A method, device, equipment and storage medium for determining a long period wave

Abstract

The present invention provides a method, device, equipment and storage medium for determining a longer period wave, belonging to the field of wave analysis technology, and the method includes: constructing a wave spectrum model based on measured data of a specific sea area; determining multi-category inferred statistical characteristics of waves based on the wave spectrum model; obtaining wave observation data of the specific sea area within a certain period of time, and determining multi-category observed statistical characteristics of the waves based on the wave observation data; determining whether the wave observation data is a longer period wave based on the multi-category inferred statistical characteristics and the multi-category observed statistical characteristics. The present invention realizes multi-parameter and multi-dimensional identification of longer period waves through multi-category inferred statistical characteristics, thereby improving the accuracy of determining longer period waves.

Description

Technical Field

[0001] The present invention relates to the field of wave analysis technology, and in particular to a method, device, equipment and storage medium for determining a relatively long period wave. Background Art

[0002] Longer period waves are different from general wind waves or short period waves. They have a stronger ability to penetrate wave-permeable breakwaters. The presence of longer period waves can cause violent movement of ships in the port, causing great harm to moored ships and dock operations, and even accidents. Therefore, accurately identifying longer period waves in the sea is a prerequisite for ensuring the safety of port operations.

[0003] The traditional method of determining a long-period wave is to obtain measured wave data and analyze and determine the average period of the measured wave data. When the average period is greater than 10S, the wave is considered to be a long-period wave. The existing technology only uses a single wave period to determine a long-period wave. The judgment condition is single, resulting in a low accuracy rate for determining a long-period wave. For example, if the average period is 11S, but its ability to penetrate a wave-permeable breakwater is not strong, it is obviously unreasonable to regard this wave as a long-period wave.

[0004] Therefore, there is an urgent need to provide a method, device, equipment and storage medium for determining a longer period wave to achieve accurate determination of a longer period wave. Summary of the invention

[0005] In view of this, it is necessary to provide a method, device, equipment and storage medium for determining longer period waves, so as to solve the technical problem in the prior art that longer period waves are determined through a single average period, resulting in low accuracy in determining longer period waves.

[0006] On the one hand, in order to solve the above technical problems, the present invention provides a method for determining a long period wave, comprising:

[0007] Construct wave spectrum model based on measured data in specific sea areas;

[0008] Determine the multi-classification statistical characteristics of waves based on the wave spectrum model;

[0009] Acquire wave observation data of the specific sea area within a certain period of time, and determine multiple types of observation statistical characteristics of the waves based on the wave observation data;

[0010] Based on the multiple categories of inference statistical features and the multiple categories of observation statistical features, it is determined whether the wave observation data is a longer period wave.

[0011] In a possible implementation, the wave spectrum model is:

[0012]

[0013] In the formula, is the wave spectral density function; Wave frequency; is the effective wave height; is the spectrum peak period; is the peak enhancement factor.

[0014] In a possible implementation, the determining whether the wave observation data is a long-period wave based on the multiple categories of inference statistical features and the multiple categories of observation statistical features includes:

[0015] Determine whether a difference between a characteristic value of the observed statistical feature and a characteristic value of the inferred statistical feature is less than a preset difference;

[0016] If it is less than, then the wave observation data is a longer period wave; if not, then the wave observation data is not a longer period wave.

[0017] In a possible implementation, the derived statistical features include derived wave height statistical features and derived period statistical features, the derived wave height statistical features include a first proportional coefficient between the average wave height and the zero-order moment of the spectrum, a second proportional coefficient between the root mean square wave height and the zero-order moment of the spectrum, a third proportional coefficient between the effective wave height and the zero-order moment of the spectrum, and a fourth proportional coefficient between the 1 / 10 large wave average wave height and the zero-order moment of the spectrum; the derived period statistical features include a first period proportional coefficient between the average period and the zero first-order spectrum period, a second period proportional coefficient between the average period and the zero second-order spectrum period, a third period proportional coefficient between the spectrum peak period and the effective wave height period, and a fourth period proportional coefficient between the spectrum peak period and the average period.

[0018] In a possible implementation, after determining whether the wave observation data is a long-period wave based on the multiple-category inference statistical features and the multiple-category observation statistical features, the method further includes:

[0019] Determining multiple types of wave group characteristics based on the wave observation data;

[0020] Determining group parameters of the wave observation data based on the multiple types of wave group characteristics;

[0021] When the group parameter is greater than a threshold parameter, it is determined that the verification result of the wave observation data is a longer period wave.

