CN118052376A - Sensitive data anomaly detection system and method based on agricultural Internet of things - Google Patents
Sensitive data anomaly detection system and method based on agricultural Internet of things Download PDFInfo
- Publication number
- CN118052376A CN118052376A CN202410453193.XA CN202410453193A CN118052376A CN 118052376 A CN118052376 A CN 118052376A CN 202410453193 A CN202410453193 A CN 202410453193A CN 118052376 A CN118052376 A CN 118052376A
- Authority
- CN
- China
- Prior art keywords
- data
- agricultural
- abnormal
- agricultural planting
- value
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 84
- 238000001514 detection method Methods 0.000 title claims abstract description 18
- 230000005856 abnormality Effects 0.000 claims abstract description 50
- 230000002159 abnormal effect Effects 0.000 claims description 134
- 230000008635 plant growth Effects 0.000 claims description 77
- 238000011156 evaluation Methods 0.000 claims description 75
- 230000008569 process Effects 0.000 claims description 56
- 235000013399 edible fruits Nutrition 0.000 claims description 50
- 238000004364 calculation method Methods 0.000 claims description 47
- 230000007613 environmental effect Effects 0.000 claims description 45
- 230000012010 growth Effects 0.000 claims description 44
- 241000196324 Embryophyta Species 0.000 claims description 39
- 241000592344 Spermatophyta Species 0.000 claims description 34
- 239000002689 soil Substances 0.000 claims description 24
- 229910001385 heavy metal Inorganic materials 0.000 claims description 18
- 235000015097 nutrients Nutrition 0.000 claims description 18
- 238000004590 computer program Methods 0.000 claims description 14
- 230000015654 memory Effects 0.000 claims description 14
- 238000012544 monitoring process Methods 0.000 claims description 13
- 238000001556 precipitation Methods 0.000 claims description 9
- 238000003860 storage Methods 0.000 claims description 9
- 230000008636 plant growth process Effects 0.000 claims description 6
- 238000012545 processing Methods 0.000 description 5
- 238000012271 agricultural production Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 230000006855 networking Effects 0.000 description 3
- 238000000638 solvent extraction Methods 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 238000013500 data storage Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 230000008676 import Effects 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 239000004065 semiconductor Substances 0.000 description 2
- 238000009360 aquaculture Methods 0.000 description 1
- 244000144974 aquaculture Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000010413 gardening Methods 0.000 description 1
- 244000144972 livestock Species 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000009304 pastoral farming Methods 0.000 description 1
- 238000009374 poultry farming Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a sensitive data abnormality detection system and method based on an agricultural Internet of things, which belong to the field of Internet of things.
Description
Technical Field
The invention belongs to the field of Internet of things, and particularly relates to a sensitive data anomaly detection system and method based on an agricultural Internet of things.
Background
The internet of things is widely applied to agricultural production such as livestock and poultry farming, facility gardening and aquaculture, and becomes one of important means for acquiring data, in the agricultural internet of things, sensing equipment periodically measures equipment states and environmental parameters, and acquired time series data have characteristics such as infinity, dynamic property and space-time correlation and are transmitted to a data processing system in a data stream form, but the following problems exist: the prior art cannot comprehensively analyze the growth data and future environmental data of agricultural plants in the agricultural planting process, so that the abnormal early warning efficiency and early warning speed in the agricultural planting process are reduced, the mastering capability of abnormal conditions in the agricultural planting process is reduced, and the problems in the prior art are all solved;
for example, in chinese patent with application publication number CN106302487a, a method and a device for real-time detecting and processing of data flow abnormality of agricultural internet of things are disclosed, in the method, firstly, the size of a sliding window is determined according to a characteristic period of collected data and a time interval of collection, and then a measurement value and a prediction interval collected by a sensor at the current moment are predicted according to historical collected data in the sliding window; and comparing the actual measured value actually acquired at the current moment with the predicted interval, and if the actual measured value does not fall into the predicted interval, indicating that the data actually acquired at the current moment is abnormal data, thereby realizing the detection of the abnormal data. In addition, after the data at the current moment is judged to be the abnormal data, the obtained predicted value is utilized to replace the abnormal data, so that the abnormal data can be processed, the accuracy of the acquired data stream is effectively improved, and powerful data support is provided for automatic control and effective data analysis of the equipment;
The problems proposed in the background art exist in the above patents: the prior art cannot comprehensively analyze the growth data of agricultural plants and the future environmental data in the agricultural planting process, reduces the abnormal early warning efficiency and early warning speed in the agricultural planting process, reduces the mastering capability of the abnormal situation in the agricultural planting process, and designs the sensitive data abnormal detection system and method based on the agricultural Internet of things in order to solve the problems.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a sensitive data abnormality detection system and method based on the agricultural Internet of things, which are characterized in that the environment data and the future weather data in the agricultural planting process are acquired through an Internet of things module, the agricultural plant growth evaluation network is constructed based on the agricultural plant growth data and the future weather data, the agricultural plant growth condition is evaluated in real time, the agricultural planting environment evaluation network is constructed based on the environment data and the future weather data in the agricultural planting process, the environmental influence coefficient is evaluated, the evaluated agricultural plant growth condition and environmental influence coefficient are substituted into an agricultural planting abnormality judgment strategy to carry out agricultural planting abnormality early warning according to the obtained agricultural planting abnormality judgment result, the agricultural plant growth data and the future environment data in the agricultural planting process are comprehensively analyzed, the abnormal early warning efficiency and early warning speed in the agricultural planting process are improved, the grasping capability of the abnormal condition in the agricultural planting process is improved, and the quality of agricultural production is further improved.
