A method and device for updating a hydrological model, an electronic device and a medium

Abstract

The application relates to the technical field of hydrodynamics, in particular to a hydrological model updating method and device, an electronic equipment and a medium, which comprises the following steps: obtaining an input data type, input data and a target output data type, determining a to-be-updated data type according to the contribution rate of the input data type to the target output data type, then obtaining first data according to the to-be-updated data type, determining respective time nodes of the first data and second data and obtaining an interval duration according to the time nodes, obtaining a specific value according to the input data, the first data and the interval duration of the to-be-updated data type in a historical hydrological model, finally updating the input data according to the specific value, and constructing a current hydrological model according to the updated input data, the input data type and the target output data type. In this way, the deviation between the predicted hydrological condition and the actual hydrological condition can be reduced when the hydrological model is constructed to predict the hydrological condition.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of hydrodynamics, in particular to a method and device for updating a hydrological model, an electronic device and a medium. BACKGROUND

[0002] A hydrological model is a scientific model that generalizes complex hydrological phenomena and processes through simulation methods. In recent years, in order to achieve accurate prediction of future hydrological conditions, the modeling method of the hydrological model has been continuously developed and optimized, but there are still situations where people's livelihood is damaged by flood disasters in different places. One of the main reasons for the current situation is that the current modeling method leads to a large model error in the final hydrological model. Generally speaking, when applying a hydrological model to predict future hydrological conditions, the data input for calculation should be the real-time data, but in fact, due to the high stability of some input data, and the need for a large amount of manpower for field survey, such as field survey of dam foundation horizontal displacement of flood discharge dam, it cannot be guaranteed that field survey is conducted every time. The technical personnel who build the hydrological model are prone to make minor changes to the historical data based on their own experience, or even start building the current model without making any changes, but this leads to the fact that the real-time data is in the survey blind area, and if there are unknown factors that make the real-time data significantly different from the historical data, it is easy to cause the hydrological conditions reflected by the obtained hydrological model to deviate from the actual situation.

[0003] Therefore, how to provide a solution to the above technical problems is a problem that those skilled in the art are eager to solve. SUMMARY

[0004] The purpose of the present application is to provide a method and device for updating a hydrological model, an electronic device and a medium, for solving at least one of the above technical problems.

[0005] The above invention purpose of the present application is realized by the following technical scheme:

[0006] In a first aspect, the present application provides a method for updating a hydrological model, which adopts the following technical scheme:

[0007] A method for updating a hydrological model, comprising:

[0008] obtaining all input data types required for building a current hydrological model, all input data types corresponding to each input data type and a target output data type;

[0009] determining a data type to be updated from all input data types according to the contribution rate of each input data type to the target output data type;

[0010] performing data tracing on a same kind of hydrological model that is built most recently according to the data type to be updated, to obtain first data corresponding to the data type to be updated.

[0011] determining time nodes corresponding to the first data and the second data respectively, and obtaining an interval length from the two time nodes, wherein the second data is input data corresponding to a data type to be updated in constructing the current hydrological model;

[0012] obtaining a specific value of the data type to be updated at the time node corresponding to the second data according to input data of the same type of historical hydrological model constructed in the past, the first data and the interval length;

[0013] updating all input data required for constructing the current hydrological model according to the specific value, and constructing the current hydrological model according to the updated input data, all input data types and the target output data type.

[0014] In another possible implementation manner, the determining of the data type to be updated from all the input data types according to the contribution rate of each input data type to the target output data type further includes:

[0015] determining a test data interval according to the input data type, the test data interval being an input data interval corresponding to the input data type in the contribution rate analysis;

[0016] performing multiple sampling in all the test data intervals to obtain multiple groups of test data;

[0017] inputting each group of test data into a test black box in sequence to obtain multiple target output data corresponding to the target output data type, the test black box being a mathematical method used in constructing the current hydrological model;

[0018] determining the contribution rate of each input data type to the target output data type according to different target output data.

[0019] In another possible implementation manner, the determining of the data type to be updated from all the input data types according to the contribution rate of each input data type to the target output data type includes:

[0020] attaching a level label to each input data type according to the contribution rate of each input data type;

[0021] determining a data change period of each input data type, and attaching a period label to each input data type based on the data change period;

[0022] The key value corresponding to the input data type is determined based on the period label and the level label corresponding to the same input data type, and the key value is the update value corresponding to the input data type.

[0023] Based on the key values ​​corresponding to each of the input data types, the data type to be updated is determined from all the input data types.

[0024] In another possible implementation, the step of determining the data type to be updated from all the input data types based on the contribution rate of each input data type to the target output data type further includes:

[0025] Obtain the current time, which is the time node corresponding to the determination of the data type to be updated from all the input data types;

[0026] The update information is determined based on the current time, the data type to be updated, the input data type, and the target output data type;

[0027] The updated information is matched with the historical updated information. If the matching degree exceeds a preset matching threshold, the historical updated information is merged into the updated information. The historical updated information is the updated information determined before the current time based on the historical input data type, the historical target output data type, the historical current time, and the historical data type to be updated.

[0028] The final data type to be updated is determined based on the updated information.

[0029] In another possible implementation, the step of constructing the current hydrological model based on all the updated input data, all the input data types, and the target output data type further includes:

[0030] If the input data corresponding to the updated data type is the specific value, then the data type to be updated will be included as necessary training content.

[0031] If the input data corresponding to the updated data type to be updated is the second data, then the data type to be updated will be regarded as non-essential training content. Both the essential training content and the non-essential training content are training content references for technicians who construct the current hydrological model.

[0032] In another possible implementation, the method further includes:

[0033] If the risk factor of the output data corresponding to the target output data type exceeds the preset standard, then local road network information is obtained;

[0034] Based on the current road network information, several escape routes are planned, and escape prompts are generated according to the escape routes.

