A water level sequence data monitoring and early warning method based on big data

Abstract

The application discloses a water level sequence data monitoring and early warning method based on big data, and relates to the technical field of water level monitoring and early warning; some defects still exist in the prior art, mainly including data quality and abnormality detection problems, disaster hidden danger early warning lag problems, insufficient response speed and decision support capability of the early warning system and the like; the method comprises the following steps: step one, obtaining hydrological, geographical and meteorological data of a specific area through a big data platform; step two, performing data screening on the hydrological data, geographical data and meteorological data; the application effectively identifies and processes abnormal data through systematic feature screening, statistical analysis and abnormality detection, thereby ensuring the data accuracy and reliability of the early warning system; the abnormal coefficients of the hydrological, geographical and meteorological data are calculated, high-risk time periods and regions are identified in a timely manner, earlier disaster early warning is realized, and the problem of early warning lag is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of water level monitoring and early warning, and in particular to a water level sequence data monitoring and early warning method based on big data. Background Art

[0002] Existing big data-based water level series data monitoring and early warning methods utilize large-scale data collection and analysis technologies to provide timely and accurate flood warnings and risk assessments by deeply mining and predicting trends in real-time data from water level monitoring points. These methods typically combine meteorological data, historical water level data, and environmental parameters, using machine learning and big data analysis models to model water level changes and predict future water level trends.

[0003] However, this method still has some defects, mainly including being limited by data loss, sensor failure or untimely real-time data processing, failing to detect potential disaster risks in advance when water levels change rapidly, precipitation exceeds the standard or extreme weather, the existing system may lack sufficient decision support or respond quickly enough during emergency response and provide early warning of floods, and lack the ability to deal with natural disasters caused by other water levels. Summary of the Invention

[0004] In order to solve the technical problems raised by the above background technology, the present invention is proposed. An embodiment of the present invention provides a water level series data monitoring and early warning method based on big data.

[0005] The purpose of the present invention can be achieved through the following technical solution: A water level sequence data monitoring and early warning method based on big data, comprising:

[0006] Step 1: Obtain hydrological, geographical and meteorological data of a specific area through a big data platform;

[0007] Step 2: Screen the hydrological, geographic, and meteorological data. Specifically, visualize the hydrological, geographic, and meteorological data, using line graphs to show the temporal trends of water levels and flow rates, and mark peaks and periods of sharp change. Use heat maps to display the spatial distribution of precipitation and meteorological data, checking whether heavy rainfall areas overlap with low-lying terrain. Also, calculate basic statistics and identify extreme values ​​and key characteristic points of sharp fluctuations.

[0008] Step 3: Obtain the time points corresponding to the data with obvious feature markers through big data, and obtain all the data at the corresponding time points; if floods or flash floods have occurred at the corresponding time points, obtain the comparison data set 1 by statistics; if floods or flash floods do not occur at the corresponding time points, obtain the comparison data set 2 by statistics; at each time point, filter out the data of disasters that have occurred at that time point from the comparison data set 1, and calculate the mean of all the comparison data sets 1 to obtain the mean set, compare the comparison data set 1 with the comparison data set 2 to obtain the difference data set between the two; then compare the difference data set with the mean corresponding to the data, and obtain the difference over-limit value set for the difference data set that is greater than or equal to the mean corresponding to the data; and obtain the difference critical value set for the difference data set that is less than the mean corresponding to the data;

[0009] Step 4: retrieve the difference data set, difference over-boundary value set, and difference critical value set obtained in step 3; then classify the three to obtain the difference data set, difference over-boundary value set, and difference critical value corresponding to hydrology, geography, and meteorology; analyze the hydrological difference data set, hydrological difference over-boundary value set, and hydrological difference critical value set to obtain the hydrological anomaly coefficient value; analyze the geographic difference data set, geographic difference over-boundary value set, and geographic difference critical value set to obtain the geographic anomaly coefficient value; analyze the meteorological difference data set, meteorological difference over-boundary value set, and meteorological difference critical value set to obtain the meteorological anomaly coefficient value;

[0010] Step 5: Analyze the real-time hydrological data, geographic data and meteorological data collected in the corresponding area to obtain the regional water level coefficient and ratio B.

