Method, device, medium and product for determining meteorological service index for whole growth period of crops

Abstract

The application discloses a kind of methods, equipment, medium and product for determining crop whole growth period meteorological service index, and relates to meteorological service index analysis field.The method comprises: obtaining the information data of crop in whole growth period in target planting area;Using Spearman rank correlation analysis method, according to crop quality data and coding meteorological data set in growth period, meteorological factor screening is carried out, and relevant factor is obtained;Using grey correlation analysis method, according to crop quality data and relevant factor, grey correlation degree is determined, and relevant factor is screened based on grey correlation degree, and core meteorological factor is obtained;Based on preset growth period time sequence benchmark and core meteorological factor, upper and lower limit threshold of meteorological elements in each growth period is counted, to determine each growth period meteorological service index.The application can improve the accuracy of crop and meteorological condition relationship.

Description

Technical Field

[0001] This application relates to the field of meteorological service indicator analysis, and in particular to a method, equipment, medium and product for determining meteorological service indicators for the entire growth period of crops. Background Technology

[0002] Currently, some research has been conducted on the relationship between crops and meteorological conditions in agricultural production. Examples include the classification and evaluation of meteorological disaster indices related to continuous rain in an apple-producing area; the screening of suitable meteorological factors for quinoa in a basin and the construction of agricultural meteorological indicators; the study of observation indicators and suitable sowing period indicators for peanut growth in saline-alkali land; and the determination of suitable temperature, humidity, and soil temperature meteorological conditions for tomato growth in solar greenhouses. However, the above studies have the following limitations: 1. The research methods and indicator construction logic differ for open-field crops and single specific facility crops.

[0003] 2. The use of single multiple regression or simple correlation analysis methods often results in insufficient accuracy and robustness in factor selection when faced with the typical characteristics of agricultural meteorological data, such as small sample size, multi-factor coupling, nonlinearity, and multicollinearity.

[0004] 3. There is a lack of systematic image identification standards for reproductive period, and the determination of reproductive period relies on subjective experience.

[0005] Therefore, it is crucial to determine meteorological service indicators for the entire growth period of crops in order to improve the accuracy of the relationship between crops and meteorological conditions. Summary of the Invention

[0006] The purpose of this application is to provide a method, equipment, medium, and product for determining meteorological service indicators for the entire growth period of crops, which can improve the accuracy of the relationship between crops and meteorological conditions.

[0007] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a method for determining meteorological service indicators for the entire growth period of crops, including: Acquire information data on crops within the target planting area throughout their entire growth period; the information data includes meteorological data, crop quality data, and field observation data; the entire growth period refers to the entire growth process of crops from transplanting to fruit ripening and harvesting; the entire growth period includes multiple growth periods obtained by dividing the entire growth process into growth period time segments based on a preset growth period timeline. Spearman rank correlation analysis was used to screen meteorological factors based on the crop quality data and the coded meteorological dataset for different growth stages to obtain relevant factors. The coded meteorological dataset for different growth stages was obtained by labeling the meteorological elements and corresponding growth stage time periods contained in the meteorological data according to the coding format of growth stage-meteorological element. The grey relational analysis method is used to determine the grey relational degree based on the crop quality data and the relevant factors, and the relevant factors are screened based on the grey relational degree to obtain the core meteorological factors. Based on the preset fertility period timeline benchmark and the core meteorological factors, the upper and lower limits of meteorological elements in each fertility period are statistically determined to identify meteorological service indicators for each fertility period.

[0008] In one embodiment, the preset reproductive period timeline includes: reproductive period timeline data and reproductive period image recognition atlas; The reproductive period time series data includes the start and end dates of each reproductive period determined based on preset definition standards; the reproductive period image recognition atlas includes feature images corresponding to the growth status of crops within each reproductive period.

[0009] In one embodiment, Spearman's rank correlation analysis is used to screen meteorological factors based on the crop quality data and the meteorological dataset coded by growth stage, resulting in relevant factors, including: Using the crop quality data as the dependent variable and the meteorological dataset coded by growth stage as the independent variable, Spearman rank correlation analysis was used to screen meteorological factors according to preset screening criteria to obtain relevant factors.