[0035] In another possible implementation, the step of generating escape prompts based on the escape path further includes:

[0036] Obtain user feedback information, including the target escape route selected by the user in the escape prompt;

[0037] The number of users who tend to choose the target escape route is determined based on the user feedback information;

[0038] The optimal escape route is determined based on the starting and ending points of the target escape path, the number of users, and all escape paths.

[0039] Secondly, this application provides a device for updating a hydrological model, which adopts the following technical solution:

[0040] An apparatus for updating a hydrological model, comprising:

[0041] The data acquisition module is used to acquire all the input data types required to build the current hydrological model, the corresponding input data for each of the input data types, and the target output data type.

[0042] The contribution rate determination module is used to determine the data type to be updated from all the input data types based on the contribution rate of each input data type to the target output data type.

[0043] The data tracing module is used to trace the data of the most recently built hydrological model based on the data type to be updated, and obtain the first data corresponding to the data type to be updated.

[0044] The interval determination module is used to determine the time nodes corresponding to the first data and the second data respectively, and to determine the interval duration obtained from the two time nodes, wherein the second data is the input data corresponding to the data type to be updated when constructing the current hydrological model;

[0045] The data simulation module is used to obtain the specific value of the data type to be updated at the time node corresponding to the second data based on the input data of all the same historical hydrological models built in history, the first data, and the interval duration.

[0046] The data update module is used to update all input data required to construct the current hydrological model according to the specific value, and to construct the current hydrological model according to all the updated input data, all the input data types, and the target output data type.

[0047] In another possible implementation, the device further includes: an interval determination module, an interval sampling module, a black-box testing module, and a contribution rate module, wherein,

[0048] The interval determination module is used to determine the test data interval based on the input data type, wherein the test data interval is the input data interval corresponding to the input data type when performing contribution rate analysis.

[0049] The interval sampling module is used to perform multiple samplings within all the test data intervals to obtain multiple sets of test data.

[0050] The test black box module is used to input each set of test data into the test black box in sequence to obtain multiple target output data corresponding to the target output data type. The test black box is the mathematical method used to construct the current hydrological model.

[0051] The contribution rate module is used to determine the contribution rate of different input data types to the target output data type based on different target output data.

[0052] In another possible implementation, when the first contribution rate module determines the data type to be updated from all the input data types based on the contribution rate of each input data type to the target output data type, it is specifically used for:

[0053] A rating label is attached to the corresponding input data type based on the contribution rate of each input data type;

[0054] Determine the corresponding data change period for each input data type, and attach a period label to the corresponding input data type based on the data change period;

[0055] The key value corresponding to the input data type is determined based on the period label and the level label corresponding to the same input data type, and the key value is the update value corresponding to the input data type.

[0056] Based on the key values ​​corresponding to each of the input data types, the data type to be updated is determined from all the input data types.

[0057] In another possible implementation, the apparatus further includes: a time acquisition module, an information determination module, a matching information module, and a type determination module, wherein,

[0058] The time acquisition module is used to acquire the current time, which is the time node corresponding to the determination of the data type to be updated from all the input data types;

[0059] The information determination module is used to determine update information based on the current time, the data type to be updated, the input data type, and the target output data type.

[0060] The matching information module is used to match the updated information with historical updated information. If the matching degree exceeds a preset matching threshold, the historical updated information is merged into the updated information. The historical updated information is the updated information determined before the current time based on the historical input data type, the historical target output data type, the historical current time, and the historical data type to be updated.

[0061] The type determination module is used to determine the final data type to be updated based on the update information.

[0062] In another possible implementation, the device further includes: a necessary training module and a non-necessary training module, wherein,

[0063] The necessary training module is used to include the data type being updated as necessary training content.

[0064] The non-essential training module is used to treat the data type to be updated as non-essential training content. Both the essential training content and the non-essential training content are used as training content references for technicians who construct the current hydrological model.

[0065] In another possible implementation, the apparatus further includes: a road network acquisition module and a route planning module, wherein,

[0066] The road network acquisition module is used to acquire local road network information;

[0067] The route planning module is used to plan several escape routes based on the current road network information and generate escape prompts based on the escape routes.

[0068] In another possible implementation, the device further includes: a user feedback acquisition module, a user preference determination module, and an escape route recommendation module, wherein,

[0069] The user feedback acquisition module is used to acquire user feedback information, including the target escape path selected by the user in the escape prompt;

[0070] The user preference determination module is used to determine the number of users who prefer the target escape route based on the user feedback information.

[0071] The recommended escape route module is used to determine the optimal escape route based on the starting and ending positions of the target escape route, the number of users, and all escape routes.

[0072] Thirdly, this application provides an electronic device that adopts the following technical solution:

[0073] At least one processor;

[0074] Memory;

[0075] At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, the at least one application being configured to: execute one of the above-described methods for updating a hydrological model.

[0076] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution:

[0077] A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the above-described method for updating a hydrological model.

[0078] In summary, this application includes at least one of the following beneficial technical effects:

[0079] This application provides a method, apparatus, electronic device, and medium for updating a hydrological model. Compared with related technologies, in this application, the data type to be updated is determined from the input data types by the contribution rate of the current hydrological model's input data type to the target output data type. First data is obtained by tracing the data of the most recently built hydrological model of the same type based on the data type to be updated. The time nodes corresponding to the first data and the input data of the data type to be updated in the current hydrological model are determined, i.e., the time nodes corresponding to the first data and the second data, respectively, thus obtaining the interval between the time nodes. Based on the input data of the historically built hydrological model of the same type, the first data, and the interval, the specific value of the data type to be updated corresponding to the second data's time node is simulated. Finally, the input data of the current hydrological model is updated using the specific value, and the current hydrological model is constructed based on the updated input data, the input data type, and the target output data type. Thus, the current hydrological model is updated using highly realistic simulated input data, reducing the possibility of distortion in the current hydrological model construction. Attached Figure Description

[0080] Fig. 1 This is a flowchart illustrating a method for updating a hydrological model according to one embodiment of this application;

[0081] Fig. 2 This is a schematic diagram of the structure of an updated hydrological model device according to one embodiment of this application;

[0082] Fig. 3This is a schematic diagram of the structure of an electronic device for updating a hydrological model, according to one embodiment of this application. Implementation

[0083] The following combination Figs. 1 to 3 This application will be described in further detail.

