CN-121981344-A - Agricultural production management system based on cloud platform

CN121981344ACN 121981344 ACN121981344 ACN 121981344ACN-121981344-A

Abstract

The invention discloses an agricultural production management system based on a cloud platform, which relates to the technical field of production management, and comprises the steps of receiving geographic information data, crop growth state data and weather forecast data of an area to be operated, discretizing the area to be operated into an operation grid node set, calculating operation urgency, generating a preference reference vector based on the operation urgency, generating a first operation instruction set by adopting a two-stage evolution strategy according to the operation grid node set and the preference reference vector, calculating vertical Euclidean distance between individuals in an initial population and the preference reference vector by utilizing a preference distance strategy, guiding the initial population to converge to an interested area, optimizing in the interested area by utilizing a preference area sequencing strategy, outputting the first operation instruction set, monitoring the execution state and environmental parameters of the first operation instruction set, triggering transfer learning and rescheduling when a disturbance event is detected, generating a rescheduling initial population, generating by the rescheduling initial population, and issuing a second operation instruction set to an agricultural machinery terminal.

Inventors

HAN YU
GUO XIAOHUA
YU XISHENG
GUO XIAOYUN
WANG XI
LIU LIQUN
WEN PENGXIANG

Assignees

利良华玫生物科技(成都)有限公司

Dates

Publication Date: 20260505
Application Date: 20260409

Claims (10)