[0010] In one embodiment, a grey relational analysis method is used to determine the grey relational degree based on the crop quality data and the relevant factors, including: Using the crop quality data as a reference sequence and each of the relevant factors as a comparison sequence, the reference sequence and the comparison sequence are initialized respectively to obtain the initialized data. Based on the initialized data, calculate the absolute difference between the reference sequence and the comparison sequence at the same time point: ; in, For the reference sequence in the 1st Initial values ​​at each time point; For the first The comparison sequence in the th ... Initialized values ​​at each time point For the first The reference sequence at time point 1 and the 1st time point The absolute difference between the comparison sequences; Determine the absolute difference matrix by iterating through all time points and all comparison sequences; In the absolute difference matrix, the maximum value among all absolute differences is taken as the global maximum value. The minimum value among all absolute differences is taken as the global minimum. ; Based on global maximum value and global minimum value Calculate the first one one by one The comparison sequence in the th ... Correlation coefficient at each time point : ; in, For resolution; For the The grey relational degree is obtained by taking the arithmetic mean of the correlation coefficients at all time points in the comparison sequence. : ; in, This represents the total number of time points.

[0011] In one embodiment, based on the preset fertility period timeline benchmark and the core meteorological factors, upper and lower limit thresholds of meteorological elements within each fertility period are statistically analyzed to determine meteorological service indicators for each fertility period, including: Based on the preset fertility period timeline benchmark and the core meteorological factors, the 80% guarantee rate method is used to statistically analyze the observed values ​​of meteorological elements in each historical year, and obtain the upper and lower limit thresholds of meteorological elements in each fertility period to determine the meteorological service indicators for each fertility period.

[0012] In one embodiment, the meteorological service indicators include: average air temperature, diurnal temperature range, average ground temperature, average 5cm ground temperature, carbon dioxide concentration, sunshine duration, total irradiance, and photosynthetically active irradiance.

[0013] In one embodiment, it further includes: The disaster type identification result is determined based on the meteorological disaster occurrence records contained in the field observation data; The disaster stress state is determined based on the meteorological service indicators for each reproductive period and the upper and lower limits of the corresponding meteorological elements. Based on the preset growth period timeline, the disaster type identification results, and the disaster stress status, control measures to cope with meteorological disasters are determined for crops at different growth stages.

[0014] Secondly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for determining meteorological service indicators for the entire growth period of crops as described above.

[0015] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for determining meteorological service indicators for the entire growth period of crops as described above.

[0016] Fourthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the method described above for determining meteorological service indicators for the entire growth period of crops.

[0017] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a method, equipment, medium, and product for determining meteorological service indicators for the entire growth period of crops. It involves acquiring information data on crops throughout their entire growth period in a target planting area; employing Spearman's rank correlation analysis to screen meteorological factors based on crop quality data and coded meteorological datasets for each growth period, thus identifying relevant factors. This process quickly eliminates meteorological factors not significantly correlated with crop quality data, narrowing the scope of subsequent analysis; and using grey relational analysis to determine the grey relational degree based on crop quality data and relevant factors, further screening relevant factors to obtain core meteorological factors. This process can further refine core controlling factors and provide a precise importance ranking; and based on a preset growth period time series benchmark and core meteorological factors, statistically determining the upper and lower limits of meteorological elements within each growth period to determine meteorological service indicators for each growth period. This improves the accuracy of the relationship between crops and meteorological conditions. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart illustrating the method for determining meteorological service indicators for the entire growth period of crops; Figure 2 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0022] In one exemplary embodiment, such as Figure 1 As shown, a method for determining meteorological service indicators for the entire growth period of crops is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both. In this embodiment, the method is illustrated using a server as an example.

