Drought disaster early warning method and device and related equipment

Abstract

The invention provides a drought disaster early warning method and device and related equipment, and particularly relates to the technical field of drought disaster early warning, and the method comprises the steps: obtaining the actual water storage capacity of a target drainage basin at a first time, the precipitation of the target drainage basin in a target time period, and a water distribution function of the target drainage basin, and obtaining the water storage capacity of the target drainage basin according to the water distribution function; predicting target water consumption, wherein the target water consumption is used for representing the water consumption of the target drainage basin in the target time period under a set confidence interval; according to the actual water storage amount, the precipitation amount and the target water consumption amount, the predicted water storage amount of the target drainage basin at the second time is obtained through calculation; and analyzing the predicted water storage according to a set drought early warning rule to obtain drought early warning information. According to the invention, more accurate and reliable drought early warning information can be formed.

Description

Technical Field

[0001] The present disclosure relates to the technical field of drought disaster warning, and specifically relates to a drought disaster warning method, device and related equipment. Background Art

[0002] Drought is an important factor affecting the rational utilization of water resources, the balance of the ecosystem and social and economic development. Timely and accurate drought warning is crucial for reducing drought disaster losses.

[0003] However, in the existing field of drought warning, it is mainly based on meteorological drought warning, focusing more on the selection of drought indicators and the improvement of traditional warning models. However, due to the complex environmental conditions in the analysis area, the uneven spatio-temporal distribution of water resources, the complexity of the actual distribution of water volume and the water flow law (hydrological cycle, transmission, land vegetation), and the uncertainty of water use behavior and boundaries, it leads to difficulties such as large errors in drought warning judgment and delayed warning response.

[0004] That is to say, the accuracy and timeliness of the warning information obtained based on the drought disaster warning scheme provided by the existing technology are poor. Summary of the Invention

[0005] The purpose of the present disclosure is to provide a drought disaster warning method, device and related equipment, which are used to solve the technical problem of poor accuracy and timeliness of the drought disaster warning information obtained by the existing technology.

[0006] In a first aspect, the present application provides a drought disaster warning method, and the method includes: Obtain the actual water storage volume of the target basin at a first time, the precipitation in the target period of the target basin, and the water use distribution function of the target basin, where the target period is a period from the first time to a second time, and the second time is the current time or a future time after the current time, and the water use distribution function is used to predict the probability distribution of the water consumption volume of the target basin in the target period; Predict the target water consumption volume according to the water use distribution function, where the target water consumption volume is used to represent the water consumption volume of the target basin in the target period under a set confidence interval; Calculate the predicted water storage volume of the target basin at the second time according to the actual water storage volume, the precipitation and the target water consumption volume; Analyze the predicted water storage volume according to the set drought warning rules to obtain drought warning information.

[0007] In a second aspect, the present application provides a drought disaster warning device, and the device includes: An acquisition module, configured to acquire the actual water storage of a target basin at a first time, the precipitation of the target basin within a target period, and the water use distribution function of the target basin, where the target period is a period from the first time to a second time, and the second time is the current time or a future time after the current time, and the water use distribution function is used to predict the probability distribution of the water use amount of the target basin within the target period; A prediction module, configured to predict a target water use amount according to the water use distribution function, where the target water use amount is used to represent the water use amount of the target basin within the target period under a set confidence interval; A calculation module, configured to calculate a predicted water storage of the target basin at the second time according to the actual water storage, the precipitation, and the target water use amount; An analysis module, configured to analyze the predicted water storage according to a set drought warning rule to obtain drought warning information.

[0008] In a third aspect, the present application provides an electronic device, including a processor, a memory, and a computer program stored on the memory and executable on the processor. When the computer program is executed by the processor, the steps of the method described in the first aspect are implemented.

[0009] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in the first aspect are implemented.

[0010] In a fifth aspect, the present application provides a computer program product, including computer instructions. When the computer instructions are executed by a processor, the steps of the method described in the first aspect are implemented.

[0011] The present application abstracts a basin as a "virtual lake", and correspondingly constructs a water balance model of the "virtual lake" to simplify a complex basin water system into a "precipitation - water use - water storage" dynamic relationship model, so as to grasp the dynamic balance of basin water volume from a macroscopic perspective and provide a more comprehensive perspective for drought warning. Then, by obtaining real information such as the actual water storage of the target basin at the first time and the precipitation of the target basin within the target period, and combining with the predicted target water use amount of the target basin within the target period, through a simple formula of water storage + precipitation - water use, the predicted water storage of the target basin at the second time is quickly calculated. Finally, the predicted water storage is analyzed in combination with a set drought warning rule to form more timely and accurate drought warning information. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 is a schematic flowchart of a drought disaster warning method provided by an embodiment of the present disclosure; Figure 2 It is a schematic diagram of a virtual lake provided by an embodiment of the present disclosure; Figure 3 It is a schematic structural diagram of a drought disaster warning device provided by an embodiment of the present disclosure; Figure 4 It is a schematic diagram of an electronic device provided by an embodiment of the present disclosure. Detailed implementation manners

[0013] Next, the technical solutions in the embodiments of the present disclosure will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, rather than all the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present disclosure.

