CN115372551B - System for monitoring quality of gastrodia elata armillaria mellea strains - Google Patents
System for monitoring quality of gastrodia elata armillaria mellea strains Download PDFInfo
- Publication number
- CN115372551B CN115372551B CN202210857378.8A CN202210857378A CN115372551B CN 115372551 B CN115372551 B CN 115372551B CN 202210857378 A CN202210857378 A CN 202210857378A CN 115372551 B CN115372551 B CN 115372551B
- Authority
- CN
- China
- Prior art keywords
- value
- growth
- gastrodia elata
- analysis
- marking
- 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.)
- Active
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0098—Plants or trees
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/24—Earth materials
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/24—Earth materials
- G01N33/246—Earth materials for water content
Landscapes
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Food Science & Technology (AREA)
- Medicinal Chemistry (AREA)
- General Health & Medical Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- Geology (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Environmental & Geological Engineering (AREA)
- Remote Sensing (AREA)
- Botany (AREA)
- Wood Science & Technology (AREA)
- Sampling And Sample Adjustment (AREA)
Abstract
The application relates to the field of cultivation of armillaria mellea of gastrodia elata, which is used for solving the problem that the existing quality monitoring system of armillaria mellea of gastrodia elata can not analyze the growth state of gastrodia elata through the combination adaptability of gastrodia elata and armillaria mellea, and particularly relates to a quality monitoring system of armillaria mellea of gastrodia elata, which comprises a monitoring platform, wherein the monitoring platform is in communication connection with an environment detection module, a growth analysis module, a characteristic analysis module and a storage module; the environment detection module is used for detecting and analyzing the growing environment of the gastrodia elata through the humidity data SD, the temperature data WD, the water content data HS and the acid-base data SJ, obtaining an environment coefficient HJ for growing the gastrodia elata, comparing the environment coefficient HJ with an environment threshold HJMax, and judging whether the growing environment of the gastrodia elata is qualified or not through a comparison result; according to the application, the environment detection module is used for monitoring and analyzing the growing environment of the gastrodia elata, so that the problem that the growth progress of the gastrodia elata is influenced due to the fact that the gastrodia elata grows in an abnormal environment for a long time is avoided.
Description
Technical Field
The application relates to the field of cultivation of armillaria mellea of gastrodia elata, in particular to a system for monitoring quality of armillaria mellea strains of gastrodia elata.
Background
The gastrodia elata is a dry tuber of gastrodia elata belonging to orchidaceae, has the effects of calming endogenous wind and relieving spasm, suppressing liver yang, dispelling wind and dredging collaterals, and is mainly used for treating liver wind internal movement, convulsion, dizziness, headache, limb numbness, hand and foot paralysis, rheumatalgia and the like.
The strain cultivation of the gastrodia elata armillaria mellea has high requirements on the environment and the combination time of the gastrodia elata and the armillaria mellea, and in the strain cultivation process, the existing gastrodia elata armillaria mellea strain quality monitoring system can only monitor the growth environment of the gastrodia elata, but can not analyze the growth state of the gastrodia elata through the combination adaptability of the gastrodia elata and the armillaria mellea, and can not monitor the influence characteristics of the growth of the gastrodia elata according to the growth state of the gastrodia elata.
The application provides a solution to the technical problem.
Disclosure of Invention
The application aims to solve the problem that the existing gastrodia elata armillaria mellea strain quality monitoring system cannot analyze the growth state of gastrodia elata through the combination adaptability of gastrodia elata and armillaria mellea, and provides a gastrodia elata armillaria mellea strain quality monitoring system.
The aim of the application can be achieved by the following technical scheme: the system for monitoring the quality of the armillaria mellea strains of the gastrodia elata comprises a monitoring platform, wherein the monitoring platform is in communication connection with an environment detection module, a growth analysis module, a characteristic analysis module and a storage module;
the environment detection module is used for detecting and analyzing the growing environment of the gastrodia elata through the humidity data SD, the temperature data WD, the water content data HS and the acid-base data SJ, obtaining an environment coefficient HJ for growing the gastrodia elata, comparing the environment coefficient HJ with an environment threshold HJMax, and judging whether the growing environment of the gastrodia elata is qualified or not through a comparison result;
the growth analysis module is used for carrying out growth state analysis on gastrodia elata qualified in growth environment, selecting a plurality of groups of gastrodia elata as analysis objects u, wherein u=1, 2, …, m and m are positive integers, each analysis object u consists of a detection object i, i=1, 2, …, n and n are positive integers, calculating the height value, the leaf length value and the capsule length value of the detection object to obtain a growth coefficient sZi of the detection object, carrying out combination analysis on the combination growth coefficient sZi and the combination time on the analysis objects u to obtain growth expression values SBu of the analysis objects u, comparing the growth expression values SBu of the analysis objects u with a growth expression threshold SBmin one by one, and marking the analysis objects u as growth qualified objects or growth unqualified objects through comparison results;
the characteristic analysis module is used for comparing and analyzing the growth characteristics of the unqualified growth objects, marking the analysis object with the largest growth expression value as a forward object, and marking the analysis object with the smallest growth expression value as a reverse object;
the characteristic analysis module comprises a gray level analysis unit and a weight analysis unit; the gray level analysis unit is used for comparing the gray level value of the image of the detection object i in the forward object with the gray level value of the detection object i in the reverse object, and the weight analysis unit is used for comparing the weight value of the detection object i in the forward object with the weight value of the detection object i in the reverse object; and judging the influence characteristics and the irrelevant characteristics through the comparison result of the gray level analysis unit and the weight analysis unit, and sending the influence characteristics to a monitoring platform, wherein the monitoring platform monitors the influence characteristics of the growth of the gastrodia elata after receiving the influence characteristics.