In order to achieve the above purpose, the invention provides the following technical scheme, namely a sensitive data anomaly detection method based on the agricultural Internet of things, which comprises the following specific steps:
acquiring agricultural seed plant growth data and image data through an Internet of things module, and simultaneously acquiring environmental data and weather data at future time in the agricultural planting process;
constructing an agricultural seed plant growth evaluation network based on the agricultural seed plant growth data and the image data, and evaluating the agricultural seed plant growth condition in real time;
an agricultural planting environment evaluation network is constructed based on environment data in the agricultural planting process and weather data at the future moment, and environmental influence coefficients are evaluated;
substituting the estimated growth condition of the agricultural seed plants and the environmental impact coefficient into an agricultural planting abnormality judgment strategy to judge the agricultural planting abnormality;
And carrying out early warning on the abnormal agricultural planting according to the obtained judging result of the abnormal agricultural planting.
The method for acquiring the agricultural seed plant growth data and the image data through the internet of things module, and simultaneously acquiring the environmental data and the weather data at the future moment in the agricultural planting process comprises the following specific steps:
S11, acquiring the range of an agricultural planting area to be monitored, uniformly dividing the agricultural planting area into a plurality of monitoring subareas, and collecting plant growth data at the center of the subareas, wherein the plant growth data comprise plant height data and plant average diameter data, and simultaneously collecting image data of agricultural plant fruits, and importing the image data of the fruits into image processing software to output pixel values of all pixel points, wherein the dividing number of the monitoring subareas is divided according to the monitoring requirements, and the area is preferably 100-200 square meters;
S12, acquiring agricultural planting soil environment data through an agricultural planting environment data acquisition terminal, wherein the agricultural planting soil environment data comprises the concentration and heavy metal content of various nutrients in soil, and acquiring weather data at the future moment through a weather acquisition terminal, wherein the weather data at the future moment comprises temperature data and precipitation data at the future moment;
And S13, storing the acquired agricultural seed plant growth data, image data, environment data and weather data at the future moment in a storage module for retrieval at any time.
It is specifically stated herein that the agricultural seed plant growth evaluation network includes the following specific contents:
S21, acquiring plant growth data and image data of agricultural plant fruits at the center positions of all the subregions;
s22, substituting plant growth data of the center positions of all the subregions and standard growth data of the plants in the corresponding period into a calculation formula of abnormal values of the growth data to calculate the abnormal values of the growth data, wherein the calculation formula of the abnormal values of the growth data is as follows: Where n is the number of subregions,/> Height of plant growth at center of ith sub-region,/>For the standard height of the plants in the corresponding period,/>Mean diameter data of the plant for the central position of the ith sub-region,/>Standard average diameter data for the corresponding period of seed plants;
S23, substituting the image data of the agricultural seed plant fruits at the center positions of all the subregions and the standard image data of the seed plant fruits at the corresponding periods into a fruit outlier calculation formula to calculate the fruit outlier, wherein the fruit outlier calculation formula is as follows: wherein m is the number of pixels of the standard image of the plant fruits in the corresponding period,/> For the pixel value of the j-th pixel point of the standard image of the plant fruits of the corresponding period, the pixel value of the j-th pixel point is/The pixel value of the j pixel point of the plant fruit image at the center position of the i sub-area;
s24, substituting the obtained abnormal value of the growth data and the obtained abnormal value of the fruit into a growth evaluation value calculation formula to calculate a growth evaluation value, wherein the growth evaluation value calculation formula is as follows: Wherein/> Generating a data outlier duty cycle,/>Is the ratio of abnormal fruit values, wherein/>。
It is specifically described herein that the agricultural planting environment evaluation network includes the following specific steps:
S31, acquiring environmental data and weather data at future time in the agricultural planting process of the central positions of all the subareas;
S32, importing the acquired environmental data in the agricultural planting process into an environmental outlier calculation formula for calculation, wherein the environmental outlier calculation formula is as follows: Wherein d is the nutrient and heavy metal species in the soil,/> Is the specific value of the nutrient and heavy metal species in the s-th soil at the central position of the i-th sub-area,Is the median value of the safety range of nutrient and heavy metal species in the s-th soil,/>Is the maximum value of the safety range of nutrient and heavy metal species in the s-th soil,/>Is the minimum value of the safe range of nutrient and heavy metal species in the s-th soil;
S33, importing the acquired weather data at the future time in the agricultural planting process into a future weather abnormal value calculation formula to calculate a future weather abnormal value, wherein the future weather abnormal value calculation formula is as follows: Wherein T is the period monitoring duration of the future period,/> Temperature value at t moment of weather forecast,/>Is the standard temperature value in the plant growth process,/>Precipitation at time t of weather forecast,/>Is a precipitation standard value required in the plant growth process;
S34, substituting the calculated environment abnormal value and future weather abnormal value into an environment evaluation value calculation formula to calculate an environment evaluation value, wherein the environment evaluation value calculation formula is as follows: Wherein, the method comprises the steps of, wherein, Is the duty ratio coefficient of environment anomaly value,/>For the future weather anomaly value, wherein/>。
The agricultural planting abnormality judging strategy comprises the following specific contents:
Obtaining a calculated growth evaluation value and an environment evaluation value, substituting the obtained growth evaluation value and environment evaluation value into an agricultural planting abnormal judgment value calculation formula to calculate an agricultural planting abnormal judgment value, wherein the agricultural planting abnormal judgment value calculation formula is as follows: Where exp () is the power of e.