[0014] In the prior art, it is mainly based on meteorological drought warning, which mostly focuses on the selection of drought indicators and the improvement of traditional warning models. It is found in application that the above methods have at least the following three defects.

[0015] Defect 1: When judging the drought situation, the prior art often relies on meteorological drought indices (such as the Standardized Precipitation Index (SPI)). Most of them are judged based on single-point meteorological data or local water volume monitoring data, focusing on single-point or local areas to judge the drought situation. Most methods only rely on single indicators such as rainfall or soil moisture, and cannot comprehensively reflect the complexity of drought. They lack comprehensive consideration of the entire basin water system and comprehensive water volume balance analysis, etc., and cannot accurately grasp the overall water volume supply and demand dynamic balance within the basin.

[0016] Defect 2: Due to the influence of human activities and the complexity of the social system, the water use behavior is complex and changeable, the water use data is incomplete, and the authenticity and reliability of the statistically obtained water use volume are not high. The water use situation information in historical drought data is difficult to be effectively applied, resulting in a lack of comprehensive water use data support when judging the drought trend, and the timeliness and accuracy of drought disaster warning are insufficient. For example, when calculating the total water use volume, simply adding the water use volumes of each department ignores the spatio-temporal heterogeneity of water use behavior, such as the complex characteristics of seasonality, suddenness, and regional differences of water use in different industries. Taking agricultural irrigation water use as an example, its water use volume fluctuates significantly in different seasons and different growth stages of crops. The uncertainty of water use behavior affects the accurate grasp of the complex dynamic relationship among precipitation, water use, and water storage, resulting in obvious delays and unreliability in drought warning and inability to provide timely and effective support for decision-making.

[0017] Defect 3: The model construction is complex and redundant and cannot clearly and comprehensively reflect the water volume changes in the basin. It fails to effectively relate key factors such as precipitation, water use, and water storage in a concise manner, resulting in a lag in drought early warning and inability to meet the needs of rapid decision-making. In the face of increasingly complex and variable climate conditions and growing water demand, it cannot quickly capture the changing trend of the water volume in the basin and provide timely and effective basis for water resources management and drought resistance decision-making.

[0018] To address the above-mentioned defects in the existing technology, this application proposes a drought disaster early warning method, device, and related equipment. By abstracting the basin as a "virtual lake", the complex basin water system is simplified into a quantifiable analysis model, which integrates the entire process of the water cycle at the basin scale, can more intuitively analyze the water supply-demand balance relationship, solves the problem of spatial heterogeneity, significantly shortens the early warning time, improves the accuracy of early warning, and provides strong support for dynamic drought assessment and early warning.

[0019] Specifically, the embodiments of the present disclosure provide a drought disaster early warning method, as Figure 1 shown, the drought disaster early warning method includes: Step 101, obtain the actual water storage volume of the target basin at the first time, the precipitation in the target basin during the target period, and the water use distribution function of the target basin.

[0020] Among them, the target period is the period from the first time to the second time, and the second time is the current time or a future time after the current time. The water use distribution function is used to predict the probability distribution of the water use volume in the target basin during the target period.

[0021] A basin refers to an area where all precipitation converges into the same water body (such as a river, lake, or ocean). The boundary of the basin is usually determined by topographic features (such as ridges, hills, etc.). After the precipitation converges in this area, it finally flows into the same river or water body.

[0022] In this application, the above-mentioned target basin can be any basin that meets the foregoing basin definition, for example: the Yangtze River Basin, the Yellow River Basin, the Xiangjiang River Basin, the Baima Lake Basin, etc.

[0023] When the second time is the current date, the above-mentioned first time can be understood as the nearest monitoring date to the current date. For example, when the current date is set to the 4th, if the water storage volume of the target basin is monitored once a day, the above-mentioned first time can be understood as the day before the current date, that is, the 3rd; similarly, if the monitoring is carried out once every three days, the above-mentioned first time can be understood as the 1st.

[0024] In the case where the second time is a future time after the current time (i.e., a certain future date), the above-mentioned first time can be understood as the current date.

[0025] That is to say, the solution of the present application not only supports the early warning of real-time drought disasters in the target basin at the current time, but also supports the early warning of drought disasters in the target basin at future times.

[0026] Among them, the actual water storage volume of the target basin at the first time is: the sum of the water storage volumes of multiple water storage bodies included in the target basin at the first time. Among them, the multiple water storage bodies included in the target basin can be surface water storage bodies such as rivers, lakes, reservoirs, and ponds. The water storage volume of such surface water storage bodies at the first time can be obtained through water conservancy data, or calculated or estimated according to long-term observation data of hydrological data. In addition, the multiple water storage bodies included in the target basin can also be underground water storage bodies, and the water storage volume of the underground water storage bodies at the first time can be obtained through the monitoring data of groundwater monitoring wells.

[0027] The precipitation in the target basin during the target time period is: the total precipitation in the geographical area corresponding to the target basin during the target time period. In application, the precipitation in the target basin during the target time period can be obtained according to meteorological forecast data.

