Genetic diversity analysis method for cabbage germplasm resources

Abstract

The invention discloses a genetic diversity analysis method for Chinese cabbage germplasm resources, and relates to the field of Chinese cabbage germplasm resources, and the method comprises the following steps: analyzing and processing to obtain a monitoring sensing basic performance evaluation value of each sub-planting area; screening each normal sub-planting area; performing comprehensive treatment to obtain an environment characteristic value of each normal sub-planting area; and performing statistics to obtain Chinese cabbage hereditary sampling data of various qualitative analysis areas. By monitoring the sensor performance in real time, the data accuracy is ensured, the analysis cost is reduced, and resources are concentrated in a sensor performance normal area. Environmental characteristics are analyzed, environmental influence is reduced, and genetic diversity evaluation efficiency is improved. Environmental influence factors are introduced, Chinese cabbage features are accurately evaluated, and data analysis errors are corrected. Sampling is customized based on the environment characteristic value, resource allocation is optimized, the detection cost is reduced, and detection deviation is avoided.

Description

Technical Field

[0001] The invention relates to the field of cabbage germplasm resources, and in particular to a method for analyzing genetic diversity of cabbage germplasm resources. Background Art

[0002] Genetic diversity is an important component of biodiversity. Analyzing genetic diversity helps to screen out germplasm with strong stress resistance, thereby improving the safety and stability of agricultural production. Genetic diversity analysis is an important tool for the research and utilization of cabbage germplasm resources, and is of great significance for promoting the genetic improvement of vegetable crops and ensuring food safety.

[0003] Existing technologies, such as the invention patent with announcement number: CN110428401B, are a method for measuring and analyzing phenotypic fruit point data of pear germplasm resources. The method collects pear surface images, pre-processes the images to remove noise in the images, and then counts the fruit point data in the images. The data is analyzed to obtain the fruit point density. The fruit point data of pear germplasm resources is measured based on machine vision, which improves the statistical efficiency of fruit point data in pear germplasm research and increases the types of statistical data.

[0004] The prior art, such as the invention patent with announcement number: CN109118488B, is a method for identifying the cold resistance of new anthurium germplasm. The method determines two morphological indicators, namely the leaf cold injury index and the petiole cold injury index, of the new anthurium germplasm and three different cold-resistant varieties when the whole plant is subjected to cold injury, as well as two physiological indicators, namely the leaf relative conductivity change rate and the chlorophyll fluorescence parameter Fv / Fm change rate, and combines the principal component and cluster analysis methods to identify the new anthurium germplasm. The indicators used in this method reflect the cold injury morphological changes of the whole plant of anthurium, as well as the damage to the membrane system and the photosynthetic system, avoiding the instability of single compound indicators.

[0005] Based on the above technical solutions, it can be seen that in the field of germplasm resource data processing, the existing technology only performs simple processing on the data and then directly analyzes it. However, in actual applications, germplasm resource data is often affected by multiple factors. If the influence of these factors is ignored and the germplasm resource data is directly processed and analyzed, the accuracy of the analysis results will be reduced, and even erroneous analysis results will be caused. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention provides a method for analyzing the genetic diversity of cabbage germplasm resources. To achieve the above purpose, the present invention is implemented through the following technical scheme: A method for analyzing the genetic diversity of cabbage germplasm resources, comprising:

[0007] The cabbage planting area is divided into sub-planting areas, and the basic performance data of the monitoring sensors in each sub-planting area are counted in real time. The basic performance evaluation value of the monitoring sensors in each sub-planting area is obtained through analysis and processing.

[0008] Based on the analysis and processing of the basic performance evaluation values ​​of the monitoring sensors of each sub-planting area, the sensor performance evaluation results of each sub-planting area are obtained, and the sub-planting areas with normal sensor performance evaluation results are screened and recorded as normal sub-planting areas.

[0009] The basic performance evaluation value of monitoring sensors in each normal sub-planting area is statistically analyzed and processed to generate the monitoring transmission correction factor of each normal sub-planting area, and the environmental sensor data of each normal sub-planting area is obtained and comprehensively processed to obtain the environmental characteristic value of each normal sub-planting area.

[0010] Based on the environmental characteristic values ​​of each normal sub-planting area, the environmental impact results of each normal sub-planting area are generated, and the environmental impact results include normal impact and abnormal impact. Each normal sub-planting area with a normal impact statistically is recorded as various quality analysis areas, and the cabbage genetic sampling data of various quality analysis areas are statistically obtained.

[0011] The genetic sampling data of cabbage in various quality analysis areas are processed and the genetic diversity analysis results are output and displayed.

[0012] As a preferred technical solution, the basic performance data of the monitoring sensors of each sub-planting area are collected in real time, and the basic performance evaluation value of the monitoring sensors of each sub-planting area is obtained through analysis and processing. The specific process is as follows:

[0013] The basic performance data of the monitoring sensors in each sub-planting area include real-time power, real-time resolution, real-time temperature and real-time delay.

[0014] Extract basic performance verification data of monitoring sensors from the database, including power verification value, resolution verification value, processor temperature verification value and delay verification value.

[0015] The basic performance data of the monitoring sensors in each sub-planting area and the basic performance verification data are comprehensively processed and analyzed to obtain the basic performance evaluation value of the monitoring sensors in each sub-planting area.

[0016] As a preferred technical solution, the sensor performance evaluation results of each sub-planting area are obtained by analyzing and processing the basic performance evaluation values ​​of the monitoring sensors of each sub-planting area. The specific process is as follows:

[0017] The monitoring sensing basic performance evaluation threshold of the monitoring sensor is extracted from the database.

[0018] The monitoring sensor basic performance evaluation value of each sub-planting area is compared with the monitoring sensor basic performance evaluation threshold. If the monitoring sensor basic performance evaluation value of a sub-planting area is less than the monitoring sensor basic performance evaluation threshold, the sensor performance evaluation result of the monitoring sensor in the sub-planting area is judged to be abnormal.

