Water conservancy monitoring data dynamic early warning method and system, electronic equipment and storage medium

Abstract

The invention relates to the technical field of water conservancy data monitoring and early warning, and discloses a water conservancy monitoring data dynamic early warning method and system, electronic equipment and a storage medium, and the method comprises the steps: collecting water level data and rainfall data of a reservoir or a river channel in real time, and calculating a sliding window difference value of the water level and the rainfall; setting a basic early warning threshold value of the water level according to the sliding window difference value; according to a flood season and a non-flood season, combining historical data and water level data and rainfall data which are acquired in real time, and dynamically adjusting an early warning threshold value by adopting a regression algorithm model based on a support vector machine; and performing fuzzification processing on the water level data and the rainfall data which are acquired in real time and the adjusted water level height early warning threshold, making a decision according to a fuzzy rule, and judging the type of released early warning. By introducing the decision algorithm based on the regression algorithm model and the fuzzy logic, the dynamic adjustment of the early warning threshold and the intelligence of the early warning decision are realized, the accuracy and adaptability of the early warning system are improved, and the false alarm and missing alarm conditions are reduced.

Description

Technical Field

[0001] The present application relates to the technical field of water conservancy data monitoring and early warning, and in particular to a water conservancy monitoring data dynamic early warning method and system, electronic equipment, and storage medium. Background Art

[0002] As an important tool for water management and disaster warning, the existing water data monitoring system usually relies on various sensors and monitoring equipment to collect key hydrological parameters such as water level, rainfall, flow and water quality in real time or regularly. These systems have improved the efficiency and quality of hydrological data collection to a certain extent, and provided valuable decision-making support for water management departments. However, with the continuous improvement of water management needs and the rapid development of technology, the existing system has gradually exposed a series of problems.

[0003] Specifically, the existing water conservancy data monitoring system has the problem of insufficient timeliness in data collection. Due to limitations in sensor performance, data transmission network or equipment maintenance, some data cannot be collected in real time, resulting in a certain time difference between the monitoring data and the actual hydrological situation. This not only affects the real-time nature of the data, but may also prevent decision makers from obtaining the latest hydrological information at critical moments.

[0004] In addition, the existing system also has obvious shortcomings in data processing capabilities. With the continuous increase in hydrological monitoring points and the increase in monitoring frequency, the amount of data that the system needs to process has increased dramatically. However, the data processing capabilities of many existing systems have not kept up with this trend, resulting in low data processing efficiency and even possible data loss or processing errors. This not only affects the accuracy and completeness of the data, but also limits the application value of the data in water management.

[0005] In terms of early warning mechanisms, the existing systems also have imperfections. At present, many systems still use fixed early warning thresholds for early warning, and cannot dynamically adjust the early warning thresholds based on real-time hydrological data. This static early warning mechanism cannot adapt to the complex and changing hydrological environment, resulting in the accuracy and timeliness of early warnings being affected. Especially in emergency situations such as extreme weather or sudden floods, if the early warning system cannot issue early warning information in a timely and accurate manner, it will pose a serious threat to the safety of people's lives and property.

[0006] In summary, the existing water conservancy data monitoring system has obvious deficiencies in data collection, data processing and early warning mechanism. Among them, the imperfection of the early warning mechanism is particularly prominent, because the early warning system is an important part of water conservancy management, and its accuracy and timeliness are directly related to the safety of people's lives and property. Summary of the invention

[0007] In view of this, the purpose of the embodiments of the present application is to provide a dynamic early warning method and system for water conservancy monitoring data, an electronic device, and a storage medium to solve the obvious deficiencies of existing water conservancy data monitoring systems in terms of data collection, data processing, and early warning mechanisms.

