LSSVM-based salt tide forecasting method and system, and medium

Abstract

The invention provides an LSSVM-based salt tide forecasting method and system and a medium, and the method comprises the steps: obtaining real-time monitoring data in a monitoring region, carrying out the preprocessing of the monitoring data, and obtaining optimized data; calculating a forecast section diameter tide influence phase difference based on the optimization data to obtain analysis data; constructing an LSSVM model, and transmitting the analysis data to the LSSVM model to obtain a forecast result; evaluating the forecast result based on an evaluation rule to obtain an evaluation result; dynamically adjusting the forecasting result based on the evaluation result to obtain new forecasting information, and transmitting the new forecasting information to the terminal in real time; the monitoring data of the monitoring area is analyzed through the LSSVM model, so that the salt tide forecasting result is accurately analyzed, the salt tide forecasting result is evaluated, the forecasting result is dynamically adjusted, and the forecasting precision is improved.

Description

Technical Field

[0001] The present application relates to the technical field of saltwater tide warning, and in particular to a saltwater tide forecasting method, system and medium based on LSSVM. Background Art

[0002] Salt tide forecast is to forecast the chlorine content in the estuary area. The correct forecast of salt tide in the estuary area plays an important role in safeguarding the estuary ecosystem and the normal water intake in the estuary area.

[0003] For example, patent CN117113854A states that the main method currently used to forecast saltwater tides in estuary areas is numerical simulation, which is performed by establishing a one-dimensional or multi-dimensional numerical model for calculation and forecasting. Numerical simulation requires a large amount of terrain, hydrology, tides, salinity, etc. as a basis, and a certain amount of manpower and material resources are required for model calibration.

[0004] One-dimensional or multi-dimensional numerical model forecasting methods are cumbersome to operate, inefficient, and require a lot of data, which is not conducive to the calculation of saltwater tide forecasts. In addition, it is difficult to accurately evaluate the saltwater tide forecast results based on the monitoring data of the monitoring area in the existing saltwater tide forecasting methods, resulting in abnormal evaluation results and affecting the accuracy of the saltwater tide forecast. Summary of the invention

[0005] The purpose of this application is to provide a saltwater tide forecasting method, system and medium based on LSSVM, analyze the monitoring data of the monitoring area through the LSSVM model, so as to accurately analyze the saltwater tide forecast results, evaluate the saltwater tide forecast results, dynamically adjust the forecast results, and improve the forecast accuracy.

[0006] The present application also provides a saltwater tide forecasting method based on LSSVM, including: Obtain real-time monitoring data within the monitoring area, pre-process the monitoring data, and obtain optimized data; Calculate the phase difference of the impact of runoff tide on the forecast section based on the optimized data to obtain the analysis data; Construct the LSSVM model, transfer the analysis data to the LSSVM model, and obtain the forecast results; Evaluate the forecast result based on the evaluation rule to obtain an evaluation result; The forecast results are dynamically adjusted based on the evaluation results to obtain new forecast information, which is then transmitted to the terminal in real time.

[0007] Optionally, in the saltwater tide forecasting method based on LSSVM described in the present application, real-time monitoring data in the monitoring area is obtained, and the monitoring data is preprocessed to obtain optimized data; specifically including: Set up a monitoring area and set up a number of monitoring points within the monitoring area; Acquire real-time monitoring data of each monitoring point, the monitoring data including upstream flow data, downstream tide level data, downstream tidal range data, regional wind speed data, wind direction data and salinity data of the forecast section; Analyze the real-time monitoring data of each monitoring point, clean the real-time monitoring data of the same monitoring point, and obtain cleaned monitoring data; Analyze the monitoring data after cleaning to see whether it meets the set condition information; If the set condition information is met, the optimized data is obtained based on the cleaned monitoring data; If the set condition information is not met, the cleaning parameters are adjusted and the real-time monitoring data is cleaned again.