[0011] As a preferred embodiment of the present invention, the specific process of calculating the mean of all comparison data sets to obtain the mean set is as follows:

[0012] If in the comparison data set 1, for a certain feature label data F, at time point t i There is m i Each piece of data represents a vector x ij , x ij Contains multiple attributes; calculates the time point t at which all disasters occur i The mean of all attributes corresponding to the feature label data F is obtained by statistics; for a certain attribute A, the mean is calculated as follows: Output the mean of attribute A A ; Among them, A(X ij ) is the vector x ij The value of attribute A in It represents the sum of attribute A at all time points when disasters occur; Indicates the total number of data at all disaster occurrence points in time.

[0013] As a preferred embodiment of the present application, the specific process of analyzing the hydrological difference data set, the hydrological difference over-limit value set and the hydrological difference critical value set to obtain the hydrological anomaly coefficient value is as follows:

[0014] According to the established formula: Output the hydrological anomaly coefficient value Ks, wherein Di1 is the difference value of the i1th record in the hydrological difference data set, Wi1 is the preset weight of the i1th record, and n1 is the total number of records in the hydrological difference data set;

[0015] Further calculate the contribution value of the hydrological difference over-limit value set and the hydrological difference critical value set: calculate the weighted mean value of the hydrological difference over-limit value set: Output the weighted over-limit mean value μ 超界 ; calculate the weighted mean value of the hydrological difference critical value set: Output the weighted critical mean value μ 临界 ; the hydrological anomaly coefficient value Ks = ω1 × μ 超界 + ω2 × μ 临界 , ω1 and ω2 are weight factors, ω1 + ω2 = 1, and ω1 / ω2 = λ, λ is an adjustment coefficient.

[0016] As a preferred embodiment of the present application, the specific process of analyzing the geographical difference data set, the geographical difference over-limit value set and the geographical difference critical value set to obtain the geographical anomaly coefficient value is as follows:

[0017] Obtain the vector x i2 for each record data, which contains the slope value S i2 , the elevation H i2 and the terrain roughness P i2 , assign a weight W i2 to each record, and output the assigned weight W i2 of the i2th record according to the established formula: W i2 = a1 × S i2 + a2 × (Hmax-H i2 ) + a3 × P i2 , wherein a1, a2 and a3 are preset weight coefficients, and Hmax is the maximum elevation of the study area.

[0018] According to the established formula: Output the geographical anomaly coefficient value Kd, wherein is the difference value of the i2th record in the geographical difference data set, is the absolute difference value of the geographical difference over-limit value and the critical value, n2 is the total number of records in the geographical difference data set, λ1 is an adjustment coefficient, and m1 is the number of overlapping records of the geographical difference over-limit value set and the critical value set.

[0019] As a preferred embodiment of the present invention, the specific process of analyzing the meteorological difference data set, the meteorological difference over-limit value set and the meteorological difference critical value set to obtain the meteorological anomaly coefficient value is as follows:

[0020] By establishing the formula:

[0021]

[0022] The meteorological anomaly coefficient value Kq is obtained. Contribute to the part that exceeds the threshold value, is the difference value of the i3th record in the meteorological difference exceeding threshold value set, To perform logarithmic transformation on over-limit values, W i3 The preset weight of the i3th record, is the total weight of the part exceeding the threshold; Contribute to the critical value part, is the difference value of the j3th record in the meteorological difference critical value set, To perform square root transformation on the critical value, W j3 The preset weight of the j3th record, is the total weight of the critical value part, ω3 and ω4 are weight factors, satisfying ω3+ω4=1, ω3 / ω4=λ2, λ2 is the adjustment coefficient.

