A hydrological and meteorological intelligent forecast management method and system

Abstract

The present invention provides a hydrological and meteorological intelligent forecasting management method and system, wherein the method includes: S1, obtaining hydrological and meteorological coupling forecast information; S2, based on a basin flow prediction model, predicting the basin flow of a hydropower station according to the hydrological and meteorological coupling forecast information; S3, assisting users in operating and managing the hydropower station based on the basin flow prediction results. The present invention is based on a basin flow prediction model, predicting the basin flow of a hydropower station according to the hydrological and meteorological coupling forecast information. The hydrological and meteorological coupling forecast information is more comprehensive than basic basin weather forecast information, improving the accuracy of basin flow prediction, eliminating the need for manual prediction, and reducing labor costs; assisting users in operating and managing the hydropower station based on the basin flow prediction results, further reducing labor costs and improving the efficiency of hydropower station operation and management.

Description

Technical Field

[0001] The present invention relates to the technical field of hydrological and meteorological intelligent forecast management, and in particular to a hydrological and meteorological intelligent forecast management method and system. Background Art

[0002] At present, in order to carry out operation and management of hydropower stations in advance, it is necessary to predict the basin flow of hydropower stations and implement it based on the basin flow prediction results.

[0003] However, traditional basin flow prediction methods are mostly based on basic basin weather forecast information and require manual implementation by multiple staff members. In addition, traditional operation and management methods require staff to manually manage the operation of hydropower stations based on the basin flow prediction results after the basin flow prediction is completed. Therefore, the overall labor cost is large, which reduces the efficiency of basin flow prediction and even reduces the efficiency of hydropower station operation and management.

[0004] Therefore, a solution is urgently needed. Summary of the Invention

[0005] One of the purposes of the present invention is to provide a hydrological and meteorological intelligent forecasting management method, which is based on the basin flow prediction model and performs basin flow prediction for hydropower stations according to the hydrological and meteorological coupling forecast information. The hydrological and meteorological coupling forecast information is more comprehensive than the basic basin weather forecast information, which improves the accuracy of basin flow prediction, eliminates the need for manual prediction, and reduces labor costs; it assists users in operating and managing hydropower stations based on the basin flow prediction results, assists users in operating and managing hydropower stations, further reduces labor costs, and improves the efficiency of hydropower station operation and management.

[0006] An embodiment of the present invention provides a hydrological and meteorological intelligent forecast management method, comprising:

[0007] Obtain hydrological and meteorological coupled forecast information;

[0008] Based on the basin flow prediction model and the hydrological and meteorological coupled forecast information, basin flow prediction for hydropower stations is carried out;

[0009] Assist users to manage the operation of hydropower stations based on basin flow prediction results.

[0010] Optionally, the hydro-meteorological coupled forecast information includes at least information on hydro-meteorological forecasting performed by coupling numerical weather forecasting with a land surface hydrological model.

[0011] Optionally, the steps for constructing the watershed flow prediction model are as follows:

[0012] Based on big data technology, mine target multi-source data;

[0013] Preprocess the target multi-source data;

[0014] Based on the preprocessing results, the watershed flow prediction model is trained.

[0015] Optionally, the auxiliary user performs operation management of the hydropower station based on the basin flow prediction result, including:

[0016] Visualize the basin flow prediction results to obtain a visualization model;

[0017] Displaying a visual model to the user;

[0018] Based on the viewing history of the visual model by the user, multiple decision basis source areas are divided from the visual model;

[0019] When the set of regional attribute relationships between the decision basis source areas meets at least one timing condition in the timing condition library, if the user uses the virtual selector to select the first target basis content from the decision basis source area, and the user continues to use the virtual selector to move in the first unique direction at a moving speed exceeding the first speed threshold, and moves a distance reaching the first distance threshold, the movement filter boxes of other decision basis source areas other than the one from which the first target basis content was selected are activated;

[0020] Determine the target decision basis source area from other decision basis source areas;

[0021] Determine the relevance between each of the plurality of candidate basis contents in the target decision basis source area and the first target basis content based on the relevance database of the timing conditions that the regional attribute relationship set meets;

[0022] Generate a content selection table;

[0023] Set the content selection table in the mobile filter frame of the target decision basis source area;

[0024] If the user continues to use the virtual selector to move in the second unique direction at a moving speed that does not exceed the second speed threshold, the control based on the content selection table starts to indicate the first candidate based content to be selected, and each time the user continues to use the virtual selector to move in the second unique direction for a distance exceeding the second distance threshold, the control based on the content selection table continues to indicate the next candidate based content to be selected;

[0025] When the time duration for which the user stops moving the virtual selector exceeds a time threshold, the to-be-selected reference content currently indicated in the reference selection table is used as the second target reference content;

[0026] Based on the suggestion knowledge base, provide management decision suggestions to users according to the first goal-based content and the second goal-based content;

[0027] When the user completes the management strategy based on the management decision suggestion, the hydropower station is operated and managed accordingly based on the management strategy.

