Regional waterlogging monitoring and prediction method, platform, medium and equipment

Abstract

The present invention provides a method, platform, medium and equipment for regional waterlogging monitoring and prediction. The scheme includes obtaining all monitoring point data, and selecting key points according to the horizontal coordinate value; obtaining the coordinate information of each key point, and automatically calculating and generating the center distance; obtaining the center distance, setting the high-level priority, the medium-level priority and the low-level priority; obtaining the high-level priority, the medium-level priority and the low-level priority, and automatically predicting the next ten minutes, one hour and four hours, and updating the prediction data group; obtaining all data of the prediction data group, and automatically calculating the short-term maximum value, the medium-term maximum value and the long-term maximum value; and displaying the long-term maximum value, the medium-term maximum value and the short-term maximum value in layers according to the priority of the key points. The scheme automatically screens and grades key points, combines distributed prediction and calculation, and performs online distributed forecasting and display of waterlogging data in the observation area.

Description

Technical Field

[0001] The present invention relates to the technical field of meteorological risk prediction, and more specifically, to a method, platform, medium and equipment for regional waterlogging monitoring and prediction. Background Art

[0002] With the development of science and technology and the progress of society, more and more power electronic equipment is being applied to the prediction of industrial and agricultural production. As an environmental factor affecting production activities, meteorological conditions will also contribute to the variables of production activities if they can be automatically predicted and displayed. Therefore, the prediction of urban flood risk is very important for people's current lives, especially the accurate and efficient prediction of the most likely situation.

[0003] Prior to the technology of the present invention, the existing technology mainly collected waterlogging data for a region by manually selecting key nodes, and combined with linear programming methods to achieve prediction of regional waterlogging information, which showed poor targeting and low efficiency. Summary of the invention

[0004] In view of the above problems, the present invention proposes a regional waterlogging monitoring and prediction method, platform, medium and equipment, which automatically screens and classifies key points, combines distributed prediction and calculation, and performs online distributed forecasting and display of waterlogging data in the observation area.

[0005] According to a first aspect of an embodiment of the present invention, a method for regional waterlogging monitoring and prediction is provided.

[0006] In one or more embodiments, preferably, the regional waterlogging monitoring and prediction method comprises:

[0007] Obtain all monitoring point data and select key points based on the horizontal coordinate value;

[0008] Obtaining the coordinate information of each key point, and automatically calculating and generating the center distance;

[0009] Obtaining the center distance, setting a high gear priority, a middle gear priority, and a low gear priority;

[0010] Obtaining the high gear priority, the middle gear priority and the low gear priority, automatically predicting the next ten minutes, one hour and four hours, and updating the prediction data group;

[0011] Acquire all data of the forecast data group and automatically calculate the short-term maximum value, the medium-term maximum value and the long-term maximum value;

[0012] According to the priorities of the key points, the long-term maximum value, the medium-term maximum value and the short-term maximum value are displayed in layers.

[0013] In one or more embodiments, preferably, the step of acquiring all monitoring point data and selecting key points according to the horizontal coordinate values ​​specifically includes:

[0014] Acquire all monitoring point data, and extract the horizontal coordinates of the monitoring point data;

[0015] According to the degree of aggregation of the horizontal axis, it is divided into several clusters of monitoring points based on experience;

[0016] An average value is calculated for each of the monitoring point clusters to generate key points.

[0017] In one or more embodiments, preferably, the acquiring the coordinate information of each key point and automatically calculating and generating the center distance specifically includes:

[0018] Get the horizontal axis position of each key point;

[0019] Extract monitoring points within 1 km around each key point as monitoring points near the corresponding key point;

[0020] The first calculation formula is used to calculate the collection deviation of each key point;

[0021] Eliminate the measurement data of the key points whose acquisition deviation exceeds the preset value, and update all the key points;

[0022] Calculate the partition center position after each collection using the second calculation formula according to the updated key points;

[0023] Calculate the center distance using the third calculation formula;

[0024] The first calculation formula is:

[0025]

[0026] Among them, d i is the collection deviation of the ith key point, x i is the horizontal axis position of the i-th key point, x ij is the horizontal axis position of the jth monitoring point adjacent to the i-th key point, and n is the number of monitoring points;

[0027] The second calculation formula is:

[0028]

[0029] Among them, n k is the total number of key points collected for the kth time, u k is the partition center position obtained by the k-th acquisition, x iis the horizontal axis position of the i-th key point;

[0030] The third calculation formula is:

[0031] E=|x i -u k | 2

[0032] Wherein, E is the center distance of the i-th key point.