[0008] Optionally, in the saltwater tide forecasting method based on LSSVM described in the present application, the phase difference of the radial tide influence of the forecast section is calculated based on the optimized data to obtain the analysis data, which specifically includes: Obtain optimized data, and analyze the phase difference of the impact of the runoff tide on the forecast section based on the optimized data; Compare the phase difference of the impact of the runoff tide on the forecast section with the set phase difference threshold to obtain the deviation rate; Determining whether the deviation rate is greater than or equal to a set deviation rate threshold; If it is greater than or equal to the set deviation rate threshold, feedback information is generated, and the phase difference of the impact of runoff on the forecast section is adjusted based on the feedback information; If it is less than the set deviation rate threshold, the analysis data is obtained.

[0009] Optionally, in the saltwater tide forecasting method based on LSSVM described in the present application, an LSSVM model is constructed, and the analysis data is transmitted to the LSSVM model to obtain a forecast result, which specifically includes: Obtain historical monitoring data and historical forecast results based on big data, and generate training sets based on historical monitoring data and historical forecast results; Iteratively train the initial model based on the training set to obtain the training results; Determining whether the training result converges; If converged, the final LSSVM model is generated, and the prediction results are obtained based on the final LSSVM model; If it does not converge, the model parameters are dynamically adjusted until the model converges.

[0010] Optionally, in the saltwater tide forecasting method based on LSSVM described in the present application, the forecast result is evaluated based on the evaluation rule to obtain the evaluation result, which specifically includes: Obtain hydrological and meteorological station information in the monitoring area and analyze historical assessment results in the monitoring area; Analyze whether the historical assessment results of the monitoring area meet the set assessment accuracy; If the set evaluation accuracy is met, an evaluation rule is established based on the historical evaluation results of the monitoring area, and the current forecast results are evaluated according to the evaluation rule to obtain the evaluation result; If the set evaluation accuracy is not met, the corresponding historical evaluation results will be discarded.

[0011] Optionally, in the saltwater tide forecasting method based on LSSVM described in the present application, the forecast result is dynamically adjusted based on the evaluation result to obtain new forecast information, and the new forecast information is transmitted to the terminal in real time, specifically including: Obtain the current evaluation results, and analyze the deviation information of the forecast results based on the current evaluation results; Determining whether the deviation information is greater than or equal to a set deviation threshold; If it is greater than or equal to the set deviation threshold, a correction parameter is generated based on the deviation information of the forecast result; Dynamically correct the LSSVM model parameters based on the correction parameters and obtain the final prediction results; If it is less than the set deviation threshold, the final forecast information is generated and transmitted to the terminal in real time.

[0012] In a second aspect, the present application provides a saltwater tide forecasting system based on LSSVM, the system comprising: a memory and a processor, the memory comprising a program based on the LSSVM saltwater tide forecasting method, the program based on the LSSVM saltwater tide forecasting method being executed by the processor to implement the following steps: Obtain real-time monitoring data within the monitoring area, pre-process the monitoring data, and obtain optimized data; Calculate the phase difference of the impact of runoff tide on the forecast section based on the optimized data to obtain the analysis data; Construct the LSSVM model, transfer the analysis data to the LSSVM model, and obtain the forecast results; Evaluate the forecast result based on the evaluation rule to obtain an evaluation result; The forecast results are dynamically adjusted based on the evaluation results to obtain new forecast information, which is then transmitted to the terminal in real time.

[0013] Optionally, in the LSSVM-based saltwater tide forecasting system described in the present application, real-time monitoring data in the monitoring area is obtained, and the monitoring data is preprocessed to obtain optimized data; specifically including: Set up a monitoring area and set up a number of monitoring points within the monitoring area; Acquire real-time monitoring data of each monitoring point, the monitoring data including upstream flow data, downstream tide level data, downstream tidal range data, regional wind speed data, wind direction data and salinity data of the forecast section; Analyze the real-time monitoring data of each monitoring point, clean the real-time monitoring data of the same monitoring point, and obtain cleaned monitoring data; Analyze the monitoring data after cleaning to see whether it meets the set condition information; If the set condition information is met, the optimized data is obtained based on the cleaned monitoring data; If the set condition information is not met, the cleaning parameters are adjusted and the real-time monitoring data is cleaned again.

