CN115237982A - Agricultural big data management system and method based on cloud computing - Google Patents
Agricultural big data management system and method based on cloud computing Download PDFInfo
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Abstract
The invention belongs to the field of agriculture, relates to a data analysis technology, and aims to solve the problem that the existing agricultural industry big data management system cannot perform targeted adjustment on an area with abnormal output, in particular to an agricultural big data management system and method based on cloud computing, wherein the agricultural big data management system comprises an agricultural management platform, a region analysis module is used for monitoring and analyzing agricultural resources of a region through region analysis, dividing the region for performing agricultural big data management into monitoring regions, acquiring land data, water source data and output data of the monitoring regions, and marking the monitoring regions as areas with abnormal output or areas with normal output through the numerical value of a resource coefficient; the agricultural resource analysis system can perform zoning analysis on the regions for agricultural resource analysis, analyze the agricultural product output conditions of the regions according to the cultivated land data, the water source data and the output data of each region, and further evaluate the resource utilization and the overall output level in each region.
Description
Technical Field
The invention belongs to the field of agriculture, relates to a data analysis technology, and particularly relates to an agricultural big data management system and method based on cloud computing.
Background
The agricultural big data is a data set which is generated after self characteristics such as agricultural regionality, seasonality, diversity, periodicity and the like are fused, has wide sources, various types, complex structure and potential value, and is difficult to be processed and analyzed by a common method; the agricultural big data is the practice of big data concepts, technologies and methods in agriculture, relates to various links such as ploughing, sowing, fertilizing, killing insects, harvesting, storing, breeding and the like, and is data analysis and mining across industries, professions and businesses and data visualization.
The existing agricultural big data management system cannot search the reason of the abnormal output region through regional analysis, cannot adjust the abnormal output region in a targeted manner, and further reduces the abnormal output region efficiency.
In view of the above technical problem, the present application proposes a solution.
Disclosure of Invention
The invention aims to provide an agricultural big data management system and method based on cloud computing, which are used for solving the problem that the existing agricultural big data management system cannot adjust the area with abnormal output in a targeted manner;
the technical problems to be solved by the invention are as follows: how to provide an agricultural big data management system capable of carrying out targeted adjustment on areas with abnormal yield.
The purpose of the invention can be realized by the following technical scheme:
an agricultural big data management system based on cloud computing comprises an agricultural management platform, wherein a region analysis module is used for monitoring and analyzing agricultural resources of regions through region analysis, dividing the regions subjected to agricultural big data management into monitoring regions, acquiring land data, water source data and output data of the monitoring regions, carrying out numerical calculation on the land data, the water source data and the output data of the monitoring regions to obtain resource coefficients of the monitoring regions, and marking the monitoring regions as areas with different production or areas with correct production according to the numerical values of the resource coefficients;
the meteorological analysis module is used for carrying out meteorological analysis on the areas with the abnormal production, acquiring wind data, rainfall data and acid-base data of the areas with the abnormal production in the last quarter, carrying out numerical calculation to obtain meteorological coefficients, and judging whether the meteorology of the areas with the abnormal production meets requirements or not according to the numerical values of the meteorological coefficients;
the biological analysis module is used for carrying out biological resource analysis on the productive area.
As a preferred embodiment of the present invention, the land data of the monitoring area is a total area value of land for cultivation in the monitoring area;
the water source data of the monitoring area is the total area value of fresh water resources in the monitoring area;
the output data of the monitoring area is the total weight of the crops produced in the previous quarter monitoring area.
As a preferred embodiment of the present invention, the specific process of marking the monitoring area as a parturient area or a parturient area includes: acquiring a resource threshold value through a storage module, and comparing the resource coefficients of the monitoring area with the resource threshold value one by one:
if the resource coefficient is less than or equal to the resource threshold value, judging that the resource output of the corresponding monitoring area is unqualified, marking the corresponding monitoring area as a production-different area, sending a weather analysis signal to an agricultural management platform by a regional analysis module, and sending the weather analysis signal to a weather analysis module by the agricultural management platform after receiving the weather analysis signal;
if the resource coefficient is larger than the resource threshold value, judging that the resource output of the corresponding monitoring area is qualified, and marking the corresponding resource area as a productive area.