[0033] In one or more embodiments, preferably, obtaining the center distance and setting the high gear priority, the middle gear priority and the low gear priority specifically include:

[0034] Obtaining the center distance of each key point;

[0035] Setting a first center margin and a second center margin;

[0036] When the center distance is greater than the first center margin, it is set to the highest priority;

[0037] When the center distance is greater than the second center margin and not greater than the first center margin, it is set as a mid-range priority;

[0038] When the center distance is not greater than the second center margin, it is set to low gear priority.

[0039] In one or more embodiments, preferably, obtaining the high gear priority, the middle gear priority and the low gear priority, automatically predicting the next ten minutes, one hour and four hours, and updating the prediction data group specifically include:

[0040] Obtain historical data of key points corresponding to low-level priorities, and extract 1 hour of data as short-term historical data;

[0041] Obtain the historical data of key points corresponding to the mid-level priority, and extract 10 hours of data as mid-level historical data;

[0042] Obtain historical data of key points corresponding to high-level priorities, and extract 10 days of data as long-term historical data;

[0043] Calculate the ten-minute forecast using the fourth calculation formula;

[0044] Calculate the one-hour forecast using the fifth calculation formula;

[0045] Calculate the four-hour forecast using the sixth calculation formula;

[0046] storing the data of the ten-minute forecast, the one-hour forecast and the four-hour forecast in the forecast data group;

[0047] When the data of each of the ten-minute forecast, the one-hour forecast and the four-hour forecast obtain the corresponding measured data, the forecast data group is updated;

[0048] The fourth calculation formula is:

[0049] X=w1Σx+a

[0050] Wherein, X is the ten-minute forecast, Σx is the short-term historical data, w1 is the first weight parameter, and a is the first basic parameter;

[0051] The fifth calculation formula is:

[0052] Y=w2Σy+b

[0053] Wherein, Y is the one-hour forecast, Σy is the mid-hour historical data, w2 is the second weight parameter, and b is the second basic parameter;

[0054] The sixth calculation formula is:

[0055] Z=w3Σz+c

[0056] Among them, Z is the four-hour forecast, Σz is the long-term historical data, w3 is the third weight parameter, and c is the third basic parameter.

[0057] In one or more embodiments, preferably, the acquiring of all data of the forecast data set and automatic calculation of short-term maximum value, medium-term maximum value and long-term maximum value specifically includes:

[0058] Acquire all data of the prediction data group;

[0059] Calculating the short-term maximum value for the key point corresponding to the low gear priority using the seventh calculation formula;

[0060] Calculating the mid-term maximum value for the key points corresponding to the mid-level priority using the eighth calculation formula;

[0061] Calculating the long-term maximum value for the key point corresponding to the high priority level using the ninth calculation formula;

[0062] The seventh calculation formula is:

[0063] A=max(x 1k , x 2k , ..., x 10k )

[0064] Where A is the short-term maximum value, x 1k , x 2k , ..., x10k is the predicted value of the 1st, 2nd, ..., 10th minute interval after the kth sampling;

[0065] The eighth calculation formula is:

[0066] B=max(y 1k ,y 2k , ..., y 4k )

[0067] Where B is the mid-term maximum value, y 1k ,y 2k , ..., y 4k is the predicted value of the first, second, ..., fourth quarter-hour interval after the k-th sampling;

[0068] The ninth calculation formula is:

[0069] C=max(z 1k , z 2k , ..., z 4k )

[0070] Where C is the long-term maximum value, z 1k , z 2k , ..., z 4k It is the predicted value for the 1st, 2nd, ..., 4th hour interval after the kth sampling.

[0071] In one or more embodiments, preferably, the display of the long-term maximum value, the medium-term maximum value and the short-term maximum value in layers according to the priority of the key points specifically includes:

[0072] The long-term maximum value calculated for the key point corresponding to the high priority level is displayed with the key point corresponding to the high priority level as the center of the circle;

[0073] The mid-term maximum value calculated for the key point corresponding to the mid-level priority is displayed with the key point corresponding to the mid-level priority as the center of the circle;

[0074] The short-term maximum value calculated for the key point corresponding to the low gear priority is displayed with the key point corresponding to the low gear priority as the center of the circle;

[0075] When the display range of the long-term maximum value covers the medium-term maximum value, only the long-term maximum value is displayed;

[0076] When the display range of the mid-term maximum value covers the short-term maximum value, only the mid-term maximum value is displayed.