1. The agricultural production management system based on the cloud platform is characterized by comprising a modeling module, a planning module and a management module; The modeling module is used for receiving geographic information data, crop growth state data and weather forecast data of an area to be operated, discretizing the area to be operated into an operation grid node set, calculating operation urgency according to the weather forecast data, and generating preference reference vectors based on the operation urgency; The planning module is used for generating a first operation instruction set by adopting a two-stage evolution strategy according to the operation grid node set and the preference reference vector, wherein the two-stage evolution strategy comprises a preference distance strategy and a preference region ordering strategy, the first stage uses the preference distance strategy to calculate the vertical Euclidean distance between an individual in the initial population and the preference reference vector, the initial population is guided to converge to a region of interest, and the second stage uses the preference region ordering strategy to perform optimizing in the region of interest, and the first operation instruction set is output; The management module is used for monitoring the execution state and the environmental parameters of the first operation instruction set, triggering transfer learning and re-planning when a disturbance event is detected, generating a re-planning initial population, generating and transmitting a second operation instruction set to the agricultural machine terminal based on the re-planning initial population.
2. The cloud platform-based agricultural production management system of claim 1, wherein said modeling module comprises a discretization mapping unit and an urgency resolution unit; The discretization mapping unit is used for receiving geographic information data and crop growth state data of an area to be worked, discretizing the area to be worked into a working grid node set containing attribute information, and specifically comprises the following steps: Extracting the boundary vertex coordinates of the land block in the geographic information data, determining the internal area of the land block by utilizing a polygon scanning line filling algorithm, and dividing the internal area of the land block into rectangular sub-grids with preset sizes, wherein the preset sizes are set according to the minimum operation breadth of the agricultural machinery; Building a space attribute matrix corresponding to the rectangular sub-grids one by one, traversing crop growth state data, and mapping soil moisture content values, crop height values and leaf area indexes of discrete sampling points to the central point of each rectangular sub-grid by using an inverse distance weight interpolation method; Each element in the space attribute matrix is defined as a working grid node, and the attribute information of the working grid node comprises the geographic center coordinates of the node, the working weight coefficient of the node and the passing resistance coefficient of the node, wherein the working weight coefficient is positively related to a normal distribution value of a crop growth stage.
3. The cloud platform-based agricultural production management system of claim 2, wherein the urgency resolution unit is configured to calculate a job urgency from weather forecast data, and generate a preference reference vector based on the job urgency, and specifically comprises: Analyzing the time resolution of weather forecast data, extracting a rainfall probability time sequence curve in a future preset period, and intercepting the rainfall probability time sequence curve by setting a rainfall probability threshold value to obtain a starting time point of rainfall event occurrence; calculating the time difference from the current system time to the starting time point to obtain a safe operation time window; The standard operation working hours of all operation grid nodes are accumulated to obtain the predicted total operation time length, the ratio of the predicted total operation time length to the safe operation time window is calculated, normalization processing is carried out, and the comparison value is truncated to be the same Obtaining operation urgency coefficients in the interval; Constructing a reference coordinate system of a multi-target optimization space, wherein the reference coordinate system comprises an operation cost shaft and an operation aging shaft; Calculating a deflection angle of a preference reference vector in a base coordinate system based on a work urgency coefficient, wherein the mathematical expression of the deflection angle is as follows: ; Wherein, the In order to be able to deflect the angle, Is a preset sensitivity coefficient, and , Is an operation urgency coefficient; according to the trigonometric function relation, respectively calculating a horizontal axis projection component and a vertical axis projection component of the unit length under the deflection angle; And combining the horizontal axis projection component and the vertical axis projection component according to the axial sequence to generate a preference reference vector.
4. The agricultural production management system based on the cloud platform as claimed in claim 3, wherein the planning module comprises an initialization coding unit, a region guiding unit and a fine optimizing unit; the initialization coding unit is used for constructing an initial population according to the operation grid node set and calculating an objective function value, and specifically comprises the following steps: Mapping a node ID sequence in a working grid node set into chromosome gene chains by adopting an integer coding rule, randomly generating a plurality of gene chains to form an initial population, wherein each independent gene chain in the initial population is defined as an individual; Analyzing the operation weight coefficient and the passing resistance coefficient of the operation grid node, calculating an operation cost target value and an operation aging target value corresponding to each individual in the initial population by combining the rated parameters of the agricultural machinery, and constructing a target function value vector based on the operation cost target value and the operation aging target value.
5. The cloud platform-based agricultural production management system of claim 4, wherein the region guide unit is configured to execute a first stage of preference distance policy according to a preference reference vector, and specifically comprises: carrying out normalization processing on the objective function value vector, and calculating the vertical Euclidean distance between the normalized objective function value vector and the preference reference vector in the multi-objective optimization space; Setting a preference radius threshold, traversing the initial population, reserving individuals with vertical Euclidean distances smaller than the preference radius threshold, forming a region-of-interest sub-population, judging individuals with vertical Euclidean distances larger than or equal to the preference radius threshold as inferior individuals, and eliminating the inferior individuals.
6. The cloud platform based agricultural production management system of claim 5, wherein said fine optimization unit is configured to execute a second stage of preference region ordering strategy within a region of interest and generate a first job instruction set, specifically comprising: Non-dominant ranking of individuals in the sub-population of the region of interest, and assigning a pareto grade value to each individual; Calculating the projection density value of the individual under the same pareto grade value in the direction of the preference reference vector; selecting a preferred elite individual by adopting a double-sequencing screening method, performing crossover and mutation operations on the preferred elite individual to generate a child individual, and feeding the child individual back to a region guiding unit for distance screening until the iteration number reaches a preset iteration number threshold; Defining a set of the last generation of optimal elite individuals after reaching a preset iteration number threshold as an optimal elite solution set, decoding a gene chain in the optimal elite solution set, and converting the gene chain into a first operation instruction set, wherein the first operation instruction set comprises an operation time stamp, a driving path coordinate and an agricultural machinery action code.
7. The cloud platform-based agricultural production management system of claim 6, wherein the selecting the preferred elite individual by the double ranking screening method comprises the following specific logic: the individuals are ranked according to the pareto grade value from small to large preferentially, when the pareto grade value is the same, the individuals are ranked according to the projection density value from small to large in a second order, and the individuals are positioned in front after the ranking is selected The individual at the position is marked as a preferred elite individual, wherein, The scale is reserved for the preset population.
8. The agricultural production management system based on the cloud platform as claimed in claim 7, wherein said management module comprises a status monitoring unit, a migration learning unit and an instruction generation unit; the state monitoring unit is used for monitoring the execution state and environmental parameters of the first operation instruction set and identifying disturbance events, and specifically comprises the following steps: Collecting operation progress data returned by the agricultural machine in real time, comparing the operation progress data with a preset time node in a first operation instruction set, and calculating a progress deviation rate; Receiving updated weather forecast data in real time, judging whether the initial time point of rainfall event occurs in advance, and calculating the reduction amount of a safe operation time window; When the progress deviation rate is larger than a preset deviation threshold value or the reduction amount of the safety operation time window is larger than a preset safety threshold value, the current operation environment is judged to be suddenly changed, and a disturbance event signal is triggered.
9. The cloud platform-based agricultural production management system of claim 8, wherein the migration learning unit is configured to perform migration learning and re-planning on disturbance events to generate a re-planned initial population, and specifically comprises: constructing a source domain knowledge space, wherein the source domain knowledge space comprises an optimal elite solution set before a disturbance event occurs and a corresponding residual job task sequence which is not executed in a first job instruction set; constructing a target domain constraint space, wherein the target domain constraint space comprises a residual safe operation time window after a disturbance event occurs, the number of residual available agricultural machines and an updated environment passing resistance coefficient; and establishing a feature mapping function from a source domain knowledge space to a target domain constraint space, extracting effective gene segments corresponding to the residual operation task sequences from the optimal elite solution set by using the feature mapping function, mapping the effective gene segments to the target domain constraint space, and generating a re-planning initial population.
10. The cloud platform-based agricultural production management system of claim 9, wherein the instruction generating unit is configured to generate and issue a second job instruction set, and specifically includes: based on the initial population re-planning, carrying out evolution iteration in a target domain constraint space; Because the initial population is re-planned to contain the optimization characteristics of the source domain, only the intersection and variation operation of the preset fine tuning times is needed to be executed, and a corrected elite solution set after convergence is output; and decoding the corrected elite solution set to generate a second operation instruction set, wherein the second operation instruction set comprises an updated operation time stamp, a travel path coordinate and an agricultural machine action code, and the second operation instruction set is issued to an agricultural machine terminal to replace the first operation instruction set.

