delivery rate R of the agricultural product from the producing place to the delivery destination, respectively.
The transportation route and transportation cost calculation module 130 is configured to calculate an optimal transportation route from the place of origin to the delivery destination according to the address of the place of origin and the location information of the delivery destination acquired by the destination information acquisition module 110, thereby obtaining a transportation distance d from the place of origin to the delivery destination, and further calculate and acquire a transportation cost F (ton/yuan/km) based on the transportation distance d.
The attrition information acquiring module 140 is used for acquiring the sales attrition ratio LT of the agricultural product at the delivery destination.
The delivery destination information acquisition module 110, the agricultural product information acquisition module 120, the transportation route and transportation cost calculation module 130, and the wearout information acquisition module 140 described above are merely exemplary embodiments of the present invention, and may be combined into, for example, one module, e.g., a parameter acquisition module. At the parameter acquiring module, the parameters acquired by the modules are acquired as described above.
The optimal delivery destination calculation module 150 is configured to determine the optimal delivery destination of the agricultural product according to an objective function based on the delivery destination information acquired by the delivery destination information acquisition module 110, the purchase price S, the daily price P, and the delivery rate R acquired by the agricultural product information acquisition module 120, the transportation distance d and the transportation cost F acquired by the transportation route and transportation cost calculation module 130, and the sales-to-loss ratio LT acquired by the loss information acquisition module 140. The objective function (i.e., the objective function representing the revenue) is:
in the above objective function, i denotes a producing area of the agricultural produce, j denotes a delivery destination, k denotes a sale date of the agricultural produce at the delivery destination, and t denotes a category of the agricultural produce.
And more specifically, the parameter information S obtained by combining the above modules 110 and 140itRepresenting the purchasing cost of the t categories at the i producing area; mjtkRepresents the cost of t items on the kth day of j delivery destination (market); pjtkShowing the daily price of the agricultural products of the t class in the j market in the market; cjtRepresents the cost (Yuan/ton) of t categories in j market; LT represents the k-th market sales loss ratio of the t categories; rijtThe delivery quantity of t products from the i producing area to the j market is represented; fitjRepresenting the transportation cost of t categories from i origin to j market; dijRepresenting the transport distance from the i origin to the j market.
Based on the above definitions for the respective parameters, in the above objective function,
In this way, the optimal destination calculation module 150 calculates a destination corresponding to the maximum value of the objective function as an optimal destination by using a genetic algorithm (ga-VRPTW algorithm), thereby realizing intelligent scheduling of agricultural products.
By utilizing the device for intelligently scheduling agricultural product resources, the technical problems of low data processing precision, unreasonable parameter setting, low result accuracy and the like in the prior art are solved. The device for intelligently scheduling agricultural product resources can integrate data of multiple dimensions, and calculate the maximum value of the target function for determining the delivery destination market by using agricultural big data and machine learning, so that the optimal delivery market of the agricultural products on the market is determined, and the intelligent scheduling of the agricultural products is realized.
According to the device for intelligently scheduling agricultural product resources, disclosed by the invention, data of parameters of multiple dimensions are integrated, the accuracy of objective function calculation is improved, and the calculated result is more in line with the actual situation, so that agricultural product scheduling is optimized, the production and marketing contradiction is adjusted, the agricultural product loss is reduced, and the economic benefit is improved.
Reference is now made to fig. 3, which is a block diagram illustrating a computer system suitable for implementing an apparatus for intelligently scheduling agricultural product resources in accordance with an embodiment of the present invention. The apparatus for intelligently scheduling agricultural resources shown in fig. 3 is only an example, and should not bring any limitation to the function and the scope of the embodiments of the present invention.
As shown in fig. 3, the computer system 300 includes a Central Processing Unit (CPU)301 that can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)302 or a program loaded from a storage section 308 into a Random Access Memory (RAM) 303. In the RAM 303, various programs and data necessary for the operation of the system 300 are also stored. The CPU 301, ROM302, and RAM 303 are connected to each other via a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.
The following components are connected to the I/O interface 305: an input portion 306 including a keyboard, a mouse, and the like; an output section 303 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 308 including a hard disk and the like; and a communication section 309 including a network interface card such as a LAN card, a modem, or the like. The communication section 309 performs communication processing via a network such as an internet. A drive 310 is also connected to the I/O interface 305 as needed. A removable medium 311 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 310 as necessary, so that a computer program read out therefrom is mounted into the storage section 308 as necessary.