[0077] According to a second aspect of an embodiment of the present invention, a regional waterlogging monitoring and prediction platform is provided.

[0078] In one or more embodiments, preferably, the regional waterlogging monitoring and prediction platform includes:

[0079] Point acquisition module, used to obtain all monitoring point data and select key points according to the horizontal coordinate value;

[0080] A center distance analysis module is used to obtain the coordinate information of each key point and automatically calculate and generate the center distance;

[0081] Selecting a calculation module, used for obtaining the center distance, setting a high gear priority, a middle gear priority and a low gear priority;

[0082] A prediction calculation module, used to obtain the high gear priority, the middle gear priority and the low gear priority, automatically make predictions for the next ten minutes, one hour and four hours, and update the prediction data group;

[0083] A prediction output module, used to obtain all data of the prediction data group and automatically calculate the short-term maximum value, the medium-term maximum value and the long-term maximum value;

[0084] A layered display module is used to display the long-term maximum value, the medium-term maximum value and the short-term maximum value in layers according to the priority of the key points.

[0085] According to a third aspect of an embodiment of the present invention, there is provided a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method as described in any one of the first aspect of the embodiment of the present invention is implemented.

[0086] According to a fourth aspect of an embodiment of the present invention, there is provided an electronic device, comprising a memory and a processor, wherein the memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement any one of the methods described in the first aspect of the embodiment of the present invention.

[0087] The technical solution provided by the embodiments of the present invention may have the following beneficial effects:

[0088] In the embodiment of the present invention, a distributed waterlogging display of key points is adopted, and different priority settings are combined to perform online distributed prediction and display of the observation area.

[0089] In the embodiment of the present invention, more accurate waterlogging monitoring data and prediction data are obtained by automatically screening key points.

[0090] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or 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, claims, and drawings.

[0091] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0093] Figure 1 It is a flow chart of a method for regional waterlogging monitoring and prediction according to an embodiment of the present invention.

[0094] Figure 2 The present invention is a flowchart of obtaining all monitoring point data and selecting key points according to horizontal coordinate values ​​in a method for regional waterlogging monitoring and prediction according to an embodiment of the present invention.

[0095] Figure 3 It is a flowchart of obtaining coordinate information of each key point in a regional waterlogging monitoring and prediction method in one embodiment of the present invention, and automatically calculating and generating the center distance.

[0096] Figure 4 It is a flowchart of obtaining the center distance, setting high gear priority, middle gear priority and low gear priority in a method for regional waterlogging monitoring and prediction according to an embodiment of the present invention.

[0097] Figure 5 It is a flowchart of obtaining the high-gear priority, the middle-gear priority and the low-gear priority in a regional waterlogging monitoring and prediction method according to an embodiment of the present invention, automatically making predictions for the next ten minutes, one hour and four hours, and updating the prediction data group.

[0098] Figure 6 It is a flowchart of obtaining all data of the prediction data group and automatically calculating the short-term maximum value, the medium-term maximum value and the long-term maximum value in a regional waterlogging monitoring and prediction method according to an embodiment of the present invention.

[0099] Figure 7It is a flowchart of displaying the long-term maximum value, the medium-term maximum value and the short-term maximum value in layers according to the priority of the key points in a regional waterlogging monitoring and prediction method according to an embodiment of the present invention.

[0100] Figure 8 It is a structural diagram of a regional waterlogging monitoring and prediction platform according to an embodiment of the present invention.

[0101] Fig. 9 It is a structural diagram of an electronic device in one embodiment of the present invention. DETAILED DESCRIPTION

[0102] In some of the processes described in the specification and claims of the present invention and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0103] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0104] With the development of science and technology and the progress of society, more and more power electronic equipment is being applied to the prediction of industrial and agricultural production. As an environmental factor affecting production activities, meteorological conditions will also contribute to the variables of production activities if they can be automatically predicted and displayed. Therefore, the prediction of urban flood risk is very important for people's current lives, especially the accurate and efficient prediction of the most likely situation.

[0105] Prior to the technology of the present invention, the existing technology mainly collected waterlogging data for a region by manually selecting key nodes, and combined with linear programming methods to achieve prediction of regional waterlogging information, which showed poor targeting and low efficiency.