[0008] According to a first aspect of an embodiment of the present application, a method for dynamic early warning of water conservancy monitoring data is provided, comprising: Collect water level data and rainfall data of reservoirs or rivers in real time; Calculate the sliding window difference of water level and rainfall based on the real-time collected water level data and rainfall data; According to the sliding window difference and expert experience, a basic warning threshold of the water level is set; According to the flood season and the non-flood season, combined with historical data and real-time collected water level data and rainfall data, a regression algorithm model based on a support vector machine is used to dynamically adjust the warning threshold, specifically including: first, extracting the water level data and rainfall data of the flood season and the non-flood season from the historical data and performing data cleaning to form a training set; then, combining the initial warning level provided by the basic warning threshold, using the regression algorithm model to train the training set, obtain the dynamic adjustment parameters of the warning threshold, and thus obtain a trained regression algorithm model; finally, inputting the real-time data into the trained regression algorithm model to obtain the dynamically adjusted water level height warning threshold; The real-time collected water level data, rainfall data and the adjusted water level height warning threshold are fuzzy processed, and decisions are made based on fuzzy rules to determine the type of warning to be issued.

[0009] According to a second aspect of an embodiment of the present application, a water conservancy monitoring data dynamic early warning system is provided, comprising: The collection module is used to collect water level data and rainfall data of reservoirs or rivers in real time; A calculation module is used to calculate the sliding window difference of water level and rainfall according to the real-time collected water level data and rainfall data; A setting module, used for setting a basic warning threshold of the water level according to the sliding window difference and expert experience; The dynamic threshold module is used to dynamically adjust the warning threshold according to the flood season and non-flood season, combined with historical data and real-time collected water level data and rainfall data, using a regression algorithm model based on a support vector machine. Specifically, it includes: first, extracting the water level data and rainfall data of the flood season and non-flood season from the historical data and performing data cleaning to form a training set; then, combining the initial warning water level provided by the basic warning threshold, using the regression algorithm model to train the training set, and obtaining the dynamic adjustment parameters of the warning threshold, thereby obtaining a trained regression algorithm model; finally, inputting the real-time data into the trained regression algorithm model to obtain the dynamically adjusted water level height warning threshold; The early warning module is used to perform fuzzy processing on the real-time collected water level data, rainfall data and the adjusted water level height early warning threshold, make decisions based on fuzzy rules, and determine the type of early warning to be issued.

[0010] According to a third aspect of an embodiment of the present application, there is provided an electronic device, including: one or more processors; A memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in the first aspect.

[0011] According to a third aspect of an embodiment of the present application, a computer-readable storage medium is provided, on which computer instructions are stored. When the instructions are executed by a processor, the steps of the method described in the first aspect are implemented.

[0012] The technical solution provided by the embodiments of the present application may have the following beneficial effects: It can be seen from the above embodiments that the water level data and rainfall data collected in real time by the present invention provide real-time monitoring capabilities for water conservancy safety. According to the water level data and rainfall data collected in real time, the sliding window difference of the water level and rainfall is calculated; according to the sliding window difference, the basic warning threshold of the water level is set; according to the flood season and the non-flood season, the warning threshold is dynamically adjusted by combining historical data and the water level data and rainfall data collected in real time with the regression algorithm model based on the support vector machine to adapt to different environmental and condition changes. The real-time collected water level data, rainfall data and the adjusted water level height warning threshold are fuzzy processed, and decisions are made according to fuzzy rules to determine the type of warning to be issued. The present application can automatically adjust the warning threshold according to the hydrological data monitored in real time, and issue water level abnormality warning, rainfall abnormal value warning and rainfall mutation warning based on the decision algorithm of fuzzy logic, thereby improving the accuracy and timeliness of the warning. Through this improvement, the present invention aims to solve the problem that the existing system cannot adapt to the complex and changeable hydrological environment, and provide more scientific and effective decision support for water conservancy management departments.

[0013] This application realizes the dynamic adjustment of warning thresholds and intelligent warning decisions by introducing a regression algorithm model based on SVM and a decision algorithm based on fuzzy logic, thereby improving the accuracy and adaptability of the warning system and reducing false alarms and missed alarms.