[0084] This specific embodiment is merely an explanation of this application and is not intended to limit it. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they are within the scope of this application.

[0085] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0086] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0087] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0088] The hydrological models used in this application are all hydrodynamic mathematical models. Hydrodynamic mathematical models are mathematical models established using fundamental equations from fluid mechanics, reflecting the hydrological conditions of a specific region. Examples include the impulse and modified impulse calculus model proposed by U.S. Army engineer Tu. Currently, hydrodynamic mathematical models are often simulated numerically using computers to facilitate the visualization of hydrological conditions. Hydrological models obtained through computer modeling have numerous applications in real-world scenarios. For instance, regarding the flood evolution process of a dam break in the Zhanghe Reservoir, numerical simulations of the dam break flood mathematical model can be used to visualize the hydrological conditions of the Zhanghe River.

[0089] This application provides a method for updating a hydrological model, executed by an electronic device. This electronic device can be a server or a terminal device. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, tablet, laptop, desktop computer, etc., but is not limited to these. The terminal device and the server can be directly or indirectly connected via wired or wireless communication. This application does not impose any limitations on this. Fig. 1 As shown, the method includes steps S10, S20, S30, S40, S50, S60, and S70, wherein:

[0090] Step S10: Obtain all the input data types required to construct the current hydrological model, the input data corresponding to each of the input data types, and the target output data type.

[0091] The current hydrological model is a digital model constructed using any hydrodynamic mathematical model, used to simulate hydrological conditions according to the requirements of relevant technical personnel; the input data type is the hydrological data type that exists in the initial stage of the simulated hydrological process and is used to simulate future hydrological processes, such as the coordinates of the distributary, the bottom elevation of the reservoir, and the maximum opening of the sluice gate; the target output data type is the hydrological data obtained by calculating the input data based on the hydrodynamic mathematical model, such as the changes in water level and flow rate, flow velocity, and water quality factor concentration at the river cross-section.

[0092] In this embodiment of the application, the output data type, all input data types, and input data corresponding to all input data types required for constructing the current hydrological model are obtained, and any output data type is selected as the target output data type.

[0093] Step S20: Determine the data type to be updated from all input data types based on the contribution rate of each input data type to the target output data type.

[0094] The contribution rate is a quantitative indicator that measures the influence of input data on output data in a hydrological model.

[0095] Since the hydrological process simulated by the hydrological model is obtained by calculating and simulating the input data using certain mathematical methods, the simulated hydrological process will reflect different hydrological conditions as the input data changes. When analyzing the influence of different input data types on hydrological conditions, the contribution rate can better reflect the influence of different input data types on the output. When constructing the hydrological model, it provides a reliable basis for relevant technicians to adjust the input data, improves the efficiency of simulation evaluation, and further assists in optimizing the input data in the hydrological model.

[0096] In this application embodiment, the contribution rate calculation method may include: local parameter sensitivity analysis method, qualitative global sensitivity analysis method, and quantitative global sensitivity analysis method. The quantitative global sensitivity analysis method includes RSA method, GLUE method, and RSMobol method. The specific calculation method can be selected according to actual needs, and this application embodiment does not limit it. Specifically, when determining the data type to be updated from all input data types, the input data type corresponding to the maximum contribution rate can be directly used as the data type to be updated. Alternatively, a level label can be attached to the input data type by determining the data change period corresponding to the input data type in a preset data change period. The level label is then attached to the input data type using the contribution rate. Afterwards, a multi-layer feedforward neural network is used to evaluate the update value of all input data types, and the data type to be updated is determined based on the update value corresponding to different input data types.

[0097] Step S30: Based on the data type to be updated, perform data tracing on the most recently built hydrological model of the same type in history to obtain the first data corresponding to the data type to be updated.

[0098] Among them, the most recently built hydrological model of the same type refers to the hydrological model that was successfully built in the past and is of the same type as the current hydrological model, and is the hydrological model whose building time is closest to that of the current hydrological model. Since in most cases, after the application of a computer-simulated hydrological model is completed, the part that is retained / recorded only includes the input data type, input data, and target output data type, and other files related to the hydrological model are not completely retained / recorded, in this embodiment of the application, it is assumed that the recording situation of the most recently built hydrological model of the same type is the same as in most cases, that is, only the input data type, input data, and target output data type are retained / recorded.

[0099] In this embodiment of the application, in the most recently built hydrological model of the same type as the current hydrological model, the same input data type as the data type to be updated in the current hydrological model is determined, and the input data corresponding to the same input data type is used as the first data, thereby completing the original data tracing of the data type to be updated in the current hydrological model.

[0100] Step S40: Determine the time nodes corresponding to the first data and the second data respectively, and determine the interval duration obtained from the two time nodes.

[0101] In this embodiment of the application, the time represented by the first data in the most recent historical hydrological model of the same type is taken as the time node corresponding to the first data, and the time represented by the second data in the current hydrological model is taken as the time node corresponding to the second data, and the time difference between the first data and the second data is determined as the interval duration.

[0102] Step S50: Based on the input data, first data, and interval duration of the data type to be updated in all historical hydrological models of the same type built in history, obtain the specific value of the data type to be updated at the time node corresponding to the second data.

[0103] In this application, among all historical hydrological models of the same type established in the past, the most recent historical hydrological model of the same type is selected for analysis. These historical hydrological models are of the same type and are referred to as "historical hydrological models of the same type" below. Due to the large quantity and complex types of historical hydrological data, in order to obtain historical hydrological conditions with high similarity when simulating hydrological situations, and to facilitate the subsequent establishment of the current hydrological model, hydrological time series similarity analysis algorithms, such as the DTW algorithm, are introduced.