[0106] In the embodiment of the present invention, a method, platform, medium and device for regional waterlogging monitoring and prediction are provided. The solution automatically screens and classifies key points, combines distributed prediction and calculation, and performs online distributed forecasting and display of waterlogging data in the observed area.

[0107] According to a first aspect of an embodiment of the present invention, a method for regional waterlogging monitoring and prediction is provided.

[0108] Figure 1 It is a flow chart of a method for regional waterlogging monitoring and prediction according to an embodiment of the present invention.

[0109] In one or more embodiments, preferably, the regional waterlogging monitoring and prediction method comprises:

[0110] S101, obtaining all monitoring point data, and selecting key points according to the horizontal coordinate values;

[0111] S102, obtaining the coordinate information of each key point, and automatically calculating and generating the center distance;

[0112] S103, obtaining the center distance, setting a high gear priority, a middle gear priority, and a low gear priority;

[0113] S104, obtaining the high gear priority, the middle gear priority and the low gear priority, automatically performing predictions for the next ten minutes, one hour and four hours, and updating the prediction data set;

[0114] S105, obtaining all data of the forecast data group, and automatically calculating the short-term maximum value, the medium-term maximum value, and the long-term maximum value;

[0115] S106. Displaying the long-term maximum value, the medium-term maximum value, and the short-term maximum value in layers according to the priorities of the key points.

[0116] In the embodiment of the present invention, the observation area determined each time is automatically divided through a hierarchical, layered and distributed setting, and automatic waterlogging prediction and display are performed in combination with all current monitoring data.

[0117] Figure 2 The present invention is a flowchart of obtaining all monitoring point data and selecting key points according to horizontal coordinate values ​​in a method for regional waterlogging monitoring and prediction according to an embodiment of the present invention.

[0118] like Figure 2 As shown, in one or more embodiments, preferably, the step of acquiring all monitoring point data and selecting key points according to the horizontal coordinate values ​​specifically includes:

[0119] S201, acquiring all monitoring point data, and extracting the horizontal coordinates of the monitoring point data;

[0120] S202, dividing into a number of monitoring point clusters based on experience according to the degree of aggregation of the horizontal axis;

[0121] S203, calculating the average value of each monitoring point cluster to generate key points.

[0122] In the embodiment of the present invention, in order to select key points, when setting, firstly a longer direction in the monitoring area is set as the horizontal coordinate, and the horizontal coordinate is extracted based on this to determine the key point information.

[0123] Figure 3 It is a flowchart of obtaining coordinate information of each key point in a regional waterlogging monitoring and prediction method in one embodiment of the present invention, and automatically calculating and generating the center distance.

[0124] like Figure 3 As shown, in one or more embodiments, preferably, the obtaining of the coordinate information of each key point and automatically calculating and generating the center distance specifically includes:

[0125] S301, obtaining the horizontal axis position of each key point;

[0126] S302, extracting monitoring points within 1 km around each key point as monitoring points near the key point;

[0127] S303, calculating the collection deviation of each key point using the first calculation formula;

[0128] S304, eliminating the measurement data of the key points whose acquisition deviation exceeds the preset value, and updating all the key points;

[0129] S305, calculating the partition center position after each acquisition using a second calculation formula according to the updated key points;

[0130] S306, calculating the center distance using a third calculation formula;

[0131] The first calculation formula is:

[0132]

[0133] Among them, d i is the collection deviation of the ith key point, x i is the horizontal axis position of the i-th key point, x ij is the horizontal axis position of the jth monitoring point adjacent to the i-th key point, and n is the number of monitoring points;

[0134] The second calculation formula is:

[0135]

[0136] Among them, n k is the total number of key points collected for the kth time, u k is the partition center position obtained by the k-th acquisition, x i is the horizontal axis position of the i-th key point;

[0137] The third calculation formula is:

[0138] E=|x i -u k | 2

[0139] Wherein, E is the center distance of the i-th key point.

[0140] In an embodiment of the present invention, in order to screen out the calculated values ​​of key points that are not accurate enough, a specific center distance calculation is first performed. During this calculation process, the nodes with large deviations are eliminated through a preset margin. After automatic screening based on these margins, the center distance of each point in the entire monitoring interval can be obtained.

[0141] Figure 4 It is a flowchart of obtaining the center distance, setting high gear priority, middle gear priority and low gear priority in a method for regional waterlogging monitoring and prediction according to an embodiment of the present invention.

