CN115879645A - Agricultural product supply chain optimization decision method and device - Google Patents

Agricultural product supply chain optimization decision method and device Download PDF

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CN115879645A
CN115879645A CN202310061593.1A CN202310061593A CN115879645A CN 115879645 A CN115879645 A CN 115879645A CN 202310061593 A CN202310061593 A CN 202310061593A CN 115879645 A CN115879645 A CN 115879645A
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agricultural product
supply chain
week
contract
data information
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杜利平
陈泫宇
梁家瑞
张涵
王金权
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University of Science and Technology Beijing USTB
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University of Science and Technology Beijing USTB
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Abstract

The invention relates to the technical field of intelligent agriculture, in particular to an agricultural product supply chain optimization decision method and device, wherein the method comprises the following steps: s1, acquiring initial data information of agricultural products; s2, performing data integration on the initial data information to obtain integrated agricultural product data information; and S3, determining a supply chain of the agricultural product by adopting a pre-constructed agricultural product supply chain optimization decision model according to the integrated agricultural product data information. Compared with the traditional decision making by the experience of farmers, the agricultural product supply chain decision making method can help managers to make better decision making for the agricultural product supply chain.

Description

Agricultural product supply chain optimization decision method and device
Technical Field
The invention relates to the technical field of intelligent agriculture, in particular to an agricultural product supply chain optimization decision method and device.
Background
The intelligent agriculture is an important component of the intelligent economy, and is a main component of the intelligent economy and a main way for realizing the future advantages, living after the economic development and overtaking the strategy.
Wisdom agriculture is to use internet of things to traditional agriculture, and the agricultural production is controlled through mobile platform or computer platform to application sensor and software, makes traditional agriculture more have "wisdom". In addition to accurate sensing, control and decision management, the intelligent agriculture also comprises contents in aspects of agricultural electronic commerce, food source tracing and anti-counterfeiting, agricultural leisure travel, agricultural information service and the like in a broad sense.
At present, with the development of the Internet of things (IOA) of agriculture, the relationship between the production and the sale of fresh agricultural products has changed fundamentally, and because of the special characteristics of the fresh agricultural products, such as the easy decay and the existence of multi-cycle production, how to better determine the supply chain of the agricultural products becomes an urgent problem to be solved.
Disclosure of Invention
The invention provides an agricultural product supply chain optimization decision method and device, which are used for carrying out optimization decision on an agricultural product supply chain. The technical scheme is as follows:
in one aspect, an agricultural product supply chain optimization decision method is provided, and the method includes:
s1, acquiring initial data information of agricultural products;
s2, performing data integration on the initial data information to obtain integrated agricultural product data information;
and S3, determining a supply chain of the agricultural product by adopting a pre-constructed agricultural product supply chain optimization decision model according to the integrated agricultural product data information.
Optionally, the S2 performs data integration on the initial data information to obtain integrated agricultural product data information, and specifically includes:
processing abnormal data in the initial data information based on a stationary random process, wherein the stationary random process comprises the following steps: wide stationary inspection and strict stationary time series inspection;
and integrating the data information of the agricultural product without the abnormal data into week-related data information by using a preset ARMA time series model.