[0104] In this embodiment, DTW (Dynamic Time Warp Distance) is used to perform hydrological time series similarity mining on the input data corresponding to the data type to be updated in the same historical hydrological model. This yields the similarity between all historical hydrological phenomena simulated by the same historical hydrological model and the hydrological phenomena simulated by the most recent historical hydrological model of the same type. The input data used to build the same historical hydrological model is determined from the historical hydrological phenomena with the highest similarity. The change curve of the first data is simulated within the interval based on the determined input data, and the last value in the change curve is used as the specific value of the data type to be updated at the time node corresponding to the second data.

[0105] Step S60: Update all input data required to construct the current hydrological model according to the specific values, and construct the current hydrological model based on all updated input data, all input data types, and the target output data type.

[0106] In this embodiment, the error range is determined based on the data type to be updated. For example, the input data type is river width, average water level, and river height. When the river width is 10m and the river height is 5m, if the data type to be updated is average water level, the error range corresponding to the data type to be updated can be [-0.1m, +0.1m]. As the river width and river height increase, the error range of the average water level should also increase accordingly. When the river width is 50m and the river height is 10m, if the data type to be updated is average water level, the error range corresponding to the data type to be updated can be expanded to [-0.5m, +0.5m].

[0107] Determine the difference between the specific value and the second data. If the difference is within the error range, retain all the original input data and use all the input data, all the input data types, and the target output data type as the construction parameters of the current hydrological model to construct the current hydrological model.

[0108] If the obtained difference exceeds the error range, the specific value will replace the second data corresponding to the data type to be updated in all input data types of the current hydrological model, so as to update the input data of the current hydrological model, and construct the current hydrological model through the target output data type, the updated input data, and the input data type corresponding to the input data.

[0109] In this embodiment, the input data types that have a high impact on the output data in the current hydrological model are used as the data to be updated. The historical hydrological processes that are similar to the hydrological conditions simulated by the current hydrological model are accurately determined in a large number of complex historical hydrological processes. Then, the changes of the first data within the time interval are simulated by the similar historical hydrological processes. The specific values ​​of the input data corresponding to the data types to be updated when the current hydrological model is established are highly restored. Finally, the current hydrological model is updated by restoring the true values ​​of the input data and other input data, which reduces the possibility of distortion in the construction of the hydrological model due to the blind spots of the field data.

[0110] One possible implementation of this application embodiment includes steps S1B2 (not shown in the figure), S2B2 (not shown in the figure), S3B2 (not shown in the figure), and S4B2 (not shown in the figure) before step S20, wherein...

[0111] Step S1B2: Determine the test data range based on the input data type.

[0112] The test data interval is the data sampling interval corresponding to the input data type when determining the contribution rate of the current input data type. When calculating the contribution rate of different input data types, historical hydrological data existing in the region reflected by the hydrological conditions simulated by the current hydrological model can be selected as a reference to make the input data corresponding to the current hydrological data type more closely match the hydrological conditions simulated by the current hydrological model. Since the hydrological conditions simulated by the current hydrological model are generally only similar to one type of historical hydrological conditions when selecting the test data interval from the hydrological data corresponding to historical hydrological conditions, the number of test data intervals is defaulted to 1 in this embodiment. In addition, the specific values ​​of the left and right endpoints of the test data interval are selected according to the method described in step S20.

[0113] In this embodiment, in the hydrological situation simulated by the current hydrological model, historical hydrological data corresponding to the region is determined, and historical hydrological data corresponding to each input data type of the current hydrological model are selected from the historical hydrological data. The hydrological data that can represent the maximum fluctuation of the hydrological situation is used as the left and right endpoints of the test data interval. For example, in the example of the precipitation test data interval in step S20, when setting the annual precipitation of XX County in 2021, it was found that the maximum precipitation in XX County from 1980 to 2020 was 1500mm and the minimum precipitation was 200mm. When setting the test data interval in 2021, it is considered that the maximum precipitation of 1500mm and the minimum precipitation of 200mm reflect the maximum fluctuation of the hydrological phenomenon of precipitation from 1980 to 2020. Therefore, the maximum precipitation of 1500mm is used as the right endpoint of the test data interval, and the minimum precipitation of 200mm is used as the left endpoint of the test data interval, resulting in the test data interval [200mm] for the input data type of precipitation. 1500mm).

[0114] After determining the test data range corresponding to the input data type, the input data type is matched with the preset test range to obtain the test data range. For example, in the example of determining the left and right endpoints of the precipitation test range in this step, to obtain the test data range, the test data range [200mm, 1500mm] should be matched with the input data type precipitation beforehand, resulting in the preset test data range as: input data type - precipitation, test data range - [200mm, 1500mm]. Then, the precipitation data is filtered within the preset test data range according to the input data type precipitation, finally obtaining the test data range [200mm, 1500mm] corresponding to the precipitation.

[0115] Step S2B2 involves sampling multiple times within all test data intervals to obtain multiple sets of test data.

[0116] When sampling within the test data range, the sampling method used should be adjusted according to the actual situation, and it should correspond to the specific method used in the subsequent step S3B2 for contribution rate analysis of the input data, in order to improve the accuracy of the contribution rate of each input data type. The Monte Carlo sampling method used in this step is a widely used sampling method, and its specific sampling principle will not be detailed here.

[0117] In this embodiment, the Monte Carlo sampling method is used to sample within the determined test data range, and the sampled data is divided into multiple groups of test data. The sampling method and the method of grouping the sampled data are the same as those described in step S20, and will not be repeated here.

[0118] Step S3B2: Input each set of test data into the test black box in sequence to obtain multiple target output data corresponding to the target output data type.