[0028] Optionally, the area difference between the two regions into which the target decision is divided by the ray starting from the current moving position of the virtual selector and moving in the first unique direction of the virtual selector does not exceed an area difference threshold.

[0029] Optionally, the content to be selected is displayed in the content selection table in order from the first direction to the second direction according to the relevance from large to small;

[0030] The steps for determining the first direction are as follows:

[0031] Constructing a movement vector based on the user continuing to use the movement starting point and the first unique direction of the virtual selector when the user uses the virtual selector to select the first target basis content from the decision basis source area;

[0032] When the horizontal component of the motion vector exceeds the vertical component of the motion vector, the vertical upward direction is taken as the first direction; otherwise, the horizontal leftward direction is taken as the first direction;

[0033] The second direction is the opposite direction of the first direction.

[0034] Optionally, based on the viewing history of the visual model by the user, a plurality of decision basis source areas are divided from the visual model, including:

[0035] Perform a time-series representation of the viewing history to obtain a sequence of historical items;

[0036] Determining a plurality of history item clusters from the history item sequence; wherein each history item cluster satisfies a cluster constraint;

[0037] Based on the division rules of the cluster constraints that each historical item cluster complies with, a plurality of decision basis source areas are divided from the visualization model according to each historical item cluster.

[0038] An embodiment of the present invention provides a hydrological and meteorological intelligent forecast management system, comprising:

[0039] Acquisition module, used to obtain hydrological and meteorological coupled forecast information;

[0040] The prediction module is used to predict the basin flow of hydropower stations based on the basin flow prediction model and the hydrological and meteorological coupling forecast information;

[0041] The management module is used to assist users in operating and managing hydropower stations based on basin flow prediction results.

[0042] Optionally, the hydro-meteorological coupled forecast information includes at least information on hydro-meteorological forecasting performed by coupling numerical weather forecasting with a land surface hydrological model.

[0043] Optionally, the steps for constructing the watershed flow prediction model are as follows:

[0044] Based on big data technology, mine target multi-source data;

[0045] Preprocess the target multi-source data;

[0046] Based on the preprocessing results, the watershed flow prediction model is trained.

[0047] Optionally, the management module assists the user in operating and managing the hydropower station based on the basin flow prediction results, including:

[0048] Visualize the basin flow prediction results to obtain a visualization model;

[0049] Displaying a visual model to the user;

[0050] Based on the viewing history of the visual model by the user, multiple decision basis source areas are divided from the visual model;

[0051] When the set of regional attribute relationships between the decision basis source areas meets at least one timing condition in the timing condition library, if the user uses the virtual selector to select the first target basis content from the decision basis source area, and the user continues to use the virtual selector to move in the first unique direction at a moving speed exceeding the first speed threshold, and moves a distance reaching the first distance threshold, the movement filter boxes of other decision basis source areas other than the one from which the first target basis content was selected are activated;

[0052] Determine the target decision basis source area from other decision basis source areas;

[0053] Determine the relevance between each of the plurality of candidate basis contents in the target decision basis source area and the first target basis content based on the relevance database of the timing conditions that the regional attribute relationship set meets;

[0054] Generate a content selection table;

[0055] Set the content selection table in the mobile filter frame of the target decision basis source area;

[0056] If the user continues to use the virtual selector to move in the second unique direction at a moving speed that does not exceed the second speed threshold, the control based on the content selection table starts to indicate the first candidate based content to be selected, and each time the user continues to use the virtual selector to move in the second unique direction for a distance exceeding the second distance threshold, the control based on the content selection table continues to indicate the next candidate based content to be selected;

[0057] When the time duration for which the user stops moving the virtual selector exceeds a time threshold, the to-be-selected reference content currently indicated in the reference selection table is used as the second target reference content;

[0058] Based on the suggestion knowledge base, provide management decision suggestions to users according to the first goal-based content and the second goal-based content;

[0059] When the user completes the management strategy based on the management decision suggestion, the hydropower station is operated and managed accordingly based on the management strategy.

[0060] Optionally, the area difference between the two regions into which the target decision is divided by the ray starting from the current moving position of the virtual selector and moving in the first unique direction of the virtual selector does not exceed an area difference threshold.

