Water budget data processing method

Abstract

The invention relates to the technical field of water resource management, in particular to a water budget data processing method. The technical scheme comprises the following steps: acquiring historical data of a management area; and obtaining the increment of the area management unit in the management area after the budgeting time period and the corresponding data label. And performing matching according to the data labels corresponding to the region management units to obtain paired region management units. Obtaining the soil state of the area management unit in the management area and the weather state in the budget time period, and matching the soil state and the weather state with the existing soil state and weather state to obtain a matching time period. And taking the water expenditure data and the water income data of the matched area management unit in the matched time period as the water expenditure data and the water income data of the area management unit in the management area, and then calculating to obtain the water transfer amount in the management area. According to the invention, the existing area management unit is used for replacing a newly added area management unit for calculation, so that a large amount of survey work can be reduced, and the calculation accuracy is improved at the same time.

Description

Technical Field

[0001] This invention relates to the field of water resource management technology, and specifically to a method for processing water budget data. Background Technology

[0002] Water budgeting, also known as water balance, is a management tool and concept similar to financial budgeting. It refers to the systematic quantitative accounting and analysis of all water inflows (supply) and outflows (consumption) in a specific region over a specific period (usually one year). Simply put, it's a "water income and expenditure ledger." An ideal and sustainable water budget state is: total water supply ≥ total water consumption. If the state of "total water supply < total water consumption" persists for a long period, it will lead to groundwater over-extraction, river drying up, lake shrinkage, and water scarcity. Groundwater over-extraction refers to the continuous decline of groundwater levels, forming funnel-shaped depressions, leading to land subsidence, seawater intrusion, etc. River drying up and lake shrinkage can lead to ecosystem collapse. Water scarcity will affect economic and social development and people's lives.

[0003] Achieving sustainable water resource management: This is the most fundamental goal. Through quantitative analysis, we ensure that our development and utilization of water resources do not exceed their regenerative capacity, avoiding "consuming our ancestors' resources and jeopardizing future generations' future." Addressing the water crisis: Against the backdrop of global climate change and population growth, many regions face water shortages. Water budgeting can help identify water scarcity risks and develop response strategies in advance. Optimizing water resource allocation: Like managing funds, water budgeting can guide us to allocate limited water resources most effectively among different users (agriculture, industry, domestic, and ecological) to maximize benefits.

[0004] The existing water budget data requires real-time monitoring of various regions to supplement the basic data for the water budget. This process increases the monitoring workload and also increases the monitoring cost. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a method for processing water budget data, used to process and analyze data collected during water budget processing. The method for processing water budget data includes the following steps: S1. Obtain historical data of the management area and analyze it to obtain the regional management unit, the data tags of each regional management unit, and the corresponding water revenue and water expenditure data; S2. Obtain the increment of the regional management unit within the management area and its corresponding data label after a preset budget time period; S3. Match the paired regional management units based on the data tags corresponding to each regional management unit; S4. Obtain the soil conditions and weather conditions of the regional management units within the management area during the budgeted time period, and match them with the existing soil conditions and weather conditions to obtain the matching time period. S5. Use the water expenditure data and water revenue data of the matching area management unit in the matching time period as the water expenditure and water revenue data of the area management unit in the management area, and then calculate the water transfer volume in the management area.

[0006] Preferred method for calculating budget time period includes: constructing a plane coordinate system, where the horizontal axis is time and the vertical axis is water volume; constructing a deficit curve; constructing a water diversion curve, where the time period between the intersection of the deficit curve and the water diversion curve and the current time point is used as the budget time period.

[0007] Preferred method for constructing the deficit curve includes: obtaining initial water expenditure and initial water revenue at various future time points; calculating the initial budget deficit Q. t Then, obtain the corresponding deficit-filling amounts for each time period. Initial budget deficit Q t The deficit is calculated by summing the deficit amount with the amount to fill the deficit. The deficit amount is then inserted into a plane coordinate system according to time points and connected in chronological order to obtain the deficit curve.