As a preferred embodiment of the present invention, the wind data of the production area on the production day is the maximum wind level in the production area on the day;
the rainfall data of the production areas on the production day is the total rainfall in the production areas on the same day;
the acquisition process of the acid-base data of the productive area in the last quarter on each production day comprises the following steps: and acquiring the pH value of the air in the productive area once every hour, marking the absolute value of the difference value between the pH value and the seven as the pH value, and marking the pH value with the maximum production day value as the acid-base data of the production day.
As a preferred embodiment of the present invention, the specific process of determining whether the weather in the parturient area meets the requirement includes: acquiring a meteorological threshold through a storage module, marking production days with meteorological coefficients smaller than the meteorological threshold as gas-in days, and marking the ratio of the number of the gas-in days to the number of the production days in the last quarter as a gas-in ratio;
establishing a meteorological set by the meteorological coefficients of all production days in the previous quarter, calculating the variance of the meteorological set to obtain a meteorological expression value, acquiring a weather threshold and a meteorological expression threshold through a storage module, and comparing the weather ratio and the meteorological expression value of a production area with the weather threshold and the meteorological expression threshold respectively:
if the gas-to-gas ratio is greater than the gas-to-gas threshold and the weather performance value is smaller than the weather performance threshold, determining that the weather of the area with the different production meets the requirement, sending a biological resource analysis signal to an agricultural management platform by a weather analysis module, and sending the biological resource analysis signal to a biological analysis module by the agricultural management platform after receiving the biological resource analysis signal;
otherwise, judging that the weather in the area with the abnormal production meets the requirements, sending the weather adjusting signal to the agricultural management platform by the weather analysis module, and sending the weather adjusting signal to the mobile phone terminal of the manager by the agricultural management platform after receiving the weather adjusting signal.
As a preferred embodiment of the present invention, the specific process of the biological analysis module for performing the biological resource analysis on the xenogenesis area comprises: acquiring fertilizer data and pesticide data of a previous quarter of a production area, wherein the fertilizer data of the production area is the total weight of fertilizers used by crops of the previous quarter of the production area, the pesticide data of the production area is the total weight of pesticides used by the crops of the previous quarter of the production area, and the biological coefficient of the production area is obtained by carrying out numerical calculation on the fertilizer data and the pesticide data; obtaining a biological threshold value through a storage module, and comparing the biological coefficient of the parturient area with the biological threshold value:
if the biological coefficient is less than or equal to the biological threshold value, the biological resources in the xenogenesis area are judged not to meet the requirements, the biological analysis module sends a biological resource shortage signal to the agricultural management platform, and the agricultural management platform receives the biological resource shortage signal and sends the biological resource shortage signal to a mobile phone terminal of a manager;
if the biological coefficient is larger than the biological threshold value, the biological resources of the xenogenesis area meet the requirements, the biological analysis module sends the pesticide signals to the agricultural management platform, and the agricultural management platform sends the received pesticide signals to a mobile phone terminal of a manager.
An agricultural big data management method based on cloud computing comprises the following steps:
the method comprises the following steps: monitoring and analyzing the agricultural resources of the region through regional analysis to obtain a resource coefficient of the monitoring region, and marking the monitoring region as a production area or a production area according to the numerical value of the resource coefficient;
step two: carrying out meteorological analysis on the parturient area to obtain a meteorological coefficient, a gas-to-gas ratio and a meteorological expression value of the parturient area, judging whether the meteorology of the parturient area meets the requirements or not according to the numerical value of the gas-to-gas ratio and the meteorological expression value, and sending a biological resource analysis signal to a biological analysis module through an agricultural management platform when the meteorology of the parturient area meets the requirements;
step three: and (4) carrying out biological resource analysis on the productive area to obtain a biological coefficient of the productive area, and judging whether the biological resource of the productive area meets the requirement or not according to the numerical value of the biological coefficient.