[0014] In addition, the present invention also has an intelligent early warning decision-making function. It can not only monitor data, but also perform fuzzy processing on the real-time collected water level data, rainfall data, and the adjusted water level height early warning threshold, make decisions based on fuzzy rules, and determine the type of early warning to be issued. The intelligent decision-making process reduces the need for human intervention and reduces the risk of false alarms and missed alarms caused by operational errors. By reducing false alarms and missed alarms, the present invention can improve the work efficiency of water safety managers, allowing them to focus on issues that really need attention.

[0015] In summary, the present invention significantly improves the accuracy and efficiency of the early warning system through real-time monitoring and intelligent processing of water conservancy data. It provides more effective protection for water conservancy safety, ensures the rational use of water resources and the efficient implementation of disaster prevention and mitigation work. By reducing false alarms and missed alarms, the present invention can also save resources and avoid unnecessary emergency responses and economic losses. In short, the present invention has significant beneficial effects in the field of water conservancy safety and has important practical application value for improving the level of water conservancy management and ensuring public safety.

[0016] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0018] Figure 1 The present invention is a flow chart showing a method for dynamic early warning of water conservancy monitoring data according to an exemplary embodiment.

[0019] Figure 2 It is a block diagram of a dynamic early warning system for water conservancy monitoring data according to an exemplary embodiment.

[0020] Figure 3 The diagram is a schematic structural diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0021] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of systems and methods consistent with some aspects of the present application as detailed in the appended claims.

[0022] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0023] Figure 1 is a flow chart of a method for dynamic early warning of water conservancy monitoring data according to an exemplary embodiment. Figure 1 As shown, the method is applied in a terminal and may include the following steps: Step 1: Collect water level data and rainfall data of reservoirs or rivers in real time.

[0024] Specifically, sensors are used to collect real-time water level data and rainfall data every minute, thus providing real-time monitoring capabilities for water conservancy safety.

[0025] Step 2: Calculate the sliding window difference of water level and rainfall based on the real-time collected water level data and rainfall data.

[0026] Specifically, the sliding window size is set to n minutes; based on the water level data collected in real time, the sliding window difference of the water level is calculated, and the water level sliding window difference is the difference between the current water level and the water level in the previous n minutes; based on the rainfall data collected in real time, the sliding window difference of the rainfall is calculated, and the rainfall sliding window difference is the difference between the current rainfall and the rainfall in the previous n minutes.

[0027] In this embodiment, the sliding window size is set to 5 minutes (that is, the collection interval is not limited to 5 minutes and can be set by yourself). For example, the water level sliding window difference is the difference between the current water level and the water level 5 minutes ago, and the rainfall sliding window difference is the difference between the current rainfall and the rainfall 5 minutes ago.

[0028] Step 3: According to the sliding window difference and expert experience, a basic warning threshold of the water level is set.

[0029] Specifically, assuming that the water level sliding window difference is the difference between the current water level and the water level in the previous 5 minutes, the basic warning threshold of the water level will be set based on these differences and expert experience. For example, the suspicious threshold can be set to a sliding window difference of 0.6m to 1m, and the error threshold is a sliding window difference of more than 1m.

[0030] The basic warning value here is a combination of the sliding window difference of real-time data and the warning value set by expert experience. It is used as a reference value for subsequent model calculations and can more accurately set the subsequent dynamic warning threshold. The sliding window difference can capture the short-term trend of water level changes, while expert experience provides insights into historical data and long-term trends. This combination ensures that the warning threshold can respond to immediate hydrological changes while taking into account long-term seasonal and cyclical changes.

[0031] Step 4: According to the flood season and non-flood season, combined with the historical data after data cleaning and the water level data and rainfall data collected in real time, a regression algorithm model based on support vector machine (SVM) is used to dynamically adjust the warning threshold.