[0023] As a preferred embodiment of the present invention, the specific process of analyzing the real-time collected hydrological data, geographic data and meteorological data in the corresponding area to obtain the regional water level coefficient is as follows:

[0024] By formula: Output area water level coefficient CSZ, T1 time series length, Q1 is the amount of water flowing in or out per unit time, R1 is the rainfall per unit area, K1 s is the soil permeability coefficient, S1 is the terrain slope, E1 is the evaporation, A1 is the normalization coefficient, which standardizes the net change to unit area; Q1 t1 +R1 t1 -E1 t1 is the net change in water volume, K1 s ×S1 is the correction of water flow rate due to terrain and soil.

[0025] As a preferred embodiment of the present invention, the specific process of analyzing the real-time collected hydrological data, geographic data and meteorological data in the corresponding area to obtain the ratio B is as follows:

[0026] Compare the regional water level value SWZ calculated in step 4 with the regional water level coefficient CSZ to obtain the ratio between the regional water level coefficient CSZ and the regional water level value SWZ, recorded as B; then compare the ratio B with the preset warning interval, and the preset warning intervals include warning interval one, warning interval two, and warning interval three; if the ratio B belongs to warning interval one, it is a level three warning, and a level three alarm is issued to notify relevant personnel and departments to ensure that they pay attention to water level changes and prepare to respond to possible flood risks; if the ratio B belongs to warning interval two, it is a level two warning, and a level two alarm is issued. Relevant personnel and departments should strengthen inspections and initiate emergency response measures; if the ratio B belongs to warning interval three, it is a level one warning, and a level one alarm is issued, and all relevant departments are immediately notified to conduct a comprehensive emergency response.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] 1. The present invention combines multi-dimensional data such as hydrology, geography and meteorology, and provides an accurate flood prediction and early warning method through systematic feature screening, statistical analysis and anomaly detection. Steps one to four identify possible high-risk periods through in-depth analysis of historical and real-time data, especially in cases of rapid water level changes, excessive precipitation or extreme weather, so that potential disaster risks can be discovered in advance. By calculating the anomaly coefficients of hydrology, geography and meteorology, a comprehensive regional water level value is obtained, and an alarm is issued in a timely manner according to the set early warning interval to ensure that relevant personnel and departments can take appropriate preventive measures according to different warning levels, such as strengthening inspections, deploying flood control facilities, evacuating personnel, etc., thereby effectively reducing disaster losses.

[0029] 2. The present invention has comprehensive hydrological monitoring and early warning capabilities, which not only improves the monitoring accuracy of water level changes, but also enhances the early identification and decision-making support capabilities of disaster risks. Through the comprehensive analysis of multi-source data, it can quickly capture abnormal fluctuations in real-time data changes, and quantify the risk level through accurate abnormal coefficient values. This intelligent early warning system can greatly improve the response speed and accuracy of disaster prevention and mitigation, and avoid huge losses caused by delayed response. In addition, combined with the analysis of regional characteristics, this method is not only suitable for flood warnings, but can also effectively respond to natural disasters caused by other water levels. Overall, this method provides governments at all levels and relevant departments with a set of efficient and scientific disaster prevention and mitigation decision-making tools, improves the efficiency and accuracy of emergency response, and optimizes the allocation and utilization of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. The following drawings are not intentionally scaled to the actual size, and the focus is on illustrating the main purpose of the present invention.

[0031] Figure 1 A diagram showing the steps of the method of the present invention. DETAILED DESCRIPTION

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts also fall within the scope of protection of the present invention.

[0033] As used herein and in the claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" are not intended to refer to the singular but may include the plural. Generally speaking, the terms "comprises" and "include" only indicate the inclusion of the steps and elements specifically identified, and these steps and elements do not constitute an exclusive list. A method or apparatus may also include other steps or elements.

[0034] Although the present invention has made various references to certain modules in the system according to an embodiment of the present invention, however, any number of different modules can be used and run on the user terminal and / or server. The modules are only illustrative, and different aspects of the system and method can use different modules.

