A construction area sea wave prediction platform based on multi-dimensional data analysis

Abstract

The present invention belongs to the technical field of ocean wave parameter prediction, and specifically relates to an ocean wave prediction platform for construction areas based on multi-dimensional data analysis. It undertakes the prediction task of the significant wave height data of ocean waves in the target construction area for a preset future period on the current day. On the one hand, it considers the seabed terrain differences between each historical reference day and the current day in the target construction area, corrects the monitored significant wave height values of ocean waves in the corresponding preset future period for each historical reference day, and predicts the significant wave height of ocean waves in the target construction area for the preset future period on the current day for the first time. On the other hand, based on the immediate prediction of the significant wave height of ocean waves, it corrects the prediction deviation of the meteorological conditions in the preset future period on the current day, and predicts the significant wave height of ocean waves in the target construction area for the preset future period on the current day for the second time. It determines the adaptation weights of the first prediction and the second prediction, obtains and feeds back the comprehensive prediction value of the significant wave height of ocean waves in the target construction area for the preset future period on the current day, realizes the effective integration of historical reference and immediate prediction, and improves the accuracy and reliability of the prediction results.

Description

Technical Field

[0001] The present invention belongs to the technical field of ocean wave parameter prediction, and particularly relates to an ocean wave prediction platform for a construction area based on multi-dimensional data analysis. Background Art

[0002] The changes in ocean waves have an important impact on the safety, project progress and economic benefits of a construction area. Especially in the fields of marine engineering, port construction, offshore platform installation, etc., factors such as the intensity, frequency and period of ocean waves directly determine the feasibility and safety of construction. Accurately predicting the ocean wave conditions in the construction area is crucial for the smooth progress of the project.

[0003] As one of the commonly used key indicators in the fields of ocean engineering, shipping, fishery, meteorological warning, etc., the significant wave height of ocean waves not only directly affects the safety of activities such as offshore operations, ship navigation and offshore engineering construction, but also its prediction accuracy plays an irreplaceable role in ensuring personnel safety, optimizing resource allocation and reducing operating costs. Therefore, accurately predicting the significant wave height of ocean waves is of great significance.

[0004] In the prior art, there are also some related solutions involving the prediction of the significant wave height of ocean waves. For example, the method for predicting the significant wave height of ocean waves based on ERA-Interim disclosed in Chinese Patent Publication No. CN104021434A obtains original data, performs data preprocessing, selects a suitable sea level pressure field, corrects the prediction model using the data in the first time period in ERA-Interim, evaluates the prediction model using the data in the second time period later than the first time period in ERA-Interim, and predicts the significant wave height of ocean waves using the prediction model. By using the long-term stable ERA-Interim reanalysis data source of the European Centre for Medium-Range Weather Forecasts, extracting the data for predicting the significant wave height of ocean waves therefrom, and supplementing it with the method of principal component analysis, it can not only predict the significant wave height of ocean waves at multiple times, but also has strong operability and high prediction accuracy.

[0005] Another Chinese patent publication number CN104050514A is a method for predicting the long-term trend of significant wave height based on reanalysis data, and its steps include: 1. Collect ERA-Interim meteorological forecast data at each time, 2. Obtain the coordinates of each grid point, 3. Calculate the SLP anomaly value and standard deviation, 4. SLP anomaly value principal component analysis, 5. Box-Cox transformation of sea area data, 6. Calculate the prediction factor of significant wave height, 7. Calculate the standard deviation of significant wave height and prediction factor, 8. Bring the prediction factor into the prediction model, 9. Bring the significant wave height lag value into the model, 10. SLP field prediction based on EOF, 11. Optimize and select prediction factors, 12. Model predicts significant wave height, 13. Evaluate the prediction level, 14. Calculate the long-term trend of significant wave height, and 15. Draw a long-term trend chart of significant wave height. The long-term trend of significant wave height at multiple times can be predicted with high accuracy.

[0006] Although the above scheme proposes some solutions for predicting the effective wave height of waves, the existing technology still has the following limitations, specifically: 1. The existing technology for predicting the effective wave height of waves in a preset period in the future usually relies on the same historical situation as a reference, but ignores the impact of the historical and current differences in seabed topography on wave characteristics, resulting in deviations between the prediction results and the actual situation.

[0007] 2. The existing technology has an instant prediction method for predicting the effective wave height of waves in a preset period of time in the future, which mainly introduces meteorological boundary conditions into the physical model constructed at the prediction location. However, it ignores the problem of meteorological prediction errors, which may reduce the accuracy of the prediction of the effective wave height of waves, and thus affect the safety, economy and feasibility of offshore operations.