[0032] Specifically, firstly, water level data and rainfall data in flood season and non-flood season are extracted from historical data, and data cleaning is performed, including filtering and outlier detection, to form a training set; then, in combination with the initial warning level provided by the basic warning threshold, the training set is trained using a regression algorithm model to obtain dynamic adjustment parameters of the warning threshold, thereby obtaining a trained regression algorithm model; finally, real-time data is input into the trained regression algorithm model to obtain a dynamically adjusted water level height warning threshold.

[0033] Algorithm formula: SVM regression algorithm model:

[0034] Among them, αi is the weight corresponding to the support vector, Ki(x, xi) is the kernel function (Gaussian kernel function), and b is the bias term.

[0035] For example, a flood warning system needs to be designed for a city in the Yangtze River Basin. The system needs to dynamically adjust the warning threshold according to water level and rainfall data during the flood season and non-flood season to ensure that an alarm is issued in time before a flood occurs.

[0036] Data parameters: (1) Flood season: April to October every year.

[0037] (2) Non-flood season: January to March and November to December every year.

[0038] (3) Historical data: Contains water level and rainfall data from the past five years, recorded every minute, and the corresponding historical warning thresholds.

[0039] (4) Real-time data: current water level and rainfall data every minute, as well as calculation of 5-minute window water level difference and rainfall difference.

[0040] The specific implementation steps of step 4 are described in detail as follows: 1. Data extraction and cleaning: (1) Extract the flood season and non-flood season water level and rainfall data for the past five years from the historical database.

[0041] (2) Use filtering and outlier detection to clean the data and form a training set.

[0042] (3) Ensure that the dataset contains enough sample points, for example, 1000 sample points are extracted during the flood season and 1000 sample points are extracted during the non-flood season.

[0043] 2. SVM regression algorithm model training: (1) Use the extracted data set to train the SVM regression algorithm model.

[0044] The Gaussian kernel function (RBF) is selected as the kernel function, that is, , where γ is the kernel function parameter.

[0045] (2) Select the optimal model parameters, including the penalty parameter C and the kernel function parameter γ, through methods such as cross-validation.

[0046] Select appropriate penalty parameters C and kernel function parameters γ for different combinations to obtain the parameter configuration with the highest accuracy and the strongest generalization ability. The penalty parameters here are only used in the training and verification process of the model.

[0047] (3) After training is completed, the weight αi and bias term b corresponding to the support vector are obtained.

[0048] The dynamic adjustment parameters mentioned above include the penalty parameter C, the kernel function parameter γ, the weight αi and the bias term b.

[0049] 3. Dynamically adjust the warning threshold: (1) Input real-time data into the trained SVM regression algorithm model.

[0050] (2) Calculate the current warning threshold, which is the predicted value output by the model.

[0051] (3) Determine whether a warning needs to be issued based on the comparison between the current warning threshold and the actual 5-minute window water level difference data.

[0052] Assuming that the 5-minute window water level warning threshold for the current flood season is 0.67 meters through the SVM model, and the real-time monitoring data shows that the current 5-minute window water level difference is 0.51 meters, the system will not issue a warning. If the real-time 5-minute window water level difference reaches or exceeds 0.67 meters, the system will issue a corresponding alarm according to the set warning mechanism.

[0053] Algorithm formula application examples: Assume that the trained SVM model output is:

[0054] Among them, x1, x2, x3 are support vectors, K is the Gaussian kernel function, and b is the bias term.

[0055] When the real-time data "x real-time" is input, the calculation is:

[0056] According to the calculation results, if f(x real-time) is greater than or equal to the set warning threshold, an early warning is issued.

[0057] The SVM regression algorithm model calculates and outputs the current warning threshold based on the input real-time 5-minute window difference data and the flood season and non-flood season characteristics in the historical data. This warning threshold is obtained by the model based on a comprehensive analysis of historical data and real-time data, and can reflect the current water conditions and seasonal characteristics.