As a preferred embodiment of the present application, the process for obtaining humidity data SD of a gastrodia elata growing environment includes: obtaining a soil humidity value of gastrodia elata growth, obtaining a maximum value and a minimum value of a gastrodia elata growth soil humidity range through a storage module, marking an average value of the maximum value and the minimum value of the gastrodia elata growth soil humidity range as a humidity standard value, and marking an absolute value of a difference value between the soil humidity value and the humidity standard value as humidity data SD;
the process for obtaining the temperature data WD of the gastrodia elata growing environment comprises the following steps: obtaining a soil temperature value of gastrodia elata growth, obtaining a maximum value and a minimum value of a gastrodia elata growth soil temperature range through a storage module, marking an average value of the maximum value and the minimum value of the gastrodia elata growth soil temperature range as a temperature standard value, and marking an absolute value of a difference value between the soil temperature value and the temperature standard value as temperature data WD;
the acquisition process of the water content data HS of the gastrodia elata growing environment comprises the following steps: acquiring the water content of soil in which gastrodia elata grows, acquiring the maximum value and the minimum value of the water content range of the gastrodia elata growing soil through a storage module, marking the average value of the maximum value and the minimum value of the water content range of the gastrodia elata growing soil as a water content standard value, and marking the absolute value of the difference value between the water content of the soil and the water content standard value as water content data HS;
the process for acquiring the acid-base data SJ of the gastrodia elata growing environment comprises the following steps: and acquiring the pH value of the soil in which the gastrodia elata grows, acquiring the maximum value and the minimum value of the acid-base range of the gastrodia elata growing soil through a storage module, marking the maximum value and the minimum value of the acid-base range of the gastrodia elata growing soil as acid-base standard values, and marking the absolute value of the difference value between the pH value of the soil and the acid-base standard values as acid-base data SJ.
As a preferred embodiment of the present application, the comparison process of the environmental coefficient HJ and the environmental threshold HJmax includes:
if the environmental coefficient HJ is smaller than the environmental threshold HJMax, judging that the growth environment of the gastrodia elata is qualified, and sending an environment qualification signal to a monitoring platform by an environment detection module;
if the environmental coefficient HJ is greater than or equal to the environmental threshold HJMax, the growth environment of the gastrodia elata is judged to be unqualified, and the environment detection module sends an environment adjustment signal to the monitoring platform.
As a preferred embodiment of the present application, the process of performing the binding analysis of the analysis object u includes: acquiring the combination time of the detection object i and the armillaria mellea through a storage module, establishing a rectangular coordinate system by taking the combination time as an abscissa and taking a growth coefficient SZi as an ordinate, marking the detection object i as an analysis point in a first quadrant of the rectangular coordinate system according to the combination time and the numerical value of the growth coefficient SZi, marking the analysis point with the maximum numerical value of the ordinate as a high point, acquiring a growth threshold SZmin through the storage module, and carrying out left connection analysis on the high point: marking an analysis point positioned at the left side of the high point as a left point, marking the absolute value of the difference value between the left point abscissa value and the high point abscissa value as a left distance, marking the analysis point with the smallest left distance value as a judgment point, and comparing the ordinate value of the judgment point with a growth threshold SZmin: if the ordinate value of the judgment point is smaller than or equal to the growth threshold value, marking the judgment point as a left pole, and ending the left connection analysis; if the ordinate value of the judgment point is larger than the growth threshold, marking the analysis point with the second smallest left distance value as the judgment point, comparing the ordinate value of the judgment point with the growth threshold SZmin again, and so on until the ordinate value of the judgment point is smaller than or equal to the growth threshold SZmin and marking the corresponding judgment point as the left pole; and carrying out right connection analysis on the high points in a left connection analysis mirror image mode to obtain right poles, connecting the left poles with the right poles and the high points to obtain a triangle, and marking the numerical value of the area of the triangle as a growth expression value SBu of the analysis object u.
As a preferred embodiment of the present application, the comparison process of the growth expression value SBu of the analysis subject u with the growth expression threshold SBmin includes: if the growth expression value SBu of the analysis object u is smaller than or equal to the growth expression threshold SBmin, marking the corresponding analysis object u as a growth disqualified object; if the growth expression value SBu of the analysis object u is larger than the growth expression threshold SBmin, the corresponding analysis object u is marked as a growth qualified object.