[0061] Optionally, the content to be selected is displayed in the content selection table in order from the first direction to the second direction according to the relevance from large to small;

[0062] The steps for determining the first direction are as follows:

[0063] Constructing a movement vector based on the user continuing to use the movement starting point and the first unique direction of the virtual selector when the user uses the virtual selector to select the first target basis content from the decision basis source area;

[0064] When the horizontal component of the motion vector exceeds the vertical component of the motion vector, the vertical upward direction is taken as the first direction; otherwise, the horizontal leftward direction is taken as the first direction;

[0065] The second direction is the opposite direction of the first direction.

[0066] Optionally, the management module divides the visualization model into multiple decision basis source areas based on the user's viewing history of the visualization model, including:

[0067] Perform a time-series representation of the viewing history to obtain a sequence of historical items;

[0068] Determining a plurality of history item clusters from the history item sequence; wherein each history item cluster satisfies a cluster constraint;

[0069] Based on the division rules of the cluster constraints that each historical item cluster complies with, a plurality of decision basis source areas are divided from the visualization model according to each historical item cluster.

[0070] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0071] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0073] Figure 1 Schematic diagram of a hydrological and meteorological intelligent forecast management method according to an embodiment of the present invention;

[0074] Figure 2 Schematic diagram of a hydrological and meteorological intelligent forecast management system in an embodiment of the present invention. DETAILED DESCRIPTION

[0075] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0076] The embodiment of the present invention provides a hydrological and meteorological intelligent forecast management method, such as Figure 1 As shown, including:

[0077] S1. Obtaining hydrological and meteorological coupled forecast information;

[0078] S2. Based on the basin flow prediction model and the hydrological and meteorological coupled forecast information, the basin flow of the hydropower station is predicted;

[0079] S3, assisting users to manage the operation of hydropower stations based on basin flow prediction results;

[0080] The hydro-meteorological coupled forecast information at least includes: information on hydro-meteorological forecasting performed by coupling numerical weather forecasting with land surface hydrological models.

[0081] In the above technical solution, numerical weather forecasts refer to high-resolution meteorological forecast data, including at least precipitation, temperature, air pressure, and wind speed, typically derived from meteorological stations. Land surface hydrological models, such as precipitation-runoff models, simulate hydrological processes within a watershed, and can be implemented based on the SWAT hydrological model. The coupling mechanism of numerical weather forecasts involves feeding the numerical weather forecasts into the land surface hydrological model, which then simulates hydrological processes based on the numerical weather forecasts, obtaining simulation parameters such as soil moisture and groundwater levels. These simulation parameters are then fed back to the numerical weather forecast for further forecast optimization. Thus, the hydrometeorological information generated by the coupling of numerical weather forecasts with the land surface hydrological model can include precipitation, temperature, air pressure, wind speed, soil moisture, groundwater levels within the watershed, and optimized forecast results from the numerical weather forecast. The watershed flow prediction model can use this coupled hydrometeorological forecast information to predict watershed flows for hydropower stations. When the basin flow forecast results are obtained, users can conduct operation management of the hydropower station based on the basin flow forecast results. During management, scenario analysis can be conducted on future climate change and extreme weather events (such as heavy rain, drought, etc.), and whether the operation of the hydropower station can cope with them can be evaluated. It can also simulate the basin flow in the next few days, weeks or even months, and predict the reservoir water level and power generation potential under different scenarios.

[0082] This application is based on the basin flow prediction model and conducts basin flow prediction for hydropower stations according to the hydro-meteorological coupling forecast information. The hydro-meteorological coupling forecast information is more comprehensive than the basic basin weather forecast information, which improves the accuracy of basin flow prediction and eliminates the need for manual prediction, reducing labor costs. It assists users in operating and managing hydropower stations based on the basin flow prediction results, assisting users in operating and managing hydropower stations, further reducing labor costs and improving the efficiency of hydropower station operation and management.

[0083] In one embodiment, the steps for constructing the watershed flow prediction model are as follows:

[0084] Based on big data technology, mine target multi-source data;

[0085] Preprocess the target multi-source data;

[0086] Based on the preprocessing results, the watershed flow prediction model is trained.

[0087] In the above technical solution, the target multi-source data includes at least:

[0088] The target multi-source data at least includes: a large amount of hydrological and meteorological information and river basin flow information at the same time in history; when preprocessing the target multi-source data, data cleaning can be performed to improve the credibility of the training data; after the preprocessing is completed, the preprocessing results are used as training samples to train the river basin flow prediction model. The trained river basin flow prediction model can be combined with the training samples according to the hydrological and meteorological coupling forecast information to predict the river basin flow of the hydropower station.