Optionally, the mathematical expression of the agricultural product supply chain optimization decision model of S3 is shown as the following formula (1):
Figure SMS_1
/>
Figure SMS_3
represents a maximum of a benefit>
Figure SMS_8
For the contract price in contract agriculture for week h, <' > based on the number of hours>
Figure SMS_13
Selling the price of produce by wholesale for week h, based on the value of the produce, and>
Figure SMS_5
a price for selling produce by retail for the h week, based on the total volume of produce sold by the retailer>
Figure SMS_9
For the agricultural supply on week h under contract>
Figure SMS_14
Deliver volume for the nth week of wholesale>
Figure SMS_15
Deliver size for week h retail>
Figure SMS_2
The planting area of the crop in the jth week>
Figure SMS_6
In order to achieve a production cost per hectare, device for selecting or keeping>
Figure SMS_10
For the operating costs of the Internet of things devices per hectare, based on the operating conditions>
Figure SMS_12
For depreciation cost of the Internet of things equipment, wherein->
Figure SMS_4
Represents the operation cost of the Internet of things equipment required for planting agricultural products per hectare and the operating cost of the Internet of things equipment required for planting agricultural products per hectare>
Figure SMS_7
Per person per week employment costs, <' > based on the total number of people>
Figure SMS_11
Representing the number of retail booths in week H, let J = {1,2,.., n } be the set of planting weeks, where n represents the total number of weeks in the planting season, let H = {1,2,..., v } be the set of sales weeks, v be the total number of weeks in the sales season, J and H be indices of production and sales weeks, J ∈ J, H ∈ H.
Optionally, the constraints of the agricultural product supply chain optimization decision model include at least one of:
Figure SMS_16
the constraint condition (2) expresses the planting area within the specified time in contract agriculture
Figure SMS_17
The maximum planting area of farmers can not be exceeded;
Figure SMS_18
the constraint (3) expresses that the farmer production must reach the minimum product supply stipulated in the contract agriculture,
Figure SMS_19
for a product percentage meeting contractual agricultural requirements, is selected>
Figure SMS_20
The h week minimum supply percentage agreed in contract agriculture;
Figure SMS_21
the specific meaning of the constraint condition (4) is the production cost and daily operation cost of the agricultural Internet of things equipment, and the total amount of available funds is not exceeded
Figure SMS_22
。
Optionally, in the step S3, determining a supply chain of the agricultural product by using a pre-constructed agricultural product supply chain optimization decision model according to the integrated agricultural product data information, specifically including:
the agricultural product supply chain optimization decision model calculates the maximum sales amount of the sales mode only depending on contract agriculture
Figure SMS_23
And maximum sales in combination with contract agriculture and traditional retail wholesale>
Figure SMS_24
；
When in use
Figure SMS_25
When the agricultural product supply chain optimization decision model is used, the supply chain of the agricultural product is decided to adopt a sale mode only depending on contract agriculture;
when the temperature is higher than the set temperature
Figure SMS_26
And then, the agricultural product supply chain optimization decision model decides that the supply chain of the agricultural product adopts a sale mode combining contract agriculture and traditional retail wholesale.
In another aspect, an agricultural product supply chain optimization decision-making device is provided, the device comprising:
the acquisition module is used for acquiring initial data information of agricultural products;
the integration module is used for carrying out data integration on the initial data information to obtain integrated agricultural product data information;
and the determining module is used for determining the supply chain of the agricultural product by adopting a pre-constructed agricultural product supply chain optimization decision model according to the integrated agricultural product data information.
Optionally, the mathematical expression of the agricultural product supply chain optimization decision model is shown in the following formula (1):
Figure SMS_27
Figure SMS_30
represents the maximum value of the benefit, is>
Figure SMS_34
For the contract price in contract agriculture for week h, <' > based on the number of hours>
Figure SMS_38
Selling the price of produce by wholesale for week h, based on the value of the produce, and>
Figure SMS_29
a price for selling produce by retail for the h week, based on the total volume of produce sold by the retailer>
Figure SMS_35
For the agricultural supply on week h under contract>
Figure SMS_39
Deliver volume for the nth week of wholesale>
Figure SMS_41
Deliver size for week h retail>
Figure SMS_28
The planting area of the crop in the jth week>
Figure SMS_33
For production costs per hectare, based on the sum of the production costs>
Figure SMS_37
For the operation cost of the Internet of things equipment per hectare, the operation cost is judged>
Figure SMS_40
For depreciation cost of the Internet of things equipment, wherein->
Figure SMS_31
Represents the operation cost of the Internet of things equipment required for planting agricultural products per hectare, and>
Figure SMS_32
per person per week employment costs, <' > based on the total number of people>
Figure SMS_36
Representing the number of retail booths in week H, let J = {1, 2.. And n } be the set of planting weeks, where n represents the total number of weeks in a planting period, let H = {1, 2.. And v } be the set of sales weeks, v be the total number of weeks in a sales period, J and H be the indices of production and sales weeks, J ∈ J, H ∈ H.