[0019] If the monitoring sensor basic performance evaluation value of a sub-planting area is greater than or equal to the monitoring sensor basic performance evaluation threshold, the sensor performance evaluation result of the monitoring sensor in the sub-planting area is determined to be normal.

[0020] As a preferred technical solution, the statistical evaluation value of the monitoring sensor basic performance of each normal sub-planting area and the analysis and processing to generate the monitoring transmission correction factor of each normal sub-planting area specifically include:

[0021] The basic performance evaluation value of the monitoring sensor of each normal sub-planting area is retrieved, and mapped and matched with the transmission correction factor corresponding to each monitoring sensor basic performance evaluation value interval pre-stored in the database to obtain the monitoring transmission correction factor of each normal sub-planting area.

[0022] As a preferred technical solution, the environmental sensor data of each normal sub-planting area is obtained and comprehensively processed to obtain the environmental characteristic value of each normal sub-planting area, specifically including:

[0023] The environmental sensing data of each normal sub-planting area includes the average day and night temperature difference, average humidity, average soil pH and average light intensity during the planting growth cycle.

[0024] The environmental sensor verification data of the sub-planting area is extracted from the database, including the average day and night temperature difference verification value, the average humidity verification value, the average soil pH verification value and the average light intensity verification value.

[0025] The environmental sensing data, environmental sensing verification data and monitoring transmission correction factors of each normal sub-planting area are jointly analyzed and processed to obtain the environmental characteristic values ​​of each normal sub-planting area.

[0026] As a preferred technical solution, the environmental impact results of each normal sub-planting area are generated based on the environmental characteristic values ​​of each normal sub-planting area, specifically including:

[0027] Based on the environmental characteristic values ​​of each normal sub-planting area, a comparison is performed with the environmental characteristic thresholds pre-stored in the database. If the environmental characteristic value of a normal sub-planting area is greater than or equal to the environmental characteristic threshold, the environmental impact result of the normal sub-planting area is determined to be an abnormal impact.

[0028] If the environmental characteristic value of a normal sub-planting area is less than the environmental characteristic threshold, the environmental impact result of the normal sub-planting area is determined to be a normal impact, and the normal sub-planting area is recorded as a germplasm analysis area.

[0029] As a preferred technical solution, the statistics obtained the cabbage genetic sampling data of various quality analysis areas, and the specific processing conditions are:

[0030] Genetic images of cabbage in various quality analysis areas are obtained by photographing through monitoring sensors in various quality analysis areas.

[0031] The environmental characteristic values ​​of various quality analysis areas are counted, and mapped and matched with the number of cabbage genetic sampling images corresponding to each environmental characteristic value interval pre-stored in the database to obtain the amount of cabbage genetic sampling data in various quality analysis areas.

[0032] According to the number of cabbage genetic sampling images in various quality analysis areas, the cabbage genetic images uploaded in various quality analysis areas are sampled to obtain the genetic sampling images of each cabbage, and the cabbage characteristic data of various quality analysis areas are obtained by processing and analysis, including the cabbage characteristic average data and cabbage characteristic extreme data of various quality analysis areas.

[0033] The cabbage characteristic standard data of the cabbage variety is extracted from the database.

[0034] The average cabbage characteristic data of various quality analysis areas include average cabbage plant height, average cabbage head area, average cabbage leaf chroma and average cabbage petiole length in various quality analysis areas.

[0035] The cabbage characteristic extreme data of various quality analysis areas include the maximum cabbage plant height, the minimum cabbage plant height, the maximum cabbage head area, the minimum cabbage head area, the maximum cabbage leaf chroma, the minimum cabbage leaf chroma, the maximum cabbage petiole length and the minimum cabbage petiole length in various quality analysis areas.

[0036] As a preferred technical solution, the cabbage genetic sampling data of various quality analysis areas are processed, and the specific processing conditions are:

[0037]

[0038] Among them, V i is the evaluation value of the discrete degree of cabbage characteristics in the i-th germplasm analysis area, A i is the evaluation value of the plant height characteristic dispersion degree of the i-th germplasm analysis area, B i is the evaluation value of the dispersion degree of the bulb area characteristics in the i-th germplasm analysis area, C i is the leaf chromaticity characteristic dispersion evaluation value of the i-th germplasm analysis area, D iis the evaluation value of the discrete degree of petiole length characteristics of the i-th germplasm analysis area, i is the germplasm analysis area number, i=1,2,3,...,n, n is the total number of germplasm analysis areas, Swish function is the activation function, Swish(x)=x*Sigmoid(x).

[0039] As a preferred technical solution, the obtained genetic diversity analysis results are output and displayed, specifically including:

[0040] Based on the discrete degree evaluation values ​​of cabbage characteristics in various quality analysis areas, a comprehensive analysis was performed with the environmental influencing factors of various quality analysis areas to obtain the comprehensive characteristic discrete degree evaluation values ​​of various quality analysis areas.

[0041] The comprehensive characteristic discrete degree evaluation values ​​of various qualitative analysis regions are compared with the comprehensive characteristic discrete degree evaluation thresholds pre-stored in the database to obtain the genetic diversity analysis results of various qualitative analysis regions.

[0042] If the comprehensive characteristic dispersion degree evaluation value of a certain germplasm analysis area is greater than or equal to the comprehensive characteristic dispersion degree evaluation threshold, the genetic diversity analysis result of the germplasm resources in the germplasm analysis area is determined to be diverse, and the germplasm area is recorded as a genetically diverse germplasm area. The characteristic dispersion degree evaluation values ​​of each genetically diverse germplasm area are counted and transmitted to the management terminal in order from high to low for output display.

[0043] If the comprehensive characteristic dispersion evaluation value of a certain germplasm analysis area is less than the comprehensive characteristic dispersion evaluation threshold, the genetic diversity analysis result of the germplasm resources in the germplasm analysis area is determined to be single and transmitted to the management terminal for output and display.

[0044] As a preferred technical solution, the environmental impact factors of the various quality analysis areas, the specific analysis process includes:

[0045] The environmental characteristic values ​​of various quality analysis areas are statistically mapped and matched with the environmental impact factors corresponding to each environmental characteristic value interval pre-stored in the database to obtain the environmental impact factors of various quality analysis areas. The environmental impact factors are used to correct the impact of the environment on the cabbage genetic sampling data.

[0046] Compared with the prior art, the embodiments of the present invention have at least the following beneficial effects:

[0047] (1) The present invention provides a method for analyzing the genetic diversity of cabbage germplasm resources. By real-time detection of the performance of monitoring sensors, the accuracy and availability of environmental sensor data of various germplasm analysis areas and cabbage characteristic data uploaded by the monitoring sensors are ensured, the additional analysis costs caused by abnormal data quality are reduced, the analysis efficiency is improved, and the sub-planting areas with abnormal initial sensor performance evaluation results are screened, and the analysis resources are concentrated on normal sub-planting areas to reduce resource waste.

[0048] (2) The present invention analyzes and processes the environmental characteristics of each normal seed planting area, screens the areas with abnormal environmental impact results, reduces the impact of the environment on the genetic diversity assessment of cabbage germplasm resources, concentrates the analysis resources on the areas with normal environmental impact results, and improves the analysis efficiency.

[0049] (3) The present invention eliminates the influence of the environment on the cabbage genetic sampling data by adding environmental influencing factors, more accurately evaluates the discrete degree of cabbage characteristics, helps correct the errors of data analysis, and improves the accuracy of the analysis results of the genetic diversity of cabbage germplasm resources.

[0050] (4) The present invention obtains the amount of cabbage genetic sampling data in various germplasm analysis areas based on environmental characteristic values, and performs customized data sampling on various germplasm analysis areas, which can effectively realize more scientific and reasonable cabbage characteristic detection for germplasm analysis areas with high environmental characteristic values ​​and low high environmental characteristic values, respectively, thereby reducing the cost of cabbage characteristic detection, optimizing resource allocation, and avoiding over-detection or under-detection.

[0051] Of course, any product implementing the present invention does not necessarily need to achieve all of the above advantages at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] In the description of the present invention, it should be understood that the terms "opening", "upper", "lower", "thickness", "top", "middle", "length", "inside", "all around" and the like indicating orientation or positional relationship are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the components or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0055] See also Figure 1 As shown, the embodiment of the present invention provides a method for analyzing the genetic diversity of cabbage germplasm resources, comprising:

[0056] The cabbage planting area is divided into sub-planting areas, and the basic performance data of the monitoring sensors in each sub-planting area are counted in real time. The basic performance evaluation value of the monitoring sensors in each sub-planting area is obtained through analysis and processing.

[0057] The real-time statistics of the basic performance data of the monitoring sensors of each sub-planting area are analyzed and processed to obtain the basic performance evaluation value of the monitoring sensors of each sub-planting area. The specific process is as follows:

[0058] The basic performance data of the monitoring sensors of each sub-planting area include real-time power, real-time resolution, real-time temperature and real-time delay, and the basic performance data of the monitoring sensors of each sub-planting area are read through a data interface.

[0059] Real-time power refers to the amount of electrical energy consumed by the monitoring sensor during operation. By monitoring the real-time power, the energy consumption of the monitoring sensor can be evaluated. Abnormal changes in power may indicate that the monitoring sensor is faulty or damaged.

[0060] Real-time resolution refers to the minimum change in each physical quantity that the monitoring sensor can detect per unit time. High resolution means that the sensor can capture more subtle changes and improve the accuracy of monitoring data.

[0061] Real-time temperature refers to the instantaneous temperature of the monitoring sensor during operation. Too high or too low a temperature may affect the performance of the monitoring sensor.

[0062] Real-time latency refers to the time interval from when a monitoring sensor detects a signal to when the signal is processed and the result is output. Low latency means that the monitoring sensor can respond quickly to external changes.

[0063] Extract basic performance verification data of monitoring sensors from the database, including power verification value, resolution verification value, processor temperature verification value and delay verification value.

[0064] The basic performance data of the monitoring sensors in each sub-planting area and the basic performance verification data are comprehensively processed and analyzed to obtain the basic performance evaluation value of the monitoring sensors in each sub-planting area. The specific processing and analysis process is as follows:

[0065]

[0066] Among them, Z x is the basic performance evaluation value of the monitoring sensor in the ith sub-planting area, w x is the real-time power of the monitoring sensor in the x-th sub-planting area, f x is the real-time resolution of the monitoring sensor in the x-th sub-planting area, t x is the real-time temperature of the monitoring sensor in the x-th sub-planting area, l x is the real-time delay of the monitoring sensor of the x-th sub-planting area, w0 is the power check value, f0 is the resolution check value, t0 is the processor temperature check value, l0 is the delay check value, x is the sub-planting area number, x=1,2,3,...,y, y is the total number of sub-planting areas, it should be understood that the softplus function is a built-in function in Pytnon, softplus(x)=lg(1+e x ).

[0067] It should be noted that the basic performance data of the monitoring sensors in each sub-planting area include real-time power, real-time resolution, real-time temperature and real-time delay. There is a certain correlation between these parameters. The monitoring sensor generates heat when working, so an increase in real-time power may lead to an increase in real-time temperature. Conversely, excessive temperature may also lead to an increase in power consumption. The ability of the monitoring sensor to process data affects its response time, that is, real-time delay. Higher power consumption may be used to achieve faster processing speeds, thereby reducing delays. Increasing resolution usually requires higher processing power and power consumption. Therefore, an increase in real-time resolution will lead to an increase in real-time power. At the same time, a higher real-time resolution will increase processing time, resulting in an increase in real-time delay. Excessive temperature of the monitoring sensor will affect the performance of its electronic components, resulting in slower processing speeds, thereby increasing real-time delays. These parameters affect each other, and changes in one parameter will cause changes in one or more parameters.