[0058] Early warning judgment: The system compares the calculated early warning threshold with the real-time 5-minute window water level difference data. If the real-time 5-minute window water level difference reaches or exceeds the early warning threshold, the system will automatically trigger the early warning mechanism and issue a corresponding alarm. At the same time, the system can also predict future water conditions based on the changing trend of the early warning threshold.

[0059] To explain the calculation process of f(x real time) more specifically, assume the following data: (1) The specific values ​​of the support vectors x1, x2, and x3 are (5-minute window water level difference: 0.49 m, rainfall: 50 mm), (5-minute window water level difference: 0.51 m, rainfall: 75 mm), and (5-minute window water level difference: 0.54 m, rainfall: 100 mm).

[0060] (2) The Gaussian kernel function parameter γ is 0.1.

[0061] (3) The bias term b is 0.5.

[0062] When the input real-time data "x real-time" is (water level: 0.43 meters, rainfall: 60 mm), it can be calculated:

[0063] in, Represents the square of the Euclidean distance between vector (a, b) and vector (c, d).

[0064] In this way, a specific f(xrealtime) value can be obtained. If the calculated result is 0.6 meters (this value is only an example, the actual result will vary depending on the model parameters and data), and the current real-time 5-minute window water level is 0.43 meters, then the system will not issue an early warning because f(xrealtime) (0.6 meters) is greater than the real-time data (0.43 meters). However, if the real-time water level continues to rise and reaches or exceeds 0.6 meters, the system will issue a corresponding alarm according to the set early warning mechanism.

[0065] The system can dynamically adjust the warning threshold based on real-time water level and rainfall data and the flood and non-flood season characteristics of historical data, thereby ensuring that alarms are issued in time before floods occur and providing strong technical support for flood prevention work.

[0066] Step 5: Fuzzy the water level data and rainfall data collected in real time, make decisions based on fuzzy rules, and determine the type of warning to be issued.

[0067] The fuzzy processing in this embodiment adopts Gaussian function, and the fuzzy rules are set according to historical data and expert experience.

[0068] Algorithm formula: Fuzzification: μA(x) = exp(-((x - μA)² / (2σA²))) Among them, μA is the mean of fuzzy set A, and σA is the standard deviation of fuzzy set A. Specific embodiment: Suppose you are designing a flood warning system for a city in the Yangtze River Basin. The system needs to dynamically adjust the warning threshold based on water level and rainfall data during the flood season and non-flood season to ensure timely warnings before floods occur. In step 4, the warning threshold has been dynamically adjusted using the SVM regression algorithm model. Now, based on the adjusted dynamic adjustment warning threshold, combined with the fuzzy logic-based decision algorithm, water level anomaly warnings, rainfall anomaly warnings, and rainfall mutation warnings are issued.

[0070] Data parameters: (1) Flood season: June to September every year.

[0071] (2) Non-flood season: January to May and October to December every year.

[0072] (3) Historical data: Contains water level and rainfall data from the past five years, recorded every minute.

[0073] (4) Data collected in real time: current water level and rainfall data every minute, as well as calculation of water level difference and rainfall difference in a 5-minute window.

[0074] The specific implementation of step 5 is as follows: (1) Fuzzy processing: 1) Fuzzify the water level data and rainfall data collected in real time. The mean and standard deviation of the fuzzy set are set based on historical data and expert experience, and can be adjusted according to actual hydrological conditions. Specifically, assume that the fuzzy set we set includes three fuzzy sets: "low", "medium", and "high".

[0075] 2) For the water level data, the means and standard deviations of the three fuzzy sets are set as follows: Low water level (A1): mean μA1 = 0.35 m, standard deviation σA1 = 0.05 m; Medium water level (A2): mean μA2 = 0.45 m, standard deviation σA2 = 0.05 m; High water level (A3): mean μA3 = 0.55 m, standard deviation σA3 = 0.05 m; 3) For rainfall data, the means and standard deviations of the three fuzzy sets are set as follows: Light rainfall (B1): mean μB1 = 10 mm, standard deviation σB1 = 5 mm; Moderate rainfall (B2): mean μB2 = 30 mm, standard deviation σB2 = 5 mm; Heavy rainfall (B3): mean μB3 = 50 mm, standard deviation σB3 = 5 mm; (2) Fuzzy rule setting: 1) Based on historical data and expert experience, the following fuzzy rules are set: For the water level and rainfall data of the same measuring station, if the water level is "high" and the rainfall is "heavy", an abnormal water level warning will be issued.

