CN116385124A - Agricultural socialization integrated service cloud platform based on big data analysis traceability - Google Patents

Agricultural socialization integrated service cloud platform based on big data analysis traceability Download PDF

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CN116385124A
CN116385124A CN202310374655.4A CN202310374655A CN116385124A CN 116385124 A CN116385124 A CN 116385124A CN 202310374655 A CN202310374655 A CN 202310374655A CN 116385124 A CN116385124 A CN 116385124A
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陈放侠
张海林
田梓涵
刘俊杰
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Xuzhou Hongquan Internet Of Things Technology Co ltd
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Abstract

The invention belongs to the technical field of agricultural socialization integrated services, and relates to an agricultural socialization integrated service cloud platform based on traceability of big data analysis. By arranging the agricultural machinery leasing confirmation module, the cultivated land information monitoring module, the cloud database, the leasing agricultural machinery returning time analysis module, the leasing agricultural machinery intelligent processing module, the reservation customer matching module and the target customer screening module, the agricultural machinery leasing service system is further perfected, the problem that the land reclamation effect is poor due to insufficient knowledge of customers on agricultural machinery equipment is effectively solved, the leasing agricultural machinery is subjected to targeted analysis and processing by analyzing the leasing agricultural machinery predicting returning time, the efficient management of agricultural machinery leasing is realized, the convenience and optimization of assigning the agricultural machinery are realized by combining the reservation time and the distance of reservation customers, the problem of 'people' or 'machines and the like' is effectively solved, and the use efficiency of the agricultural machinery is greatly improved.

Description

Agricultural socialization integrated service cloud platform based on big data analysis traceability
Technical Field
The invention belongs to the technical field of agricultural socialization integrated services, and relates to an agricultural socialization integrated service cloud platform based on traceability of big data analysis.
Background
In recent years, with the great development of farmland hosting and land circulation, the demand of agricultural machinery is greatly increased, the large-scale and industrialization of agricultural machinery is promoted, the increase of social conservation quantity and the acceleration of product upgrading and upgrading are promoted, and meanwhile, the unstable agricultural income caused by the influence of natural disasters is bound to reduce the purchase of the agricultural machinery. In this case, the cost of purchasing the agricultural machinery at a time is too high for the farmers, and the risk of bearing the cost is larger, so that the farmers prefer to rent the agricultural machinery, and in order to reduce the agricultural production cost, it is important how to develop the agricultural machinery renting service.
At present, in the domestic agricultural machinery industry, such groups or organizations have moved to rent agricultural machinery for operation in the farming season, but the agricultural machinery has small scale and no pertinence, and has some defects, particularly in the following steps: (1) The customers leasing the agricultural machinery take the agricultural machinery as the main, the agricultural machinery is not high in cultural level, the knowledge of the agricultural machinery is lacking, the proper agricultural machinery model can not be selected for the soil which needs to be cultivated, the problems of low agricultural machinery efficiency, poor land reclamation effect and the like exist in the cultivation process, on one hand, the unnecessary cost is increased, and on the other hand, the enthusiasm of the agricultural machinery leasing by the customers is reduced.
(2) When renting the agricultural machinery, the customer can cause the problem that the return time of the agricultural machinery is inconsistent with the return time appointed by the platform in advance due to weather factors, and the agricultural machinery renting service at the present stage cannot timely process the agricultural machinery or provide targeted service, so that the problems of imperfect and inflexible service exist, the state of renting the agricultural machinery cannot be mastered in time, and the efficient management of agricultural machinery renting is not facilitated.
(3) After the last customer returns the agricultural machine, the agricultural machine leasing service in the current stage assigns the agricultural machine only according to the sequence of the reservation time of the subsequent reservation customer, the agricultural machine model, the reservation time and the distance required by the reservation customer can not be combined and considered to realize the convenience and the optimization of the agricultural machine assignment, certain limitations exist, the embarrassing situation of 'people and the like' or 'machines and the like' can not be avoided, and further the requirements of the customer on using the agricultural machine for operation conveniently, rapidly and economically can not be met, and the use efficiency of the agricultural machine is greatly reduced.
Disclosure of Invention
In view of this, in order to solve the problems set forth in the above background art, an agricultural socialization integrated service cloud platform is now proposed that is traceable based on big data analysis.
The aim of the invention can be achieved by the following technical scheme: an agricultural socialization integrated service cloud platform based on big data analysis traceability, comprising: the agricultural machinery leasing confirmation module is used for acquiring the agricultural machinery type, leasing time and land information which are needed to be leased by the appointed clients, wherein the land information comprises land compactness, land complexity, land area and land geographic position, and confirming the model number, reference return time and leasing of the leasing agricultural machinery.
And the cultivated land information monitoring module is used for monitoring the position of the leased agricultural machine in a set time period to obtain the working efficiency of the leased agricultural machine.
The cloud database is used for storing the width data and the unit duration operation price of each type of agricultural machine, storing the corresponding suitable land compactness, the suitable land complexity, the suitable land area and the maximum rainfall born by each type of agricultural machine during normal operation, storing the rainfall after each rainfall in the historical time period, the waiting operation correction duration of each type of agricultural machine and storing the reservation time and the land information of each reservation customer.
