Agricultural irrigation water quality early warning method and system

Abstract

The application discloses a kind of agricultural irrigation water quality early warning method and system.The application combines irrigation water quality condition and crop growth risk, screens early warning index and early warning crop, sets dynamic grading early warning rule, obtains water quality monitoring point, regional grid, river entry outlet position and water quality monitoring data, judges whether to trigger early warning, analyzes early warning range and early warning source.The system includes data acquisition module, water monitoring module, water quality early warning module, collaborative disposal module and comprehensive display module.The application solves the problem of lagging of agricultural irrigation water risk, the problem of insufficient early warning research and judgment and untimely response of agricultural irrigation water, and the problem of multi-head supervision and lack of information exchange between departments of agricultural irrigation water.

Description

Technical Field

[0001] This invention relates to the field of water quality early warning technology, specifically to a method and system for early warning of water quality for agricultural irrigation. Background Technology

[0002] Agricultural irrigation is a crucial link in food production, and the quality of irrigation water has a significant impact on crop growth, making it a critical factor in ensuring food security and sustainable land development. With the rapid development of industrial and agricultural production, the discharge of large amounts of industrial wastewater has led to a certain degree of pollution of surface water and groundwater, affecting the safety of agricultural irrigation water.

[0003] Although the safety of agricultural irrigation water quality is receiving increasing attention, most regions still rely on traditional regulatory methods, with imperfect regulatory mechanisms, weak risk prevention capabilities, and low levels of informatization. Existing regulatory means are insufficient to meet actual needs.

[0004] In addition, since the supervision of irrigation water quality involves multiple competent authorities, the lack of clear division of responsibilities and coordination mechanisms will inevitably lead to problems such as information asymmetry, slow response speed, and low timeliness of disposal, making it impossible to deal with water pollution issues.

[0005] In response to the relatively weak capacity for monitoring agricultural irrigation water quality, it is of great significance to construct an early warning system for irrigation water quality and to use information technology to compensate for the shortcomings of traditional environmental management in monitoring irrigation water. This will improve the safety and management level of agricultural irrigation water.

[0006] Patent specification CN 108872516 A discloses a water quality monitoring and early warning system, including a data acquisition module, a data processing module, and a water quality early warning module. The data acquisition module is used to monitor and collect real-time water quality data. The data processing module is used to process the collected water quality data. The water quality early warning module is used to issue water quality early warnings based on the processed water quality data. The data processing module includes a first processing submodule, a second processing submodule, a third processing submodule, and a data evaluation submodule. The first processing submodule is used to repair missing water quality data. The second processing submodule is used to repair abnormal water quality data. The third processing submodule is used to denoise the water quality data repaired by the first and second processing submodules. The data evaluation submodule is used to evaluate the denoised water quality data.

[0007] The water quality monitoring and early warning system disclosed in the aforementioned patent technology is not actually integrated with the specific scenario of agricultural irrigation, and it is also unable to make targeted and timely early warning rule adjustments based on the actual growth conditions of crops, resulting in poor flexibility.

[0008] The following are some of the current problems in the supervision of water quality safety for agricultural irrigation:

[0009] 1. Risks associated with agricultural irrigation water use are delayed: Current technologies do not consider strengthening the safety of agricultural irrigation water quality from the perspective of early warning.

[0010] 2. Insufficient early warning and assessment of agricultural irrigation water use, and untimely response: Existing technologies do not provide a comprehensive early warning and analysis scheme for the sudden and short-term characteristics of agricultural irrigation water quality safety incidents.

[0011] 3. Multiple regulatory bodies oversee agricultural irrigation water use, resulting in a lack of information sharing among departments: Existing technologies do not consider the coordination among multiple competent authorities in the supervision of agricultural irrigation water quality. Summary of the Invention

[0012] To address the aforementioned technical problems and shortcomings in this field, this invention provides a method and system for early warning of agricultural irrigation water quality, helping managers to achieve closed-loop management of irrigation water from real-time monitoring, hierarchical dynamic early warning, analysis and judgment to collaborative disposal, thereby improving the regulatory capacity and risk prevention level of irrigation water.

[0013] A method for early warning of water quality for agricultural irrigation water includes the following steps:

[0014] S1. Combining irrigation water quality with crop growth risks, and based on regional crop planting conditions and water environment pollution characteristics, screening early warning indicators and early warning crops;

[0015] S2. Establish early warning rules: Establish dynamic graded early warning rules for irrigation water quality. First, determine the early warning level and the basic early warning concentration value of each early warning indicator in different early warning levels. Then, use the analytic hierarchy process to assess the tolerance of the early warning crop to water pollution under the current actual growth conditions. Based on the obtained pollution tolerance coefficient, dynamically adjust the early warning concentration value to generate the actual early warning concentration value.

[0016] S3. Obtain the location of water quality monitoring points: Set up water quality monitoring points at the irrigation water intake and obtain the latitude and longitude of the water quality monitoring points;

[0017] S4. Divide the region into grids: Divide the region into several grids, using the grids as the basic early warning objects, and extract the latitude and longitude of the center position of each grid;

[0018] S5. Obtain the location of river outlets: Obtain the latitude and longitude of river outlets within the region;

[0019] S6. Obtain water quality monitoring data: Obtain water quality monitoring data from each water quality monitoring point;

[0020] S7. Determine whether an early warning has been triggered: Based on the water quality monitoring data obtained in step S6 and the actual early warning concentration value generated in step S2, determine whether the water quality at each water quality monitoring point has triggered an early warning. If an early warning has been triggered, determine the early warning indicators, early warning level, and early warning crops.

[0021] S8. Warning Range Analysis: Establish a warning range analysis model. Based on the location information of water quality monitoring points and regional grids that trigger the warning, and using the Geohash algorithm, analyze the grid objects covered by the warning range and generate the warning grid.

[0022] S9. Early Warning Source Analysis: Establish an early warning source analysis model. Based on the location information of water quality monitoring points and river outfalls that trigger early warnings, and using the Geohash algorithm, analyze the suspected pollution sources that trigger early warnings.

[0023] The basic warning concentration value in this invention can be set based on existing data such as experience and literature.