[0028] It should be noted that the water use distribution function is generated based on multiple groups of historical data in the historical drought period of the target basin. Each group of historical data includes at least the historical water storage transformation volume and historical rainfall in the target basin under the corresponding historical drought event. Among them, the historical water storage transformation volume is used to indicate the adjustment value of the water storage volume in the target basin under the corresponding historical drought event, that is, the difference between the first water storage volume and the second water storage volume. The first water storage volume indicates the water storage volume at the start time of the corresponding historical drought event in the target basin, and the second water storage volume indicates the water storage volume at the end time of the corresponding historical drought event in the target basin; the historical rainfall is used to indicate the total rainfall in the target basin under the corresponding historical drought event.

[0029] Based on the historical rainfall minus the corresponding historical water storage transformation volume, the historical water use volume of the target basin under the corresponding historical drought event can be obtained.

[0030] The present invention abstracts the basin as a "virtual lake" with the characteristics of "precipitation - water use - water storage" to inversely obtain relatively reliable historical water use volumes through accurately measurable historical rainfall and historical water storage transformation volumes, which can avoid the related defects existing in directly counting historical water use volumes (such as defects such as overly simplified statistical models and incomplete statistical channels), so as to obtain relatively accurate historical water use volumes, and form a water use distribution function based on this, which can ensure the accuracy of the subsequent obtained target water use volumes.

[0031] Step 102: Predict the target water consumption according to the water consumption distribution function.

[0032] The target water consumption is used to represent the water consumption of the target basin in the target time period under a set confidence interval.

[0033] Exemplarily, the set confidence interval can be 90% - 100%, or 95% - 100%.

[0034] Step 103: Calculate the predicted water storage of the target basin at the second time according to the actual water storage, the precipitation and the target water consumption.

[0035] Specifically, the predicted water storage of the target basin at the second time = actual water storage + precipitation - target water consumption.

[0036] Step 104: Analyze the predicted water storage according to the set drought warning rules to obtain drought warning information.

[0037] In one example, the set drought warning rules may include multiple consecutive warning intervals. The multiple consecutive warning intervals are, in ascending order of value: the first warning interval corresponding to the critical state, the second warning interval corresponding to the mild drought state, the third warning interval corresponding to the moderate drought state, and the fourth warning interval corresponding to the severe drought state.

[0038] In this example, based on the warning interval where the predicted water storage is located, drought warning information corresponding to the state of this warning interval is generated. For example, if the predicted water storage is in the second warning interval, drought warning information indicating that the target basin is in a mild drought state is generated.

[0039] Furthermore, different warning prompt messages can also be associated and set for different states (used to remind users to take corresponding drought prevention measures to reduce the negative impact brought by drought). For example, when the drought warning information indicates that the target basin is in a mild drought state, the drought warning information can carry a warning prompt message indicating that the user should appropriately adjust the water use plan, and when the drought warning information indicates that the target basin is in a moderate drought state, the drought warning information can carry a warning prompt message suggesting that the user activate the emergency water conservation and water transfer plan.

[0040] This application abstracts a river basin as a "virtual lake" and correspondingly constructs a water balance model for the "virtual lake" to simplify the complex river basin water system into a dynamic relationship model of "precipitation - water use - water storage", so as to grasp the dynamic balance of the river basin water volume from a macroscopic perspective and provide a more comprehensive perspective for drought warning. Then, by obtaining real information such as the actual water storage of the target river basin at the first time and the precipitation of the target river basin during the target period, and combining the predicted target water use of the target river basin during the target period, through the simple formula of water storage + precipitation - water use, the predicted water storage of the target river basin at the second time is quickly calculated. Finally, the predicted water storage is analyzed in combination with the set drought warning rules to form more timely and accurate drought warning information.

[0041] Exemplarily, the "virtual lake" corresponding to "precipitation - water use - water storage" can be as Figure 2 shown, Figure 2 where P represents the rainfall of the "virtual lake" from time t - 1 to time t, W represents the water use of the "virtual lake" from time t - 1 to time t, V t-1 represents the water storage of the "virtual lake" at time t - 1, and V t represents the water storage of the "virtual lake" at time t.

[0042] In one embodiment, before obtaining the actual water storage of the target river basin at the first time, the precipitation of the target river basin during the target period, and the water use distribution function of the target river basin, the method further includes: According to multiple groups of historical data of the target river basin during historical drought periods, multiple historical water uses of the target river basin during historical drought periods are inversely derived, where the multiple groups of historical data correspond one - to - one with multiple historical drought events during the historical drought periods, and each group of historical data includes the historical water storage transformation amount and historical rainfall of the target river basin under the corresponding historical drought event; Cluster the multiple historical water uses according to the time dimension and / or drought level dimension to obtain at least two clusters; Analyze the at least two clusters respectively to obtain at least two distribution functions associated with the at least two clusters one - to - one. The distribution function is used to predict the probability distribution of the water use of the target river basin during the period corresponding to its associated cluster, and the at least two distribution functions include the water use distribution function of the target river basin.

[0043] In the present invention, the inversion operation is specifically: subtracting the historical water storage transformation amount from the historical rainfall of the target river basin under the corresponding historical drought event to obtain the historical water use of the target river basin under the corresponding historical drought event. This indirect way of obtaining historical water use has higher data accuracy compared with the ways of directly counting historical water use or predicting historical water use by models.

[0044] Clustering the multiple historical water consumption amounts according to the time dimension can aggregate different historical water consumption amounts corresponding to similar times, so as to reflect different water use situations of the target basin at different times through different clusters obtained by aggregation, thereby further improving the accuracy of the target water consumption amount generated subsequently.