[0089] The embodiment of the present invention mines target multi-source data based on big data, preprocesses the mined target multi-source data, and trains a watershed flow prediction model based on the preprocessing results, thereby improving the comprehensiveness and training accuracy of the watershed flow prediction model.

[0090] In one embodiment, the auxiliary user performs operation management of the hydropower station based on the basin flow prediction result, including:

[0091] S301, visualizing the basin flow prediction results to obtain a visualization model;

[0092] In S301, the basin flow prediction results include at least: the flow at different flow monitoring points in the hydropower station basin at different times in the future, the precipitation distribution and precipitation intensity distribution in the hydropower station basin in the future; when performing visualization processing, the basin flow prediction results are displayed in the form of a digital model based on GIS technology to obtain a visualization model; in the visualization model, the future flow change trend in the hydropower station basin and the future water storage change in the hydropower station reservoir can be viewed;

[0093] S302, displaying the visual model to the user;

[0094] In S302, the user may be an operation manager of a hydropower station, etc. When the visualization model is displayed to the user, the user may view it through a display device such as a visualization large screen;

[0095] S303: Based on the viewing history of the visualization model by the user, a plurality of decision basis source areas are divided from the visualization model;

[0096] In S303, when a user views a visualization model, a viewing history is generated. The viewing history includes at least: the viewing content, viewing time, operations performed by the user during the viewing, and the operation time, etc.; based on the viewing history, a decision basis source area is divided, and the decision basis source area includes the content based on which the user makes decisions on the operation and management of the hydropower station;

[0097] S304: When the set of regional attribute relationships between the decision basis source areas meets at least one timing condition in the timing condition library, if the user uses the virtual selector to select the first target basis content from the decision basis source area, and the user continues to use the virtual selector to move in the first unique direction at a speed exceeding the first speed threshold and moves a distance exceeding the first distance threshold, then activate the movement filter boxes of other decision basis source areas other than the one from which the first target basis content was selected;

[0098] In S304, the regional attribute relationship set includes a plurality of regional attribute relationships between the source areas of the pairwise decision basis, and the regional attribute relationship includes at least: the two areas are of the same area type, and there is a decision logic association between the two areas (for example, if the user wants to make a decision on the water discharge scheduling of a reservoir, he needs to first check the display area of ​​the future water level change of the reservoir, and then check the location distribution area of ​​the responsible personnel around the reservoir, and finally dispatch the responsible personnel nearby to perform the water discharge operation of the reservoir, then there is a decision logic association between the display area of ​​the future water level change of the reservoir and the location distribution area of ​​the responsible personnel around the reservoir); the timing condition refers to the use of The conditions under which the user can currently make decisions on the operation and management of the hydropower station. For example, if the timing condition is that there is a decision logic association between the user's viewing of the future water level change display area of ​​the reservoir and the distribution area of ​​the responsible personnel around the reservoir, it means that the user wants to make a decision on the reservoir discharge scheduling, and the user can currently make a decision on the operation and management of the hydropower station; the introduction of the regional attribute relationship set meets the timing condition, and the next step is performed when the regional attribute relationship set meets the timing condition, which reduces the auxiliary resources of the system and improves the accuracy and efficiency of the assistance to the user; the virtual selector can be an operation pointer, etc.; the first goal The basis content is the content that at least one user in the decision basis source area can use as a basis for making a hydropower station operation and management decision; confirmation refers to the user confirming the selection through the virtual selector; the first speed threshold is the speed at which the virtual pointer moves faster; the first distance threshold is the distance at which the virtual pointer moves longer; the first unique direction is the unique direction in which the virtual pointer continues to move; when the user selects the first target basis content, continues to use the virtual selector to continue moving in the first unique direction at a speed exceeding the first speed threshold, and moves a distance reaching the first distance threshold, it means that the user wants to select a content in other decision basis source areas to be used together with the first target basis content as a basis for the operation and management decision of the hydropower station, and the user needs to be assisted in quickly selecting the content (generally, the area of ​​the hydropower station basin is very large, and the corresponding scope of the constructed visualization model is also large. When the user selects the content, it may be necessary to move the virtual selector a very long distance, which is very inconvenient and reduces the user's hydropower station management decision efficiency. Therefore, such assistance is needed), triggering assistance further reduces system resources and further improves the accuracy and efficiency of user assistance;

[0099] S305: Determine a target decision basis source region from other decision basis source regions; wherein a difference in area between two regions divided by a moving filter frame of the target decision basis source region along a ray from the current moving position of the virtual selector in a first unique direction of continuous movement of the virtual selector does not exceed an area difference threshold;