[0024] Step S2 specifically includes the following steps:

[0025] S21. Based on the growth conditions of the early warning crop, establish a multi-level assessment index system for the pollution tolerance of the early warning crop, including primary and secondary indicators. The primary indicators include crop characteristic index A1, climate condition index A2, soil condition index A3, and irrigation condition index A4. Each primary indicator includes secondary indicators. The crop characteristic index A1 includes crop type secondary indicator A11 and crop growth stage secondary indicator A12. The climate condition index A2 includes temperature secondary indicator A21, light secondary indicator A22, and diurnal temperature range secondary indicator A23. The soil condition index A3 includes soil permeability secondary indicator A31, soil pH secondary indicator A32, and soil fertility secondary indicator A33. The irrigation condition index A4 includes irrigation water consumption secondary indicator A41 and irrigation method secondary indicator A42.

[0026] S22. For the established multi-level assessment index system of crop pollution tolerance for early warning, construct a comparison matrix and determine the weights of each primary and secondary index:

[0027] The comparison matrix represents the relative importance of each indicator at each level relative to the indicator at the next higher level. It compares the relative importance of each pair of secondary indicators under the same primary indicator. The comparison matrix M is represented as follows:

[0028]

[0029] Among them, a ij It represents the ratio of the degree of influence of any two secondary indicators under the same primary indicator on its primary indicator;

[0030] The eigenvectors are obtained by taking the largest eigenvalue λmax of the comparison matrix. After normalization, the relative importance of each secondary indicator to its primary indicator is calculated. Then, a hierarchical single ranking is performed, that is, the weight ranking of the secondary indicators is performed under each primary indicator.

[0031] Based on the hierarchical single ranking, after normalization, the weight values ​​of the relative importance of all secondary indicators in the multi-level assessment index system of the tolerance of crops to pollution in the early warning are calculated, and the hierarchical overall ranking is carried out, that is, the weight ranking of all secondary indicators in the multi-level assessment index system of the tolerance of crops to pollution in the early warning.

[0032] S23. Assign scores to each secondary indicator based on the current actual growth conditions of the warning crop. The higher the score, the stronger the pollution tolerance of the warning crop, and vice versa. Sum the scores of each secondary indicator with their weight values ​​to obtain the comprehensive pollution tolerance index of the warning crop.

[0033] Pollution tolerance coefficient of early warning crops = Comprehensive pollution tolerance index / Standard pollution tolerance index;

[0034] The standard pollution tolerance index is obtained by weighted summation of the median scores of each secondary indicator and their respective weights.

[0035] Actual warning concentration value = basic warning concentration value × pollution tolerance coefficient.

[0036] A pollution tolerance coefficient greater than 1 indicates that the crop under warning has a high degree of pollution tolerance, and the actual warning concentration value can be appropriately adjusted upwards; conversely, it needs to be adjusted downwards.

[0037] In step S8, the warning range is related to the warning level. The higher the warning level, the larger the warning range. The warning range is automatically configured and calculated using the Geohash algorithm. The grid objects to be warned and alerted within the regulatory area are generated by inputting the regulatory area range.

[0038] In step S9, the source analysis of the early warning starts from the water quality monitoring point that triggered the early warning and analyzes the upstream water quality. The range between the first qualified water quality monitoring point traced upstream and the water quality monitoring point that triggered the early warning is taken as the pollution interval. Then, based on the Geohash algorithm, the suspected pollution sources located within the pollution interval are analyzed to further narrow down the source tracing range.

[0039] The present invention also provides an agricultural irrigation water quality early warning system, including a data acquisition module, a water use monitoring module, a water quality early warning module, a collaborative treatment module, and a comprehensive display module;

[0040] The water quality early warning module is connected to the data acquisition module and the water use monitoring module. It is used to receive information on the regional grid, water quality monitoring points, river discharge outlets and crop growth conditions generated by the data acquisition module, as well as water consumption data and water quality monitoring data generated by the water use monitoring module. It analyzes the early warning range and suspected pollution sources and generates early warning events.

[0041] The collaborative handling module is connected to the water quality early warning module to receive water quality early warning information generated by the water quality early warning module and realize closed-loop handling of early warning events;

[0042] The integrated display module is connected to the data acquisition module, water use monitoring module, water quality early warning module, and collaborative response module. It is used to receive information on the location of regional grids, water quality monitoring points, and river discharge outlets generated by the data acquisition module; water consumption and water quality monitoring data generated by the water use monitoring module; water quality early warning information generated by the water quality early warning module; and progress information on the handling of early warning events generated by the collaborative response module.

[0043] The data acquisition module includes a regional grid management submodule, an irrigation water intake management submodule, a water quality monitoring point management submodule, a river discharge outlet management submodule, and a crop growth condition management submodule, which are used to uniformly input and manage the basic data and monitoring data required for water quality early warning;

[0044] The regional grid management submodule manages the divided regional grids in a unified manner, including information such as grid number, name, center location latitude and longitude, and grid boundaries;

[0045] The irrigation intake management submodule manages the basic information of irrigation intakes in a unified manner. The basic information includes the name of the irrigation intake, the grid it is located in, the irrigation area, and the latitude and longitude. It supports the input, editing, and deletion of irrigation intake information.

[0046] The water quality monitoring site management submodule manages the basic information of water quality monitoring sites in a unified manner, including site number, site name, river channel, latitude and longitude, and monitoring method. It supports the input, editing, and deletion of information.

[0047] The river outfall management submodule manages the basic information of river outfalls in a unified manner, including the name, type and latitude and longitude of the outfall, and supports the input, editing and deletion of information;

[0048] The crop growth condition management submodule collects and manages information on the growth conditions of crops under early warning, including crop characteristics, climate conditions, soil conditions, and irrigation conditions.

[0049] The water usage monitoring module includes a water usage monitoring submodule, a water quality monitoring submodule, a data query submodule, a data statistics submodule, and a data analysis submodule, which are used to monitor and statistically analyze the water usage data of each irrigation intake and the water quality data of the water quality monitoring points in real time.