In particular, the steps described above with reference to the flow diagrams may be implemented as computer software programs, according to embodiments of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in FIG. 1. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 309, and/or installed from the removable medium 311. The computer program performs the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 301.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. The computer readable storage medium may be, for example, and not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods, apparatus, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules and units thereof may also be provided in a processor, which may be described as: a processor includes a delivery destination information acquisition module, an agricultural product information acquisition module, a transportation route and transportation cost calculation module, a loss information acquisition module, and an optimal delivery destination calculation module. Here, the names of these modules do not constitute a limitation to the module and its unit itself in some cases, and for example, a destination information acquisition module may also be described as a "market-wide information acquisition module".
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to comprise:
s1: delivery destination information acquisition step
Acquiring information of a delivery destination, wherein the delivery destination is a delivery market, the information of the delivery destination comprises position information of the delivery destination and various cost information of the delivery destination, and the various cost information comprises daily cost M of agricultural products (of various types) at the delivery destination and market cost C (yuan/ton) of the agricultural products at the delivery destination.
It should be noted that, in particular, the daily fee M of the agricultural product at the delivery destination includes a labor fee for sale, a packaging fee, an unloading fee, a miscellaneous fee, and the like, which are generated during sale in the market, and may be different every day. The market fee C includes a fixed fee charged by the market such as a market management fee, a booth fee, a parking space fee, and the like.
S2: agricultural product information acquisition step
A purchase price S of the agricultural product at a producing place and a daily price P of the agricultural product at a delivery destination and an amount R of the agricultural product from the producing place to the delivery destination are respectively obtained.
S3: transportation route and transportation cost calculation step
An optimum transport route from the place of origin to the delivery destination is calculated from the address of the place of origin and the location information of the delivery destination acquired in step S1, thereby obtaining a transport distance d from the place of origin to the delivery destination, and based on the transport distance d, a transport cost F (ton/yuan/km) is further calculated and acquired.
S4: loss information acquisition step
The sales loss ratio LT of the agricultural product at the delivery destination is acquired.
It should be noted that the sequence of the above steps S1-S4 is not fixed, but may be changed, that is, the sequence of the above steps S1 to S4 may be changed appropriately without affecting the implementation of the present invention. For example, it may be performed in the order of step S4, step S2, step S1, step S4, and the like.
And the above-described steps S1 to S4 of the present invention are only exemplary embodiments of the present invention, and may be combined into, for example, one step, e.g., a parameter acquisition step. In this parameter acquisition step, the respective parameters described in the above-described steps S1 to S4 are acquired.
S5: optimal delivery destination calculation step
The optimum delivery destination of the agricultural product is determined according to an objective function based on the delivery destination information acquired in step S1, the purchase price S, the daily price P, and the delivery volume R acquired in step S2, the transportation distance d and the transportation cost F acquired in step S3, and the sales loss ratio LT acquired in step S4. The objective function (i.e., the objective function representing the revenue) is:
in the above objective function, i denotes a producing area of the agricultural produce, j denotes a delivery destination, k denotes a sale date of the agricultural produce at the delivery destination, and t denotes a category of the agricultural produce.
And more specifically, in conjunction with the respective parameter information, S, obtained in steps S1-S4itRepresenting the purchasing cost of the t categories at the i producing area; mjtkRepresents the cost of the t item on the kth day of j delivery destination (market); pjtkShowing the daily price of the agricultural products of the t class in the j market in the market; cjtRepresents the cost (Yuan/ton) of t categories in j market; LT represents the k-th market sales loss ratio of t types; rijtThe delivery quantity of t products from the i producing area to the j market is represented; fitjRepresenting the transportation cost of t categories from i origin to j market; dijRepresenting the transport distance from the i origin to the j market.
Based on the above definitions for the respective parameters, in the above objective function,
Therefore, the delivery destination corresponding to the maximum value of the objective function is calculated by using a genetic algorithm (ga-VRPTW algorithm) to be used as the optimal delivery destination, so that the intelligent scheduling of the agricultural products is realized.
By utilizing the method and the device for intelligently scheduling agricultural product resources, the technical problems of low data processing precision, unreasonable parameter setting, low result accuracy and the like in the prior art are solved. The method and the device for intelligently scheduling agricultural product resources can integrate data of multiple dimensions, and calculate the maximum value of an objective function for determining a delivery destination market by using agricultural big data and machine learning, so that the optimal delivery market of agricultural products on the market is determined, and the intelligent scheduling of the agricultural products is realized.