[0119] Among them, the test black box refers to the mathematical method used to construct the current hydrological model. The current hydrological model, the historical hydrological models of the same type, and the most recently constructed hydrological models of the same type are all hydrological models constructed using computers through the test black box.

[0120] In this embodiment of the application, the mathematical method used to build the current hydrological model is used as a test black box. For example, if the mathematical method used to build the current hydrological model is the Muskingan model, which assumes that there is a linear relationship between the channel storage V of the river section and the outflow Q and inflow I: V=K[xI+(1-x)Q], where K and x are empirical coefficients and 0<=x<=0.5, then the linear relationship V=K[xI+(1-x)Q] is used as a test black box.

[0121] Multiple sets of test data are sequentially input into the test black box to obtain the output data corresponding to the target output data type. This output data is then used as the target output data, corresponding to the input data type. For example, if the test black box is the pulse and modified pulse calculus model proposed by U.S. Army engineer Tu, then all sets of test data are sequentially input into the pulse and modified pulse calculus model to obtain the target output data corresponding to the target output data type.

[0122] Step S4B2: Determine the contribution rate of different input data types to the target output data type based on different target output data.

[0123] In this embodiment, the first-order influence index formula in RSMsobol (a quantitative global sensitivity analysis method combined with surface response) is used to calculate the first-order influence index of all target output data to obtain the first-order influence index of all input data types. The obtained first-order influence index is used as the contribution rate of the target output data type to different input data types.

[0124] Specifically, first-order sensitivity can represent the contribution rate of a single input parameter in a hydrological model to the variance of the model output.

[0125] One possible implementation of this application embodiment includes step S201 (not shown in the figure), step S202 (not shown in the figure), step S203 (not shown in the figure), and step S204 (not shown in the figure), wherein,

[0126] Step S201: Attach a level label to the corresponding input data type based on the contribution rate of each input data type.

[0127] In this embodiment, a grade label is attached to each input data type based on its contribution rate. For example, if the target output data is the change in river runoff, and the contribution rate of glacial meltwater is 53% and that of ice meltwater is 27%, and the ratio of the contribution rates of the two input data types is 1.9 or 0.5, then grade labels are attached to the two input data types, glacial meltwater and ice meltwater, based on the ratio of their contribution rates. The attached results are: Input data type - glacial meltwater - grade label - 2; Input data type - glacial water - grade label - 1.

[0128] Step S202: Determine the corresponding data change period for each input data type, and attach a period label to the corresponding input data type based on the data change period.

[0129] In this embodiment of the application, the real data type in all input data types is matched with the real data type in the preset real data period, and the data change period corresponding to the real data type in the real data period when the matching degree is 1 is taken as the change period of the input data.

[0130] The preset field data period establishes the correspondence between the field data type and the data change period. For example, the preset field data period is: Input data type - Northern Hemisphere continental glacier meltwater: Data change period - melting period May to September; Input data type - Northern Hemisphere maritime glacier meltwater: Data change period - melting period April to October. When the field data type is Northern Hemisphere continental glacier meltwater, the corresponding data change period within the preset field data period is the melting period May to September.

[0131] Based on the starting time point and the periodic change time within the data change cycle corresponding to the hydrological conditions simulated by the current hydrological model, a periodic label is added to the input data type. For example, the preset field data cycle is: input data type - Northern Hemisphere continental glacial meltwater: data change cycle - melting period May - September; input data type - Northern Hemisphere maritime glacial meltwater: data change cycle - melting period April - October, and the starting time point is 2000.4.1. In this case, if the input data type is Northern Hemisphere maritime glacial meltwater, the periodic label "fast" is added to the input data type; if the input data type is Northern Hemisphere continental glacial meltwater, the periodic label "slow" is added to the input data type.

[0132] Step S203: Determine the key value corresponding to the input data type based on the period label and level label corresponding to the same input data type.

[0133] Step S204: Based on the key values ​​corresponding to each of the input data types, determine the data type to be updated from all the input data types.

[0134] The key value is the update value corresponding to the input data type.

[0135] In this embodiment, the input data type is input into the trained neural network model to obtain the key value of the corresponding input data type. Accordingly, the training samples used in the training process of the trained neural network model are: historical input data types as input variables, the level labels corresponding to the historical input data types, and the period labels corresponding to the historical input data; and historical input data types and their corresponding key values ​​as output variables. The key values ​​corresponding to the historical input data types are determined beforehand by evaluating the update value of the corresponding historical input data types based on the changes brought about by different historical input data types to the historical target output data types. Then, the key values ​​of the historical input data are determined based on the evaluation value of the historical input data types.

[0136] The input data type with the highest update value is determined based on the key values ​​corresponding to different input data types, and this obtained input data type is used as the data type to be updated. When determining the input data type with the highest update value based on the key values ​​corresponding to different input data types, the following two methods are included, but are not limited to: taking the maximum value among the key values ​​corresponding to different input data types and using the input data type corresponding to the maximum key value as the data type to be updated; or, if there are cases where the maximum key value is approximately equal to the second-largest key value among different input data types, then the input data types corresponding to both the maximum and second-largest key values ​​are used as the data types to be updated. Similarly, if there are multiple key values ​​among the obtained key values ​​that are approximately equal to the maximum key value, then all key values ​​approximately equal to the maximum key value, along with the input data type corresponding to the maximum key value, are used as the data types to be updated.

[0137] One possible implementation of this application embodiment includes, after step S20, steps S21 (not shown in the figure), S22 (not shown in the figure), S23 (not shown in the figure), S24 (not shown in the figure), and S25 (not shown in the figure), wherein...

[0138] Step S21: Obtain the current time.

[0139] Step S22: Determine the update information based on the current time, the data type to be updated, the input data type, and the target output data type.

[0140] Among them, the historical update type represents the update record of the current hydrological model. The record format in the update record includes, but is not limited to, one of the examples. The specific record format is not restricted.

