Big Data-Based Water Service Intelligent Management Evaluation Method, System and Medium

Abstract

The invention of this application provides a water service intelligent management evaluation method, system and medium based on big data. The method includes: obtaining the hydrogeomorphic, geological, hydro-meteorological and soil crop data of a region and processing them to obtain indexes of water storage surplus and deficit, drought index, water body storage surplus rate, waterlogging trend and flood and drought prevention degree of the region, and then judging the regional hydrological conditions. If it is the flood season, the hydropower production benefit index is obtained according to the expected increment of reservoir capacity, production capacity index and hydropower installation energy efficiency, and the hydropower production effectiveness is judged. If it is the dry season, the quality inspection of the net water source before purification in the region is carried out, and the water demand supply surplus and deficit degree index is obtained according to the indexes of hierarchical purification treatment and purification water supply efficiency, and the water source supply and demand surplus and deficit is judged; thus, the flood and drought conditions of the region are analyzed and identified through the regional hydrological water service big data, and the hydropower production in the flood season and the net water supply supply and demand conditions in the dry season are judged, realizing the intelligent evaluation of the hydrological, hydropower and water supply water service conditions of the region through big data.

Description

Technical Field

[0001] The present application relates to the technical field of water management. Specifically, it relates to a water service intelligent management evaluation method, system and medium based on big data. Background Art

[0002] Water management includes activities such as statistics, distribution, treatment and protection of water resources, flood control and drought relief, hydropower, water purification, water supply, etc. The purpose is to ensure the sustainable utilization of water resources. Traditional water management relies on manual monitoring, survey statistics and data analysis. Due to the large number of elements and parameters involved in water management, traditional processing means have the disadvantages of poor comprehensiveness, low efficiency, low accuracy and poor fault tolerance. With the development of big data and artificial intelligence, using big data for data analysis and evaluation of water service information can improve management effectiveness and evaluation accuracy. However, there is currently a lack of technology for intelligent information collection, analysis and evaluation management of water services through big data.

[0003] In view of the above problems, there is an urgent need for an effective technical solution at present. Summary of the Invention

[0004] The purpose of the present application is to provide a water service intelligent management evaluation method, system and medium based on big data, which can analyze and identify the drought and flood conditions of a region through regional hydrographic and water service big data, and judge the hydropower production capacity during the flood season and the supply and demand situation of water purification and water supply during the dry season, so as to realize the intelligent evaluation of the water service situation of regional hydrology, hydropower and water supply through big data.

[0005] The present application also provides a water service intelligent management evaluation method based on big data, including the following steps:

[0006] Obtain the hydrogeological and geomorphic information of a preset region and extract the hydrogeomorphic feature data and hydrogeological feature data, and obtain the hydro-meteorological monitoring data and soil-crop water condition detection data of the preset region within a preset seasonal period;

[0007] Process the regional water storage surplus and deficit data according to the hydro-meteorological monitoring data, and process the regional drought index data according to the soil-crop water condition detection data;

[0008] Process the regional water body storage surplus rate index and the regional waterlogging trend coefficient respectively according to the hydrogeomorphic feature data and the hydrogeological feature data combined with the regional water storage surplus and deficit data, and process the regional flood control and drought resistance degree index combined with the regional drought index data, and then judge the regional hydrological conditions of the preset region within the preset seasonal period through threshold comparison;

[0009] If the preset area is in the flood season, obtain the expected reservoir capacity increment data, the planned production capacity index data, and the hydropower installation energy efficiency data, and process them to obtain the hydropower production benefit index of the reservoir power station in the preset area, and judge the hydropower production effectiveness through threshold comparison;

[0010] If the preset area is in the dry season, obtain the water source storage and supply volume data and the net water quality characteristic data of the water source in the preset area, and conduct a comparative quality inspection based on the net water quality characteristic data and the preset water quality index characteristic data to obtain the water source quality inspection level;

[0011] Obtain the purified water supply efficiency data of the water source supply station in the preset area and the regional water demand index data, and purify the pre-net water of the water source in the preset area according to the regional water demand index data to obtain purified domestic water and purified production water respectively. Process according to the regional water demand index data of the purified domestic water and purified production water, combined with the corresponding purified water supply efficiency data, the water source quality inspection level, and the water source storage and supply volume data to obtain the water demand supply profit and loss degree index of the preset area, and then judge the water source supply and demand profit and loss situation through threshold comparison.

[0012] Optionally, in the big data-based water service intelligent management evaluation method described in this application, the obtaining of the hydrogeological and geomorphic information of the preset area and the extraction of hydrogeomorphic characteristic data and hydrogeological characteristic data, and the obtaining of the hydro-meteorological monitoring data and soil crop water condition detection data of the preset area within a preset season period include:

[0013] Obtain the hydrogeological and geomorphic information of the preset area and extract hydrogeomorphic characteristic data and hydrogeological characteristic data;

[0014] The hydrogeomorphic characteristic data includes surface basin water storage data, water area geomorphic proportion data, river and lake storage distribution data, and reservoir storage water volume data, and the hydrogeological characteristic data includes water body layer resistance data, rock layer void looseness data, terrain convergence degree data, and soil water storage rate data;

[0015] Obtain the hydro-meteorological monitoring data and soil crop water condition detection data of the preset area within a preset season period;

[0016] The hydro-meteorological monitoring data includes regional evaporation data, regional precipitation data, and regional runoff surplus data, and the soil crop water condition detection data includes soil particle size data, crop water stress rate data, and crop growth index data.

[0017] Optionally, in the big data-based water service intelligent management evaluation method described in this application, the processing of the hydro-meteorological monitoring data to obtain regional water storage profit and loss data, and the processing of the soil crop water condition detection data to obtain regional drought index data include:

[0018] Process the regional evaporation data, regional precipitation data, and regional runoff surplus data to obtain regional water storage surplus / deficit data;

[0019] Process the soil particle size data, crop water stress rate data, and crop growth index data to obtain regional drought index data;

[0020] The calculation formula for the regional water storage surplus / deficit data is:

[0021] d m = ι 1 z b + ι 2 v e + ι 3 u d ;

[0022] Where d m is the regional water storage surplus / deficit data, z b , v e , u d are the regional precipitation data, regional evaporation data, and regional runoff surplus data respectively, and ι 1 , ι 2 , ι 3 are preset characteristic coefficients;

[0023] The calculation formula for the regional drought index data is:

[0024]

[0025] Where p μ is the regional drought index data, a r , y d , w p are the soil particle size data, crop water stress rate data, and crop growth index data respectively, and υ 1 , υ 2 , υ 3 , are preset characteristic coefficients.

[0026] Optionally, in the water service intelligent management evaluation method based on big data described in this application, the processing based on the hydrogeomorphic feature data and hydrogeological feature data in combination with the regional water storage surplus / deficit data respectively obtains a regional water body storage surplus / deficit rate index and a regional waterlogging trend coefficient, and combines with the regional drought index data to process and obtain a regional flood control and drought resistance degree index, and then judges the regional hydrological conditions in a preset region during a preset seasonal period through threshold comparison, including:

[0027] Process the surface watershed water storage data, reservoir storage water volume data, combined with the water body layer resistance data, soil water storage rate data, and the regional water storage surplus and deficit data through a preset water body storage surplus and deficit test model to obtain the regional water body storage surplus rate index;

[0028] Process the water area geomorphology proportion data, river and lake endowment distribution data, combined with the rock layer porosity looseness data and terrain confluence degree data through a preset waterlogging trend prediction model to obtain the regional waterlogging trend coefficient;

[0029] Process the regional water body storage surplus rate index, regional waterlogging trend coefficient, and regional drought index data through a preset flood and drought prevention degree identification model to obtain the regional flood and drought prevention degree index of a preset region within a preset season period;

[0030] Compare the regional flood and drought prevention degree index with a preset flood and drought prevention threshold to determine the regional flood season or dry season condition within the preset season period;

[0031] The calculation formula of the regional flood and drought prevention degree index is:

[0032]

[0033] Among them, e ξ is the regional flood and drought prevention degree index, r λ is the regional water body storage surplus rate index, ε K is the regional waterlogging trend coefficient, p μ is the regional drought index data, is the geographical dimension coefficient of the preset region, ψ 1 、ψ 2 、ψ 3 are preset characteristic coefficients.

[0034] Optionally, in the big data-based water service intelligent management evaluation method described in this application, if the preset region is in the flood season, obtain the expected reservoir capacity increment data, planned production capacity index data, and hydropower installation energy efficiency data, and process them to obtain the hydropower production benefit index of the preset region's reservoir power station, and judge the hydropower production effectiveness through threshold comparison, including:

[0035] If the preset region is in the flood season within the preset season period, obtain the expected reservoir capacity increment data of the preset region's reservoir within the preset flood season time period;

[0036] Obtain the planned production capacity index data and hydropower installation energy efficiency data of the preset region's reservoir power station within the preset flood season time period;

[0037] The hydropower installation energy efficiency data includes power generation efficiency data, unit reservoir capacity water energy conversion data, and unit reservoir capacity potential energy conversion data;

[0038] Based on the expected reservoir capacity increment data, combined with the power generation efficiency data, the water energy conversion data per unit reservoir capacity, the potential energy conversion data per unit reservoir capacity, and the planned production capacity index data, it is processed through a preset hydropower production efficiency evaluation model to obtain the hydropower production benefit index of the reservoir power station in the preset area during the preset flood season period;

[0039] Compare the hydropower production benefit index with a preset hydropower production efficiency threshold to judge the hydropower production effectiveness of the reservoir power station in the preset area during the preset flood season period;

[0040] The calculation formula of the hydropower production benefit index is:

[0041]

[0042] where, n φ is the hydropower production benefit index, Δc r is the expected reservoir capacity increment data, f ρ , k h , l q are the power generation efficiency data, the water energy conversion data per unit reservoir capacity, and the potential energy conversion data per unit reservoir capacity respectively, P β is the planned production capacity index data, and δ, λ, μ are preset characteristic coefficients.