Optionally, the constraints of the agricultural product supply chain optimization decision model include at least one of:
Figure SMS_42
the constraint condition (2) expresses the planting area within the time specified in contract agriculture
Figure SMS_43
The maximum planting area of farmers can not be exceeded;
Figure SMS_44
the constraint (3) expresses that the farmer production must reach the minimum product supply stipulated in the contract agriculture,
Figure SMS_45
for a product percentage meeting contractual agricultural requirements, is selected>
Figure SMS_46
The h week minimum supply percentage agreed in contract agriculture;
Figure SMS_47
the specific meaning of the constraint condition (4) is the production cost and daily operation cost of the agricultural Internet of things equipment, and the total amount of available funds is not exceeded
Figure SMS_48
。
In another aspect, an electronic device is provided, and the electronic device includes a processor and a memory, where the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the agricultural product supply chain optimization decision method.
In another aspect, a computer-readable storage medium is provided, in which at least one instruction is stored, and the at least one instruction is loaded and executed by a processor to implement the agricultural product supply chain optimization decision method.
The technical scheme provided by the invention has the beneficial effects that at least:
the method can help farmers to deal with complex decision-making problems in the agricultural product supply chain, and compared with the traditional decision-making by the experience of farmers, the method can help managers to make better decisions for the agricultural product supply chain, especially the joint decision of production and sales, and finally aims to reduce unnecessary loss of farmers, maximize the income and implement the rural pleasure strategy.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart of a decision-making method for agricultural product supply chain optimization according to an embodiment of the present invention;
FIG. 2 is a block diagram of an agricultural product supply chain optimization decision-making apparatus according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
As shown in fig. 1, an embodiment of the present invention provides an agricultural product supply chain optimization decision method, including:
s1, acquiring initial data information of agricultural products;
s2, performing data integration on the initial data information to obtain integrated agricultural product data information;
and S3, determining a supply chain of the agricultural product by adopting a pre-constructed agricultural product supply chain optimization decision model according to the integrated agricultural product data information.
An agricultural product supply chain optimization decision method according to an embodiment of the present invention is described in detail below.
The agricultural product supply chain optimization decision method provided by the embodiment of the invention comprises the following steps:
s1, acquiring initial data information of agricultural products;
the initial data information is parameter information required by a subsequent agricultural product supply chain optimization decision model, and includes but is not limited to: crop planting area per week, contract agricultural supply, traditional retail distribution, and wholesale distribution.
The initial data information can be input by a manager who uses the agricultural product supply chain optimization decision model of the embodiment of the invention to help farmers determine the supply chain of the agricultural product.
The contract agriculture is a novel agricultural planting sale mode, is called order agriculture, is different from a traditional 'planting before selling' production mode, plays an important role in the aspects of ensuring reasonable income of farmers, resisting market risks and the like, and embodies the contract agriculture advantages. One party signing a contract is an enterprise or an intermediary organization including brokers and sales outlets, and the other party is a farmer or a representative of a group of farmers. Contract agriculture has marketability, contractuality, predictability and risk. The quantity, quality and minimum protection price of the purchased agricultural products specified in the order ensure that the two parties enjoy corresponding rights, obligations and restrictions and cannot be damaged unilaterally. That is, how many contracts to produce and how many acres to plant.
S2, performing data integration on the initial data information to obtain integrated agricultural product data information;
optionally, the S2 performs data integration on the initial data information to obtain integrated agricultural product data information, which specifically includes:
processing abnormal data in the initial data information based on a stationary stochastic process, wherein the stationary stochastic process comprises the following steps: wide stationary inspection and strict stationary time series inspection;
and integrating the data information of the agricultural product from which the abnormal data is removed into week-related data information by using a preset ARMA time series model.