[0100] In S305, the mobile filter box is a rectangular box that minimally encloses the other decision basis source area; the area difference threshold may be, for example, 8 square centimeters; when a ray passes through the mobile filter box, it divides it into two areas; if the area difference between the two areas does not exceed the area difference threshold, it means that the ray nearly bisects the mobile filter box, indicating that the content the user wants to select is within the other decision basis source area of ​​the mobile filter box, i.e., the target decision basis source area; utilizing the positional relationship between the ray starting from the current moving position of the virtual selector and continuously moving in the first unique direction of the virtual selector and the mobile filter box, it is quickly determined which other decision basis source area the content the user wants to select originates from, thereby greatly improving the working efficiency of the system;

[0101] S306: Determine the relevance between each of the plurality of candidate basis contents in the target decision basis source area and the first target basis content based on the relevance database of the timing conditions that the regional attribute relationship set meets;

[0102] In S306, the timing condition is pre-set with a correlation library, which contains correlations between two different basis contents. The timing condition that the regional attribute relationship set meets represents how the user wants to make a decision on the operation and management of the hydropower station. Based on this, the correlation between each of the candidate basis contents and the first target basis content can be determined. For example, the timing condition represents that the user wants to make a decision on the water discharge scheduling of the reservoir. The first target basis content that has been selected is the water level change of the reservoir in the next 10 hours. If the candidate basis content is the current distance ranking between the person in charge of the reservoir and the reservoir, the correlation between the two is 10. If the candidate basis is the daily power generation of the hydropower station, the correlation between the two is 0. Therefore, the correlation library can be preset in advance to facilitate the rapid determination of the correlation between each of the multiple candidate basis contents in the target decision basis source area and the first target basis content, thereby improving the work efficiency of the system.

[0103] S307: Generate a content selection table; wherein the content selection table displays the content to be selected in descending order of relevance from the first direction to the second direction;

[0104] In S307, the selection criteria are displayed in the content selection table in descending order of relevance from the first direction to the second direction, so that the user can quickly select the selection criteria that is more likely to meet his / her selection intention, thereby improving the user experience and the efficiency of the user's hydropower station management decision-making.

[0105] S308, setting the content selection table in the mobile filter frame of the target decision basis source area;

[0106] In S308, when the user wants to select content in other decision basis source areas, the user will first view the corresponding other decision basis source areas and then move the virtual selector. Therefore, the content selection table can be set in the mobile filter box of the target decision basis source area, so that the user can directly view it, which improves the humanization;

[0107] S309: If the user continues to use the virtual selector to move in the second unique direction at a speed that does not exceed the second speed threshold, the control starts to indicate the first candidate content to be selected according to the content selection table, and each time the user continues to use the virtual selector to move in the second unique direction for a distance exceeding the second distance threshold, the control continues to indicate the next candidate content to be selected according to the content selection table;

[0108] In S309, the second speed threshold is a speed representing a slower movement of the virtual pointer; the second unique direction is a unique direction in which the virtual pointer continues to move at this time; the second distance threshold is a distance representing a shorter distance in which the virtual pointer moves; if the user uses the virtual selector to continuously move in the second unique direction at a movement speed that does not exceed the second speed threshold, it means that the user knows that the system has begun to assist it, and at this time the user can continue to use the virtual selector to select a to-be-selected basis content in the to-be-selected basis selection table by controlling it. First, the to-be-selected basis content selection table is controlled to start indicating the first to-be-selected basis content to be selected, and the to-be-selected basis content can be displayed in a jump manner; whenever the user continues to use the virtual selector to move in the second unique direction for a distance of the second distance threshold, the to-be-selected basis content selection table is controlled to continue indicating the next to-be-selected basis content to be selected; the second unique direction is different from the first unique direction;

[0109] S310: When the duration of the user stopping the movement of the virtual selector exceeds a duration threshold, the to-be-selected reference content currently indicated in the reference selection table is used as the second target reference content;

[0110] In S310, the duration threshold is a duration value representing a longer duration of time during which the user stops moving the virtual selector; when the duration during which the user stops moving the virtual selector exceeds the duration threshold, it indicates that the user confirms the currently indicated candidate basis content and selects it as the second target basis content;

[0111] S311. Based on the suggestion knowledge base, and according to the first objective basis content and the second objective basis content, provide management decision suggestions to the user;

[0112] In S311, the suggestion knowledge base contains management decision suggestions corresponding to different basis contents. The management decision suggestions can be pre-set based on the expert experience of hydropower station operation management. Therefore, based on the suggestion knowledge base, management decision suggestions can be made to the user according to the first target basis contents and the second target basis contents, thereby providing further assistance to the user.

[0113] S312. When the user completes the management strategy based on the management decision suggestion, the hydropower station is operated and managed accordingly based on the management strategy.