[0050] The water consumption monitoring submodule enables the input and editing of irrigation water consumption data at irrigation intakes, and performs statistical analysis on the water consumption data, displaying the statistical results in a list format, including the total annual water consumption and the water consumption for each month, and supports the export of water consumption data;

[0051] The water quality monitoring submodule provides a unified display of irrigation water quality monitoring data. The water quality monitoring data is updated synchronously according to the monitoring frequency of the water quality monitoring points. The displayed information includes the monitoring values ​​of various indicators at the water quality monitoring points, the water quality category, and the compliance status.

[0052] The data query submodule is used to query water consumption and water quality monitoring data according to conditions, and supports exporting the queried data;

[0053] The data statistics submodule is used to perform statistical analysis on regional water consumption and water quality. Water consumption statistics are used to count and rank the water consumption of the regional grid based on the water consumption data of each irrigation intake. Water quality statistics are used to count the compliance rate of irrigation water quality, the proportion of water quality categories, the main water quality exceeding indicators, and the main water quality exceeding points.

[0054] The data analysis submodule performs multidimensional analysis based on water quality monitoring data, including single-site water quality trend analysis and multi-site water quality comparative analysis. Single-site water quality trend analysis analyzes the trend of various indicators at a single water quality monitoring point over time. Multi-site water quality comparative analysis compares the changing trends of single indicators at multiple water quality monitoring points.

[0055] The water quality early warning module includes an early warning rule setting submodule, an early warning analysis submodule, an early warning information submodule, and an early warning notification submodule;

[0056] The early warning rule setting submodule sets early warning rules, which serve as the criteria for determining whether water quality monitoring data triggers early warning conditions. The early warning rule setting submodule and the crop growth condition management submodule are used to receive early warning crop growth condition data generated by the crop growth condition management submodule, calculate the pollution tolerance coefficient, and dynamically adjust the actual early warning concentration value.

[0057] The steps for setting early warning rules are as follows: First, select an early warning indicator from all monitoring indicators to choose the one for which an early warning rule needs to be set; second, select an early warning level based on the determined early warning level; third, set trigger conditions to determine whether the early warning information will be triggered once or multiple times; fourth, set a basic early warning concentration value for each type of crop; fifth, establish an assessment index system for the tolerance of crops to water pollution, setting primary and secondary indicators and determining the weights of each primary and secondary indicator; sixth, based on the secondary indicator data obtained from the crop growth condition management submodule, assign scores, weight, and sum the results, compare the sum with the standard pollution tolerance index representing the average score, calculate the pollution tolerance coefficient of the crop, and multiply it by the basic early warning concentration value to generate the actual early warning concentration value.

[0058] The early warning analysis submodule is used to establish an early warning range analysis model and an early warning source analysis model. It combines the grid location information from the regional grid management submodule, the water quality monitoring point location information from the water quality monitoring point management submodule, and the river discharge outlet location information from the river discharge outlet management submodule to analyze the early warning range and suspected pollution sources.

[0059] The steps for establishing the early warning range analysis model are as follows: First, input the latitude and longitude of the water quality monitoring points; second, input the latitude and longitude of the center location of the regional grid; third, set the scope of the monitoring area; fourth, run the Geohash algorithm to obtain the list of grids within the early warning range of the monitoring area.

[0060] The steps for establishing the early warning source analysis model are as follows: First, input the latitude and longitude of the water quality monitoring points; second, input the latitude and longitude of the river discharge outlets; third, starting from the water quality monitoring point that triggered the early warning, analyze the upstream water quality, and take the range between the first qualified water quality monitoring point traced upstream and the water quality monitoring point that triggered the early warning as the pollution interval; fourth, run the Geohash algorithm to obtain a list of river discharge outlets suspected of being pollution sources within the pollution interval.

[0061] The early warning information submodule, based on the water quality monitoring data from the water quality monitoring submodule and combined with the set early warning rules, determines whether the water quality at each monitoring point has triggered an early warning. If an early warning is triggered, an early warning message is generated, and the early warning grid and suspected pollution sources are automatically analyzed through the early warning range analysis model and the early warning source analysis model. The early warning message includes the early warning location, early warning indicator, early warning level, early warning value, early warning crop, early warning time, early warning grid, and suspected source.

[0062] The early warning notification submodule manages the information of personnel who need to receive early warning information, including the name, contact information, and grid to which the person to be notified belongs. After an early warning occurs, the early warning information will be accurately pushed to the relevant personnel within the early warning grid.

[0063] The collaborative response module includes a process setting submodule, an early warning response submodule, a daily inspection submodule, and an event statistics submodule;

[0064] The process setting submodule is used to build a multi-department collaborative handling process for early warning events. It allows you to set the departments and personnel involved in handling early warning events, customize handling nodes, the responsible department for each node, and the handling time limit, and build a process framework for handling early warning events.

[0065] The early warning handling submodule is connected to the early warning information submodule and is used to obtain early warning information generated by the early warning information submodule to form early warning events for handling.

[0066] The daily inspection submodule is used for daily inspection work record keeping and problem reporting, and enables the handling of agricultural irrigation water problems found during the inspection process;

[0067] The event statistics submodule performs statistical analysis on the overall handling status of early warning events, including the number of early warning events and the progress of handling, the frequency of participation of various departments in the collaborative handling of early warning events, and the number and trend of early warning events handled each month.

[0068] The comprehensive display module includes a map display submodule, a statistics display submodule, and an early warning display submodule, which are used to visually display the overview of regional agricultural irrigation water quality monitoring and early warning.

[0069] The map display submodule uses a GIS map to display multiple selectable overlays of elements related to agricultural irrigation water quality monitoring. These layers include farmland distribution, regional grid distribution, river distribution, water quality monitoring point distribution, irrigation water intake distribution, and river discharge outlet distribution.

[0070] The statistics display submodule provides statistics on basic information including the planting area of ​​various crops in the region, the number of water quality monitoring points, the water quality compliance rate, irrigation water consumption, the number of water quality warnings, and the progress of handling warning events, and displays the statistical results in combination with charts.

[0071] The early warning display submodule is used to call the early warning range and early warning source results analyzed by the water quality early warning module, and to visualize the analysis results based on a GIS map.