As a preferred embodiment of the present application, the process of comparing the image gray value of the detection object i in the forward object and the reverse object by the gray analysis unit includes: image shooting is carried out on a detection object i in a forward object, the obtained image is marked as a forward image, the average gray value of the forward image is obtained through an image processing technology and marked as forward gray ZHi, the obtained image is marked as a reverse image, the average gray value of the reverse image is obtained through an image processing technology and marked as reverse gray value FHi, n gray difference values HC are obtained through gray difference calculation of the arranged forward object and the reverse object one by one, the n gray difference values HC are summed and averaged to obtain a gray influence coefficient of an analysis object, a gray influence threshold is obtained through a storage module, and the gray influence coefficient is compared with the gray influence threshold: if the gray scale influence coefficient is larger than or equal to the gray scale influence threshold value, marking the gray scale value of the image as an influence characteristic; and if the gray scale influence coefficient is smaller than the gray scale influence threshold value, marking the gray scale value of the image as an irrelevant feature.
As a preferred embodiment of the present application, the process of comparing the weight value of the detection object i in the forward object and the reverse object by the weight analysis unit includes: the method comprises the steps of marking a weight value of a detection object i in a forward object as a forward weight ZZi, marking a weight value of a detection object i in a reverse object as a reverse weight FZi, carrying out weight difference calculation on the arranged forward object and the reverse object one by one to obtain n weight difference values ZC, summing the n weight difference values ZC to average to obtain a weight influence coefficient of an analysis object, obtaining a weight influence threshold through a storage module, and comparing the weight influence coefficient with the weight influence threshold: if the weight impact coefficient is greater than or equal to the weight impact threshold, the weight value is marked as an impact feature, and if the weight impact coefficient is less than the weight impact threshold, the weight value is marked as an irrelevant feature.
Compared with the prior art, the application has the beneficial effects that:
1. the environment detection module is used for monitoring and analyzing the environment in which the gastrodia elata grows, and the deviation degree of the environment coefficient to the gastrodia elata growing environment and the proper environment is achieved, so that the environment can be timely adjusted when the gastrodia elata growing environment is abnormal, and the gastrodia elata is prevented from growing in the abnormal environment for a long time, and the growth progress of the gastrodia elata is prevented from being influenced.
2. The growth analysis module calculates a growth coefficient by combining various indexes and parameters of the growth of the gastrodia elata, then combines the growth coefficient with the combination time to obtain a growth expression value, so that the combination adaptability of an analysis object u and the armillaria mellea is analyzed, the growth expression value is used for reflecting the growth state of the gastrodia elata and the comprehensive capacity of the combination adaptability of the armillaria mellea, the actual growth state of the gastrodia elata can be analyzed according to the numerical value of the growth expression value, and the characteristic comparison is performed on the gastrodia elata with abnormal actual growth state.
3. The characteristic analysis module can compare the performance characteristics of the forward object and the reverse object, so that the influence characteristics of the analysis object are obtained by analysis according to the overall difference of comparison results, the influence characteristics are abnormal characteristics which can be displayed after the gastrodia elata grows abnormally, the initial gastrodia elata growth state can be monitored by monitoring the abnormal characteristics, and the regulation is timely carried out when the abnormality occurs, so that the normal growth of the gastrodia elata is ensured.
Drawings
The present application is further described below with reference to the accompanying drawings for the convenience of understanding by those skilled in the art.
FIG. 1 is a schematic block diagram of a first embodiment of the present application;
fig. 2 is a schematic block diagram of a second embodiment of the present application.
Detailed Description
The technical solutions of the present application will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Example 1
Referring to fig. 1, a system for monitoring quality of armillaria mellea strains of gastrodia elata comprises a monitoring platform, wherein the monitoring platform is in communication connection with an environment detection module, a growth analysis module and a storage module.
The environment detection module detects and analyzes the growth environment of the gastrodia elata through humidity data SD, temperature data WD, water content data HS and acid-base data SJ:
acquiring humidity data SD of a gastrodia elata growing environment: obtaining a soil humidity value of gastrodia elata growth, obtaining a maximum value and a minimum value of a gastrodia elata growth soil humidity range through a storage module, wherein the humidity value adopts relative humidity, the gastrodia elata growth soil humidity range is usually 70% -90%, the humidity value is directly obtained through a humidity sensor, the humidity sensor is a device capable of sensing external humidity changes and converting the humidity into useful signals through physical or chemical property changes of device materials, the average value of the maximum value and the minimum value of the gastrodia elata growth soil humidity range is marked as a humidity standard value, and the absolute value of the difference value between the soil humidity value and the humidity standard value is marked as humidity data SD;
obtaining temperature data WD of a gastrodia elata growing environment: acquiring a soil temperature value of gastrodia elata growth, wherein the soil temperature value is directly acquired by a temperature sensor, and the temperature sensor (temperature transducer) is a sensor capable of sensing temperature and converting the temperature into a usable output signal; obtaining the maximum value and the minimum value of the temperature range of the gastrodia elata growing soil through a storage module, marking the average value of the maximum value and the minimum value of the temperature range of the gastrodia elata growing soil as a temperature standard value, and marking the absolute value of the difference value between the soil temperature value and the temperature standard value as temperature data WD;
acquiring water content data HS of a gastrodia elata growing environment: the soil moisture content of gastrodia elata growth is obtained, and the soil moisture content measurement refers to quantitative determination of various liquid moisture in the soil. Ice and partial mineral crystal water are sometimes included, and the method can be generally divided into two main types, namely a sampling method and an in-situ measurement method, and the soil water content is measured by adopting the sampling method; obtaining the maximum value and the minimum value of the water content range of the gastrodia elata growing soil through a storage module, marking the average value of the maximum value and the minimum value of the water content range of the gastrodia elata growing soil as a water content standard value, and marking the absolute value of the difference value between the water content of the soil and the water content standard value as water content data HS;