The specific steps for carrying out the early warning of the abnormal agricultural planting according to the obtained judging result of the abnormal agricultural planting are as follows:
S51, comparing the calculated abnormal agricultural planting judgment value with a set abnormal agricultural planting judgment threshold;
and S52, if the obtained abnormal agricultural planting judgment value is larger than or equal to the set abnormal agricultural planting judgment threshold, carrying out abnormal agricultural planting early warning on the manager, and if the obtained abnormal agricultural planting judgment value is smaller than the set abnormal agricultural planting judgment threshold, not carrying out abnormal agricultural planting early warning on the manager.
The growth data abnormal value duty ratio coefficient, the fruit abnormal value duty ratio coefficient, the environment abnormal value duty ratio coefficient, the future weather abnormal value duty ratio coefficient and the agricultural planting abnormal judgment threshold value are as follows: acquiring 5000 groups of agricultural plant growth data, image data, environmental data in the agricultural planting process and weather data at the future time, meanwhile, acquiring fruits obtained by planting, manually classifying superior and inferior products, substituting the agricultural plant growth data, the image data, the environmental data in the agricultural planting process and the weather data at the future time into an agricultural planting abnormal judgment value calculation formula to calculate an agricultural planting abnormal judgment value, importing the calculated agricultural planting abnormal judgment value and classification result into fitting software, and outputting growth data abnormal value proportion coefficient, fruit abnormal value proportion coefficient, environmental abnormal value proportion coefficient, weather abnormal value proportion coefficient at the future and agricultural planting abnormal judgment threshold value which accord with the highest judgment accuracy.
The utility model provides a sensitive data anomaly detection system based on agriculture thing networking, its is realized based on the above-mentioned sensitive data anomaly detection method based on agriculture thing networking, and it specifically includes data acquisition module, vegetation evaluation module, environment evaluation module, agriculture planting anomaly judgment module, anomaly early warning module and total accuse module, data acquisition module is used for acquireing agriculture kind plant growth data and image data through thing networking module, acquires environment data and future moment weather data in the agriculture planting process simultaneously, vegetation evaluation module is used for constructing agriculture kind plant growth evaluation network based on agriculture kind plant growth data and image data, carries out real-time evaluation to agriculture kind plant growth condition, environment evaluation module is used for constructing agriculture planting environment evaluation network based on environment data and future moment weather data in the agriculture planting process, carries out the evaluation to environmental impact coefficient.
The method specifically needs to be described, the agricultural planting abnormality judging module is used for substituting the estimated agricultural planting growth condition and the environmental impact coefficient into the agricultural planting abnormality judging strategy to judge the agricultural planting abnormality, and the abnormality early warning module is used for carrying out agricultural planting abnormality early warning according to the obtained agricultural planting abnormality judging result.
The general control module is used for controlling the operation of the data acquisition module, the plant growth evaluation module, the environment evaluation module, the agricultural planting abnormality judgment module and the abnormality early warning module.
An electronic device, comprising: a processor and a memory, wherein the memory stores a computer program for the processor to call;
the processor executes the sensitive data anomaly detection method based on the agricultural Internet of things by calling the computer program stored in the memory.
A computer readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a method for detecting anomalies in sensitive data based on the internet of things of agriculture as described above.
Compared with the prior art, the invention has the beneficial effects that:
According to the invention, the agricultural plant growth data and the image data are acquired through the Internet of things module, the environment data and the future weather data in the agricultural planting process are acquired simultaneously, the agricultural plant growth evaluation network is constructed based on the agricultural plant growth data and the image data, the agricultural plant growth condition is evaluated in real time, the agricultural planting environment evaluation network is constructed based on the environment data and the future weather data in the agricultural planting process, the environmental impact coefficient is evaluated, the agricultural planting abnormality judgment is carried out by substituting the evaluated agricultural plant growth condition and the environmental impact coefficient into the agricultural planting abnormality judgment strategy, the agricultural planting abnormality early warning is carried out according to the obtained agricultural planting abnormality judgment result, the agricultural plant growth data and the future environment data in the agricultural planting process are comprehensively analyzed, the abnormality early warning efficiency and the early warning speed in the agricultural planting process are improved, the mastering capability of the abnormal condition in the agricultural planting process is improved, and the quality of agricultural production is further improved.
Drawings
FIG. 1 is a schematic overall flow chart of a sensitive data anomaly detection method based on the agricultural Internet of things;
fig. 2 is a schematic diagram of an overall framework of the sensitive data anomaly detection system based on the agricultural internet of things.
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.