[0014] Optionally, in the saltwater tide forecasting system based on LSSVM described in the present application, the phase difference of the radial tide influence of the forecast section is calculated based on the optimized data to obtain the analysis data, which specifically includes: Obtain optimized data, and analyze the phase difference of the impact of the runoff tide on the forecast section based on the optimized data; Compare the phase difference of the impact of the runoff tide on the forecast section with the set phase difference threshold to obtain the deviation rate; Determining whether the deviation rate is greater than or equal to a set deviation rate threshold; If it is greater than or equal to the set deviation rate threshold, feedback information is generated, and the phase difference of the impact of runoff on the forecast section is adjusted based on the feedback information; If it is less than the set deviation rate threshold, the analysis data is obtained.

[0015] In a third aspect, the present application also provides a computer-readable storage medium, which includes a saltwater tide forecasting method program based on LSSVM. When the saltwater tide forecasting method program based on LSSVM is executed by a processor, the steps of the saltwater tide forecasting method based on LSSVM as described in any one of the above items are implemented.

[0016] As can be seen from the above, the present application provides a saltwater tide forecasting method, system and medium based on LSSVM, which obtains real-time monitoring data in the monitoring area, pre-processes the monitoring data, and obtains optimized data; calculates the phase difference of the tidal influence of the forecast section based on the optimized data to obtain analysis data; constructs an LSSVM model, transmits the analysis data to the LSSVM model, and obtains a forecast result; evaluates the forecast result based on the evaluation rule to obtain an evaluation result; dynamically adjusts the forecast result based on the evaluation result to obtain new forecast information, and transmits the new forecast information to the terminal in real time; analyzes the monitoring data of the monitoring area through the LSSVM model, thereby accurately analyzing the saltwater tide forecast result, evaluating the saltwater tide forecast result, dynamically adjusting the forecast result, and improving the forecast accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A flow chart of a saltwater tide forecasting method based on LSSVM provided in an embodiment of the present application; Figure 2 A flow chart of a real-time monitoring data preprocessing method based on the LSSVM saltwater tide forecasting method provided in an embodiment of the present application; Figure 3 A flow chart of a method for acquiring analytical data based on the LSSVM saltwater tide forecasting method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0020] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0021] Example 1 Please refer to Figure 1 , Figure 1 1 is a flow chart of a saltwater tide forecasting method based on LSSVM in some embodiments of the present application. The saltwater tide forecasting method based on LSSVM is used in a terminal device, and the saltwater tide forecasting method based on LSSVM includes the following steps: S101, acquiring real-time monitoring data in the monitoring area, and preprocessing the monitoring data to obtain optimized data; S102, calculating the phase difference of the impact of the runoff tide on the forecast section based on the optimized data to obtain analysis data; S103, constructing an LSSVM model, transmitting the analysis data to the LSSVM model, and obtaining a prediction result; S104, evaluating the forecast result based on the evaluation rule to obtain an evaluation result; S105, dynamically adjusting the forecast result based on the evaluation result, obtaining new forecast information, and transmitting the new forecast information to the terminal in real time.

[0022] It should be noted that the monitoring data in the monitoring area is obtained in real time, and the monitoring data is analyzed according to the LSSVM model, so as to accurately predict the saltwater tide state in the monitoring area, and then accurately warn of the occurrence of saltwater tide in the monitoring area, thereby improving the accuracy of warning.

[0023] Please refer to Figure 2 , Figure 2 This is a flow chart of a real-time monitoring data preprocessing method based on the LSSVM saltwater tide forecasting method in some embodiments of the present application. According to an embodiment of the present invention, real-time monitoring data in the monitoring area is obtained, and the monitoring data is preprocessed to obtain optimized data; specifically, it includes: S201, setting a monitoring area and setting a number of monitoring points in the monitoring area; S202, obtaining real-time monitoring data of each monitoring point, the monitoring data including upstream flow data, downstream tide level data, downstream tidal range data, regional wind speed data, wind direction data and salinity data of the forecast section; S203, analyzing the real-time monitoring data of each monitoring point, and cleaning the real-time monitoring data of the same monitoring point to obtain cleaned monitoring data; S204, analyzing whether the cleaned monitoring data meets the set condition information; S205, if the set condition information is met, then the optimized data is obtained based on the cleaned monitoring data; if the set condition information is not met, then the cleaning parameters are adjusted and the real-time monitoring data is cleaned a second time.

[0024] It should be noted that multiple monitoring points are set in the monitoring area, and different monitoring points correspond to different monitoring data. The monitoring data of different monitoring points are cleaned and optimized to improve the monitoring accuracy of the monitoring data.