And the leasing agricultural machinery return time analysis module is used for calculating weather delay factors and correction duration of each day in the residual time period and analyzing the predicted return time of the leasing agricultural machinery.
And the intelligent renting agricultural machine processing module is used for processing the renting agricultural machine according to the expected return time of the renting agricultural machine.
The reservation client matching module is used for extracting land information of each reservation client in the cloud database, obtaining a rental agricultural machine matching coefficient of each reservation client, screening out each reservation client matched with the rental agricultural machine, and marking the reservation client as each preparation client.
And the target client screening module is used for calculating the distance comparison coefficient and the time comparison coefficient of each preparation client so as to screen out the target client, and the platform immediately sends reservation success information to the target client.
Preferably, the method for confirming the model number, the reference return time and the rent of the leasing agricultural machine comprises the following specific analysis processes: according to the agricultural machinery types required to be leased by the appointed clients, extracting agricultural machinery of each type which is the same as the agricultural machinery types required to be leased by the appointed clients from a cloud database, marking the agricultural machinery of each type as each reference agricultural machinery, extracting the appropriate land compactness, the appropriate land complexity and the appropriate land area corresponding to each reference agricultural machinery in the cloud database in normal operation, and marking the agricultural machinery of each reference agricultural machinery as alpha respectively h 、β h 、
Figure BDA0004169916600000033
h represents the number of the h reference agricultural machine, h=1, 2, l, by the formula->
Figure BDA0004169916600000031
Obtaining model matching coefficients of each reference agricultural machine, wherein alpha, beta and +>
Figure BDA0004169916600000032
Respectively represent the land compactness, the land complexity and the land area of the appointed clients, ρ 1 、ρ 2 、ρ 3 The weight ratio factors of the model matching coefficients corresponding to the set land compactness, land complexity and land area are respectively represented, the model matching coefficients of the reference agricultural machines are compared, and the model of the reference agricultural machine corresponding to the maximum value of the model matching coefficients is taken as the model of the leasing agricultural machine.
According to the type and model of the leasing agricultural machine, extracting the appropriate land compactness corresponding to the leasing agricultural machine in normal operation from a cloud database, and respectively marking the appropriate land complexity as alpha 0 、β 0 From the formula
Figure BDA0004169916600000041
Obtaining a reference operation duration of the leased agricultural machine, wherein v 0 A, representing standard working efficiency of corresponding model of set leasing agricultural machinery 1 、a 2 Respectively representing the set land compactness and the weight duty factor of the land complexity corresponding to the reference returning duration, and pi and e both represent natural constants.
Analyzing the reference operation days of the leasing agricultural machinery according to the reference operation time of the leasing agricultural machinery
Figure BDA0004169916600000042
And reference job remaining duration->
Figure BDA0004169916600000043
γ 0 Representing the set standard operation time length of one day of the leasing agricultural machine, and combining the leasing time of the leasing agricultural machine to obtain the reference return time t of the leasing agricultural machine 1 。
According to the unit time length operation price of each type of agricultural machine stored in the cloud database, extracting the unit time length operation price m of the leasing agricultural machine stored in the cloud database, and calculating (t) by the formula b=m 1 -t 0 ) And obtaining the rent of the rented agricultural machinery.
As a preferable mode, the work efficiency of the leasing agricultural machine is as follows: the position of the leasing agricultural machine is monitored through a GPS positioning system installed on the leasing agricultural machine, and the operation advancing track of the leasing agricultural machine is displayed on a map, so that the total operation advancing distance eta of each unit time point in the set time period of the leasing agricultural machine is obtained i I represents the number of the i-th unit time point within the set period, i=1, 2, g, according to the breadth data of each model of agricultural machine stored in the cloud database, extracting the width data c of the leasing agricultural machinery to obtain the total area of the reclaimed land area at each unit time point in the set time period
Figure BDA0004169916600000051
The calculation formula is +.>
Figure BDA0004169916600000052
And then is->
Figure BDA0004169916600000053
Obtaining the work efficiency of leasing the agricultural machinery, wherein t' i 、t′ i-1 Respectively indicates the ith time point, the ith-1 time points and the +.>
Figure BDA0004169916600000054
The total area of the reclaimed land area at the i-1 th time point in the set time period is represented, and g represents the total number of unit time points in the set time period.
Preferably, the weather delay factor and the correction duration of each day in the remaining time period are calculated, and the specific analysis process is as follows: according to the land geographic position in the land information of the appointed client, the rainfall of each day in the local future time period is obtained from the local weather bureau, and the rainfall w of each day in the local residual time period is selected j J represents the number of the j th day in the remaining time period, j=1, 2, and q, and then the maximum rainfall born by the leasing agricultural machine in normal operation is extracted from the cloud database according to the type and model of the leasing agricultural machine
Figure BDA0004169916600000057
Rainfall w 'after each rainfall in historical period of leasing agricultural machinery' s And waiting for the job correction period mu s S represents the number of the s-th rainfall in the history period, s=1, 2,..and f, by the formula ∈>
Figure BDA0004169916600000055
Obtaining weather delay factors of each day in the residual time period, wherein lambda represents a weight ratio factor of the set rainfall corresponding to the weather delay factors, and the weather delay factors are represented by a formula
Figure BDA0004169916600000056
And obtaining the correction duration of each day in the residual time period, wherein f represents the total rainfall times in the historical time period of the leased agricultural machinery.