[0008] Preferred: Initial budget deficit Where t is the prediction time length, i.e., the time period between the current time and the prediction time point, and Y... S It is the amount of water income, Y Z It refers to the amount of water expenditure.

[0009] Preferred method for constructing water diversion curve includes: calculating and obtaining the water diversion volume. Where q is the water diversion flow rate and t is the water diversion time; then the water diversion volume is inserted into the coordinate system in time order and connected in time order to construct the water diversion curve.

[0010] Preferred: Incremental data for regional management units within the management area includes: passive change data and autonomous change data.

[0011] Preferred method for calculating incremental data of autonomous changes includes: obtaining the current incremental data value A0 and the current parameter derivative A0', and obtaining the incremental data value B of the previous period. t and its corresponding parameter derivative B t ', then calculate the predicted incremental data value. Where B0 is the incremental data value at the current time point of the previous period, and t min It is the smallest unit of time, where t is the prediction time point. To take the sign of the integer.

[0012] Preferred method for matching data labels corresponding to regional management units includes: calculating and obtaining a matching index R, and using the obtained regional management unit data with the maximum matching index Rmax as the paired regional management unit.

[0013] Preferred: Matching Index Where i is the tag number of the area management unit, I is the total number of tags in the area management unit, i = 1, 2, ... I; K i It is the tag value of the existing area management unit numbered i, k i It is the tag value of the newly added regional management unit with the number i; g i It is the label adjustment factor for region management unit number i.

[0014] Preferred: Adjustment coefficient , where w i It is the weight of the region management unit with label number i.

[0015] Preferred: Water expenditure data of matching regional management units Where j is the number of the regional management unit within the management area, J is the total number of regional management units within the management area, and y zj For the unit water expenditure data of the area management unit numbered j within the management area, N j It is the unit quantity of the area management unit numbered j within the management area.

[0016] Preferred: Matching regional management unit water revenue data Where j is the number of the regional management unit within the management area, J is the total number of regional management units within the management area, and y sj This refers to the water revenue data for the area management unit numbered j within the management area.

[0017] The technical effects and advantages of this invention are as follows: By managing and analyzing water expenditure and revenue data within a managed area, it ensures normal water budgeting and supply, preventing inconvenience to daily life and production caused by insufficient water regulation, while also avoiding energy waste and resource allocation problems caused by excessive water regulation. Replacing newly added regional management unit data with existing regional management units for calculations results in accurate calculations without requiring geographical surveys, reducing significant survey work and improving calculation accuracy. Dividing the managed area into regional management units allows for modular calculations, minimizing differences between regional management units, ensuring accurate tag data matching, and facilitating analysis and calculation. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating a water budget data processing method proposed in this invention.

[0019] Figure 2 This is a flowchart illustrating the budget time period calculation method in a water budget data processing method proposed in this invention.

[0020] Figure 3 This is a flowchart illustrating the method for constructing a deficit curve in a water budget data processing method proposed in this invention. Detailed Implementation

[0021] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the invention, and should not be construed as limiting the invention. Rather, embodiments of the invention include all variations, modifications, and equivalents falling within the spirit and scope of the appended claims.