[0022] In a possible implementation, the multiple types of wave group characteristics include wave group height characteristics and wave group length characteristics, the wave group height characteristics include GF parameters and GFH parameters, and the wave group length characteristics include GLF parameters, average company length parameters and average total company length parameters.

[0023] In a possible implementation, determining the group parameters of the wave observation data based on the multiple types of wave group characteristics includes:

[0024] A weighted sum of the GF parameter, the GFH parameter, the GLF parameter, the average company length parameter and the average total company length parameter is calculated based on preset weights, and the weighted sum is used as the group parameter.

[0025] On the other hand, the present invention also provides a device for determining a relatively long period wave, comprising:

[0026] A wave spectrum model building unit, used to build a wave spectrum model based on measured data of a specific sea area;

[0027] An inferred statistical feature determination unit, used for determining multiple types of inferred statistical features of waves based on the wave spectrum model;

[0028] An observation statistical feature determination unit, used to obtain wave observation data of the specific sea area within a certain period of time, and determine multiple types of observation statistical features of the waves based on the wave observation data;

[0029] A longer period wave determination unit is used to determine whether the wave observation data is a longer period wave based on the multiple categories of inference statistical features and the multiple categories of observation statistical features.

[0030] On the other hand, the present invention also provides a relatively long period wave determination device, comprising a memory and a processor, wherein:

[0031] The memory is used to store programs;

[0032] The processor is coupled to the memory and is used to execute the program stored in the memory to implement the steps in the method for determining a longer period wave described in any one of the possible implementations above.

[0033] On the other hand, the present invention also provides a computer-readable storage medium for storing computer-readable programs or instructions, which, when executed by a processor, can implement the steps in the method for determining a longer period wave described in any of the above possible implementations.

[0034] The beneficial effects of the present invention are as follows: the method for determining longer period waves provided by the present invention builds an accurate wave spectrum model based on measured data of characteristic sea areas, and determines multiple categories of inferred statistical features based on the wave spectrum model. The inferred statistical features can characterize the characteristics of longer period waves, and then multiple categories of observed statistical features are determined based on the acquired wave observation data, so that the longer period waves can be judged by observing statistical features and inferred statistical features. Compared with the prior art that identifies longer period waves by a single parameter, the average period, the present invention uses multiple categories of inferred statistical features to identify longer period waves by multiple parameters and dimensions, thereby improving the accuracy of determining longer period waves. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0036] Figure 1 A schematic flow chart of an embodiment of a method for determining a longer period wave provided by the present invention;

[0037] Figure 2 This is a flow chart of an embodiment of step S103 of the present invention;

[0038] Figure 3 A schematic diagram of the structure of an embodiment of the present invention for verifying the determination result of a longer period wave based on wave group characteristics;

[0039] Figure 4 A schematic diagram of the structure of an embodiment of a device for determining a relatively long period wave provided by the present invention;

[0040] Figure 5 A schematic structural diagram of an embodiment of a device for determining a longer period wave provided by the present invention. DETAILED DESCRIPTION

[0041] 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0042] It should be understood that the schematic drawings are not drawn to scale. The flowchart used in the present invention shows the operations implemented according to some embodiments of the present invention. It should be understood that the operations of the flowchart can be implemented out of order, and the steps without logical context can be reversed in order or implemented simultaneously. In addition, those skilled in the art, under the guidance of the content of the present invention, can add one or more other operations to the flowchart, and can also remove one or more operations from the flowchart. Some of the block diagrams shown in the accompanying drawings are functional entities, which do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor systems and / or microcontroller systems.

[0043] Reference to an "embodiment" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiment may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0044] The present invention provides a method, device, equipment and storage medium for determining a relatively long period wave, which are described below respectively.

[0045] Figure 1 A schematic flow chart of an embodiment of a method for determining a longer period wave provided by the present invention is as follows: Figure 1 As shown, the method for determining the longer period wave includes:

[0046] S101. Constructing a wave spectrum model based on measured data of a specific sea area;

[0047] S102, determining the multi-classification statistical characteristics of the waves based on the wave spectrum model;

[0048] S103, obtaining wave observation data of a specific sea area within a certain period of time, and determining multiple types of observation statistical characteristics of waves based on the wave observation data;

[0049] S104. Determine whether the wave observation data is a longer period wave based on multi-class inference statistical characteristics and multi-class observation statistical characteristics.