[0035] Flowcharts are used in this disclosure to illustrate the operations performed by systems according to embodiments of the present invention. It should be understood that the preceding or following operations do not necessarily need to be performed in exact order. Instead, various steps may be processed in reverse order or simultaneously, as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0036] Below, the exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described herein.

[0037] See also Figure 1 As shown in FIG, a water level series data monitoring and early warning method based on big data, the specific steps are as follows:

[0038] Step 1: Obtain hydrological, geographic, and meteorological data for a specific location through big data. Hydrological data includes water level, flow, precipitation, evaporation, sediment content, and groundwater level. Geographic data includes topography, land cover types (forest, grassland, bare land, cultivated land, water bodies, etc.), water system distribution, geological conditions, and water conservancy facilities. Meteorological data includes temperature, precipitation, wind speed and direction, weather types, and historical extremes.

[0039] Step two: Obtain the hydrological data, geographic data and meteorological data obtained in step one for data screening, specifically:

[0040] Visualize the hydrological data, geographic data and meteorological data, and draw a line graph to show the time series of water level, flow or precipitation over time; mark the peak point and rapid rising section in the graph; if the water level rises significantly within a few hours, highlight these time periods in red in the graph; then draw a heat map for spatial distribution analysis: for example, rainfall data is mapped to a heat map through geographic coordinates, showing the area with concentrated rainfall; mark the rainfall intensity in the graph by color depth to compare whether the strong rainfall area overlaps with the low-lying area;

[0041] Calculate statistical indicators, including basic statistics: mean, variance, extreme value; obtain obvious feature standards (obvious feature standards are determined by experts in the field combined with professional experience in hydrology, geography and meteorology to determine which values or changes can be directly defined) including: extreme points, time periods of sharp fluctuations, trends;

[0042] Hydrological data: warning water level screening: extract the time points where the water level exceeds the set warning value (such as 5 meters) and mark the obvious features; further analyze the flow or rainfall in that period for screening: screen records corresponding to time points that exceed the historical mean P times (P = 1, 2, 3, …) and mark the obvious features;

[0043] Geographical data: terrain screening: screen terrain data with a slope greater than the corresponding slope threshold (such as slope > 30°) or terrain data with an elevation less than the corresponding elevation, which may lead to flash flood risk, and mark the obvious features;

[0044] Meteorological data: extreme weather screening: screen out time points with high wind speed (such as wind speed > 10 m / s) or heavy rainfall (such as rainfall > 50 mm / h); mark the obvious features;

[0045] Further analyze the feature changes in consecutive time periods to identify potential high-risk periods: such as continuous rainfall detection: detect whether the continuous three-hour rainfall exceeds the corresponding rainfall threshold (such as cumulative 3-hour rainfall > 100 mm) and mark the obvious features; water level rapid change detection: detect time periods with rapid water level rise within a short period of time (such as water level rise > 1 meter within 1 hour) and mark the obvious features.

[0046] Step three: Obtain data corresponding to the obvious feature markers for analysis:

[0047] Obtain the corresponding time point of the data with obvious feature markers through big data, and obtain all the data at the corresponding time point; if floods, mountain torrents or other water level natural disasters have occurred at the corresponding time point, it is recorded as comparison data set 1; if floods, mountain torrents or other water level natural disasters have not occurred at the corresponding time point, it is recorded as comparison data set 2;

[0048] At each time point, the data of disasters that occurred at that time point will be filtered out from the comparison data set 1, and the mean of all comparison data sets 1 will be calculated:

[0049] If in the comparison data set 1, for a certain feature label data F, at time point t i (i=1,2,...,n) there are m i Each piece of data can be represented as a vector x ij (j=1,2,...,m i ), where x ij Contains multiple attributes (such as rainfall, water level, wind speed, etc.); calculates the time point t of all disasters i The mean of all attributes corresponding to the feature label data F is obtained by statistics; for example, for a certain attribute A (such as rainfall), the mean calculation formula is: Output attribute A mean; where A(X ij ) is the vector x ij The value of attribute A in It represents the sum of attribute A at all time points when disasters occur; Indicates the total number of data at all disaster-occurring time points;

[0050] Compare the difference between the comparison data set 1 and the comparison data set 2 to obtain the difference data set between the two; then compare the difference data set with the mean set corresponding to the data, and record the difference data set that is greater than or equal to the mean set corresponding to the data as the difference over-limit value set; record the difference data set that is less than the mean set corresponding to the data as the difference critical value set;

[0051] Step 4: Retrieve the difference data set, difference over-limit value set, and difference critical value set obtained in step 3; then classify the three to obtain the difference data set, difference over-limit value set, and difference critical value corresponding to hydrology, geography, and meteorology;

[0052] The hydrological anomaly coefficient value is obtained by analyzing the hydrological difference data set, the hydrological difference over-limit value set, and the hydrological difference critical value set. It should be noted that the hydrological difference data set includes all hydrological difference data, the hydrological difference over-limit value set includes data with hydrological difference values ​​greater than or equal to the corresponding mean, and the hydrological difference critical value set includes data with hydrological difference values ​​less than the corresponding mean.

[0053] The calculation process of the hydrological anomaly coefficient value Ks (used to quantify the degree of anomaly in hydrological data) is as follows: Output the hydrological anomaly coefficient value Ks, where Di1 is the difference value of the i1th record in the hydrological difference dataset, and Wi1 is the preset weight of the i1th record, which is usually set based on the importance or impact of the data. For example, if the data is associated with a specific warning water level, a higher weight is set; if the data reflects an extreme point or a violent fluctuation segment, a higher weight is set; n1 is the total number of records in the hydrological difference dataset;

[0054] Then calculate the contribution value of the hydrological difference over-limit value set and the hydrological difference critical value set: calculate the weighted mean of the hydrological difference over-limit value set: Output weighted over-bound mean μ 超界 ; Calculate the weighted mean of the hydrological difference critical value set: Output weighted critical mean μ 临界 If the over-limit value contributes more to the total weight, Ks tends to a larger value, indicating the degree of anomaly; if the critical value contributes more, Ks tends to a smaller value, indicating that the degree of anomaly is low or close to the mean; the hydrological anomaly coefficient value Ks = ω1×μ 超界 +ω2×μ 临界 , where ω1 and ω2 are weight factors, satisfying ω1+ω2=1, ω1 / ω2=λ, λ is the adjustment coefficient used to balance the contributions of the two parts;

[0055] The geographic anomaly coefficient value is obtained by analyzing the geographic difference data set, the geographic difference over-limit value set and the geographic difference critical value set; similarly: each record data is represented by a vector x i2 =(Si2, Hi2, Pi2...) indicates that it contains the slope value S i2 、Altitude H i2 and terrain roughness P i2 etc., and assign a weight W to each record i2 , by establishing the formula: W i2 =a1×S i2 +a2×(Hmax-H i2 )+a3×P i2 Output the weight W assigned to the i2th record i2 , where a1, a2, and a3 are preset weight coefficients reflecting the relative importance of slope, altitude, and terrain roughness (which can be adjusted according to actual conditions); Hmax is the maximum altitude of the study area;

[0056] The calculation process of the geographic anomaly coefficient value Kd (used to quantify the degree of anomaly in geographic data) is as follows: Output geographic anomaly coefficient value Kd, where is the difference value of the i2th record in the geographic difference dataset, is the absolute difference between the upper limit value and the critical value of the geographical difference, n2 is the total number of records in the geographical difference data set, λ1 is an adjustment coefficient for balancing the contributions of the two parts, and m1 is the number of overlapping records in the upper limit value set and the critical value set of the geographical difference;