[0072] Compared with the prior art, the beneficial effects of this invention are as follows:

[0073] 1. By combining irrigation water quality status with crop growth risks, and taking into account the current actual growth conditions of crops, dynamic early warning of irrigation water quality will be implemented based on classification. This will enhance the accuracy and flexibility of pre-emptive prevention and early warning of crop irrigation water risks, strengthen the safety line of irrigation water quality, and solve the problems of the lag and inflexibility of agricultural irrigation water risks.

[0074] 2. Based on the Geohash algorithm, analyze the impact range and suspected pollution sources of early warning points, and combine GIS technology to intuitively display the analysis results. This helps managers quickly grasp key information, take timely and effective preventive measures, avoid agricultural economic losses, and solve the problems of insufficient early warning analysis and untimely response for agricultural irrigation water.

[0075] 3. Establishing business processes for water quality supervision, early warning and response, and daily inspections will enable data and business processes to flow between competent authorities, which will help improve the ability of competent authorities to coordinate the supervision of agricultural irrigation water quality and solve the problems of multiple supervisions and lack of information sharing between departments. Attached Figure Description

[0076] Figure 1 A flowchart of the agricultural irrigation water quality early warning method in a specific implementation;

[0077] Figure 2 This is a schematic diagram of the crop pollution tolerance assessment index system for the agricultural irrigation water quality early warning method in a specific implementation method.

[0078] Figure 3 This is a block diagram of the agricultural irrigation water quality early warning system in a specific implementation. Detailed Implementation

[0079] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention.

[0080] like Figure 3 As shown, an agricultural irrigation water quality early warning system includes a data acquisition module 1, a water use monitoring module 2, a water quality early warning module 3, a collaborative treatment module 4, and a comprehensive display module 5.

[0081] The data acquisition module 1 includes a regional grid management submodule 11, an irrigation water intake management submodule 12, a water quality monitoring point management submodule 13, a river discharge outlet management submodule 14, and a crop growth condition management submodule 15, which are used to uniformly input and manage the basic data and monitoring data required for water quality early warning.

[0082] The regional grid management submodule 11 manages the divided regional grids in a unified manner, including information such as grid number, name, center location latitude and longitude, and grid boundary.

[0083] The irrigation intake management submodule 12 manages the basic information of irrigation intakes in a unified manner. The basic information includes the name of the irrigation intake, the grid it is located in, the irrigation area, and the latitude and longitude. It supports the input, editing, and deletion of irrigation intake information.

[0084] The water quality monitoring point management submodule 13 manages the basic information of water quality monitoring points in a unified manner, including the point number, point name, river where it is located, latitude and longitude, and monitoring method. It supports the input, editing and deletion of information.

[0085] The submodule 14 for managing river outfalls provides unified management of basic information about river outfalls, including the name, type, and latitude and longitude of the outfall. It supports the input, editing, and deletion of this information.

[0086] The crop growth condition management submodule 15 collects and manages information on the growth conditions of crops under early warning, including crop characteristics, climate conditions, soil conditions and irrigation conditions.

[0087] The water usage monitoring module 2 includes a water usage monitoring submodule 21, a water quality monitoring submodule 22, a data query submodule 23, a data statistics submodule 24, and a data analysis submodule 25, which are used to monitor and statistically analyze the water usage data of each irrigation water intake and the water quality data of the water quality monitoring points in real time.

[0088] The water consumption monitoring submodule 21 enables the input and editing of irrigation water consumption data at irrigation intakes, and performs statistical analysis on the water consumption data, displaying the statistical results in a list format, including the total annual water consumption and the water consumption for each month, and supports the export of water consumption data.

[0089] The water quality monitoring submodule 22 provides a unified display of irrigation water quality monitoring data. The water quality monitoring data is updated synchronously according to the monitoring frequency of the water quality monitoring points. The displayed information includes the monitoring values ​​of various indicators at the water quality monitoring points, water quality categories, and compliance status.

[0090] The data query submodule 23 is used to query water consumption and water quality monitoring data according to conditions, and supports exporting the queried data.

[0091] The data statistics submodule 24 is used to perform statistical analysis on regional water consumption and water quality. The water consumption statistics are based on the water consumption data of each irrigation intake to count and rank the water consumption of the regional grid. The water quality statistics are used to count the compliance rate of irrigation water quality, the proportion of water quality categories, the main water quality exceeding indicators, and the main water quality exceeding points.

[0092] The data analysis submodule 25 performs multi-dimensional analysis based on water quality monitoring data, including single-site water quality trend analysis and multi-site water quality comparison analysis. The single-site water quality trend analysis analyzes the trend of various indicators at a single water quality monitoring point over time. The multi-site water quality comparison analysis compares the changing trends of single indicators at multiple water quality monitoring points.

[0093] The water quality early warning module 3 is connected to the data acquisition module 1 and the water use monitoring module 2. It is used to receive the regional grid, water quality monitoring points, river discharge outlet location information and crop growth condition information generated by the data acquisition module 1, as well as the water consumption data and water quality monitoring data generated by the water use monitoring module 2, analyze the early warning range and suspected pollution sources, and generate early warning events.

[0094] Specifically, the water quality early warning module 3 includes an early warning rule setting submodule 31, an early warning analysis submodule 32, an early warning information submodule 33, and an early warning notification submodule 34.

[0095] The early warning rule setting submodule 31 sets the early warning rules, which serve as the criteria for determining whether water quality monitoring data triggers early warning conditions. The early warning rule setting submodule 31 and the crop growth condition management submodule 15 are used to receive the early warning crop growth condition data generated by the crop growth condition management submodule 15, calculate the pollution tolerance coefficient, and dynamically adjust the actual early warning concentration value.

[0096] The steps for setting early warning rules are as follows: First, select an early warning indicator from all monitoring indicators to choose the one for which an early warning rule needs to be set; second, select an early warning level based on the determined early warning level; third, set trigger conditions to determine whether the early warning information will be triggered once or multiple times; fourth, set a basic early warning concentration value for each type of crop; fifth, establish an assessment index system for the tolerance of crops to water pollution, setting primary and secondary indicators and determining the weights of each primary and secondary indicator; sixth, based on the secondary indicator data obtained from the crop growth condition management submodule 15, assign scores, weighted summation, and compare with the standard pollution tolerance index representing the average score to calculate the pollution tolerance coefficient of the crop, multiplying it by the basic early warning concentration value to generate the actual early warning concentration value.

[0097] The early warning analysis submodule 32 is used to establish an early warning range analysis model and an early warning source analysis model. It combines the grid location information of the regional grid management submodule 11, the water quality monitoring point location information of the water quality monitoring point management submodule 13, and the river discharge outlet location information of the river discharge outlet management submodule 14 to analyze the early warning range and suspected pollution sources.

[0098] The steps for establishing the early warning range analysis model are as follows: First, input the latitude and longitude of the water quality monitoring points; second, input the latitude and longitude of the center location of the regional grid; third, set the scope of the monitoring area; fourth, run the Geohash algorithm to obtain the list of grids within the early warning range of the monitoring area.

[0099] The steps for establishing the early warning source analysis model are as follows: First, input the latitude and longitude of the water quality monitoring points; second, input the latitude and longitude of the river discharge outlets; third, starting from the water quality monitoring point that triggered the early warning, analyze the upstream water quality situation, and take the range between the first qualified water quality monitoring point traced upstream and the water quality monitoring point that triggered the early warning as the pollution interval; fourth, run the Geohash algorithm to obtain a list of river discharge outlets suspected of being pollution sources within the pollution interval.

[0100] The early warning information submodule 33, based on the water quality monitoring data from the water quality monitoring submodule 22 and combined with the set early warning rules, determines whether the water quality at each water quality monitoring point has triggered an early warning. If an early warning is triggered, an early warning information is generated, and the early warning grid and suspected pollution sources are automatically analyzed through the early warning range analysis model and the early warning source analysis model. The early warning information includes the early warning location, early warning indicator, early warning level, early warning value, early warning crop, early warning time, early warning grid, and suspected source.

[0101] The early warning notification submodule 34 manages the information of personnel who need to receive early warning information, including the name, contact information and grid of the person to be notified. After an early warning occurs, the early warning information will be accurately pushed to the relevant personnel within the early warning grid.

[0102] The collaborative handling module 4 is connected to the water quality early warning module 3 and is used to receive water quality early warning information generated by the water quality early warning module 3 to realize closed-loop handling of early warning events.

[0103] Specifically, the collaborative handling module 4 includes a process setting submodule 41, an early warning handling submodule 42, a daily inspection submodule 43, and an event statistics submodule 44.

[0104] The process setting submodule 41 is used to build a multi-department collaborative handling process for early warning events. It allows setting the departments and personnel involved in handling early warning events, customizing handling nodes, the responsible department for each node, and the handling time limit, thus building a process framework for handling early warning events.

[0105] The early warning handling submodule 42 is connected to the early warning information submodule 33 and is used to obtain the early warning information generated by the early warning information submodule 33 and form an early warning event for handling.

[0106] The daily inspection submodule 43 is used for daily inspection work record keeping and problem reporting, and enables the handling of agricultural irrigation water problems found during the inspection process.

[0107] The event statistics submodule 44 performs statistical analysis on the overall handling status of early warning events, including the number of early warning events and the progress of handling, the frequency of participation of various departments in the collaborative handling of early warning events, and the number and trend of early warning events handled each month.

[0108] The integrated display module 5 is connected to the data acquisition module 1, water use monitoring module 2, water quality early warning module 3, and collaborative response module 4. It is used to receive the regional grid, water quality monitoring point, and river discharge outlet location information generated by the data acquisition module 1, the water use and water quality monitoring data generated by the water use monitoring module 2, the water quality early warning information generated by the water quality early warning module 3, and the early warning event handling progress information generated by the collaborative response module 4.

[0109] Specifically, the comprehensive display module 5 includes a map display submodule 51, a statistics display submodule 52, and an early warning display submodule 53, which are used to visually display the overview of regional agricultural irrigation water quality monitoring and early warning.

[0110] The map display submodule 51 uses a GIS map to display multiple layers of elements related to the monitoring of water quality for agricultural irrigation water. These layers include farmland distribution, regional grid distribution, river distribution, water quality monitoring point distribution, irrigation water intake distribution, and river discharge outlet distribution.

[0111] The statistics display submodule 52 provides statistics on basic information including the planting area of ​​various crops in the region, the number of water quality monitoring points, the water quality compliance rate, irrigation water consumption, the number of water quality warnings, and the progress of handling warning events, and displays the statistical results in combination with charts.

[0112] The early warning display submodule 53 is used to call the early warning range and early warning source results analyzed by the water quality early warning module (3), and to visualize the analysis results based on the GIS map.

[0113] The aforementioned agricultural irrigation water quality early warning system implements agricultural irrigation water quality early warning methods, such as... Figure 1 As shown, the steps include:

[0114] S1. Combining irrigation water quality with crop growth risks, and based on regional crop planting conditions and water environment pollution characteristics, screening early warning indicators and early warning crops;

[0115] S2. Establish early warning rules: Establish dynamic graded early warning rules for irrigation water quality. First, determine the early warning level and the basic early warning concentration value of each early warning indicator in different early warning levels. Then, use the analytic hierarchy process to assess the tolerance of the early warning crop to water pollution under the current actual growth conditions. Based on the obtained pollution tolerance coefficient, dynamically adjust the early warning concentration value to generate the actual early warning concentration value.

[0116] S3. Obtain the location of water quality monitoring points: Set up water quality monitoring points at the irrigation water intake and obtain the latitude and longitude of the water quality monitoring points;

[0117] S4. Divide the region into grids: Divide the region into several grids, using the grids as the basic early warning objects, and extract the latitude and longitude of the center position of each grid;

[0118] S5. Obtain the location of river outlets: Obtain the latitude and longitude of river outlets within the region;

[0119] S6. Obtain water quality monitoring data: Obtain water quality monitoring data from each water quality monitoring point;

[0120] S7. Determine whether an early warning has been triggered: Based on the water quality monitoring data obtained in step S6 and the actual early warning concentration value generated in step S2, determine whether the water quality at each water quality monitoring point has triggered an early warning. If an early warning has been triggered, determine the early warning indicators, early warning level, and early warning crops.