[0025] Please refer to Figure 3 , Figure 3 It is a flow chart of a method for obtaining analytical data based on the LSSVM saltwater tide forecasting method in some embodiments of the present application. According to an embodiment of the present invention, the phase difference of the radial tide influence of the forecast section is calculated based on the optimized data to obtain analytical data, which specifically includes: S301, obtaining optimized data, and analyzing the phase difference of the impact of the runout tide on the forecast section based on the optimized data; S302, comparing the phase difference of the impact of the runoff tide on the forecast section with the set phase difference threshold to obtain a deviation rate; S303, determining whether the deviation rate is greater than or equal to a set deviation rate threshold; S304, if it is greater than or equal to the set deviation rate threshold, feedback information is generated, and the phase difference of the impact of runoff on the forecast section is adjusted based on the feedback information; S305: If the deviation rate is less than the set threshold, the analysis data is obtained.

[0026] It should be noted that the phase difference of the impact of tidal current on the forecast section is analyzed based on the monitoring data, and the accuracy of the phase difference of the impact of tidal current on the forecast section is improved by analyzing the phase difference and making dynamic adjustments.

[0027] According to an embodiment of the present invention, a LSSVM model is constructed, and analysis data is transmitted to the LSSVM model to obtain a forecast result, which specifically includes: Obtain historical monitoring data and historical forecast results based on big data, and generate training sets based on historical monitoring data and historical forecast results; Iteratively train the initial model based on the training set to obtain the training results; Determine whether the training results converge; If converged, the final LSSVM model is generated, and the prediction results are obtained based on the final LSSVM model; If it does not converge, the model parameters are dynamically adjusted until the model converges.

[0028] It should be noted that by establishing a model training set through big data, the initial model is continuously trained to obtain an accurate LSSVM model. During the model training process, the model parameters are continuously adjusted to improve the model training effect.

[0029] According to an embodiment of the present invention, the forecast result is evaluated based on the evaluation rule to obtain the evaluation result, which specifically includes: Obtain hydrological and meteorological station information in the monitoring area and analyze historical assessment results in the monitoring area; Analyze whether the historical assessment results of the monitoring area meet the set assessment accuracy; If the set evaluation accuracy is met, an evaluation rule is established based on the historical evaluation results of the monitoring area, and the current forecast results are evaluated according to the evaluation rule to obtain the evaluation result; If the set evaluation accuracy is not met, the corresponding historical evaluation results will be discarded.

[0030] It should be noted that the assessment accuracy of historical assessment results is analyzed based on the historical assessment results of the monitoring area, so that corresponding assessment rules are established according to the assessment accuracy to improve the accuracy of the assessment results.

[0031] According to an embodiment of the present invention, dynamically adjusting the forecast result based on the evaluation result to obtain new forecast information, and transmitting the new forecast information to the terminal in real time, specifically includes: Obtain the current evaluation results, and analyze the deviation information of the forecast results based on the current evaluation results; Determine whether the deviation information is greater than or equal to the set deviation threshold; If it is greater than or equal to the set deviation threshold, a correction parameter is generated based on the deviation information of the forecast result; Dynamically correct the LSSVM model parameters based on the correction parameters and obtain the final prediction results; If it is less than the set deviation threshold, the final forecast information is generated and transmitted to the terminal in real time.

[0032] It should be noted that by analyzing whether the current evaluation results are biased, the LSSVM model parameters are continuously corrected to improve the accuracy of the forecast results.

[0033] Example 2 In a second aspect, an embodiment of the present application provides a saltwater tide forecasting system based on LSSVM, the system comprising: a memory and a processor, the memory comprising a program based on the LSSVM saltwater tide forecasting method, and the program based on the LSSVM saltwater tide forecasting method is executed by the processor to implement the following steps: Obtain real-time monitoring data within the monitoring area, pre-process the monitoring data, and obtain optimized data; Calculate the phase difference of the impact of runoff tide on the forecast section based on the optimized data to obtain the analysis data; Construct the LSSVM model, transfer the analysis data to the LSSVM model, and obtain the forecast results; Evaluate the forecast result based on the evaluation rule to obtain an evaluation result; The forecast results are dynamically adjusted based on the evaluation results to obtain new forecast information, which is then transmitted to the terminal in real time.