According to the method and the device for intelligently scheduling agricultural product resources, disclosed by the invention, data of parameters of multiple dimensions are integrated, the accuracy of objective function calculation is improved, and the calculated result is more in line with the actual situation, so that agricultural product scheduling is optimized, the contradiction between production and sales is adjusted, the agricultural product loss is reduced, and the economic benefit is improved.
The above description is only an example of the present application and is not intended to limit the present invention, and it is obvious to those skilled in the art that various modifications and variations can be made in the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (12)
1. A method for intelligently scheduling agricultural commodity resources, the method comprising:
a delivery destination information acquisition step of acquiring delivery destination information including position information of the delivery destination and cost information of the delivery destination;
an agricultural product information acquisition step of acquiring a purchase price S of the agricultural product at a producing place and a daily price P of the agricultural product at the delivery destination, and an amount R of shipment of the agricultural product from the producing place to the delivery destination, respectively;
a transportation route and transportation cost calculation step of calculating an optimal transportation route from the address of the origin and the location information of the delivery destination, thereby obtaining a transportation distance d and a transportation cost F from the origin to the delivery destination;
a loss information acquisition step of acquiring a sales loss ratio LT of the agricultural product at the delivery destination; and
an optimum delivery destination calculation step of determining an optimum delivery destination of the agricultural product according to an objective function based on the delivery destination information, the purchase price S, the daily price P, the delivery amount R, the distance d, the transportation cost F, and the sales loss ratio LT.
2. The method of claim 1, wherein,
in the delivery destination information acquiring step, the cost information of the delivery destination includes a daily cost M of the agricultural commodity at the delivery destination and a market cost C of the agricultural commodity at the delivery destination.
3. The method of claim 2, wherein,
the objective function is:
in the objective function, i represents the origin of the agricultural commodity, j represents the delivery destination, k represents a sale date of the agricultural commodity at the delivery destination, and t represents a category of the agricultural commodity.
4. The method of claim 3, wherein, in the objective function,
5. The method according to any one of claims 1 to 4, wherein,
and calculating the delivery destination corresponding to the maximum value of the target function by using a genetic algorithm to serve as an optimal delivery destination.
6. An apparatus for intelligently scheduling agricultural commodity resources, the apparatus comprising:
a delivery destination information acquisition module for acquiring delivery destination information including location information of the delivery destination and cost information of the delivery destination;
an agricultural product information acquisition module for respectively acquiring a purchase price S of the agricultural product at a producing place and a daily price P of the agricultural product at the delivery destination, and an amount R of shipment of the agricultural product from the producing place to the delivery destination;
a transportation route and transportation cost calculation module for calculating an optimal transportation route from the address of the origin and the location information of the delivery destination, thereby obtaining a transportation distance d and a transportation cost F from the origin to the delivery destination;
a loss information acquisition module for acquiring a sales loss ratio LT of the agricultural product at the delivery destination; and
an optimal delivery destination calculation module for determining an optimal delivery destination of the agricultural commodity according to an objective function based on the delivery destination information, the purchase price S, the daily price P, the delivery amount R, the distance d, the transportation cost F, and the sales loss ratio LT.
7. The apparatus of claim 6, wherein,
the cost information of the delivery destination includes a daily cost M of the agricultural commodity at the delivery destination and a market cost C of the agricultural commodity at the delivery destination.
8. The apparatus of claim 7, wherein,
the objective function is:
in the objective function, i represents the origin of the agricultural commodity, j represents the delivery destination, k represents a sale date of the agricultural commodity at the delivery destination, and t represents a category of the agricultural commodity.
9. The apparatus of claim 8, wherein, in the objective function,
10. The apparatus according to any one of claims 6 to 9, wherein,
the optimal delivery destination calculation module calculates the delivery destination corresponding to the maximum value of the objective function by using a genetic algorithm, and the optimal delivery destination is used as the optimal delivery destination.
11. An apparatus for intelligently scheduling agricultural commodity resources, the apparatus comprising:
one or more processors; and
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-5.
12. A computer-readable medium, wherein,
the medium has stored thereon a program that is executed to implement the method of any one of claims 1-5.
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