[0057] The meteorological anomaly coefficient value is obtained by analyzing the meteorological difference data set, the upper limit value set and the critical value set of the meteorological difference, specifically as follows:

[0058] Through the formula: Output the meteorological anomaly coefficient value Kq, wherein is the contribution of the upper limit value part, is the difference value of the i3th record in the upper limit value set of the meteorological difference, is the logarithmic transformation of the upper limit value, which amplifies the influence of high-value abnormal points and reduces the interference of small values, W i3 is the preset weight of the i3th record, is the total weight of the upper limit value part; is the contribution of the critical value part, is the difference value of the j3th record in the critical value set of the meteorological difference, is the square root transformation of the critical value, which reduces the influence of high-value abnormal points and enhances the relative contribution of small values, W j3 is the preset weight of the j3th record, is the total weight of the critical value part, ω3 and ω4 are weight factors, satisfying ω3+ω4=1 and ω3 / ω4=λ2, λ2 is an adjustment coefficient for balancing the contributions of the two parts;

[0059] The hydrological anomaly coefficient value Ks, the geographical anomaly coefficient value Kd and the meteorological anomaly coefficient value Kq calculated are then comprehensively calculated: through the formula: SWZ=Ks×b1+Kd×b2+Kq×b3, the regional water level value SWZ is output, b1, b2 and b3 are all preset weight coefficients, and b1+b2+b3=1;

[0060] Step five: The regional water level coefficient is obtained by analyzing the hydrological data, geographical data and meteorological data collected in the corresponding region in real time, specifically as follows:

[0061] Through the formula: Output the regional water level coefficient CSZ, wherein T1 is the time series length, Q1 is the water inflow or outflow per unit time, R1 is the rainfall per unit area, K1 s is the soil permeability coefficient, S1 is the terrain slope, E1 is the evaporation amount, and A1 is the normalization coefficient for normalizing the net change to a unit area; Q1 t1 +R1 t1 -E1 t1is the net change in water volume, K1 s ×S1 is the correction of water velocity due to topography and soil;

[0062] Compare the regional water level value SWZ calculated in step 4 with the regional water level coefficient CSZ to obtain the ratio between the regional water level coefficient CSZ and the regional water level value SWZ, recorded as B; then compare the ratio B with the preset warning interval, and the preset warning intervals include warning interval one, warning interval two, and warning interval three; if the ratio B belongs to warning interval one, it is a level three warning, and a level three alarm is issued to notify relevant personnel and departments to ensure that they pay attention to water level changes and prepare to respond to possible flood risks; if the ratio B belongs to warning interval two, it is a level two warning, and a level two alarm is issued. Relevant personnel and departments should strengthen inspections and initiate emergency response measures, such as deploying flood control facilities in advance and restricting the entry and exit of people; if the ratio B belongs to warning interval three, it is a level one warning, and a level one alarm is issued. All relevant departments are immediately notified to carry out a comprehensive emergency response, including evacuation of personnel, blockade of dangerous areas, emergency rescue, etc.

[0063] In addition, it will be understood by those skilled in the art that various aspects of the present invention may be illustrated and described by a number of patentable categories or situations, including any new and useful process, machine, product or combination of substances, or any new and useful improvements thereto. Accordingly, various aspects of the present invention may be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software may be referred to as "data blocks", "modules", "engines", "units", "components" or "systems". In addition, various aspects of the present invention may be represented as a computer product located in one or more computer-readable media, which includes computer-readable program code.

[0064] Unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It should also be understood that terms such as those defined in common dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology and should not be interpreted in an idealized or highly formal sense, unless expressly defined as such herein.

[0065] The above is an illustration of the present invention and should not be considered as limiting thereof. Although several exemplary embodiments of the present invention have been described, it will be readily understood by those skilled in the art that many modifications may be made to the exemplary embodiments without departing from the novel teachings and advantages of the present invention. Therefore, all such modifications are intended to be included within the scope of the present invention as defined by the claims. It should be understood that the above is an illustration of the present invention and should not be considered as being limited to the specific embodiments disclosed, and modifications to the disclosed embodiments and other embodiments are intended to be included within the scope of the appended claims. The present invention is defined by the claims and their equivalents.