[0045] Clustering the multiple historical water consumption amounts according to the drought level dimension can aggregate different historical water consumption amounts corresponding to the same drought level, so as to reflect different water use situations of the target basin at different drought levels through different clusters obtained by aggregation, thereby further improving the accuracy of the target water consumption amount generated subsequently.

[0046] Clustering the multiple historical water consumption amounts according to the time dimension can aggregate different historical water consumption amounts corresponding to similar times and the same drought level, so as to reflect different water use situations of the target basin at different times and different drought levels through different clusters obtained by aggregation, thereby further improving the accuracy of the target water consumption amount generated subsequently.

[0047] It should be noted that the time dimension in the present invention only involves dates and / or months.

[0048] In one example, the foregoing at least two clusters may include a cluster corresponding to the agricultural spring plowing time (February - May), a cluster corresponding to the summer time (June - September), and a cluster corresponding to the peak industrial production time (October - December).

[0049] After determining the foregoing at least two clusters, based on at least one historical water consumption amount included in each cluster, a corresponding distribution function is constructed to obtain at least two distribution functions.

[0050] After that, the coincidence degree (referring to the intersection - union ratio of the two) between the target time period and the corresponding times of different clusters is calculated, and the cluster with the highest corresponding coincidence degree is determined as the target cluster, and the distribution function corresponding to the target cluster is determined as the foregoing water use distribution function.

[0051] In one embodiment, calculating the predicted water storage amount of the target basin at the second time according to the actual water storage amount, the precipitation amount, and the target water consumption amount includes: Comparing the industrial and agricultural output values of the target basin during the target time period with the industrial and agricultural output values of the target basin under the historical drought event corresponding to the water use distribution function to obtain an industrial and agricultural water use fluctuation coefficient; and comparing the total population of the target basin during the target time period with the total population of the target basin under the historical drought event corresponding to the water use distribution function to obtain a residential water use fluctuation coefficient; Modify the target water consumption according to the industrial and agricultural water consumption fluctuation coefficient and the domestic water consumption fluctuation coefficient to obtain the modified water consumption; Calculate the predicted water storage of the target basin at the second time according to the actual water storage, the precipitation, and the modified water consumption.

[0052] Among them, the industrial and agricultural output value of the target basin during the target period is: during the target period, the total industrial and agricultural output value of the industrial water use area and the agricultural water use area associated with the target basin, where the industrial water use area associated with the target basin is the industrial water use area that uses the water resources of the target basin. Similarly, the agricultural water use area associated with the target basin is the agricultural water use area that uses the water resources of the target basin.

[0053] The industrial and agricultural output value of the target basin under the historical drought event corresponding to the water use distribution function is: the average value of multiple historical industrial and agricultural output values, and multiple historical industrial and agricultural output values correspond one by one to multiple historical drought events in the cluster corresponding to the water use distribution function. Within each historical drought event, the industrial and agricultural output value of the industrial water use area and the agricultural water use area associated with the target basin is the corresponding historical industrial and agricultural output value.

[0054] The total population of the target basin during the target period is: during the target period, the number of people included in the domestic water use area associated with the target basin, where the domestic water use area associated with the target basin is the domestic water use area that uses the water resources of the target basin.

[0055] The total population of the target basin under the historical drought event corresponding to the water use distribution function is: the average value of multiple historical total populations, and multiple historical total populations correspond one by one to multiple historical drought events in the cluster corresponding to the water use distribution function. Within each historical drought event, the number of people included in the domestic water use area associated with the target basin is the corresponding historical total population.

[0056] Calculate the difference between the industrial and agricultural output value of the target basin during the target period and the industrial and agricultural output value of the target basin under the historical drought event corresponding to the water use distribution function. This difference can be defined as the industrial and agricultural difference. Subsequently, the ratio of the industrial and agricultural difference to the industrial and agricultural output value of the target basin under the historical drought event corresponding to the water use distribution function can be calculated to obtain the industrial and agricultural difference ratio, and the product of the industrial and agricultural difference ratio and the corresponding industrial and agricultural impact coefficient is determined as the industrial and agricultural water consumption fluctuation coefficient.

[0057] Similarly, calculate the difference between the total population in the target basin during the target period and the total population in the target basin under the historical drought event corresponding to the water use distribution function. This difference can be defined as the population difference. Subsequently, the ratio of the population difference to the total population in the target basin under the historical drought event corresponding to the water use distribution function can be calculated to obtain the population difference ratio, and the product of the population difference ratio and the corresponding living impact coefficient is determined as the residential water use fluctuation coefficient.

[0058] Among them, the industrial and agricultural impact coefficient is used to represent the proportion of industrial and agricultural water use in the total water use of the target basin, and the living impact coefficient is used to represent the proportion of residential water use in the total water use of the target basin.

[0059] Specifically, the target water use is corrected according to the industrial and agricultural water use fluctuation coefficient and the residential water use fluctuation coefficient to obtain the corrected water use, including: Determine the sum of the industrial and agricultural water use fluctuation coefficient, the residential water use fluctuation coefficient, and the standard coefficient as the correction coefficient, and determine the product of the correction coefficient and the target water use as the corrected water use. The standard coefficient is 1.