Preferably, the predicted return time of the leasing agricultural machine comprises the following specific analysis processes: analyzing the estimated operation time gamma of the leasing agricultural machine according to the weather delay factors and the correction time of each day in the residual time period 2 The calculation formula is as follows:
Figure BDA0004169916600000061
wherein->
Figure BDA0004169916600000062
Indicating the total area of the land area which is reclaimed at the current time, and analyzing the estimated operation days of the leasing agricultural machinery according to the estimated operation time of the leasing agricultural machinery
Figure BDA0004169916600000063
And the predicted remaining duration +.>
Figure BDA0004169916600000064
And combining the current time to obtain the estimated return time t of the leased agricultural machinery 2 。
Preferably, the processing of the leasing agricultural machinery comprises the following specific analysis processes: comparing the predicted return time of the leasing agricultural machine with the reference return time, if the predicted return time of the leasing agricultural machine is not greater than the reference return time, not processing the leasing agricultural machine, and if the predicted return time of the leasing agricultural machine is greater than the reference return time, and is close to the reference return time, sending early warning information to a designated client by a short message mode by a platform to remind the designated client to pay the renewal fee b' within a specified time period, wherein the calculation formula is as follows: b' = (t 2 -t 1 ) M, if the appointed client finishes the renewal in the specified time period, not processing the leasing agricultural machinery, and if the appointed client does not finish the renewal in the specified time period, remotely sending an engine locking instruction to stop the operation of the agricultural machinery.
Preferably, the screening of each preparation client comprises the following specific analysis processes: extracting land information of each reservation customer stored in the cloud database, and respectively marking the land compactness, the land complexity and the land area of each reservation customer as alpha' n 、β′ n 、
Figure BDA0004169916600000071
n denotes the number of the n-th reservation customer, n=1, 2,..k, k, represented by the formula
Figure BDA0004169916600000072
Obtaining matching coefficients of leasing agricultural machinery of each reserved customer, wherein p is 1 、p 2 、p 3 The weight ratio factors of the rental agriculture machinery matching coefficients corresponding to the set land compactness, land complexity and land area are respectively represented, the rental agriculture machinery matching coefficients of all reservation clients are compared with the set suitable rental agriculture machinery matching coefficient range, and all reservation clients with the rental agriculture machinery matching coefficients within the set suitable rental agriculture machinery matching coefficient range are screened and recorded as all preparation clients.
Preferably, the calculating the distance contrast coefficient and the time contrast coefficient of each preparation customer comprises the following specific analysis processes: extracting the land geographic position and reservation time of each preparation client through the land information and reservation time of each reservation client stored in the cloud database, and recording the land geographic position of each preparation client and the land geographic position of the appointed client into a map to obtain the distance d between the land geographic position of each preparation client and the land geographic position of the appointed client n′ N 'represents the number of the n' th preparatory client, n '=1', 2',..k', represented by the formula
Figure BDA0004169916600000073
Obtaining a distance contrast coefficient of each preparation client, wherein k' represents the total number of the preparation clients, and comparing the reservation time of each preparation client with the expected return time of the appointed client leasing agricultural machinery to obtain a time contrast coefficient kappa of each preparation client n′ The calculation formula is ∈>
Figure BDA0004169916600000081
Wherein->
Figure BDA0004169916600000082
Represents the nth'The reservation time of each preliminary client, t ", represents the set reference reservation interval duration.
Preferably, the screening target clients include the following specific analysis processes: obtaining a comprehensive matching coefficient x through the distance comparison coefficient and the time comparison coefficient of each preparation client n′ The calculation formula is that
Figure BDA0004169916600000083
Wherein y is 1 、y 2 And the weight ratio factors of the comprehensive matching coefficients corresponding to the set distance comparison coefficient and the time comparison coefficient are represented, the comprehensive matching coefficients of all the preparation clients are compared, and the preparation client with the largest comprehensive matching coefficient is selected as the target client.
Compared with the prior art, the invention has the following beneficial effects: (1) According to the invention, the land information of the client is acquired, the model of the client leasing agricultural machinery is analyzed and confirmed, the model of the agricultural machinery suitable for land reclamation of the client is selected for the client, the problem of poor land reclamation effect caused by insufficient knowledge of the client on the agricultural machinery equipment is effectively solved, unnecessary expenditure is avoided, the agricultural production cost is reduced to a certain extent, the enthusiasm of the client leasing the agricultural machinery is improved, and the energy of the client is greatly saved.
(2) According to the invention, the expected return time of the leasing agricultural machine is analyzed by considering the working efficiency and weather factors of the leasing agricultural machine, and the leasing agricultural machine is analyzed and processed in a targeted manner in time, so that the efficient management of the leasing of the agricultural machine is realized, the leasing service of the agricultural machine is more perfect, and the leasing service is more targeted and flexible, so that the state of the leasing agricultural machine can be mastered in the first time, and the fluency and timeliness of subsequent arrangement are facilitated.