acquiring acid-base data SJ of a gastrodia elata growing environment: the pH value of the soil for gastrodia elata growth is obtained by directly collecting the pH value of the soil by a pH meter, wherein the pH meter is an instrument for measuring the pH value of a solution, and the pH meter works by utilizing the principle of a primary battery, and the electromotive force between two electrodes of the primary battery is related to the self attribute of the electrode and the concentration of hydrogen ions in the solution according to Nernst's law; the pH value of the soil layer is preferably between 5.3 and 6, the maximum value and the minimum value of the acid-base range of the gastrodia elata growing soil are obtained through a storage module, the maximum value and the minimum value of the acid-base range of the gastrodia elata growing soil are marked as acid-base standard values, and the absolute value of the difference value between the pH value of the soil and the acid-base standard value is marked as acid-base data SJ;
by the formulaObtaining environment coefficients HJ of gastrodia elata growth, wherein alpha 1, alpha 2, alpha 3 and alpha 4 are all proportional coefficients, alpha 1 is larger than alpha 2 is larger than alpha 3 is larger than alpha 4 is larger than 1, the environment coefficients HJ are numerical values for reflecting the deviation degree of the whole environment of gastrodia elata growth and the standard growth environment, and the larger the numerical values of the environment coefficients HJ are, the larger the deviation degree of the corresponding gastrodia elata growth environment and the standard environment is, and the environment is not suitable for gastrodia elata growth;
the environment threshold value HJMax is obtained through the storage module, and the environment coefficient HJ is compared with the environment threshold value HJMax: if the environment coefficient HJ is smaller than the environment threshold HJMax, judging that the growth environment of the gastrodia elata is qualified, and the corresponding environment is suitable for the growth of the gastrodia elata, and sending an environment qualification signal to a monitoring platform by an environment detection module; if the environmental coefficient HJ is greater than or equal to the environmental threshold HJMax, the growth environment of the gastrodia elata is judged to be unqualified, the corresponding environment is unsuitable for the growth of the gastrodia elata, and the environment detection module sends an environment adjustment signal to the monitoring platform.
The growth analysis module is used for carrying out growth state analysis on the gastrodia elata qualified in growth environment, and the growth state analysis process comprises the following steps: selecting a plurality of groups of gastrodia elata as analysis objects u with u=1, 2, …, m and m being positive integers from gastrodia elata with qualified growth environment, wherein each analysis object u consists of a detection object i, i=1, 2, …, n and n are positive integers, the height of the detection object i is obtained and marked as GDi, the leaf length of the detection object i is obtained and marked as YCi, the capsule length value of the detection object i is obtained and marked as SCi, and the growth coefficient SZi of the detection object is obtained through a formula szi=β1×gd+β2× YCi +β3×sci, wherein β1, β2 and β3 are all proportional coefficients, and β3 > β2 > β1 > 1; acquiring the time of combining the detection object i with the armillaria mellea through a storage module; the method comprises the steps of carrying out combination analysis on an analysis object u, wherein the combination analysis aims at carrying out comprehensive analysis on the growth coefficient and the combination adaptability of the gastrodia elata, and the combination analysis process comprises the following steps: establishing a rectangular coordinate system by taking the combination time as an abscissa and the growth coefficient SZi as an ordinate, marking a point of a detection object i in a first quadrant of the rectangular coordinate system according to the combination time and the value of the growth coefficient SZi, marking the marked point as an analysis point, marking the analysis point with the maximum ordinate value as a high point, obtaining a growth threshold SZmin by a storage module, and carrying out left-side connection analysis on the high point, wherein the growth state of the detection object at the high point is the best: marking an analysis point positioned at the left side of the high point as a left point, marking the absolute value of the difference value between the left point abscissa value and the high point abscissa value as a left distance, marking the analysis point with the smallest left distance value as a judgment point, and comparing the ordinate value of the judgment point with a growth threshold SZmin: if the ordinate value of the judgment point is smaller than or equal to the growth threshold value, marking the judgment point as a left pole, and ending the left connection analysis; if the ordinate value of the judgment point is larger than the growth threshold, marking the analysis point with the second smallest left distance value as the judgment point, comparing the ordinate value of the judgment point with the growth threshold SZmin again, and so on until the ordinate value of the judgment point is smaller than or equal to the growth threshold SZmin, marking the corresponding judgment point as a left pole, wherein the purpose of left connection analysis is to screen the analysis point positioned at the critical position of the growth threshold; carrying out right connection analysis on the high points in a left connection analysis mirror image mode to obtain right poles, wherein a time interval formed by the abscissa of the left poles and the right poles is an adaptation interval for combining an analysis object u with armillaria mellea, so that the larger the absolute value of the difference value of the abscissa of the left poles and the right poles is, the better the adaptability of the gastrodia elata to the armillaria mellea is indicated, the left poles, the right poles and the high points are connected to obtain a triangle, the numerical value of the area of the triangle is marked as a growth representation value SBu of the analysis object u, the growth representation value SBu is a comprehensive feedback numerical value for reflecting the adaptability of the growth state of the gastrodia elata and the armillaria mellea, the larger the numerical value of the growth representation value SBu is indicated to indicate that the actual growth state of the gastrodia elata is better, the growth representation threshold SBmin is obtained through a storage module, and the growth representation value SBu of the analysis object u is compared with the growth representation threshold SBmin one by one. If the growth expression value SBu of the analysis object u is smaller than or equal to the growth expression threshold SBmin, marking the corresponding analysis object u as a growth disqualified object; if the growth expression value SBu of the analysis object u is larger than the growth expression threshold SBmin, the corresponding analysis object u is marked as a growth qualified object.