[0043] Optionally, in the big data-based water service intelligent management evaluation method described in this application, if the preset area is in the dry season, obtain the water source storage and supply volume data and the net front water quality characteristic data of the water source in the preset area, and conduct a comparison quality inspection based on the net front water quality characteristic data and the preset water quality index characteristic data to obtain the water source quality inspection level, including:

[0044] If the preset area is in the dry season during the preset season period, obtain the water source storage and supply volume data of the preset area during the preset dry period;

[0045] Obtain the net front water quality characteristic data of the water source in the preset area, including ammonia nitrogen and phosphorus content detection data, PH detection data, suspended solid content detection data, and bacterial population detection data;

[0046] Conduct a comparison quality inspection based on the net front water quality characteristic data and the preset water quality index characteristic data to obtain the water source quality inspection level;

[0047] The calculation formula of the water source quality inspection level is:

[0048]

[0049] where, I W is the water source quality inspection level, x q , s g, d y , t h are the detection data of ammonia nitrogen and phosphorus content, pH detection data, suspended solid content detection data, and flora quantity detection data respectively, and X c , S a , D u , T k are the standard data of ammonia nitrogen and phosphorus content, pH standard data, suspended solid content standard data, and flora quantity standard data respectively, and η 1 , η 2 , η 3 , η 4 , γ 1 , γ 2 , γ 3 , γ 3 are preset characteristic coefficients.

[0050] Optionally, in the big data-based water service intelligent management evaluation method described in this application, obtaining the purification and water supply efficiency data of the water supply station of the water source in the preset area and the regional water demand index data, and purifying the raw water of the water source in the preset area according to the regional water demand index data to obtain purified domestic water and purified production water respectively, and processing according to the regional water demand index data of the purified domestic water and purified production water in combination with the corresponding purification and water supply efficiency data, as well as the water source quality inspection level and water source storage and supply volume data to obtain the water demand supply profit and loss degree index of the preset area, and then judging the water source supply and demand profit and loss situation through threshold comparison, including:

[0051] Obtaining the purification and water supply efficiency data of the water supply station of the water source in the preset area, including level purification efficiency data and level water supply efficiency data;

[0052] Obtaining the regional water demand index data of the preset area during the preset dry period, including domestic water demand data, domestic water demand level data, production water demand data, and production water demand level data;

[0053] Purifying the raw water of the water source in the preset area by the water supply station of the water source in the preset area according to the regional water demand index data respectively to obtain purified domestic water and purified production water;

[0054] Processing according to the domestic water demand data, domestic water demand level data in combination with the corresponding domestic water level purification efficiency data and domestic water level water supply efficiency data, and production water demand data, production water demand level data in combination with the corresponding production water level purification efficiency data and production water level water supply efficiency data, in combination with the water source quality inspection level and water source storage and supply volume data to obtain the water demand supply profit and loss degree index of the preset area during the preset dry period;

[0055] Compare the water demand - supply profit - loss degree index with the preset water source supply profit - loss threshold to determine the water source supply - demand profit - loss situation in the preset area during the preset dry period time period;

[0056] The calculation formula of the water demand - supply profit - loss degree index is as follows:

[0057]

[0058] Among them, f γ is the water demand - supply profit - loss degree index, b z , m z , g x , z g are respectively the domestic water demand level data, domestic water demand quantity data, domestic water level purification efficiency data, domestic water level water supply efficiency data, p s , h f , n q , a r are respectively the industrial water demand level data, industrial water demand quantity data, industrial water level purification efficiency data, industrial water level water supply efficiency data, I W is the water source quality inspection level, ct is the water source storage and supply quantity data, π 1 , π 2 , κ 1 , κ 2 , χ 1 , χ 2 , are preset characteristic coefficients.

[0059] In a second aspect, the present application provides a water service intelligent management evaluation system based on big data. The system includes: a memory and a processor. The memory includes a program of the water service intelligent management evaluation method based on big data. When the program of the water service intelligent management evaluation method based on big data is executed by the processor, the following steps are implemented:

[0060] Obtain the hydrogeological and geomorphic information of the preset area and extract the hydrogeomorphic feature data and hydrogeological feature data, and obtain the hydro - meteorological monitoring data and soil - crop water condition detection data of the preset area during the preset season time period;

[0061] Process the hydro - meteorological monitoring data to obtain the regional water storage profit - loss quantity data, and process the soil - crop water condition detection data to obtain the regional drought index data;

[0062] Based on the hydrological and geomorphic feature data and the hydrogeological feature data, combined with the regional water storage surplus and deficit data, process them to obtain the regional water body storage surplus rate index and the regional waterlogging trend coefficient respectively, and combine with the regional drought index data to process and obtain the regional flood control and drought resistance degree index, and then judge the regional hydrological conditions in the preset area during the preset season period through threshold comparison;

[0063] If the preset area is in the flood season, obtain the expected increment data of the reservoir capacity, the planned production capacity index data and the hydroelectric installation energy efficiency data, and process them to obtain the hydroelectric production benefit index of the reservoir power station in the preset area, and judge the hydroelectric production effectiveness through threshold comparison;

[0064] If the preset area is in the dry season, obtain the water source storage and supply volume data and the net pre-water quality characteristic data of the water source in the preset area, and conduct a comparative quality inspection according to the net pre-water quality characteristic data and the preset water quality index characteristic data to obtain the water source quality inspection level;

[0065] Obtain the purified water supply efficiency data of the water source supply station in the preset area and the regional water demand index data, and purify the pre-water of the water source in the preset area according to the regional water demand index data to obtain purified domestic water and purified production water respectively. According to the regional water demand index data of the purified domestic water and purified production water, combined with the corresponding purified water supply efficiency data, the water source quality inspection level and the water source storage and supply volume data, process them to obtain the water demand supply surplus and deficit degree index of the preset area, and then judge the water source supply and demand surplus and deficit situation through threshold comparison.

[0066] Optionally, in the water service intelligent management evaluation method based on big data described in this application, the obtaining of the hydrogeological and geomorphic information of the preset area and the extraction of the hydrological and geomorphic feature data and the hydrogeological feature data, and the obtaining of the hydrometeorological monitoring data and the soil and crop water condition detection data in the preset area during the preset season period include:

[0067] Obtain the hydrogeological and geomorphic information of the preset area and extract the hydrological and geomorphic feature data and the hydrogeological feature data;

[0068] The hydrological and geomorphic feature data includes surface basin water storage data, water area geomorphic proportion data, river and lake storage distribution data and reservoir storage water volume data, and the hydrogeological feature data includes water body layer resistance data, rock layer void looseness data, terrain confluence degree data and soil water storage rate data;

[0069] Obtain the hydrometeorological monitoring data and the soil and crop water condition detection data in the preset area during the preset season period;

[0070] The hydrometeorological monitoring data includes regional evaporation data, regional precipitation data, and regional runoff surplus data. The soil and crop water condition detection data includes soil particle size data, crop water stress rate data, and crop growth index data.

[0071] In a third aspect, the present application also provides a computer-readable storage medium, which includes a program for the water service intelligent management evaluation method based on big data. When the program for the water service intelligent management evaluation method based on big data is executed by a processor, it realizes the steps of the water service intelligent management evaluation method based on big data as described in any one of the above.

[0072] As can be seen from the above, the water service intelligent management evaluation method, system, and medium provided by the present application obtain the water storage profit and loss amount, drought index, water body storage profit rate, waterlogging trend, and flood control and drought resistance degree index of the region by acquiring and processing the hydrogeomorphic and geological feature data, hydrometeorological and soil and crop water condition data of the region, and then judge the regional hydrological conditions. If it is the flood season, the hydroelectric power production benefit index of the reservoir power station is obtained by processing the expected increment data of the reservoir capacity, the planned production capacity index data, and the hydroelectric power installation energy efficiency data, and the hydroelectric power production effectiveness is judged. If it is the dry season, the quality inspection of the regional net water source before is obtained to get the water source quality inspection level, and purification treatment is carried out according to the water source water supply station. The water demand supply profit and loss degree index is obtained by processing the water demand index data in combination with the purification water supply efficiency data and the water source quality inspection level, and the water source supply and demand profit and loss situation is judged. Therefore, the drought and flood conditions of the region are analyzed and identified through the regional hydrological and water service big data, and the hydroelectric power production during the flood season and the water supply and demand situation of the purified water supply during the dry season are judged, realizing the intelligent evaluation of the hydrological, hydroelectric power, and water supply situations of the region through big data.

[0073] Other features and advantages of the present application will be described in the subsequent description. Moreover, some of them will become obvious from the description or be understood by implementing the embodiments of the present application. The objectives and other advantages of the present application can be achieved and obtained by the structures specifically pointed out in the written description and the drawings. Description of the Drawings

[0074] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required to be used in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present application, and thus should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0075] Figure 1 It is a flowchart of the water service intelligent management evaluation method based on big data provided by the embodiments of the present application;

[0076] Figure 2 Flow chart for obtaining hydrogeomorphic feature data, hydrogeological feature data, hydro-meteorological monitoring data, and soil crop water condition detection data of the water service intelligent management evaluation method based on big data provided by the embodiments of the present application;

[0077] Figure 3 Flow chart for obtaining regional drought index data of the water service intelligent management evaluation method based on big data provided by the embodiments of the present application;

[0078] Figure 4 Flow chart for obtaining the regional flood control and drought resistance index of the water service intelligent management evaluation method based on big data provided by the embodiments of the present application. Detailed implementation manners

[0079] Next, the technical solutions in the embodiments of the present application will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Components of the embodiments of the present application described and illustrated in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application claimed, but merely represents selected embodiments of the present application. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative efforts fall within the scope of protection of the present application.