[0142] like Figure 4 As shown, in one or more embodiments, preferably, obtaining the center distance and setting the high gear priority, the middle gear priority and the low gear priority specifically include:

[0143] S401, obtaining the center distance of each key point;

[0144] S402, setting a first center margin and a second center margin;

[0145] S403, when the center distance is greater than the first center margin, setting it as the highest priority;

[0146] S404: when the center distance is greater than the second center margin and not greater than the first center margin, set it to a medium priority;

[0147] S405: When the center distance is not greater than the second center margin, set it to low gear priority.

[0148] In an embodiment of the present invention, two distance margins are set in order to determine the center distance of each key point. After the two distance margins are set, the priorities of different gears are automatically determined according to the margin values. These priorities are the key to performing hierarchical distributed analysis. The center of this area is analyzed first because its data is more representative.

[0149] Figure 5 It is a flowchart of obtaining the high-gear priority, the middle-gear priority and the low-gear priority in a regional waterlogging monitoring and prediction method according to an embodiment of the present invention, automatically making predictions for the next ten minutes, one hour and four hours, and updating the prediction data group.

[0150] like Figure 5 As shown, in one or more embodiments, preferably, obtaining the high gear priority, the middle gear priority and the low gear priority, automatically predicting the next ten minutes, one hour and four hours, and updating the prediction data group specifically include:

[0151] S501, obtaining historical data of key points corresponding to low-level priorities, and extracting 1 hour of data as short-term historical data;

[0152] S502, obtaining historical data of key points corresponding to the mid-level priority, and extracting 10 hours of data as mid-level historical data;

[0153] S503, obtaining historical data of key points corresponding to the high-level priority, and extracting 10 days of data as long-term historical data;

[0154] S504, using the fourth calculation formula to calculate the ten-minute forecast,

[0155] S505, calculating a one-hour forecast using the fifth calculation formula;

[0156] S506, calculating a four-hour forecast using the sixth calculation formula;

[0157] S507, storing the data of the ten-minute forecast, the one-hour forecast, and the four-hour forecast into the forecast data group;

[0158] S508, when each of the ten-minute forecast, the one-hour forecast and the four-hour forecast data obtains corresponding measured data, the forecast data group is updated;

[0159] The fourth calculation formula is:

[0160] X=w1Σx+a

[0161] Wherein, X is the ten-minute forecast, Σx is the short-term historical data, w1 is the first weight parameter, and a is the first basic parameter;

[0162] The fifth calculation formula is:

[0163] Y=w2Σy+b

[0164] Wherein, Y is the one-hour forecast, Σy is the mid-hour historical data, w2 is the second weight parameter, and b is the second basic parameter;

[0165] The sixth calculation formula is:

[0166] Z=w3Σz+c

[0167] Among them, Z is the four-hour forecast, Σz is the long-term historical data, w3 is the third weight parameter, and c is the third basic parameter.

[0168] In the embodiment of the present invention, the parameter setting of each prediction data in the hierarchical prediction is set according to the empirical value. In practice, it is necessary to determine the optimal parameters through an intelligent learning algorithm to update the weight parameters and basic parameters in real time.

[0169] Figure 6 It is a flowchart of obtaining all data of the prediction data group and automatically calculating the short-term maximum value, the medium-term maximum value and the long-term maximum value in a regional waterlogging monitoring and prediction method according to an embodiment of the present invention.

[0170] like Figure 6 As shown, in one or more embodiments, preferably, the obtaining of all data of the forecast data group and automatic calculation of short-term maximum value, medium-term maximum value and long-term maximum value specifically includes:

[0171] S601, obtaining all data of the prediction data group;

[0172] S602, calculating the short-term maximum value for the key point corresponding to the low gear priority using the seventh calculation formula;

[0173] S603, calculating the mid-term maximum value for the key points corresponding to the mid-level priority using the eighth calculation formula;

[0174] S604, calculating the long-term maximum value for the key point corresponding to the high priority level using the ninth calculation formula;

[0175] The seventh calculation formula is:

[0176] A=max(x 1k , x 2k , ..., x 10k )

[0177] Where A is the short-term maximum value, x 1k , x 2k , ..., x 10k is the predicted value of the 1st, 2nd, ..., 10th minute interval after the kth sampling;

[0178] The eighth calculation formula is:

[0179] B=max(y 1k ,y 2k , ..., y 4k )

[0180] Where B is the mid-term maximum value, y 1k ,y 2k , ..., y 4k is the predicted value of the first, second, ..., fourth quarter-hour interval after the k-th sampling;

[0181] The ninth calculation formula is:

[0182] C=max(z 1k , z 2k , ..., z 4k )

[0183] Where C is the long-term maximum value, z 1k , z 2k , ..., z 4k It is the predicted value for the 1st, 2nd, ..., 4th hour interval after the kth sampling.