The invention has the following beneficial effects:
1. the region analysis module can be used for carrying out regional analysis on the regions for carrying out agricultural resource analysis, analyzing the agricultural product output conditions of the regions according to the cultivated land data, the water source data and the output data of each region, evaluating the resource utilization and the overall output level in each region, and carrying out factor troubleshooting in time when the resource utilization is not matched with the output level;
2. the meteorological analysis module can be used for carrying out meteorological analysis on the parturient areas, monitoring and analyzing the overall meteorology of the zen data through the wind data, the rainfall data and the acid-base data of the parturient areas, judging whether factors causing the output of the parturient areas not to meet the requirements are related to meteorological abnormity, and taking corresponding measures in time when the meteorological abnormity happens to ensure that the output of agricultural products of the subsequent parturient areas meets the requirements;
3. biological resource analysis can be carried out on the area where the crop is produced through the biological analysis module, whether the biological resources of the area where the crop is produced are sufficient or not is judged through chemical fertilizer data and pesticide data, the biological resources of the area where the crop is produced are supplemented in time when the biological resources are insufficient, and the influence of the biological resources on the output of the regional crops is reduced.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a block diagram of a system according to a first embodiment of the present invention;
FIG. 2 is a flowchart of a method according to a second embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described below clearly and completely in conjunction with the embodiments, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Big agricultural data is the practice of big data concept, technology and method in agriculture, the big agricultural data relates to links such as ploughing, sowing, fertilizing, killing insects, harvesting, storing and breeding, is data analysis and mining of cross-industry, cross-specialty and cross-business, and is data visualization, the big agricultural data consists of structured data and unstructured data, along with the development and construction of agriculture and the application of Internet of things, the unstructured data shows a rapidly increasing trend, the quantity of the unstructured data greatly exceeds that of the structured data, the big data or massive data refers to the information that the related data quantity is huge in scale, so that the information can not pass through mainstream software tools, and the information can be captured, managed, processed and collated into more active purposes for helping enterprise operation and decision making in reasonable time;
from the field, the method takes the agricultural field as a core (covering sub-industries such as planting industry, forestry industry, animal husbandry industry and the like), gradually expands the agricultural field to relevant upstream and downstream industries (feed production, chemical fertilizer production, agricultural machinery production, slaughtering industry, meat processing industry and the like), and integrates data of macroscopic economic backgrounds, including statistical data, import and export data, price data, production data, meteorological data and the like; from the region, domestic regional data is taken as a core, and international agricultural data is used as effective reference for reference; the system not only comprises national level data, but also covers provincial and municipal data, even local and municipal level data, and provides a foundation for accurate regional research; from the granularity, the data includes not only statistical data, but also basic information, investment information, stockholder information, patent information, import and export information, recruitment information, media information, GIS coordinate information and the like of the economic principal involved in agriculture.
Example one
As shown in fig. 1, an agricultural big data management system based on cloud computing comprises an agricultural management platform, wherein the agricultural management platform is in communication connection with a regional analysis module, a meteorological analysis module, a biological analysis module and a storage module, the agricultural management platform is in one-way connection with the regional analysis module and the storage module, and the agricultural management platform is in two-way connection with the meteorological analysis module and the biological analysis module.
The region analysis module is used for monitoring and analyzing the agricultural resources of the region through regional analysis: dividing an area subjected to agricultural big data management into monitoring areas i, i =1,2, \8230, wherein n and n are positive integers, obtaining land data TDi, water source data SYi and output data CCi of the monitoring areas i, the land data TDi of the monitoring areas i is a total land area value used for cultivation in the monitoring areas i, the water source data SYi of the monitoring areas i is a total area value of fresh water resources in the monitoring areas, the output data CCi of the monitoring areas i is a total crop weight output in the monitoring areas i in the previous quarter, and obtaining a resource coefficient ZYi of the monitoring areas i through a formula ZYi = (alpha 1 x CCi)/(alpha 2 x TDi + alpha 3 x SYi), wherein the resource coefficient is a numerical value reflecting the rationality of resources and output of the monitoring areas, and the higher numerical value of the resource coefficient indicates that the resources and the output of the corresponding monitoring areas are higher; wherein alpha 1, alpha 2 and alpha 3 are all proportionality coefficients, and alpha 1 is more than alpha 2 and more than alpha 3 is more than 1; acquiring a resource threshold ZYmin through a storage module, and comparing the resource coefficients ZYi of the monitoring area i with the resource threshold ZYmin one by one: if the resource coefficient ZYi is less than or equal to the resource threshold value Zymin, judging that the resource output of the corresponding monitoring area i is unqualified, marking the corresponding monitoring area as a production-different area, sending a weather analysis signal to an agricultural management platform by a region analysis module, and sending the weather analysis signal to a weather analysis module by the agricultural management platform after receiving the weather analysis signal; if the resource coefficient ZYi is larger than the resource threshold value Zymin, judging that the resource output of the corresponding monitoring area i is qualified, and marking the corresponding resource area as a productive area; the regional analysis is carried out to the area that carries out agricultural resource analysis, and farming land data, water source data and the output data to each region carry out the analysis to regional agricultural product output condition, and then appraise resource utilization and whole output level in each region, in time carry out the factor investigation when resource utilization mismatches with output level.