[0008] 3. The existing technology has a method of predicting the effective wave height of waves by combining historical reference and instant prediction. However, there is a lack of effective analysis of the weight distribution between history and the present, which may not only lead to the prediction results failing to accurately reflect the actual situation of the waves, but also lead to the lack of adaptability of the prediction results in different sea areas and different time periods. Summary of the invention

[0009] In order to overcome the shortcomings of the background technology, an embodiment of the present invention provides a construction area wave prediction platform based on multi-dimensional data analysis, which can effectively solve the problems involved in the above-mentioned background technology.

[0010] The purpose of the present invention can be achieved through the following technical solutions: a construction area wave prediction platform based on multidimensional data analysis, including: a data prediction task acceptance module, a historical reference day retrieval module, a primary prediction module, a secondary prediction module, a comprehensive prediction value feedback module and a cloud database.

[0011] The data prediction task acceptance module is connected to the historical reference day retrieval module and the secondary prediction module respectively, the historical reference day retrieval module is connected to the primary prediction module, the primary prediction module and the secondary prediction module are both connected to the comprehensive prediction numerical feedback module, and the cloud database is connected to the historical reference day retrieval module and the secondary prediction module respectively.

[0012] The data prediction task acceptance module is used to accept the task of predicting the effective wave height data of waves in the target construction area in the future preset period of the day, and define the specified time before the future preset period as the inspection period.

[0013] The historical reference day retrieval module is used to extract the wave height monitoring data and meteorological monitoring data of the target construction area during the inspection period of the day, and to retrieve the reference days with similar data scenarios between the historical inspection period of the target construction area and the inspection period of the day, which are recorded as historical reference days.

[0014] The primary prediction module is used to extract the monitoring values ​​of the effective wave height of waves corresponding to each historical reference day in the preset future period, consider the difference in seabed topography between each historical reference day and the current day in the target construction area, correct the monitoring values ​​of the effective wave height of waves corresponding to each historical reference day in the preset future period, and then predict the effective wave height of waves in the preset future period of the target construction area on that day.

[0015] The secondary prediction module is used to construct a physical model of the seabed topography in the target construction area on the day, import the weather forecast data for the preset period in the future of the day to make an immediate prediction of the effective wave height, correct the forecast deviation of the weather conditions for the preset period in the future of the day, and then make a secondary prediction of the effective wave height in the target construction area for the preset period in the future of the day.

[0016] The comprehensive prediction value feedback module is used to determine the adaptation weights of the first and second predictions, and to accumulate the products of the effective wave heights of waves in the target construction area in the preset time period in the future on the same day with the first and second predictions and their corresponding adaptation weights, so as to obtain the comprehensive prediction value of the effective wave height of waves in the target construction area in the preset time period in the future on the same day and give feedback.

[0017] The cloud database is used to store preset overall data scenario similarity thresholds, preset permitted meteorological deviation coefficient thresholds, and preset meteorological parameter grade scoring rules.

[0018] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) The present invention retrieves reference days with similar data scenarios between the historical inspection period of the target construction area and the inspection period of the current day, and conducts a forecast based on them, thereby helping to more accurately capture the laws and trends of wave changes, making it closer to the actual wave changes, thereby improving the accuracy of historical reference forecasts.

[0019] (2) The present invention considers the difference in seabed topography between each historical reference day and the current day in the target construction area, and makes a refined correction to the monitoring value of the effective wave height of waves in the future preset period corresponding to each historical reference day, thereby realizing a one-time prediction of the effective wave height of waves in the future preset period in the target construction area on the current day, significantly improving the accuracy of the prediction and providing strong support for the safety planning of construction activities.

[0020] (3) The present invention is based on the real-time prediction of the effective wave height of ocean waves, corrects the forecast deviation of meteorological conditions in a preset period of time in the future on the same day, and then realizes the secondary prediction of the effective wave height of ocean waves in the preset period of time in the future on the same day in the target construction area. It not only improves the prediction accuracy and dynamic adaptability, but also optimizes the prediction process and decision support, and reduces the construction risk and cost.

[0021] (4) The present invention determines the adaptation weights of the primary prediction and the secondary prediction, combines the primary and secondary predictions of the effective wave heights of the target construction area in the preset period of time in the future on the same day, obtains and feeds back the comprehensive prediction value of the effective wave height of the target construction area in the preset period of time in the future on the same day, and effectively integrates the historical reference with the real-time prediction, thereby improving the accuracy and reliability of the prediction results. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0023] Figure 1 It is a schematic diagram of module connection of the present invention.

[0024] Figure 2 A logical schematic diagram for specifically retrieving each historical reference date for the present invention.

[0025] Figure 3 The present invention is a logical schematic diagram for correcting the forecast deviation of meteorological conditions for a preset time period in the future on the same day. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] Reference Figure 1 As shown, the present invention provides a construction area wave prediction platform based on multidimensional data analysis, including: a data prediction task acceptance module, a historical reference day retrieval module, a primary prediction module, a secondary prediction module, a comprehensive prediction value feedback module and a cloud database.