[0141] In this embodiment, the time when the data type to be updated is determined is taken as the current time. Then, the current time, the data type to be updated, the input data type, and the target input data type are integrated and recorded accordingly. The recorded content is used as update information. For example, if the input data types are A, B, and C, the target output data type is D, the data type to be updated is A, and the time when the data type to be updated is determined is 2000.01.01.9:00, then 2000.01.01.9:00 is taken as the current time, and the update information is: current time - 2000.01.01.9:00; input data types - A, B, C; data type to be updated - A; target output data type - D.

[0142] Step S23: Match the updated information with the historical updated information. If the matching degree exceeds the preset matching threshold, merge the historical updated information into the updated information.

[0143] Step S24: Determine the final data type to be updated based on the update information.

[0144] The historical update information refers to the update information determined before the current time based on the historical input data type, the historical target output data type, the historical current time, and the historical data type to be updated. The preset matching threshold should be set according to the actual situation, and the specific value is not limited. In this application embodiment, the method is described using a preset matching threshold of 90% to more clearly explain the method included in the implementation of this application.

[0145] In this embodiment of the application, it is determined whether there is historical update information before the current time. If not, the data type to be updated in the update information is used as the final data type to be updated when establishing the current hydrological model.

[0146] If historical update information exists prior to the current time, a matching order "1" is appended to the target output data type, a matching order "2" is appended to the input data type, and a matching order "3" is appended to the current time. Based on the matching order, the target data type, input data type, and current time in the update information are matched with the corresponding information in the historical update information. If the matching degree in the first and second matches is greater than the preset matching threshold of 90%, a third match is performed. If, during the third match, the update information and the current time in the historical update information belong to the same data cycle node, then the matching degree during the third match is considered to be greater than the preset matching degree threshold of 90%.

[0147] For example, the preset field data period is represented as input data type-B: data change period - ablation period May to September; input data type-A: data change period - ablation period April to October.

[0148] The update information 'a' corresponding to the current time is: Current time - 2000.05.01.09:00; Input data types - A, B, C; Data type to be updated - A; Target output data type - D.

[0149] The historical update information b that appeared before the current time is: current time - 2001.06.01.19:00; input data types - A, B, C; data type to be updated - A; target output data type - D. At this time, the matching degree of the update information a and the historical update information b in the first and second matching is greater than 90%, and the data cycle node of input data type A in update information a is the same as the data cycle node of input data type B in historical update information b, that is, both are ablation period. Therefore, it is determined that the matching degree in the third matching is greater than 90%.

[0150] After determining that the matching degree of the third match is greater than 90% of the preset matching threshold, the data types to be updated in the historical update information are merged with the data types to be updated in the update information, and the merged data types to be updated are taken as the final data types to be updated.

[0151] One possible implementation of this application embodiment includes steps S61 (not shown in the figure) and S62 (not shown in the figure) after step S60, wherein,

[0152] Step S61: If the input data corresponding to the updated data type to be updated is a specific value, then the data type to be updated will be used as necessary training content.

[0153] Step S62: If the input data corresponding to the updated data type to be updated is the second data, then the data type to be updated is treated as non-essential training content.

[0154] The necessary and unnecessary training content are both for reference when training technicians who are building the current hydrological model.

[0155] In this embodiment, it is determined whether the data corresponding to the updated data type to be updated is a specific value. If so, it is considered that the input data corresponding to the data type to be updated previously deviated significantly from the actual data. The data type to be updated in the current hydrological model is then included as necessary training content. This is so that when training the technicians who constructed the current hydrological model, the relevant personnel who determined the training content can be prompted to add the necessary training content to the training content. If not, the data corresponding to the updated data type to be updated is considered as secondary data. The data type to be updated in the current hydrological model is then included as non-necessary training content. This is so that when training the technicians who constructed the current hydrological model, the relevant personnel who determined the training content can skip the non-necessary training content.

[0156] One possible implementation of this application embodiment includes steps S611 (not shown in the figure) and S621 (not shown in the figure), wherein...

[0157] Step S611: If the output data corresponding to the target output data type exceeds the preset safety range, then obtain the local road network information.

[0158] The preset safety interval is the set of output data corresponding to the situation where the current hydrological conditions do not cause losses to the public. The preset safety interval should be adjusted according to the actual situation. In order to make the explanation clear, this application will use the preset safety interval of [70m, 200m] in the example to introduce the method.

[0159] In this embodiment, if the output data corresponding to the target output data type in the current hydrological model exceeds the preset safety range, the area with a threat to people's livelihood in the current hydrological model is designated as the danger center, and the area without a threat to people's livelihood is designated as the destination. The road from the danger center to the destination is then obtained as the local road network information. For example, if the target output data type of the current hydrological model is the reservoir water level, and the preset safety range is defined by the check flood level of 200m and the dead water level of 70m, then the preset safety range is [70m, 200m]. If the output data exceeds [70m, 200m], the area around the reservoir with a threat to people's livelihood is designated as A, and the area without a threat to people's livelihood is designated as B. Then, the roads distributed between A and B are obtained.

[0160] Step S621: Plan an escape route based on the current road network information and output data, and generate escape prompts based on the escape route.

[0161] In this embodiment, roads impassable to people are marked in the hydrological data represented by the specific numerical values ​​of the output data. These marked roads are discarded from the current road network information, and the remaining roads connecting to the destination are used as escape routes. Finally, an escape prompt is generated based on the geographical location of the escape route. The specific form of the escape prompt is not limited. For example, if a danger center will cause flooding to block intersection XX in area A and street XX in area B within 10 seconds, then the roads connecting to intersection XX in area A or street XX in area B within the current road network information are discarded. The remaining roads connecting to the destination within the current road network information are retained, and these retained roads are used as escape routes. An escape prompt is then generated based on the location of the escape route.

[0162] One possible implementation of this application embodiment includes, after step S621, steps S62A (not shown in the figure), S62B (not shown in the figure), and S62C (not shown in the figure), wherein...