The weather analysis module receives the weather analysis signal and then carries out weather analysis on the production area: the method comprises the steps of obtaining wind power data FL, rainfall data JY and acid-base data SJ of a productive area in each production day in the previous quarter, wherein the wind power data FL of the productive area in the production day is the maximum wind power grade in the productive area in the current day, the rainfall data JY of the productive area in the production day is the total rainfall in the productive area in the current day, and the obtaining process of the acid-base data SJ of the productive area in each production day in the previous quarter comprises the following steps: acquiring the pH value of air in a production area once every hour, marking the absolute value of the difference value between the pH value and seven as a pH value, marking the pH value with the maximum production day value as pH data SJ of the production day, wherein the pH value is a method for representing the hydrogen ion concentration, the pH value is a negative value of a common logarithm of the hydrogen ion concentration (activity) in an aqueous solution and is generally called as pH or pH value, a qualitative method can be used for measuring the hydrogen ion activity index by using a pH indicator and a pH test paper, quantitative pH measurement needs to be carried out by using a pH meter, a meteorological coefficient QX of the production area in the production day is obtained by a formula QX = beta 1 FL + beta 2 JY + beta 3 SJ, the meteorological coefficient is a numerical value reflecting the overall meteorological goodness of the production area in the production day, the larger the numerical value of the meteorological coefficient is, the overall meteorological difference of the production area in the corresponding production day is, and the influence of the meteorological phenomenon on the production of the production area is larger; wherein beta 1, beta 2 and beta 3 are proportionality coefficients, and beta 3 is more than beta 2 and more than beta 1 and more than 1; acquiring a meteorological threshold value QXmax through a storage module, marking production days with meteorological coefficients QX smaller than the meteorological threshold value as weather-in days, marking the ratio of the number of the weather-in days to the number of the production days in the last quarter as a weather-in ratio, establishing a meteorological set by the meteorological coefficients of all the production days in the last quarter, calculating the variance of the meteorological set to obtain a meteorological expression value, acquiring the weather-in threshold value and the meteorological expression threshold value through the storage module, and comparing the weather-in ratio and the meteorological expression value of a productive area with the weather-in threshold value and the meteorological expression threshold value respectively: if the gas-to-gas ratio is greater than the gas-to-gas threshold and the weather performance value is smaller than the weather performance threshold, determining that the weather of the area with the different production meets the requirement, sending a biological resource analysis signal to an agricultural management platform by a weather analysis module, and sending the biological resource analysis signal to a biological analysis module by the agricultural management platform after receiving the biological resource analysis signal; otherwise, judging that the weather in the area with the abnormal production meets the requirements, sending a weather adjusting signal to the agricultural management platform by the weather analysis module, and sending the weather adjusting signal to the mobile phone terminal of the manager by the agricultural management platform after receiving the weather adjusting signal; carry out meteorological analysis to the area of parturition, monitor the analysis through the wind-force data, rainfall data and the acid-base data of the area of parturition to the whole meteorological of buddhist's data, and then judge whether relevant with meteorological abnormality to the factor that leads to the unsatisfied requirement of output in the area of parturition, in time take counter-measures when meteorological abnormality, guarantee follow-up agricultural product output in the area of parturition satisfies the requirement.