Example 1. Referring to fig. 1, an embodiment of the present invention is provided: the technical problems solved by the embodiment are as follows: the prior art cannot comprehensively analyze the growth data and future environmental data of agricultural plants in the agricultural planting process, so that the abnormal early warning efficiency and early warning speed in the agricultural planting process are reduced, and the mastering capability of abnormal conditions in the agricultural planting process is reduced;
The sensitive data anomaly detection method based on the agricultural Internet of things comprises the following specific steps:
acquiring agricultural seed plant growth data and image data through an Internet of things module, and simultaneously acquiring environmental data and weather data at future time in the agricultural planting process;
constructing an agricultural seed plant growth evaluation network based on the agricultural seed plant growth data and the image data, and evaluating the agricultural seed plant growth condition in real time;
an agricultural planting environment evaluation network is constructed based on environment data in the agricultural planting process and weather data at the future moment, and environmental influence coefficients are evaluated;
substituting the estimated growth condition of the agricultural seed plants and the environmental impact coefficient into an agricultural planting abnormality judgment strategy to judge the agricultural planting abnormality;
Carrying out abnormal agricultural planting early warning according to the obtained abnormal agricultural planting judgment result;
in this embodiment, it needs to be specifically described that, obtaining, by the internet of things module, agricultural seed plant growth data and image data, and simultaneously obtaining environmental data and weather data at a future time in an agricultural planting process includes the following specific steps:
S11, acquiring the range of an agricultural planting area to be monitored, uniformly dividing the agricultural planting area into a plurality of monitoring subareas, and collecting plant growth data at the center of the subareas, wherein the plant growth data comprise plant height data and plant average diameter data, and simultaneously collecting image data of agricultural plant fruits, and importing the image data of the fruits into image processing software to output pixel values of all pixel points, wherein the dividing number of the monitoring subareas is divided according to the monitoring requirements, and the area is preferably 100-200 square meters;
here, the Python code example realizes the functions of acquiring the range of the agricultural planting area, dividing the monitoring subarea, acquiring the plant growth data and the fruit image data, and importing the image processing software to output pixel values:
import numpy as np
# acquisition of agricultural planting area Range
agricultural_zone = {'x_min': 0, 'x_max': 100, 'y_min': 0, 'y_max': 100}
# Dividing monitoring subarea
num_subareas = 5
subareas = []
x_step = (agricultural_zone['x_max'] - agricultural_zone['x_min']) / num_subareas
y_step = (agricultural_zone['y_max'] - agricultural_zone['y_min']) / num_subareas
for i in range(num_subareas):
for j in range(num_subareas):
subarea = {
'x_min': agricultural_zone['x_min'] + i * x_step,
'x_max': agricultural_zone['x_min'] + (i + 1) * x_step,
'y_min': agricultural_zone['y_min'] + j * y_step,
'y_max': agricultural_zone['y_min'] + (j + 1) * y_step
}
subareas.append(subarea)
# Acquisition of plant growth data and fruit image data
for subarea in subareas:
center_x = (subarea['x_min'] + subarea['x_max']) / 2
center_y = (subarea['y_min'] + subarea['y_max']) / 2
Plant_height=np.random.unitorm (10, 50) # simulated seed plant height data
Plant diameter=np.random.uniform (5, 20) # mock seed plant mean diameter data
Simulation acquisition of fruit image data
Random_image=np.random.random (0, 256, (100, 100, 3), dtype=np.uint8) # generates random image data
Processing fruit image data by# import image processing software
# Partial code omitting image processing software processing here
print(f"Subarea: {subarea}, Center: ({center_x}, {center_y}), Plant Height: {plant_height}, Plant Diameter: {plant_diameter}")
The code uses numpy library to generate random plant height and diameter data and simulate to generate random fruit image data;
S12, acquiring agricultural planting soil environment data through an agricultural planting environment data acquisition terminal, wherein the agricultural planting soil environment data comprises the concentration and heavy metal content of various nutrients in soil, and acquiring weather data at the future moment through a weather acquisition terminal, wherein the weather data at the future moment comprises temperature data and precipitation data at the future moment;
s13, storing the obtained agricultural seed plant growth data, image data, environment data and weather data at future time in a storage module for retrieval at any time;
In this embodiment, it should be specifically described that the agricultural seed plant growth evaluation network includes the following specific matters:
S21, acquiring plant growth data and image data of agricultural plant fruits at the center positions of all the subregions;
s22, substituting plant growth data of the center positions of all the subregions and standard growth data of the plants in the corresponding period into a calculation formula of abnormal values of the growth data to calculate the abnormal values of the growth data, wherein the calculation formula of the abnormal values of the growth data is as follows: Where n is the number of subregions,/> Height of plant growth at center of ith sub-region,/>For the standard height of the plants in the corresponding period,/>Mean diameter data of the plant for the central position of the ith sub-region,/>Standard average diameter data for the corresponding period of seed plants;
S23, substituting the image data of the agricultural seed plant fruits at the center positions of all the subregions and the standard image data of the seed plant fruits at the corresponding periods into a fruit outlier calculation formula to calculate the fruit outlier, wherein the fruit outlier calculation formula is as follows: wherein m is the number of pixels of the standard image of the plant fruits in the corresponding period,/> For the pixel value of the j-th pixel point of the standard image of the plant fruits of the corresponding period, the pixel value of the j-th pixel point is/The pixel value of the j pixel point of the plant fruit image at the center position of the i sub-area;
s24, substituting the obtained abnormal value of the growth data and the obtained abnormal value of the fruit into a growth evaluation value calculation formula to calculate a growth evaluation value, wherein the growth evaluation value calculation formula is as follows: Wherein/> Generating a data outlier duty cycle,/>Is the ratio of abnormal fruit values, wherein/>；
In this embodiment, it should be specifically described that the agricultural planting environment evaluation network includes the following specific steps:
S31, acquiring environmental data and weather data at future time in the agricultural planting process of the central positions of all the subareas;
S32, importing the acquired environmental data in the agricultural planting process into an environmental outlier calculation formula for calculation, wherein the environmental outlier calculation formula is as follows: Wherein d is the nutrient and heavy metal species in the soil,/> Is the specific value of the nutrient and heavy metal species in the s-th soil at the central position of the i-th sub-area,Is the median value of the safety range of nutrient and heavy metal species in the s-th soil,/>Is the maximum value of the safety range of nutrient and heavy metal species in the s-th soil,/>Is the minimum value of the safe range of nutrient and heavy metal species in the s-th soil;