[0050] It should be understood that the wave observation data is also measured data, and the difference between the measured data in step S101 and the wave observation data in step S103 is the data time.

[0051] In a specific embodiment of the present invention, the specific sea area may be the sea area where the ADIPALA observation station of Indonesia is located.

[0052] Since the wave spectrum model constructed in step S101 is a wave spectrum model representing waves with a longer period, the measured data in step S101 should be data that meets certain conditions. Specifically, the measured data in step S101 are: 1. Wave data with an effective wave height greater than 1m; 2. Wave data with an average period greater than 12S, to ensure that the constructed wave spectrum model effectively represents waves with a longer period.

[0053] Furthermore, in order to avoid the adverse effects of noise data on the constructed wave spectrum model, in some embodiments of the present invention, before constructing the wave spectrum model, the measured data needs to be preprocessed to eliminate data that is obviously unreasonable due to environmental factors or signal interference.

[0054] Compared with the prior art, the method for determining longer period waves provided in the embodiment of the present invention builds an accurate wave spectrum model based on the measured data of the characteristic sea area, and determines multiple categories of inferred statistical features based on the wave spectrum model. The inferred statistical features can characterize the characteristics of longer period waves, and then determine multiple categories of observed statistical features based on the acquired wave observation data, so that the longer period waves can be judged by observing statistical features and inferred statistical features. Compared with the prior art that identifies longer period waves by a single parameter of the average period, the embodiment of the present invention uses multiple categories of inferred statistical features to identify longer period waves by multiple parameters and dimensions, thereby improving the accuracy of determining longer period waves.

[0055] In some embodiments of the present invention, the wave spectrum model is:

[0056]

[0057] In the formula, is the wave spectral density function; Wave frequency; is the effective wave height; is the spectrum peak period; is the peak enhancement factor.

[0058] Since the statistical characteristics are derived to characterize the characteristics of longer period waves, Figure 2 As shown, step S103 is specifically as follows:

[0059] S201, determining whether the difference between the characteristic value of the observed statistical feature and the characteristic value of the inferred statistical feature is less than a preset difference;

[0060] S202. If it is less than, the wave observation data is a longer period wave; if not, the wave observation data is not a longer period wave.

[0061] It should be understood that the preset difference can be set or adjusted according to experience or actual application scenarios, and is not specifically limited here.

[0062] In some embodiments of the present invention, deducing statistical features includes deducing wave height statistical features and deducing period statistical features. The deduced wave height statistical features include a first proportional coefficient between the average wave height and the zero-order moment of the spectrum, a second proportional coefficient between the root mean square wave height and the zero-order moment of the spectrum, a third proportional coefficient between the effective wave height and the zero-order moment of the spectrum, and a fourth proportional coefficient between the 1 / 10 large wave average wave height and the zero-order moment of the spectrum; deducing period statistical features includes a first period proportional coefficient between the average period and the zero first-order spectrum period, a second period proportional coefficient between the average period and the zero second-order spectrum period, a third period proportional coefficient between the spectrum peak period and the effective wave height period, and a fourth period proportional coefficient between the spectrum peak period and the average period.

[0063] Specifically, the first proportionality coefficient for:

[0064]

[0065]

[0066] Second proportionality factor for:

[0067]

[0068] The third proportionality factor for:

[0069]

[0070] Fourth proportionality factor for:

[0071]

[0072] In the formula, is the average wave height; is the zero-order moment of the spectrum; is the mean square wave height; is the effective wave height; It is 1 / 10 of the average wave height of large waves.

[0073] Specifically, the first cycle proportionality coefficient is:

[0074]

[0075]

[0076] The second period proportionality coefficient is:

[0077]

[0078]

[0079] The third period proportionality coefficient is:

[0080]

[0081] The fourth cycle proportionality coefficient is:

[0082]

[0083] In the formula, is the zero first-order spectrum period; is the first moment of the spectrum; is the zero second-order spectral period; is the second moment of the spectrum; is the effective wave height period; is the average period.

[0084] In a specific embodiment of the present invention, when the specific sea area is the ADIPALA sea area of ​​Indonesia, the first proportional coefficient is 2.438, the second proportional coefficient is 2.761, the third proportional coefficient is 3.928, the fourth proportional coefficient is 4.90, the first period proportional coefficient is 0.967, the second period proportional coefficient is 1.071, the third period proportional coefficient is 1.095, and the fourth period proportional coefficient is 1.391.