Example two
Referring to fig. 2, the difference between the present embodiment and the first embodiment is that: the monitoring platform is also in communication connection with a characteristic analysis module; in the first embodiment, the analysis objects that are qualified for growth and unqualified for growth are determined according to the value of the growth expression value SBu of the analysis object, but only the result analysis is corresponded to the corresponding analysis process; in this embodiment, the feature analysis module performs inverse pushing and comparison on the expression factors of the growth abnormality through the growth state detection result, so as to screen out the expression features of the gastrodia elata when the growth abnormality occurs, and thus the overall process monitoring can be performed on the growth of the gastrodia elata aiming at the expression features.
The feature analysis module compares and analyzes the growth characteristics of the unqualified growth objects, marks the analysis object with the largest growth expression value as a forward object, marks the analysis object with the smallest growth expression value as a reverse object, arranges the detection objects i in the forward object and the reverse object according to the sequence of the combination time from small to large, carries out one-to-one matching on the detection objects in the forward object and the reverse object after the arrangement is completed, and has similar combination time of the two detection objects after the matching, so that the two detection objects after the matching are subjected to feature comparison, and whether the features are influence features or not can be judged according to the result of the feature comparison.
The characteristic analysis module comprises a gray level analysis unit and a weight analysis unit; the gray level analysis unit compares the gray level value of the image of the detection object i in the forward object and the backward object: the method comprises the steps of performing image shooting on a detection object i in a forward object, marking the obtained image as a forward image, obtaining an average gray value of the forward image through an image processing technology, marking the average gray value as a forward gray value ZHi, wherein the image processing technology mainly comprises the steps of performing image digitization, image enhancement and restoration, image data encoding, image segmentation, image recognition and the like on image information, performing image shooting on the detection object i in a reverse object, marking the obtained image as a reverse image, obtaining the average gray value of the reverse image through the image processing technology, marking the obtained image as a reverse gray value FHi, performing gray difference calculation on the arranged forward object and the reverse object one by one to obtain n gray difference values HC, wherein the gray difference represents a gray difference between two detection objects with the largest difference in growth state, and in characteristic analysis, performing inverse inference to obtain a monitoring factor causing growth abnormality by taking the difference of the growth state as a result, so that the larger value of the gray difference between the two detection objects with different growth states represents the larger gray difference of the two detection objects, performing quality monitoring on rhizoma gastrodiae in the growth process, performing average comparison on a gray difference value to obtain a gray coefficient, and obtaining a gray coefficient by comparing the gray difference value with a threshold value and the gray coefficient: if the gray scale influence coefficient is larger than or equal to the gray scale influence threshold value, marking the gray scale value of the image as an influence characteristic; and if the gray scale influence coefficient is smaller than the gray scale influence threshold value, marking the gray scale value of the image as an irrelevant feature.
The weight analysis unit is used for comparing the weight value of the detection object i in the forward object and the backward object: the method comprises the steps of marking the weight value of a detection object i in a forward object as forward weight ZZi, marking the weight value of the detection object i in a reverse object as reverse weight FZi, carrying out weight difference calculation on the arranged forward object and the reverse object one by one to obtain n weight difference ZC, wherein the weight difference represents the weight difference between two detection objects with the largest growth state difference, in the characteristic analysis, reversely pushing the monitoring factors causing abnormal growth by taking the difference of the growth state as a result, so that the larger the value of the weight difference is, the larger the weight difference of the two detection objects with different growth states is, the weight of the detection objects becomes the main characteristic of quality monitoring on gastrodia elata in the growth process, summing and averaging the n weight difference ZC to obtain the weight influence coefficient of an analysis object, obtaining the weight influence threshold value through a storage module, and comparing the weight influence coefficient with the weight influence threshold value: if the weight impact coefficient is greater than or equal to the weight impact threshold, the weight value is marked as an impact feature, and if the weight impact coefficient is less than the weight impact threshold, the weight value is marked as an irrelevant feature.
The characteristic analysis module sends the influence characteristics to the monitoring platform, and the monitoring platform monitors the influence characteristics of the growth of the gastrodia elata in real time after receiving the influence characteristics, and the characteristics to be compared can be added or replaced according to actual conditions in actual growth monitoring of the gastrodia elata.