S33, importing the acquired weather data at the future time in the agricultural planting process into a future weather abnormal value calculation formula to calculate a future weather abnormal value, wherein the future weather abnormal value calculation formula is as follows: Wherein T is the period monitoring duration of the future period,/> Temperature value at t moment of weather forecast,/>Is the standard temperature value in the plant growth process,/>Precipitation at time t of weather forecast,/>Is a precipitation standard value required in the plant growth process;
S34, substituting the calculated environment abnormal value and future weather abnormal value into an environment evaluation value calculation formula to calculate an environment evaluation value, wherein the environment evaluation value calculation formula is as follows: Wherein, the method comprises the steps of, wherein, Is the duty ratio coefficient of environment anomaly value,/>For the future weather anomaly value, wherein/>；
In this embodiment, it should be specifically described that the agricultural planting abnormality determination policy includes the following specific contents:
Obtaining a calculated growth evaluation value and an environment evaluation value, substituting the obtained growth evaluation value and environment evaluation value into an agricultural planting abnormal judgment value calculation formula to calculate an agricultural planting abnormal judgment value, wherein the agricultural planting abnormal judgment value calculation formula is as follows: wherein exp () is the power of e;
The specific steps of carrying out the early warning of the abnormal agricultural planting according to the obtained judging result of the abnormal agricultural planting are as follows:
S51, comparing the calculated abnormal agricultural planting judgment value with a set abnormal agricultural planting judgment threshold;
S52, if the obtained abnormal agricultural planting judgment value is larger than or equal to a set abnormal agricultural planting judgment threshold, carrying out abnormal agricultural planting early warning on the manager, and if the obtained abnormal agricultural planting judgment value is smaller than the set abnormal agricultural planting judgment threshold, not carrying out abnormal agricultural planting early warning on the manager;
The growth data abnormal value duty ratio coefficient, the fruit abnormal value duty ratio coefficient, the environment abnormal value duty ratio coefficient, the future weather abnormal value duty ratio coefficient and the agricultural planting abnormal judgment threshold value are as follows: acquiring 5000 groups of agricultural plant growth data, image data, environmental data in the agricultural planting process and weather data at the future moment, classifying the superior and inferior products by manpower of the planted fruits, substituting the agricultural plant growth data, the image data, the environmental data in the agricultural planting process and the weather data at the future moment into an agricultural planting abnormal judgment value calculation formula to calculate an agricultural planting abnormal judgment value, importing the calculated agricultural planting abnormal judgment value and classification result into fitting software, and outputting growth data abnormal value proportion coefficient, fruit abnormal value proportion coefficient, environmental abnormal value proportion coefficient, weather abnormal value proportion coefficient at the future and agricultural planting abnormal judgment threshold value which accord with the highest judgment accuracy;
In this embodiment, it needs to be specifically described that the benefits of this embodiment compared with the prior art are: the method comprises the steps of acquiring agricultural plant growth data and image data through an Internet of things module, acquiring environment data and future weather data in the agricultural planting process, constructing an agricultural plant growth evaluation network based on the agricultural plant growth data and the image data, evaluating the agricultural plant growth condition in real time, constructing an agricultural planting environment evaluation network based on the environment data in the agricultural planting process and the future weather data, evaluating environmental impact coefficients, substituting the evaluated agricultural plant growth condition and environmental impact coefficients into an agricultural planting abnormality judgment strategy to perform agricultural planting abnormality judgment, comprehensively analyzing the agricultural plant growth data and the future environment data in the agricultural planting process according to the obtained agricultural planting abnormality judgment result, improving the abnormality early warning efficiency and early warning speed in the agricultural planting process, improving the grasping capability of the abnormal condition in the agricultural planting process, and further improving the quality of agricultural production.
Example 2. As shown in fig. 2, the system for detecting abnormal sensitive data based on the agriculture internet of things is realized based on the method for detecting abnormal sensitive data based on the agriculture internet of things, and specifically comprises a data acquisition module, a plant growth evaluation module, an environment evaluation module, an agriculture planting abnormality judgment module, an abnormality early warning module and a master control module, wherein the data acquisition module is used for acquiring agriculture plant growth data and image data through the internet of things module, simultaneously acquiring environment data and future time weather data in the agriculture planting process, the plant growth evaluation module is used for constructing an agriculture plant growth evaluation network based on the agriculture plant growth data and the image data, carrying out real-time evaluation on the agriculture plant growth condition, and the environment evaluation module is used for constructing an agriculture planting environment evaluation network based on the environment data and the future time weather data in the agriculture planting process, and evaluating the environment influence coefficient; the agricultural planting abnormality judgment module is used for substituting the estimated agricultural planting growth condition and environmental impact coefficient into an agricultural planting abnormality judgment strategy to judge the agricultural planting abnormality, and the abnormality early warning module is used for carrying out agricultural planting abnormality early warning according to the obtained agricultural planting abnormality judgment result; the total control module is used for controlling the operation of the data acquisition module, the plant growth evaluation module, the environment evaluation module, the agricultural planting abnormality judgment module and the abnormality early warning module.
Example 3. The present embodiment provides an electronic device including: a processor and a memory, wherein the memory stores a computer program for the processor to call;
The processor executes the sensitive data anomaly detection method based on the agricultural Internet of things by calling the computer program stored in the memory.
The electronic device can generate larger difference due to different configurations or performances, and can comprise one or more processors (Central Processing Units, CPU) and one or more memories, wherein at least one computer program is stored in the memories, and the computer program is loaded and executed by the processors to realize the sensitive data anomaly detection method based on the agricultural Internet of things. The electronic device can also include other components for implementing the functions of the device, for example, the electronic device can also have wired or wireless network interfaces, input-output interfaces, and the like, for inputting and outputting data. The present embodiment is not described herein.
Example 4. The present embodiment proposes a computer-readable storage medium having stored thereon an erasable computer program;
When the computer program runs on the computer equipment, the computer equipment is caused to execute the sensitive data anomaly detection method based on the agricultural Internet of things.