[0022] Example 1 refer to Figure 1 This embodiment proposes a method for processing water budget data, used to process and analyze data collected for water budget processing. The method for processing water budget data may include the following steps: S1. Obtain historical data for the management area and analyze it to obtain data tags for each management unit, as well as corresponding water revenue and water expenditure data. The management area is the sum of all objects within the area defined in this invention; it can refer to a specific region, such as a country, a river basin, a city, or a piece of farmland. When managing the management area, it is necessary to record and store the various water revenue and water expenditure data in real time for later data retrieval and analysis. Water revenue refers to all sources of water entering the management area. Water sources are diverse, including precipitation, surface water inflow, groundwater inflow, and inter-basin water transfer. Precipitation is the most significant natural water inflow, including rain and snow. Surface water inflow is water flowing in from upstream rivers and lakes. Groundwater inflow is water flowing in from outside the area through underground aquifers; this is generally not included in water revenue and should be used with caution. Inter-basin water transfer is water transferred from other river basins through artificial engineering projects; this is a result we need to calculate, so it is not included in the initial statistical calculations and will not be elaborated upon here. Water expenditure refers to all water that leaves the area or is consumed. It varies depending on geographical conditions, population distribution, and other factors, and its data changes over time, forming the main body of the data analyzed in this application. The large number of parameters affecting water expenditure makes its analysis challenging due to the sheer volume of data. Water expenditure can include evapotranspiration, surface water outflow, groundwater outflow, and human water use. Evapotranspiration is the natural process of water returning to the atmosphere from the earth's surface, water bodies, and plants (through transpiration), and it is one of the largest expenditure items. Surface water outflow is the amount of water that flows out of the area into downstream rivers and lakes. Groundwater outflow is the amount of water that flows out of the area through underground aquifers. Human water use mainly includes agricultural irrigation, industrial water use, domestic water use, and ecological water use. Agricultural irrigation is usually the largest water user. Industrial water use includes manufacturing and energy production. Domestic water use includes water used by households, businesses, and public services. Ecological water use is the amount of water that must be retained to maintain the health of ecosystems such as rivers and wetlands. Human water use is the purpose and significance of this application. A regional management unit is the smallest analytical object for water revenue and / or water expenditure. It can be a user, a piece of farmland, an ecological green space, etc., and will not be listed here. Historical data for the management area is obtained, including the characteristics of each area, the corresponding water expenditure and water revenue data. This data is then analyzed and categorized to obtain the water expenditure and water revenue data for the regional management unit. For example, when analyzing agricultural irrigation water use, we need to categorize the amount of farmland, its geographical location, soil composition, the types of crops planted in each time period, soil moisture content, and irrigation water usage. This allows us to obtain the irrigation amount per acre of farmland in a specific time period and location; details will not be elaborated here. Data tags for regional management units describe the characteristic data of that unit and can be used to annotate it.For example, a green space within a city might have sandy soil with 25% clay content, a south-facing slope of 3 degrees, and be planted with ryegrass. This is just a simple example and may not be universally applicable; other specific scenarios will not be elaborated upon here. The water revenue and expenditure data here are unit-based data for regional management units. For example, the amount of irrigation water or rainfall per acre of a green space during the budgeted period. Again, this is just a simple example and not universally applicable; other specific scenarios will not be elaborated upon here.

