Partitioned early warning method and system for mountain torrent disaster in small watershed of mountainous area

Abstract

The invention discloses a mountainous area small watershed mountain torrent disaster zoning early warning method and system, relates to the technical field of mountain torrent early warning, is used for improving the problem that various early warning indexes of an existing early warning system are lack of association, and utilizes real-time monitoring of to-be-monitored indexes of a mountainous area small watershed to trigger judgment through a threshold value of the to-be-detected indexes so as to realize early warning of mountain torrent disasters. The method comprises the following steps: acquiring real-time data of a to-be-monitored index, calculating to obtain an abnormal degree coefficient, evaluating the overall safety coefficient of the small watershed in the mountainous area, normalizing the change rate of the real-time data unit time of the to-be-monitored index with the real-time data of the to-be-monitored index, and calculating the abnormal degree coefficient of the to-be-monitored index according to the normalized change rate of the real-time data unit time of the to-be-monitored index. Calculating a safety coefficient score according to the abnormal degree coefficient and the real-time data of the to-be-monitored indexes, performing weighted average calculation on the real-time data of the to-be-monitored indexes according to the weight values of the to-be-monitored indexes to obtain an overall safety coefficient, and comprehensively judging an alarm response mode through the abnormal degree coefficient and the overall safety coefficient. The problem that various data indexes of an existing early warning system are lack of association is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of flash flood warning, and more specifically, to a method and system for zoning flash flood disaster warning in a small watershed in a mountainous area. Background Art

[0002] Flash floods refer to sudden floods caused by heavy rainfall, snowmelt or other factors in a short period of time in mountainous or hilly areas. Such floods often form quickly within a short period of time after rainfall, and there is a lack of sufficient warning time. Flash floods are usually concentrated in specific valleys or gullies, with a relatively small impact range, but extremely destructive power in the local area. Due to the influence of mountainous terrain, water flows quickly and flows at a high speed, carrying a large amount of silt and gravel, causing serious damage to the surrounding environment and buildings. Flash floods may trigger secondary disasters such as mudslides and landslides, further exacerbating losses. Therefore, the accuracy and necessity of flash flood disaster warnings are particularly important.

[0003] Deficiencies of existing technologies: Existing flash flood warning methods and system models are often based on separate monitoring and analysis of factors such as rainfall and river water levels. This leads to insufficient applicability under different terrain and climate conditions, which may lead to delayed and inaccurate warnings. Natural disasters are often sudden and accidental, and early warning requires a comprehensive analysis of various data in areas prone to flash floods.

[0004] In view of the above problems, the present invention proposes a solution. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides a zoning early warning system for mountain torrent disasters in small watersheds in mountainous areas to solve the problems arising from the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for zoning early warning of mountain torrent disasters in small watersheds in mountainous areas comprises the following steps:

[0008] Step 1: Monitor the indicators to be monitored in small watersheds in mountainous areas, including flow, rainfall, water level, and sediment concentration in the water;

[0009] Step 2: According to the indicators to be monitored in the small watershed in the mountainous area, determine whether the real-time data of the indicators to be monitored triggers the set threshold, and then calculate the abnormal degree coefficient. Then determine the weight coefficient between various types of real-time data, and calculate the overall safety factor of the small watershed in the mountainous area by weighted average based on the real-time data of the monitoring area;

[0010] Step 3: Based on the calculated overall safety factor and abnormality degree coefficient, use fuzzy logic to adjust the alarm response method.

[0011] In a preferred embodiment, in step 2, confirming the abnormality degree coefficient of a small watershed in a mountainous area includes the following steps: after determining that the received monitored indicator exceeds a threshold, collecting real-time data of the monitored indicator to calculate the Z-score statistical indicator to obtain the abnormality degree coefficient.