(3) According to the invention, land information of each reservation client is acquired to be matched with the reservation client suitable for the model of the leasing agricultural machine, convenience and optimization of assigning the agricultural machine are realized by combining the reservation time and the distance of the reservation client, the problem of 'people and the like' or 'machines and the like' is effectively solved, the requirements of the clients on conveniently, rapidly and economically using the agricultural machine to carry out the operation are rapidly met, and the use efficiency of the agricultural machine is greatly improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a system according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the invention provides a traceable agricultural socialization integrated service cloud platform based on big data analysis, and the specific modules are distributed as follows: the system comprises an agricultural machinery leasing confirmation module, a cultivated land information monitoring module, a cloud database, a leasing agricultural machinery returning time analysis module, a leasing agricultural machinery intelligent processing module, a reservation client matching module and a target client screening module. The connection relation between the modules is as follows: the agricultural machinery lease confirmation module, the farmland information monitoring module, the lease agricultural machinery return time analysis module, the lease agricultural machinery intelligent processing module and the lease agricultural machinery return time analysis module are connected, the reservation client matching module is connected with the lease agricultural machinery intelligent processing module, the target client screening module is connected with the reservation client matching module, and the agricultural machinery lease confirmation module, the agricultural machinery information monitoring module, the lease agricultural machinery return time analysis module, the reservation client matching module and the target client screening module are all connected with the cloud database.
The agricultural machinery leasing confirmation module is used for acquiring agricultural machinery types, leasing time and land information which are needed to be leased by specified clients, wherein the land information comprises land compactness, land complexity, land area and land geographic position, and confirming the model of leasing agricultural machinery, reference return time and leasing.
Specifically, the model, the pre-return time and the rent of the leasing agricultural machinery are confirmed, and the specific analysis process is as follows: according to the agricultural machinery types required to be leased by the appointed clients, extracting agricultural machinery of each type which is the same as the agricultural machinery types required to be leased by the appointed clients from a cloud database, marking the agricultural machinery of each type as each reference agricultural machinery, extracting the appropriate land compactness, the appropriate land complexity and the appropriate land area corresponding to each reference agricultural machinery in the cloud database in normal operation, and marking the agricultural machinery of each reference agricultural machinery as alpha respectively h 、β h 、
Figure BDA0004169916600000101
h represents the number of the h reference agricultural machine, h=1, 2, l, by the formula->
Figure BDA0004169916600000102
Obtaining model matching coefficients of each reference agricultural machine, wherein alpha, beta and +>
Figure BDA0004169916600000103
Respectively represent the land compactness, the land complexity and the land area of the appointed clients, ρ 1 、ρ 2 、ρ 3 The weight ratio factors of the model matching coefficients corresponding to the set land compactness, land complexity and land area are respectively represented, the model matching coefficients of the reference agricultural machines are compared, and the model of the reference agricultural machine corresponding to the maximum value of the model matching coefficients is taken as the model of the leasing agricultural machine.
According to the type and model of the leasing agricultural machine, extracting the appropriate land compactness corresponding to the leasing agricultural machine in normal operation from a cloud database, and respectively marking the appropriate land complexity as alpha 0 、β 0 From the formula
Figure BDA0004169916600000111
Obtaining a reference operation duration of the leased agricultural machine, wherein v 0 Rental agricultural machine pair indicating settingStandard working efficiency of the corresponding model, a 1 、a 2 Respectively representing the set land compactness and the weight duty factor of the land complexity corresponding to the reference returning duration, and pi and e both represent natural constants.
Analyzing the reference operation days of the leasing agricultural machinery according to the reference operation time of the leasing agricultural machinery
Figure BDA0004169916600000112
And reference job remaining duration->
Figure BDA0004169916600000113
γ 0 Representing the set standard operation time length of one day of the leasing agricultural machine, and combining the leasing time of the leasing agricultural machine to obtain the reference return time t of the leasing agricultural machine 1 。
According to the unit time length operation price of each type of agricultural machine stored in the cloud database, extracting the unit time length operation price m of the leasing agricultural machine stored in the cloud database, and calculating (t) by the formula b=m 1 -t 0 ) And obtaining the rent of the rented agricultural machinery.
It should be noted that, the above-mentioned agricultural machinery category, lease time and land information required to be leased by the specified customer are obtained by the specified customer manual input platform.
It should be further noted that the above soil compactness refers to a composite index of soil strength, and its size can affect perforation and growth of crop root system, which is an important physical property index of soil for evaluating soil ploidy. The soil complexity refers to the soil quality, structure and water content of the soil, and is one of important indexes for evaluating the soil ploidy.
According to the concrete embodiment of the invention, the land information of the customer is acquired, the model of the agricultural machinery leased by the customer is analyzed and confirmed, the model of the agricultural machinery suitable for land reclamation of the customer is selected for the customer, the problem that the land reclamation efficiency and quality are reduced because the customer knows the agricultural machinery equipment insufficiently is effectively solved, the customer can conveniently, quickly and economically use the agricultural machinery for operation, unnecessary expenditure is avoided, the agricultural production cost is reduced to a certain extent, the enthusiasm of the customer leasing the agricultural machinery is improved, and the customer energy is greatly saved.