[0060] In this embodiment, by comparing the industrial and agricultural output value and the total population in the target period with the historical industrial and agricultural output value and the historical population in the corresponding historical period, the difference between the water use situation in the target period and the water use situation in the corresponding historical period is determined. Accordingly, the industrial and agricultural water use fluctuation coefficient and the residential water use fluctuation coefficient are formed, and the target water use is corrected, so as to obtain a more accurate water use prediction result (i.e., the corrected water use), and further improve the accuracy of the predicted water storage finally obtained.

[0061] In one embodiment, according to the actual water storage, the precipitation, and the corrected water use, the predicted water storage of the target basin at the second time is calculated, including: Compare the temperature in the target basin during the target period with the temperature in the target basin under the historical drought event corresponding to the water use distribution function to obtain the temperature optimization coefficient; Optimize the actual water storage and the corrected water use respectively according to the temperature optimization coefficient to obtain the optimized water storage and the optimized water use; Calculate the predicted water storage of the target basin at the second time according to the optimized water storage, the precipitation, and the optimized water use.

[0062] The air temperature of the target basin during the target period is: the average of the daily air temperatures of the target basin during the target period. The air temperature of the target basin under the historical drought event corresponding to the water use distribution function is: the average of the historical daily air temperatures of the target basin during the historical period corresponding to the water use distribution function.

[0063] The air temperature optimization coefficient is used to represent the data deviation caused by the air temperature difference between the air temperature of the target basin during the target period and the air temperature of the target basin under the historical drought event corresponding to the water use distribution function, on the water storage volume and water use volume of the target basin during the target period.

[0064] In one example, a large model can be used to analyze multiple sets of sample data to determine the air temperature impact parameter, which is used to represent the impact of air temperature on the water body evaporation situation of the target basin. The multiple sets of sample data are collected from the target basin, and each set of sample data includes an air temperature value and a water body evaporation volume, and the water body evaporation volume is obtained based on the water body evaporation model.

[0065] In this example, comparing the air temperature of the target basin during the target period with the air temperature of the target basin under the historical drought event corresponding to the water use distribution function to obtain the air temperature optimization coefficient includes: determining the difference between the air temperature of the target basin during the target period and the air temperature of the target basin under the historical drought event corresponding to the water use distribution function as the air temperature difference, and fusing (such as multiplying) the air temperature difference and the aforementioned air temperature impact parameter to obtain the air temperature optimization coefficient.

[0066] In this embodiment, considering that the air temperature will also affect the water storage volume and water use volume of the target basin, that is, by comparing the air temperature of the target basin during the target period with its air temperature during the corresponding historical period, the air temperature optimization coefficient is generated, so as to optimize the actual water storage volume and the corrected water use volume respectively according to the air temperature optimization coefficient, in order to obtain more accurate optimized water storage volume and optimized water use volume, and further improve the data reliability of the predicted water storage volume obtained.

[0067] Among them, optimizing the actual water storage volume and the corrected water use volume respectively according to the air temperature optimization coefficient to obtain the optimized water storage volume and the optimized water use volume includes: Determining the product of the air temperature optimization coefficient and the actual water storage volume as the optimized water storage volume, and determining the product of the air temperature optimization coefficient and the optimized water use volume as the optimized water use volume.

[0068] In one embodiment, analyzing the predicted water storage volume according to the set drought warning rule to obtain the drought warning information includes: Analyzing the historical water storage deviation information of the target basin to obtain the water storage prediction deviation parameter; Rectify the predicted water storage volume according to the water storage prediction deviation parameter to obtain the target water storage volume; Analyze the target water storage volume according to the drought warning rule to obtain drought warning information.

[0069] Among them, the historical water storage volume deviation information of the target basin may be: N historical water storage volume deviation data in the previous N water storage volume prediction operations of the target basin. The historical water storage volume deviation data is the difference between the corresponding historical predicted water storage volume and the historical actual water storage volume, and N is a positive integer.

[0070] The above-mentioned water storage prediction deviation parameter is the weighted average of N historical water storage volume deviation data. Among them, among the N historical water storage volume deviation data, the closer the historical time corresponding to the historical water storage volume deviation data is to the first time, the higher the calculation weight of the historical water storage volume deviation data.

[0071] Rectifying the predicted water storage volume according to the water storage prediction deviation parameter to obtain the target water storage volume includes: determining the sum value of the water storage prediction deviation parameter and the predicted water storage volume as the target water storage volume.

[0072] In this embodiment, by introducing the data deviation existing in the past prediction operations, the currently predicted water storage volume is dynamically rectified to further improve the prediction accuracy of the obtained target water storage volume.

[0073] In one embodiment, before analyzing the target water storage volume according to the drought warning rule to obtain drought warning information, it includes: According to multiple drought warning critical water storage volumes of the target basin in historical drought periods, where the multiple drought warning critical water storage volumes correspond one by one to multiple historical drought events in the historical drought periods, and the drought warning critical water storage volume is the water storage volume of the target basin before changing from a non-drought state to a drought state under the corresponding historical drought event; Determine the average value of the multiple drought warning critical water storage volumes as the target critical water storage volume; Generate the drought warning rule according to the target critical water storage volume.