Claims

1. A water level series data monitoring and early warning method based on big data, characterized in that: include: Step 1: Obtain hydrological, geographical and meteorological data of a specific area through a big data platform; Step 2: Screen the hydrological, geographic, and meteorological data. Specifically, visualize the hydrological, geographic, and meteorological data, using line graphs to show the temporal trends of water levels and flow rates, and mark peaks and periods of sharp change. Use heat maps to display the spatial distribution of precipitation and meteorological data, checking whether heavy rainfall areas overlap with low-lying terrain. Also, calculate basic statistics and identify extreme values ​​and key characteristic points of sharp fluctuations. Step 3: Obtain the time points corresponding to the data with obvious feature markers through big data, and obtain all the data at the corresponding time points; if floods or flash floods have occurred at the corresponding time points, obtain the comparison data set 1 by statistics; if floods or flash floods do not occur at the corresponding time points, obtain the comparison data set 2 by statistics; at each time point, filter out the data of disasters that have occurred at that time point from the comparison data set 1, and calculate the mean of all the comparison data sets 1 to obtain the mean set, compare the comparison data set 1 with the comparison data set 2 to obtain the difference data set between the two; then compare the difference data set with the mean corresponding to the data, and obtain the difference over-limit value set for the difference data set that is greater than or equal to the mean corresponding to the data; and obtain the difference critical value set for the difference data set that is less than the mean corresponding to the data; Step 4: Retrieve the difference data set, difference over-limit value set, and difference critical value set obtained in step 3; then classify the three to obtain the difference data set, difference over-limit value set, and difference critical value corresponding to hydrology, geography, and meteorology; analyze the hydrological difference data set, hydrological difference over-limit value set, and hydrological difference critical value set to obtain the hydrological anomaly coefficient value; The geographic anomaly coefficient values ​​were obtained by analyzing the geographic difference data set, the geographic difference over-limit value set and the geographic difference critical value set; The meteorological anomaly coefficient values ​​are obtained by analyzing the meteorological difference data set, the meteorological difference over-limit value set and the meteorological difference critical value set; Then the hydrological anomaly coefficient value, geographical anomaly coefficient value and meteorological anomaly coefficient value are comprehensively calculated to obtain the regional water level value; Step 5: Collect hydrological data, geographic data and meteorological data in the corresponding area in real time and analyze them to obtain the regional water level coefficient; compare the obtained regional water level value with the regional water level coefficient to obtain the ratio B between the regional water level coefficient and the regional water level value, compare the ratio B with the preset warning interval, and issue a warning.

2. The water level series data monitoring and early warning method based on big data according to claim 1 is characterized in that: And calculate the mean of all comparison data sets to obtain the specific process of the mean set: If in the comparison data set 1, for a certain feature label data F, at time point t i There is m i Each piece of data represents a vector x ij , x ij Contains multiple attributes; calculates the time point t at which all disasters occur i The mean of all attributes corresponding to the feature label data F is obtained by statistics; for a certain attribute A, the mean is calculated as follows: Output the mean of attribute A A ;in, is the vector x ij The value of attribute A in It represents the sum of attribute A at all time points when disasters occur; Indicates the total number of data at all disaster occurrence points in time.