[0023] S2. Obtain the increment of regional management units within the management area and their corresponding data tags after a preset budget time period. The preset budget time period is the length of time we need to predict, which can be determined according to the actual situation. For example, it can be a week, half a month, a month, etc., which are artificially set budget time periods, or it can be determined according to the water revenue regulation period. For example, city A needs to divert water from the Yangtze River to maintain the water supply for urban households, businesses, and public services. The average time required for transportation from the Yangtze River to city A is calculated using existing methods. Of course, in actual data management, water is transported through pipelines or rivers, and there is a time delay in transportation, which needs to be calculated. Reference Figure 2 Specific calculation methods may include: constructing a planar coordinate system, where the horizontal coordinate x can be time, its origin can be the current time point, and the vertical coordinate y represents the volume or weight of water, with consistent units throughout. (Reference) Figure 3 This process obtains the initial water expenditure and initial water revenue at various future time points. The initial water expenditure can be an initial forecast of water expenditure, based on the specific conditions of the management area, or initial forecast data. It can utilize existing technology, which will not be elaborated upon here. For example, irrigating a patch of greenery with a planned daily water consumption of 1 ton is a simple example and not representative of all scenarios; other scenarios will not be detailed here. The initial water revenue can be the initially predicted water availability within the management area. This does not include the amount of water transferred; it can include water obtained through rainfall, etc., which will not be elaborated upon here. Then, the initial budget deficit Q is calculated. t The initial budget deficit Where t is the prediction time length, i.e., the time interval between the current time and the prediction time point, which is the value on the horizontal axis, Y. S It is the amount of water income, Y ZThis refers to the amount of water expenditure. Then, the deficit-filling amount for each time period is obtained, which can be positive or negative. For example, if a reservoir needs to continuously inject water for storage, this injection is to fill previous deficits or to prepare for future water use; we define this as the deficit-filling amount. Specific details are not elaborated here. Next, a deficit curve is constructed. This deficit curve can be a calculated deficit, where the deficit is the sum of the initial budget deficit and the deficit-filling amount. For example, for a reservoir used for farmland irrigation, if the daily water expenditure is 1 ton, the surface water inflow is 1 ton, and 1 ton of groundwater needs to be stored, then the daily deficit is 1 ton. This is just a simple example and may not be universally applicable; other data settings are not elaborated here. Then, the deficit is plotted into a plane coordinate system according to time points to obtain the deficit curve. A water diversion curve is constructed, and the time period between the intersection of the deficit curve and the water diversion curve and the current time point is used as the budget time period. The water diversion curve construction method includes: calculating the water diversion volume. Where q is the water diversion flow rate and t is the water diversion time, with the diversion time and prediction time being the same. Then, the diverted water volume is sequentially inserted into the coordinate system to construct the water diversion curve. As is well known, budgeting water revenue and expenditure data relies on data such as weather, geographical environment, and population changes. However, these data are constantly changing; the longer the budget period, the worse the controllability of data changes, and the greater the error in the budgeted data. The budget period calculated using this method can, under permissible conditions—that is, maximizing the reduction of the budget period while ensuring sufficient water supply—to minimize the uncontrollability of data changes and ensure data accuracy. The increment of regional management units within the management area refers to the change in regional management units within the management area. Its value can be positive or negative; negative values ​​may not be matched, depending on the actual situation. The incremental data changes of regional management units within the management area can be divided into passive changes and autonomous changes. Passive changes are planning-related data, which can be understood as data that changes according to plans, and can be obtained from planning reports. For example, data such as the number of acres of newly developed farmland or the number of factories planned to close or open in an industrial zone are readily available. However, obtaining data on autonomous changes is more complex. For instance, population changes can be predicted based on trends or calculated using the current base population and cyclical patterns. Specific calculation methods may include obtaining the current incremental data value A0 and the current parameter derivative A0', and obtaining the incremental data value B from the previous period. t and its corresponding parameter derivative B t ', then calculate the predicted incremental data value. Where B0 is the incremental data value at the current time point of the previous period. This formula excludes cases where the parameter at the current time point of the previous period remains strictly unchanged; such a situation is practically nonexistent in reality. Where t... min This is the smallest unit of time, which can be in days, meaning its value can be 1. Other value settings are also possible, but will not be elaborated upon here. `t` is the prediction time point, and `t-1` is the previous prediction time point. The period here can be a year, but other values ​​are also possible, depending on the specific circumstances. As is well known, some autonomously changing data exhibits periodicity, and this period is often calculated on an annual basis. For example, tourist populations may be related to local specialties, environment, ethnic customs, or holiday arrangements. These factors all vary annually, and while the general situation remains the same each year, there are always variations. To ensure integer representation, consider this: For example, if a city has 1000 tourists at the current time this year, with a population parameter derivative of 1.1, and we want to predict the number three days later, last year at the current time, the number was 900 (population parameter derivative 1.05), the number two days later was 1060 (population parameter derivative 1.1), and the number three days later was 1.12. We can calculate the number of tourists three days later as 1318. This is just a simple example and may not be universally applicable; other scenarios will not be elaborated upon here. This method considers both data changes within the current period and the data development patterns during the week. Using the current period's data as a base and refining it with data from the previous period, this comprehensive approach of combining the development patterns of the previous period's data with the current period's data results in more accurate prediction parameters.