[0074] In this embodiment, by counting multiple drought warning critical water storage volumes under multiple historical drought events and determining their average value as the target critical water storage volume, the water storage volume corresponding to the drought critical state is standardized to avoid the influence of human factors, and a more reliable drought warning rule is generated accordingly.

[0075] For example, the drought warning rules can be as follows: when the water storage volume of the target basin is greater than or equal to the target critical water storage volume, it is determined that the target basin is in a critical state; when the water storage volume of the target basin is within the first numerical range, it is determined that the target basin is in a mild drought state; when the water storage volume of the target basin is within the second numerical range, it is determined that the target basin is in a moderate drought state; when the water storage volume of the target basin is within the third numerical range, it is determined that the target basin is in a severe drought state. Among them, the first numerical range is [0.6 times of the target critical water storage volume, target critical water storage volume), the second numerical range is [0.4 times of the target critical water storage volume, 0.6 times of the target critical water storage volume), and the third numerical range is [0, 0.4 times of the target critical water storage volume).

[0076] Generally speaking, based on the above scheme, the present invention starts from the overall basin, takes the basin as the analysis unit, abstracts the basin as a "virtual lake", and abstracts the complex water system in the basin as an overall unit similar to a lake, overcoming the difficulties that traditional drought warnings mainly rely on meteorological warnings, often focusing on single points or local areas, with complex actual distribution and flow laws of basin water volume and uncertain boundaries, enabling the dynamic balance of basin water volume to be grasped macroscopically and providing a more comprehensive perspective for drought warnings.

[0077] Based on the basic principle of water balance, a dynamic relationship model of "precipitation - water use - water storage" is constructed, which clearly, intuitively and accurately expresses the dynamic change process of water volume. In the present invention, precipitation is regarded as the incoming water, and the total water use volume is regarded as water use. Through the "virtual lake", precipitation is regarded as the total inflow, simplifying the complex hydrological process and overcoming the difficulties in simulating complex hydrological processes such as runoff generation, confluence and infiltration. At the same time, all water use (evaporation, leakage, vegetation interception, water intake for production and life, etc.) is integrated as the outflow. Based on historical drought events, the water use situation in the same period of previous years is inverted. Based on the principle of water balance, a dynamic water balance relationship model of "precipitation - water use - water storage" is constructed. Through the constructed water balance model of the basin "virtual lake", the dynamic change relationship between precipitation, water use and water storage can be described, the water storage volume can be calculated quickly and accurately, combined with real-time precipitation data and future precipitation forecasts, the water supply - demand balance can be corrected timely and accurately by using the constructed water balance model, and the drought trend of the basin can be judged and warned according to the preset drought indicators.

[0078] Using historical drought data, the total water consumption under different drought levels is inverted, water use patterns are extracted, and the warning accuracy is improved. By using the water balance relationship of "precipitation - water use - water storage" and the historical drought situations, the water use conditions in the same period of previous years are inversely calculated. The comprehensive water consumption of agriculture, industry, and domestic use in the same period of previous years is inversely deduced, so as to obtain relatively accurate total water consumption data. The water use patterns extracted by historical inversion include the dual effects of drought levels and seasonality, making full use of the hidden information in historical data, overcoming the estimation of complex water use behaviors and the inaccuracy of industry water use statistics, providing a richer data basis for accurately predicting the current drought situation, and improving the reliability of drought warnings.

[0079] The water storage situation is obtained by combining real-time precipitation data and the water use patterns inversely calculated by date (month) in history. By obtaining real-time precipitation data and combining the historical water use patterns in the same period that have been analyzed, the current water storage situation is calculated according to the water balance principle. Based on the water storage status of the "virtual lake", according to the corrected water storage situation and the supply-demand balance situation corrected by the historical water demand elasticity, the dynamic water use and drought level data are coupled. And through feedback to correct the supply-demand balance, using the corrected water storage volume and updating the drought level, it breaks through the limitations of traditional one-way causal models, and improves the operability and timeliness of drought warnings.

[0080] See Figure 3 , Figure 3 which is a drought disaster warning device provided by an embodiment of the present disclosure. As Figure 3 shown, the drought disaster warning device 300 includes: An acquisition module 301, configured to acquire the actual water storage volume of the target basin at a first time, the precipitation in the target time period of the target basin, and the water use distribution function of the target basin, where the target time period is a time period from the first time to a second time, and the second time is the current time or a future time after the current time, and the water use distribution function is used to predict the probability distribution of the water consumption of the target basin in the target time period; A prediction module 302, configured to predict a target water consumption according to the water use distribution function, where the target water consumption is used to represent the water consumption of the target basin in the target time period under a set confidence interval; A calculation module 303, configured to calculate a predicted water storage volume of the target basin at the second time according to the actual water storage volume, the precipitation, and the target water consumption; An analysis module 304, configured to analyze the predicted water storage volume according to the set drought warning rules to obtain drought warning information.