[0080] It should be noted that similar reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of the present application, the terms "first", "second", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0081] Please refer to Figure 1 , Figure 1 which is a flow chart of the water service intelligent management evaluation method based on big data in some embodiments of the present application. The water service intelligent management evaluation method based on big data is used in terminal devices such as computers, mobile phone terminals, etc. The water service intelligent management evaluation method based on big data includes the following steps:

[0082] S11. Obtain the hydrogeological and geomorphic information of a preset area and extract hydrogeomorphic feature data and hydrogeological feature data, and obtain hydro-meteorological monitoring data and soil crop water condition detection data of the preset area within a preset seasonal period;

[0083] S12. Process the hydro-meteorological monitoring data to obtain regional water storage surplus and deficit data, and process the soil crop water condition detection data to obtain regional drought index data;

[0084] S13. Process according to the hydrological and geomorphic feature data and the hydrogeological feature data in combination with the regional water storage surplus and deficit data to respectively obtain the regional water body storage surplus rate index and the regional waterlogging trend coefficient, and process in combination with the regional drought index data to obtain the regional flood control and drought resistance degree index, and then judge the regional hydrological conditions in the preset area during the preset seasonal period through threshold comparison;

[0085] S14. If the preset area is in the flood season, obtain the expected reservoir capacity increment data, the planned production capacity index data and the hydropower installation energy efficiency data, and process to obtain the hydropower production benefit index of the reservoir power station in the preset area, and judge the hydropower production effectiveness through threshold comparison;

[0086] S15. If the preset area is in the dry season, obtain the water source storage and supply volume data and the net front water quality feature data of the water source in the preset area, and conduct a comparative quality inspection according to the net front water quality feature data and the preset water quality index feature data to obtain the water source quality inspection level;

[0087] S16. Obtain the purified water supply efficiency data of the water source supply station in the preset area and the regional water demand index data, and purify the net front water of the water source in the preset area according to the regional water demand index data to respectively obtain purified domestic water and purified production water. Process according to the regional water demand index data of the purified domestic water and the purified production water in combination with the corresponding purified water supply efficiency data, the water source quality inspection level and the water source storage and supply volume data to obtain the water demand supply surplus and deficit degree index of the preset area, and then judge the water source supply and demand surplus and deficit situation through threshold comparison.

[0088] Among them, for the technology of realizing intelligent analysis and evaluation of hydrological states and water service activities such as drought and flood degree, hydropower production capacity, water quality detection, and purified water supply in a region through big data technology, data is acquired on the hydrological geomorphology, geology, meteorology, and soil crop information of a preset region within a preset seasonal period. Then, the water storage profit and loss status and drought status of the region are processed and identified. Based on the hydrological geology and geomorphology information, the water storage profit and loss rate and waterlogging trend of the water body in the region are analyzed and processed to obtain the degree index of flood prevention and drought resistance, thereby identifying the hydrological flood and dry conditions of the region in the seasonal period. If the region is in the flood season, the hydropower situation of the regional reservoir is processed according to the reservoir capacity increment, production capacity index, and installation energy efficiency parameters to identify the hydropower production effectiveness status, and then the hydropower generation effectiveness status in the flood season of the region is analyzed and judged. If the region is in the dry season, the water quality and purified water supply status of the region are analyzed and judged. The quality inspection level of the water source quality is obtained through quality inspection of the water quality before purification in the dry season, and then the regional purification water supply station conducts purification treatment according to the demand for production and domestic water and the water quality demand level in the region. By obtaining the purification efficiency and water supply efficiency of the corresponding levels of production and domestic water, and combining the water demand and the water source storage supply volume for processing, the supply profit and loss degree of hierarchical purified water supply in the region is obtained, which reflects the purification supply and demand status of the water source storage for production and domestic water, thereby analyzing and judging the water supply status in the dry season of the region, and realizing intelligent analysis and evaluation of the water service conditions of hydrology, hydropower, and water supply in the region through big data.

[0089] Please refer to Figure 2 , Figure 2 is a flowchart of obtaining hydrological geomorphological feature data, hydrogeological feature data, hydrometeorological monitoring data, and soil crop water condition detection data in a water service intelligent management and evaluation method based on big data in some embodiments of the present application. According to an embodiment of the present invention, the obtaining of the hydrogeological geomorphology information of a preset region and the extraction of hydrological geomorphological feature data and hydrogeological feature data, and the obtaining of hydrometeorological monitoring data and soil crop water condition detection data of the preset region within a preset seasonal period are specifically as follows:

[0090] S21. Obtain the hydrogeological geomorphology information of a preset region and extract hydrological geomorphological feature data and hydrogeological feature data;

[0091] S22. The hydrological geomorphological feature data includes surface basin water storage data, water area geomorphology proportion data, river and lake storage distribution data, and reservoir storage water volume data, and the hydrogeological feature data includes water body layer resistance data, rock layer void looseness data, terrain confluence degree data, and soil water storage rate data;

[0092] S23. Obtain the hydrometeorological monitoring data and soil crop water condition detection data of the preset region within a preset seasonal period;

[0093] S24. The hydrometeorological monitoring data includes regional evaporation data, regional precipitation data, and regional runoff surplus data, and the soil-crop water condition detection data includes soil particle size data, crop water stress rate data, and crop growth index data.

[0094] Among them, to evaluate the water resource storage and flood or drought conditions in a region, first, relevant data on the hydrological resources information of the region is collected. Since the hydrological conditions are related to the stored water volume in the region, the topographies of rivers and lakes, water distribution, and geological layers, water body layers, terrain confluence, and soil water storage degree in the region, and are also related to the net surplus of precipitation, runoff inflow and outflow in the region within a season. At the same time, the soil particle size, crop water content, and growth conditions of the soil-crop can also reflect the drought and flood degree of the regional water resources indirectly. Therefore, characteristic data of hydrological geomorphology and geology are extracted, including data on the total stored water volume of each basin on the surface, the proportion of water area geomorphology in the regional geomorphology, the distribution of water storage in rivers and lakes, and the water storage capacity of reservoirs. The geological data includes data on the blocking ability of water-blocking in the water body layer, the porosity and looseness of the water body rock layer, the confluence degree of the terrain trend, and the water storage rate of the soil. The hydrometeorological monitoring data of the region within a season includes the evaporation, precipitation, and total surplus of runoff inflow and outflow in the region. The soil-crop water condition detection data includes data on soil particle size, crop water stress rate, and crop growth index.

[0095] Please refer to Figure 3 , Figure 3 is a flowchart for obtaining regional drought index data of the water service intelligent management evaluation method based on big data in some embodiments of the present application. According to an embodiment of the present invention, the regional water storage surplus and deficit data is obtained by processing the hydrometeorological monitoring data, and the regional drought index data is obtained by processing the soil-crop water condition detection data. Specifically:

[0096] S31. Obtain regional water storage surplus and deficit data according to the regional evaporation data, regional precipitation data, and regional runoff surplus data;

[0097] S32. Process the soil particle size data, crop water stress rate data, and crop growth index data to obtain regional drought index data;

[0098] The calculation formula for the regional water storage surplus and deficit data is:

[0099] d m =ι 1 z b +ι 2 v e +ι 3 u d ;

[0100] Among them, d mis the regional water storage surplus and deficit data, z b , v e , u d are the regional precipitation data, regional evaporation data, and regional runoff surplus data respectively, ι 1 , ι 2 , ι 3 are preset characteristic coefficients (the characteristic coefficients are obtained by querying the preset hydrological and water service information database);

[0101] The calculation formula for the regional drought index data is:

[0102]

[0103] where p μ is the regional drought index data, a r , y d , w p are the soil particle size data, crop water stress rate data, and crop growth index data respectively, υ 1 , υ 2 , υ 3 , are preset characteristic coefficients (the characteristic coefficients are obtained by querying the preset hydrological and water service information database).

[0104] Among them, the water storage surplus and deficit of the region is the net remaining amount of the inflow and precipitation in the region minus evaporation and outflow within a time period, reflecting the remaining amount of water resources in the region during the time period. The regional water storage surplus and deficit can be obtained through calculation, and the index data of the drought degree of the region in the seasonal time period can be detected by calculating based on the soil and crop water condition detection data, which is used to assist in evaluating the drought index of the region in the seasonal time period.