The biological analysis module receives the biological resource analysis signal and then carries out biological resource analysis on the parturition area: acquiring chemical fertilizer data HF and pesticide data NY of a previous quarter of a production area, wherein the chemical fertilizer data HF of the production area is the total weight of chemical fertilizers used by crops of the previous quarter of the production area, the pesticide data NY of the production area is the total weight of pesticides used by crops of the previous quarter of the production area, and obtaining a biological coefficient SW of the production area through a formula SW = gamma 1 HF + gamma 2 NY, wherein the biological coefficient is a numerical value for reflecting the sufficiency degree of biological resources of the production area, and the larger the numerical value of the biological coefficient is, the more sufficient the biological resources of the production area are; wherein gamma 1 and gamma 2 are proportional coefficients, and gamma 2 is more than gamma 1 and more than 1; obtaining a biological threshold value SWmin through a storage module, and comparing the biological coefficient SW of the parturition area with the biological threshold value SWmin: if the biological coefficient SW is less than or equal to the biological threshold value SWmin, judging that the biological resources in the xenogenesis area do not meet the requirements, sending a biological resource shortage signal to an agricultural management platform by a biological analysis module, and sending the biological resource shortage signal to a mobile phone terminal of a manager by the agricultural management platform after receiving the biological resource shortage signal; if the biological coefficient SW is larger than a biological threshold value SWmin, judging that the biological resources in the xenogenesis area meet requirements, sending an agricultural insect signal to an agricultural management platform by a biological analysis module, and sending the received agricultural insect signal to a mobile phone terminal of a manager by the agricultural management platform; and (4) performing biological resource analysis on the areas with different production, judging whether the biological resources in the areas with different production are sufficient through the chemical fertilizer data and the pesticide data, and supplementing the biological resources in the areas with different production in time when the biological resources in the areas with different production are insufficient, so that the influence of the biological resources on the output of regional crops is reduced.
Example two
As shown in fig. 2, an agricultural big data management method based on cloud computing includes the following steps:
the method comprises the following steps: monitoring and analyzing the agricultural resources of the region through regional analysis to obtain a resource coefficient of the monitoring region, and marking the monitoring region as a production area or a production area according to the numerical value of the resource coefficient;
step two: carrying out meteorological analysis on the area with the parity to obtain meteorological coefficients, gas-to-liquid ratios and meteorological expression values of the area with the parity, judging whether the meteorology of the area with the parity meets requirements or not according to the numerical values of the gas-to-liquid ratios and the meteorological expression values, and sending a biological resource analysis signal to a biological analysis module through an agricultural management platform when the meteorology of the area with the parity meets the requirements;
step three: and carrying out biological resource analysis on the productive area to obtain a biological coefficient of the productive area, and judging whether the biological resource of the productive area meets the requirement or not according to the numerical value of the biological coefficient.
During work, agricultural resources of regions are monitored and analyzed through regional analysis, resource coefficients of the monitored regions are obtained, the monitored regions are marked as areas with different production or areas with correct production through the numerical values of the resource coefficients, resource utilization and the overall production level in each region are evaluated, and factor troubleshooting is performed in time when the resource utilization is not matched with the production level; carrying out meteorological analysis on the productive area to obtain a meteorological coefficient, a gas-to-liquid ratio and a meteorological expression value of the productive area, judging whether the meteorology of the productive area meets requirements or not according to the numerical value of the gas-to-liquid ratio and the meteorological expression value, sending a biological resource analysis signal to a biological analysis module through an agricultural management platform when the meteorology of the productive area meets the requirements, judging whether factors causing the output of the productive area to not meet the requirements are related to meteorological abnormity or not, and timely taking corresponding measures when the meteorology is abnormal to ensure that the output of agricultural products of the subsequent productive area meets the requirements; and carrying out biological resource analysis on the productive area to obtain a biological coefficient of the productive area, judging whether the biological resource of the productive area meets the requirement or not according to the numerical value of the biological coefficient, and supplementing the biological resource of the productive area in time when the biological resource of the productive area is insufficient, so that the influence of the biological resource on the output of regional crops is reduced.