For example, the computer readable storage medium can be Read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), compact disk Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM), magnetic tape, floppy disk, optical data storage device, and the like.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
It should be understood that determining B from a does not mean determining B from a alone, but can also determine B from a and/or other information.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions in accordance with embodiments of the present invention are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by way of wired or/and wireless networks from one website site, computer, server, or data center to another. Computer readable storage media can be any available media that can be accessed by a computer or data storage devices, such as servers, data centers, etc. that contain one or more collections of available media. Usable media may be magnetic media (e.g., floppy disks, hard disks, magnetic tape), optical media (e.g., DVD), or semiconductor media. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the partitioning of units is merely one way of partitioning, and there may be additional ways of partitioning in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, 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 present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. 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 only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form 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 and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (10)
1. The sensitive data anomaly detection method based on the agricultural Internet of things is characterized by comprising the following specific steps of:
acquiring agricultural seed plant growth data and image data through an Internet of things module, and simultaneously acquiring environmental data and weather data at future time in the agricultural planting process;
constructing an agricultural seed plant growth evaluation network based on the agricultural seed plant growth data and the image data, and evaluating the agricultural seed plant growth condition in real time;
an agricultural planting environment evaluation network is constructed based on environment data in the agricultural planting process and weather data at the future moment, and environmental influence coefficients are evaluated;
substituting the estimated growth condition of the agricultural seed plants and the environmental impact coefficient into an agricultural planting abnormality judgment strategy to judge the agricultural planting abnormality;
And carrying out early warning on the abnormal agricultural planting according to the obtained judging result of the abnormal agricultural planting.
2. The method for detecting abnormal sensitive data based on the agricultural Internet of things according to claim 1, wherein the steps of acquiring the growth data and the image data of agricultural plants and simultaneously acquiring the environmental data and the weather data at the future time in the agricultural planting process through the Internet of things module comprise the following specific steps:
S11, acquiring the range of an agricultural planting area to be monitored, uniformly dividing the agricultural planting area into a plurality of monitoring subareas, and acquiring plant growth data at the center of the subareas in the monitoring subareas, wherein the plant growth data comprise plant height data and plant average diameter data, and simultaneously acquiring image data of agricultural plant fruits, and importing the image data of the fruits into image processing software to output pixel values of all pixel points;
S12, acquiring agricultural planting soil environment data through an agricultural planting environment data acquisition terminal, wherein the agricultural planting soil environment data comprises the concentration and heavy metal content of various nutrients in soil, and acquiring weather data at the future moment through a weather acquisition terminal, wherein the weather data at the future moment comprises temperature data and precipitation data at the future moment;
And S13, storing the acquired agricultural seed plant growth data, image data, environment data and weather data at the future moment in a storage module.
3. The method for detecting abnormal sensitive data based on the agricultural Internet of things according to claim 2, wherein the agricultural plant growth evaluation network comprises the following specific contents:
S21, acquiring plant growth data and image data of agricultural plant fruits at the center positions of all the subregions;
s22, substituting plant growth data of the center positions of all the subregions and standard growth data of the plants in the corresponding period into a calculation formula of abnormal values of the growth data to calculate the abnormal values of the growth data, wherein the calculation formula of the abnormal values of the growth data is as follows: Where n is the number of subregions,/> Height of plant growth at center of ith sub-region,/>For the standard height of the plants in the corresponding period,/>Mean diameter data of the plant for the central position of the ith sub-region,/>Standard average diameter data for the corresponding period of seed plants;
S23, substituting the image data of the agricultural seed plant fruits at the center positions of all the subregions and the standard image data of the seed plant fruits at the corresponding periods into a fruit outlier calculation formula to calculate the fruit outlier, wherein the fruit outlier calculation formula is as follows: wherein m is the number of pixels of the standard image of the plant fruits in the corresponding period,/> For the pixel value of the j-th pixel point of the standard image of the plant fruits of the corresponding period, the pixel value of the j-th pixel point is/The pixel value of the j pixel point of the plant fruit image at the center position of the i sub-area;
s24, substituting the obtained abnormal value of the growth data and the obtained abnormal value of the fruit into a growth evaluation value calculation formula to calculate a growth evaluation value, wherein the growth evaluation value calculation formula is as follows: Wherein/> Generating a data outlier duty cycle,/>Is the ratio of abnormal fruit values, wherein/>。
4. The method for detecting abnormal sensitive data based on the agricultural internet of things according to claim 3, wherein the agricultural planting environment evaluation network comprises the following specific steps:
S31, acquiring environmental data and weather data at future time in the agricultural planting process of the central positions of all the subareas;
S32, importing the acquired environmental data in the agricultural planting process into an environmental outlier calculation formula for calculation, wherein the environmental outlier calculation formula is as follows: Wherein d is the nutrient and heavy metal species in the soil,/> Is the specific value of the nutrient and heavy metal species in the s-th soil at the central position of the i-th sub-area,Is the median value of the safety range of nutrient and heavy metal species in the s-th soil,/>Is the maximum value of the safety range of nutrient and heavy metal species in the s-th soil,/>Is the minimum value of the safe range of nutrient and heavy metal species in the s-th soil;
S33, importing the acquired weather data at the future time in the agricultural planting process into a future weather abnormal value calculation formula to calculate a future weather abnormal value, wherein the future weather abnormal value calculation formula is as follows: Wherein T is the period monitoring duration of the future period,/> Temperature value at t moment of weather forecast,/>Is the standard temperature value in the plant growth process,/>Precipitation at time t of weather forecast,/>Is a precipitation standard value required in the plant growth process;
S34, substituting the calculated environment abnormal value and future weather abnormal value into an environment evaluation value calculation formula to calculate an environment evaluation value, wherein the environment evaluation value calculation formula is as follows: Wherein/> Is the duty ratio coefficient of environment anomaly value,/>For the future weather anomaly value, wherein/>。
5. The method for detecting abnormal sensitive data based on the agricultural Internet of things according to claim 4, wherein the agricultural planting abnormality judgment strategy comprises the following specific contents:
Obtaining a calculated growth evaluation value and an environment evaluation value, substituting the obtained growth evaluation value and environment evaluation value into an agricultural planting abnormal judgment value calculation formula to calculate an agricultural planting abnormal judgment value, wherein the agricultural planting abnormal judgment value calculation formula is as follows: Where exp () is the power of e.