[0012] In a preferred embodiment, in step 2, determining the weight coefficient of the indicator to be monitored in the small watershed in the mountainous area includes the following steps: after receiving the real-time data of the indicator to be monitored in the small watershed in the mountainous area, calculating the rate of change of the indicator to be monitored per unit time, calculating the score of the indicator to be monitored by summing the normalized rate of change of the indicator to be monitored per unit time and the real-time data value of the indicator to be monitored, and assigning weights to the scores of the indicator to be monitored according to the priority diagram method, and determining the weight value of the indicator to be monitored;

[0013] The real-time data of the indicators to be monitored are weighted averaged according to the weight values ​​to obtain the overall safety factor.

[0014] In a preferred embodiment, in step three, using fuzzy logic to adjust the alarm response mode includes the following steps:

[0015] After receiving the overall safety factor and the abnormality degree coefficient of the small watershed in the mountain area, the overall safety factor and the abnormality degree coefficient are defined as input variables and divided into different fuzzy sets respectively;

[0016] The alarm response mode is defined as the output variable and divided into fuzzy sets;

[0017] Formulate fuzzy rules to describe the impact of overall safety factor and abnormality degree factor on alarm response mode;

[0018] Perform fuzzy reasoning based on fuzzy rules to determine the alarm response method.

[0019] An early warning system for flash flood disaster zoning in a small watershed in a mountainous area, used to implement the above-mentioned early warning method for flash flood disaster zoning in a small watershed in a mountainous area, comprising a data monitoring module, a data processing module and an alarm module;

[0020] The data monitoring module is used to monitor the indicators to be monitored in the small watershed in the mountainous area, including: rainfall, flow, sediment concentration in the water, and water level, and send the real-time data of the indicators to be monitored to the data processing module for the data processing module to determine the overall safety factor and abnormality degree coefficient of the small watershed in the mountainous area;

[0021] After receiving the real-time data of the indicators to be monitored, the data processing module first determines whether the rainfall, flow, sediment concentration in the water, and water level exceed the corresponding set thresholds. If the thresholds are exceeded, the abnormality degree coefficient is calculated, and the weight coefficients between the indicators to be monitored are determined. The overall safety factor of the small watershed in the mountainous area is calculated by weighted average based on the real-time data of the indicators to be monitored. Finally, the overall safety factor and the abnormality degree coefficient are sent to the alarm module;

[0022] After receiving the overall safety factor and abnormality degree coefficient, the alarm module uses fuzzy logic to perform graded response processing.

[0023] The technical advantages of the method and system for early warning of flash flood disasters in small watersheds in mountainous areas of the present invention are as follows:

[0024] By integrating multiple indicators such as flow, rainfall, water level, and sediment concentration in the water, it is possible to more comprehensively and accurately assess the safety status of small watersheds in mountainous areas, avoiding the shortcoming that a single indicator cannot fully reflect disaster risks.

[0025] Using the Z-score standard score method for real-time data anomaly detection helps to identify data that deviates from the normal range and improve the sensitivity of the early warning system, especially to enable timely response when abnormalities occur in key data.

[0026] Based on the real-time data change rate and normalization processing, the safety factor of each monitoring indicator is calculated by weighting through the priority diagram method, so that important indicators occupy a higher weight in the calculation process, thereby ensuring the accuracy and representativeness of the safety factor assessment.

[0027] Through the fuzzy rule reasoning system, the alarm response method is flexibly adjusted according to the overall safety factor and the abnormality degree coefficient, to achieve refined and graded early warning processing, adapt to different flash flood disaster risk scenarios, and provide three response modes of "no warning, early warning, and alarm". BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a framework diagram of a flash flood disaster early warning system for small watersheds in mountainous areas of the present invention;

[0029] Figure 2 The present invention is a flow chart of a method for early warning of flash flood disasters in small watersheds in mountainous areas. DETAILED DESCRIPTION

[0030] The following will be combined with the accompanying 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 ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0031] The purpose of the present invention is to solve the disadvantage that there is a lack of connection between the warning indicators of the flash flood warning system, which may lead to the flash flood outbreak and the warning system fails to play a role in time, thereby proposing a flash flood disaster zoning warning method and system for small watersheds in mountainous areas.