[0068] Based on the analysis and processing of the basic performance evaluation values ​​of the monitoring sensors of each sub-planting area, the sensor performance evaluation results of each sub-planting area are obtained, and the sub-planting areas with normal sensor performance evaluation results are screened and recorded as normal sub-planting areas.

[0069] Based on the analysis and processing of the basic performance evaluation values ​​of the monitoring sensors in each sub-planting area, the sensor performance evaluation results of each sub-planting area are obtained. The specific process is as follows:

[0070] Extract monitoring sensor basic performance evaluation thresholds from the database.

[0071] The monitoring sensor basic performance evaluation threshold is processed as follows: in a specific embodiment, the monitoring sensor basic performance evaluation threshold is the genetic diversity analysis method of the cabbage germplasm resources involved in the embodiment of the present invention, which is directly set in the database during process design. The method for setting the monitoring sensor basic performance evaluation threshold includes multiple methods, such as obtaining it through statistical analysis, including statistically analyzing the monitoring sensor basic performance evaluation value under the historical high monitoring sensor basic performance state (for example, the real-time delay of the monitoring sensor is less than 10ms), and averaging the monitoring sensor basic performance evaluation values ​​obtained multiple times to obtain the monitoring sensor basic performance evaluation threshold, or experts judge the boundary between the high monitoring sensor basic performance evaluation value and the low monitoring sensor basic performance evaluation value through the corresponding historical data to set it, thereby adjusting the threshold.

[0072] The monitoring sensor basic performance evaluation value of each sub-planting area is compared with the monitoring sensor basic performance evaluation threshold. If the monitoring sensor basic performance evaluation value of a sub-planting area is less than the monitoring sensor basic performance evaluation threshold, the sensor performance evaluation result of the monitoring sensor in the sub-planting area is judged to be abnormal.

[0073] If the monitoring sensor basic performance evaluation value of a sub-planting area is greater than or equal to the monitoring sensor basic performance evaluation threshold, the sensor performance evaluation result of the monitoring sensor in the sub-planting area is determined to be normal.

[0074] The basic performance evaluation value of monitoring sensors in each normal sub-planting area is statistically analyzed and processed to generate the monitoring transmission correction factor of each normal sub-planting area, and the environmental sensor data of each normal sub-planting area is obtained and comprehensively processed to obtain the environmental characteristic value of each normal sub-planting area.

[0075] The statistical evaluation value of the basic performance of monitoring sensors in each normal sub-planting area and the analysis and processing to generate the monitoring transmission correction factor of each normal sub-planting area specifically include:

[0076] The basic performance evaluation value of the monitoring sensor of each normal sub-planting area is retrieved, and mapped and matched with the transmission correction factor corresponding to each monitoring sensor basic performance evaluation value interval pre-stored in the database to obtain the monitoring transmission correction factor of each normal sub-planting area.

[0077] The environmental sensor data of each normal sub-planting area is obtained and comprehensively processed to obtain the environmental characteristic value of each normal sub-planting area, specifically including:

[0078] Environmental sensing data of each normal sub-planting area is obtained through the built-in integrated temperature sensing module, humidity sensing module, pH sensing module and light intensity sensing module of the monitoring sensors of each normal sub-planting area.

[0079] The environmental sensing data of each normal sub-planting area includes the average day and night temperature difference, average humidity, average soil pH and average light intensity during the planting growth cycle.

[0080] The average day-night temperature difference refers to the average difference between the highest temperature during the day and the lowest temperature at night in each normal sub-planting area during the planting growth cycle. The temperature difference affects the metabolic process of cabbage, thereby affecting the growth rate of cabbage.

[0081] Average humidity refers to the average value of air humidity during the planting and growing cycle. Humidity affects the transpiration and water absorption of cabbage, which in turn affects the growth conditions. High humidity may lead to the occurrence of diseases such as mold and bacterial infection.

[0082] Average soil pH refers to the average value of soil acidity and alkalinity during the growing cycle. pH affects the absorption of nutrients by cabbage because the availability of nutrients in the soil varies at different pH values. pH also determines the activity of soil microorganisms, thus affecting soil fertility and cabbage health.

[0083] Average light intensity refers to the average amount of light energy received by cabbage during the planting and growth cycle. Light is the energy source for cabbage to carry out photosynthesis and directly affects the growth of cabbage.

[0084] The environmental sensor verification data of the sub-planting area is extracted from the database, including the average day and night temperature difference verification value, the average humidity verification value, the average soil pH verification value and the average light intensity verification value.

[0085] The environmental sensor data, environmental sensor verification data and monitoring transmission correction factor of each normal sub-planting area are jointly analyzed and processed to obtain the environmental characteristic value of each normal sub-planting area. The specific processing process includes:

[0086]

[0087] Among them, H m is the environmental characteristic value of the mth normal sub-planting area, T m is the average day-night temperature difference of the mth normal sub-planting area during the planting growth cycle, W m is the average humidity of the mth normal sub-planting area during the planting growth cycle, pH m is the average soil pH of the mth normal sub-planting area during the planting growth cycle, g m is the average light intensity of the mth normal sub-planting area during the planting growth cycle, T0 is the average day and night temperature difference calibration value, W0 is the average humidity calibration value, pH0 is the average soil pH calibration value, g0 is the average light intensity calibration value, and Z mis the basic performance evaluation value of the monitoring sensor of the mth normal sub-planting area, m is the number of the normal sub-planting area, m = 1, 2, 3, ..., p, p is the total number of normal sub-planting areas, it should be understood that the softplus function is a built-in function in Pytnon, softplus(x) = lg(1+e x ).

[0088] It should be noted that the environmental sensor data of each normal sub-planting area includes the average day and night temperature difference, average humidity, average soil pH and average light intensity during the planting growth cycle. There is a certain correlation between these parameters. A large day and night temperature difference will cause the air humidity to increase at night, because colder air can often hold more water. During the day, the temperature rise will reduce the humidity. A large day and night temperature difference is usually associated with stronger daytime light and colder nights, which will affect the photosynthesis and respiration of cabbage. Soil moisture can affect the pH value of the soil because moisture can change the chemical balance in the soil. High humidity will reduce the penetration of sunlight, thereby reducing the light intensity. The soil pH value can affect the absorption of nutrients by cabbage, thereby affecting the growth of cabbage and the efficiency of photosynthesis, which will indirectly affect the utilization of light intensity. Changes in light intensity will directly affect temperature changes. These parameters affect each other, and changes in one parameter will cause changes in one or more other parameters, which work together on the growth environment of cabbage.

[0089] Based on the environmental characteristic values ​​of each normal sub-planting area, the environmental impact results of each normal sub-planting area are generated, and the environmental impact results include normal impact and abnormal impact. Each normal sub-planting area with a normal impact statistically is recorded as various quality analysis areas, and the cabbage genetic sampling data of various quality analysis areas are statistically obtained.

[0090] Based on the environmental characteristic values ​​of each normal sub-planting area, the environmental impact results of each normal sub-planting area are generated, including:

[0091] Based on the environmental characteristic values ​​of each normal sub-planting area, a comparison is performed with the environmental characteristic thresholds pre-stored in the database. If the environmental characteristic value of a normal sub-planting area is greater than or equal to the environmental characteristic threshold, the environmental impact result of the normal sub-planting area is determined to be an abnormal impact.

[0092] If the environmental characteristic value of a normal sub-planting area is less than the environmental characteristic threshold, the environmental impact result of the normal sub-planting area is determined to be a normal impact, and the normal sub-planting area is recorded as a germplasm analysis area.

[0093] The environmental characteristic threshold is processed as follows: in a specific embodiment, the environmental characteristic threshold is directly set in the database during process design of the genetic diversity analysis method of cabbage germplasm resources involved in the embodiment of the present invention. There are many methods for setting the environmental characteristic threshold, such as obtaining it through statistical analysis, including statistically analyzing the environmental characteristic values ​​under historical high environmental impact states, and averaging the environmental characteristic values ​​obtained multiple times to obtain the environmental characteristic threshold, or experts judge the boundary between the high environmental impact state and the low environmental impact state through corresponding historical data to set the threshold, thereby adjusting the threshold.

[0094] The statistics obtained the cabbage genetic sampling data of various quality analysis areas, and the specific processing conditions were:

[0095] Genetic images of cabbage in various quality analysis areas are obtained by photographing through monitoring sensors in various quality analysis areas.

[0096] The environmental characteristic values ​​of various quality analysis areas are statistically analyzed, and mapped and matched with the cabbage genetic sampling data corresponding to each environmental characteristic value interval pre-stored in the database to obtain the cabbage genetic sampling data of various quality analysis areas.

[0097] For example, in another embodiment of the invention, the environmental characteristic value of a certain germplasm analysis area is 0.25, which is matched with the environmental characteristic value interval containing 0.25 pre-stored in the database, and the amount of cabbage genetic sampling data obtained for the germplasm analysis area is 1,000 images.

[0098] According to the number of cabbage genetic sampling images in various quality analysis areas, the cabbage genetic images uploaded in various quality analysis areas are sampled to obtain the genetic sampling images of each cabbage, and the cabbage characteristic data of various quality analysis areas are obtained by processing and analysis, including the cabbage characteristic average data and cabbage characteristic extreme data of various quality analysis areas.

[0099] The cabbage characteristic standard data of the cabbage variety is extracted from the database.

[0100] The cabbage characteristic standard data include cabbage standard plant height, cabbage standard head area, cabbage standard leaf chromaticity and cabbage standard petiole length.

[0101] The genetic sampling images of each cabbage are identified by edge detection technology, the top, bottom and outline of each cabbage in various quality analysis areas are obtained, and the color information of each cabbage in various quality analysis areas is extracted by color space conversion technology, and the cabbage characteristic average data and cabbage characteristic extreme data of various quality analysis areas are obtained. The cabbage characteristic average data of various quality analysis areas include the average plant height of cabbage in various quality analysis areas, the average cabbage head area, the average cabbage leaf chroma and the average cabbage petiole length of cabbage in various quality analysis areas, and the cabbage characteristic extreme data of various quality analysis areas include the maximum plant height of cabbage, the minimum plant height of cabbage, the maximum cabbage head area, the minimum cabbage head area, the maximum cabbage leaf chroma, the minimum cabbage leaf chroma, the maximum cabbage petiole length and the minimum cabbage petiole length of cabbage in various quality analysis areas.

[0102] Based on the average data and extreme data of cabbage characteristics in various quality analysis areas, the discrete degree evaluation values ​​of each characteristic in various quality analysis areas were obtained through analysis and processing. The specific processing conditions are as follows:

[0103]

[0104]

[0105] Among them, A i is the evaluation value of the plant height characteristic dispersion degree of the i-th germplasm analysis area, B i is the evaluation value of the dispersion degree of the bulb area characteristics in the i-th germplasm analysis area, C i is the leaf chromaticity characteristic dispersion evaluation value of the i-th germplasm analysis area, D i is the evaluation value of the petiole length characteristic dispersion of the i-th germplasm analysis area, σa i is the average plant height of Chinese cabbage in the i-th germplasm analysis area, σb i is the average cabbage head area of ​​the i-th germplasm analysis area, σc i is the average leaf chromaticity of cabbage in the i-th germplasm analysis area, σd i is the average petiole length of the cabbage in the i-th germplasm analysis area, a i,max is the maximum plant height of Chinese cabbage in the i-th germplasm analysis area, a i,min is the minimum plant height of Chinese cabbage in the i-th germplasm analysis area, b i,max is the maximum cabbage head area of ​​the i-th germplasm analysis area, b i,min is the minimum cabbage head area of ​​the i-th germplasm analysis area, c i,max is the maximum chromaticity of cabbage leaves in the i-th germplasm analysis area, c i,min is the minimum chromaticity of cabbage leaves in the i-th germplasm analysis area, d i,max is the maximum petiole length of the cabbage in the i-th germplasm analysis area, d i,minis the minimum petiole length of cabbage in the i-th germplasm analysis area, a0 is the standard plant height of cabbage, b0 is the standard head area of ​​cabbage, c0 is the standard leaf chromaticity of cabbage, d0 is the standard petiole length of cabbage, i is the germplasm analysis area number, i=1,2,3,...,n, n is the total number of germplasm analysis areas, Sigmoid is the activation function,

[0106]

[0107] The processing is performed based on the cabbage genetic sampling data of various quality analysis areas, and the specific processing conditions are:

[0108]

[0109] Among them, V i is the evaluation value of the discrete degree of cabbage characteristics in the i-th germplasm analysis area, A i is the evaluation value of the plant height characteristic dispersion degree of the i-th germplasm analysis area, B i is the evaluation value of the dispersion degree of the bulb area characteristics in the i-th germplasm analysis area, C i is the leaf chromaticity characteristic dispersion evaluation value of the i-th germplasm analysis area, D i is the evaluation value of the discrete degree of petiole length characteristics of the i-th germplasm analysis area, i is the germplasm analysis area number, i=1,2,3,...,n, n is the total number of germplasm analysis areas, Swish function is the activation function, Swish(x)=x*Sigmoid(x).

[0110] The genetic sampling data of cabbage in various quality analysis areas are processed and the genetic diversity analysis results are output and displayed.

[0111] The obtained genetic diversity analysis results are output and displayed, specifically including:

[0112] Based on the discrete degree evaluation values ​​of cabbage characteristics in various quality analysis areas, a comprehensive analysis was performed with the environmental influencing factors of various quality analysis areas to obtain the comprehensive characteristic discrete degree evaluation values ​​of various quality analysis areas.

[0113] The environmental impact factors of the various quality analysis areas, the specific analysis process includes:

[0114] The environmental characteristic values ​​of various quality analysis areas are statistically mapped and matched with the environmental impact factors corresponding to each environmental characteristic value interval pre-stored in the database to obtain the environmental impact factors of various quality analysis areas. The environmental impact factors are used to correct the impact of the environment on the cabbage genetic sampling data.

[0115] The specific processing method of the comprehensive characteristic discrete degree evaluation value of the various quality analysis areas is as follows:

[0116] J i = Mish(V i *δH i )

[0117] Among them, J i is the evaluation value of the comprehensive characteristic dispersion degree of the i-th germplasm analysis area, V i is the evaluation value of the dispersion degree of cabbage characteristics in the i-th germplasm analysis area, δH i is the environmental influencing factor of the i-th germplasm analysis area, Mish function is the activation function, Mish(x)=x*tanh[softplus(x)].

[0118] It should be noted that the environment will affect the degree of dispersion of cabbage characteristics. If different cabbage individuals of the same variety grow in different environments, their characteristic differences may be caused by the environment rather than genetic differences. In the case of differences in the growth environment, the degree of dispersion of cabbage characteristics cannot represent genetic differences. Therefore, the embodiment of the present invention more accurately evaluates the degree of dispersion of cabbage comprehensive characteristics by correcting the influence of environmental factors.

[0119] The comprehensive characteristic discrete degree evaluation values ​​of various qualitative analysis regions are compared with the comprehensive characteristic discrete degree evaluation thresholds pre-stored in the database to obtain the genetic diversity analysis results of various qualitative analysis regions.

[0120] The comprehensive characteristic dispersion degree evaluation threshold is processed as follows: in a specific embodiment, the comprehensive characteristic dispersion degree evaluation threshold is directly set in the database during process design of the genetic diversity analysis method of cabbage germplasm resources involved in the embodiment of the present invention. There are many methods for setting the comprehensive characteristic dispersion degree evaluation threshold, such as obtaining it through statistical analysis, including statistically analyzing the comprehensive characteristic dispersion degree evaluation value under historical high comprehensive characteristic dispersion degrees (for example, the difference between the maximum plant height and the minimum plant height of cabbage exceeds 10 cm), and averaging the comprehensive characteristic dispersion degree evaluation values ​​obtained multiple times to obtain the comprehensive characteristic dispersion degree evaluation threshold, or experts judge the boundary between high comprehensive characteristic dispersion degrees and low comprehensive characteristic dispersion degrees through corresponding historical data to set the threshold, thereby adjusting the threshold.

[0121] If the comprehensive characteristic discrete degree evaluation value of a certain germplasm analysis area is greater than or equal to the comprehensive characteristic discrete degree evaluation threshold, the genetic diversity analysis result of the germplasm resources in the germplasm analysis area is determined to be diverse, and the germplasm area is recorded as a genetically diverse germplasm area. The comprehensive characteristic discrete degree evaluation values ​​of each genetically diverse germplasm area are counted and transmitted to the management terminal in order from high to low for output display.

[0122] If the comprehensive characteristic dispersion evaluation value of a certain germplasm analysis area is less than the comprehensive characteristic dispersion evaluation threshold, the genetic diversity analysis result of the germplasm resources in the germplasm analysis area is determined to be single and transmitted to the management terminal for output and display.

[0123] In another embodiment, the overall characteristic dispersion evaluation value of the cabbage planting area can be generated by statistically analyzing the comprehensive characteristic dispersion evaluation values ​​of various quality analysis areas, and compared with the overall dispersion evaluation threshold value pre-stored in the database to obtain the genetic diversity analysis result of the cabbage planting area. The specific processing process is:

[0124]

[0125] Among them, J is the overall characteristic dispersion evaluation value of the cabbage planting area, J i is the evaluation value of the comprehensive characteristic discrete degree of the i-th germplasm analysis area, i is the germplasm analysis area number, i=1,2,3,...,n, n is the total number of germplasm analysis areas, Sigmoid is the activation function,

[0126] The overall dispersion evaluation threshold is processed as follows: in a specific embodiment, the overall dispersion evaluation threshold is directly set in the database during process design of the genetic diversity analysis method of cabbage germplasm resources involved in the embodiment of the present invention. There are many methods for setting the overall dispersion evaluation threshold, such as obtaining it through statistical analysis, including statistically analyzing the overall dispersion evaluation value under historical high characteristic dispersion (for example, the difference between the maximum plant height and the minimum plant height of cabbage exceeds 10 cm), and averaging the overall dispersion evaluation values ​​obtained multiple times to obtain the environmental characteristic threshold, or experts judge the boundary between high characteristic dispersion and low characteristic dispersion through corresponding historical data to set the threshold, thereby adjusting the threshold.

[0127] If the overall characteristic dispersion evaluation value of the cabbage planting area is greater than or equal to the overall dispersion evaluation threshold, the genetic diversity analysis result of the germplasm resources in the cabbage planting area is determined to be diverse and transmitted to the management terminal for output and display.

[0128] If the overall characteristic dispersion evaluation value of the cabbage planting area is less than the overall dispersion evaluation threshold, the genetic diversity analysis result of the germplasm resources in the cabbage planting area is determined to be single and transmitted to the management terminal for output and display.

[0129] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0130] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can understand and use the present invention well. As long as they do not deviate from the structure of the present invention or exceed the scope defined by the present invention, they should all belong to the protection scope of the present invention.

Claims

1. A method for analyzing the genetic diversity of cabbage germplasm resources, characterized in that: include: The cabbage planting area is divided into various sub-planting areas, basic performance data of monitoring sensors in each sub-planting area are counted in real time, and basic performance evaluation values ​​of monitoring sensors in each sub-planting area are obtained through analysis and processing; Based on the analysis and processing of the monitoring sensor basic performance evaluation value of each sub-planting area, the sensor performance evaluation result of each sub-planting area is obtained, and the sub-planting areas with normal sensor performance evaluation results are screened and recorded as normal sub-planting areas; The basic performance evaluation value of monitoring sensors in each normal sub-planting area is counted, and the monitoring transmission correction factor of each normal sub-planting area is generated through analysis and processing, and the environmental sensor data of each normal sub-planting area is obtained for comprehensive processing to obtain the environmental characteristic value of each normal sub-planting area; Based on the environmental characteristic values ​​of each normal sub-planting area, the environmental impact results of each normal sub-planting area are generated, wherein the environmental impact results include normal impact and abnormal impact, each normal sub-planting area with a normal impact statistically is recorded as various quality analysis areas, and the cabbage genetic sampling data of various quality analysis areas are statistically obtained; The genetic sampling data of cabbage in various quality analysis areas are processed and the genetic diversity analysis results are output and displayed.

2. The method for analyzing genetic diversity of Chinese cabbage germplasm resources according to claim 1, characterized in that: The real-time statistics of the basic performance data of the monitoring sensors of each sub-planting area are analyzed and processed to obtain the basic performance evaluation value of the monitoring sensors of each sub-planting area. The specific process is as follows: The basic performance data of the monitoring sensors in each sub-planting area include real-time power, real-time resolution, real-time temperature and real-time delay; Extract basic performance verification data of monitoring sensors from the database, including power verification value, resolution verification value, processor temperature verification value and delay verification value; The basic performance data of the monitoring sensors in each sub-planting area and the basic performance verification data are comprehensively processed and analyzed to obtain the basic performance evaluation value of the monitoring sensors in each sub-planting area.

3. The method for analyzing genetic diversity of Chinese cabbage germplasm resources according to claim 1, characterized in that: The sensor performance evaluation results of each sub-planting area are obtained by analyzing and processing the basic performance evaluation values ​​of the monitoring sensors of each sub-planting area. The specific process is as follows: Extracting a monitoring sensor basic performance evaluation threshold of the monitoring sensor from a database; The monitoring sensor basic performance evaluation value of each sub-planting area is compared with the monitoring sensor basic performance evaluation threshold. If the monitoring sensor basic performance evaluation value of a sub-planting area is less than the monitoring sensor basic performance evaluation threshold, the sensor performance evaluation result of the monitoring sensor of the sub-planting area is determined to be abnormal; If the monitoring sensor basic performance evaluation value of a sub-planting area is greater than or equal to the monitoring sensor basic performance evaluation threshold, the sensor performance evaluation result of the monitoring sensor in the sub-planting area is determined to be normal.

4. The method for analyzing genetic diversity of Chinese cabbage germplasm resources according to claim 1, characterized in that: The statistical evaluation value of the basic performance of monitoring sensors in each normal sub-planting area and the analysis and processing to generate the monitoring transmission correction factor of each normal sub-planting area specifically include: The basic performance evaluation value of the monitoring sensor of each normal sub-planting area is retrieved, and mapped and matched with the transmission correction factor corresponding to each monitoring sensor basic performance evaluation value interval pre-stored in the database to obtain the monitoring transmission correction factor of each normal sub-planting area.

5. The method for analyzing genetic diversity of Chinese cabbage germplasm resources according to claim 1, characterized in that: The environmental sensor data of each normal sub-planting area is obtained and comprehensively processed to obtain the environmental characteristic value of each normal sub-planting area, specifically including: The environmental sensor data of each normal sub-planting area includes the average day and night temperature difference, average humidity, average soil pH and average light intensity during the planting growth cycle; Extract environmental sensor verification data of the sub-planting area from the database, including average day and night temperature difference verification value, average humidity verification value, average soil pH verification value and average light intensity verification value; The environmental sensing data, environmental sensing verification data and monitoring transmission correction factors of each normal sub-planting area are jointly analyzed and processed to obtain the environmental characteristic values ​​of each normal sub-planting area.

6. The method for analyzing genetic diversity of Chinese cabbage germplasm resources according to claim 1, characterized in that: The generating of the environmental impact results of each normal sub-planting area based on the environmental characteristic values ​​of each normal sub-planting area specifically includes: Based on the environmental characteristic values ​​of each normal sub-planting area, a comparison is performed with the environmental characteristic threshold value pre-stored in the database. If the environmental characteristic value of a normal sub-planting area is greater than or equal to the environmental characteristic threshold value, the environmental impact result of the normal sub-planting area is determined to be an abnormal impact; If the environmental characteristic value of a normal sub-planting area is less than the environmental characteristic threshold, the environmental impact result of the normal sub-planting area is determined to be a normal impact, and the normal sub-planting area is recorded as a germplasm analysis area.

7. The method for analyzing genetic diversity of Chinese cabbage germplasm resources according to claim 1, characterized in that: The statistics obtained the cabbage genetic sampling data of various quality analysis areas, and the specific processing conditions were: Genetic images of cabbage in various quality analysis areas are obtained by photographing with monitoring sensors in various quality analysis areas; Counting the environmental characteristic values ​​of various quality analysis areas, mapping and matching them with the number of cabbage genetic sampling images corresponding to each environmental characteristic value interval pre-stored in the database, and obtaining the amount of cabbage genetic sampling data in various quality analysis areas; According to the number of cabbage genetic sampling images in various quality analysis areas, the cabbage genetic images uploaded in various quality analysis areas are sampled to obtain the cabbage genetic sampling images, and the cabbage characteristic data of various quality analysis areas are obtained by processing and analyzing, including cabbage characteristic average data and cabbage characteristic extreme data of various quality analysis areas; Extracting cabbage characteristic standard data of the cabbage variety from the database; The average cabbage characteristic data of various quality analysis areas include average cabbage plant height, average cabbage head area, average cabbage leaf chromaticity and average cabbage petiole length in various quality analysis areas; The cabbage characteristic extreme data include the maximum cabbage plant height, the minimum cabbage plant height, the maximum cabbage head area, the minimum cabbage head area, the maximum cabbage leaf chroma, the minimum cabbage leaf chroma, the maximum cabbage petiole length and the minimum cabbage petiole length in various quality analysis areas.

8. The method for analyzing genetic diversity of Chinese cabbage germplasm resources according to claim 1, characterized in that: The cabbage genetic sampling data of various quality analysis areas are processed, and the specific processing conditions are: Among them, V i is the evaluation value of the discrete degree of cabbage characteristics in the i-th germplasm analysis area, A i is the evaluation value of the plant height characteristic dispersion degree of the i-th germplasm analysis area, B i is the evaluation value of the dispersion degree of the bulb area characteristics in the i-th germplasm analysis area, C i is the leaf chromaticity characteristic dispersion evaluation value of the i-th germplasm analysis area, D i is the evaluation value of the discrete degree of petiole length characteristics of the i-th germplasm analysis area, i is the germplasm analysis area number, i=1,2,3,...,n, n is the total number of germplasm analysis areas, Swish function is the activation function, Swish(x)=x*Sigmoid(x).

9. The method for analyzing genetic diversity of Chinese cabbage germplasm resources according to claim 8, characterized in that: The obtained genetic diversity analysis results are output and displayed, specifically including: Based on the evaluation values ​​of the discrete degree of cabbage characteristics in various quality analysis areas, comprehensive analysis and processing are performed with the environmental impact factors of various quality analysis areas obtained by analysis to obtain the evaluation values ​​of the discrete degree of comprehensive characteristics of various quality analysis areas; The comprehensive characteristic dispersion evaluation values ​​of various qualitative analysis regions are compared with the comprehensive characteristic dispersion evaluation thresholds pre-stored in the database to obtain the genetic diversity analysis results of various qualitative analysis regions; If the comprehensive characteristic dispersion evaluation value of a certain germplasm analysis area is greater than or equal to the comprehensive characteristic dispersion evaluation threshold, the genetic diversity analysis result of the germplasm resources in the germplasm analysis area is determined to be diverse, and the germplasm area is recorded as a genetically diverse germplasm area. The characteristic dispersion evaluation values ​​of each genetically diverse germplasm area are counted and transmitted to the management terminal in descending order for output display; If the comprehensive characteristic dispersion evaluation value of a certain germplasm analysis area is less than the comprehensive characteristic dispersion evaluation threshold, the genetic diversity analysis result of the germplasm resources in the germplasm analysis area is determined to be single and transmitted to the management terminal for output and display.

10. The method for analyzing genetic diversity of cabbage germplasm resources according to claim 9, characterized in that: The environmental impact factors of the various quality analysis areas, the specific analysis process includes: The environmental characteristic values ​​of various quality analysis areas are statistically mapped and matched with the environmental impact factors corresponding to each environmental characteristic value interval pre-stored in the database to obtain the environmental impact factors of various quality analysis areas. The environmental impact factors are used to correct the impact of the environment on the cabbage genetic sampling data.

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