6. The method for detecting abnormal sensitive data based on the agricultural Internet of things according to claim 5, wherein the specific steps of performing the early warning of abnormal agricultural planting according to the obtained judging result of abnormal agricultural planting are as follows:
S51, comparing the calculated abnormal agricultural planting judgment value with a set abnormal agricultural planting judgment threshold;
and S52, if the obtained abnormal agricultural planting judgment value is larger than or equal to the set abnormal agricultural planting judgment threshold, carrying out abnormal agricultural planting early warning on the manager, and if the obtained abnormal agricultural planting judgment value is smaller than the set abnormal agricultural planting judgment threshold, not carrying out abnormal agricultural planting early warning on the manager.
7. The system for detecting the abnormality of the sensitive data based on the agricultural Internet of things is realized based on the method for detecting the abnormality of the sensitive data based on the agricultural Internet of things according to any one of claims 1-6, and is characterized by comprising a data acquisition module, a plant growth evaluation module, an environment evaluation module, an agricultural planting abnormality judgment module, an abnormality early warning module and a general control module, wherein the data acquisition module is used for acquiring the plant growth data and the image data of the agricultural plants through the Internet of things module, simultaneously acquiring the environment data and the future time weather data in the agricultural planting process, the plant growth evaluation module is used for constructing an agricultural plant growth evaluation network based on the agricultural plant growth data and the image data, evaluating the agricultural plant growth condition in real time, and the environment evaluation module is used for constructing the agricultural planting environment evaluation network based on the environment data in the agricultural planting process and the future time weather data, and evaluating the environmental influence coefficient.
8. The system for detecting abnormal sensitive data based on the agricultural Internet of things according to claim 7, wherein the abnormal agricultural planting judging module is used for substituting the estimated growth condition of the agricultural seed plants and the environmental impact coefficient into an abnormal agricultural planting judging strategy to judge abnormal agricultural planting, and the abnormal early warning module is used for early warning abnormal agricultural planting according to the obtained abnormal agricultural planting judging result; the total control module is used for controlling the operation of the data acquisition module, the plant growth evaluation module, the environment evaluation module, the agricultural planting abnormality judgment module and the abnormality early warning module.
9. An electronic device, comprising: a processor and a memory, wherein the memory stores a computer program for the processor to call;
The method for detecting the abnormality of the sensitive data based on the agricultural Internet of things is characterized in that the processor executes the method for detecting the abnormality of the sensitive data based on the agricultural Internet of things according to any one of claims 1 to 6 by calling a computer program stored in the memory.
10. A computer readable storage medium storing instructions which, when executed on a computer, cause the computer to perform a method of detecting anomalies in sensitive data based on the internet of things of any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410453193.XA CN118052376B (en) | 2024-04-16 | 2024-04-16 | Sensitive data anomaly detection system and method based on agricultural Internet of things |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410453193.XA CN118052376B (en) | 2024-04-16 | 2024-04-16 | Sensitive data anomaly detection system and method based on agricultural Internet of things |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN118052376A true CN118052376A (en) | 2024-05-17 |
| CN118052376B CN118052376B (en) | 2024-06-18 |
Family
ID=91046862
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410453193.XA Active CN118052376B (en) | 2024-04-16 | 2024-04-16 | Sensitive data anomaly detection system and method based on agricultural Internet of things |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118052376B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118261492A (en) * | 2024-05-30 | 2024-06-28 | 绵阳梓兴食品科技有限公司 | Nondestructive testing method and system for comprehensive quality of fresh pork |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005013786A (en) * | 2003-06-23 | 2005-01-20 | Gifu Prefecture | Estimation method and estimation program of amount of heavy metal flowing out of soil |
| CN204796339U (en) * | 2015-06-25 | 2015-11-25 | 重庆电子工程职业学院 | Information -based vegetable greenhouse based on thing networking |
| KR101822410B1 (en) * | 2016-07-22 | 2018-01-29 | 아인정보기술 주식회사 | Apparatus for diagnosing growth state of crop organ using crop organ image |
| US20180132423A1 (en) * | 2016-11-16 | 2018-05-17 | The Climate Corporation | Identifying management zones in agricultural fields and generating planting plans for the zones |
| CN110400314A (en) * | 2019-08-01 | 2019-11-01 | 新疆傲阳科技有限公司 | A kind of agricultural Internet of Things Integrated Service System |
| CN110545531A (en) * | 2019-09-20 | 2019-12-06 | 河南工业大学 | Crop growth monitoring method and system based on big data and cloud computing |
| CN111652756A (en) * | 2020-07-03 | 2020-09-11 | 张玉红 | Green wisdom green house planting environment monitoring management system |
| CN113095619A (en) * | 2021-03-04 | 2021-07-09 | 广东省科学院广州地理研究所 | Method and system for simulating vegetation productivity space pattern based on climate and soil |