[0105] Please refer to Figure 4 , Figure 4 is the flowchart of obtaining the regional flood control and drought resistance degree index of the water service intelligent management evaluation method based on big data in some embodiments of the present application. According to the embodiments of the present invention, the above-mentioned hydrological and geomorphic feature data and hydrogeological feature data are combined with the regional water storage surplus and deficit data for processing to obtain the regional water body storage surplus rate index and the regional waterlogging trend coefficient respectively, and the regional flood control and drought resistance degree index is obtained by combining the processing of the regional drought index data, and then the regional hydrological condition in the preset seasonal time period of the preset region is judged through threshold comparison. Specifically:

[0106] S41. According to the surface basin water storage data, reservoir storage capacity data, combined with the water body layer resistance data, soil water storage rate data, and the regional water storage surplus and deficit data, process through a preset water body storage surplus test model to obtain the regional water body storage surplus rate index;

[0107] S42. Process the water area landform proportion data, the distribution data of the water storage capacity in rivers and lakes, combined with the looseness data of rock layer voids and the terrain confluence degree data through a preset waterlogging trend prediction model to obtain the regional waterlogging trend coefficient;

[0108] S43. Process the regional water body storage and surplus rate index, the regional waterlogging trend coefficient, and the regional drought index data through a preset flood and drought prevention and control degree identification model to obtain the regional flood and drought prevention and control degree index within a preset season period for a preset region;

[0109] S44. Compare the regional flood and drought prevention and control degree index with a preset flood and drought prevention and control threshold to judge the regional flood season or dry season condition within the preset season period;

[0110] The calculation formula for the regional flood and drought prevention and control degree index is:

[0111]

[0112] where, e ξ is the regional flood and drought prevention and control degree index, r λ is the regional water body storage and surplus rate index, ε K is the regional waterlogging trend coefficient, p μ is the regional drought index data, is the preset regional geographical dimension coefficient, ψ 1 、ψ 2 、ψ 3 are preset characteristic coefficients (the regional geographical dimension coefficient and characteristic coefficients are obtained by querying a preset hydrological and water affairs information database).

[0113] Among them, the storage and surplus rate index of the regional water body can be calculated through the calculation formula of a preset water body storage and profit and loss test model, that is, it reflects the surplus rate of the water resource retention of the regional water body. The trend detection result of waterlogging occurrence in the region can be calculated through the formula of the preset waterlogging trend prediction model. Then, according to the regional water body storage and surplus rate index, the regional waterlogging trend coefficient, and the regional drought index data, it is processed through the calculation formula of the preset flood and drought prevention and control degree identification model to obtain the regional flood and drought prevention and control degree index within a preset season period for a preset region, that is, to detect and evaluate the index result of flood prevention and drought resistance in the region within the season period. Then, it is compared with the preset flood and drought prevention and control threshold. According to the threshold comparison result, the degree of flood or drought in the region can be evaluated, that is, it can be judged whether the region is in the flood season or the dry season. The flood and drought prevention and control threshold is a segmented threshold, divided into multiple threshold interval segments. The threshold interval segment where the threshold comparison falls is correspondingly used to identify the flood season interval segment, the normal interval segment, and the dry season interval segment, so as to obtain the judgment of the flood season or dry season of the region. Among them, the calculation formula for the regional water body storage and surplus rate index is:

[0114]

[0115] Among them, r λ is the regional water body storage and surplus rate index, h s , m y , d m are respectively the surface basin water storage data, reservoir storage capacity data, and regional water storage surplus and deficit data, g c , q r are respectively the water layer resistance data and soil water storage rate data, ζ 1 , ζ 2 , ζ 3 , θ 1 , θ 2 , τ are preset characteristic coefficients (the characteristic coefficients are obtained by querying the preset hydrological and water service information database);

[0116] The calculation formula of the regional waterlogging trend coefficient is:

[0117]

[0118] Among them, ε K is the regional waterlogging trend coefficient, s n , t w , b p , m c are respectively the water area landform proportion data, river and lake storage distribution data, rock layer void looseness data, and terrain confluence degree data, ω 1 , ω 2 , ω 3 , σ 1 , σ 2 , are preset characteristic coefficients (the characteristic coefficients are obtained by querying the preset hydrological and water service information database).

[0119] According to the embodiment of the present invention, if the preset area is in the flood season, the expected reservoir capacity increment data, the planned production capacity index data, and the hydropower installation energy efficiency data are obtained and processed to obtain the hydropower production benefit index of the reservoir power station in the preset area, and the hydropower production effectiveness is judged by threshold comparison, specifically:

[0120] If the preset area is in the flood season during the preset season period, the expected reservoir capacity increment data of the preset reservoir in the preset flood season time period is obtained;

[0121] The planned production capacity index data and the hydropower installation energy efficiency data of the preset reservoir power station in the preset flood season time period are obtained;

[0122] The hydropower installation energy efficiency data includes power generation efficiency data, unit reservoir capacity water energy conversion data, and unit reservoir capacity potential energy conversion data;

[0123] Based on the expected increment data of the reservoir capacity, combined with the power generation efficiency data, the water energy conversion data per unit reservoir capacity, the potential energy conversion data per unit reservoir capacity, and the planned production capacity index data, it is processed through a preset hydropower production efficiency evaluation model to obtain the hydropower production benefit index of the reservoir power station in the preset area during the preset flood season period;

[0124] Compare the hydropower production benefit index with a preset hydropower production efficiency threshold to judge the hydropower production effectiveness of the reservoir power station in the preset area during the preset flood season period;

[0125] The calculation formula of the hydropower production benefit index is:

[0126]

[0127] where n φ is the hydropower production benefit index, Δc r is the expected increment data of the reservoir capacity, f ρ , k h , l q are the power generation efficiency data, the water energy conversion data per unit reservoir capacity, and the potential energy conversion data per unit reservoir capacity respectively, P β is the planned production capacity index data, and δ, λ, μ are preset characteristic coefficients (the characteristic coefficients are obtained by querying a preset hydrological and water affairs information database).

[0128] Among them, if the area is judged to be in the flood season during the preset season period, it is necessary to use the flood situation for hydropower generation operations to obtain the maximum hydropower production benefit. Since hydropower generation is affected by the reservoir capacity and the hydropower installation situation, and there are also planned production capacity indicators for the regional hydropower energy efficiency, therefore, to evaluate the hydropower production effectiveness of the flood situation, it is necessary to combine the expected increment of the reservoir capacity and the hydropower installation energy efficiency indicators for processing. The hydropower installation energy efficiency data includes the power generation efficiency, the water energy impact per unit reservoir capacity, and the conversion data of the potential energy difference into electric energy. At the same time, combined with the planned production capacity index, it is processed through the calculation formula of the preset hydropower production efficiency evaluation model to obtain the hydropower production benefit index during the flood season period, and then the threshold comparison is used to judge the level of production effectiveness. If the hydropower production benefit index is greater than the preset hydropower production efficiency threshold, it is judged that the effectiveness is compliant, otherwise the effectiveness is poor, so as to realize the evaluation of the production effectiveness of the reservoir power station in the regional flood season.

[0129] According to an embodiment of the present invention, if the preset area is in the dry season, obtain the water source storage supply data and the net front water quality characteristic data of the water source in the preset area, and compare and quality inspect the net front water quality characteristic data with the preset water quality index characteristic data to obtain the water source quality inspection level, specifically:

[0130] If the preset area is in the dry season during the preset season period, obtain the water source storage supply data of the preset area during the preset dry period;

[0131] Obtain the pre-cleaning water quality characteristic data of the water source in the preset area, including ammonia nitrogen and phosphorus content detection data, pH detection data, suspended solid content detection data, and bacterial population detection data;

[0132] Compare and quality-check the pre-cleaning water quality characteristic data with the preset water quality index characteristic data to obtain the water source quality inspection level;

[0133] The calculation formula for the water source quality inspection level is:

[0134]

[0135] Among them, I W is the water source quality inspection level, x q , s g , d y , t h are respectively the ammonia nitrogen and phosphorus content detection data, pH detection data, suspended solid content detection data, and bacterial population detection data, X c , S a , D u , T k are respectively the ammonia nitrogen and phosphorus content standard data, pH standard data, suspended solid content standard data, and bacterial population standard data, η 1 , η 2 , η 3 , η 4 , γ 1 , γ 2 , γ 3 , γ 3 are preset characteristic coefficients (the characteristic coefficients are obtained by querying the preset hydrological and water service information database).

[0136] Among them, if the area is judged to be in the dry season during the preset seasonal period, the water quality status of the area and the purification and water supply effectiveness of the purified water are the key tasks for evaluating the water service in the dry season area to judge and give early warnings about the insufficient water supply situation. First, quality-check the water source quality before purification at the water purification station in the area. By comparing and quality-checking the obtained pre-cleaning water quality characteristic data, including ammonia nitrogen and phosphorus content detection data, pH detection data, suspended solid content detection data, and bacterial population detection data, with the preset water quality index data, obtain the quality inspection level of the water source, and then perform hierarchical purification and water supply according to the required water quality level of water supply and demand.

[0137] According to an embodiment of the present invention, obtain the purification and water supply efficiency data of the water supply station for the water source in the preset area and the regional water demand index data, purify the raw water of the water source in the preset area according to the regional water demand index data to obtain purified domestic water and purified production water respectively, and process according to the regional water demand index data of the purified domestic water and purified production water in combination with the corresponding purification and water supply efficiency data, as well as the water source quality inspection level and water source storage and supply volume data to obtain the water demand supply profit and loss degree index of the preset area, and then judge the water source supply and demand profit and loss situation through threshold comparison, specifically as follows:

[0138] Obtain the purification and water supply efficiency data of the water supply station for the water source in the preset area, including level purification efficiency data and level water supply efficiency data;

[0139] Obtain the regional water demand index data of the preset area during the preset dry period, including domestic water demand data, domestic water demand level data, production water demand data, and production water demand level data;

[0140] Purify the raw water of the water source in the preset area by the water supply station in the preset area according to the regional water demand index data respectively to obtain purified domestic water and purified production water;

[0141] Process according to the domestic water demand data, domestic water demand level data in combination with the corresponding domestic water level purification efficiency data and domestic water level water supply efficiency data, and production water demand data, production water demand level data in combination with the corresponding production water level purification efficiency data and production water level water supply efficiency data, in combination with the water source quality inspection level and water source storage and supply volume data to obtain the water demand supply profit and loss degree index of the preset area during the preset dry period;

[0142] Compare the water demand supply profit and loss degree index with the preset water source supply and demand profit and loss threshold to judge the water source supply and demand profit and loss situation of the preset area during the preset dry period;

[0143] The calculation formula of the water demand supply profit and loss degree index is:

[0144]

[0145] where f γ is the water demand supply profit and loss degree index, b z , m z , g x , z g are respectively the domestic water demand level data, domestic water demand data, domestic water level purification efficiency data, domestic water level water supply efficiency data, p s , h f , n q , a rThey are respectively production water demand level data, production water demand quantity data, production water level purification efficiency data, and production water level water supply efficiency data, I W is the water source quality inspection level, c t is the water source storage and supply quantity data, π 1 π 2 κ 1 κ 2 χ 1 χ 2 is a preset characteristic coefficient (the characteristic coefficient is obtained by querying a preset hydrographic and water service information database).