[0114] In S312, after viewing the management decision suggestion, the user will start to make a management decision based on the management decision suggestion, and finally complete the management strategy, and then perform corresponding operation management of the hydropower station based on the management strategy.

[0115] The various technical steps of the embodiments of the present invention, when working together, achieve the following beneficial effects:

[0116] After the user selects the first target basis content, the system will assist him / her in quickly selecting the second target basis content at the most appropriate time, improving convenience and being more humane; after selecting the second target basis content, the system will give management decision suggestions to provide further assistance.

[0117] In one embodiment, the steps of determining the first direction are as follows:

[0118] Constructing a movement vector based on the user continuing to use the movement starting point and the first unique direction of the virtual selector when the user uses the virtual selector to select the first target basis content from the decision basis source area;

[0119] When the horizontal component of the motion vector exceeds the vertical component of the motion vector, the vertical upward direction is taken as the first direction; otherwise, the horizontal leftward direction is taken as the first direction;

[0120] The second direction is the opposite direction of the first direction.

[0121] In the above technical solution, a plane rectangular coordinate system is established with the starting point of movement as the origin, the horizontal direction as the x-axis, and the vertical direction as the y-axis. The movement vector can be expressed as (x, y) in the plane rectangular coordinate system, where x and y are the projection lengths of the movement vector on the x-axis and y-axis respectively; the horizontal component of the movement vector is the absolute value of x, and the vertical component of the movement vector is the absolute value of y; when the horizontal component of the movement vector exceeds the vertical component of the movement vector, it means that the movement of the virtual selector is closer to horizontal movement, then according to the content selection table, the candidates need to be indicated in sequence from top to bottom in the vertical direction; otherwise, the candidates need to be indicated in sequence from left to right in the horizontal direction.

[0122] The embodiment of the present invention reasonably determines the first direction and the second direction, so that when a user views the content selection table, he or she can select each candidate content along the most appropriate candidate direction according to the content selection table.

[0123] In one embodiment, the method of dividing the visualization model into multiple decision basis source areas based on the viewing history of the visualization model by the user includes:

[0124] Perform a time-series representation of the viewing history to obtain a sequence of historical items;

[0125] Determining a plurality of history item clusters from the history item sequence; wherein each history item cluster satisfies a cluster constraint;

[0126] Based on the division rules of the cluster constraints that each historical item cluster complies with, a plurality of decision basis source areas are divided from the visualization model according to each historical item cluster.

[0127] In the above technical solution, when the viewing history is represented in time series, the viewing history is split into multiple historical items, and each historical item is sorted in the order of the time of historical generation to obtain a historical item sequence; multiple sets of one-to-one corresponding cluster constraints and division rules are set. When there is a historical item cluster that meets the cluster constraint in the historical item sequence, the decision basis source area is divided according to the corresponding historical item cluster based on the corresponding division rule. For example: if the cluster constraint is that each historical item in the same historical item cluster reflects that the user has paid attention to the same model area in the visualization model for more than 10 minutes, it means that the user may want to select a decision basis from it, and the corresponding division rule is to divide the model area into a decision basis source area.

[0128] The embodiment of the present invention introduces cluster constraints and division rules to quickly determine multiple historical item clusters from the historical item sequence, and based on the division rules of the cluster constraints that each historical item cluster complies with, multiple decision-making basis source areas are divided from the visualization model according to each historical item cluster, thereby improving the division accuracy and efficiency of the decision-making basis source areas.

[0129] The embodiment of the present invention provides a hydrological and meteorological intelligent forecast management system, such as Figure 2 As shown, including:

[0130] Acquisition module 1, used to obtain hydrological and meteorological coupled forecast information;

[0131] Prediction module 2 is used to predict the basin flow of the hydropower station based on the basin flow prediction model and the hydrological and meteorological coupling forecast information;

[0132] Management module 3 is used to assist users in operating and managing hydropower stations based on basin flow prediction results.

[0133] The hydrological and meteorological coupled forecast information at least includes: information on hydrological and meteorological forecasting performed by coupling numerical weather forecasts with land surface hydrological models.

[0134] The steps for constructing the watershed flow prediction model are as follows:

[0135] Based on big data technology, mine target multi-source data;

[0136] Preprocess the target multi-source data;

[0137] Based on the preprocessing results, the watershed flow prediction model is trained.