The formulas are all formulas obtained by collecting a large amount of data for software simulation and selecting a formula close to a true value, and coefficients in the formulas are set by a person skilled in the art according to actual conditions; such as: formula (VI)Collecting a plurality of groups of sample data by a person skilled in the art and setting a corresponding environmental coefficient for each group of sample data; substituting the set environmental coefficient and the acquired sample data into a formula, forming a quaternary once equation set by any four formulas, screening the calculated coefficient and taking an average value to obtain values of alpha 1, alpha 2, alpha 3 and alpha 4 of 3.85, 2.58, 2.31 and 1.42 respectively;
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 the corresponding environment coefficient is preliminarily set for each group of sample data by a person skilled in the art; as long as the proportional relation between the parameter and the quantized value is not affected, for example, the environmental coefficient is in direct proportion to the value of the temperature data;
when the environment detection module is used, the environment detection module is used for detecting and analyzing the growth environment of the gastrodia elata through humidity data SD, temperature data WD, water-containing data HS and acid-base data SJ to obtain an environment coefficient HJ for growth of the gastrodia elata, judging whether the growth environment of the gastrodia elata is qualified through the numerical value of the environment coefficient, analyzing the growth state of the gastrodia elata qualified in the growth environment by adopting the growth analysis module, combining the growth coefficient with the combination time to obtain a growth expression value, and analyzing the actual growth state of the gastrodia elata through the numerical value of the growth expression value; and performing characteristic analysis on the two groups of detection objects with the largest difference in growth states.
The preferred embodiments of the application disclosed above are intended only to assist in the explanation of the application. The preferred embodiments are not intended to be exhaustive or to limit the application 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 application and the practical application, to thereby enable others skilled in the art to best understand and utilize the application. The application is limited only by the claims and the full scope and equivalents thereof.
Claims (5)
1. The system for monitoring the quality of the armillaria mellea strains of the gastrodia elata comprises a monitoring platform, and is characterized in that the monitoring platform is in communication connection with an environment detection module, a growth analysis module, a characteristic analysis module and a storage module;
the environment detection module is used for detecting and analyzing the growing environment of the gastrodia elata through the humidity data SD, the temperature data WD, the water content data HS and the acid-base data SJ, obtaining an environment coefficient HJ for growing the gastrodia elata, comparing the environment coefficient HJ with an environment threshold HJMax, and judging whether the growing environment of the gastrodia elata is qualified or not through a comparison result;
the growth analysis module is used for carrying out growth state analysis on gastrodia elata qualified in growth environment, selecting a plurality of groups of gastrodia elata as analysis objects u, wherein u=1, 2, …, m and m are positive integers, each analysis object u consists of a detection object i, i=1, 2, …, n and n are positive integers, calculating the height value, the leaf length value and the capsule length value of the detection object to obtain a growth coefficient sZi of the detection object, carrying out combination analysis on the combination growth coefficient sZi and the combination time on the analysis objects u to obtain growth expression values SBu of the analysis objects u, comparing the growth expression values SBu of the analysis objects u with a growth expression threshold SBmin one by one, and marking the analysis objects u as growth qualified objects or growth unqualified objects through comparison results;
the characteristic analysis module is used for comparing and analyzing the growth characteristics of the unqualified growth objects, marking the analysis object with the largest growth expression value as a forward object, and marking the analysis object with the smallest growth expression value as a reverse object;
the characteristic analysis module comprises a gray level analysis unit and a weight analysis unit; the gray level analysis unit is used for comparing the gray level value of the image of the detection object i in the forward object with the gray level value of the detection object i in the reverse object, and the weight analysis unit is used for comparing the weight value of the detection object i in the forward object with the weight value of the detection object i in the reverse object; judging the influence features and the irrelevant features through the comparison results of the gray level analysis unit and the weight analysis unit, and sending the influence features to a monitoring platform, wherein the monitoring platform monitors the influence features of gastrodia elata growth after receiving the influence features;
the process of performing a binding analysis on the analysis object u includes: acquiring the combination time of the detection object i and the armillaria mellea through a storage module, establishing a rectangular coordinate system by taking the combination time as an abscissa and taking a growth coefficient SZi as an ordinate, marking the detection object i as an analysis point in a first quadrant of the rectangular coordinate system according to the combination time and the numerical value of the growth coefficient SZi, marking the analysis point with the maximum numerical value of the ordinate as a high point, and acquiring a growth threshold SZmin through the storage module;
left-hand connection analysis was performed on high points: marking an analysis point positioned at the left side of the high point as a left point, marking the absolute value of the difference value between the left point abscissa value and the high point abscissa value as a left distance, marking the analysis point with the smallest left distance value as a judgment point, and comparing the ordinate value of the judgment point with a growth threshold SZmin: if the ordinate value of the judgment point is smaller than or equal to the growth threshold value, marking the judgment point as a left pole, and ending the left connection analysis; if the ordinate value of the judgment point is larger than the growth threshold, marking the analysis point with the second smallest left distance value as the judgment point, comparing the ordinate value of the judgment point with the growth threshold SZmin again, and so on until the ordinate value of the judgment point is smaller than or equal to the growth threshold SZmin and marking the corresponding judgment point as the left pole;
carrying out right connection analysis on the high point in a left connection analysis mirror image mode to obtain a right pole, connecting the left pole with the right pole and the high point to obtain a triangle, and marking the numerical value of the area of the triangle as a growth expression value SBu of the analysis object u;
the comparison of the growth expression value SBu of the analysis subject u with the growth expression threshold SBmin comprises:
if the growth expression value SBu of the analysis object u is smaller than or equal to the growth expression threshold SBmin, marking the corresponding analysis object u as a growth disqualified object;
if the growth expression value SBu of the analysis object u is larger than the growth expression threshold SBmin, the corresponding analysis object u is marked as a growth qualified object.