In a feasible implementation manner, the embodiment of the present invention calls an internally embedded pynorm, which is a Python Integrated Development Environment (Python Integrated Development Environment, python ide) with a whole set of tools that can help users improve their efficiency when developing using Python language, and the embodiment of the present invention can be a community version, version number 2022.1, and integrates data.
The ARMA smoothness is a necessary condition of an ARMA time series model, so for a time series, firstly, it is ensured that an n-order differential sequence applying autoregressive is smooth, therefore, data or n-order differential of the data needs to be smoothly checked, and a common method is used for smoothing a random process.
The abnormal data in the initial data information is processed based on the stationary random process, and an adfuller function is introduced by using a statunmodel module in python, and the principle is as follows:
in mathematics, a stationary stochastic process is a stochastic process where the probability distribution at a fixed time and location is the same as the probability distribution at all times and locations: i.e. the statistical properties of the random process do not change over time. Thus, the parameters mathematical expectations and variances also do not change with time and location.
In the test, the first step utilizes a broad stationary method, which is mathematically defined as:
for time series
Figure SMS_49
If, for any t, k, m, the following is satisfied:
Figure SMS_50
then call the time series
Figure SMS_51
The width is smooth.
In the above formula
Figure SMS_54
And &>
Figure SMS_56
Respectively represents a time series parameter->
Figure SMS_59
And &>
Figure SMS_53
Is desired. />
Figure SMS_57
Represents a time sequence parameter->
Figure SMS_60
And &>
Figure SMS_61
Is greater than or equal to>
Figure SMS_52
Representing a time sequence>
Figure SMS_55
And &>
Figure SMS_58
The covariance of (a).
The second step is that: the time sequence for the over-width stability test is carried out
Figure SMS_62
For the order of time series check, a strict stationary time series check is performed, and the mathematical definition is as follows:
Figure SMS_63
Figure SMS_64
is a strictly steady process parameter>
Figure SMS_65
Is a characteristic equation of a time series. If the root of the above equation is outside the unit circle->
Figure SMS_66
Then sequence>
Figure SMS_67
Also a strict stationary time series.
After the abnormal data are processed, the data information of the agricultural products with the abnormal data removed is integrated into week-related data information through a preset ARMA time sequence model based on data time correlation and by means of calculation of a Flink distributed computing platform embedded in the system.
The week-related data information is in a data form required by a subsequent agricultural product supply chain optimization decision model.
And S3, determining a supply chain of the agricultural product by adopting a pre-constructed agricultural product supply chain optimization decision model according to the integrated agricultural product data information.
Optionally, the mathematical expression of the agricultural product supply chain optimization decision model of S3 is as shown in the following formula (1):
Figure SMS_68
Figure SMS_70
represents a maximum benefit (in units of dollars), which may be a total benefit maximum within a contractual period of a contractual agriculture, and ` withholds `>
Figure SMS_75
For the contract price (in dollars) in contract agriculture for the h week, a weekly contract price, which is greater than or equal to the contract price of the h week>
Figure SMS_79
Selling the price (in units) of produce in wholesale mode for week h, and/or>
Figure SMS_72
Sell price (in dollars) of produce and/or based on sale at retail for week h>
Figure SMS_76
Contract agricultural supply (in tons) for week h,
Figure SMS_80
based on the nth week of the batch delivery (in tons),. Sup.>
Figure SMS_82
Retail delivery in tons for week h>
Figure SMS_69
The planting area (unit is hectare) of the crop in the jth week>
Figure SMS_74
For a production cost per hectare (unit is Yuan),. Sup.>
Figure SMS_78
For per hectare thing networking facilityThe spare operating cost (in units of yuan),. Or->
Figure SMS_81
For depreciation cost of the Internet of things equipment, wherein->
Figure SMS_71
Expresses the operation cost (unit is Yuan) of the Internet of things equipment needed for planting agricultural products per hectare and/or the plant condition>
Figure SMS_73
Per person per week employment costs (in dollars),. Or->
Figure SMS_77
Representing the number of retail booths in units of units for the H week, let J = {1, 2..., n } be the set of planting weeks, where n represents the total number of weeks in a planting season, let H = {1, 2...., v } be the set of sales weeks, v be the total number of weeks in a sales season, J and H be the indices of production and sales weeks, J ∈ J, H ∈ H.