[0146] Among them, due to the differences in water quality, supply and demand volume of production water demand and domestic water demand in the region, it is necessary to supply purified water according to the demand level based on the quality of the water source detected by quality inspection. For example, if the water source quality inspection level is 4, the production water demand level in the region is 3, and the domestic water demand level in the region is 1, then it is necessary to carry out corresponding purification and water supply according to the demand level. Moreover, the purification and supply efficiency of different levels of purified water and supply water also have differences. Therefore, to evaluate whether the quality and storage volume of the regional water source during the dry season meet the water use requirements of different levels in the region, it is calculated according to the domestic water demand volume, demand level combined with the corresponding domestic water level purification efficiency and water supply efficiency, and production water demand volume, demand level combined with the corresponding production water level purification efficiency and water supply efficiency, as well as the water source quality inspection level and storage and supply volume, to obtain the water demand supply profit and loss degree index of the region during the dry period, that is, the supply abundance and deficiency of production and domestic water demand. Finally, it is compared with the preset water source supply profit and loss threshold. If it is greater than or equal to the threshold, the supply is abundant; if it is less than the threshold, the supply is deficient, and an alarm needs to be issued for the water supply situation.

[0147] The second aspect of the present invention also discloses a water service intelligent management and evaluation system based on big data, including a memory and a processor. The memory includes a water service intelligent management and evaluation method program based on big data. When the water service intelligent management and evaluation method program based on big data is executed by the processor, the following steps are implemented:

[0148] Obtain the hydrogeological and geomorphic information of a preset region and extract hydrogeomorphic feature data and hydrogeological feature data, and obtain the hydro-meteorological monitoring data and soil and crop water condition detection data of the preset region within a preset season period;

[0149] Process the hydro-meteorological monitoring data to obtain regional water storage profit and loss volume data, and process the soil and crop water condition detection data to obtain regional drought index data;

[0150] ​Based on the hydrological and geomorphic feature data and the hydrogeological feature data, combined with the regional water storage surplus and deficit data, process them to obtain the regional water body storage surplus rate index and the regional waterlogging trend coefficient respectively, and combine with the regional drought index data to process and obtain the regional flood control and drought resistance degree index, and then judge the regional hydrological conditions in the preset region during the preset seasonal period through threshold comparison;

[0151] If the preset region is in the flood season, obtain the expected reservoir capacity increment data, the planned production capacity index data and the hydropower installation energy efficiency data, and process them to obtain the hydropower production benefit index of the reservoir power station in the preset region, and judge the hydropower production effectiveness through threshold comparison;

[0152] If the preset region is in the dry season, obtain the water source storage and supply volume data and the net pre-quality feature data of the water source in the preset region, and conduct a comparison quality inspection based on the net pre-quality feature data and the preset water quality index feature data to obtain the water source quality inspection level;

[0153] Obtain the purified water supply efficiency data of the water source supply station in the preset region and the regional water demand index data, and purify the pre-net water of the water source in the preset region according to the regional water demand index data to obtain purified domestic water and purified production water respectively. Based on the regional water demand index data of the purified domestic water and the purified production water, combined with the corresponding purified water supply efficiency data, the water source quality inspection level and the water source storage and supply volume data, process them to obtain the water demand supply surplus and deficit degree index of the preset region, and then judge the water source supply and demand surplus and deficit situation through threshold comparison.

[0154] Among them, for the technology of realizing intelligent analysis and evaluation of hydrological states and water service activities such as drought and flood degree, hydropower production capacity, water quality detection, and purified water supply in a region through big data technology, data is acquired for the hydrogeomorphic, geological, meteorological, and soil crop information of a preset region within a preset seasonal period, and then the water resource storage profit and loss status and drought status of the region are processed and identified. According to the hydrogeological and geomorphic information, the water storage profit and loss rate and waterlogging trend of the water body in the region are analyzed and processed to obtain the degree index of flood prevention and drought resistance, so as to identify the hydrological flood and dry conditions of the region in the seasonal period. If the region is in the flood season, the hydropower situation of the regional reservoir is processed according to the reservoir capacity increment, production capacity index, and installation energy efficiency parameters to identify the hydropower production effectiveness status, so as to analyze and judge the hydropower generation effectiveness status in the flood season region. If the region is in the dry season, the water quality and purified water supply status of the region are analyzed and judged. The quality inspection level of the water source quality is obtained through quality inspection of the water quality before purification in the dry season, and then the regional purification water supply station conducts purification treatment according to the demand for production and domestic water and the water quality demand level in the region. By obtaining the purification efficiency and water supply efficiency of the corresponding levels of production and domestic water, and combining the water demand and the water source storage and supply volume for processing, the supply profit and loss degree of hierarchical purified water supply in the region is obtained, that is, it reflects the purification supply and demand status of the water source storage for production and domestic water in the region, so as to analyze and judge the water supply status in the dry season region, and realize intelligent analysis and evaluation of the water service conditions of hydrology, hydropower, and water supply in the region through big data.

[0155] According to an embodiment of the present invention, the obtaining of the hydrogeological and geomorphic information of the preset region and the extraction of hydrogeomorphic feature data and hydrogeological feature data, and the obtaining of the hydro-meteorological monitoring data and soil crop water condition detection data of the preset region within the preset seasonal period are specifically as follows:

[0156] Obtain the hydrogeological and geomorphic information of the preset region and extract hydrogeomorphic feature data and hydrogeological feature data;

[0157] The hydrogeomorphic feature data includes surface basin water storage data, water area geomorphic proportion data, river and lake storage distribution data, and reservoir storage water volume data, and the hydrogeological feature data includes water body layer resistance data, rock layer void looseness data, terrain convergence degree data, and soil water storage rate data;

[0158] Obtain the hydro-meteorological monitoring data and soil crop water condition detection data of the preset region within the preset seasonal period;

[0159] The hydro-meteorological monitoring data includes regional evaporation data, regional precipitation data, and regional runoff surplus data, and the soil crop water condition detection data includes soil particle size data, crop water stress rate data, and crop growth index data.

[0160] Among them, to evaluate the water resource storage and flood or drought conditions in the evaluation area, relevant data of the hydrological resources in the area are first collected. Since the hydrological conditions are related to the stored water volume in the area, the topographic features of rivers and lakes, the water distribution, as well as the geological layer, water body layer, terrain confluence, and soil water storage degree in the area, and are also related to the net remaining storage of precipitation, runoff inflow and outflow in the area within a season. At the same time, the soil particle size, crop water content, and growth conditions of soil crops can also reflect the drought and flood degree of water resources in the area from the side. Therefore, characteristic data of hydrogeomorphology and geology are extracted, including the total stored water volume in each basin on the surface, the proportion of water area topography in the regional topography, the distribution of stored water volume in rivers and lakes, and the data of reservoir storage capacity. Geological data include the data of the blocking ability of water blocking in the water body layer, the porosity looseness of the water body rock layer, the confluence degree of the terrain trend, and the water storage rate of the soil. The hydrometeorological monitoring data in the area within a season include the evaporation amount, precipitation amount, and total surplus amount of runoff collection and outflow in the area. The soil crop water condition detection data include the data of soil particle size, crop water stress rate, and crop growth index.

[0161] According to an embodiment of the present invention, the regional water storage surplus and deficit data are obtained by processing the hydrometeorological monitoring data, and the regional drought index data are obtained by processing the soil crop water condition detection data, specifically:

[0162] The regional water storage surplus and deficit data are obtained by processing the regional evaporation amount data, regional precipitation amount data, and regional runoff surplus amount data;

[0163] The regional drought index data are obtained by processing the soil particle size data, crop water stress rate data, and crop growth index data;

[0164] The calculation formula for the regional water storage surplus and deficit data is:

[0165] d m =ι 1 z b +ι 2 v e +ι 3 u d ;

[0166] Among them, d m is the regional water storage surplus and deficit data, z b 、v e 、u d are the regional precipitation amount data, regional evaporation amount data, and regional runoff surplus amount data respectively, and ι 1 、ι 2 、ι 3 are preset characteristic coefficients (the characteristic coefficients are obtained by querying a preset hydrological and water service information database);

[0167] The calculation formula for the regional drought index data is:

[0168]

[0169] Among them, p μ is the regional drought index data, and a r , yd, w p are the soil particle size data, crop water stress rate data, and crop growth index data respectively. υ 1 , υ 2 , υ 3 , are preset characteristic coefficients (the characteristic coefficients are obtained by querying the preset hydrological and water affairs information database).