[0028] The data prediction task acceptance module is connected to the historical reference day retrieval module and the secondary prediction module respectively, the historical reference day retrieval module is connected to the primary prediction module, the primary prediction module and the secondary prediction module are both connected to the comprehensive prediction numerical feedback module, and the cloud database is connected to the historical reference day retrieval module and the secondary prediction module respectively.

[0029] The data prediction task undertaking module is used to undertake the task of predicting the effective wave height data of waves in the target construction area in the future preset period of the day, and define the specified time before the future preset period as the inspection period.

[0030] The historical reference day retrieval module is used to extract the wave height monitoring data and meteorological monitoring data of the target construction area during the inspection period of the day, and retrieve each reference day with similar data context between the historical inspection period of the target construction area and the inspection period of the day, and record them as each historical reference day.

[0031] Specifically, the wave height monitoring data includes the wave height monitoring values ​​at each unit time point.

[0032] The meteorological monitoring data include average air pressure value, average temperature value, wind direction angle range, average wind speed value and total precipitation.

[0033] It should be noted that the above-mentioned wave height monitoring data can be obtained through shore wave measurement radar or buoy wave measurement, and the meteorological monitoring data and the meteorological forecast data mentioned below can be obtained through the meteorological station in the target construction area or meteorological satellite.

[0034] Reference Figure 2 As shown, specifically, the specific retrieval process of each historical reference day includes: taking each diary of the same season as the current day in each historical year as each historical concurrent day, extracting the wave height monitoring data and meteorological monitoring data in the inspection period of each historical concurrent day in the target construction area, respectively analyzing the similarity of the wave height data context and meteorological data context of each historical concurrent day inspection period relative to the current day inspection period, multiplying the similarity of the wave height data context with the similarity of the meteorological data context, and obtaining the overall data context similarity of each historical concurrent day inspection period relative to the current day inspection period; if the overall data context similarity of a historical concurrent day inspection period relative to the current day inspection period is greater than or equal to the preset overall data context similarity threshold stored in the cloud database, it means that the data context of the historical concurrent day inspection period of the target construction area is similar to that of the current day inspection period, and the historical concurrent day is used as the historical reference day to retrieve each historical reference day.

[0035] Specifically, analyzing the similarity of the sea wave height data scenarios of each historical same - period day's inspection period relative to the current day's inspection period includes: calculating the cosine similarity for the sea wave height monitoring values at each unit time point within each historical same - period day and the current day's inspection period to obtain the similarity of the sea wave height change trends of each historical same - period day relative to the current day's inspection period.

[0036] It should be noted that the specific calculation process for obtaining the similarity of the sea wave height change trends of each historical same - period day relative to the current day's inspection period is as follows: Denote the sea wave height monitoring values at each unit time point within the inspection period of each historical same - period day as , where is the number of each historical same - period day, , is the number of each unit time point within the inspection period, , and denote the sea wave height monitoring values at each unit time point within the current day's inspection period as . Analyze the similarity of the sea wave height change trends of each historical same - period day relative to the current day's inspection period by the formula .

[0037] Cut the inspection period into each inspection sub - period, obtain the significant wave heights of the sea waves for each historical same - period day and the current day in each inspection sub - period, construct the significant wave height sequences of the sea waves for each historical same - period day and the current day in the order of time sequence of the inspection sub - periods, calculate the autocorrelation coefficients of the significant wave height sequences of the sea waves for each historical same - period day and the current day at each preset lag order, and compare and analyze the similarity of the significant wave height change trends of each historical same - period day relative to the current day's inspection period.

[0038] It should be noted that the specific process for obtaining the significant wave heights of the sea waves for each historical same - period day and the current day in each inspection sub - period is as follows: Taking a certain historical same - period day and a certain inspection sub - period as an example, arrange the sea wave height monitoring values at each unit time point within this historical same - period day and this inspection sub - period in ascending order, calculate the mean value of the sea wave height monitoring values ranked in the last one - third to obtain the significant wave height of the sea waves for this historical same - period day and this inspection sub - period. Similarly, obtain the significant wave heights of the sea waves for each historical same - period day and the current day in each inspection sub - period.

[0039] It should be noted that the calculation process for the autocorrelation coefficients of the significant wave height sequences of the sea waves for each historical same - period day and the current day at each preset lag order is as follows: Taking a certain historical same - period day as an example, calculate the mean value for each element in the significant wave height sequence of this historical same - period day, where is the number of each element in the significant wave height sequence, to obtain the average significant wave height of the inspection sub - period of this historical same - period day. The significant wave height sequence of this historical same - period day at the preset lag order The specific calculation formula for the autocorrelation coefficient is , where is the length of the significant wave height sequence of the sea wave, that is, the number of elements in the significant wave height sequence of the sea wave. The specific preset lag orders include . Substitute the above formula to obtain the autocorrelation coefficients of the significant wave height sequence of the sea wave on the historical same-day at each preset lag order. Similarly, calculate the autocorrelation coefficients of the significant wave height sequences of the sea wave on each historical same-day and the current day at each preset lag order.