[0184] In an embodiment of the present invention, in order to complete the extraction of the corresponding maximum value, the maximum values ​​of three different time dimensions are extracted respectively. During the longest time extraction process, not too much prediction data is selected. This is because the time cost of long-term prediction is high, so the density of prediction points is relatively low, otherwise the calculation cannot be completed.

[0185] Figure 7 It is a flowchart of displaying the long-term maximum value, the medium-term maximum value and the short-term maximum value in layers according to the priority of the key points in a regional waterlogging monitoring and prediction method according to an embodiment of the present invention.

[0186] like Figure 7 As shown, in one or more embodiments, preferably, the display of the long-term maximum value, the medium-term maximum value and the short-term maximum value in layers according to the priority of the key points specifically includes:

[0187] S701, calculating the long-term maximum value obtained from the key point corresponding to the high priority level, and displaying the long-term maximum value with the key point corresponding to the high priority level as the center of the circle;

[0188] S702, calculating the mid-term maximum value obtained from the key point corresponding to the mid-level priority, and displaying the mid-term maximum value with the key point corresponding to the mid-level priority as the center of the circle;

[0189] S703, calculating the short-term maximum value obtained at the key point corresponding to the low-gear priority, and displaying the short-term maximum value with the key point corresponding to the low-gear priority as the center of the circle;

[0190] S704: When the display range of the long-term maximum value covers the mid-term maximum value, only the long-term maximum value is displayed;

[0191] S705: When the display range of the mid-term maximum value covers the short-term maximum value, only the mid-term maximum value is displayed.

[0192] In the embodiment of the present invention, when displaying, the key point information includes two parts, a horizontal coordinate and a vertical coordinate, and the vertical coordinate is displayed with half of the most longitudinal direction of the corresponding key point location as the radius.

[0193] According to a second aspect of an embodiment of the present invention, a regional waterlogging monitoring and prediction platform is provided.

[0194] Figure 8 It is a structural diagram of a regional waterlogging monitoring and prediction platform according to an embodiment of the present invention.

[0195] In one or more embodiments, preferably, the regional waterlogging monitoring and prediction platform includes:

[0196] The point acquisition module 801 is used to obtain all monitoring point data and select key points according to the horizontal coordinate value;

[0197] The center distance analysis module 802 is used to obtain the coordinate information of each key point and automatically calculate and generate the center distance;

[0198] Selecting a calculation module 803, for obtaining the center distance, setting a high gear priority, a middle gear priority, and a low gear priority;

[0199] A prediction calculation module 804 is used to obtain the high gear priority, the middle gear priority and the low gear priority, automatically make predictions for the next ten minutes, one hour and four hours, and update the prediction data group;

[0200] The prediction output module 805 is used to obtain all the data of the prediction data group and automatically calculate the short-term maximum value, the medium-term maximum value and the long-term maximum value;

[0201] The layered display module 806 is used to display the long-term maximum value, the medium-term maximum value and the short-term maximum value in layers according to the priorities of the key points.

[0202] In the embodiment of the present invention, in accordance with the above method settings, a waterlogging data prediction system for different gears and distributed areas is automatically performed.

[0203] According to a third aspect of an embodiment of the present invention, there is provided a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method as described in any one of the first aspect of the embodiment of the present invention is implemented.

[0204] According to a fourth aspect of an embodiment of the present invention, an electronic device is provided. Fig. 9 It is a structural diagram of an electronic device in one embodiment of the present invention. Fig. 9 The electronic device shown is a general-purpose regional waterlogging monitoring and prediction device. The electronic device can be a smart phone, a tablet computer, or other device. As shown, the electronic device 900 includes a processor 901 and a memory 902. The processor 901 is electrically connected to the memory 902. The processor 901 is the control center of the terminal 900, and uses various interfaces and lines to connect various parts of the entire terminal. By running or calling a computer program stored in the memory 902, and calling the data stored in the memory 902, the various functions of the terminal are executed and the data is processed, thereby monitoring the terminal as a whole.

[0205] In this embodiment, the processor 901 in the electronic device 900 will load the instructions corresponding to the processes of one or more computer programs into the memory 902 according to the following steps, and the processor 901 will run the computer program stored in the memory 902 to realize various functions: obtain all monitoring point data, and select key points according to the horizontal coordinate value; obtain the coordinate information of each key point, and automatically calculate and generate the center distance; obtain the center distance, set the high gear priority, the mid-gear priority and the low gear priority; obtain the high gear priority, the mid-gear priority and the low gear priority, and automatically predict the next ten minutes, one hour and four hours, and update the prediction data group; obtain all data of the prediction data group, and automatically calculate the short-term maximum value, the medium-term maximum value and the long-term maximum value; according to the priority of the key point, display the long-term maximum value, the medium-term maximum value and the short-term maximum value in a layered manner.

[0206] The memory 902 may be used to store computer programs and data. The computer programs stored in the memory 902 include instructions that can be executed in the processor. The computer programs may constitute various functional modules. The processor 901 executes various functional applications and data processing by calling the computer programs stored in the memory 902.

[0207] The technical solution provided by the embodiments of the present invention may have the following beneficial effects:

[0208] In the embodiment of the present invention, a distributed waterlogging display of key points is adopted, and different priority settings are combined to perform online distributed prediction and display of the observation area.

[0209] In the embodiment of the present invention, more accurate waterlogging monitoring data and prediction data are obtained by automatically screening key points.

[0210] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0211] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0212] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0213] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0214] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for monitoring and predicting regional waterlogging, characterized in that: The method includes: Obtain all monitoring point data and select key points based on the horizontal coordinate value; Obtain the coordinate information of each key point and automatically calculate the center distance; Obtaining the center distance, setting a high gear priority, a middle gear priority, and a low gear priority; Obtaining the high gear priority, the middle gear priority and the low gear priority, automatically predicting the next ten minutes, one hour and four hours, and updating the prediction data group, wherein the four-hour prediction corresponds to the high gear priority, the one-hour prediction corresponds to the middle gear priority, and the ten-minute prediction corresponds to the low gear priority; Acquire all data of the prediction data group, perform automatic short-term maximum value, medium-term maximum value and long-term maximum value calculation, calculate the short-term maximum value for the low gear priority, calculate the medium-term maximum value for the medium gear priority, and calculate the long-term maximum value for the high gear priority; According to the priority of the key points, the long-term maximum value, the medium-term maximum value and the short-term maximum value are displayed in layers; The acquisition of all monitoring point data and selection of key points according to the horizontal coordinate values ​​specifically includes: Acquire all monitoring point data, and extract the horizontal coordinates of the monitoring point data; According to the degree of aggregation of the horizontal axis, it is divided into several clusters of monitoring points based on experience; Calculating the average value of each monitoring point cluster to generate key points; The step of obtaining the coordinate information of each key point and automatically calculating and generating the center distance specifically includes: Get the horizontal axis position of each key point; Extract monitoring points within 1 km around each key point as monitoring points near the corresponding key point; The first calculation formula is used to calculate the collection deviation of each key point; Eliminate the measurement data of the key points whose acquisition deviation exceeds the preset value, and update all the key points; Calculate the partition center position after each collection using the second calculation formula according to the updated key points; Calculate the center distance using the third calculation formula; The first calculation formula is: ； in, d i is the collection deviation of the i-th key point, x i is the horizontal axis position of the i-th key point, x ij is the number of points near the i-th key point j The horizontal axis position of the monitoring point, n is the number of monitoring points; The second calculation formula is: ； in, n k is the total number of key points collected for the kth time, u k is the partition center position obtained by the k-th acquisition, x i is the horizontal axis position of the i-th key point; The third calculation formula is: E =| x i -u k | 2 in, E is the center distance of the i-th key point; The obtaining of the center distance and setting of the high gear priority, the middle gear priority and the low gear priority specifically include: Obtaining the center distance of each key point; Setting a first center margin and a second center margin; When the center distance is greater than the first center margin, a high gear priority is set; When the center distance is greater than the second center margin and not greater than the first center margin, it is set as the middle gear priority; When the center distance is not greater than the second center margin, it is set to low gear priority.