[0163] Step S62A: Obtain user feedback information.

[0164] Step S62B: Determine the number of users who prefer to select the target escape route based on user feedback information.

[0165] The user feedback information refers to the target escape route selected by the user in the escape prompts.

[0166] In this embodiment of the application, the escape route selected by the user in the user feedback information is obtained, and users who specify / navigate the same escape route are regarded as users with the tendency to select the target escape route, and the number of users with the tendency to select the target escape route is calculated.

[0167] Step S62C: Determine the optimal escape route based on the starting and ending positions of the target escape path, the number of users, and all escape paths.

[0168] In this embodiment, the endpoint of the target escape path connected to the destination is taken as the starting position, and the other endpoint is taken as the arrival position. When the number of users who tend to choose the same target escape path is greater than the width value of a certain segment in the corresponding target escape path, the corresponding segment in the target escape path is taken as an obstacle area, and the route connecting the starting position and the arrival position in the target escape path is planned by informedRRT (path planning algorithm) as the optimal escape route.

[0169] Specifically, when planning the optimal escape route using informed RRT, an elliptical sampling region is created with the starting position and the destination position as vertices. Obstacle areas within the elliptical sampling region are removed, and a search tree is built within the elliptical sampling region with the starting position as the starting point. Finally, the optimal escape route is determined by the path formed by connecting the starting point and the root node in the search tree.

[0170] The above embodiments describe a method for updating a hydrological model from the perspective of process flow. The following embodiments describe a device for updating a hydrological model from the perspective of virtual modules or virtual units. For details, please refer to the following embodiments.

[0171] This application provides an embodiment of a hydrological model updating device 20, such as... Fig. 2 As shown, the hydrological model updating device 20 may specifically include: a data acquisition module 21, a contribution rate determination module 22, a data tracing module 23, an interval determination module 24, a data simulation module 25, and a data updating module 26, wherein,

[0172] The data acquisition module 21 is used to acquire all the input data types required to construct the current hydrological model, the input data corresponding to each of the input data types, and the target output data type.

[0173] The contribution rate determination module 22 is used to determine the data type to be updated from all input data types based on the contribution rate of each input data type to the target output data type.

[0174] Data tracing module 23 is used to trace the data of the most recently built hydrological model in history according to the data type to be updated, and obtain the first data corresponding to the data type to be updated.

[0175] The interval determination module 24 is used to determine the time nodes corresponding to the first data and the second data, and to determine the interval duration obtained from the two time nodes, wherein the second data is the input data corresponding to the data type to be updated when constructing the current hydrological model;

[0176] The data simulation module 25 is used to obtain the specific value of the data type to be updated at the corresponding time node of the second data based on the input data, the first data and the interval duration of all the same historical hydrological models built in the past.

[0177] The data update module 26 is used to update all input data required to build the current hydrological model according to specific values, and to build the current hydrological model based on all updated input data, all input data types, and target output data types.

[0178] In another possible implementation of this application embodiment, the apparatus 20 further includes: a range determination module, a range sampling module, a black-box testing module, and a contribution rate module, wherein...

[0179] The interval determination module is used to determine the test data interval based on the input data type. The test data interval is the input data interval corresponding to the input data type when performing contribution rate analysis.

[0180] The interval sampling module is used to perform multiple samplings within all test data intervals to obtain multiple sets of test data.

[0181] The test black box module is used to input each set of test data into the test black box in sequence to obtain multiple target output data corresponding to the target output data type. The test black box is the mathematical method used to construct the current hydrological model.

[0182] The contribution rate module is used to determine the contribution rate of different input data types to the target output data type based on different target output data.

[0183] Another possible implementation of this application embodiment is that when the contribution rate determination module 22 determines the data type to be updated from all input data types based on the contribution rate of each input data type to the target output data type, it is specifically used for:

[0184] A grade label is attached to the corresponding input data type based on the contribution rate of each input data type;

[0185] Determine the corresponding data change period for each input data type, and attach a period label to the corresponding input data type based on the data change period;

[0186] The key value corresponding to the input data type is determined based on the period label and level label corresponding to the same input data type. The key value is the update value corresponding to the input data type.

[0187] Based on the key values ​​corresponding to each of the input data types, the data type to be updated is determined from all the input data types.

[0188] In another possible implementation of this application embodiment, the apparatus 20 further includes: a time acquisition module, an information determination module, a matching information module, and a type determination module, wherein...

[0189] The Time Acquisition module is used to obtain the current time, which is the time node corresponding to the determination of the data type to be updated from all input data types;

[0190] The information determination module is used to determine the update information based on the current time, the data type to be updated, the input data type, and the target output data type.

[0191] The matching information module is used to match the updated information with the historical updated information. If the matching degree exceeds the preset matching threshold, the historical updated information is merged into the updated information. The historical updated information is the updated information determined before the current time based on the historical input data type, the historical target output data type, the historical current time, and the historical data type to be updated.

[0192] The type determination module is used to determine the final data type to be updated based on the update information.

[0193] In another possible implementation of this application embodiment, the apparatus 20 further includes: a necessary training module and an unnecessary training module, wherein...

[0194] The necessary training module is used to include the data type update as a required training content.

[0195] The "Non-Essential Training" module is used to include data types to be updated as non-essential training content. Both essential and non-essential training content serve as references for training personnel building the current hydrological model.

[0196] In another possible implementation of this application embodiment, the apparatus 20 further includes: a road network acquisition module and a route planning module, wherein...

[0197] The road network acquisition module is used to acquire local road network information;

[0198] The route planning module is used to plan several escape routes based on the current road network information and generate escape prompts based on the escape routes.

[0199] In another possible implementation of this application embodiment, the device 20 further includes: a user feedback acquisition module, a user preference determination module, and an escape route recommendation module, wherein...