[0023] Specifically, such as Figure 1 As shown, this application provides a method for determining meteorological service indicators for the entire growth period of crops, including: Step 100: Obtain information data on crops throughout their entire growth period in the target planting area. The information data includes meteorological data, crop quality data, and field observation data; the entire growth period refers to the entire growth process of crops from transplanting to fruit ripening and harvesting; the entire growth period includes multiple growth periods obtained by dividing the entire growth process into growth period time segments based on a preset growth period timeline.

[0024] The preset growth period timeline includes: growth period timeline data and growth period image identification atlas; wherein, the growth period timeline data includes the start and end dates of each growth period determined based on preset definition standards; the growth period image identification atlas includes feature images corresponding to the growth status of crops within each growth period.

[0025] Step 200: Using Spearman's rank correlation analysis, meteorological factors are screened based on crop quality data and coded meteorological datasets for different growth stages to obtain relevant factors. The coded meteorological datasets for different growth stages are obtained by labeling the meteorological elements and corresponding growth stage time periods contained in the meteorological data according to the coding format of growth stage-meteorological element.

[0026] Spearman's rank correlation analysis was used to screen meteorological factors based on crop quality data and coded meteorological datasets for different growth stages, resulting in relevant factors, including: Using crop quality data as the dependent variable and meteorological datasets coded by growth stage as the independent variable, Spearman rank correlation analysis was used to screen meteorological factors according to preset screening criteria to obtain relevant factors.

[0027] Step 300: Using grey relational analysis, determine the grey relational degree based on crop quality data and related factors, and screen related factors based on the grey relational degree to obtain core meteorological factors.

[0028] In one embodiment, a grey relational analysis method is used to determine the grey relational degree based on crop quality data and related factors, including: Using crop quality data as a reference sequence and each relevant factor as a comparison sequence, the reference sequence and the comparison sequences are initialized to obtain initialized data.

[0029] Based on the initialized data, calculate the absolute difference between the reference sequence and the comparison sequence at the same time point: .

[0030] in, For the reference sequence in the 1st Initial values ​​at each time point; For the first The comparison sequence in the th ... Initialized values ​​at each time point For the first The reference sequence at time point 1 and the 1st time point The absolute difference between the comparison sequences.

[0031] The absolute difference matrix is ​​determined by iterating through all time points and all comparison sequences; the maximum value among all absolute differences in the absolute difference matrix is ​​then taken as the global maximum value. The minimum value among all absolute differences is taken as the global minimum. .

[0032] Based on global maximum value and global minimum value Calculate the first one one by one The comparison sequence in the th ... Correlation coefficient at each time point : .

[0033] in, For resolution; for the first The grey relational degree is obtained by taking the arithmetic mean of the correlation coefficients at all time points in the comparison sequence. : .

[0034] in, This represents the total number of time points.

[0035] Step 400: Based on the preset fertility period time series benchmark and core meteorological factors, the upper and lower limits of meteorological elements in each fertility period are statistically determined to determine the meteorological service indicators for each fertility period.

[0036] Specifically, based on a preset fertility period timeline benchmark and core meteorological factors, upper and lower limit thresholds for meteorological elements within each fertility period are statistically analyzed to determine meteorological service indicators for each fertility period, including: Based on the preset fertility period timeline benchmark and core meteorological factors, the 80% guarantee rate method is used to statistically analyze the observed values ​​of meteorological elements in each historical year, and obtain the upper and lower limit thresholds of meteorological elements in each fertility period in order to determine the meteorological service indicators for each fertility period.

[0037] Meteorological service indicators include: average temperature, diurnal temperature range, average ground temperature, average 5cm ground temperature, carbon dioxide concentration, sunshine duration, total irradiance, and photosynthetically active irradiance.

[0038] As an optional implementation method, the method for determining meteorological service indicators for the entire crop growth period further includes: The disaster type identification results are determined based on the meteorological disaster occurrence records included in the field observation data; the disaster stress status is determined based on the meteorological service indicators of each growth stage and the upper and lower limits of the corresponding meteorological elements; and the control measures to deal with meteorological disasters are determined for crops at different growth stages based on the preset growth stage time series benchmark, the disaster type identification results and the disaster stress status.