The farmland information monitoring module is used for monitoring the position of the leasing agricultural machine in a set time period to obtain the working efficiency of the leasing agricultural machine.
Specifically, the work efficiency of the leasing agricultural machine is as follows: the position of the leasing agricultural machine is monitored through a GPS positioning system installed on the leasing agricultural machine, and the operation advancing track of the leasing agricultural machine is displayed on a map, so that the total operation advancing distance eta of each unit time point in the set time period of the leasing agricultural machine is obtained i I represents the number of the i-th unit time point within the set period, i=1, 2, g, according to the breadth data of each model of agricultural machine stored in the cloud database, extracting the width data c of the leasing agricultural machinery to obtain the total area of the reclaimed land area at each unit time point in the set time period
Figure BDA0004169916600000121
The calculation formula is +.>
Figure BDA0004169916600000122
And then is->
Figure BDA0004169916600000123
Obtaining the work efficiency of leasing the agricultural machinery, wherein t' i 、t′ i-1 Respectively indicates the ith time point, the ith-1 time points and the +.>
Figure BDA0004169916600000124
The total area of the reclaimed land area at the i-1 th time point in the set time period is represented, and g represents the total number of unit time points in the set time period.
It should be noted that, the above-mentioned set time period refers to a time period obtained by advancing the current time by a set time length, and a time period between the set time and the current time is referred to as a set time period.
For example, when the current time is 5 pm and the specified time is 1 pm after 4 hours of forward movement, the time period between 1 pm and 5 pm is recorded as the set time period.
The cloud database is used for storing the width data and the unit duration operation price of each type of agricultural machine, storing the corresponding suitable land compactness, the suitable land complexity, the suitable land area and the maximum rainfall born by each type of agricultural machine during normal operation, storing the rainfall after each rainfall in the historical time period and the waiting operation correction duration of each type of agricultural machine, and storing the reservation time and the land information of each reservation customer.
The leasing agricultural machinery return time analysis module is used for calculating weather delay factors and correction duration of each day in the residual time period and analyzing the actual return time of the leasing agricultural machinery.
On the basis of the scheme, the weather delay factors and the correction duration of each day in the residual time period are calculated, and the specific analysis process is as follows: according to the land geographic position in the land information of the appointed client, the rainfall of each day in the local future time period is obtained from the local weather bureau, and the rainfall w of each day in the local residual time period is selected j J represents the number of the j th day in the remaining time period, j=1, 2, and q, and then the maximum rainfall born by the leasing agricultural machine in normal operation is extracted from the cloud database according to the type and model of the leasing agricultural machine
Figure BDA0004169916600000131
Rainfall w 'after each rainfall in historical period of leasing agricultural machinery' s And waiting for the job correction period mu s S represents the number of the s-th rainfall in the history period, s=1, 2,..and f, by the formula ∈>
Figure BDA0004169916600000132
Obtaining weather delay factors of each day in the residual time period, wherein lambda represents a weight ratio factor of the set rainfall corresponding to the weather delay factors, and the weather delay factors are represented by a formula
Figure BDA0004169916600000141
Obtaining the correction time length of each day in the residual time period, wherein f represents the history time of leasing the agricultural machineryTotal number of rainfall in the segment.
Still more specifically, the rental agricultural machine predicts the return time, and the specific analysis process is as follows: analyzing the estimated operation time gamma of the leasing agricultural machine according to the weather delay factors and the correction time of each day in the residual time period 2 The calculation formula is as follows:
Figure BDA0004169916600000142
wherein->
Figure BDA0004169916600000143
Indicating the total area of the land area which is reclaimed at the current time, and analyzing the estimated operation days of the leasing agricultural machinery according to the estimated operation time of the leasing agricultural machinery
Figure BDA0004169916600000144
And the predicted remaining duration +.>
Figure BDA0004169916600000145
And obtaining the estimated return time t2 of the leased agricultural machinery by combining the current time.
And the intelligent renting agricultural machine processing module is used for processing the renting agricultural machine according to the expected return time of the renting agricultural machine.
Specifically, the processing of the leasing agricultural machinery comprises the following specific analysis processes: comparing the predicted return time of the leasing agricultural machine with the reference return time, if the predicted return time of the leasing agricultural machine is not greater than the reference return time, not processing the leasing agricultural machine, and if the predicted return time of the leasing agricultural machine is greater than the reference return time, and is close to the reference return time, sending early warning information to a designated client by a short message mode by a platform to remind the designated client to pay the renewal fee b' within a specified time period, wherein the calculation formula is as follows: b' = (t 2 -t 1 ) M, if the appointed client finishes the renewal in the specified time period, not processing the leasing agricultural machinery, and if the appointed client does not finish the renewal in the specified time period, remotely sending a locking command to stop the leasing agricultural machinery.