[0121] S8. Warning Range Analysis: Establish a warning range analysis model. Based on the location information of water quality monitoring points and regional grids that trigger the warning, and using the Geohash algorithm, analyze the grid objects covered by the warning range and generate the warning grid.

[0122] S9. Early Warning Source Analysis: Establish an early warning source analysis model. Based on the location information of water quality monitoring points and river outfalls that trigger early warnings, and using the Geohash algorithm, analyze the suspected pollution sources that trigger early warnings.

[0123] Step S2 specifically includes the following steps:

[0124] S21. Based on the growth conditions of the early warning crops, establish a multi-level assessment index system for the pollution tolerance of the early warning crops, such as... Figure 2 As shown, it includes primary and secondary indicators. The primary indicators include crop characteristic indicator A1, climate condition indicator A2, soil condition indicator A3, and irrigation condition indicator A4. Each primary indicator includes secondary indicators. The crop characteristic indicator A1 includes crop type secondary indicator A11 and crop growth stage secondary indicator A12. The climate condition indicator A2 includes temperature secondary indicator A21, light intensity secondary indicator A22, and diurnal temperature range secondary indicator A23. The soil condition indicator A3 includes soil permeability secondary indicator A31, soil pH secondary indicator A32, and soil fertility secondary indicator A33. The irrigation condition indicator A4 includes irrigation water consumption secondary indicator A41 and irrigation method secondary indicator A42.

[0125] S22. For the established multi-level assessment index system of crop pollution tolerance for early warning, construct a comparison matrix and determine the weights of each primary and secondary index:

[0126] The comparison matrix represents the relative importance of each indicator at each level relative to the indicator at the next higher level. It compares the relative importance of each pair of secondary indicators under the same primary indicator. The comparison matrix M is represented as follows:

[0127]

[0128] Among them, a ij It represents the ratio of the degree of influence of any two secondary indicators under the same primary indicator on its primary indicator;

[0129] The eigenvectors are obtained by taking the largest eigenvalue λmax of the comparison matrix. After normalization, the relative importance of each secondary indicator to its primary indicator is calculated. Then, a hierarchical single ranking is performed, that is, the weight ranking of the secondary indicators is performed under each primary indicator.

[0130] Based on the hierarchical single ranking, after normalization, the weight values ​​of the relative importance of all secondary indicators in the multi-level assessment index system of the tolerance of crops to pollution in the early warning are calculated, and the hierarchical overall ranking is carried out, that is, the weight ranking of all secondary indicators in the multi-level assessment index system of the tolerance of crops to pollution in the early warning.

[0131] S23. Assign scores to each secondary indicator based on the current actual growth conditions of the warning crop. The higher the score, the stronger the pollution tolerance of the warning crop, and vice versa. Sum the scores of each secondary indicator with their weight values ​​to obtain the comprehensive pollution tolerance index of the warning crop.

[0132] Pollution tolerance coefficient of early warning crops = Comprehensive pollution tolerance index / Standard pollution tolerance index;

[0133] The standard pollution tolerance index is obtained by weighted summation of the median scores of each secondary indicator and their respective weights.

[0134] Actual warning concentration value = basic warning concentration value × pollution tolerance coefficient.

[0135] In step S8, the warning range is related to the warning level. The higher the warning level, the larger the warning range. The warning range is automatically configured and calculated using the Geohash algorithm. The grid objects to be warned and alerted within the regulatory area are generated by inputting the regulatory area range.

[0136] In step S9, the source analysis of the early warning starts from the water quality monitoring point that triggered the early warning and analyzes the upstream water quality. The range between the first qualified water quality monitoring point traced upstream and the water quality monitoring point that triggered the early warning is taken as the pollution interval. Then, based on the Geohash algorithm, the suspected pollution sources located within the pollution interval are analyzed to further narrow down the source tracing range.

[0137] Furthermore, it should be understood that after reading the above description of the present invention, those skilled in the art can make various alterations or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims.

Claims

1. An agricultural irrigation water quality early warning method characterized by, The method comprises the following steps: S1, combining the irrigation water quality condition and the crop growth risk, screening the early warning index and the early warning crop based on the regional crop planting condition and the water environment quality pollution characteristic; S2, establishing an early warning rule: establishing an irrigation water quality dynamic grading early warning rule, first determining the early warning level and the basic early warning concentration value of each early warning index in different early warning levels; Then, the tolerance degree of the early warning crop to water quality pollution under the current actual growth condition is evaluated by the analytic hierarchy process, the early warning concentration value is dynamically adjusted according to the obtained pollution tolerance coefficient, and the actual early warning concentration value is generated; S3, obtaining the position of the water quality monitoring point: setting the water quality monitoring point at the irrigation water intake, and obtaining the longitude and latitude of the water quality monitoring point; S4, dividing the regional grid: dividing the region into several grids, taking the grid as the basic early warning object, and extracting the longitude and latitude of the center position of each grid; S5, obtaining the position of the river intake: obtaining the longitude and latitude of the river intake in the region; S6, obtaining water quality monitoring data: obtaining the water quality monitoring data of each water quality monitoring point; S7, determining whether the early warning is triggered: according to the water quality monitoring data obtained in step S6, combining the actual early warning concentration value generated in step S2, determining whether the water quality of each water quality monitoring point triggers the early warning, if the early warning is triggered, determining the early warning index, the early warning level and the early warning crop; S8, early warning range analysis: establishing an early warning range analysis model, according to the position information of the water quality monitoring point and the regional grid which triggers the early warning, based on the Geohash algorithm, analyzing the grid object covered by the early warning range, and generating the early warning grid; S9, early warning source analysis: establishing an early warning source analysis model, according to the position information of the water quality monitoring point and the river intake which triggers the early warning, based on the Geohash algorithm, analyzing the suspected pollution source which triggers the early warning.

2. The agricultural irrigation water quality early warning method according to claim 1, characterized by, Step S2 specifically comprises the following steps: S21, based on the growth condition of the early warning crop, a multi-level evaluation index system of the pollution tolerance degree of the early warning crop is established, including first-level indexes and second-level indexes, the first-level indexes include crop characteristic index A1, climate condition index A2, soil condition index A3 and irrigation condition index A4; each first-level index includes a second-level index, the crop characteristic index A1 includes crop type second-level index A11 and crop growth stage second-level index A12, the climate condition index A2 includes temperature second-level index A21, illumination second-level index A22 and daily temperature difference second-level index A23, the soil condition index A3 includes soil air permeability second-level index A31, soil acid-base value second-level index A32 and soil fertility second-level index A33, and the irrigation condition index A4 includes irrigation water quantity second-level index A41 and irrigation mode second-level index A42; S22, for the established multi-level evaluation index system of the pollution tolerance degree of the early warning crop, a comparison matrix is constructed to determine the weight of each first-level index and second-level index: The comparison matrix represents the relative importance of each index at each level relative to the index at the previous level, and the relative importance of each second-level index under the same first-level index is compared, and the comparison matrix M is represented as: wherein a ij represents the ratio of the influence degree of any two secondary indexes under the same primary index on the primary index they are in. According to the maximum eigenvalue λmax of the comparison matrix, the eigenvector is obtained, and after normalization processing, the relative importance of each secondary index to the primary index is calculated, and the hierarchical single sorting is performed, that is, the weight ordering of the secondary index under each primary index is performed; On the basis of hierarchical single sorting, the weight value of the relative importance of all secondary indexes in the multi-level evaluation index system of the early warning crop pollution tolerance degree is obtained after normalization processing, and hierarchical total sorting is performed, that is, the weight ordering of all secondary indexes in the multi-level evaluation index system of the early warning crop pollution tolerance degree is performed; S23, according to the current actual growth conditions of the early warning crop, each secondary index is scored, the higher the score, the stronger the pollution tolerance of the early warning crop, and vice versa, the score of each secondary index is weighted and summed with the weight value, and the comprehensive pollution tolerance degree index of the early warning crop is obtained; The pollution tolerance coefficient of the early warning crop = the comprehensive pollution tolerance degree index / the standard pollution tolerance degree index; The standard pollution tolerance degree index is obtained by weighting and summing the median value of the score of each secondary index and the weight value; The actual early warning concentration value = the basic early warning concentration value × the pollution tolerance coefficient.

3. The agricultural irrigation water quality early warning method according to claim 1, characterized by, In step S8, the early warning range is related to the early warning level, the higher the early warning level, the larger the early warning range, and the early warning range is calculated by the Geohash algorithm, and the grid object to be early warned in the supervision area is generated by inputting the supervision area range.

4. The agricultural irrigation water quality early warning method of claim 1, wherein In step S9, the early warning source analysis takes the water quality monitoring point triggering the early warning as the starting point, analyzes the upstream water quality, takes the range between the first standard water quality monitoring point traced to the upstream and the water quality monitoring point triggering the early warning as the pollution interval, and analyzes the suspected pollution source located in the pollution interval based on the Geohash algorithm to further narrow down the source tracing range.

5. An agricultural irrigation water quality early warning system characterized by, The agricultural irrigation water quality early warning system is used to realize the agricultural irrigation water quality early warning method of claim 1; It comprises a data acquisition module (1), a water use monitoring module (2), a water quality early warning module (3), a collaborative disposal module (4) and a comprehensive display module (5); The water quality early warning module (3) is connected with the data acquisition module (1) and the water use monitoring module (2), used to receive the regional grid, water quality monitoring point, position information of river inlet and crop growth condition information generated by the data acquisition module (1), and the water quality monitoring data generated by the water use monitoring module (2), analyze the early warning range and the suspected pollution source, and generate the early warning event; The collaborative disposal module (4) is connected with the water quality early warning module (3), used to receive the water quality early warning information generated by the water quality early warning module (3), and realize the closed loop disposal of the early warning event; The comprehensive display module (5) is connected with the data acquisition module (1), the water use monitoring module (2), the water quality early warning module (3) and the collaborative disposal module (4), used to receive the regional grid, water quality monitoring point, position information of river inlet generated by the data acquisition module (1), the water use amount and water quality monitoring data generated by the water use monitoring module (2), the water quality early warning information generated by the water quality early warning module (3), and the early warning event disposal progress information generated by the collaborative disposal module (4).

6. The agricultural irrigation water quality early warning system of claim 5, wherein, The data acquisition module (1) comprises a regional grid management submodule (11), an irrigation water intake management submodule (12), a water quality monitoring point management submodule (13), an entry river outlet management submodule (14) and a crop growth condition management submodule (15), and is used for unified entry and management of basic data and monitoring data required for water quality early warning; The regional grid management submodule (11) is used for unified management of the divided regional grids, and the included information comprises a grid number, a name, central position longitude and latitude and a grid boundary; The irrigation water intake management submodule (12) is used for unified management of basic information of the irrigation water intake, and the basic information comprises an irrigation water intake name, a grid, an irrigation area and longitude and latitude, and supports irrigation water intake information entry, editing and deletion; The water quality monitoring point management submodule (13) is used for unified management of basic information of the water quality monitoring point, and the included information comprises a point number, a point name, a river, longitude and latitude and a monitoring mode, and supports information entry, editing and deletion; The entry river outlet management submodule (14) is used for unified management of basic information of the entry river outlet, and the included information comprises an entry river outlet name, an outlet type and longitude and latitude, and supports information entry, editing and deletion; The crop growth condition management submodule (15) is used for acquisition and management of growth condition information of the early warning crop, and comprises crop characteristics, climate conditions, soil conditions and irrigation conditions.