[0076] If the rainfall is "heavy" and lasts for more than 2 hours, an abnormal rainfall warning will be issued.

[0077] If the rainfall suddenly changes from "light" to "heavy" within 1 hour, a rainfall mutation warning will be issued.

[0078] Table 1 Fuzzy rule setting example reference table:

[0079] (3) Decision-making algorithm: 1) Perform fuzzy processing on real-time data to obtain the membership degree of each data point to the fuzzy set.

[0080] 2) Make decisions based on the set fuzzy rules and membership.

[0081] 3) If the decision result meets the warning conditions, the corresponding warning information will be issued.

[0082] For example, assuming that the current real-time water level is 0.52 meters and the real-time rainfall is 45 millimeters, the membership of the water level and rainfall fuzzy sets are calculated according to the fuzzy processing formula: Table 2 Reference table of water level fuzzy processing embodiment:

[0083] Table 3 Reference table of rainfall fuzzy processing embodiment:

[0084] According to the set fuzzy rules, decisions are made in combination with the membership. Specifically, if the membership of the "high" fuzzy set of water level data exceeds the membership of other sets, and the membership of the "large" fuzzy set of rainfall data also exceeds the membership of other sets, then a water level abnormality warning is issued. Similarly, other types of warnings can be judged based on the specific values ​​of fuzzy rules and membership.

[0085] Table 4 Reference table of intelligent decision-making implementation examples:

[0086] In this way, the flood warning system can release corresponding warning information in a timely manner based on real-time data and dynamically adjusted warning thresholds, combined with fuzzy logic decision-making algorithms, thereby effectively preventing flood disasters.

[0087] It can be seen from the above embodiments that the water level data and rainfall data collected in real time by the present invention provide real-time monitoring capabilities for water conservancy safety. According to the water level data and rainfall data collected in real time, the sliding window difference of the water level and rainfall is calculated; according to the sliding window difference, the basic warning threshold of the water level is set; according to the flood season and the non-flood season, the warning threshold is dynamically adjusted by combining historical data and the water level data and rainfall data collected in real time, so as to adapt to different environmental and condition changes. The water level data and rainfall data collected in real time and the adjusted water level height warning threshold are fuzzy processed, and decisions are made according to fuzzy rules to determine the type of warning to be issued. The present application can automatically adjust the warning threshold according to the hydrological data monitored in real time, and issue water level abnormality warning, rainfall abnormal value warning and rainfall mutation warning based on the decision algorithm of fuzzy logic, thereby improving the accuracy and timeliness of the warning. Through this improvement, the present invention aims to solve the problem that the existing system cannot adapt to the complex and changeable hydrological environment, and provide more scientific and effective decision support for water conservancy management departments.

[0088] This application realizes the dynamic adjustment of warning thresholds and intelligent warning decisions by introducing a regression algorithm model based on SVM and a decision algorithm based on fuzzy logic, thereby improving the accuracy and adaptability of the warning system and reducing false alarms and missed alarms.

[0089] Corresponding to the aforementioned embodiment of the method for dynamic early warning of water conservancy monitoring data, the present application also provides an embodiment of a dynamic early warning system for water conservancy monitoring data.