It should be noted that the operation of stopping the operation of the leasing agricultural machine by remotely sending the machine locking instruction is completed through an intelligent control lock installed on the leasing agricultural machine, and the intelligent control lock is an intelligent lock based on the property of the internet of things and comprises a central control unit and a wireless mobile communication system, wherein the central control unit interacts with a background management system through a wireless mobile communication module. When the appointed client does not complete the follow-up in the specified time period, the background management system sends a machine locking instruction to the central control unit through the wireless mobile communication system, and after the central control unit receives the instruction, the power supply of the leased agricultural machine is cut off, so that machine locking is completed.
According to the method and the system for managing the agricultural machinery leasing, in the specific embodiment of the invention, the working efficiency and weather factors of the leasing agricultural machinery are taken into consideration, the predicted return time of the leasing agricultural machinery is analyzed, and the leasing agricultural machinery is analyzed and processed in a targeted manner in time, so that the efficient management of the agricultural machinery leasing is realized, the agricultural machinery leasing service is more perfect, and the method and the system are more targeted and flexible, so that the state of the leasing agricultural machinery can be mastered in the first time, and the smoothness and timeliness of subsequent arrangement are facilitated.
The reservation client matching module is used for extracting land information of each reservation client in the cloud database, obtaining a leasing agricultural machinery matching index of each reservation client, screening out each reservation client matched with the leasing agricultural machinery, and marking the reservation client as each preparation client.
Specifically, the screening of each preparation customer comprises the following specific analysis processes: extracting land information of each reservation customer stored in the cloud database, and respectively marking the land compactness, the land complexity and the land area of each reservation customer as alpha' n 、β′ n 、
Figure BDA0004169916600000161
n represents the number of the n-th reservation customer, n=1, 2,..k, k, by the formula +.>
Figure BDA0004169916600000162
Obtaining matching coefficients of leasing agricultural machinery of each reserved customer, wherein p is 1 、p 2 、p 3 Respectively representing the weight duty ratio of the set land compactness, land complexity and corresponding leasing agricultural machinery matching coefficient of land areaAnd (3) comparing the rental agriculture machinery matching coefficients of the reservation clients with the set suitable rental agriculture machinery matching coefficient ranges, screening the reservation clients with the rental agriculture machinery matching coefficients within the set suitable rental agriculture machinery matching coefficient ranges, and marking the reservation clients as the preparation clients.
The target client screening module is used for calculating the distance comparison coefficient and the time comparison coefficient of each preparation client so as to screen out the target client, and the platform immediately sends reservation success information to the target client.
Specifically, the distance contrast coefficient and the time contrast coefficient of each preparation customer are specifically analyzed by the following steps: extracting the land geographic position and reservation time of each preparation client through the land information and reservation time of each reservation client stored in the cloud database, and recording the land geographic position of each preparation client and the land geographic position of the appointed client into a map to obtain the distance d between the land geographic position of each preparation client and the land geographic position of the appointed client n′ N 'represents the number of the n' th preparatory client, n '=1', 2',..k', represented by the formula
Figure BDA0004169916600000171
Obtaining a distance contrast coefficient of each preparation client, wherein k' represents the total number of the preparation clients, and comparing the reservation time of each preparation client with the actual return time of the agricultural machinery leased by the appointed client to obtain a time contrast coefficient kappa of each preparation client n′ The calculation formula is ∈>
Figure BDA0004169916600000172
Wherein->
Figure BDA0004169916600000173
The reservation time of the nth 'prepared client is represented, and t' represents the set reference reservation interval duration.
The screening target clients specifically comprise the following analysis processes: obtaining a comprehensive matching coefficient x through the distance comparison coefficient and the time comparison coefficient of each preparation client n′ The calculation formula is that
Figure BDA0004169916600000174
Wherein y is 1 、y 2 And the weight ratio factors of the comprehensive matching coefficients corresponding to the set distance comparison coefficient and the time comparison coefficient are represented, the comprehensive matching coefficients of all the preparation clients are compared, and the preparation client with the largest comprehensive matching coefficient is selected as the target client.
According to the concrete embodiment of the invention, land information of each reservation client is acquired to be matched with the reservation client suitable for the model of the leasing agricultural machine, convenience and optimization of assigning the agricultural machine are realized by combining the reservation time and the distance of the reservation client, the problem of 'people and the like' or 'machines and the like' is effectively solved, the requirements of the clients on conveniently, quickly and economically using the agricultural machine to carry out the operation are rapidly met, and the use efficiency of the agricultural machine is greatly improved.
The foregoing is merely illustrative and explanatory of the principles of this invention, as various modifications and additions may be made to the specific embodiments described, or similar arrangements may be substituted by those skilled in the art, without departing from the principles of this invention or beyond the scope of this invention as defined in the claims.