7. The agricultural irrigation water quality early warning system of claim 6, wherein, The water use monitoring module (2) comprises a water use amount monitoring submodule (21), a water quality monitoring submodule (22), a data query submodule (23), a data statistical submodule (24) and a data analysis submodule (25), and is used for real-time monitoring and statistical analysis of water use amount data of each irrigation water intake and water quality monitoring data of the water quality monitoring point; The water use amount monitoring submodule (21) realizes irrigation water use amount data entry and editing of the irrigation water intake, and performs statistics on the water use amount data, so as to display statistical results in a list form, comprising annual total water use amount and monthly water use amount, and supports water use amount data export; The water quality monitoring submodule (22) uniformly displays irrigation water quality monitoring data, and the water quality monitoring data is synchronously updated according to the monitoring frequency of the water quality monitoring point, and the display information comprises monitoring values of each index of the water quality monitoring point, a water quality category and a standard reaching condition; The data query submodule (23) is used for water use amount and water quality monitoring data query according to conditions, and supports query data export; The data statistical submodule (24) is used for statistical analysis of regional water use amount and water quality, and water use amount statistics are performed on the water use amount of the regional grid according to the water use amount data of each irrigation water intake, and ranking is performed; water quality statistics are performed on irrigation water quality standard reaching rate, water quality category proportion, main water quality exceeding index and main water quality exceeding point; The data analysis submodule (25) performs multi-dimensional analysis based on the water quality monitoring data, comprising single station water quality trend analysis and multi-station water quality comparison analysis; The single station water quality trend analysis performs trend analysis on changes of each index of a single water quality monitoring point with time. Multi-site water quality comparative analysis compares the change trend of a single index of multiple water quality monitoring points.

8. The agricultural irrigation water quality early warning system of claim 7, wherein, The water quality early warning module (3) comprises an early warning rule setting submodule (31), an early warning analysis submodule (32), an early warning information submodule (33), and an early warning notification submodule (34); The early warning rule setting submodule (31) sets the early warning rule as a criterion for judging whether the water quality monitoring data triggers the early warning condition; the early warning rule setting submodule (31) and the crop growth condition management submodule (15) are used to receive the early warning crop growth condition data generated by the crop growth condition management submodule (15), calculate the pollution tolerance coefficient, and dynamically adjust the actual early warning concentration value; The setting steps of the early warning rule are as follows: first, select an early warning index from all monitoring indexes to set the early warning rule for the current early warning index; Second, select an early warning level according to the determined early warning level to set an early warning level for the current early warning rule; Third, set the trigger condition to determine whether the early warning information is triggered once or multiple times; Fourth, set the basic early warning concentration value for each early warning crop to set the basic early warning concentration value of the corresponding early warning index; Fifth, establish an evaluation index system for the tolerance degree of early warning crops to water pollution, set primary and secondary indexes, and determine the weights of the primary and secondary indexes; Sixth, according to the secondary index data obtained from the crop growth condition management submodule (15), assign scores, sum the weighted scores, and compare them with the standard pollution tolerance degree index representing the average score to calculate the pollution tolerance coefficient of the early warning crop, and multiply it by the basic early warning concentration value to generate the actual early warning concentration value; The early warning analysis submodule (32) is used to establish an early warning range analysis model and an early warning source analysis model, and analyze the early warning range and suspected pollution sources in combination with the grid location information of the regional grid management submodule (11), the water quality monitoring point location information of the water quality monitoring point management submodule (13), and the river intake location information of the river intake management submodule (14); The establishment steps of the early warning range analysis model are as follows: first, input the latitude and longitude of the water quality monitoring point; Second, input the central position latitude and longitude of the regional grid; Third, set the supervision area range; fourth, run the Geohash algorithm to obtain the grid list within the early warning range in the supervision area; The establishment steps of the early warning source analysis model are as follows: first, input the latitude and longitude of the water quality monitoring point; Second, input the latitude and longitude of the river intake; third, take the water quality monitoring point triggering the early warning as the starting point to analyze the upstream water quality, and take the range between the first compliant water quality monitoring point traced upstream and the water quality monitoring point triggering the early warning as the pollution interval; Fourth, run the Geohash algorithm to obtain the list of suspected pollution sources of the river intake in the pollution interval; The early warning information submodule (33) judges whether the water quality of each water quality monitoring point triggers the early warning based on the water quality monitoring data of the water quality monitoring submodule (22) and in combination with the set early warning rule; If the early warning is triggered, early warning information is generated, and the early warning grid and suspected pollution source are automatically analyzed by the early warning range analysis model and the early warning source analysis model; The early warning information content includes early warning point, early warning index, early warning level, early warning value, early warning crop, early warning time, early warning grid and suspected source; The early warning notification submodule (34) manages the information of personnel who need to push the early warning information, including the name, contact information and belonging grid of the notification personnel. After the early warning occurs, the early warning information is accurately pushed to the relevant personnel in the early warning grid.

9. The agricultural irrigation water quality early warning system of claim 8, wherein, The collaborative disposal module (4) includes a process setting submodule (41), an early warning disposal submodule (42), a daily patrol submodule (43) and an event statistics submodule (44); The process setting submodule (41) is used to build a multi-department collaborative disposal process of early warning events, and can set the departments and personnel participating in the disposal of early warning events, customize disposal nodes, responsible departments of each node and disposal time limit, and build a process framework for the disposal of early warning events; The early warning disposal submodule (42) is connected with the early warning information submodule (33) and is used to obtain the early warning information generated by the early warning information submodule (33) to form an early warning event for disposal; The daily patrol submodule (43) is used for the account record and problem reporting of daily patrol work, and realizes the disposal of agricultural irrigation water problems found in the patrol process; The event statistics submodule (44) statistically analyzes the overall disposal status of early warning events, counts the number of early warning events and the disposal progress, counts the frequency of each department participating in the collaborative disposal of early warning events, and counts the number of early warning event disposal and the change trend of each month.

10. The agricultural irrigation water quality early warning system of claim 9, wherein, The comprehensive display module (5) includes a map display submodule (51), a statistical display submodule (52) and an early warning display submodule (53), which are used for visual display of the agricultural irrigation water quality monitoring and early warning overview of the region; The map display submodule (51) realizes multi-layer selectable superposition display of agricultural irrigation water quality supervision related elements based on GIS map, and the layers include farmland distribution, regional grid distribution, river distribution, water quality monitoring point distribution, irrigation water intake distribution and river intake distribution; The statistical display submodule (52) counts the basic overview including the planting area of various crops in the region, the number of water quality monitoring points, the water quality standard reaching rate, the irrigation water volume, the number of water quality early warnings, and the disposal progress of early warning events, and displays the statistical results in combination with charts; The early warning display submodule (53) is used to call the early warning range and early warning source results analyzed by the water quality early warning module (3), and visually display the analysis results based on GIS map.

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