[0040] It should also be noted that the specific process of comparing and analyzing the similarity of the change trends of the significant wave height of the sea wave within the inspection time period of each historical same-day relative to the current day is as follows: Take the difference between the autocorrelation coefficients of the significant wave height sequences of the sea wave on each historical same-day and the current day at each preset lag order, make the absolute value of the calculated difference, and further conduct a ratio analysis with the autocorrelation coefficient of the significant wave height sequence of the sea wave on the current day at the corresponding preset lag order to obtain the absolute deviation rate of the autocorrelation coefficient of the significant wave height sequence of the sea wave on each historical same-day relative to the current day at each preset lag order. Calculate the average deviation rate of the change trends of the significant wave height of the sea wave within the inspection time period of each historical same-day relative to the current day through mean calculation, and substitute its negative value into the natural exponential function to obtain the similarity of the change trends of the significant wave height of the sea wave within the inspection time period of each historical same-day relative to the current day.

[0041] Accumulate the similarity of the sea wave height change trend and the similarity of the significant wave height change trend to obtain the similarity of the sea wave height data scenario of the inspection time period of each historical same-day relative to the inspection time period of the current day.

[0042] Specifically, the analysis of the similarity of the meteorological data scenario of the inspection time period of each historical same-day relative to the inspection time period of the current day includes: calculating the relative deviation rates of the average air pressure value, average temperature value, wind direction angle interval, average wind speed value, and total precipitation within the inspection time period of each historical same-day relative to the inspection time period of the current day and accumulating them, and substituting the negative value of the accumulated value into the natural exponential function to obtain the similarity of the meteorological data scenario of the inspection time period of each historical same-day relative to the inspection time period of the current day.

[0043] It should be noted that the specific calculation process of the relative deviation rates of the average air pressure value, average temperature value, wind direction angle interval, average wind speed value, and total precipitation within the inspection time period of each historical same-day relative to the inspection time period of the current day is as follows: Calculate the absolute difference of the average air pressure value within the inspection time period of each historical same-day relative to the inspection time period of the current day, and conduct a ratio analysis of the absolute difference with the average air pressure value within the inspection time period of the current day to obtain the relative deviation rate of the average air pressure value within the inspection time period of each historical same-day relative to the inspection time period of the current day.

[0044] The calculation processes of the relative deviation rates of the average air temperature value, average wind speed value, and total precipitation in the inspection period of each historical same - period day with respect to the average value within the inspection period of the current day are all the same as the calculation process of the relative deviation rate of the average air pressure value within the inspection period of each historical same - period day with respect to the average value within the inspection period of the current day, and will not be elaborated here.

[0045] Obtain the non - overlapping intervals of the wind direction angle intervals in the inspection period of each historical same - period day and the wind direction angle interval in the inspection period of the current day, and perform ratio analysis on the length of the non - overlapping interval and the length of the wind direction angle interval in the inspection period of the current day to obtain the relative deviation rate of the inspection period of each historical same - period day with respect to the wind direction angle interval in the inspection period of the current day.

[0046] In the embodiment of the present invention, by retrieving each reference day whose data situation in the historical inspection period of the target construction area is similar to that in the inspection period of the current day, a primary prediction work is carried out based on this, which helps to more accurately capture the laws and trends of sea - wave changes, making the sea - wave change situation closer to the actual situation, thereby improving the accuracy of historical reference prediction.

[0047] The primary prediction module is used to extract the monitored values of the significant wave height of the sea - wave in the future preset period corresponding to each historical reference day, consider the differences in the seabed topography between each historical reference day and the current day in the target construction area, correct the monitored values of the significant wave height of the sea - wave in the future preset period corresponding to each historical reference day, and then primarily predict the significant wave height of the sea - wave in the future preset period of the current day in the target construction area.

[0048] Specifically, the correction of the monitored values of the significant wave height of the sea - wave in the future preset period corresponding to each historical reference day includes: taking the monitored values of each seabed topography parameter as independent variables, as the numbers of each seabed topography parameter, , taking the significant wave height of the sea - wave in the future preset period as the dependent variable to construct a multiple linear regression model, , where is the regression coefficient of the th seabed topography parameter, is the intercept, is the error value.

[0049] Collect the monitored values of each seabed topography parameter of each historical reference day in the target construction area, substitute them together with the corresponding significant wave height of the sea - wave in the future preset period into the multiple linear regression model for fitting, determine the specific values corresponding to the intercept, error value, and the regression coefficients of each seabed topography parameter, and use the multiple linear regression model as the correlation model between the seabed topography and the significant wave height of the sea - wave in the target construction area.