Claims (9)

1. The utility model provides a agricultural socialization integrated services cloud platform based on big data analysis traceability which characterized in that: the system comprises:
the agricultural machinery leasing confirmation module is used for acquiring the agricultural machinery type, leasing time and land information which are required to be leased by a specified customer, wherein the land information comprises land compactness, land complexity, land area and land geographic position, and confirming the model, reference return time and leasing of the leasing agricultural machinery;
the farmland information monitoring module is used for monitoring the position of the leased agricultural machinery in a set time period to obtain the working efficiency of the leased agricultural machinery;
the cloud database is used for storing the width data and the unit duration operation price of each type of agricultural machine, storing the corresponding suitable land compactness, the suitable land complexity, the suitable land area and the maximum rainfall born by each type of agricultural machine during normal operation, storing the rainfall after each rainfall in the historical time period, the waiting operation correction duration of each type of agricultural machine and storing the reservation time and the land information of each reservation customer;
the leasing agricultural machinery return time analysis module is used for calculating weather delay factors and correction duration of each day in the residual time period and analyzing predicted return time of the leasing agricultural machinery;
the intelligent renting agricultural machine processing module is used for processing the renting agricultural machine according to the expected return time of the renting agricultural machine;
the reservation client matching module is used for extracting land information of each reservation client in the cloud database to obtain a lease agricultural machinery matching coefficient of each reservation client, screening out each reservation client matched with the lease agricultural machinery, and marking the reservation client as each preparation client;
and the target client screening module is used for calculating the distance comparison coefficient and the time comparison coefficient of each preparation client so as to screen out the target client, and the platform immediately sends reservation success information to the target client.
2. The agricultural socialization integrated service cloud platform based on big data analysis traceability of claim 1, wherein: the specific analysis process of the method for confirming the model, the reference return time and the rent of the leasing agricultural machine comprises the following steps: according to the agricultural machinery types required to be leased by the appointed clients, extracting agricultural machinery of each type which is the same as the agricultural machinery types required to be leased by the appointed clients from a cloud database, marking the agricultural machinery of each type as each reference agricultural machinery, extracting the appropriate land compactness, the appropriate land complexity and the appropriate land area corresponding to each reference agricultural machinery in the cloud database in normal operation, and marking the agricultural machinery of each reference agricultural machinery as alpha respectively h 、β h 、
Figure FDA0004169916590000021
h represents the number of the h reference agricultural machine, h=1, 2, l, by the formula->
Figure FDA0004169916590000022
Obtaining each reference agricultural machineModel matching coefficients of (2), wherein α, β,/-are>
Figure FDA0004169916590000023
Respectively represent the land compactness, the land complexity and the land area of the appointed clients, ρ 1 、ρ 2 、ρ 3 Respectively representing the weight ratio factors of model matching coefficients corresponding to the set land compactness, land complexity and land area, comparing the model matching coefficients of all the reference agricultural machines, and taking the model of the reference agricultural machine corresponding to the maximum value of the model matching coefficients as the model of the leasing agricultural machine;
according to the type and model of the leasing agricultural machine, extracting the appropriate land compactness corresponding to the leasing agricultural machine in normal operation from a cloud database, and respectively marking the appropriate land complexity as alpha 0 、β 0 From the formula
Figure FDA0004169916590000024
Obtaining a reference operation duration of the leased agricultural machine, wherein v 0 A, representing standard working efficiency of corresponding model of set leasing agricultural machinery 1 、a 2 Respectively representing the set land compactness and the weight duty factor of the land complexity corresponding to the reference returning duration, wherein pi and e represent natural constants;
analyzing the reference operation days of the leasing agricultural machinery according to the reference operation time of the leasing agricultural machinery
Figure FDA0004169916590000031
And reference job remaining duration->
Figure FDA0004169916590000032
γ 0 Representing the set standard operation time length of one day of the leasing agricultural machine, and combining the leasing time of the leasing agricultural machine to obtain the reference return time t of the leasing agricultural machine 1 ；
According to the unit time length operation price of each type of agricultural machine stored in the cloud database, extracting the unit time length operation price m of the leasing agricultural machine stored in the cloud database, and using a public companyFormula b=m (t 1 -t 0 ) And obtaining the rent of the rented agricultural machinery.
3. The agricultural socialization integrated service cloud platform based on big data analysis traceability of claim 2, wherein: the work efficiency of the leasing agricultural machine is characterized in that the specific analysis process comprises the following steps: the position of the leasing agricultural machine is monitored through a GPS positioning system installed on the leasing agricultural machine, and the operation advancing track of the leasing agricultural machine is displayed on a map, so that the total operation advancing distance eta of each unit time point in the set time period of the leasing agricultural machine is obtained i I represents the number of the i-th unit time point within the set period, i=1, 2, g, according to the breadth data of each model of agricultural machine stored in the cloud database, extracting the width data c of the leasing agricultural machinery to obtain the total area of the reclaimed land area at each unit time point in the set time period
Figure FDA0004169916590000033
The calculation formula is as follows
Figure FDA0004169916590000034
And then is->
Figure FDA0004169916590000035
Obtaining the work efficiency of leasing the agricultural machinery, wherein t' i 、t′ i-1 Respectively indicates the ith time point, the ith-1 time points and the +.>
Figure FDA0004169916590000036
The total area of the reclaimed land area at the i-1 th time point in the set time period is represented, and g represents the total number of unit time points in the set time period.