[0081] In one embodiment, the drought disaster warning device 300 further includes: A historical inversion module, configured to inversely obtain multiple historical water consumption amounts of the target basin during historical drought periods according to multiple groups of historical data of the target basin during historical drought periods, wherein the multiple groups of historical data correspond one-to-one to multiple historical drought events during the historical drought periods, and each group of historical data includes the historical water storage transformation amount and the historical rainfall amount of the target basin under the corresponding historical drought event; A clustering module, configured to cluster the multiple historical water consumption amounts according to the time dimension and / or the drought level dimension to obtain at least two clusters; A cluster analysis module, configured to analyze the at least two clusters respectively to obtain at least two distribution functions associated one-to-one with the at least two clusters, where the distribution function is used to predict the probability distribution of the water consumption amount of the target basin during the time period corresponding to the associated cluster, and the at least two distribution functions include the water use distribution function of the target basin.

[0082] In one embodiment, the calculation module 303 includes: A coefficient calculation unit, configured to compare the industrial and agricultural output value of the target basin during the target time period with the industrial and agricultural output value of the target basin under the historical drought event corresponding to the water use distribution function to obtain an industrial and agricultural water use fluctuation coefficient; and compare the total population of the target basin during the target time period with the total population of the target basin under the historical drought event corresponding to the water use distribution function to obtain a domestic water use fluctuation coefficient; A data correction unit, configured to correct the target water consumption amount according to the industrial and agricultural water use fluctuation coefficient and the domestic water use fluctuation coefficient to obtain a corrected water consumption amount; A calculation unit, configured to calculate the predicted water storage amount of the target basin at the second time according to the actual water storage amount, the precipitation amount, and the corrected water consumption amount.

[0083] In one embodiment, the calculation unit is specifically configured to: Compare the temperature of the target basin during the target time period with the temperature of the target basin under the historical drought event corresponding to the water use distribution function to obtain a temperature optimization coefficient; Optimize the actual water storage amount and the corrected water consumption amount respectively according to the temperature optimization coefficient to obtain an optimized water storage amount and an optimized water consumption amount; Calculate the predicted water storage amount of the target basin at the second time according to the optimized water storage amount, the precipitation amount, and the optimized water consumption amount.

[0084] In one embodiment, the analysis module 304 includes: A deviation analysis unit, configured to analyze the historical water storage deviation information of the target basin to obtain a water storage prediction deviation parameter; A rectification unit for rectifying the predicted water storage volume according to the water storage prediction deviation parameter to obtain a target water storage volume; An early warning unit for analyzing the target water storage volume according to the drought early warning rule to obtain drought early warning information.

[0085] In one embodiment, the drought disaster early warning device 300 further includes a rule generation module, and the rule generation module is specifically configured to: According to a plurality of drought warning critical water storage volumes in the target basin during historical drought periods, where the plurality of drought warning critical water storage volumes correspond one-to-one to a plurality of historical drought events during the historical drought periods, and the drought warning critical water storage volume is the water storage volume of the target basin before changing from a non-drought state to a drought state under the corresponding historical drought event; Determine the average value of the plurality of drought warning critical water storage volumes as the target critical water storage volume; Generate the drought early warning rule according to the target critical water storage volume.

[0086] The drought disaster early warning device 300 provided by the embodiments of the present disclosure can implement each process in the embodiments of the above drought disaster early warning method. To avoid repetition, it will not be elaborated here.

[0087] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device and a readable storage medium.

[0088] Figure 4 The schematic block diagram of an example electronic device 400 that can be used to implement the embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are only examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0089] As Figure 4As shown, device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to computer programs stored in a read-only memory (ROM) 402 or computer programs loaded from a storage unit 408 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of device 400 can also be stored. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0090] Multiple components in device 400 are connected to the I / O interface 405, including: an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a magnetic disk, an optical disc, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows device 400 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0091] The computing unit 401 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include but are not limited to a central processing unit (CPU), a graphic process unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 401 executes the various methods and processes described above, such as the drought disaster warning method. For example, in some embodiments, the drought disaster warning method can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as the storage unit 408. In some embodiments, part or all of the computer program can be loaded and / or installed onto device 400 via the ROM 402 and / or the communication unit 409. When the computer program is loaded into the RAM 403 and executed by the computing unit 401, one or more steps of the drought disaster warning method described above can be executed. Alternatively, in other embodiments, the computing unit 401 can be configured to execute the drought disaster warning method by any other appropriate means (e.g., by means of firmware).

[0092] The various embodiments of the systems and techniques described above in this specification can be implemented in digital electronic circuitry, integrated circuit systems, field-programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems-on-chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which may be a special-purpose or general-purpose programmable processor that receives data and instructions from, and transmits data and instructions to, a storage system, at least one input device, and at least one output device.

[0093] The program code for implementing the methods of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that the program codes, when executed by the processor or controller, cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on the remote machine or server.

[0094] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0095] As used herein, the term "machine-readable medium" refers to any computer program product, apparatus, and / or device (e.g., a magnetic disk, an optical disk, a memory, a programmable logic device (PLD)) that provides machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal that provides machine instructions and / or data to a programmable processor.

[0096] In order to provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0097] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0098] A computer system can include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The relationship between the client and the server is generated by computer programs that run on the respective computers and have a client-server relationship with each other. The server can be a cloud server, can also be a server of a distributed system, or a server integrated with a blockchain.

[0099] An embodiment of this application also provides a computer program product, including computer instructions, which when executed by a processor, implement each process of the method embodiment shown above Figure 1 and can achieve the same technical effects. To avoid repetition, details are not described herein again.

[0100] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and no limitations are imposed herein.