[0039] In practical applications, taking greenhouse-grown melons as an example, the methods mentioned in this application specifically include: Step 1: Set up fixed observation plots in the core planting area of ​​greenhouse melons, carry out field positioning observations throughout the entire growth period, and establish a standardized growth period definition standard and image recognition system, that is, preset growth period timeline benchmark.

[0040] First, fixed observation plots were set up in the solar greenhouse of the core melon growing area. Plants with uniform growth were selected in each plot as fixed observation samples. The growth status of the entire growth period was tracked and recorded in detail, and the time nodes and population proportions of each morphological characteristic were recorded.

[0041] Based on the "Agricultural Meteorological Observation Specifications" and local variety standards (such as the Weifang City local standard "Qingzhou Silver Melon" (DB3707 / T058-2022)), and combined with the growth characteristics of silver melon, the entire growth period after transplanting is divided into 5 key growth periods. The definition standards for each growth period (preset definition standards) are shown in Table 1.

[0042] Table 1. Criteria for Defining Different Growth Stages of Silver Melon

[0043] The criterion for determining whether a population has entered its growth stage is that 50% of the plants in the plot have reached the corresponding growth form. Based on this criterion, the specific start and end dates of the five growth stages of greenhouse melons in each year and batch are determined throughout the entire process from transplanting to fruit ripening and harvesting, forming complete growth stage timeline data.

[0044] Images were taken at the general developmental stages of each reproductive period, and the characteristic images that best fit the standards of each developmental stage were selected to form a standardized reproductive period image identification atlas.

[0045] Through the above operations, this step established the division criteria and specific determination dates for five reproductive periods (hereinafter referred to as "reproductive period timeline benchmarks", i.e. reproductive period timeline data), as well as standard feature images for five reproductive periods (hereinafter referred to as "standard image library", i.e. reproductive period image identification atlas). The reproductive period timeline benchmarks will provide time coordinates for subsequent steps such as data segmentation collection, statistical analysis by reproductive period, and determination of product release timing.

[0046] Step Two: Based on the reproductive period timeline established in Step One, the entire growth process is divided into 5 time periods, and the following three types of data are collected for each time period: 1. Meteorological data inside the greenhouse: A DZZ4 meteorological station was set up in the greenhouse to continuously collect meteorological data throughout the entire growth period, with a collection frequency of no less than once every 10 minutes. The collected elements included: average air temperature (T), maximum air temperature (Ta), minimum air temperature (Ti), diurnal temperature range (Td), accumulated temperature ≥10℃ (K), average surface temperature (Ts), average 5cm ground temperature (T5), average 10cm ground temperature (T10), total irradiance (E), photosynthetically active irradiance (PAR), total sunshine hours (S, sourced from a nearby national meteorological observation station), CO2 concentration, and average relative humidity (RH), totaling 13 meteorological elements. After collection, the continuous meteorological data was divided into five subsets according to the five growth period time periods determined in step one, and the average and extreme values ​​of each meteorological element within each growth period were calculated.

[0047] Based on this, all meteorological factors were labeled according to the coding format of "growth period - meteorological element" (e.g., X1_T represents the average temperature during the seedling stage, X2_E represents the total irradiance during the vine extension stage, X4_CO2 represents the CO2 concentration during the fruit enlargement stage, where X1 to X5 correspond to the 5 growth periods determined in step one, and X6 represents the entire growth period), forming a meteorological dataset coded by growth period, totaling 13 categories of meteorological elements × 6 time periods = 78 factors. This coding format explicitly embeds the time-series baseline of the growth period in step one into each meteorological factor, so that all subsequent analyses can be traced back to a specific growth stage. This provides a time-series labeled data foundation for the factor screening in step three and the correlation analysis in step four, and also provides time-series data sources for the threshold statistics by growth period in step five.

[0048] 2. Crop quality data: The yield per mu, the soluble solids content of the fruit, and the transverse diameter of the fruit were selected as quality indicators.

[0049] The transverse diameter of the fruit was measured using vernier calipers, and the soluble solids content was measured using a refractometer. The yield per mu was obtained by actual harvesting and measurement throughout the region during the color change and ripening period.