2. The system for monitoring the quality of armillaria mellea strains of gastrodia elata according to claim 1, wherein the process for acquiring the humidity data SD of the growing environment of the gastrodia elata comprises the following steps: obtaining a soil humidity value of gastrodia elata growth, obtaining a maximum value and a minimum value of a gastrodia elata growth soil humidity range through a storage module, marking an average value of the maximum value and the minimum value of the gastrodia elata growth soil humidity range as a humidity standard value, and marking an absolute value of a difference value between the soil humidity value and the humidity standard value as humidity data SD;
the process for obtaining the temperature data WD of the gastrodia elata growing environment comprises the following steps: obtaining a soil temperature value of gastrodia elata growth, obtaining a maximum value and a minimum value of a gastrodia elata growth soil temperature range through a storage module, marking an average value of the maximum value and the minimum value of the gastrodia elata growth soil temperature range as a temperature standard value, and marking an absolute value of a difference value between the soil temperature value and the temperature standard value as temperature data WD;
the acquisition process of the water content data HS of the gastrodia elata growing environment comprises the following steps: acquiring the water content of soil in which gastrodia elata grows, acquiring the maximum value and the minimum value of the water content range of the gastrodia elata growing soil through a storage module, marking the average value of the maximum value and the minimum value of the water content range of the gastrodia elata growing soil as a water content standard value, and marking the absolute value of the difference value between the water content of the soil and the water content standard value as water content data HS;
the process for acquiring the acid-base data SJ of the gastrodia elata growing environment comprises the following steps: and acquiring the pH value of the soil in which the gastrodia elata grows, acquiring the maximum value and the minimum value of the acid-base range of the gastrodia elata growing soil through a storage module, marking the maximum value and the minimum value of the acid-base range of the gastrodia elata growing soil as acid-base standard values, and marking the absolute value of the difference value between the pH value of the soil and the acid-base standard values as acid-base data SJ.
3. The system for monitoring the quality of armillaria mellea strains of gastrodia elata according to claim 1, wherein the comparison process of the environmental coefficient HJ and the environmental threshold HJmax comprises:
if the environmental coefficient HJ is smaller than the environmental threshold HJMax, judging that the growth environment of the gastrodia elata is qualified, and sending an environment qualification signal to a monitoring platform by an environment detection module;
if the environmental coefficient HJ is greater than or equal to the environmental threshold HJMax, the growth environment of the gastrodia elata is judged to be unqualified, and the environment detection module sends an environment adjustment signal to the monitoring platform.
4. The system for monitoring the quality of armillaria mellea strains of gastrodia elata according to claim 1, wherein the process of comparing the image gray values of the detection object i in the forward object and the reverse object by the gray analysis unit comprises the following steps: image shooting is carried out on a detection object i in a forward object, the obtained image is marked as a forward image, the average gray value of the forward image is obtained through an image processing technology and marked as forward gray ZHi, the obtained image is marked as a reverse image, the average gray value of the reverse image is obtained through an image processing technology and marked as reverse gray value FHi, n gray difference values HC are obtained through gray difference calculation of the arranged forward object and the reverse object one by one, n gray difference values HC are summed and averaged to obtain a gray influence coefficient of an analysis object, the gray influence coefficient of a gray set is obtained through variance calculation of the gray set, a gray influence threshold is obtained through a storage module, and the gray influence coefficient is compared with the gray influence threshold: if the gray scale influence coefficient is larger than or equal to the gray scale influence threshold value, marking the gray scale value of the image as an influence characteristic; and if the gray scale influence coefficient is smaller than the gray scale influence threshold value, marking the gray scale value of the image as an irrelevant feature.