[0034] It should be noted that the monitoring data in the monitoring area is obtained in real time, and the monitoring data is analyzed according to the LSSVM model, so as to accurately predict the saltwater tide state in the monitoring area, and then accurately warn of the occurrence of saltwater tide in the monitoring area, thereby improving the accuracy of warning.

[0035] According to an embodiment of the present invention, real-time monitoring data in a monitoring area is obtained, and the monitoring data is preprocessed to obtain optimized data; specifically, the following steps are included: Set up a monitoring area and set up a number of monitoring points within the monitoring area; Obtain real-time monitoring data for each monitoring point, including upstream flow data, downstream tide data, downstream tidal range data, regional wind speed data, wind direction data and salinity data of the forecast section; Analyze the real-time monitoring data of each monitoring point, clean the real-time monitoring data of the same monitoring point, and obtain cleaned monitoring data; Analyze the monitoring data after cleaning to see whether it meets the set condition information; If the set condition information is met, the optimized data is obtained based on the cleaned monitoring data; If the set condition information is not met, the cleaning parameters are adjusted and the real-time monitoring data is cleaned again.

[0036] It should be noted that multiple monitoring points are set in the monitoring area, and different monitoring points correspond to different monitoring data. The monitoring data of different monitoring points are cleaned and optimized to improve the monitoring accuracy of the monitoring data.

[0037] According to an embodiment of the present invention, the phase difference of the impact of the runout tide on the forecast section is calculated based on the optimized data to obtain the analysis data, which specifically includes: Obtain optimized data, and analyze the phase difference of the impact of the runoff tide on the forecast section based on the optimized data; Compare the phase difference of the impact of the runoff tide on the forecast section with the set phase difference threshold to obtain the deviation rate; Determine whether the deviation rate is greater than or equal to a set deviation rate threshold; If it is greater than or equal to the set deviation rate threshold, feedback information is generated, and the phase difference of the impact of runoff on the forecast section is adjusted based on the feedback information; If it is less than the set deviation rate threshold, the analysis data is obtained.

[0038] It should be noted that the phase difference of the impact of tidal current on the forecast section is analyzed based on the monitoring data, and the accuracy of the phase difference of the impact of tidal current on the forecast section is improved by analyzing the phase difference and making dynamic adjustments.

[0039] According to an embodiment of the present invention, a LSSVM model is constructed, and analysis data is transmitted to the LSSVM model to obtain a forecast result, which specifically includes: Obtain historical monitoring data and historical forecast results based on big data, and generate training sets based on historical monitoring data and historical forecast results; Iteratively train the initial model based on the training set to obtain the training results; Determine whether the training results converge; If converged, the final LSSVM model is generated, and the prediction results are obtained based on the final LSSVM model; If it does not converge, the model parameters are dynamically adjusted until the model converges.

[0040] It should be noted that by establishing a model training set through big data, the initial model is continuously trained to obtain an accurate LSSVM model. During the model training process, the model parameters are continuously adjusted to improve the model training effect.

[0041] According to an embodiment of the present invention, the forecast result is evaluated based on the evaluation rule to obtain the evaluation result, which specifically includes: Obtain hydrological and meteorological station information in the monitoring area and analyze historical assessment results in the monitoring area; Analyze whether the historical assessment results of the monitoring area meet the set assessment accuracy; If the set evaluation accuracy is met, an evaluation rule is established based on the historical evaluation results of the monitoring area, and the current forecast results are evaluated according to the evaluation rule to obtain the evaluation result; If the set evaluation accuracy is not met, the corresponding historical evaluation results will be discarded.

[0042] It should be noted that the assessment accuracy of historical assessment results is analyzed based on the historical assessment results of the monitoring area, so that corresponding assessment rules are established according to the assessment accuracy to improve the accuracy of the assessment results.

[0043] According to an embodiment of the present invention, dynamically adjusting the forecast result based on the evaluation result to obtain new forecast information, and transmitting the new forecast information to the terminal in real time, specifically includes: Obtain the current evaluation results, and analyze the deviation information of the forecast results based on the current evaluation results; Determine whether the deviation information is greater than or equal to the set deviation threshold; If it is greater than or equal to the set deviation threshold, a correction parameter is generated based on the deviation information of the forecast result; Dynamically correct the LSSVM model parameters based on the correction parameters and obtain the final prediction results; If it is less than the set deviation threshold, the final forecast information is generated and transmitted to the terminal in real time.