[0050] Each crop quality data is recorded according to the corresponding planting batch and sampling date to ensure that it can be correlated with meteorological data in the time dimension, thus providing dependent variable data for steps three and four.

[0051] 3. Field observation data: Observations were conducted on the plants at designated observation points according to the five growth stages defined in Step 1, recording biomass morphological indicators (vine length, number of leaves, number of flowers and fruits) for each growth stage. Simultaneously, the occurrence of disasters was recorded throughout the entire process, including the timing, percentage of affected plants, damage level, and yield loss of events such as low temperatures, prolonged cloudy days, blizzards, fruit cracking, and flower and fruit drop. This data will be provided in Step 6 as field evidence for disaster type identification and the development of control measures.

[0052] Step 3: Preliminary screening by reproductive stage using Spearman's rank correlation analysis.

[0053] The crop quality data (yield per mu, soluble solids content, and fruit diameter) collected in step two were used as dependent variables, and the coded meteorological dataset for different growth stages (78 factors, all carrying the growth stage labels assigned in step one) formed in step two were used as independent variables. The Spearman rank correlation coefficient rs and the significance p-value were calculated for each factor.

[0054] Spearman's rank correlation analysis is a nonparametric statistical method. Its advantages lie in its lack of strict requirements on data distribution and its insensitivity to outliers, making it particularly suitable for the typical characteristics of agricultural meteorological data, such as small samples and nonlinearity. Through this step of the analysis, meteorological factors that are not significantly correlated with quality indicators can be quickly eliminated, narrowing down the scope of subsequent analysis.

[0055] The screening criteria were: p<0.05 was considered statistically significant, and p<0.01 was considered highly statistically significant; factors with p>0.05 were removed, and those with statistically significant correlations were retained for the next step of analysis.

[0056] After screening, a total of 13 factors significantly related to yield per acre were identified (such as average soil temperature during the seedling stage, total sunshine hours during the vine extension stage, and total irradiance during the flowering and fruit setting stage). A total of 16 factors significantly related to soluble solids content and 15 factors significantly related to fruit diameter were also identified. Each retained factor carries a growth stage label assigned in step one (e.g., X1_Ts represents average soil temperature during the seedling stage, and X3_E represents total irradiance during the flowering and fruit setting stage). This label will serve as a key identifier in the grey relational analysis in step four to distinguish the differentiated contributions of the same meteorological element at different growth stages.

[0057] Step 4: Screening and ranking of core factors by reproductive stage using grey relational analysis.

[0058] Using the crop quality data (yield per mu, soluble solids content, and fruit diameter) from step two as reference sequences, and the significantly relevant factors (carrying the growth period label from step one) selected in step three as comparison sequences, the grey relational degree between each factor and the quality indicators was calculated and ranked.

[0059] Grey relational analysis can quantify the degree of influence of multiple independent variables on the dependent variable in scenarios with small samples, multi-factor coupling, nonlinearity, and multicollinearity, and is particularly suitable for the typical characteristics of agricultural meteorological data. Through the analysis in this step, the core controlling factors can be further extracted from the significant factors screened in step three, and a precise ranking of importance can be given.

[0060] The specific calculation process is as follows: 1. Initialize the original data.

[0061] 2. Calculate the absolute difference between the reference sequence and each comparison sequence at the same time.

[0062] Using the data after initialization in step 1, calculate the reference sequence and each comparison sequence at the same time point using the following formula. The absolute difference of (k=1,2,…,N): .

[0063] This process is repeated for all time points (k=1 to N) and all comparison sequences (i=1 to M, where M is the number of significantly correlated factors selected in step three), forming an absolute difference matrix. This matrix has N rows (number of time points) × M columns (number of comparison sequences), and each element in the matrix... Indicates the first The reference sequence at time point 1 and the 1st time point The absolute difference between the comparison sequences.

[0064] 3. Calculate the correlation coefficient: In the absolute difference matrix generated in step 2, the maximum value among all N×M absolute differences is taken as... max, take the minimum value among all N×M absolute differences as the maximum value. min.