[0170] Among them, the water storage surplus or deficit of the region is the net remaining amount of the inflow, precipitation minus evaporation, and outflow in the region during a time period, reflecting the remaining amount of water resources retained in the region during the time period. The water storage surplus or deficit of the region can be obtained through calculation, and the index data of the drought degree of the region in the seasonal time period can be detected by calculating according to the soil and crop water condition detection data, which is used to assist in evaluating the drought index of the region in the seasonal time period.

[0171] According to the embodiment of the present invention, the hydrological and geomorphic feature data and the hydrogeological feature data are combined with the regional water storage surplus or deficit data for processing to respectively obtain the regional water body storage surplus rate index and the regional waterlogging trend coefficient, and the regional flood control and drought resistance degree index is obtained by combining with the regional drought index data processing, and then the regional hydrological condition in the preset seasonal time period of the preset region is judged by threshold comparison, specifically:

[0172] According to the surface water storage data of the river basin, the reservoir storage capacity data, combined with the water layer resistance data, the soil water storage rate data, and the regional water storage surplus or deficit data, are processed through a preset water body storage surplus test model to obtain the regional water body storage surplus rate index;

[0173] According to the water area geomorphic proportion data, the river and lake storage distribution data, combined with the rock layer porosity looseness data and the terrain convergence degree data, are processed through a preset waterlogging trend prediction model to obtain the regional waterlogging trend coefficient;

[0174] According to the regional water body storage surplus rate index, the regional waterlogging trend coefficient, and the regional drought index data, are processed through a preset flood control and drought resistance degree identification model to obtain the regional flood control and drought resistance degree index in the preset seasonal time period of the preset region;

[0175] According to the threshold comparison between the regional flood control and drought resistance degree index and the preset flood control and drought resistance threshold, the flood season or dry season condition of the region in the preset seasonal time period is judged;

[0176] The calculation formula of the regional flood control and drought resistance degree index is:

[0177]

[0178] Among them, e ξ is the regional flood control and drought resistance index, r λ is the regional water body storage surplus rate index, ε K is the regional waterlogging trend coefficient, p μ is the regional drought index data, is the preset regional geographical dimension coefficient, ψ 1 , ψ 2 , ψ 3 are preset characteristic coefficients (the regional geographical dimension coefficient and characteristic coefficients are obtained by querying the preset hydrological and water affairs information database).

[0179] Among them, the storage surplus rate index of the regional water body can be calculated through the calculation formula of the preset water body storage surplus and deficit test model, that is, it reflects the surplus rate of the water resource retention of the regional water body. The trend detection result of waterlogging occurrence in the region can be calculated through the formula of the preset waterlogging trend prediction model. Then, according to the regional water body storage surplus rate index, the regional waterlogging trend coefficient, and the regional drought index data, it is processed through the calculation formula of the preset flood control and drought resistance degree identification model to obtain the regional flood control and drought resistance index of the preset region within the preset seasonal period, that is, to detect and evaluate the index result of flood control and drought resistance in the region during the seasonal period. Then, it is compared with the preset flood control and drought resistance threshold. According to the threshold comparison result, the degree of flood or drought in the region can be evaluated, that is, it can be judged whether the region is in the flood season or the dry season. The flood control and drought resistance threshold is a segmented threshold, divided into multiple threshold interval segments. The threshold interval segment where the threshold comparison falls is correspondingly identified as the flood season interval segment, the normal interval segment, and the dry season interval segment, so as to obtain the judgment of the flood season or dry season of the region. Among them, the calculation formula of the regional water body storage surplus rate index is:

[0180]

[0181] Among them, r λ is the regional water body storage surplus rate index, h s , m y , d m are respectively the surface basin water storage data, reservoir storage water volume data, and regional water storage surplus and deficit data, g c , q r are respectively the water layer resistance data and soil water storage rate data, ζ 1 , ζ 2 , ζ 3 , θ 1 , θ 2 , τ are preset characteristic coefficients (the characteristic coefficients are obtained by querying the preset hydrological and water affairs information database);

[0182] The calculation formula for the regional waterlogging trend coefficient is as follows:

[0183]

[0184] where ε K is the regional waterlogging trend coefficient, s n , t w , b p , m c are respectively the proportion data of water area landform, the distribution data of river and lake endowment storage, the looseness data of rock stratum voids, and the terrain convergence degree data, ω 1 , ω 2 , ω 3 , σ 1 , σ 2 , are preset characteristic coefficients (the characteristic coefficients are obtained by querying the preset hydrological and water affairs information database).

[0185] According to the embodiments of the present invention, if the preset region is in the flood season, then obtain the expected increment data of reservoir capacity, the planned production capacity index data, and the hydroelectric installation energy efficiency data, and process them to obtain the hydroelectric production benefit index of the reservoir power station in the preset region, and judge the hydroelectric production effect through threshold comparison, specifically:

[0186] If the preset region is in the flood season within the preset season period, then obtain the expected increment data of the reservoir capacity of the preset region within the preset flood season time period;

[0187] Obtain the planned production capacity index data and the hydroelectric installation energy efficiency data of the reservoir power station in the preset region within the preset flood season time period;

[0188] The hydroelectric installation energy efficiency data includes power generation efficiency data, unit reservoir capacity water energy conversion data, and unit reservoir capacity potential energy conversion data;

[0189] According to the expected increment data of the reservoir capacity, combine the power generation efficiency data, the unit reservoir capacity water energy conversion data, the unit reservoir capacity potential energy conversion data, and the planned production capacity index data, and process them through a preset hydroelectric production effect evaluation model to obtain the hydroelectric production benefit index of the reservoir power station in the preset region within the preset flood season time period;

[0190] According to the threshold comparison between the hydroelectric production benefit index and the preset hydroelectric production effect threshold, judge the hydroelectric production effect of the reservoir power station in the preset region within the preset flood season time period;

[0191] The calculation formula for the hydroelectric production benefit index is as follows:

[0192]

[0193] where n φis the hydropower production efficiency index, Δc r is the expected incremental data of reservoir capacity, f ρ , k h , l q are respectively the power generation efficiency data, the water energy conversion data per unit reservoir capacity, and the potential energy conversion data per unit reservoir capacity, P β is the planned production capacity index data, and δ, λ, μ are preset characteristic coefficients (the characteristic coefficients are obtained by querying the preset hydrological and water service information database).

[0194] Among them, if the region is judged to be in the flood season during the preset season period, it is necessary to use the flood situation for hydropower generation operations to obtain the maximum hydropower production efficiency. Since hydropower generation is affected by the reservoir capacity and the hydropower installation situation, and there are also planned production capacity indicators for the regional hydropower energy efficiency, therefore, to evaluate the flood situation hydropower production effectiveness, it is necessary to combine the expected increment of the reservoir capacity and the hydropower installation energy efficiency indicators for processing. The hydropower installation energy efficiency data includes power generation efficiency, the conversion data of the water energy impact and potential energy difference per unit reservoir capacity into electric energy. At the same time, combined with the planned production capacity index, it is processed through the calculation formula of the preset hydropower production efficiency evaluation model to obtain the hydropower production efficiency index during the flood season period, and then the effectiveness of the production is judged by comparing with the threshold. If the hydropower production efficiency index is greater than the preset hydropower production efficiency threshold, it is judged that the effectiveness is compliant, otherwise the effectiveness is poor, so as to realize the evaluation of the production effectiveness of the reservoir hydropower station in the flood season of the region.

[0195] According to the embodiment of the present invention, if the preset region is in the dry season, the water source storage supply data and the net front water quality characteristic data of the water source in the preset region are obtained, and the water source quality inspection level is obtained by comparing and quality inspecting the net front water quality characteristic data with the preset water quality index characteristic data. Specifically:

[0196] If the preset region is in the dry season during the preset season period, the water source storage supply data of the preset region during the preset dry period are obtained;

[0197] The net front water quality characteristic data of the water source in the preset region are obtained, including ammonia nitrogen and phosphorus content detection data, PH detection data, suspended solid content detection data, and bacterial flora amount detection data;

[0198] The water source quality inspection level is obtained by comparing and quality inspecting the net front water quality characteristic data with the preset water quality index characteristic data;

[0199] The calculation formula of the water source quality inspection level is:

[0200]

[0201] Among them, I W is the water source quality inspection level, x q , s g , d y, t h are respectively the detection data of ammonia nitrogen and phosphorus content, PH detection data, suspended solid content detection data, and bacterial population detection data, X c , S a , D u , T k are respectively the standard data of ammonia nitrogen and phosphorus content, PH standard data, suspended solid content standard data, and bacterial population standard data, η 1 , η 2 , η 3 , η 4 , γ 1 , γ 2 , γ 3 , γ 3 are preset characteristic coefficients (the characteristic coefficients are obtained by querying the preset hydrological and water service information database).

[0202] Among them, if the region is judged to be in the dry season during the preset seasonal period, the regional water quality status and the purification and water supply effectiveness are the assessment priorities of the regional water service during the dry season to judge and warn of the insufficient water supply situation. First, the water quality of the water source before purification in the regional water purification station is inspected. By comparing the obtained water quality characteristic data before purification, including the detection data of ammonia nitrogen and phosphorus content, PH detection data, suspended solid content detection data, and bacterial population detection data, with the preset water quality index data, the inspection level of the water source is obtained. Then, hierarchical purification and water supply are carried out according to the required water quality level of water supply and demand.