| CN214278765U (en) * | 2021-04-12 | 2021-09-24 | 四川职业技术学院 | Constant temperature control device for planting |
| WO2021221704A1 (en) * | 2020-04-29 | 2021-11-04 | Pollen Systems Corporation | Crop management method and apparatus with autonomous vehicles |
| CN115220382A (en) * | 2022-07-21 | 2022-10-21 | 广东省农业科学院植物保护研究所 | Wisdom agricultural plant protection operation control system based on thing networking |
| CN117172959A (en) * | 2023-10-31 | 2023-12-05 | 杨凌职业技术学院 | Forestry ecological environment monitoring system and method |
| CN117172958A (en) * | 2023-10-23 | 2023-12-05 | 江苏新希望生态科技有限公司 | Bean sprout growth real-time data management method |
| CN117371727A (en) * | 2023-10-23 | 2024-01-09 | 贵州师范学院 | Intelligent agricultural monitoring and planning method and system based on Internet of things |
-
2024
- 2024-04-16 CN CN202410453193.XA patent/CN118052376B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005013786A (en) * | 2003-06-23 | 2005-01-20 | Gifu Prefecture | Estimation method and estimation program of amount of heavy metal flowing out of soil |
| CN204796339U (en) * | 2015-06-25 | 2015-11-25 | 重庆电子工程职业学院 | Information -based vegetable greenhouse based on thing networking |
| KR101822410B1 (en) * | 2016-07-22 | 2018-01-29 | 아인정보기술 주식회사 | Apparatus for diagnosing growth state of crop organ using crop organ image |
| US20180132423A1 (en) * | 2016-11-16 | 2018-05-17 | The Climate Corporation | Identifying management zones in agricultural fields and generating planting plans for the zones |
| CN110400314A (en) * | 2019-08-01 | 2019-11-01 | 新疆傲阳科技有限公司 | A kind of agricultural Internet of Things Integrated Service System |
| CN110545531A (en) * | 2019-09-20 | 2019-12-06 | 河南工业大学 | Crop growth monitoring method and system based on big data and cloud computing |
| WO2021221704A1 (en) * | 2020-04-29 | 2021-11-04 | Pollen Systems Corporation | Crop management method and apparatus with autonomous vehicles |
| CN111652756A (en) * | 2020-07-03 | 2020-09-11 | 张玉红 | Green wisdom green house planting environment monitoring management system |
| CN113095619A (en) * | 2021-03-04 | 2021-07-09 | 广东省科学院广州地理研究所 | Method and system for simulating vegetation productivity space pattern based on climate and soil |
| CN214278765U (en) * | 2021-04-12 | 2021-09-24 | 四川职业技术学院 | Constant temperature control device for planting |
| CN115220382A (en) * | 2022-07-21 | 2022-10-21 | 广东省农业科学院植物保护研究所 | Wisdom agricultural plant protection operation control system based on thing networking |
| CN117172958A (en) * | 2023-10-23 | 2023-12-05 | 江苏新希望生态科技有限公司 | Bean sprout growth real-time data management method |
| CN117371727A (en) * | 2023-10-23 | 2024-01-09 | 贵州师范学院 | Intelligent agricultural monitoring and planning method and system based on Internet of things |
| CN117172959A (en) * | 2023-10-31 | 2023-12-05 | 杨凌职业技术学院 | Forestry ecological environment monitoring system and method |
Non-Patent Citations (1)
| Title |
|---|
| 项荣;应义斌;蒋焕煜;: "田间环境下果蔬采摘快速识别与定位方法研究进展", 农业机械学报, no. 11, 25 November 2013 (2013-11-25) * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118261492A (en) * | 2024-05-30 | 2024-06-28 | 绵阳梓兴食品科技有限公司 | Nondestructive testing method and system for comprehensive quality of fresh pork |
Also Published As
| Publication number | Publication date |
|---|---|
| CN118052376B (en) | 2024-06-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN118052376B (en) | Sensitive data anomaly detection system and method based on agricultural Internet of things | |
| Kaburuan et al. | A design of IoT-based monitoring system for intelligence indoor micro-climate horticulture farming in Indonesia | |
| Reis et al. | Automatic detection of bunches of grapes in natural environment from color images | |
| CN111754045A (en) | Prediction system based on fruit tree growth | |
| CN117322214A (en) | Crop fertilizer accurate application method and system based on neural network | |
| CN116843236A (en) | Food storage supervision system based on artificial intelligence | |
| CN117743904A (en) | ZigBee-based feed additive nondestructive detection system and method | |
| CN117057946A (en) | Intelligent agricultural park data monitoring device and method | |
| CN110057764B (en) | Pesticide application safety management warning device and method | |
| CN115238237A (en) | Insect pest situation monitoring method, device and system | |
| CN117094532B (en) | Orchard intelligent monitoring system | |
| CN116863410B (en) | Data acquisition and processing method and system based on pest control | |
| CN117313920A (en) | Intelligent grape planting environment monitoring system | |
| Chaudhary et al. | A critical review on hybrid framework for precise farming with application of Machine Learning (ML) and Internet of Things (IoT) | |
| CN116560946A (en) | Soil pollution data pushing system based on cloud computing | |
| FAISAL | A pest monitoring system for agriculture using deep learning | |
| CN112907096B (en) | Heavy metal pollution risk assessment system | |
| CN107748531A (en) | A kind of agricultural breeding environmental monitoring system based on big data | |
| CN113628253A (en) | Method and system for accurately detecting individual health of animal and storage medium | |
| Lyimo et al. | Sensitivity Analysis of Coffee Leaf Rust Disease using Three Deep Learning Algorithms | |
| CN207937840U (en) | A kind of agricultural breeding environmental monitoring system based on big data | |
| CN117726194B (en) | Forestry environment analysis system based on big data | |
| Saito et al. | Representation of plant structure using XML and its application to cultivation management | |
| CN117909913B (en) | Agricultural non-point source pollution data management method, system, equipment and medium | |
| CN117933579B (en) | Efficient artificial grassland comprehensive management method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |