Environmental data real-time monitoring and analysis method and system for planting field

Abstract

The invention discloses an environmental data real-time monitoring and analysis method and system for a planting field, relates to the technical field of agricultural planting, and aims to solve the technical problems that hidden changes of environmental data cannot be monitored, multi-source data is difficult to comprehensively analyze, and problem diagnosis and processing are inaccurate in the prior art. Comprising the following steps: S1, monitoring planting environment data of a planting field in real time to obtain water quality data, soil physical and chemical data, soil microorganism data and crop image data corresponding to different positions; s2, arranging the four types of data according to a time sequence, and generating a water quality data change curve, a soil physical and chemical data change curve, a soil microorganism data change curve and a crop image data change curve; and S3, scoring the four types of data change curves according to corresponding parameters, the multi-source planting field environment data is collected in real time, the environment deterioration condition and position can be accurately judged through analysis, and the monitoring problem in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of agricultural planting technology, and more specifically, to a method and system for real-time monitoring and analysis of environmental data of a planting field. Background Art

[0002] With the continuous improvement of science and technology, people's impact on the ecological environment is also increasing. Environmental changes indirectly affect the continuation and development of humans and other organisms. The existing ecological monitoring system is mainly used for ecological environmental protection and ecosystem investigation. It provides guidance for the agricultural production process. By obtaining information about the changing state of the ecological environment, it can timely discover problems in the ecosystem to avoid the destruction of the ecological environment and promote the harmonious development of humans and the natural environment. At the same time, it also provides a decision-making basis for agricultural production to ensure the stability and sustainable development of agricultural production. With the development of big data technology, big data technology has gradually been applied to the field environment monitoring system.

[0003] However, the ecological monitoring system in the existing technology, from the perspective of monitoring methods, mainly obtains environmental data directly through sensors. This method can promptly alarm when there are obvious abnormalities in the environmental data, but when the environmental data is within the normal range, it is difficult to detect the potential hidden changes. For example, the slow changes in trace elements in the soil and the subtle adjustments in the structure of the microbial community will not cause the environmental data to trigger an alarm in the short term, but the long-term accumulation will have a negative impact on crop growth and sustainability, and the existing simple data judgment method cannot detect these potential problems in a timely manner.

[0004] In terms of data analysis, existing technologies are relatively simple in their analysis of environmental data. Most of them only perform simple statistics and comparisons of data, lack the ability to comprehensively analyze multi-source data, and find it difficult to explore the inherent connections and potential laws between data. This makes it impossible for agricultural producers to fully understand the changing trends of the ecological environment in the planting fields, and to predict possible problems in advance and take effective measures.

[0005] In addition, the existing monitoring system also has deficiencies in problem diagnosis and treatment. When environmental data deteriorates, it is impossible to quickly and accurately determine whether it is caused by soil factors, crop factors or other factors, and it is difficult to formulate accurate solutions for specific problems, resulting in poor pertinence and effectiveness of problem handling, affecting the yield and quality of crops, and restricting the sustainable development of agriculture. In view of this, we propose a real-time monitoring and analysis method and system for environmental data in planting fields. Summary of the invention

[0006] The purpose of the present invention is to provide a method and system for real-time monitoring and analysis of environmental data of planting fields, so as to solve the technical problems that the prior art cannot monitor implicit changes in environmental data, is difficult to comprehensively analyze multi-source data, and has inaccurate problem diagnosis and processing.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for real-time monitoring and analysis of environmental data of a planting field, comprising the following steps: S1: Real-time monitoring of the planting environment data of the planting field to obtain the corresponding water quality data, soil physical and chemical data, soil microbial data, and crop image data at different locations; S2: Arrange the above four types of data in chronological order, and generate a water quality data change curve, a soil physical and chemical data change curve, a soil microbial data change curve, and a crop image data change curve; S3: scoring the above four types of data change curves according to corresponding parameters respectively, obtaining a number of scores corresponding to the above four types of data change curves, and then obtaining a comprehensive score of the above four types of data, and arranging the above four types of comprehensive scores in a matrix format according to the chronological order to obtain a comprehensive score matrix; S4: Determine whether the data monitored in the current time period has deteriorated based on the comprehensive scoring matrix and data standard deviation, and determine the deterioration position based on the data change curve; S5: Determine whether the environmental data in the current time period has deteriorated. If so, determine whether the current soil factors and crop factors have deteriorated, and issue an alarm; The formula for determining whether the data monitored in the current time period has deteriorated in step S4 is: ; In the formula, The severity of the pollution or change in the planting fields during this period of change. is the variance of the comprehensive score of environmental data in the current time period, is the variance of the comprehensive score of environmental data during the previous period of change, is the variance of the comprehensive score of environmental data in the previous two periods of time, , is the calculation parameter, is the standard deviation of the data.

[0008] The present invention acquires multi-faceted data on water quality, soil physics and chemistry, soil microorganisms and crop images in real time, and generates corresponding data change curves for comprehensive scoring and analysis. This can effectively solve the problems of the inability to monitor hidden environmental changes and incomplete data analysis in the prior art. By using a comprehensive scoring matrix and data standard deviation, it can accurately determine whether environmental data has deteriorated and where the deterioration has occurred, comprehensively assess the impact of environmental changes on crop growth and sustainability, and provide a scientific and comprehensive decision-making basis for agricultural production, enabling producers to promptly discover potential problems and take preventive measures.

[0009] Preferably, in step S1, the planting environment data of the planting field is monitored in real time to obtain the water quality, soil physicochemical properties, soil microorganisms and crop image data corresponding to different locations, which specifically includes the following steps: S101: monitor water quality data in real time through sensors. If the water quality data monitoring fails, re-execute step S101 after waiting. If the water quality data monitoring succeeds, proceed to step S102. S102: Real-time detection of soil physical and chemical data and soil microbial data by a soil monitoring instrument. If the monitoring of soil physical and chemical data or soil microbial data fails, the process returns to step S101. If the monitoring of soil physical and chemical data and soil microbial data succeeds, the process proceeds to step S103. S103: Detecting crop image data in real time through a real-time image recognition module. If the crop image data monitoring fails, the process returns to step S101. If the crop image data monitoring succeeds, the process proceeds to step S104. S104: The above four types of data monitored in real time are stored in a local database.

[0010] Preferably, the step S3 specifically includes the following steps: S301: Obtain the maximum and minimum values ​​of the horizontal axis data of the water quality data change curve, calculate the width of the water quality data change curve, multiply the width by the score value corresponding to the pre-stored width to obtain scores of several sections of the water quality data change curve, and then obtain scores of several water quality data change curves; S302: using the same algorithm to obtain soil physical and chemical data change curve scores, soil microbial data change curve scores, and crop image data change curve scores; S303: According to the overall distribution of the data changes corresponding to the above four types of data, the scores of the change curves of the above four types of data are respectively corrected, so as to obtain the comprehensive scores of the above four types of data; S304: Arrange the comprehensive scores of water quality data, soil physical and chemical data, soil microbial data, and crop image data in a matrix format in chronological order to obtain a comprehensive score matrix.

[0011] Preferably, the scores of several water quality data change curves obtained in step S301 are performed in the following manner: assuming that the width of the water quality data change curve is , the corresponding fraction value of the pre-stored width is , then the score of a water quality data change curve is .

[0012] Preferably, the soil physical and chemical data change curve score is obtained in step S302 by the following method: assuming that the width is , the corresponding fraction value of the pre-stored width is , then the score of a certain section of soil physical and chemical data change curve is: .

[0013] Preferably, in step S303, the data change curve scores of water quality data, soil physical and chemical data, soil microbial data and crop image data are respectively corrected to obtain a comprehensive score of water quality data, a comprehensive score of soil physical and chemical data, a comprehensive score of soil microbial data and a comprehensive score of crop image data, in the following manner: Assume that the correction coefficient of the comprehensive score of water quality data is , the correction factor for the comprehensive score of soil physical and chemical data is , the correction coefficient of the comprehensive score of soil microbial data is , the correction coefficient of the comprehensive score of crop image data is ,but: Comprehensive rating of water quality data: ; Comprehensive score of soil physical and chemical data: ; Comprehensive score of soil microbial data: ; Comprehensive scoring of crop image data: .

[0014] Preferably, if the environmental data in the current time period has not deteriorated, the comprehensive score of the environmental data in the current time period is obtained according to the following formula: ; In the formula, Provides a comprehensive score for the environmental data for the current time period. For the The curves correspond to the weights of the curve scores. is the number of curves, The sum of the weights is 1, For the The curves correspond to the expected values ​​of the curve scores.

[0015] Preferably, the step S5 specifically includes the following steps: S501: If the environmental data of the current time period deteriorates, determine whether the current soil factor deteriorates. If not, further determine whether the current crop factor deteriorates. If so, proceed to step S503; if so, proceed to step S504; S502: If the current soil factors deteriorate, find the minimum score of the soil physical and chemical data change curve at the current location according to the deterioration location and issue an alarm; S503: If the current crop factor deteriorates, find the minimum score of the crop image data change curve at the current position according to the deterioration position and issue an alarm; S504: If the current soil factors and crop factors deteriorate at the same time, the comprehensive scoring matrix is ​​divided into blocks according to the location, and feature change comparison is performed. According to the feature change comparison results, it is determined that the soil factor or crop factor has changed more seriously, and then the main cause of the deterioration is determined.

[0016] A real-time monitoring and analysis system for environmental data of a planting field, comprising: The data monitoring module is used to monitor the planting environment data of the planting field in real time, obtain the corresponding water quality data, soil physical and chemical data, soil microbial data, and crop image data at different locations, and store the data in the local database; The data analysis module is used to extract four types of data from the local database, arrange the above four types of data in chronological order, and generate water quality data change curves, soil physical and chemical data change curves, soil microbial data change curves, and crop image data change curves; A curve scoring module is used to score the above four types of data change curves according to corresponding parameters, obtain a number of scores corresponding to the above four types of data change curves, and then obtain a comprehensive score of the above four types of data, and arrange the above four types of comprehensive scores in a matrix format according to the chronological order to obtain a comprehensive scoring matrix; The alarm module is used to determine whether the monitoring parameters in the current time period have deteriorated based on the comprehensive scoring matrix and the data standard deviation. If the environmental data in the current time period has deteriorated, it is used to determine whether the current soil factors or crop factors have deteriorated and issue an alarm.

[0017] Preferably, the data monitoring module includes a water quality data monitoring unit for collecting water quality data, a soil data monitoring unit for collecting soil physical and chemical data and soil microbial data, and a crop image data monitoring unit for collecting crop image data; The soil physical and chemical data include apparent soil electrical conductivity, soil salinity and effective cation concentration of foliar nutrient solution; The water quality data include dissolved oxygen concentration, conductivity, pH value and redox potential; The soil microbial data include the total number of soil microorganisms, the number of ammonia oxidizing bacteria, the number of nitrite oxidizing bacteria, the number of nitrate reducing bacteria and the number of Pseudomonas; The crop image data includes color feature parameters, clarity feature parameters, texture feature parameters and morphology feature parameters.

[0018] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention acquires multi-faceted data on water quality, soil physics and chemistry, soil microorganisms and crop images in real time, and generates corresponding data change curves for comprehensive scoring and analysis, which can effectively solve the problems of the inability to monitor implicit environmental changes and incomplete data analysis in the prior art. By using the comprehensive scoring matrix and data standard deviation, it can accurately determine whether the environmental data has deteriorated and the location of the deterioration, comprehensively evaluate the impact of environmental changes on crop growth and sustainability, provide a scientific and comprehensive decision-making basis for agricultural production, and enable producers to promptly discover potential problems and take preventive measures.

[0019] 2. On the basis of realizing the monitoring of implicit environmental changes and accurately judging the deterioration of environmental data, the present invention further optimizes the problem diagnosis mechanism. When the environmental data deteriorates, the system can quickly determine whether the problem is caused by soil factors or crop factors, and find the minimum score of the corresponding data change curve according to the deterioration position to issue an alarm. This enables agricultural producers to quickly locate the root cause of the problem. Compared with traditional methods, it greatly shortens the problem diagnosis time, gains precious time for timely remedial measures, effectively reduces the damage to crops caused by environmental deterioration, and further ensures the healthy growth of crops and stable yields.

[0020] 3. In view of the complex situation where soil factors and crop factors deteriorate at the same time, the present invention can accurately judge which change of soil factors or crop factors is more serious by performing position block division and feature change comparison on the comprehensive scoring matrix, thereby determining the main cause of the deterioration. Based on this, agricultural producers can formulate more accurate and effective solutions. The precise policy implementation method significantly improves the pertinence and effectiveness of problem solving, fundamentally reduces the adverse effects of environmental deterioration on crops, and ensures the long-term output of planting fields and the sustainable development of agriculture. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a schematic diagram of the process of the present invention; Figure 2 Schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0022] like Figure 1As shown, the present invention relates to a method for real-time monitoring and analysis of environmental data for a planting field, comprising the following steps: S1: Real-time monitoring of the planting environment data of the planting field to obtain the corresponding water quality data, soil physical and chemical data, soil microbial data, and crop image data at different locations; In the embodiment of the present invention, the soil physical and chemical data specifically include apparent soil conductivity, soil salinity, and effective cation concentration of foliar nutrient solution; Water quality data specifically include dissolved oxygen concentration, conductivity, pH value, and redox potential; Soil microbial data specifically include the total number of soil microorganisms, the number of ammonia-oxidizing bacteria, the number of nitrite-oxidizing bacteria, the number of nitrate-reducing bacteria, and the number of Pseudomonas; The crop image data specifically includes color feature parameters, clarity feature parameters, texture feature parameters, and morphological feature parameters; In an embodiment of the present invention, the step S1 monitors the planting environment data of the planting field in real time to obtain water quality data, soil physical and chemical data, soil microbial data, and crop image data corresponding to different locations, which specifically includes the following steps: S101: monitor water quality data in real time through sensors. If the water quality data monitoring fails, re-execute step S101 after waiting. If the water quality data monitoring succeeds, proceed to step S102. S102: Real-time detection of soil physical and chemical data and soil microbial data by a soil monitoring instrument. If the monitoring of soil physical and chemical data or soil microbial data fails, the process returns to step S101. If the monitoring of soil physical and chemical data and soil microbial data succeeds, the process proceeds to step S103. S103: Detecting crop image data in real time through a real-time image recognition module. If the crop image data monitoring fails, the process returns to step S101. If the crop image data monitoring succeeds, the process proceeds to step S104. S104: storing the real-time monitored water quality data, soil physical and chemical data, soil microbial data and crop image data into a local database; S2: Arrange the water quality data, soil physical and chemical data, soil microorganism data and crop image data in chronological order, and generate a water quality data change curve, a soil physical and chemical data change curve, a soil microorganism data change curve and a crop image data change curve; S3: scoring the water quality data change curve, the soil physical and chemical data change curve, the soil microorganism data change curve, and the crop image data change curve according to the corresponding parameters, and obtaining a number of water quality data change curve scores, soil physical and chemical data change curve scores, soil microorganism data change curve scores, and crop image data change curve scores, and then obtaining a comprehensive score of water quality data, a comprehensive score of soil physical and chemical data, a comprehensive score of soil microorganism data, and a comprehensive score of crop image data, and arranging the above four types of comprehensive scores in a matrix format in chronological order to obtain a comprehensive score matrix; In an embodiment of the present invention, step S3 specifically includes the following steps: S301: Obtain the maximum and minimum values ​​of the horizontal axis data of the water quality data change curve, calculate the width of the water quality data change curve, multiply the width by the score value corresponding to the pre-stored width to obtain scores of several sections of the water quality data change curve, and then obtain scores of several water quality data change curves; In the embodiment of the present invention, the step S301 obtains a plurality of water quality data change curve scores, which are performed in the following manner: assuming that the width of the water quality data change curve is , the corresponding fraction value of the pre-stored width is , then the score of a water quality data change curve is ; S302: using the same algorithm to obtain soil physical and chemical data change curve scores, soil microbial data change curve scores, and crop image data change curve scores; In the embodiment of the present invention, the soil physical and chemical data change curve score is obtained in step S302 by the following method: assuming that the width is , the corresponding fraction value of the pre-stored width is , then the score of a certain section of soil physical and chemical data change curve is: ; S303: According to the overall distribution of the corresponding data changes of water quality data, soil physical and chemical data, soil microorganism data and crop image data, respectively, the scores of the above four types of data change curves are corrected to obtain a comprehensive score of water quality data, a comprehensive score of soil physical and chemical data, a comprehensive score of soil microorganism data and a comprehensive score of crop image data; In an embodiment of the present invention, in step S303, the data change curve scores of water quality data, soil physical and chemical data, soil microbial data and crop image data are respectively corrected to obtain a comprehensive score of water quality data, a comprehensive score of soil physical and chemical data, a comprehensive score of soil microbial data and a comprehensive score of crop image data, which is performed in the following manner: Assume that the correction coefficient of the comprehensive score of water quality data is , the correction factor for the comprehensive score of soil physical and chemical data is , the correction coefficient of the comprehensive score of soil microbial data is , the correction coefficient of the comprehensive score of crop image data is ,but: Comprehensive rating of water quality data: ; Comprehensive score of soil physical and chemical data: ; Comprehensive score of soil microbial data: ; Comprehensive scoring of crop image data: ; S304: Arranging the comprehensive scores of water quality data, soil physical and chemical data, soil microbial data, and crop image data in a matrix format in chronological order to obtain a comprehensive score matrix; S4: Determine whether the data monitored in the current time period has deteriorated based on the comprehensive scoring matrix and data standard deviation, and determine the deterioration position based on the data change curve; In an embodiment of the present invention, the formula for determining whether the data monitored in the current time period deteriorates in step S4 is: ; In the formula, The severity of the pollution or change in the planting fields during this period of change. is the variance of the comprehensive score of environmental data in the current time period, is the variance of the comprehensive score of environmental data during the previous period of change, is the variance of the comprehensive score of environmental data in the previous two periods of time, , is the calculation parameter, is the data standard deviation; In an embodiment of the present invention, if the environmental data in the current time period has not deteriorated, the comprehensive score of the environmental data in the current time period is obtained according to the following formula: ; In the formula, Provides a comprehensive score for the environmental data for the current time period. For the The curves correspond to the weights of the curve scores. is the number of curves, The sum of the weights is 1, For the The curves correspond to the expected values ​​of the curve scores; The present invention acquires multi-faceted data on water quality, soil physics and chemistry, soil microorganisms and crop images in real time, and generates corresponding data change curves for comprehensive scoring and analysis. This can effectively solve the problems of the inability to monitor hidden environmental changes and incomplete data analysis in the prior art. By using a comprehensive scoring matrix and data standard deviation, it can accurately determine whether environmental data has deteriorated and where the deterioration has occurred, comprehensively assess the impact of environmental changes on crop growth and sustainability, and provide a scientific and comprehensive decision-making basis for agricultural production, enabling producers to promptly discover potential problems and take preventive measures.

[0023] S5: Determine whether the environmental data in the current time period has deteriorated. If so, determine whether the current soil factors and crop factors have deteriorated, and issue an alarm.

[0024] In an embodiment of the present invention, step S5 specifically includes the following steps: S501: If the environmental data of the current time period deteriorates, determine whether the current soil factor deteriorates. If not, further determine whether the current crop factor deteriorates. If so, proceed to step S503; if so, proceed to step S504; S502: If the current soil factors deteriorate, find the minimum score of the soil physical and chemical data change curve at the current location according to the deterioration location and issue an alarm; S503: If the current crop factor deteriorates, find the minimum score of the crop image data change curve at the current position according to the deterioration position and issue an alarm; On the basis of realizing the monitoring of implicit environmental changes and accurately judging the deterioration of environmental data, the present invention further optimizes the problem diagnosis mechanism. When environmental data deteriorates, the system can quickly determine whether the problem is caused by soil factors or crop factors, and find the minimum score of the corresponding data change curve according to the deterioration position to issue an alarm. This enables agricultural producers to quickly locate the root cause of the problem. Compared with traditional methods, it greatly shortens the problem diagnosis time, buys valuable time for timely remedial measures, effectively reduces the damage to crops caused by environmental deterioration, and further ensures the healthy growth and stable yield of crops.

[0025] S504: If the current soil factors and crop factors deteriorate at the same time, the comprehensive scoring matrix is ​​divided into blocks according to the location, and feature change comparison is performed. According to the feature change comparison results, it is determined that the soil factor or crop factor has changed more seriously, and then the main cause of the deterioration is determined.

[0026] In response to the complex situation where soil factors and crop factors deteriorate at the same time, the present invention can accurately determine which change is more serious, soil factors or crop factors, by performing position segmentation and feature change comparison on the comprehensive scoring matrix, thereby determining the main cause of the deterioration. Based on this, agricultural producers can formulate more accurate and effective solutions. For example, in response to the deterioration of soil factors, targeted soil improvement measures can be taken, including reasonable fertilization, adjustment of soil pH, etc.; in response to the deterioration of crop factors, precise disease and pest control or optimization of crop cultivation management measures can be carried out in a timely manner. This precise policy-making approach significantly improves the pertinence and effectiveness of problem handling, fundamentally reduces the adverse effects of environmental deterioration on crops, and ensures the long-term output of planting fields and the sustainable development of agriculture.

[0027] like Figure 2 As shown, a real-time monitoring and analysis system for environmental data of a planting field includes a data monitoring module, a data analysis module, a curve scoring module and an alarm module; The data monitoring module is used to monitor the planting environment data of the planting field in real time, obtain the corresponding water quality data, soil physical and chemical data, soil microbial data, and crop image data at different locations, and store the data in a local database.

[0028] The data analysis module is used to extract water quality data, soil physical and chemical data, soil microbial data and crop image data from a local database, arrange the water quality data, soil physical and chemical data, soil microbial data and crop image data in chronological order, and generate water quality data change curves, soil physical and chemical data change curves, soil microbial data change curves and crop image data change curves.

[0029] The curve scoring module is used to score the water quality data change curve, the soil physical and chemical data change curve, the soil microbial data change curve, and the crop image data change curve according to the corresponding parameters, and obtain a plurality of water quality data change curve scores, soil physical and chemical data change curve scores, soil microbial data change curve scores, and crop image data change curve scores, and then obtain a comprehensive score of water quality data, a comprehensive score of soil physical and chemical data, a comprehensive score of soil microbial data, and a comprehensive score of crop image data. The above four types of comprehensive scores are arranged in a matrix format in chronological order to obtain a comprehensive scoring matrix.

[0030] The alarm module is used to determine whether the monitoring parameters in the current time period have deteriorated based on the comprehensive scoring matrix and the data standard deviation. If the environmental data in the current time period has deteriorated, it is used to determine whether the current soil factors or crop factors have deteriorated and issue an alarm.

[0031] As another embodiment of the present invention, the data monitoring module includes a water quality data monitoring unit, a soil data monitoring unit and a crop image data monitoring unit.

[0032] The embodiments of the present invention disclose preferred embodiments, but are not limited thereto. A person skilled in the art can easily understand the spirit of the present invention based on the above embodiments and make different extensions and changes. However, as long as they do not deviate from the spirit of the present invention, they are all within the protection scope of the present invention.

Claims

1. A method for real-time monitoring and analysis of environmental data of a planting field, characterized in that: The following steps are involved: S1: Real-time monitoring of the planting environment data of the planting field to obtain the corresponding water quality data, soil physical and chemical data, soil microbial data, and crop image data at different locations; S2: Arrange the above four types of data in chronological order, and generate a water quality data change curve, a soil physical and chemical data change curve, a soil microbial data change curve, and a crop image data change curve; S3: scoring the above four types of data change curves according to corresponding parameters respectively, obtaining a number of scores corresponding to the above four types of data change curves, and then obtaining a comprehensive score of the above four types of data, and arranging the above four types of comprehensive scores in a matrix format according to the chronological order to obtain a comprehensive score matrix; S4: Determine whether the data monitored in the current time period has deteriorated based on the comprehensive scoring matrix and data standard deviation, and determine the deterioration position based on the data change curve; S5: Determine whether the environmental data in the current time period has deteriorated. If so, determine whether the current soil factors and crop factors have deteriorated, and issue an alarm; The formula for determining whether the data monitored in the current time period has deteriorated in step S4 is: ； In the formula, The severity of the pollution or change in the planting fields during this period of change. is the variance of the comprehensive score of environmental data in the current time period, is the variance of the comprehensive score of environmental data during the previous period of change, is the variance of the comprehensive score of environmental data in the previous two periods of time, , is the calculation parameter, is the standard deviation of the data.

2. A method for real-time monitoring and analysis of environmental data for a planting field according to claim 1, characterized in that: The step S1 monitors the planting environment data of the planting field in real time to obtain the water quality, soil physicochemical properties, soil microorganisms and crop image data corresponding to different locations, which specifically includes the following steps: S101: monitor water quality data in real time through sensors. If the water quality data monitoring fails, re-execute step S101 after waiting. If the water quality data monitoring succeeds, proceed to step S102. S102: Real-time detection of soil physical and chemical data and soil microbial data by a soil monitoring instrument. If the monitoring of soil physical and chemical data or soil microbial data fails, the process returns to step S101. If the monitoring of soil physical and chemical data and soil microbial data succeeds, the process proceeds to step S103. S103: Detecting crop image data in real time through a real-time image recognition module. If the crop image data monitoring fails, the process returns to step S101. If the crop image data monitoring succeeds, the process proceeds to step S104. S104: The above four types of data monitored in real time are stored in a local database.

3. A method for real-time monitoring and analysis of environmental data for a planting field according to claim 1, characterized in that: The step S3 specifically comprises the following steps: S301: Obtain the maximum and minimum values ​​of the horizontal axis data of the water quality data change curve, calculate the width of the water quality data change curve, multiply the width by the score value corresponding to the pre-stored width to obtain scores of several sections of the water quality data change curve, and then obtain scores of several water quality data change curves; S302: using the same algorithm to obtain soil physical and chemical data change curve scores, soil microbial data change curve scores, and crop image data change curve scores; S303: According to the overall distribution of the data changes corresponding to the above four types of data, the scores of the change curves of the above four types of data are respectively corrected, so as to obtain the comprehensive scores of the above four types of data; S304: Arrange the comprehensive scores of water quality data, soil physical and chemical data, soil microbial data, and crop image data in a matrix format in chronological order to obtain a comprehensive score matrix.

4. A method for real-time monitoring and analysis of environmental data for planting fields according to claim 3, characterized in that: In step S301, several water quality data change curve scores are obtained by the following method: assuming that the width of the water quality data change curve is , the corresponding fraction value of the pre-stored width is , then the score of a water quality data change curve is .

5. A method for real-time monitoring and analysis of environmental data for planting fields according to claim 4, characterized in that: The step S302 is to obtain the soil physical and chemical data change curve score by the following method: assuming that the width is , the corresponding fraction value of the pre-stored width is , then the score of a certain section of soil physical and chemical data change curve is: 。 6. A method for real-time monitoring and analysis of environmental data for planting fields according to claim 5, characterized in that: In step S303, the data change curve scores of water quality data, soil physical and chemical data, soil microbial data and crop image data are respectively corrected to obtain a comprehensive score of water quality data, a comprehensive score of soil physical and chemical data, a comprehensive score of soil microbial data and a comprehensive score of crop image data, in the following manner: Assume that the correction coefficient of the comprehensive score of water quality data is , the correction factor for the comprehensive score of soil physical and chemical data is , the correction coefficient of the comprehensive score of soil microbial data is , the correction coefficient of the comprehensive score of crop image data is ,but: Comprehensive rating of water quality data: ； Comprehensive score of soil physical and chemical data: ； Comprehensive score of soil microbial data: ； Comprehensive scoring of crop image data: 。 7. A method for real-time monitoring and analysis of environmental data for a planting field according to claim 1, characterized in that: If the environmental data in the current time period has not deteriorated, the comprehensive score of the environmental data in the current time period is obtained according to the following formula: ； In the formula, Provides a comprehensive score for the environmental data for the current time period. For the The curves correspond to the weights of the curve scores. is the number of curves, The sum of the weights is 1, For the The curves correspond to the expected values ​​of the curve scores.

8. A method for real-time monitoring and analysis of environmental data for a planting field according to claim 1, characterized in that: The step S5 specifically comprises the following steps: S501: If the environmental data of the current time period deteriorates, determine whether the current soil factor deteriorates. If not, further determine whether the current crop factor deteriorates. If so, proceed to step S503; if so, proceed to step S504; S502: If the current soil factors deteriorate, find the minimum score of the soil physical and chemical data change curve at the current location according to the deterioration location and issue an alarm; S503: If the current crop factor deteriorates, find the minimum score of the crop image data change curve at the current position according to the deterioration position and issue an alarm; S504: If the current soil factors and crop factors deteriorate at the same time, the comprehensive scoring matrix is ​​divided into blocks according to the location, and feature change comparison is performed. According to the feature change comparison results, it is determined that the soil factor or crop factor has changed more seriously, and then the main cause of the deterioration is determined.

9. A system for real-time monitoring and analysis of environmental data for a planting field, which uses the method for real-time monitoring and analysis of environmental data for a planting field according to claims 1-8, characterized in that: include: The data monitoring module is used to monitor the planting environment data of the planting field in real time, obtain the corresponding water quality data, soil physical and chemical data, soil microbial data, and crop image data at different locations, and store the data in the local database; The data analysis module is used to extract four types of data from the local database, arrange the above four types of data in chronological order, and generate water quality data change curves, soil physical and chemical data change curves, soil microbial data change curves, and crop image data change curves; A curve scoring module is used to score the above four types of data change curves according to corresponding parameters, obtain a number of scores corresponding to the above four types of data change curves, and then obtain a comprehensive score of the above four types of data, and arrange the above four types of comprehensive scores in a matrix format according to the chronological order to obtain a comprehensive scoring matrix; The alarm module is used to determine whether the monitoring parameters in the current time period have deteriorated based on the comprehensive scoring matrix and the data standard deviation. If the environmental data in the current time period has deteriorated, it is used to determine whether the current soil factors or crop factors have deteriorated and issue an alarm.

10. A real-time monitoring and analysis system for environmental data of a planting field according to claim 9, characterized in that: The data monitoring module includes a water quality data monitoring unit for collecting water quality data, a soil data monitoring unit for collecting soil physical and chemical data and soil microbial data, and a crop image data monitoring unit for collecting crop image data; The soil physical and chemical data include apparent soil electrical conductivity, soil salinity and effective cation concentration of foliar nutrient solution; The water quality data include dissolved oxygen concentration, conductivity, pH value and redox potential; The soil microbial data include the total number of soil microorganisms, the number of ammonia oxidizing bacteria, the number of nitrite oxidizing bacteria, the number of nitrate reducing bacteria and the number of Pseudomonas; The crop image data includes color feature parameters, clarity feature parameters, texture feature parameters and morphology feature parameters.

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