[0090] Figure 2 FIG. 1 is a block diagram of a water conservancy monitoring data dynamic early warning system according to an exemplary embodiment. Figure 2 The system comprises: Collection module 1, used for collecting water level data and rainfall data of reservoirs or rivers in real time; Calculation module 2, used to calculate the sliding window difference of water level and rainfall according to the real-time collected water level data and rainfall data; A setting module 3 is used to set a basic warning threshold of the water level according to the sliding window difference and expert experience; The dynamic threshold module 4 is used to dynamically adjust the warning threshold according to the flood season and non-flood season, combined with historical data and real-time collected water level data and rainfall data, using a regression algorithm model based on a support vector machine, specifically including: first, extracting the flood season and non-flood season water level data and rainfall data from the historical data, and performing data cleaning to form a training set; then, combining the initial warning water level provided by the basic warning threshold, using the regression algorithm model to train the training set, and obtain the dynamic adjustment parameters of the warning threshold, thereby obtaining a trained regression algorithm model; finally, inputting the real-time data into the trained regression algorithm model to obtain the dynamically adjusted water level height warning threshold; The early warning module 5 is used to perform fuzzy processing on the real-time collected water level data, rainfall data and the adjusted water level height early warning threshold, make decisions based on fuzzy rules, and determine the type of early warning to be issued.

[0091] Regarding the system in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0092] For the system embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The system embodiment described above is only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present application. A person of ordinary skill in the art can understand and implement it without creative work.

[0093] Accordingly, the present application also provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned dynamic early warning method for water conservancy monitoring data. Figure 3 As shown in FIG. 1 , a hardware structure diagram of a water conservancy monitoring data dynamic early warning system provided by an embodiment of the present invention is provided for any device with data processing capability, except Figure 3 In addition to the processor and memory shown, any device with data processing capability in which the system in the embodiment is located may also include other hardware according to the actual functions of the device with data processing capability, which will not be described in detail.

[0094] Accordingly, the present application also provides a computer-readable storage medium on which computer instructions are stored, and when the instructions are executed by the processor, the dynamic early warning method for water conservancy monitoring data as described above is implemented. The computer-readable storage medium can be an internal storage unit of any device with data processing capabilities described in any of the aforementioned embodiments, such as a hard disk or a memory. The computer-readable storage medium can also be an external storage device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), an SD card, a flash card (Flash Card), etc. equipped on the device. Furthermore, the computer-readable storage medium can also include both an internal storage unit and an external storage device of any device with data processing capabilities. The computer-readable storage medium is used to store the computer program and other programs and data required by any device with data processing capabilities, and can also be used to temporarily store data that has been output or is to be output.

[0095] Those skilled in the art will readily appreciate other embodiments of the present application after considering the description and practicing the contents disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The description and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the claims.

[0096] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A dynamic early warning method for water conservancy monitoring data, characterized in that: include: Collect water level data and rainfall data of reservoirs or rivers in real time; Calculate the sliding window difference of water level and rainfall based on the real-time collected water level data and rainfall data; According to the sliding window difference and expert experience, a basic warning threshold of the water level is set; According to the flood season and the non-flood season, combined with historical data and real-time collected water level data and rainfall data, a regression algorithm model based on a support vector machine is used to dynamically adjust the warning threshold, specifically including: first, extracting the water level data and rainfall data of the flood season and the non-flood season from the historical data and performing data cleaning to form a training set; then, combining the initial warning water level provided by the basic warning threshold, using the regression algorithm model to train the training set, obtain the dynamic adjustment parameters of the warning threshold, and thus obtain a trained regression algorithm model; finally, inputting the real-time data into the trained regression algorithm model to obtain the dynamically adjusted water level height warning threshold; The real-time collected water level data, rainfall data and the adjusted water level height warning threshold are fuzzy processed, and decisions are made based on fuzzy rules to determine the type of warning to be issued.

2. The method according to claim 1, characterized in that Based on the real-time collected water level data and rainfall data, the sliding window difference of water level and rainfall is calculated, including: Set the sliding window size to n minutes; Calculate the sliding window difference of the water level based on the water level data collected in real time, where the sliding window difference of the water level is the difference between the current water level and the water level in the previous n minutes; According to the real-time collected rainfall data, the sliding window difference of rainfall is calculated, and the sliding window difference of rainfall is the difference between the current rainfall and the rainfall in the previous n minutes.