The foregoing is merely illustrative and explanatory of the present invention and various modifications, additions or substitutions may be made to the specific embodiments described by those skilled in the art without departing from the scope of the invention as defined in the accompanying claims.
The formulas are all obtained by acquiring a large amount of data and performing software simulation, and a formula close to a true value is selected, and coefficients in the formulas are set by a person skilled in the art according to actual conditions; such as: formula ZYi = (α 1 × cci)/(α 2 × tdi + α 3 × syi); collecting multiple groups of sample data and setting corresponding resource coefficients for each group of sample data by a person skilled in the art; substituting the set resource coefficients and the acquired sample data into formulas, forming a ternary linear equation set by any three formulas, screening the calculated coefficients and taking the mean value to obtain values of alpha 1, alpha 2 and alpha 3 which are respectively 3.74, 2.97 and 2.65;
the size of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and the size of the coefficient depends on the number of sample data and a corresponding resource coefficient preliminarily set by a person skilled in the art for each group of sample data; it is sufficient that the proportional relationship between the parameters and the quantized values is not affected, for example, the resource coefficients are proportional to the values of the output data.
In the description herein, references to the description of "one embodiment," "an example," "a specific example," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand the invention for and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (7)
1. An agricultural big data management system based on cloud computing comprises an agricultural management platform and is characterized in that a region analysis module is used for monitoring and analyzing agricultural resources of regions through region analysis, the regions where agricultural big data management is carried out are divided into monitoring regions, land data, water source data and output data of the monitoring regions are obtained, numerical calculation is carried out on the land data, the water source data and the output data of the monitoring regions to obtain resource coefficients of the monitoring regions, and the monitoring regions are marked as areas with different production or areas with correct production according to the numerical values of the resource coefficients;
the meteorological analysis module is used for carrying out meteorological analysis on the parturient area, acquiring wind data, rainfall data and acid-base data of the parturient area in each production day in the last quarter, carrying out numerical calculation to obtain a meteorological coefficient, and judging whether the meteorology of the parturient area meets the requirements or not according to the numerical value of the meteorological coefficient;
the biological analysis module is used for carrying out biological resource analysis on the productive area.
2. The cloud computing-based agricultural big data management system is characterized in that land data of a monitoring area is a total land area value for cultivation in the monitoring area;
the water source data of the monitoring area is the total area value of the fresh water resources in the monitoring area;
the output data of the monitoring area is the total weight of the crops output in the monitoring area of the last quarter.
3. The cloud computing-based agricultural big data management system according to claim 1, wherein the specific process of marking the monitored area as a productive area or a productive area comprises the following steps: acquiring a resource threshold value through a storage module, and comparing the resource coefficients of the monitoring area with the resource threshold value one by one:
if the resource coefficient is less than or equal to the resource threshold value, judging that the resource output of the corresponding monitoring area is unqualified, marking the corresponding monitoring area as a production-different area, sending a weather analysis signal to an agricultural management platform by a regional analysis module, and sending the weather analysis signal to a weather analysis module by the agricultural management platform after receiving the weather analysis signal;
if the resource coefficient is larger than the resource threshold value, judging that the resource output of the corresponding monitoring area is qualified, and marking the corresponding resource area as a productive area.
4. The cloud-computing-based agricultural big data management system according to claim 1, wherein the wind data of a production area on a production day is the maximum wind level in the production area on the day;
the rainfall data of the production areas on the production day is the total rainfall in the production areas on the same day;
the acquisition process of the acid-base data of the productive area in the last quarter on each production day comprises the following steps: and acquiring the pH value of the air in the productive area once every hour, marking the absolute value of the difference value between the pH value and the seven as the pH value, and marking the pH value with the maximum production day value as the acid-base data of the production day.