[0032] Embodiment 1, as Figure 2 As shown, a method for zoning early warning of flash flood disasters in small watersheds in mountainous areas includes the following steps.

[0033] Step 1: Monitor the indicators to be monitored in small watersheds in mountainous areas, including flow, rainfall, water level, and sediment concentration in water for data processing;

[0034] Step 2: Set the threshold of the indicator to be monitored. The threshold can be set based on professional knowledge or obtained through data analysis and experiments, which will not be described here. After receiving the real-time data of the indicator to be monitored, the corresponding threshold trigger judgment is performed on it. If there is no real-time data trigger threshold, the abnormality degree coefficient output is 0. When it is determined that there is a real-time data trigger threshold, the abnormality degree coefficient is calculated. The specific calculation method is as follows:

[0035] Collect real-time data of the trigger threshold and calculate the Z-score (standard score) statistical indicator to measure the degree of deviation between a data point and the average value of the data set it belongs to. The calculation formula is: Z = (X-μ) / s;

[0036] Where X is the data to be calculated, μ is the mean value of the data, and s is the standard deviation. Z-score can help identify outliers. Generally speaking, data points with a Z-score greater than 3 or less than -3 are considered outliers. The larger the absolute value of the Z-score, the greater the degree of deviation of the data;

[0037] It should be noted that the standard score calculation formula given in this embodiment is a general formula and does not involve the various data information obtained by the data monitoring module of this application, and will not be described in detail here;

[0038] When the real-time data of the indicator to be monitored has multiple trigger thresholds, the abnormality degree coefficient takes the largest absolute value.

[0039] The overall safety factor of small watersheds in mountainous areas is evaluated. The specific evaluation method is as follows:

[0040] Calculate the rate of change per unit time of the real-time data of the monitored indicator, and use linear normalization to map the rate of change per unit time of the real-time data of the monitored indicator and its real-time data to the interval [0,1]. The formula is: In the formula, Normalized_Score is the normalized value.

[0041] It should be noted that the normalization formula given in this embodiment is a general formula and does not involve the various data information obtained in this application, so it will not be elaborated here.

[0042] In this embodiment, an example of calculating the score of the indicator to be monitored is as follows:

[0043] After normalizing the rate of change per unit time of the real-time data of the indicator to be monitored with its real-time data, they are marked as a and b respectively, and the score calculation formula of the indicator to be monitored is: Safety(i) = a + b. In the formula, Safety(i) is the score of the safety factor of different indicators, and i represents the serial number of each indicator.

[0044] After calculating the scores of various indicators, compare the scores of various indicators.

[0045] Obviously, the greater the data change rate per unit time and / or the larger the real-time data, the more abnormal the small watershed in the mountain area is, and the more attention it needs to be paid, that is, the more important it is, the greater the weight of the safety factor consideration should be, and the unit time can be set according to actual needs, which will not be elaborated here.

[0046] It should be noted that the rate of change per unit time of the real-time data of the indicator to be monitored and the real-time data of the indicator to be monitored can also be weighted and calculated according to actual conditions, which will not be elaborated here.

[0047] The safety factor score of the monitored indicator is calculated based on the normalized rate of change of the real-time data per unit time and its real-time data, and the safety factor scores of various types of monitored indicators are weighted according to the priority diagram method to determine the safety factor weight values ​​of various types of monitored indicators.

[0048] Specifically, the safety factor scores of various indicators to be monitored are weighted according to the priority diagram method, as shown in Table 1 below:

[0049]

[0050] Table 1

[0051] It should be noted that in Table 1, the water level, rainfall, flow rate, and sediment concentration in the water correspond one by one to the indicators to be monitored in this embodiment.