[0065] Utilizing a defined global max and min, calculate the number of mins one by one according to the following formula. The comparison sequence in the th ... Correlation coefficient at each time point : .

[0066] in, This represents the resolution, with a value of 0.5.

[0067] 4. Calculate the correlation degree: For the A comparison sequence, placing it in all The arithmetic mean of the correlation coefficients at each time point is used to obtain the grey correlation degree between the comparison sequence and the reference sequence. : .

[0068] correlation Reflects the first The average influence of a meteorological factor on quality indicators over the entire time series is measured by the value of the factor. The larger the value, the more significant the systematic influence of the factor on the quality indicators.

[0069] The correlation degree classification standard is as follows: correlation degree ≥ 0.8 is the core controlling factor (core meteorological factor); 0.7 ≤ correlation degree < 0.8 is the important influencing factor; correlation degree < 0.7 is the weak influencing factor (to be eliminated).

[0070] Because each factor retains the fertility period label assigned in step one, the analysis results can accurately reflect the differentiated contributions of the same meteorological factor to quality indicators at different fertility stages. For example, the analysis results show that the correlation between "total radiation irradiance during the vine-spreading stage" and soluble solids is 0.8670 (strong influence), while the correlation between "total radiation irradiance during the color-changing and maturing stage" and soluble solids is only 0.5807 (weak influence). This differential quantitative conclusion can only be obtained if a fertility period time series benchmark is provided in step one, coding is performed according to the benchmark in step two, and fertility period labels are retained in step three for screening.

[0071] Step 5: Use the 80% guarantee rate method to calculate the upper and lower limits of significantly relevant meteorological elements in each reproductive period, and determine the meteorological service indicators for each reproductive period.

[0072] Based on the reproductive period timeline established in step one (i.e., the specific start and end dates of the five reproductive periods), the observed values ​​corresponding to the core meteorological factors determined in step four are extracted from the meteorological dataset for each reproductive period formed in step two. For each core meteorological factor in each reproductive period, the 80th percentile of multi-year observation data is used to determine the appropriate threshold. The upper limit is the value corresponding to no more than 80% of the values ​​in all samples, and the lower limit is the value corresponding to no less than 80% of the values ​​in all samples.

[0073] The 80% guarantee rate is a core statistical method for quantifying the stability of meteorological elements in the field of agricultural meteorology. It means that in a long-term observation series, 80% of the samples reach or exceed (lower limit) / do not exceed (upper limit) this value. The introduction of this method gives the index threshold of this invention a clear probabilistic meaning and operability.

[0074] The final indicator system, calculated through this step, covers six periods: seedling stage, vine extension stage, flowering and fruit setting stage, fruit enlargement stage, color change and ripening stage, and the entire growth period. It includes eight meteorological elements: average air temperature, diurnal temperature range, average ground temperature, average 5cm ground temperature, CO2 concentration, sunshine duration, total irradiance, and photosynthetically active irradiance. Each indicator has a clear upper and lower limit value.

[0075] Step Six: Develop phased meteorological disaster control measures based on the specific characteristics of the reproductive period.

[0076] First, based on the disaster occurrence records in the field observation data from step two, identify the main types of disasters that pose a significant threat to greenhouse melons.

[0077] Based on the meteorological element thresholds for each growth period determined in step five, when the value of a certain meteorological element is lower than the lower limit or higher than the upper limit, it is determined to be a state of disaster stress.

[0078] Based on this, and combined with the growth period timeline benchmark of Step 1 (physiological characteristics and sensitive stages of plants at each growth period), differentiated control measures are formulated for the three stages of prevention, emergency response, and post-disaster recovery, targeting the damage mechanism of different disaster types to plants at different growth periods.

[0079] The types of disasters and their control measures are as follows: (a) Measures to deal with low-temperature chilling injury: Prevention: Select new greenhouse films with good light transmission and strong heat retention, repair any damage or holes in the greenhouse film in a timely manner, and seal any gaps in the greenhouse; install cold-proof skirts and windproof films around the greenhouse, and lay ground film, straw or heat-insulating blankets on the seedbed surface.

[0080] Emergency measures during the event: In case of a sharp drop in temperature, cover the greenhouse with double-layer insulation blankets in advance in the evening for multiple layers of insulation. If necessary, add heating and lighting equipment such as hot air furnaces, electric heating beds, and supplemental lights inside the greenhouse; it is strictly forbidden to open the greenhouse for ventilation in low temperature weather.

[0081] Post-disaster recovery: After the low temperatures subside, gradually increase the greenhouse temperature and ventilate slowly to prevent plants from being damaged by temperature stress.

[0082] (II) Response measures for continuous cloudy weather disasters: Routine prevention: Keep the greenhouse film clean and promptly remove dust and water from its surface; plant at a reasonable density, prune and remove old and diseased leaves in a timely manner, and improve ventilation and light transmission conditions inside the greenhouse.

[0083] Emergency measures during the event: turn on supplemental lighting; strictly control watering, and apply wood ash to absorb and reduce moisture when the soil is too wet; moderately regulate the greenhouse temperature to maintain a reasonable temperature difference between day and night and prevent excessive growth.

[0084] Post-disaster recovery: After the weather clears up, gradually increase ventilation and light exposure to prevent sudden strong light from causing the leaves to wilt due to water loss; if cold air is forecast to affect the area, lower the temperature inside the greenhouse in advance to avoid excessive temperature differences that could cause fruit cracking.

[0085] (III) Blizzard Disaster Response Measures: Prevention: In advance, inspect and reinforce the greenhouse frame, film pressing line, ground anchor and other facilities, and temporarily reinforce old greenhouses; cover with thicker insulation blankets in advance, and stock up on snow removal tools.

[0086] Emergency response: Clear snow from the roof of the shed in sections in a timely manner, and strictly prohibit violent snow removal that could puncture the shed film; reduce the opening and closing time of the shed and maintain a stable temperature inside the shed.

[0087] Post-disaster recovery: After the snow melts, promptly clear any water accumulation around the shed; gradually uncover the insulation blankets and allow the shed to slowly warm up by exposing it to sunlight; ventilate and dehumidify in a timely manner, and remove any frozen or damaged branches and leaves.

[0088] (iv) Measures to cope with high temperature stress: Daily prevention: Maintain cross-ventilation by keeping the wind at the top and sides, and open the vents in a timely manner to dissipate heat and cool down; cover with shade nets as needed according to temperature changes.

[0089] Emergency measures during the event: Increase ventilation and use physical cooling methods such as micro-spraying and misting water spraying inside the greenhouse; strengthen pruning management and retain functional leaves to shield the fruit and prevent sunburn.

[0090] Post-disaster recovery: Promptly remove prematurely aging leaves and weak branches, replenish water and fertilizer, and restore plant growth. In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 2 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores data for determining meteorological service indicators for the entire crop growth period. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network. When the computer program is executed by the processor, it implements a method for determining meteorological service indicators for the entire crop growth period.

[0091] Those skilled in the art will understand that Figure 2 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0092] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described method embodiments.

[0093] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the above-described method embodiments.

[0094] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the above-described method embodiments.

[0095] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0096] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0097] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, etc., and are not limited to these.

[0098] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0099] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for determining meteorological service indicators for the entire growth period of crops, characterized in that, include: Acquire information data on crops within the target planting area throughout their entire growth period; the information data includes meteorological data, crop quality data, and field observation data; The full growth period refers to the entire growth process of a crop from transplanting to fruit ripening and harvesting; the full growth period includes multiple growth periods obtained by dividing the entire growth process into growth period time segments based on a preset growth period timeline. Spearman rank correlation analysis was used to screen meteorological factors based on the crop quality data and the coded meteorological dataset for different growth stages to obtain relevant factors. The coded meteorological dataset for different growth stages was obtained by labeling the meteorological elements and corresponding growth stage time periods contained in the meteorological data according to the coding format of growth stage-meteorological element. The grey relational analysis method is used to determine the grey relational degree based on the crop quality data and the relevant factors, and the relevant factors are screened based on the grey relational degree to obtain the core meteorological factors. Based on the preset fertility period timeline benchmark and the core meteorological factors, the upper and lower limits of meteorological elements in each fertility period are statistically determined to identify meteorological service indicators for each fertility period.