[0203] According to the embodiment of the present invention, the purification and water supply efficiency data of the water supply station of the water source in the preset region and the regional water demand index data are obtained, and the water before purification of the water source in the preset region is purified according to the regional water demand index data to respectively obtain purified domestic water and purified production water. According to the regional water demand index data of the purified domestic water and purified production water, combined with the corresponding purification and water supply efficiency data, as well as the water source inspection level and water source storage and supply data, the water demand supply profit and loss degree index of the preset region is obtained, and then the profit and loss situation of water source supply and demand is judged by threshold comparison, specifically:

[0204] Obtain the purification and water supply efficiency data of the water supply station of the water source in the preset region, including the level purification efficiency data and the level water supply efficiency data;

[0205] Obtain the regional water demand index data of the preset region during the preset dry period, including domestic water demand data, domestic water demand level data, production water demand data, and production water demand level data;

[0206] According to the water supply station of the water source in the preset region, the water before purification of the water source in the preset region is purified respectively according to the regional water demand index data to obtain purified domestic water and purified production water;

[0207] Based on the domestic water demand data, domestic water demand level data, combined with the corresponding domestic water level purification efficiency data and domestic water level water supply efficiency data, as well as the production water demand data, production water demand level data, combined with the corresponding production water level purification efficiency data and production water level water supply efficiency data, and combined with the water source quality inspection level and water source storage and supply volume data for processing, the water demand supply profit and loss degree index of the preset area within the preset dry period time period is obtained;

[0208] According to the threshold comparison between the water demand supply profit and loss degree index and the preset water source supply profit and loss threshold, judge the water source supply and demand profit and loss situation of the preset area within the preset dry period time period;

[0209] The calculation formula of the water demand supply profit and loss degree index is:

[0210]

[0211] Among them, f γ is the water demand supply profit and loss degree index, b z , m z , g x , z g are respectively the domestic water demand level data, domestic water demand volume data, domestic water level purification efficiency data, domestic water level water supply efficiency data, p s , h f , n q , a r are respectively the production water demand level data, production water demand volume data, production water level purification efficiency data, production water level water supply efficiency data, I W is the water source quality inspection level, c t is the water source storage and supply volume data, π 1 , π 2 , κ 1 , κ 2 , χ 1 , χ 2 , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset hydrological and water service information database).

[0212] Among them, due to the differences in water quality, supply and demand volume of production water demand and domestic water demand in the region, it is necessary to supply purified water according to the required level based on the water quality of the detected water source. For example, if the water source quality inspection level is 4, the production water demand level in the region is 3, and the domestic water demand level in the region is 1, it is necessary to perform corresponding purification and water supply according to the demand level. Moreover, the efficiency of purified water and supply water at different levels also has differences. Therefore, to evaluate whether the quality and storage volume of the regional water source during the dry season meet the water use requirements of different levels in the region, calculations are carried out based on the domestic water demand volume, demand level, combined with the purification efficiency and water supply efficiency of the corresponding domestic water level, as well as the production water demand volume, demand level, combined with the purification efficiency and water supply efficiency of the corresponding production water level, and the water source quality inspection level and storage and supply storage volume, to obtain the water demand supply profit and loss degree index of the region during the dry period, that is, the supply abundance and deficiency situation of production and domestic water demand. Finally, a threshold comparison is made with the preset water source supply profit and loss threshold. If it is greater than or equal to the threshold, the supply is abundant; if it is less than the threshold, the supply is deficient, and an alarm needs to be issued for the water supply situation.

[0213] The third aspect of the present invention provides a computer-readable storage medium, which includes a program for the water service intelligent management evaluation method based on big data. When the program for the water service intelligent management evaluation method based on big data is executed by a processor, the steps of the water service intelligent management evaluation method as described in any one of the above are realized.

[0214] The water service intelligent management evaluation method, system and medium disclosed by the present invention obtain the storage water profit and loss volume, drought index, water body storage and surplus rate, waterlogging trend and flood control and drought resistance degree index of the region by acquiring and processing the hydrographic and geomorphic and geological feature data, hydro-meteorological and soil crop water condition data of the region, and then judge the regional hydrological condition. If it is the flood season, the hydroelectric power production benefit index of the reservoir power station is obtained by processing the expected reservoir capacity increment data, planned production capacity index data and hydroelectric power installation energy efficiency data, and the hydroelectric power production effect is judged. If it is the dry season, the water source before purification in the region is quality inspected to obtain the water source quality inspection level, and purification treatment is carried out according to the water source water supply station. The water demand supply profit and loss degree index is obtained by processing the water demand index data in combination with the purification and water supply efficiency data and the water source quality inspection level, and the water source supply and demand profit and loss situation is judged. Thus, the regional drought and flood conditions are analyzed and identified through the regional hydrographic and water service big data, and the hydroelectric power production during the flood season and the water supply and demand situation of purified water supply during the dry season are judged, realizing the intelligent evaluation of the hydrographic, hydroelectric power and water supply situations of the region through big data.

[0215] In several embodiments provided by the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces. The indirect coupling or communication connection of devices or units can be electrical, mechanical, or other forms.

[0216] The units described above as separate components may or may not be physically separated. The components shown as units may or may not be physical units; they can be located in one place or distributed to multiple network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0217] In addition, each functional unit in the embodiments of the present invention can be all integrated in a processing unit, or each unit can be separately used as a unit, or two or more units can be integrated in one unit; the above-mentioned integrated units can be implemented in the form of hardware, or in the form of a combination of hardware and software functional units.

[0218] Those of ordinary skill in the art can understand that all or part of the steps to implement the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a readable storage medium. When the program is executed, it executes the steps including the above method embodiments; and the foregoing storage medium includes: removable storage devices, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks or optical discs and other various media that can store program codes.

[0219] Alternatively, if the above-mentioned integrated units of the present invention are implemented in the form of software functional modules and sold or used as independent products, they can also be stored in a readable storage medium. Based on such an understanding, the technical solution of the embodiments of the present invention, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. The software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the various embodiments of the present invention. And the foregoing storage medium includes: removable storage devices, ROM, RAM, magnetic disks or optical discs and other various media that can store program codes.

Claims

1. A water affairs intelligent management evaluation method based on big data, characterized in that: The following steps are involved: Obtain the hydrogeological and geomorphological information of the preset area and extract the hydrogeological and geomorphological characteristic data and hydrogeological characteristic data, and obtain the hydrological and meteorological monitoring data and soil and crop water condition detection data of the preset area in the preset seasonal period; Obtain regional water storage surplus and deficit data based on the hydrological and meteorological monitoring data, and obtain regional drought index data based on the soil and crop water condition detection data; According to the hydrological and geomorphological characteristic data and the hydrogeological characteristic data, the regional water storage surplus and deficit data are processed in combination with the regional water storage surplus and deficit data to obtain the regional water body surplus rate index and the regional waterlogging trend coefficient, and the regional drought index data are processed in combination with the regional drought index data to obtain the regional waterlogging and drought resistance index, and then the regional hydrological conditions of the preset area in the preset seasonal period are judged by threshold comparison; If the preset area is in the flood season, the expected incremental data of reservoir capacity, planned capacity index data and hydropower installed capacity energy efficiency data are obtained and processed to obtain the hydropower capacity benefit index of the reservoir power station in the preset area, and the hydropower capacity effectiveness is judged by threshold comparison; If the preset area is in the dry season, the water source storage and supply data and the pre-cleaning water quality characteristic data of the water source in the preset area are obtained, and the pre-cleaning water quality characteristic data are compared with the preset water quality index characteristic data to obtain the water source quality inspection level; Acquire the purification water supply efficiency data and regional water demand index data of the water supply station of the preset regional water source, and purify the net front water of the preset regional water source according to the regional water demand index data to obtain purified domestic water and purified production water respectively, and process the regional water demand index data of the purified domestic water and purified production water in combination with the corresponding purification water supply efficiency data and the water source quality inspection level and water source storage and supply data to obtain the water demand supply profit and loss index of the preset regional area, and then judge the profit and loss situation of water supply and demand by threshold comparison; The method of obtaining the hydrogeological and geomorphological information of the preset area and extracting the hydrogeological and geomorphological characteristic data and the hydrogeological characteristic data, and obtaining the hydrological and meteorological monitoring data and soil and crop water condition detection data of the preset area in the preset seasonal period, includes: Obtaining hydrogeological and geomorphological information of a preset area and extracting hydrogeological and geomorphological characteristic data and hydrogeological characteristic data; The hydrological and geomorphological characteristic data include surface watershed water storage data, water area landform proportion data, river and lake storage distribution data and reservoir water storage data; the hydrogeological characteristic data include water body layer water resistance data, rock layer void looseness data, terrain convergence data and soil water storage rate data; Acquiring hydrological and meteorological monitoring data and soil and crop water condition detection data of the preset area within a preset seasonal period; The hydrological and meteorological monitoring data include regional evaporation data, regional precipitation data and regional runoff surplus data, and the soil and crop water condition detection data include soil granularity data, crop water stress rate data and crop growth index data.

2. The water affairs intelligent management and evaluation method based on big data according to claim 1 is characterized in that: The method of obtaining regional water storage surplus and deficit data according to the hydrological and meteorological monitoring data and obtaining regional drought index data according to the soil and crop water condition detection data includes: Obtaining regional water storage surplus and deficit data based on the regional evaporation data, regional precipitation data and regional runoff surplus data; Obtaining regional drought index data according to the soil granularity data, crop water stress rate data and crop growth index data; The calculation formula for the regional water storage surplus and deficit data is: ； in, is the regional water storage surplus and deficit data, , , They are regional precipitation data, regional evaporation data, and regional runoff surplus data. , , is the preset characteristic coefficient; The calculation formula of the regional drought index data is: ； in, is the regional drought indicator data, , , They are soil particle size data, crop water stress rate data, and crop growth index data. , , , , , is the preset characteristic coefficient.