Optionally, the constraints of the agricultural product supply chain optimization decision model include at least one of:
Figure SMS_83
the constraint condition (2) expresses the planting area within the time specified in contract agriculture
Figure SMS_84
Can exceed the maximum planting area of farmers;
Figure SMS_85
the constraint (3) expresses that the production of farmers must reach the minimum product supply amount specified in the contract agriculture,
Figure SMS_86
for a product percentage meeting contractual agricultural requirements>
Figure SMS_87
The h week minimum supply percentage agreed in contract agriculture; />
The constraint (3) can ensure that the yield of the farmer in the planting plan meets the requirement of the contract agriculture, that is, the production of the farmer must reach the minimum product supply amount specified in the contract agriculture to avoid default of the farmer.
Figure SMS_88
The specific meaning of the constraint condition (4) is the production cost and daily operation cost of the agricultural Internet of things equipment, and the total amount of available funds is not exceeded
Figure SMS_89
。
The constraint (4) can ensure that the farmers make decision plans within the maximum available operating capital.
Optionally, in the step S3, determining a supply chain of the agricultural product by using a pre-constructed agricultural product supply chain optimization decision model according to the integrated agricultural product data information, specifically including:
the agricultural product supply chain optimization decision model calculates the maximum sales amount of the sales mode only depending on contract agriculture
Figure SMS_90
And maximum sales when contract agriculture is combined with traditional retail wholesale>
Figure SMS_91
；
Optionally, for a sales mode that solely depends on contract agriculture, a parameter related to traditional retail distribution in the model may be set to 0.
When the temperature is higher than the set temperature
Figure SMS_92
When the agricultural product supply chain optimization decision model is used, the supply chain of the agricultural product is decided to adopt a sale mode only depending on contract agriculture;
optionally, the agricultural product supply chain optimization decision model can also display the expected sales.
When the temperature is higher than the set temperature
Figure SMS_93
And then, the agricultural product supply chain optimization decision model decides that the supply chain of the agricultural product adopts a sale mode combining contract agriculture and traditional retail wholesale.
In one possible embodiment, the agricultural product supply chain optimization decision model can also display expected sales.
In view of the close relationship between the production and the sale of the fresh agricultural products, the embodiment of the invention establishes a mathematical model between the two links of production and sale so as to solve the joint planning problem of production and sale.
The method comprises the steps of obtaining a decision, and obtaining a decision through the decision, wherein the decision comprises constraints of labor force, agricultural Internet of things equipment cost and the like, and the decision is based on price estimation and resource availability and also based on special requirements of influence of the agricultural Internet of things equipment cost. The method aims to provide an optimal decision scheme for managers to farmers so as to maximize the income of the farmers and practically prevent the scale poverty return and poverty induction under the great background of country joy. In addition, through the agricultural product supply chain optimization decision model, the influence of the decided factors on the income of farmers can be preliminarily evaluated.
As shown in fig. 2, an embodiment of the present invention further provides an agricultural product supply chain optimization decision device, where the device includes:
an obtaining module 210, configured to obtain initial data information of an agricultural product;
an integration module 220, configured to perform data integration on the initial data information to obtain integrated agricultural product data information;
a determining module 230, configured to determine a supply chain of the agricultural product by using a pre-constructed agricultural product supply chain optimization decision model according to the integrated agricultural product data information.
Optionally, the mathematical expression of the agricultural product supply chain optimization decision model is shown in the following formula (1):
Figure SMS_94
Figure SMS_98
represents a maximum of a benefit>
Figure SMS_99
For a contract price in contract agriculture on week h>
Figure SMS_104
Selling the price of produce by wholesale for week h, based on the value of the produce, and>
Figure SMS_96
a price for selling produce by retail for the h week, based on the total volume of produce sold by the retailer>
Figure SMS_100
For the agricultural supply on week h under contract>
Figure SMS_103
Delivering a batch for week h, ->
Figure SMS_107
Deliver size for week h retail>
Figure SMS_95
The planting area of the crop in the jth week>
Figure SMS_101
In order to achieve a production cost per hectare, device for selecting or keeping>
Figure SMS_106
For the operating costs of the Internet of things devices per hectare, based on the operating conditions>
Figure SMS_108
Depreciation cost for Internet of things devices, wherein &>
Figure SMS_97
Represents the operation cost of the Internet of things equipment required for planting agricultural products per hectare and the operating cost of the Internet of things equipment required for planting agricultural products per hectare>
Figure SMS_102
Per person per week employment costs, <' > based on the total number of people>
Figure SMS_105
Representing the number of retail booths in week H, let J = {1, 2.. And n } be the set of planting weeks, where n represents the total number of weeks in a planting period, let H = {1, 2.. And v } be the set of sales weeks, v be the total number of weeks in a sales period, J and H be the indices of production and sales weeks, J ∈ J, H ∈ H.
Optionally, the constraints of the agricultural product supply chain optimization decision model include at least one of:
Figure SMS_109
the constraint condition (2) expresses the planting area within the time specified in contract agriculture
Figure SMS_110
The maximum planting area of farmers can not be exceeded;
Figure SMS_111
the constraint (3) expresses that the production of farmers must reach the minimum product supply amount specified in the contract agriculture,
Figure SMS_112
for a product percentage meeting contractual agricultural requirements, is selected>
Figure SMS_113
The h week minimum supply percentage agreed in contract agriculture;
the constraint (3) can ensure that the yield of the farmer in the planting plan meets the requirement of the contract agriculture, that is, the production of the farmer must reach the minimum product supply amount specified in the contract agriculture to avoid default of the farmer.
Figure SMS_114
The specific meaning of the constraint condition (4) is the production cost and daily operation cost of the agricultural Internet of things equipment, and the total amount of available funds is not exceeded
Figure SMS_115
。
The constraint (4) can ensure that the farmers make decision plans within the maximum available operating capital.
The agricultural product supply chain optimization decision device provided by the embodiment of the invention has a functional structure corresponding to the agricultural product supply chain optimization decision method provided by the embodiment of the invention, and is not described herein again.
Fig. 3 is a schematic structural diagram of an electronic device 300 according to an embodiment of the present invention, where the electronic device 300 may generate a relatively large difference due to different configurations or performances, and may include one or more processors (CPUs) 301 and one or more memories 302, where the memory 302 stores at least one instruction, and the at least one instruction is loaded and executed by the processor 301 to implement the steps of the agricultural product supply chain optimization decision method.
In an exemplary embodiment, a computer-readable storage medium, such as a memory including instructions executable by a processor in a terminal, to perform the agricultural product supply chain optimization decision method is also provided. For example, the computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the storage medium may be a read-only memory, a magnetic disk or an optical disk.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (10)

1. An agricultural product supply chain optimization decision-making method, characterized in that the method comprises:
s1, acquiring initial data information of agricultural products;
s2, performing data integration on the initial data information to obtain integrated agricultural product data information;
and S3, determining a supply chain of the agricultural product by adopting a pre-constructed agricultural product supply chain optimization decision model according to the integrated agricultural product data information.
2. The method according to claim 1, wherein the step S2 of performing data integration on the initial data information to obtain integrated agricultural product data information specifically comprises:
processing abnormal data in the initial data information based on a stationary stochastic process, wherein the stationary stochastic process comprises the following steps: wide stationary inspection and strict stationary time series inspection;
and integrating the data information of the agricultural product without the abnormal data into week-related data information by using a preset ARMA time series model.
3. The method according to claim 1, wherein the mathematical expression of the agricultural product supply chain optimization decision model of S3 is as shown in the following formula (1):
Figure QLYQS_1
Figure QLYQS_3
represents a maximum of a benefit>
Figure QLYQS_10
For the contract price in contract agriculture for week h, <' > based on the number of hours>
Figure QLYQS_14
Selling the price of produce by wholesale for week h, based on the value of the produce, and>
Figure QLYQS_4
a price for selling produce by retail for the h week, based on the total volume of produce sold by the retailer>
Figure QLYQS_8
Based on the supply amount of the contract agriculture on the h week>
Figure QLYQS_11
Delivering a batch for week h, ->
Figure QLYQS_12
For retail delivery on week h, <' > based on>
Figure QLYQS_2
The planting area of the crop in the jth week>
Figure QLYQS_6
In order to achieve a production cost per hectare, device for selecting or keeping>
Figure QLYQS_7
For the operation cost of the Internet of things equipment per hectare, the operation cost is judged>
Figure QLYQS_9
Depreciation cost for Internet of things devices, wherein &>
Figure QLYQS_5
Represents the operation cost of the Internet of things equipment required for planting agricultural products per hectare, and>
Figure QLYQS_13
per person per week employment costs, <' > based on the total number of people>
Figure QLYQS_15
Representing the number of retail booths in week H, let J = {1, 2.. And n } be the set of planting weeks, where n represents the total number of weeks in a planting period, let H = {1, 2.. And v } be the set of sales weeks, v be the total number of weeks in a sales period, J and H be the indices of production and sales weeks, J ∈ J, H ∈ H.
4. The method of claim 3, wherein the constraints of the agricultural product supply chain optimization decision model include at least one of:
Figure QLYQS_16
the constraint condition (2) expresses the planting area within the specified time in contract agriculture
Figure QLYQS_17
The maximum planting area of farmers can not be exceeded; />
Figure QLYQS_18
The constraint (3) expresses that the production of farmers must reach the minimum product supply amount specified in the contract agriculture,
Figure QLYQS_19
for a product percentage meeting contractual agricultural requirements, is selected>
Figure QLYQS_20
The h week minimum supply percentage agreed in contract agriculture;
Figure QLYQS_21
the specific meaning of the constraint condition (4) is the production cost and daily operation cost of the agricultural Internet of things equipment, and the total amount of available funds is not exceeded
Figure QLYQS_22
。
5. The method according to claim 4, wherein the S3, according to the integrated agricultural product data information, determining a supply chain of the agricultural product by using a pre-constructed agricultural product supply chain optimization decision model, specifically includes:
the agricultural product supply chain optimization decision model calculates the maximum sales amount of the sales mode only depending on contract agriculture
Figure QLYQS_23
And maximum sales when contract agriculture is combined with traditional retail wholesale>
Figure QLYQS_24
；
When the temperature is higher than the set temperature
Figure QLYQS_25
Then, the agricultural product supply chain optimization decision model decides that the supply chain of the agricultural product adopts a sale mode only depending on contract agriculture;
when in use
Figure QLYQS_26
And then, the agricultural product supply chain optimization decision model decides that the supply chain of the agricultural product adopts a sale mode combining contract agriculture and traditional retail wholesale.
6. An agricultural product supply chain optimization decision-making device, the device comprising:
the acquisition module is used for acquiring initial data information of agricultural products;
the integration module is used for carrying out data integration on the initial data information to obtain integrated agricultural product data information;
and the determining module is used for determining the supply chain of the agricultural product by adopting a pre-constructed agricultural product supply chain optimization decision model according to the integrated agricultural product data information.