[0024] S3. Matching regional management units based on the data tags corresponding to each regional management unit. Here, the matched regional management units can be understood as those not in the current period. For example, a newly added urban green space, with sandy soil, 25% clay content, a 3-degree slope, and planted with ryegrass, can be matched with existing urban green spaces that also have sandy soil, 25% clay content, a 3-degree slope, and planted with ryegrass. The existing urban green space is then the matched regional management unit. Of course, the actual work process is not entirely similar, and specific details will not be elaborated here. For the management area initially constructed using this method, existing or standardized regional management unit models from other management areas can be incorporated into the matching database. Therefore, it is not necessary to conduct surveys and real-time measurements of each area within the management area; data can be directly referenced for matching, greatly reducing the amount of data storage and surveying work. The data tag matching method for regional management units can include: calculating the matching index. Where i is the tag number of the area management unit, I is the total number of tags in the area management unit, i = 1, 2, ... I; K i It is the tag value of the existing area management unit numbered i, k i This refers to the label value of the newly added area management unit numbered i. For example, a clay content of 25% and a slope of 3 degrees are all label values. We can assign numbers to each label by looking up the type of green space; details will not be elaborated here. For some labels without direct values, we can assign numbers based on their impact on soil water content. For example, we can sort the water-saving performance of lawns and then assign values ​​sequentially according to the sequence number: ryegrass could have a value of 1, Bermuda grass 3, and small needlegrass 2. Of course, this is just a simple example and may not be universally applicable; other cases will not be elaborated here. g i This is the adjustment factor for the label of region management unit number i. Its value can be obtained empirically or calculated. The specific calculation method for the adjustment factor is as follows: , where w i The weight of the regional management unit labeled i is determined by its value, which can be assigned based on its impact on soil moisture content; details are omitted here. The specific calculation values ​​are also omitted here. The adjustment coefficient calculated using this method can correct for numerical biases and take into account the weighting analysis. The matching index calculated using this method reflects the relationship between two regional management units, facilitating analysis and location. The obtained regional management unit data with the maximum matching index Rmax are used as paired regional management units. This method can be used as a database of existing regional management units; the more widely the method is applied, the more accurate the calculated data.

[0025] S4. Obtain the soil conditions and weather conditions for the budgeted time period for each regional management unit within the management area, and match them with existing soil and weather conditions to obtain a matching time period. The soil conditions for each regional management unit mainly refer to the soil moisture content within that unit. For regional management units where soil moisture content is irrelevant, this step can be disregarded or not calculated. For example, industrial or domestic water use is not significantly related to soil conditions, so it can be ignored during matching; only the weather conditions within the budgeted time period need to be considered. Soil conditions can be obtained through testing or from weather conditions within a preset past time period, such as the past 10 days or half a month. Details are omitted here. Weather conditions mainly include temperature, rainfall, etc., and can be obtained through weather forecasts. Details are omitted here. Existing soil and weather conditions are data stored before the current time point. In actual work, historical data needs to be stored. Details are omitted here. The specific matching calculation method is similar to that of the matching index and is omitted here. For the same matching index, the weather conditions closest to the current time period are used as the matching time period.

[0026] S5. Using the water expenditure and water revenue data of the matching area management unit within the matching time period as the water expenditure and water revenue data of the area management unit within the management area, the water diversion volume within the management area is then calculated. The water expenditure data... Where j is the number of the regional management unit within the management area, J is the total number of regional management units within the management area, and y zj This refers to the unit water expenditure data for area management unit j within the management area. This data can be obtained by matching the water expenditure of area management units within the same time period. Nj is the unit quantity of area management unit j within the management area. We can store the water expenditure and water revenue data of area management units at each time point for later use; details are not elaborated here. The water revenue data... Where j is the number of the regional management unit within the management area, J is the total number of regional management units within the management area, and y sj This is for the water revenue data of area management unit j within the management area. Then, the water volume is adjusted. Of course, the water expenditure and revenue data, as well as the water diversion volume, are all water budget data. Actual data needs to be determined based on the actual situation. The stored water expenditure and revenue data for the matching regional management units should be referenced to the actual situation, which will not be elaborated here. The water expenditure and revenue data here do not include the water diversion volume. The actual calculated water expenditure and revenue results can be used to replace the initial budget deficit calculation for optimization, thus achieving more accurate calculations. Managing and analyzing the water expenditure and revenue data of the management area ensures budget adjustments within the management area, thereby guaranteeing normal water budget and supply, avoiding inconvenience to life and production caused by insufficient water regulation, and also avoiding energy waste and resource allocation problems caused by excessive water regulation. Replacing newly added regional management units with existing ones for calculations ensures accuracy and eliminates the need for geographical surveys, reducing a significant amount of survey work and improving calculation accuracy. By dividing the management area into regional management units, modular calculations can be performed, minimizing the differences between regional management units, ensuring the accuracy of tag data matching, and facilitating analysis and calculation.