[0050] It should be noted that the above seabed terrain parameters include, but are not limited to, seabed slope, seabed roughness, etc. If the target construction area is a shallow sea area, it can be monitored by sensors arranged at the bottom of the buoy. If the target construction area is a deep sea area, it can be monitored by ultrasonic detection equipment.

[0051] Collect the monitoring values of each seabed terrain parameter in the target construction area on the current day, calculate the monitoring differences of each seabed terrain parameter between each historical reference day and the current day in the target construction area, substitute them into the correlation model between the seabed terrain and the significant wave height of the sea wave in the target construction area, so as to output the corrected values of the significant wave height of the sea wave under the influence of the seabed terrain differences between each historical reference day and the current day, and accumulate them with the monitoring values of the significant wave height of the sea wave in the corresponding future preset time period of each historical reference day, so as to realize the correction of the monitoring values of the significant wave height of the sea wave in the corresponding future preset time period of each historical reference day.

[0052] Specifically, the first prediction of the significant wave height of the sea wave in the future preset time period on the current day in the target construction area is the calculated average value of the corrected monitoring values of the significant wave height of the sea wave in the corresponding future preset time period of each historical reference day in the target construction area.

[0053] By considering the seabed terrain differences between each historical reference day and the current day in the target construction area, the embodiments of the present invention perform refined correction on the monitoring values of the significant wave height of the sea wave in the corresponding future preset time period of each historical reference day, and then realize the first prediction of the significant wave height of the sea wave in the future preset time period on the current day in the target construction area, significantly improving the prediction accuracy and providing strong support for the safe planning of construction activities.

[0054] The second prediction module is used to construct a physical model of the seabed terrain on the current day in the target construction area, import the meteorological forecast data in the future preset time period on the current day for real-time prediction of the significant wave height of the sea wave, correct the prediction deviation of the meteorological conditions in the future preset time period on the current day, and then perform a second prediction of the significant wave height of the sea wave in the future preset time period on the current day in the target construction area.

[0055] It should be noted that the above real-time prediction of the significant wave height of the sea wave specifically adopts the computational fluid dynamics method, which is a mature existing technology and can be implemented through computational fluid dynamics software. The specific prediction process is as follows: Take the meteorological forecast data in the future preset time period on the current day as boundary conditions and import them into the physical model of the seabed terrain on the current day in the target construction area that has been constructed. By solving the Navier-Stokes equation, simulate the flow of seawater and the generation, propagation and deformation process of sea waves, and then obtain the real-time predicted wave height of the sea wave at each unit time point in the future preset time period on the current day in the target construction area, so as to calculate the real-time predicted significant wave height of the sea wave.

[0056] Refer to Figure 3As shown, specifically, the correction of the predicted deviation of meteorological conditions in the future preset time period on the current day includes: randomly selecting the meteorological monitoring data corresponding to the future preset time period of each day within the historical preset cycle, performing real-time prediction processing on the significant wave height of the sea wave, and obtaining the error values of the real-time prediction relative to the actual monitoring of the significant wave height of the sea wave for each day within the historical preset cycle, which are recorded as the real-time prediction error values of each historical comparison day.

[0057] Collect the meteorological monitoring data corresponding to the future preset time period of each historical comparison day, and according to the content of the preset meteorological parameter grade scoring rule stored in the cloud database, perform scoring processing on the grades corresponding to the average air pressure value, average temperature value, wind direction angle interval, average wind speed value, and total precipitation in the meteorological monitoring data and accumulate the scored values to obtain the meteorological monitoring score corresponding to the future preset time period of each historical comparison day.

[0058] It should be noted that the above content of the preset meteorological parameter grade scoring rule specifically includes the meteorological parameter monitoring value intervals and scored values corresponding to each meteorological parameter grade, and each meteorological parameter specifically refers to the air pressure parameter, temperature parameter, wind direction angle parameter, wind speed parameter, and precipitation parameter.

[0059] Similarly, according to the meteorological forecast data for the future preset time period on the current day, which includes the predicted average air pressure value, average temperature value, wind direction angle interval, average wind speed value, and total precipitation, obtain the meteorological forecast score for the future preset time period on the current day.

[0060] Calculate the deviation coefficient between the meteorological monitoring score corresponding to the future preset time period of each historical comparison day and the meteorological forecast score for the future preset time period on the current day, screen out the historical comparison days with the deviation coefficient less than or equal to the preset allowable meteorological deviation coefficient threshold stored in the cloud database and record them as each historical reference day, extract the real-time prediction error values of each historical reference day for average calculation to obtain the predicted error value of the meteorological conditions in the future preset time period on the current day, and accumulate it with the real-time predicted significant wave height of the sea wave in the future preset time period on the current day to correct the predicted deviation of the meteorological conditions in the future preset time period on the current day.