[0101] The above specific embodiments do not constitute a limitation on the protection scope of this disclosure. 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 disclosure shall be included within the protection scope of this disclosure.

Claims

1. A drought disaster early warning method, characterized in that: The method comprises: Obtaining the actual water storage capacity of the target basin at the first time, the precipitation of the target basin in the target period, and the water use distribution function of the target basin, wherein the target period is a period from the first time to a second time, the second time is the current time or a future time after the current time, and the water use distribution function is used to predict the probability distribution of the water use of the target basin in the target period; According to the water consumption distribution function, a target water consumption is predicted, where the target water consumption is used to represent the water consumption of the target basin in the target period under a set confidence interval; Calculate the predicted water storage capacity of the target basin at the second time according to the actual water storage capacity, the precipitation and the target water consumption; The predicted water storage capacity is analyzed according to the set drought warning rules to obtain drought warning information.

2. The method according to claim 1, characterized in that: Before obtaining the actual water storage capacity of the target river basin at the first time, the precipitation of the target river basin within the target period, and the water use distribution function of the target river basin, the method further includes: Based on multiple sets of historical data of the target watershed during the historical drought period, multiple historical water consumptions of the target watershed during the historical drought period are inverted, wherein the multiple sets of historical data correspond to multiple historical drought events during the historical drought period, and each set of historical data includes the historical water storage change amount and historical rainfall of the target watershed under the corresponding historical drought event; Clustering the multiple historical water consumptions according to the time dimension and / or drought level dimension to obtain at least two clusters; The at least two clusters are analyzed separately to obtain at least two distribution functions associated with the at least two clusters one by one, and the distribution functions are used to predict the probability distribution of water consumption of the target river basin during the time period corresponding to its associated cluster. The at least two distribution functions include the water consumption distribution function of the target river basin.

3. The method according to claim 2, characterized in that Calculating the predicted water storage capacity of the target basin at the second time according to the actual water storage capacity, the precipitation, and the target water consumption includes: Compare the industrial and agricultural output value of the target basin in the target period with the industrial and agricultural output value of the target basin under the historical drought event corresponding to the water use distribution function to obtain the industrial and agricultural water use fluctuation coefficient; and compare the total population of the target basin in the target period with the total population of the target basin under the historical drought event corresponding to the water use distribution function to obtain the residential water use fluctuation coefficient; Correcting the target water consumption according to the industrial and agricultural water consumption fluctuation coefficient and the residential water consumption fluctuation coefficient to obtain a corrected water consumption; The predicted water storage capacity of the target basin at the second time is calculated based on the actual water storage capacity, the precipitation and the corrected water consumption.

4. The method according to claim 3, characterized in that Calculating the predicted water storage capacity of the target basin at the second time according to the actual water storage capacity, the precipitation, and the corrected water consumption includes: Compare the temperature of the target basin during the target period with the temperature of the target basin during the historical drought event corresponding to the water use distribution function to obtain a temperature optimization coefficient; According to the temperature optimization coefficient, the actual water storage capacity and the corrected water consumption are optimized respectively to obtain the optimized water storage capacity and the optimized water consumption; The predicted water storage capacity of the target basin at the second time is calculated based on the optimized water storage capacity, the precipitation and the optimized water consumption.

5. The method according to claim 1, characterized in that The predicted water storage capacity is analyzed according to the set drought warning rules to obtain drought warning information, including: Analyze the historical water storage deviation information of the target watershed to obtain water storage prediction deviation parameters; Correcting the predicted water storage capacity according to the water storage prediction deviation parameter to obtain a target water storage capacity; The target water storage capacity is analyzed according to the drought warning rules to obtain drought warning information.

6. The method according to claim 5, characterized in that The target water storage capacity is analyzed according to the drought warning rule to obtain drought warning information, including: According to a plurality of drought warning critical water storage capacities of the target watershed during the historical drought period, wherein the plurality of drought warning critical water storage capacities correspond one to one to a plurality of historical drought events during the historical drought period, and the drought warning critical water storage capacities are the water storage capacities of the target watershed before the target watershed changes from a non-drought state to a drought state under the corresponding historical drought events; Determine the average of the plurality of drought warning critical water storage volumes as the target critical water storage volume; The drought warning rule is generated according to the target critical water storage capacity.

7. A drought disaster early warning device, characterized in that: The device comprises: an acquisition module, used to acquire the actual water storage capacity of the target basin at the first time, the precipitation of the target basin in the target period, and the water use distribution function of the target basin, wherein the target period is a period from the first time to a second time, the second time is the current time or a future time after the current time, and the water use distribution function is used to predict the probability distribution of the water use of the target basin in the target period; A prediction module, used for predicting a target water consumption according to the water consumption distribution function, wherein the target water consumption is used to represent the water consumption of the target basin in the target period under a set confidence interval; A calculation module, configured to calculate the predicted water storage capacity of the target basin at the second time according to the actual water storage capacity, the precipitation and the target water consumption; The analysis module is used to analyze the predicted water storage capacity according to the set drought warning rules to obtain drought warning information.

8. An electronic device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the steps of the method according to any one of claims 1 to 6 when executed by the processor.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The method comprises computer instructions, which, when executed by a processor, implement the steps of the method according to any one of claims 1 to 6.

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