[0138] The management module 3 assists the user in operating and managing the hydropower station based on the basin flow prediction results, including:

[0139] Visualize the basin flow prediction results to obtain a visualization model;

[0140] Displaying a visual model to the user;

[0141] Based on the viewing history of the visual model by the user, multiple decision basis source areas are divided from the visual model;

[0142] When the set of regional attribute relationships between the decision basis source areas meets at least one timing condition in the timing condition library, if the user uses the virtual selector to select the first target basis content from the decision basis source area, and the user continues to use the virtual selector to move in the first unique direction at a moving speed exceeding the first speed threshold, and moves a distance reaching the first distance threshold, the movement filter boxes of other decision basis source areas other than the one from which the first target basis content was selected are activated;

[0143] Determine the target decision basis source area from other decision basis source areas;

[0144] Determine the relevance between each of the plurality of candidate basis contents in the target decision basis source area and the first target basis content based on the relevance database of the timing conditions that the regional attribute relationship set meets;

[0145] Generate a content selection table;

[0146] Set the content selection table in the mobile filter frame of the target decision basis source area;

[0147] If the user continues to use the virtual selector to move in the second unique direction at a moving speed that does not exceed the second speed threshold, the control based on the content selection table starts to indicate the first candidate based content to be selected, and each time the user continues to use the virtual selector to move in the second unique direction for a distance exceeding the second distance threshold, the control based on the content selection table continues to indicate the next candidate based content to be selected;

[0148] When the time duration for which the user stops moving the virtual selector exceeds a time threshold, the to-be-selected reference content currently indicated in the reference selection table is used as the second target reference content;

[0149] Based on the suggestion knowledge base, provide management decision suggestions to users according to the first goal-based content and the second goal-based content;

[0150] When the user completes the management strategy based on the management decision suggestion, the hydropower station is operated and managed accordingly based on the management strategy.

[0151] The area difference between the two areas into which the moving filter box of the source area is divided by the ray starting from the current moving position of the virtual selector and moving in the first unique direction of the virtual selector does not exceed the area difference threshold.

[0152] In the content selection table, the content to be selected is displayed in order from the first direction to the second direction according to the relevance from large to small;

[0153] The steps for determining the first direction are as follows:

[0154] Constructing a movement vector based on the user continuing to use the movement starting point and the first unique direction of the virtual selector when the user uses the virtual selector to select the first target basis content from the decision basis source area;

[0155] When the horizontal component of the motion vector exceeds the vertical component of the motion vector, the vertical upward direction is taken as the first direction; otherwise, the horizontal leftward direction is taken as the first direction;

[0156] The second direction is the opposite direction of the first direction.

[0157] The management module 3 divides the visualization model into multiple decision-making basis source areas based on the user's viewing history of the visualization model, including:

[0158] Perform a time-series representation of the viewing history to obtain a sequence of historical items;

[0159] Determining a plurality of history item clusters from the history item sequence; wherein each history item cluster satisfies a cluster constraint;

[0160] Based on the division rules of the cluster constraints that each historical item cluster complies with, a plurality of decision basis source areas are divided from the visualization model according to each historical item cluster.

[0161] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A hydrological and meteorological intelligent forecast management method, characterized in that: include: Obtain hydrological and meteorological coupled forecast information; Based on the basin flow prediction model and the hydrological and meteorological coupled forecast information, basin flow prediction for hydropower stations is carried out; Assist users to manage the operation of hydropower stations based on basin flow prediction results; The auxiliary user performs operation management of the hydropower station based on the basin flow prediction results, including: Visualize the basin flow prediction results to obtain a visualization model; Displaying visual models to users; Based on the viewing history of the visual model by the user, multiple decision basis source areas are divided from the visual model; When the set of regional attribute relationships between the decision basis source areas meets at least one timing condition in the timing condition library, if the user uses the virtual selector to select the first target basis content from the decision basis source area, and the user continues to use the virtual selector to move in the first unique direction at a moving speed exceeding the first speed threshold, and moves a distance reaching the first distance threshold, the movement filter boxes of other decision basis source areas other than the one from which the first target basis content was selected are activated; Determine the target decision basis source area from other decision basis source areas; Determine the relevance between each of the plurality of candidate basis contents in the target decision basis source area and the first target basis content based on the relevance database of the timing conditions that the regional attribute relationship set meets; Generate a content selection table; Set the content selection table in the mobile filter frame of the target decision basis source area; If the user continues to use the virtual selector to move in the second unique direction at a moving speed that does not exceed the second speed threshold, the control based on the content selection table starts to indicate the first candidate based content to be selected, and each time the user continues to use the virtual selector to move in the second unique direction for a distance exceeding the second distance threshold, the control based on the content selection table continues to indicate the next candidate based content to be selected; When the time duration for which the user stops moving the virtual selector exceeds a time threshold, the to-be-selected reference content currently indicated in the reference selection table is used as the second target reference content; Based on the suggestion knowledge base, provide management decision suggestions to users according to the first goal-based content and the second goal-based content; When the user completes the management strategy based on the management decision suggestion, the hydropower station is operated and managed accordingly based on the management strategy.