[0052] The real-time data of the indicators to be monitored are weighted averaged according to the weight values ​​of various indicators to be monitored to obtain the overall safety factor.

[0053] The present invention determines the relative weights of various indicators to be monitored by first evaluating the unit time change status of the indicators to be monitored in the small watershed in the mountainous area, and performs weighted averaging according to the relative weights. The overall safety factor calculated in this way is more representative and can better grasp the overall safety factor status of the small watershed in the mountainous area.

[0054] Step 3: Based on the overall safety factor and abnormality coefficient of the small watershed in the mountainous area, fuzzy rules are formulated for the alarm response method. The fuzzy rules are as follows:

[0055] After receiving the overall safety factor and the abnormality degree coefficient, the overall safety factor and the abnormality degree coefficient are defined as input variables, and they are divided into different fuzzy sets respectively.

[0056] For example, "Safe", "Warning", and "Danger" are for the overall safety factor, and "Normal" and "Abnormal" are for the abnormal degree coefficient.

[0057] Define the alarm response method as the output variable and divide it into fuzzy sets. For example, "No Alert ing", "Early Warning", "Alert ing" for the alarm response method.

[0058] Formulate a set of fuzzy rules to describe the impact of different input variables on output variables. The definition of rules can be based on professional knowledge or obtained through data analysis and experiments. For example:

[0059] The overall safety factor is marked as S, the abnormality coefficient is marked as A, and the alarm response method is marked as Reply. Then we can define

[0060]

[0061] Step seven, perform fuzzy reasoning based on fuzzy rules to determine the alarm response method.

[0062] It should be noted that the division of fuzzy sets can be adjusted according to actual conditions. For example, although this embodiment uses three fuzzy sets as examples, the overall safety factor, abnormality degree coefficient and alarm module response mode can actually be divided into more than three sets to facilitate better accurate responses according to different situations.

[0063] Furthermore, for the judgment of the overall safety factor and the abnormal degree coefficient, the threshold can be set according to the actual situation for judgment, which will not be elaborated here. The graded response according to different situations includes three modes: no triggering of warning, early warning, and issuing of alarm.

[0064] Embodiment 2, a mountain small watershed flash flood disaster zoning early warning system, such as Figure 1 As shown, it is used to implement a zoning early warning method for mountain torrent disasters in small watersheds in mountainous areas, including a data monitoring module, a data processing module, and an alarm module;

[0065] The functions of each module are as follows:

[0066] The data monitoring module is used to monitor the indicators to be monitored in the small watersheds in the mountainous area, including rainfall, flow, sediment concentration in the water, and water level. The real-time data of the indicators to be monitored are sent to the data processing module, which is used by the data processing module to determine the overall safety factor and abnormal degree coefficient of the small watershed in the mountainous area. At the same time, the data monitoring module is also used to collect data to establish a rainfall and water information database, which can be used for analysis to generate a flash flood disaster warning model.

[0067] After receiving the real-time data of the indicators to be monitored, the data processing module first determines whether the rainfall, flow, sediment concentration in the water, and water level exceed the corresponding set thresholds. If the thresholds are exceeded, the abnormality degree coefficient is calculated. At the same time, the weight coefficients between the indicators to be monitored are determined, and the overall safety factor of the small watershed in the mountainous area is calculated by weighted average based on the real-time data of the indicators to be monitored. Finally, the overall safety factor and the abnormality degree coefficient are sent to the alarm module.

[0068] After receiving the overall safety factor and abnormality degree coefficient, the alarm module uses fuzzy logic to perform graded response processing.

[0069] It should be noted that the data monitoring module monitors the real-time data of small watersheds in mountainous areas, which is a conventional technical means and will not be elaborated here.

[0070] After the data processing module receives the real-time data of the monitored indicators, the method and results for setting thresholds for rainfall, flow, etc. are not unique. For example, the critical rainfall can be calculated by model analysis and co-frequency estimation, and the threshold indicators can be determined comprehensively.