5. The cloud-computing-based agricultural big data management system according to claim 4, wherein the specific process of determining whether the weather of the parturient area meets the requirements comprises: acquiring a meteorological threshold value through a storage module, marking production days with meteorological coefficients smaller than the meteorological threshold value as gas-in days, and marking the ratio of the number of the gas-in days to the number of the production days in the last quarter as a gas-in ratio;
establishing a meteorological set by the meteorological coefficients of all production days in the previous quarter, calculating the variance of the meteorological set to obtain a meteorological performance value, acquiring a gas combination threshold and a meteorological performance threshold through a storage module, and comparing the gas combination ratio and the meteorological performance value of a production area with the gas combination threshold and the meteorological performance threshold respectively:
if the gas-to-gas ratio is greater than the gas-to-gas threshold and the weather performance value is smaller than the weather performance threshold, determining that the weather of the area with the different production meets the requirement, sending a biological resource analysis signal to an agricultural management platform by a weather analysis module, and sending the biological resource analysis signal to a biological analysis module by the agricultural management platform after receiving the biological resource analysis signal;
otherwise, judging that the weather in the area with the abnormal production meets the requirements, sending the weather adjusting signal to the agricultural management platform by the weather analysis module, and sending the weather adjusting signal to the mobile phone terminal of the manager by the agricultural management platform after receiving the weather adjusting signal.
6. The cloud-computing-based agricultural big data management system as claimed in claim 1, wherein the specific process of performing biological resource analysis on the xenogenic area by the biological analysis module comprises: acquiring chemical fertilizer data and pesticide data of a previous quarter of a production area, wherein the chemical fertilizer data of the production area is the total weight of chemical fertilizers used by crops of the previous quarter of the production area, the pesticide data of the production area is the total weight of pesticides used by the crops of the previous quarter of the production area, and the biological coefficient of the production area is obtained by performing numerical calculation on the chemical fertilizer data and the pesticide data; obtaining a biological threshold value through a storage module, and comparing the biological coefficient of the parturition area with the biological threshold value:
if the biological coefficient is less than or equal to the biological threshold value, the biological resources in the xenogenesis area are judged not to meet the requirements, the biological analysis module sends a biological resource shortage signal to the agricultural management platform, and the agricultural management platform receives the biological resource shortage signal and sends the biological resource shortage signal to a mobile phone terminal of a manager;
and if the biological coefficient is greater than the biological threshold value, judging that the biological resources of the parturient area meet the requirements, sending the pesticide signal to an agricultural management platform by the biological analysis module, and sending the received pesticide signal to a mobile phone terminal of a manager by the agricultural management platform.
7. An agricultural big data management method based on cloud computing is characterized by comprising the following steps:
the method comprises the following steps: monitoring and analyzing the agricultural resources of the region through regional analysis to obtain a resource coefficient of the monitored region, and marking the monitored region as a productive area or a productive area according to the numerical value of the resource coefficient;
step two: carrying out meteorological analysis on the parturient area to obtain a meteorological coefficient, a gas-to-gas ratio and a meteorological expression value of the parturient area, judging whether the meteorology of the parturient area meets the requirements or not according to the numerical value of the gas-to-gas ratio and the meteorological expression value, and sending a biological resource analysis signal to a biological analysis module through an agricultural management platform when the meteorology of the parturient area meets the requirements;
step three: and carrying out biological resource analysis on the productive area to obtain a biological coefficient of the productive area, and judging whether the biological resource of the productive area meets the requirement or not according to the numerical value of the biological coefficient.
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| CN115984028A (en) * | 2023-03-21 | 2023-04-18 | 山东科翔智能科技有限公司 | AI technology-based intelligent agricultural production data decision management system |
| CN116054167A (en) * | 2023-03-06 | 2023-05-02 | 国网山东省电力公司聊城供电公司 | Power grid comprehensive dispatching management system and method based on power distribution network flexible controller |
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| CN116054167A (en) * | 2023-03-06 | 2023-05-02 | 国网山东省电力公司聊城供电公司 | Power grid comprehensive dispatching management system and method based on power distribution network flexible controller |
| CN116054167B (en) * | 2023-03-06 | 2023-06-06 | 国网山东省电力公司聊城供电公司 | Power grid comprehensive dispatching management system and method based on power distribution network flexible controller |
| CN115984028A (en) * | 2023-03-21 | 2023-04-18 | 山东科翔智能科技有限公司 | AI technology-based intelligent agricultural production data decision management system |
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