7. The apparatus of claim 6, wherein the mathematical expression of the agricultural product supply chain optimization decision model is as shown in equation (1) below:
Figure QLYQS_27
Figure QLYQS_29
represents a maximum of a benefit>
Figure QLYQS_36
For the contract price in contract agriculture for week h, <' > based on the number of hours>
Figure QLYQS_37
Selling the price of produce by wholesale for week h, based on the value of the produce, and>
Figure QLYQS_30
a price for selling produce by retail for the h week, based on the total volume of produce sold by the retailer>
Figure QLYQS_38
Based on the supply amount of the contract agriculture on the h week>
Figure QLYQS_40
Deliver volume for the nth week of wholesale>
Figure QLYQS_41
Deliver size for week h retail>
Figure QLYQS_28
The planting area of the crop in the jth week>
Figure QLYQS_33
For production costs per hectare, based on the sum of the production costs>
Figure QLYQS_34
For the operation cost of the Internet of things equipment per hectare, the operation cost is judged>
Figure QLYQS_35
For depreciation cost of the Internet of things equipment, wherein->
Figure QLYQS_31
Represents the operation cost of the Internet of things equipment required for planting agricultural products per hectare and the operating cost of the Internet of things equipment required for planting agricultural products per hectare>
Figure QLYQS_32
Per person per week employment costs, <' > based on the total number of people>
Figure QLYQS_39
Representing the number of retail booths in week H, let J = {1,2,.., n } be the set of planting weeks, where n represents the total number of weeks in the planting season, let H = {1,2,..., v } be the set of sales weeks, v be the total number of weeks in the sales season, J and H be indices of production and sales weeks, J ∈ J, H ∈ H.
8. The apparatus of claim 7, wherein the constraints of the agricultural product supply chain optimization decision model include at least one of:
Figure QLYQS_42
the constraint condition (2) expresses the planting area within the specified time in contract agriculture
Figure QLYQS_43
The maximum planting area of farmers can not be exceeded;
Figure QLYQS_44
the constraint (3) expresses that the farmer production must reach the minimum product supply stipulated in the contract agriculture,
Figure QLYQS_45
for a product percentage meeting contractual agricultural requirements, is selected>
Figure QLYQS_46
The h week minimum supply percentage agreed in contract agriculture;
Figure QLYQS_47
the specific meaning of the constraint condition (4) is the production cost and daily operation cost of the agricultural Internet of things equipment, and the total amount of available funds is not exceeded
Figure QLYQS_48
。
9. An electronic device comprising a processor and a memory, the memory having at least one instruction stored therein, wherein the at least one instruction is loaded and executed by the processor to implement the agricultural product supply chain optimization decision method of any of claims 1-5.
10. A computer-readable storage medium having at least one instruction stored thereon, wherein the at least one instruction is loaded and executed by a processor to implement a method for agricultural product supply chain optimization decision-making according to any one of claims 1-5.
CN202310061593.1A 2023-01-21 2023-01-21 Agricultural product supply chain optimization decision method and device Pending CN115879645A (en)

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105260791A (en) * 2015-09-25 2016-01-20 苏州携优信息技术有限公司 Planting plan optimization system and method based on agricultural Internet of Things and big data analysis
CN106228325A (en) * 2016-07-20 2016-12-14 安徽朗坤物联网有限公司 Agricultural-product supply-chain based on Internet of Things management system
CN112085518A (en) * 2020-08-05 2020-12-15 河南农业大学 New agricultural product supply chain pricing method and device
CN112085516A (en) * 2020-08-05 2020-12-15 河南农业大学 Agricultural product pricing method and device and computer readable storage medium

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105260791A (en) * 2015-09-25 2016-01-20 苏州携优信息技术有限公司 Planting plan optimization system and method based on agricultural Internet of Things and big data analysis
CN106228325A (en) * 2016-07-20 2016-12-14 安徽朗坤物联网有限公司 Agricultural-product supply-chain based on Internet of Things management system
CN112085518A (en) * 2020-08-05 2020-12-15 河南农业大学 New agricultural product supply chain pricing method and device
CN112085516A (en) * 2020-08-05 2020-12-15 河南农业大学 Agricultural product pricing method and device and computer readable storage medium

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