[0061] It should be noted that the specific process of calculating the deviation coefficient between the meteorological monitoring score corresponding to the future preset time period of each historical comparison day and the meteorological forecast score for the future preset time period on the current day is as follows: take the absolute value difference between the meteorological monitoring score corresponding to the future preset time period of each historical comparison day and the meteorological forecast score for the future preset time period on the current day, and perform ratio analysis on the absolute difference value and the preset reference score to obtain the deviation coefficient between the meteorological monitoring score corresponding to the future preset time period of each historical comparison day and the meteorological forecast score for the future preset time period on the current day.

[0062] Specifically, the effective wave height of the sea waves in the future preset time period of the target construction area on the same day in the secondary prediction is the cumulative value of the prediction error value of the meteorological conditions in the future preset time period of the same day and the effective wave height of the sea waves predicted instantaneously in the future preset time period of the same day.

[0063] Based on the instantaneous prediction of the effective wave height of the sea waves, the embodiment of the present invention corrects the prediction deviation of the meteorological conditions in the future preset time period of the same day, and further realizes the secondary prediction of the effective wave height of the sea waves in the future preset time period of the target construction area on the same day, which not only improves the prediction accuracy and dynamic adaptability, but also optimizes the prediction process and decision support, and reduces the construction risk and cost.

[0064] The comprehensive prediction numerical feedback module is used to determine the adaptation weights of the primary prediction and the secondary prediction, and accumulate the products of the effective wave heights of the sea waves in the future preset time period of the target construction area in the primary and secondary predictions and their corresponding adaptation weights respectively, so as to obtain the comprehensive prediction numerical value of the effective wave height of the sea waves in the future preset time period of the target construction area and give feedback.

[0065] Specifically, the determination of the adaptation weights of the primary prediction and the secondary prediction includes: the analysis processes of the effective wave heights of the sea waves in the future preset time period of the same day in the primary and secondary prediction target construction areas are the same. Compare the effective wave heights of the sea waves in the future preset time period corresponding to each day in the historical preset cycle of the primary and secondary prediction target construction areas with the actually monitored effective wave heights of the sea waves in the future preset time period corresponding to each day in the historical preset cycle of the target construction area, determine the adaptation weights of the primary prediction and the secondary prediction in the future preset time period corresponding to each day in the historical preset cycle of the target construction area, use the date serial number as the abscissa and the adaptation weight as the ordinate to outline the change curves of the adaptation weights of the primary prediction and the secondary prediction in the future preset time period corresponding to the historical preset cycle of the target construction area, and further obtain the best fitting function of the adaptation weight change curve, and substitute the date serial number of the same day to determine the adaptation weights of the primary prediction and the secondary prediction on the same day.

[0066] It should be noted that the specific process of determining the adaptation weights of the primary prediction and the secondary prediction in the future preset time period corresponding to each day in the historical preset cycle of the target construction area is as follows: set the constraint condition that the sum of the adaptation weight of the primary prediction and the adaptation weight of the secondary prediction is 1, and generate a series of test groups accordingly. Each test group includes the test adaptation weights corresponding to the primary prediction and the secondary prediction.

[0067] Accumulate the products of the significant wave heights of the sea waves corresponding to each day in the historical preset period of the first- and second-order prediction target construction areas for the future preset time periods and the corresponding test adaptation weights set by each test group to obtain the comprehensive prediction values corresponding to each day in the historical preset period of the target construction area for the future preset time periods calculated by each test group. Take the absolute difference from the significant wave heights of the sea waves actually monitored for the corresponding days in the historical preset period of the target construction area for the future preset time periods, and select the test adaptation weights for the first-order prediction and second-order prediction set by the test group corresponding to the smallest absolute difference, which are determined as the adaptation weights for the first-order prediction and second-order prediction corresponding to each day in the historical preset period of the target construction area for the future preset time periods.

[0068] It should also be noted that the best fitting function of the above adaptation weight change curve can be obtained through the best fitting tool of Matlab software.

[0069] In the embodiment of the present invention, by determining the adaptation weights for the first-order prediction and second-order prediction, and combining the significant wave heights of the sea waves in the future preset time periods of the first- and second-order prediction target construction areas on the same day, the comprehensive prediction value of the significant wave heights of the sea waves in the future preset time periods of the target construction area on the same day is obtained and fed back, effectively integrating the historical reference and the instant prediction, thereby improving the accuracy and reliability of the prediction result.

[0070] The cloud database is used to store the preset overall data context similarity compliance threshold, store the preset permitted meteorological deviation coefficient threshold, and store the content of the preset meteorological parameter level scoring rules.