[0044] It should be noted that by analyzing whether the current evaluation results are biased, the LSSVM model parameters are continuously corrected to improve the accuracy of the forecast results.

[0045] Example 3 A third aspect of the present invention provides a computer-readable storage medium, which includes a saltwater tide forecasting method program based on LSSVM. When the saltwater tide forecasting method program based on LSSVM is executed by a processor, the steps of the saltwater tide forecasting method based on LSSVM as described above are implemented.

[0046] The invention discloses a saltwater tide forecasting method, system and medium based on LSSVM. The method comprises the following steps: obtaining real-time monitoring data in a monitoring area, pre-processing the monitoring data and obtaining optimized data; calculating the phase difference of the radial tide influence of the forecast section based on the optimized data and obtaining analysis data; constructing an LSSVM model, transmitting the analysis data to the LSSVM model and obtaining a forecast result; evaluating the forecast result based on an evaluation rule and obtaining an evaluation result; dynamically adjusting the forecast result based on the evaluation result and obtaining new forecast information, and transmitting the new forecast information to a terminal in real time; analyzing the monitoring data of the monitoring area through the LSSVM model, thereby accurately analyzing the saltwater tide forecast result, evaluating the saltwater tide forecast result, dynamically adjusting the forecast result and improving the forecast accuracy.

[0047] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0048] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0049] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0050] A person skilled in the art can understand that all or part of the steps of the above method embodiment can be completed by hardware related to program instructions, and the above program can be stored in a readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the above storage medium includes: mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc. Various media that can store program codes.

[0051] Or, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. A saltwater tide forecasting method based on LSSVM, characterized in that: include: Obtain real-time monitoring data within the monitoring area, pre-process the monitoring data, and obtain optimized data; Calculate the phase difference of the impact of runoff tide on the forecast section based on the optimized data to obtain the analysis data; Construct the LSSVM model, transfer the analysis data to the LSSVM model, and obtain the forecast results; Evaluate the forecast result based on the evaluation rule to obtain an evaluation result; The forecast results are dynamically adjusted based on the evaluation results to obtain new forecast information, which is then transmitted to the terminal in real time.

2. The saltwater tide forecasting method based on LSSVM according to claim 1, characterized in that, Obtain real-time monitoring data within the monitoring area, pre-process the monitoring data, and obtain optimized data; specifically including: Set up a monitoring area and set up a number of monitoring points within the monitoring area; Acquire real-time monitoring data of each monitoring point, the monitoring data including upstream flow data, downstream tide level data, downstream tidal range data, regional wind speed data, wind direction data and salinity data of the forecast section; Analyze the real-time monitoring data of each monitoring point, clean the real-time monitoring data of the same monitoring point, and obtain cleaned monitoring data; Analyze the monitoring data after cleaning to see whether it meets the set condition information; If the set condition information is met, the optimized data is obtained based on the cleaned monitoring data; If the set condition information is not met, the cleaning parameters are adjusted and the real-time monitoring data is cleaned again.

3. The saltwater tide forecasting method based on LSSVM according to claim 2, characterized in that, Based on the optimized data, the phase difference of the impact of the runoff tide on the forecast section is calculated to obtain the analysis data, including: Obtain optimized data, and analyze the phase difference of the impact of the runoff tide on the forecast section based on the optimized data; Compare the phase difference of the impact of the runoff tide on the forecast section with the set phase difference threshold to obtain the deviation rate; Determining whether the deviation rate is greater than or equal to a set deviation rate threshold; If it is greater than or equal to the set deviation rate threshold, feedback information is generated, and the phase difference of the impact of runoff on the forecast section is adjusted based on the feedback information; If it is less than the set deviation rate threshold, the analysis data is obtained.