2. A method for monitoring and predicting regional waterlogging according to claim 1, characterized in that: The obtaining of the high gear priority, the middle gear priority and the low gear priority, automatically predicting the next ten minutes, one hour and four hours, and updating the prediction data group specifically includes: Obtain historical data of key points corresponding to low-level priorities, and extract 1 hour of data as short-term historical data; Obtain the historical data of key points corresponding to the mid-level priority, and extract 10 hours of data as mid-level historical data; Obtain historical data of key points corresponding to high-level priorities, and extract 10 days of data as long-term historical data; Calculate the ten-minute forecast using the fourth calculation formula; Calculate the one-hour forecast using the fifth calculation formula; Calculate the four-hour forecast using the sixth calculation formula; storing the data of the ten-minute forecast, the one-hour forecast and the four-hour forecast in the forecast data group; When the data of each of the ten-minute forecast, the one-hour forecast and the four-hour forecast obtain the corresponding measured data, the forecast data group is updated; The fourth calculation formula is: X = w 1S x + a in, X For the ten-minute forecast, Σ x is the short-term historical data, w 1 is the first weight parameter, a is the first basic parameter; The fifth calculation formula is: Y = w 2S y + b in, Y For the one-hour forecast, Σ y is the historical data of the time period, w 2 is the second weight parameter, b is the second basic parameter; The sixth calculation formula is: Z = w 3S z + c Where Z is the four-hour forecast, Σ z is the long-term historical data, w 3 is the third weight parameter, c It is the third basic parameter.

3. A method for monitoring and predicting regional waterlogging according to claim 1, characterized in that: The step of obtaining all data of the forecast data group and automatically calculating the short-term maximum value, the medium-term maximum value and the long-term maximum value specifically includes: Acquire all data of the prediction data group; Calculating the short-term maximum value for the key point corresponding to the low gear priority using the seventh calculation formula; Calculating the mid-term maximum value for the key points corresponding to the mid-level priority using the eighth calculation formula; Calculating the long-term maximum value for the key point corresponding to the high priority level using the ninth calculation formula; The seventh calculation formula is: A =max( x 1k , x 2k ,……, x 10k ) in, A is the short-term maximum value, x 1k , x 2k ,……, x 10k is the predicted value of the 1st, 2nd, ..., 10th minute interval after the kth sampling; The eighth calculation formula is: B =max( y 1k , y 2k ,……, y 4k ) in, B is the mid-term maximum, y 1k , y 2k ,……, y 4k is the predicted value of the first, second, ..., fourth quarter-hour interval after the k-th sampling; The ninth calculation formula is: C =max( z 1k , z 2k ,……, z 4k ) in, C is the long-term maximum value, z 1k , z 2k ,……, z 4k It is the predicted value for the 1st, 2nd, ..., 4th hour interval after the kth sampling.

4. A method for monitoring and predicting regional waterlogging according to claim 1, characterized in that: The display of the long-term maximum value, the medium-term maximum value and the short-term maximum value in layers according to the priority of the key points specifically includes: The long-term maximum value calculated for the key point corresponding to the high priority level is displayed with the key point corresponding to the high priority level as the center of the circle; The mid-term maximum value calculated for the key point corresponding to the mid-level priority is displayed with the key point corresponding to the mid-level priority as the center of the circle; The short-term maximum value calculated for the key point corresponding to the low gear priority is displayed with the key point corresponding to the low gear priority as the center of the circle; When the display range of the long-term maximum value covers the medium-term maximum value, only the long-term maximum value is displayed; When the display range of the mid-term maximum value covers the short-term maximum value, only the mid-term maximum value is displayed.

5. A regional waterlogging monitoring and prediction platform, characterized in that: The platform is used to implement the method as claimed in claim 1, and the platform comprises: Point acquisition module, used to obtain all monitoring point data and select key points according to the horizontal coordinate value; The center distance analysis module is used to obtain the coordinate information of each key point and automatically calculate the center distance; Selecting a calculation module, used for obtaining the center distance, setting a high gear priority, a middle gear priority and a low gear priority; A prediction calculation module, used to obtain the high gear priority, the middle gear priority and the low gear priority, automatically make predictions for the next ten minutes, one hour and four hours, and update the prediction data group; A prediction output module, used to obtain all data of the prediction data group and automatically calculate the short-term maximum value, the medium-term maximum value and the long-term maximum value; A layered display module is used to display the long-term maximum value, the medium-term maximum value and the short-term maximum value in layers according to the priority of the key points.

6. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: The computer program instructions implement the method of claim 1 when executed by a processor.

7. An electronic device comprising a memory and a processor, characterized in that: The memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method according to claim 1.

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