4. The agricultural socialization integrated service cloud platform based on big data analysis traceability according to claim 3, wherein: the weather delay factors and correction time length of each day in the residual time period are calculated, and the specific analysis process is as follows: according to the specificationThe land geographic position in the customer land information is used for acquiring the rainfall of each day in a local future time period from a local weather bureau, and selecting the rainfall w of each day in a local residual time period j J represents the number of the j th day in the remaining time period, j=1, 2, and q, and then the maximum rainfall born by the leasing agricultural machine in normal operation is extracted from the cloud database according to the type and model of the leasing agricultural machine
Figure FDA0004169916590000041
Rainfall w 'after each rainfall in historical period of leasing agricultural machinery' s And waiting for the job correction period mu s S represents the number of the s-th rainfall in the history period, s=1, 2,..and f, by the formula ∈>
Figure FDA0004169916590000042
Obtaining weather delay factors of each day in the remaining time period, wherein lambda represents the weight ratio factor of the set rainfall corresponding to the weather delay factors, and the formula ∈λ is adopted>
Figure FDA0004169916590000043
And obtaining the correction duration of each day in the residual time period, wherein f represents the total rainfall times in the historical time period of the leased agricultural machinery.
5. The agricultural socialization integrated service cloud platform based on big data analysis traceability of claim 4, wherein: the estimated return time of the leasing agricultural machinery comprises the following specific analysis processes: analyzing the estimated operation time gamma of the leasing agricultural machine according to the weather delay factors and the correction time of each day in the residual time period 2 The calculation formula is as follows:
Figure FDA0004169916590000051
wherein->
Figure FDA0004169916590000052
Indicating the total area of the land area which has been reclaimed at the present time, according to the estimated work of the leasing agricultural machineryThe business duration, analyzing the expected operation days of leasing agricultural machinery
Figure FDA0004169916590000053
And the predicted remaining duration +.>
Figure FDA0004169916590000054
And combining the current time to obtain the estimated return time t of the leased agricultural machinery 2 。
6. The agricultural socialization integrated service cloud platform based on big data analysis traceability of claim 5, wherein: the specific analysis process of the leasing agricultural machine is as follows: comparing the predicted return time of the leasing agricultural machine with the reference return time, if the predicted return time of the leasing agricultural machine is not greater than the reference return time, not processing the leasing agricultural machine, and if the predicted return time of the leasing agricultural machine is greater than the reference return time, and is close to the reference return time, sending early warning information to a designated client by a short message mode by a platform to remind the designated client to pay the renewal fee b' within a specified time period, wherein the calculation formula is as follows: b' = (t 2 -t 1 ) M, if the appointed client finishes the renewal in the specified time period, not processing the leasing agricultural machinery, and if the appointed client does not finish the renewal in the specified time period, remotely sending a locking command to stop the leasing agricultural machinery.
7. The agricultural socialization integrated service cloud platform based on big data analysis traceability of claim 2, wherein: the specific analysis process of screening each preparation client is as follows: extracting land information of each reservation customer stored in the cloud database, and respectively marking the land compactness, the land complexity and the land area of each reservation customer as alpha' n 、β′ n 、
Figure FDA0004169916590000061
n represents the number of the n-th reservation customer, n=1, 2,..k, k, by the formula +.>
Figure FDA0004169916590000062
Obtaining matching coefficients of leasing agricultural machinery of each reserved customer, wherein p is 1 、p 2 、p 3 The weight ratio factors of the rental agriculture machinery matching coefficients corresponding to the set land compactness, land complexity and land area are respectively represented, the rental agriculture machinery matching coefficients of all reservation clients are compared with the set suitable rental agriculture machinery matching coefficient range, and all reservation clients with the rental agriculture machinery matching coefficients within the set suitable rental agriculture machinery matching coefficient range are screened and recorded as all preparation clients.
8. The agricultural socialization integrated service cloud platform based on big data analysis traceability of claim 7, wherein: the distance contrast coefficient and the time contrast coefficient of each preparation customer are calculated, and the specific analysis process is as follows: extracting the land geographic position and reservation time of each preparation client through the land information and reservation time of each reservation client stored in the cloud database, and recording the land geographic position of each preparation client and the land geographic position of the appointed client into a map to obtain the distance d between the land geographic position of each preparation client and the land geographic position of the appointed client n′ N 'represents the number of the n' th preparatory client, n '=1', 2',..k', represented by the formula
Figure FDA0004169916590000063
Obtaining a distance contrast coefficient of each preparation client, wherein k' represents the total number of the preparation clients, and comparing the reservation time of each preparation client with the expected return time of the appointed client leasing agricultural machinery to obtain a time contrast coefficient kappa of each preparation client n′ The calculation formula is ∈>
Figure FDA0004169916590000071
Wherein->
Figure FDA0004169916590000072
Prefix representing the nth' preparation clientAbout time, t "represents the set reference reservation interval duration.
9. The agricultural socialization integrated service cloud platform based on big data analysis traceability of claim 8, wherein: the specific analysis process of the target client is as follows: obtaining a comprehensive matching coefficient x according to the distance comparison coefficient and the time comparison coefficient of each preparation client n′ The calculation formula is that
Figure FDA0004169916590000073
Wherein y is 1 、y 2 And the weight ratio factors of the comprehensive matching coefficients corresponding to the set distance comparison coefficient and the time comparison coefficient are represented, the comprehensive matching coefficients of all the preparation clients are compared, and the preparation client with the largest comprehensive matching coefficient is selected as the target client.
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