[0071] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0072] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0073] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application of the technical solution and the invention constraints. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0074] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0075] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0076] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for zoning early warning of flash flood disasters in small watersheds in mountainous areas, characterized in that: The following steps are included: Step 1: Monitor multiple indicators of small watersheds in mountainous areas, including flow, rainfall, water level, and sediment concentration in the water; Step 2: Based on multiple indicators to be monitored in the small watershed in the mountainous area, determine whether the real-time data of the indicators to be monitored triggers the set threshold, and then calculate the abnormal degree coefficient; then determine the weight coefficient between various types of real-time data, and calculate the overall safety factor of the small watershed in the mountainous area by weighted average based on the real-time data of the monitoring area; Step 3: According to the calculated overall safety factor and abnormality degree coefficient, the alarm response mode is adjusted using fuzzy logic; The alarm response methods include no warning, early warning, and alarm.

2. The method for early warning of flash flood disasters in small watersheds in mountainous areas according to claim 1 is characterized by: In step 2, confirming the abnormality degree coefficient of the small watershed in the mountainous area includes the following steps: after determining that the received indicator to be monitored exceeds the threshold, collecting real-time data of the indicator to be monitored and calculating the Z-score statistical indicator to obtain the abnormality degree coefficient.

3. The method for early warning of flash flood disasters in small watersheds in mountainous areas according to claim 1 is characterized by: In step 2, determining the weight coefficient of the indicator to be monitored in the small watershed in the mountainous area includes the following steps: after receiving the real-time data of the indicator to be monitored in the small watershed in the mountainous area, calculating the rate of change of the indicator to be monitored per unit time, and calculating the score of the indicator to be monitored by summing the normalized rate of change of the indicator to be monitored per unit time and the real-time data value of the indicator to be monitored, and assigning weights to the scores of the indicator to be monitored according to the priority diagram method, and determining the weight value of the indicator to be monitored; The real-time data of the indicators to be monitored are weighted averaged according to the weight values ​​to obtain the overall safety factor.

4. The method for early warning of flash flood disasters in small watersheds in mountainous areas according to claim 1 is characterized by: In step three, the alarm response mode is adjusted using fuzzy logic as follows: After receiving the overall safety factor and the abnormality degree coefficient of the small watershed in the mountain area, the overall safety factor and the abnormality degree coefficient are defined as input variables and divided into different fuzzy sets respectively; The alarm response mode is defined as the output variable and divided into fuzzy sets; Formulate fuzzy rules to describe the impact of overall safety factor and abnormality degree factor on alarm response mode; Perform fuzzy reasoning based on fuzzy rules to determine the alarm response method.

5. A mountain small watershed flash flood disaster zoning early warning system, used to implement a mountain small watershed flash flood disaster zoning early warning method according to any one of claims 1 to 4, characterized in that: Including data monitoring module, data processing module and alarm module; The data monitoring module is used to monitor the indicators to be monitored in the small watershed in the mountainous area, including: rainfall, flow, sediment concentration in the water, and water level, and send the real-time data of the indicators to be monitored to the data processing module for the data processing module to determine the overall safety factor and abnormality degree coefficient of the small watershed in the mountainous area; After receiving the real-time data of the indicators to be monitored, the data processing module first determines whether the rainfall, flow, sediment concentration in the water, and water level exceed the corresponding set thresholds. If the thresholds are exceeded, the abnormality degree coefficient is calculated, and the weight coefficients between the indicators to be monitored are determined. The overall safety factor of the small watershed in the mountainous area is calculated by weighted average based on the real-time data of the indicators to be monitored. Finally, the overall safety factor and the abnormality degree coefficient are sent to the alarm module; After receiving the overall safety factor and abnormality degree coefficient, the alarm module uses fuzzy logic to perform graded response processing.

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