[0071] The data sources in the cloud database of this embodiment are shown in Table 1 below.

[0072] Table 1 Detailed Explanation of Data Sources in the Cloud Database

[0073]

[0074] The above content is only an example and explanation of the structure of the present invention. Those skilled in the art of the present technology can make various modifications or supplements to the described specific embodiments or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A construction area wave prediction platform based on multidimensional data analysis, characterized in that: include: The data prediction task acceptance module is used to accept the task of predicting the effective wave height data of the target construction area in the future preset period of the day, and define the specified time before the future preset period as the inspection period; The historical reference day retrieval module is used to extract the sea wave height monitoring data and meteorological monitoring data of the target construction area during the inspection period of the day, and retrieve the reference days with similar data scenarios between the historical inspection period of the target construction area and the inspection period of the day, and record them as historical reference days; The primary prediction module is used to extract the monitoring values ​​of the effective wave height of the waves corresponding to the preset time period in the future on each historical reference day, and to correct the monitoring values ​​of the effective wave height of the waves corresponding to the preset time period in the future on each historical reference day by considering the difference in the seabed topography between the target construction area on each historical reference day and the current day, so as to predict the effective wave height of the waves in the preset time period in the future on the target construction area on the current day. The secondary prediction module is used to construct a physical model of the seabed topography of the target construction area on the day, import the weather forecast data for the preset period in the future of the day to make an immediate prediction of the effective wave height, correct the forecast deviation of the weather conditions for the preset period in the future of the day, and then secondary predict the effective wave height of the target construction area for the preset period in the future of the day; The comprehensive prediction value feedback module is used to determine the adaptation weights of the first and second predictions, and to accumulate the products of the first and second predictions of the effective wave heights of the target construction area in the preset period in the future on the same day and the corresponding adaptation weights, so as to obtain the comprehensive prediction value of the effective wave height of the target construction area in the preset period in the future on the same day and give feedback; The cloud database is used to store the preset overall data scenario similarity threshold, the preset permitted meteorological deviation coefficient threshold, and the preset meteorological parameter grade scoring rules; Determine the adaptation weights of the primary prediction and the secondary prediction, including: the analysis process of the effective wave height of the wave in the future preset period of the same and secondary prediction target construction area is consistent, the effective wave height of the wave in the future preset period of each day in the historical preset period of the primary and secondary prediction target construction area is compared with the effective wave height of the wave actually monitored in the future preset period of each day in the historical preset period of the target construction area, determine the adaptation weights of the primary prediction and the secondary prediction of the future preset period of each day in the historical preset period of the target construction area, use the date serial number as the horizontal coordinate and the adaptation weight as the vertical coordinate to outline the adaptation weight change curve of the primary prediction and the secondary prediction of the future preset period of the target construction area in the historical preset period, further obtain the best fitting function of the adaptation weight change curve, substitute it into the date serial number of the day, so as to determine the adaptation weight of the primary prediction and the secondary prediction of the day.

2. The construction area wave prediction platform based on multidimensional data analysis according to claim 1, characterized in that: The wave height monitoring data includes the wave height monitoring values ​​at each unit time point; The meteorological monitoring data include average air pressure value, average temperature value, wind direction angle range, average wind speed value and total precipitation.

3. The construction area wave prediction platform based on multidimensional data analysis according to claim 2, characterized in that: The specific retrieval process of each historical reference day includes: taking each diary of the same season as the current day in each historical year as each historical concurrent day, extracting the wave height monitoring data and meteorological monitoring data in the inspection period of each historical concurrent day in the target construction area, respectively analyzing the similarity of the wave height data context and meteorological data context of each historical concurrent day inspection period relative to the current day inspection period, multiplying the similarity of the wave height data context with the similarity of the meteorological data context, and obtaining the overall data context similarity of each historical concurrent day inspection period relative to the current day inspection period; if the overall data context similarity of a certain historical concurrent day inspection period relative to the current day inspection period is greater than or equal to the preset overall data context similarity threshold stored in the cloud database, it means that the data context of the historical concurrent day inspection period of the target construction area is similar to that of the current day inspection period, and the historical concurrent day is used as the historical reference day to retrieve each historical reference day.

4. The construction area wave prediction platform based on multidimensional data analysis according to claim 3 is characterized by: The analysis of the similarity of the wave height data scenarios of the observation periods of the same historical days relative to the observation period of the current day includes: performing cosine similarity calculation on the wave height monitoring values ​​of the same historical days and each unit time point in the observation period of the current day to obtain the similarity of the wave height change trend of the same historical days relative to the observation period of the current day; The survey period is divided into sub-periods, and the effective wave heights of the waves on the historical days and the sub-periods of the current day are obtained. The effective wave height sequences of the waves on the historical days and the current day are constructed according to the time sequence of the sub-periods. The autocorrelation coefficients of the effective wave height sequences of the historical days and the current day at the preset lag orders are calculated, and the similarity of the changing trends of the effective wave heights of the waves on the historical days relative to the current day in the survey period is compared and analyzed. The similarity of the wave height change trend is accumulated with the similarity of the wave effective wave height change trend to obtain the similarity of the wave height data scenarios of each historical daily observation period relative to the current day's observation period.