3. The water level series data monitoring and early warning method based on big data according to claim 1 is characterized in that: The specific process of analyzing the hydrological difference data set, the hydrological difference over-limit value set and the hydrological difference critical value set to obtain the hydrological anomaly coefficient value is as follows: By establishing the formula: Output the hydrological anomaly coefficient value Ks, where Di1 is the difference value of the i1th record in the hydrological difference dataset, Wi1 is the preset weight of the i1th record, and n1 is the total number of records in the hydrological difference dataset; Then calculate the contribution value of the hydrological difference over-limit value set and the hydrological difference critical value set: calculate the weighted mean of the hydrological difference over-limit value set: Output weighted over-bound mean ; Calculate the weighted mean of the hydrological difference critical value set: Output weighted critical mean ; Hydrological anomaly coefficient value Ks=ω1× +ω2× , ω1 and ω2 are weight factors, satisfying ω1+ω2=1, and ω1 / ω2=λ, λ is the adjustment coefficient.

4. The water level series data monitoring and early warning method based on big data according to claim 3 is characterized in that: The specific process of analyzing the geographic difference data set, the geographic difference over-boundary value set and the geographic difference critical value set to obtain the geographic anomaly coefficient value is as follows: Get each record data using a vector x i2 , including the slope value S i2 、Altitude H i2 and terrain roughness P i2 , assign a weight W to each record i2 , by setting up the formula: Output the weight W assigned to the i2th record i2 , a1, a2, and a3 are all preset weight coefficients, and Hmax is the maximum altitude of the study area; By establishing the formula: Output geographic anomaly coefficient value Kd, where is the difference value of the i2th record in the geographic difference dataset, is the absolute difference between the geographical difference over-limit value and the critical value, n2 is the total number of records in the geographical difference data set, λ1 is the adjustment coefficient, and m1 is the number of overlapping records between the geographical difference over-limit value set and the critical value set.

5. The water level series data monitoring and early warning method based on big data according to claim 4 is characterized in that: The specific process of analyzing the meteorological difference data set, the meteorological difference over-limit value set and the meteorological difference critical value set to obtain the meteorological anomaly coefficient value is as follows: By establishing the formula: Output meteorological anomaly coefficient value Kq, Contribute to the part that exceeds the threshold value, is the difference value of the i3th record in the meteorological difference exceeding threshold value set, To perform logarithmic transformation on over-limit values, W i3 The preset weight of the i3th record, is the total weight of the part exceeding the threshold; Contribute to the critical value part, is the difference value of the j3th record in the meteorological difference critical value set, To perform square root transformation on the critical value, W j3 The preset weight of the j3th record, is the total weight of the critical value part, ω3 and ω4 are weight factors, satisfying ω3+ω4=1, ω3 / ω4=λ2, λ2 is the adjustment coefficient.

6. The water level series data monitoring and early warning method based on big data according to claim 1 is characterized in that: The specific process of collecting hydrological data, geographic data and meteorological data in the corresponding area in real time and analyzing them to obtain the regional water level coefficient is as follows: By formula: Output area water level coefficient CSZ, T1 time series length, Q1 is the amount of water flowing in or out per unit time, R1 is the rainfall per unit area, K1 s is the soil permeability coefficient, S1 is the terrain slope, E1 is the evaporation, and A1 is the normalization coefficient, which normalizes the net change to unit area; is the net change in water volume, Correction for water velocity due to topography and soils.

7. The water level series data monitoring and early warning method based on big data according to claim 1 is characterized in that: The specific process of comparing the obtained regional water level value with the regional water level coefficient is as follows: Compare the regional water level value SWZ calculated in step 4 with the regional water level coefficient CSZ to obtain the ratio between the regional water level coefficient CSZ and the regional water level value SWZ, recorded as B; then compare the ratio B with the preset warning interval, which includes warning interval 1, warning interval 2, and warning interval 3; if the ratio B falls within warning interval 1, it is a level 3 warning, and a level 3 alert is issued to notify relevant personnel and departments to ensure that they pay attention to water level changes and prepare to respond to possible flood risks; if the ratio B falls within warning interval 2, it is a level 2 warning, and a level 2 alert is issued. Relevant personnel and departments should strengthen inspections and initiate emergency response measures; If the ratio B falls into warning range three, it is a level one warning, a level one alert is issued, and all relevant departments are immediately notified to conduct a comprehensive emergency response.

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