Description

Agricultural production management system based on cloud platform Technical Field The invention relates to the technical field of production management, in particular to an agricultural production management system based on a cloud platform. Background With the development of the Internet of things and cloud computing technology, intelligent agriculture has been transformed from single environment monitoring to full-process production management and intelligent scheduling, meteorological, soil moisture and crop growth data can be collected in real time through a sensor network, and agricultural production activities are guided by utilizing a big data analysis technology, so that the resource utilization rate is improved, and the operation cost is reduced. At present, the invention in China with the application number 202511231552.8 discloses a cloud computing-based intelligent agricultural production scheduling method and system, which are used for predicting the growth cycle and material demand of crops by collecting meteorological data, soil data and crop image data and utilizing a weighted environmental factor model, dynamically adjusting parameters of a prediction model by a gradient descent method by comparing the deviation of the actual material usage amount and a predicted value to generate a material purchasing and inventory scheduling scheme, thereby achieving remarkable effects in solving the problem of matching the material supply and demand and macroscopic prediction of the growth cycle in agricultural production and realizing the fine management of production materials. However, the technology has the following defects that the technology lacks the fine scheduling capability of the operation path under the multi-dimensional space-time constraint, the prior art focuses on purchasing and distributing production materials, the scheduling object is static material stock and is not a dynamic agricultural machine, in actual production, compared with how many fertilizers are purchased, a farm manager urgently needs to solve how to plan the driving paths and the operation sequences of a plurality of agricultural machines in a limited meteorological window, a mechanism for discretizing an area to be operated into grid nodes and optimizing the paths is lacking, the problem of invalid empty driving and path conflict in the operation of the agricultural machines cannot be solved, a dynamic preference adjusting mechanism for coping with the change of the meteorological urgency is lacking, the agricultural production has extremely strong timeliness, an optimization target is not invariable, fixed optimization logic or only deviation-based model parameters are adopted, preference reference vectors cannot be automatically generated and switched according to real-time meteorological early warning, so that scheduling instructions meeting the requirements of the rush-time period cannot be generated in actual production, the prior art has high unstructured characteristics in the face of the response speed lag of the rescheduling of sudden disturbance, the prior art has the characteristics, the prior art has the problem that the prior art frequently has feedback cycle and the problem is difficult to solve the problem of the collision due to the fact that the feedback cycle and the offline model is frequently occurs, the optimal time-consuming time is long, and the optimal disturbance is calculated and the time is required to be corrected due to the fact that the sudden disturbance is based on the long time-consuming calculation model is calculated and is long, and is required to be calculated and is a long time-down due to the fact that the time-consuming and is required to be calculated and is lost. Disclosure of Invention The method solves the technical problems that in the prior art, dynamic balance optimization of operation timeliness and resource operation cost is difficult to realize under a complex agricultural production environment with multidimensional space-time constraint coupling, particularly, adaptive initial path planning is difficult to be carried out aiming at time-varying preference drift caused by weather window change, when sudden environment disturbance or equipment failure is encountered, the existing scheduling algorithm causes large calculation amount, slow convergence speed and delayed instruction issuing in the re-planning process due to lack of knowledge migration mechanism based on historical experience, and cannot meet the real-time response requirement under the agricultural emergency receiving rushed planting scene. In order to solve the technical problems, the invention provides the technical scheme that the agricultural production management system based on the cloud platform comprises a modeling module, a planning module and a management module; The modeling module is used for receiving geographic information data, crop growth state data and weather forecast data of an area to be