3. The water affairs intelligent management and evaluation method based on big data according to claim 2 is characterized in that: The hydrological and geomorphological characteristic data and the hydrogeological characteristic data are processed in combination with the regional water storage surplus and deficit data to obtain the regional water body surplus rate index and the regional waterlogging trend coefficient, and the regional drought index data are processed to obtain the regional waterlogging and drought resistance index, and then the regional hydrological conditions of the preset area in the preset seasonal period are judged by threshold comparison, including: The surface watershed water storage data, reservoir water storage data, combined with water layer water resistance data, soil water storage rate data and regional water storage surplus and deficit data are processed through a preset water storage surplus and deficit test model to obtain a regional water body surplus rate index; The water body landform proportion data, river and lake storage distribution data, rock stratum porosity data, and terrain confluence data are processed through a preset waterlogging trend prediction model to obtain a regional waterlogging trend coefficient; The regional water body surplus rate index, regional waterlogging trend coefficient and regional drought index data are processed by a preset waterlogging and drought resistance identification model to obtain a regional waterlogging and drought resistance index for a preset region in a preset seasonal period; According to the regional flood control and drought resistance index, a threshold comparison is performed with a preset flood control and drought resistance threshold to determine the regional flood season or dry season status within the preset seasonal period; The calculation formula of the regional flood control and drought resistance index is: ； in, is the regional flood control and drought resistance index, is the regional water body surplus rate index, is the regional waterlogging trend coefficient, is the regional drought indicator data, is the geographic dimension coefficient of the preset area, , , is the preset characteristic coefficient.

4. The water affairs intelligent management and evaluation method based on big data according to claim 3 is characterized in that: If the preset area is in the flood season, the expected reservoir capacity increment data, planned capacity index data and hydropower installed capacity energy efficiency data are obtained and processed to obtain the hydropower capacity benefit index of the reservoir power station in the preset area, and the hydropower capacity effectiveness is judged by threshold comparison, including: If the preset area is in the flood season during the preset seasonal period, then obtaining the expected incremental storage capacity data of the reservoir in the preset area during the preset flood season period; Obtaining planned capacity index data and hydropower installed capacity energy efficiency data of reservoir power stations in a preset area during the preset flood season period; The hydropower installed capacity energy efficiency data include power generation efficiency data, unit storage capacity water energy conversion data and unit storage capacity potential energy conversion data; The expected reservoir capacity increment data is combined with the power generation efficiency data, the unit reservoir capacity water energy conversion data, the unit reservoir capacity potential energy conversion data and the planned capacity index data through a preset hydropower production efficiency evaluation model to obtain the hydropower production capacity benefit index of the reservoir power station in the preset area during the preset flood season period; According to the hydropower production efficiency index, a threshold comparison is performed with a preset hydropower production efficiency threshold, to determine the hydropower production efficiency of the reservoir power station in the preset area during the preset flood season period; The calculation formula of the hydropower capacity benefit index is: ； in, is the hydropower production efficiency index, is the expected incremental data of storage capacity, , , They are power generation efficiency data, unit storage capacity water energy conversion data, unit storage capacity potential energy conversion data, To plan capacity indicator data, , , is the preset characteristic coefficient.

5. The water affairs intelligent management and evaluation method based on big data according to claim 4 is characterized in that: If the preset area is in the dry season, the water source storage and supply data and the pre-cleaning water quality characteristic data of the water source in the preset area are obtained, and the pre-cleaning water quality characteristic data are compared with the preset water quality index characteristic data to obtain the water source quality inspection level, including: If the preset area is in a dry season during the preset seasonal period, then obtaining water source storage and supply data of the preset area during the preset dry season period; Obtain the pre-clean water quality characteristic data of the water source in the preset area, including ammonia nitrogen and phosphorus detection data, pH detection data, suspended matter content detection data and bacterial flora detection data; Compare the water quality characteristic data before purification with the preset water quality index characteristic data to obtain the water source quality inspection grade; The calculation formula for the water source quality inspection level is: ； in, is the water source quality inspection level, , , , They are ammonia nitrogen and phosphorus detection data, pH detection data, suspended matter content detection data, and bacterial flora detection data. , , , They are standard data of ammonia, nitrogen and phosphorus, standard data of pH, standard data of suspended matter content and standard data of bacterial flora. , , , , , , , is the preset characteristic coefficient.

6. The water affairs intelligent management and evaluation method based on big data according to claim 5 is characterized in that: The method comprises: obtaining the purification water supply efficiency data and the regional water demand index data of the water source supply station in the preset area, purifying the pre-net water of the water source in the preset area according to the regional water demand index data, obtaining purified domestic water and purified production water respectively, processing the regional water demand index data of the purified domestic water and the purified production water in combination with the corresponding purification water supply efficiency data and the water source quality inspection level and water source storage and supply data to obtain the water demand supply profit and loss index of the preset area, and then judging the water supply and demand profit and loss situation by threshold comparison, including: Obtaining the purification and water supply efficiency data of the water source supply station in the preset area, including the level purification efficiency data and the level water supply efficiency data; Acquire regional water demand index data of the preset area during the preset dry period, including domestic water demand data, domestic water demand level data, and production water demand data, and production water demand level data; Purify the clean water of the preset regional water source according to the preset regional water source water supply station according to the regional water demand index data to obtain purified living water and purified production water respectively; According to the domestic water demand data, the domestic water demand level data combined with the corresponding domestic water level purification efficiency data and the domestic water level water supply efficiency data, as well as the production water demand data, the production water demand level data combined with the corresponding production water level purification efficiency data and the production water level water supply efficiency data, combined with the water source quality inspection level and the water source storage and supply data, the water demand supply profit and loss index of the preset area in the preset dry period is obtained; According to the water demand supply profit and loss index and the preset water supply profit and loss threshold, the water supply and demand profit and loss situation of the preset area in the preset dry period is judged; The calculation formula of the water demand supply profit and loss index is: ； in, is the water supply profitability index, , , , They are the data of living water demand level, living water demand volume, living water purification efficiency, and living water supply efficiency. , , , They are production water demand level data, production water demand data, production water level purification efficiency data, and production water level water supply efficiency data. is the water source quality inspection level, For water storage and supply data, , , , , , , is the preset characteristic coefficient.

7. The water affairs intelligent management and evaluation system based on big data is characterized by: The system includes: a memory and a processor, wherein the memory includes a program of a water affairs intelligent management and evaluation method based on big data, and when the program of the water affairs intelligent management and evaluation method based on big data is executed by the processor, the following steps are implemented: Obtain the hydrogeological and geomorphological information of the preset area and extract the hydrogeological and geomorphological characteristic data and hydrogeological characteristic data, and obtain the hydrological and meteorological monitoring data and soil and crop water condition detection data of the preset area in the preset seasonal period; Obtain regional water storage surplus and deficit data based on the hydrological and meteorological monitoring data, and obtain regional drought index data based on the soil and crop water condition detection data; According to the hydrological and geomorphological characteristic data and the hydrogeological characteristic data, the regional water storage surplus and deficit data are processed in combination with the regional water storage surplus and deficit data to obtain the regional water body surplus rate index and the regional waterlogging trend coefficient, and the regional drought index data are processed in combination with the regional drought index data to obtain the regional waterlogging and drought resistance index, and then the regional hydrological conditions of the preset area in the preset seasonal period are judged by threshold comparison; If the preset area is in the flood season, the expected incremental data of reservoir capacity, planned capacity index data and hydropower installed capacity energy efficiency data are obtained and processed to obtain the hydropower capacity benefit index of the reservoir power station in the preset area, and the hydropower capacity effectiveness is judged by threshold comparison; If the preset area is in the dry season, the water source storage and supply data and the pre-cleaning water quality characteristic data of the water source in the preset area are obtained, and the pre-cleaning water quality characteristic data are compared with the preset water quality index characteristic data to obtain the water source quality inspection level; Acquire the purification water supply efficiency data and regional water demand index data of the water supply station of the preset regional water source, and purify the net front water of the preset regional water source according to the regional water demand index data to obtain purified domestic water and purified production water respectively, and process the regional water demand index data of the purified domestic water and purified production water in combination with the corresponding purification water supply efficiency data and the water source quality inspection level and water source storage and supply data to obtain the water demand supply profit and loss index of the preset regional area, and then judge the profit and loss situation of water supply and demand by threshold comparison; The method of obtaining the hydrogeological and geomorphological information of the preset area and extracting the hydrogeological and geomorphological characteristic data and the hydrogeological characteristic data, and obtaining the hydrological and meteorological monitoring data and soil and crop water condition detection data of the preset area in the preset seasonal period, includes: Obtaining hydrogeological and geomorphological information of a preset area and extracting hydrogeological and geomorphological characteristic data and hydrogeological characteristic data; The hydrological and geomorphological characteristic data include surface watershed water storage data, water area landform proportion data, river and lake storage distribution data and reservoir water storage data; the hydrogeological characteristic data include water body layer water resistance data, rock layer void looseness data, terrain convergence data and soil water storage rate data; Acquiring hydrological and meteorological monitoring data and soil and crop water condition detection data of the preset area within a preset seasonal period; The hydrological and meteorological monitoring data include regional evaporation data, regional precipitation data and regional runoff surplus data, and the soil and crop water condition detection data include soil granularity data, crop water stress rate data and crop growth index data.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a water affairs intelligent management and evaluation method program based on big data. When the water affairs intelligent management and evaluation method program based on big data is executed by a processor, the steps of the water affairs intelligent management and evaluation method based on big data as described in any one of claims 1 to 6 are implemented.

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