5. The construction area wave prediction platform based on multidimensional data analysis according to claim 3 is characterized by: The analysis of the similarity of the meteorological data contexts of each historical period of observation relative to the current day's observation period includes: calculating the relative deviation rates of the average air pressure value, average temperature value, wind direction angle range, average wind speed value, and total precipitation of each historical period of observation relative to the current day's observation period and accumulating them, taking the negative value of the accumulated value and substituting it into the natural exponential function to obtain the similarity of the meteorological data contexts of each historical period of observation relative to the current day's observation period.

6. The construction area wave prediction platform based on multidimensional data analysis according to claim 1, characterized in that: The correction of the monitoring values ​​of the effective wave height of each historical reference day corresponding to the preset period in the future includes: is the independent variable, is the number of each seafloor topographic parameter, , based on the effective wave height of the future preset period Construct a multiple linear regression model for the dependent variable, ,in For the The regression coefficients of the seafloor topography parameters are is the intercept, is the error value; Collect the monitoring values ​​of each seabed topography parameter of the target construction area on each historical reference day, and substitute them into the multivariate linear regression model for fitting in combination with the effective wave height of the corresponding future preset period, and determine the intercept, error value, and the regression coefficient corresponding to each seabed topography parameter. In this way, the multivariate linear regression model is used as the correlation model between the seabed topography of the target construction area and the effective wave height of the wave; The monitoring values ​​of various seabed topography parameters in the target construction area on the day are collected, and the monitoring differences between various seabed topography parameters in the target construction area on each historical reference day and the current day are calculated. The differences are substituted into the correlation model between the seabed topography and the effective wave height of the waves in the target construction area to output the corrected value of the effective wave height of the waves under the influence of the difference in seabed topography between each historical reference day and the current day. The corrected value of the effective wave height of the waves under the influence of the difference in seabed topography between each historical reference day and the current day is output, and the corrected value of the effective wave height of the waves corresponding to the historical reference day in the preset future time period is accumulated, so as to realize the correction of the effective wave height monitoring value of the waves corresponding to the preset future time period on each historical reference day.

7. The construction area wave prediction platform based on multidimensional data analysis according to claim 6, characterized in that: The once-predicted effective wave height of waves in the target construction area for a preset period in the future on that day is the calculated average of the corrected effective wave height monitoring values ​​of waves in the target construction area for the preset period in the future on each historical reference day.

8. The construction area wave prediction platform based on multidimensional data analysis according to claim 2, characterized in that: The correction of the forecast deviation of meteorological conditions for a preset period of time in the future on the same day includes: randomly selecting meteorological monitoring data of a preset number of days in a preset historical period corresponding to a preset period of time in the future to perform real-time forecast processing of effective wave height of waves, and obtaining the error values ​​of the real-time forecast of the effective wave height of waves actually monitored relative to the actual monitoring on the preset number of days in the preset historical period, and recording them as the real-time forecast error values ​​of each historical reference day; Collect meteorological monitoring data for each historical reference day corresponding to a preset period in the future, and assign points to the average air pressure value, average temperature value, wind direction angle range, average wind speed value, and total precipitation in the meteorological monitoring data according to the preset meteorological parameter grade scoring rules stored in the cloud database, and add them up to obtain the meteorological monitoring score for each historical reference day corresponding to the preset period in the future; Similarly, based on the weather forecast data for the preset time period in the future of the day, the weather forecast score for the preset time period in the future of the day is obtained; Calculate the deviation coefficient between the meteorological monitoring score of each historical reference day corresponding to the future preset time period and the meteorological forecast score of the future preset time period of the day, select the historical reference diaries whose deviation coefficient is less than or equal to the preset permitted meteorological deviation coefficient threshold stored in the cloud database as each historical reference day, extract the instantaneous prediction error value of each historical reference day for average calculation to obtain the prediction error value of the meteorological conditions for the future preset time period of the day, and add it to the instantaneous prediction of the effective wave height of the wave for the future preset time period of the day to correct the prediction deviation of the meteorological conditions for the future preset time period of the day.

9. The construction area wave prediction platform based on multidimensional data analysis according to claim 8, characterized in that: The effective wave height of the waves in the target construction area for the second prediction in the preset time period in the future on the same day is the cumulative value of the forecast error value of the meteorological conditions in the preset time period in the future on the same day and the effective wave height of the waves predicted in real time in the preset time period in the future on the same day.

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