5. The system for monitoring the quality of armillaria mellea strains of gastrodia elata according to claim 1, wherein the process of comparing the weight value of the detection object i in the forward object and the reverse object by the weight analysis unit comprises the following steps: the method comprises the steps of marking a weight value of a detection object i in a forward object as a forward weight ZZi, marking a weight value of a detection object i in a reverse object as a reverse weight FZi, carrying out weight difference calculation on the arranged forward object and the reverse object one by one to obtain n weight difference values ZC, summing the n weight difference values ZC to average to obtain a weight influence coefficient of an analysis object, obtaining a weight influence threshold through a storage module, and comparing the weight influence coefficient with the weight influence threshold: if the weight impact coefficient is greater than or equal to the weight impact threshold, the weight value is marked as an impact feature, and if the weight impact coefficient is less than the weight impact threshold, the weight value is marked as an irrelevant feature.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210857378.8A CN115372551B (en) | 2022-07-20 | 2022-07-20 | System for monitoring quality of gastrodia elata armillaria mellea strains |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210857378.8A CN115372551B (en) | 2022-07-20 | 2022-07-20 | System for monitoring quality of gastrodia elata armillaria mellea strains |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115372551A CN115372551A (en) | 2022-11-22 |
| CN115372551B true CN115372551B (en) | 2023-09-19 |
Family
ID=84062571
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210857378.8A Active CN115372551B (en) | 2022-07-20 | 2022-07-20 | System for monitoring quality of gastrodia elata armillaria mellea strains |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115372551B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115979339B (en) * | 2022-12-07 | 2023-08-15 | 吉林农业科技学院 | Intelligent monitoring system for laying hen breeding environment based on big data analysis |
| CN116203333B (en) * | 2023-01-10 | 2024-03-22 | 国网山东省电力公司超高压公司 | Comprehensive evaluation system and evaluation method for aging state of composite insulator material |
| CN116764261B (en) * | 2023-08-18 | 2023-11-24 | 济宁长胜新材料股份有限公司 | Execution safety supervision system for distillation flow |
| CN117074600B (en) * | 2023-08-25 | 2024-03-29 | 徐州天意药业股份有限公司 | System and method for analyzing quality of finished double coptis chinensis products based on data test |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110610506A (en) * | 2019-09-17 | 2019-12-24 | 北京中环易达设施园艺科技有限公司 | Image processing technology-based agaricus blazei murill fruiting body growth parameter detection method |
| CN110763824A (en) * | 2019-11-26 | 2020-02-07 | 徐州斯塬网络科技有限公司 | Agricultural product soil environment monitoring system based on Internet of things |
| CN111837824A (en) * | 2020-07-31 | 2020-10-30 | 王美华 | Edible fungus planting environment regulation and control management system based on big data intelligent agriculture |
| CN114358450A (en) * | 2022-03-22 | 2022-04-15 | 广东佳焙食品股份有限公司 | Fermentation quality prediction system for mochi bread processing based on data processing |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190050741A1 (en) * | 2017-08-10 | 2019-02-14 | Iteris, Inc. | Modeling and prediction of below-ground performance of agricultural biological products in precision agriculture |
-
2022
- 2022-07-20 CN CN202210857378.8A patent/CN115372551B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110610506A (en) * | 2019-09-17 | 2019-12-24 | 北京中环易达设施园艺科技有限公司 | Image processing technology-based agaricus blazei murill fruiting body growth parameter detection method |
| CN110763824A (en) * | 2019-11-26 | 2020-02-07 | 徐州斯塬网络科技有限公司 | Agricultural product soil environment monitoring system based on Internet of things |
| CN111837824A (en) * | 2020-07-31 | 2020-10-30 | 王美华 | Edible fungus planting environment regulation and control management system based on big data intelligent agriculture |
| CN114358450A (en) * | 2022-03-22 | 2022-04-15 | 广东佳焙食品股份有限公司 | Fermentation quality prediction system for mochi bread processing based on data processing |
Non-Patent Citations (1)
| Title |
|---|
| 万寿菊生物熏蒸对连作苹果幼苗和土壤微生物的影响;王晓芳等;土壤学报(第01期);213-220 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115372551A (en) | 2022-11-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115372551B (en) | System for monitoring quality of gastrodia elata armillaria mellea strains | |
| WO2018153143A1 (en) | Method for measuring mudflat elevation by remotely sensed water content | |
| CN112417370B (en) | Mu Leqiong S matrix estimation and polarization noise analysis method for rough surface substances | |
| CN116429988B (en) | Dynamic remote sensing monitoring device for ocean plant carbon sink | |
| CN105445344A (en) | Temperature compensation method of system for detecting heavy metals in water environment | |
| CN115791891A (en) | Structural damage identification method and system based on piezoelectric impedance technology | |
| CN111879709A (en) | Method and device for detecting spectral reflectivity of lake water body | |
| CN113670823A (en) | Material surface relative humidity detection device under atmospheric environment | |
| CN110110771B (en) | Saline soil salinity estimation method based on surface image | |
| CN116912672A (en) | Unmanned survey vessel-based biological integrity evaluation method for large benthonic invertebrates | |
| CN113916181B (en) | Data processing method of surface-internal integrated deformation monitoring device | |
| CN115639335A (en) | Water quality monitoring data calibration method, system and computer readable storage medium | |
| CN113418963B (en) | Trunk freezing-thawing impedance image real-time detection method and system | |
| Kalchikhin et al. | Detection of microstructure characteristics of liquid atmospheric precipitation with the optical rain gage | |
| CN100504383C (en) | Method for measuring degree of roughness of soils | |
| CN113916193A (en) | Method for calculating hydrogeological parameters of aquifer by inversion | |
| CN116930459B (en) | Soil in-situ detection device and detection method thereof | |
| CN117571164B (en) | Automatic collection system of multi-functional concrete sample surface stress | |
| CN111898314A (en) | Lake water body parameter detection method and device, electronic equipment and storage medium | |
| CN118464810B (en) | Archaeological environment detection method and system based on spectral component analysis | |
| CN115661672B (en) | PolSAR image CFAR detection method and system based on GMM | |
| CN115753712B (en) | SIF anomaly detection method, device, system, terminal and medium based on PAR data | |
| CN117235607B (en) | Soil moisture content real-time monitoring system and monitoring method | |
| Rossel et al. | Development of an on-the-go soil sensing system for determinations of soil pH and lime requirement | |
| CN116771327A (en) | Geotechnical engineering exploration intelligent management system and method based on Internet of things |
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 |