4. The saltwater tide forecasting method based on LSSVM according to claim 3, characterized in that, Construct the LSSVM model, transfer the analysis data to the LSSVM model, and obtain the forecast results, including: Obtain historical monitoring data and historical forecast results based on big data, and generate training sets based on historical monitoring data and historical forecast results; Iteratively train the initial model based on the training set to obtain the training results; Determining whether the training result converges; If converged, the final LSSVM model is generated, and the prediction results are obtained based on the final LSSVM model; If it does not converge, the model parameters are dynamically adjusted until the model converges.

5. The saltwater tide forecasting method based on LSSVM according to claim 4, characterized in that, The forecast results are evaluated based on the evaluation rules to obtain the evaluation results, which specifically include: Obtain hydrological and meteorological station information in the monitoring area and analyze historical assessment results in the monitoring area; Analyze whether the historical assessment results of the monitoring area meet the set assessment accuracy; If the set evaluation accuracy is met, an evaluation rule is established based on the historical evaluation results of the monitoring area, and the current forecast results are evaluated according to the evaluation rule to obtain the evaluation result; If the set evaluation accuracy is not met, the corresponding historical evaluation results will be discarded.

6. The saltwater tide forecasting method based on LSSVM according to claim 5, characterized in that, Dynamically adjust the forecast results based on the evaluation results to obtain new forecast information, and transmit the new forecast information to the terminal in real time, including: Obtain the current evaluation results, and analyze the deviation information of the forecast results based on the current evaluation results; Determining whether the deviation information is greater than or equal to a set deviation threshold; If it is greater than or equal to the set deviation threshold, a correction parameter is generated based on the deviation information of the forecast result; Dynamically correct the LSSVM model parameters based on the correction parameters and obtain the final prediction results; If it is less than the set deviation threshold, the final forecast information is generated and transmitted to the terminal in real time.

7. A saltwater tide forecasting system based on LSSVM, characterized in that: The system comprises: a memory and a processor, wherein the memory comprises a program based on the LSSVM saltwater tide forecasting method, and when the program based on the LSSVM saltwater tide forecasting method is executed by the processor, the following steps are implemented: Obtain real-time monitoring data within the monitoring area, pre-process the monitoring data, and obtain optimized data; Calculate the phase difference of the impact of runoff tide on the forecast section based on the optimized data to obtain the analysis data; Construct the LSSVM model, transfer the analysis data to the LSSVM model, and obtain the forecast results; Evaluate the forecast result based on the evaluation rule to obtain an evaluation result; The forecast results are dynamically adjusted based on the evaluation results to obtain new forecast information, which is then transmitted to the terminal in real time.

8. The saltwater tide forecasting system based on LSSVM according to claim 7, characterized in that: Obtain real-time monitoring data within the monitoring area, pre-process the monitoring data, and obtain optimized data; specifically including: Set up a monitoring area and set up a number of monitoring points within the monitoring area; Acquire real-time monitoring data of each monitoring point, the monitoring data including upstream flow data, downstream tide level data, downstream tidal range data, regional wind speed data, wind direction data and salinity data of the forecast section; Analyze the real-time monitoring data of each monitoring point, clean the real-time monitoring data of the same monitoring point, and obtain cleaned monitoring data; Analyze the monitoring data after cleaning to see whether it meets the set condition information; If the set condition information is met, the optimized data is obtained based on the cleaned monitoring data; If the set condition information is not met, the cleaning parameters are adjusted and the real-time monitoring data is cleaned again.

9. The saltwater tide forecasting system based on LSSVM according to claim 8, characterized in that: Based on the optimized data, the phase difference of the impact of the runoff tide on the forecast section is calculated to obtain the analysis data, including: Obtain optimized data, and analyze the phase difference of the impact of the runoff tide on the forecast section based on the optimized data; Compare the phase difference of the impact of the runoff tide on the forecast section with the set phase difference threshold to obtain the deviation rate; Determining whether the deviation rate is greater than or equal to a set deviation rate threshold; If it is greater than or equal to the set deviation rate threshold, feedback information is generated, and the phase difference of the impact of runoff on the forecast section is adjusted based on the feedback information; If it is less than the set deviation rate threshold, the analysis data is obtained.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a saltwater tide forecasting method program based on LSSVM. When the saltwater tide forecasting method program based on LSSVM is executed by a processor, the steps of the saltwater tide forecasting method based on LSSVM as claimed in any one of claims 1 to 6 are implemented.

Images