2. The hydrological and meteorological intelligent forecast management method according to claim 1, characterized in that: The hydrological and meteorological coupled forecast information at least includes: information on hydrological and meteorological forecasting performed by coupling numerical weather forecasts with land surface hydrological models.

3. The hydrological and meteorological intelligent forecast management method according to claim 1, characterized in that: The steps for constructing the watershed flow prediction model are as follows: Based on big data technology, mine target multi-source data; Preprocess the target multi-source data; Based on the preprocessing results, the watershed flow prediction model is trained.

4. The hydrological and meteorological intelligent forecast management method according to claim 1, characterized in that: The area difference between the two areas into which the moving filter box of the source area is divided by the ray starting from the current moving position of the virtual selector and moving in the first unique direction of the virtual selector does not exceed the area difference threshold.

5. The hydrological and meteorological intelligent forecast management method according to claim 1, characterized in that: In the content selection table, the content to be selected is displayed in order from the first direction to the second direction according to the relevance from large to small; The steps for determining the first direction are as follows: Constructing a movement vector based on the user continuing to use the movement starting point and the first unique direction of the virtual selector when the user uses the virtual selector to select the first target basis content from the decision basis source area; When the horizontal component of the motion vector exceeds the vertical component of the motion vector, the vertical upward direction is taken as the first direction; otherwise, the horizontal leftward direction is taken as the first direction; The second direction is the opposite direction of the first direction.

6. The hydrological and meteorological intelligent forecast management method according to claim 1, characterized in that: Based on the viewing history of the user viewing the visualization model, a plurality of decision basis source areas are divided from the visualization model, including: Perform a time-series representation of the viewing history to obtain a sequence of historical items; Determining a plurality of history item clusters from the history item sequence; wherein each history item cluster satisfies a cluster constraint; Based on the division rules of the cluster constraints that each historical item cluster complies with, a plurality of decision basis source areas are divided from the visualization model according to each historical item cluster.

7. A hydrological and meteorological intelligent forecast management system, characterized in that: include: Acquisition module, used to obtain hydrological and meteorological coupled forecast information; The prediction module is used to predict the basin flow of hydropower stations based on the basin flow prediction model and the hydrological and meteorological coupling forecast information; Management module, used to assist users in operating and managing hydropower stations based on basin flow prediction results; The auxiliary user performs operation management of the hydropower station based on the basin flow prediction results, including: Visualize the basin flow prediction results to obtain a visualization model; Displaying visual models to users; Based on the viewing history of the visual model by the user, multiple decision basis source areas are divided from the visual model; When the set of regional attribute relationships between the decision basis source areas meets at least one timing condition in the timing condition library, if the user uses the virtual selector to select the first target basis content from the decision basis source area, and the user continues to use the virtual selector to move in the first unique direction at a moving speed exceeding the first speed threshold, and moves a distance reaching the first distance threshold, the movement filter boxes of other decision basis source areas other than the one from which the first target basis content was selected are activated; Determine the target decision basis source area from other decision basis source areas; Determine the relevance between each of the plurality of candidate basis contents in the target decision basis source area and the first target basis content based on the relevance database of the timing conditions that the regional attribute relationship set meets; Generate a content selection table; Set the content selection table in the mobile filter frame of the target decision basis source area; If the user continues to use the virtual selector to move in the second unique direction at a moving speed that does not exceed the second speed threshold, the control based on the content selection table starts to indicate the first candidate based content to be selected, and each time the user continues to use the virtual selector to move in the second unique direction for a distance exceeding the second distance threshold, the control based on the content selection table continues to indicate the next candidate based content to be selected; When the time duration for which the user stops moving the virtual selector exceeds a time threshold, the to-be-selected reference content currently indicated in the reference selection table is used as the second target reference content; Based on the suggestion knowledge base, provide management decision suggestions to users according to the first goal-based content and the second goal-based content; When the user completes the management strategy based on the management decision suggestion, the hydropower station is operated and managed accordingly based on the management strategy.

8. The hydrological and meteorological intelligent forecast management system according to claim 7, characterized in that: The hydrological and meteorological coupled forecast information at least includes: information on hydrological and meteorological forecasting performed by coupling numerical weather forecasts with land surface hydrological models.

9. The hydrological and meteorological intelligent forecast management system according to claim 7, characterized in that: The steps for constructing the watershed flow prediction model are as follows: Based on big data technology, mine target multi-source data; Preprocess the target multi-source data; Based on the preprocessing results, the watershed flow prediction model is trained.

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