[0027] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this invention can be achieved, and this is not limited herein.

[0028] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for processing water budget data, characterized in that, The processing method for the water budget data includes: S1. Obtain historical data of the management area and analyze it to obtain the regional management unit, the data tags of each regional management unit, and the corresponding water revenue and water expenditure data; S2. Obtain the increment of the regional management unit within the management area and its corresponding data label after a preset budget time period; S3. Match the paired regional management units based on the data tags corresponding to each regional management unit; S4. Obtain the soil conditions and weather conditions of the regional management units within the management area during the budgeted time period, and match them with the existing soil conditions and weather conditions to obtain the matching time period. S5. Use the water expenditure data and water revenue data of the matching area management unit in the matching time period as the water expenditure and water revenue data of the area management unit in the management area, and then calculate the water transfer volume in the management area.

2. The method for processing water budget data according to claim 1, characterized in that, The method for calculating the budget time period includes: constructing a planar coordinate system and constructing a deficit curve; constructing a water diversion curve, and using the time period between the intersection of the deficit curve and the water diversion curve and the current time point as the budget time period.

3. The method for processing water budget data according to claim 2, characterized in that, In a two-dimensional coordinate system, the horizontal axis represents time, and the vertical axis represents water volume.

4. The method for processing water budget data according to claim 2, characterized in that, The method for constructing the deficit curve includes: obtaining the initial water expenditure and initial water revenue at each future time point; and calculating the initial budget deficit Q. t Then obtain the corresponding deficit filling amount for each time period; initial budget deficit Q t The deficit is calculated by summing the deficit amount with the amount to fill the deficit. The deficit amount is then inserted into a plane coordinate system according to time points and connected in chronological order to obtain the deficit curve.

5. The method for processing water budget data according to claim 4, characterized in that, Initial budget deficit Where t is the prediction time length, Y S It is the amount of water income, Y Z It refers to the amount of water expenditure.

6. The method for processing water budget data according to claim 2, characterized in that, The method for constructing a water diversion curve includes: calculating and obtaining the water diversion volume. Where q is the water diversion flow rate and t is the water diversion time; then the water diversion volume is inserted into the coordinate system in time order and connected in time order to construct the water diversion curve.

7. The method for processing water budget data according to claim 1, characterized in that, The data label matching method for the corresponding regional management unit includes: calculating the matching index R, and using the obtained regional management unit data with the maximum matching index Rmax as the paired regional management unit.

8. The method for processing water budget data according to claim 1, characterized in that, Incremental data for regional management units within the management area includes: passive change data and autonomous change data.

9. The method for processing water budget data according to claim 1, characterized in that, Matching regional management unit water expenditure data Where j is the number of the regional management unit within the management area, J is the total number of regional management units within the management area, and y zj For the unit water expenditure data of the area management unit numbered j within the management area, N j It is the unit quantity of the area management unit numbered j within the management area.

10. A method for processing water budget data according to claim 1, characterized in that, Matching regional management unit water revenue data Where j is the number of the regional management unit within the management area, J is the total number of regional management units within the management area, and y sj This refers to the water revenue data for the area management unit numbered j within the management area.

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