[0200] The user feedback acquisition module is used to acquire user feedback information, including the target escape route selected by the user in the escape prompts;

[0201] The user preference determination module is used to determine the number of users who prefer to choose the target escape route based on user feedback information;

[0202] The recommended escape route module is used to determine the optimal escape route based on the starting and ending points of the target escape route, the number of users, and all escape routes.

[0203] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the hydrological model updating device 20 described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0204] This application provides an electronic device, such as... Fig. 3 As shown, Fig. 3 The illustrated electronic device 300 includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, via a bus 302. Optionally, the electronic device 300 may also include a transceiver 304. It should be noted that in practical applications, the transceiver 304 is not limited to one type, and the structure of this electronic device 300 does not constitute a limitation on the embodiments of this application.

[0205] Processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 301 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0206] Bus 302 may include a pathway for transmitting information between the aforementioned components. Bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 302 can be divided into address bus, data bus, control bus, etc. For ease of representation, Fig. 3 The symbol is represented by only one line, but this does not mean that there is only one bus or one type of bus.

[0207] The memory 303 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0208] The memory 303 is used to store application code that executes the solution of this application, and its execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the foregoing method embodiments.

[0209] Electronic devices include, but are not limited to: mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (such as in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Servers can also be included. Fig. 3 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0210] This application provides a computer-readable storage medium storing a computer program that, when run on a computer, enables the computer to execute the corresponding content in the aforementioned method embodiments.

[0211] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0212] The above are only some embodiments of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for updating a hydrological model, characterized in that, include: Obtain all input data types required to construct the current hydrological model, the corresponding input data for each input data type, and the target output data type; Based on the contribution rate of each input data type to the target output data type, determine the data type to be updated from all the input data types; Based on the data type to be updated, the most recently built hydrological model of the same type in history is traced to obtain the first data corresponding to the data type to be updated. Determine the time nodes corresponding to the first data and the second data respectively, and determine the interval length obtained from the two time nodes, wherein the second data is the input data corresponding to the data type to be updated when constructing the current hydrological model; Based on the input data of the data type to be updated in all the same historical hydrological models built in history, the first data, and the interval duration, the specific value of the data type to be updated at the corresponding time node of the second data is obtained; The current hydrological model is constructed by updating all input data required to build the current hydrological model based on the specific values, and by constructing the current hydrological model based on all the updated input data, all the input data types, and the target output data type.

2. The method for updating a hydrological model according to claim 1, characterized in that, The step of determining the data type to be updated from all the input data types based on the contribution rate of each input data type to the target output data type further includes: The test data interval is determined based on the input data type, and the test data interval is the input data interval corresponding to the input data type when performing contribution rate analysis. Multiple samples were taken within all the aforementioned test data intervals to obtain multiple sets of test data; Each set of test data is sequentially input into the test black box to obtain multiple target output data corresponding to the target output data type. The test black box is the mathematical method used to construct the current hydrological model. The contribution rate of different input data types to the target output data type is determined based on the different target output data.

3. The method for updating a hydrological model according to claim 1, characterized in that, The step of determining the data type to be updated from all the input data types based on the contribution rate of each input data type to the target output data type includes: A rating label is attached to the corresponding input data type based on the contribution rate of each input data type; Determine the corresponding data change period for each input data type, and attach a period label to the corresponding input data type based on the data change period; The key value corresponding to the input data type is determined based on the period label and the level label corresponding to the same input data type, and the key value is the update value corresponding to the input data type. Based on the key values ​​corresponding to each of the input data types, the data type to be updated is determined from all the input data types.

4. The method for updating a hydrological model according to claim 1, characterized in that, The step of determining the data type to be updated from all the input data types based on the contribution rate of each input data type to the target output data type further includes: Obtain the current time, which is the time node corresponding to the determination of the data type to be updated from all the input data types; The update information is determined based on the current time, the data type to be updated, the input data type, and the target output data type; The updated information is matched with the historical updated information. If the matching degree exceeds a preset matching threshold, the historical updated information is merged into the updated information. The historical updated information is the updated information determined before the current time based on the historical input data type, the historical target output data type, the historical current time, and the historical data type to be updated. The final data type to be updated is determined based on the updated information.

5. The method for updating a hydrological model according to claim 1, characterized in that, The method further includes: If the risk factor of the output data corresponding to the target output data type exceeds the preset standard, then local road network information is obtained; Based on the local road network information, several escape routes are planned, and escape prompts are generated according to the escape routes.

6. The method for updating a hydrological model according to claim 5, characterized in that, The process of generating escape prompts based on the escape route further includes: Obtain user feedback information, including the target escape route selected by the user in the escape prompt; The number of users who tend to choose the target escape route is determined based on the user feedback information. The optimal escape route is determined based on the starting and ending points of the target escape path, the number of users, and all escape paths.

7. A device for updating a hydrological model, characterized in that, include: The data acquisition module is used to acquire all the input data types required to build the current hydrological model, the corresponding input data for each of the input data types, and the target output data type. The contribution rate determination module is used to determine the data type to be updated from all the input data types based on the contribution rate of each input data type to the target output data type. The data tracing module is used to trace the data of the most recently built hydrological model based on the data type to be updated, and obtain the first data corresponding to the data type to be updated. The interval determination module is used to determine the time nodes corresponding to the first data and the second data respectively, and to determine the interval duration obtained from the two time nodes, wherein the second data is the input data corresponding to the data type to be updated when constructing the current hydrological model; The data simulation module is used to obtain the specific value of the data type to be updated at the time node corresponding to the second data based on the input data of all the same historical hydrological models built in history, the first data, and the interval duration. The data update module is used to update all input data required to construct the current hydrological model according to the specific value, and to construct the current hydrological model according to all the updated input data, all the input data types, and the target output data type.

8. An electronic device, characterized in that, include: At least one processor; Memory; At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, said at least one application being configured to: perform a method for updating a hydrological model as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed in a computer, causes the computer to perform a method for updating a hydrological model as described in any one of claims 1 to 6.

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