3. The method according to claim 1, characterized in that The regression algorithm model is as follows: Among them, αi is the weight corresponding to the support vector, Ki(x, xi) is the kernel function, x is the data point to be predicted, xi is the support vector in the training set, and b is the bias term.

4. The method according to claim 1, characterized in that: The training process of the regression algorithm model is as follows: (1) Extracting flood season and non-flood season water level and rainfall data from the historical database as training sets, and using the data sets to train the regression algorithm model; in the training, the Gaussian kernel function (RBF) is selected as the kernel function, that is, , where γ is the kernel function parameter; (2) Select the best model parameters through cross-validation method; (3) After training is completed, the weight αi and bias term b corresponding to the support vector are obtained.

5. The method according to claim 1, characterized in that The fuzzification process comprises: 1) Perform fuzzy processing on the water level and rainfall data in real-time data, and set the fuzzy sets including "low", "medium" and "high"; 2) For the water level data, the means and standard deviations of the three fuzzy sets are set as follows: Low water level (A1): mean μA1 = 0.35 m, standard deviation σA1 = 0.05 m; Medium water level (A2): mean μA2 = 0.45 m, standard deviation σA2 = 0.05 m; High water level (A3): mean μA3 = 0.55 m, standard deviation σA3 = 0.05 m; 3) For rainfall data, the means and standard deviations of the three fuzzy sets are set as follows: Light rainfall (B1): mean μB1 = 10 mm, standard deviation σB1 = 5 mm; Moderate rainfall (B2): mean μB2 = 30 mm, standard deviation σB2 = 5 mm; Heavy rainfall (B3): mean μB3 = 50 mm, standard deviation σB3 = 5 mm.

6. The method according to claim 5, characterized in that The fuzzy rule setting: If the water level is "high" and the rainfall is "heavy", an abnormal water level warning is issued; If the rainfall is "heavy" and lasts for more than 2 hours, an abnormal rainfall warning will be issued; If the rainfall suddenly changes from "light" to "heavy" within 1 hour, a sudden rainfall change warning will be issued.

7. The method according to claim 1, characterized in that The real-time collected water level data, rainfall data and adjusted water level height warning threshold are fuzzy processed, and decisions are made based on fuzzy rules to determine the type of warning to be issued, including: 1) Perform fuzzy processing on real-time data to obtain the membership degree of each data point to the fuzzy set; 2) Make decisions based on the set fuzzy rules and membership; 3) If the decision result meets the warning conditions, the corresponding warning information will be issued.

8. A dynamic early warning system for water conservancy monitoring data, characterized in that: include: The collection module is used to collect water level data and rainfall data of reservoirs or rivers in real time; A calculation module is used to calculate the sliding window difference of water level and rainfall according to the real-time collected water level data and rainfall data; A setting module, used for setting a basic warning threshold of the water level according to the sliding window difference and expert experience; The dynamic threshold module is used to dynamically adjust the warning threshold according to the flood season and non-flood season, combined with historical data and real-time collected water level data and rainfall data, using a regression algorithm model based on a support vector machine. Specifically, it includes: first, extracting the water level data and rainfall data of the flood season and non-flood season from the historical data and performing data cleaning to form a training set; then, combining the initial warning water level provided by the basic warning threshold, using the regression algorithm model to train the training set, and obtaining the dynamic adjustment parameters of the warning threshold, thereby obtaining a trained regression algorithm model; finally, inputting the real-time data into the trained regression algorithm model to obtain the dynamically adjusted water level height warning threshold; The early warning module is used to perform fuzzy processing on the real-time collected water level data, rainfall data and the adjusted water level height early warning threshold, make decisions based on fuzzy rules, and determine the type of early warning to be issued.

9. An electronic device, characterized in that: include: one or more processors; A memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having computer instructions stored thereon, characterized in that: When the instruction is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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