2. The method for determining meteorological service indicators for the entire crop growth period according to claim 1, characterized in that, The preset reproductive period timeline includes: reproductive period timeline data and reproductive period image recognition atlas; The reproductive period time series data includes the start and end dates of each reproductive period determined based on preset definition standards; the reproductive period image recognition atlas includes feature images corresponding to the growth status of crops within each reproductive period.

3. The method for determining meteorological service indicators for the entire growth period of crops according to claim 1, characterized in that, Spearman's rank correlation analysis was used to screen meteorological factors based on the crop quality data and the coded meteorological dataset for different growth stages, resulting in relevant factors, including: Using the crop quality data as the dependent variable and the meteorological dataset coded by growth stage as the independent variable, Spearman rank correlation analysis was used to screen meteorological factors according to preset screening criteria to obtain relevant factors.

4. The method for determining meteorological service indicators for the entire crop growth period according to claim 1, characterized in that, The grey relational analysis method is used to determine the grey relational degree based on the crop quality data and the relevant factors, including: Using the crop quality data as a reference sequence and each of the relevant factors as a comparison sequence, the reference sequence and the comparison sequence are initialized respectively to obtain the initialized data. Based on the initialized data, calculate the absolute difference between the reference sequence and the comparison sequence at the same time point: ; in, For the reference sequence in the 1st Initial values ​​at each time point; For the first The comparison sequence in the th ... Initialized values ​​at each time point For the first The reference sequence at time point 1 and the 1st time point The absolute difference between the comparison sequences; Determine the absolute difference matrix by iterating through all time points and all comparison sequences; In the absolute difference matrix, the maximum value among all absolute differences is taken as the global maximum value. The minimum value among all absolute differences is taken as the global minimum. ; Based on global maximum value and global minimum value Calculate the first one one by one The comparison sequence in the th ... Correlation coefficient at each time point : ； in, For resolution; For the first The grey relational degree is obtained by taking the arithmetic mean of the correlation coefficients at all time points in the comparison sequence. : ； in, This represents the total number of time points.

5. The method for determining meteorological service indicators for the entire growth period of crops according to claim 1, characterized in that, Based on the preset reproductive period timeline benchmark and the core meteorological factors, the upper and lower limits of meteorological elements within each reproductive period are statistically determined to identify meteorological service indicators for each reproductive period, including: Based on the preset fertility period timeline benchmark and the core meteorological factors, the 80% guarantee rate method is used to statistically analyze the observed values ​​of meteorological elements in each historical year, and obtain the upper and lower limit thresholds of meteorological elements in each fertility period to determine the meteorological service indicators for each fertility period.

6. The method for determining meteorological service indicators for the entire crop growth period according to claim 5, characterized in that, The meteorological service indicators include: average air temperature, diurnal temperature range, average ground temperature, average 5cm ground temperature, carbon dioxide concentration, sunshine duration, total irradiance, and photosynthetically active irradiance.

7. The method for determining meteorological service indicators for the entire growth period of crops according to claim 1, characterized in that, Also includes: The disaster type identification result is determined based on the meteorological disaster occurrence records contained in the field observation data; The disaster stress state is determined based on the meteorological service indicators for each reproductive period and the upper and lower limits of the corresponding meteorological elements. Based on the preset growth period timeline, the disaster type identification results, and the disaster stress status, control measures to cope with meteorological disasters are determined for crops at different growth stages.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the method for determining meteorological service indicators for the entire growth period of crops as described in any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the method for determining meteorological service indicators for the entire growth period of crops as described in any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the method for determining meteorological service indicators for the entire growth period of crops as described in any one of claims 1-7.

Images