[0085] Since the longer period waves in the sea area do not appear alone but always exist in the form of groups, in order to further verify the longer period waves determined in step S103, in some embodiments of the present invention, Figure 3 As shown, after step S103, the following further includes:

[0086] S301, determining multiple types of wave group characteristics based on wave observation data;

[0087] S302, determining group parameters of wave observation data based on multiple types of wave group characteristics;

[0088] S303. When the group parameter is greater than the threshold parameter, it is determined that the verification result of the wave observation data is a longer period wave.

[0089] After determining the longer period wave, the embodiment of the present invention further verifies the determination result based on the characteristics of multiple types of wave groups, and performs secondary verification on the longer period wave from the perspective of the wave group, which can further ensure the accuracy of the determined longer period wave.

[0090] Furthermore, since the wave group characteristics include wave group height characteristics and wave group length characteristics, when determining the group parameters based on these two characteristics, in order to ensure the accuracy and simplicity of the considered wave group characteristics, before step S302, it is necessary to analyze the correlation between the wave group height characteristics and the wave group length characteristics. If the correlation is not high, then when determining the group parameters, both the wave group height characteristics and the wave group length characteristics need to be considered. If the correlation is high, then when determining the group parameters, only the wave group height characteristics or the wave group length characteristics need to be considered.

[0091] It is verified by measured data that the correlation between the wave group height characteristics and the wave group length characteristics is not high. Therefore, the wave group characteristics involved in determining the group parameters in step S302 include the wave group height characteristics and the wave group length characteristics.

[0092] In a specific embodiment of the present invention, the wave group height characteristics include GF parameters and GFH parameters, and the wave group length characteristics include GLF parameters, average company length parameters and average total company length parameters.

[0093] It should be understood that: the GF parameter is a wave group height characteristic obtained based on the wave energy process line method, and the GFH parameter is a wave group height characteristic obtained based on the wave envelope method. The embodiment of the present invention obtains two different wave group height characteristics based on two methods, thereby improving the accuracy of the wave group height characteristics, and further improving the accuracy of determining longer period waves.

[0094] Similarly, the GLF parameters are wave group length characteristics obtained based on the wave envelope method, and the average company length parameters and the average total company length parameters are wave group length characteristics obtained based on the statistical theory method of the company. The embodiment of the present invention obtains two different wave group length characteristics based on two methods, which can improve the accuracy of the wave group length characteristics, and further improve the accuracy of determining longer period waves.

[0095] Among them, the GF parameters are:

[0096]

[0097] In the formula, is the zero-order moment of the smooth instantaneous wave energy process line.

[0098] Among them, the GFH parameters are:

[0099]

[0100] In the formula, Wave Envelope The standard deviation of Wave Envelope The average value of .

[0101] Among them, the GLF parameters are:

[0102]

[0103] In the formula, is the peak frequency of the spectrum; is the peak frequency of the wave packet spectrum.

[0104] Among them, the average company length parameter for:

[0105]

[0106]

[0107] In the formula, The adjacent wave height H 1 and H 2 Both exceed the threshold wave height value probability; is the wave height distribution; The adjacent wave height H 1 and H 2 joint probability density function.

[0108] Among them, the average total company length parameter is:

[0109]

[0110]

[0111] In the formula, The adjacent wave height H 1 and H 2 Both exceed the threshold wave height value probability.

[0112] In a specific embodiment of the present invention, step S302 is specifically:

[0113] The weighted sum of the GF parameter, GFH parameter, GLF parameter, average company length parameter and average total company length parameter is calculated based on the preset weights, and the weighted sum is used as the group parameter.

[0114] The embodiment of the present invention performs weighted processing on the above-mentioned multiple wave group characteristics, and represents the group parameter by a weighted numerical value, and the result is intuitive and simple.

[0115] It should be understood that the preset weights can be set or adjusted according to actual application scenarios or experience, and are not specifically limited here.

[0116] In order to better implement the method for determining a longer period wave in the embodiment of the present invention, based on the method for determining a longer period wave, the embodiment of the present invention also provides a device for determining a longer period wave, such as Figure 4 As shown, the longer period wave determination device 400 comprises:

[0117] A wave spectrum model building unit 401 is used to build a wave spectrum model based on measured data of a specific sea area;

[0118] The inferred statistical feature determination unit 402 is used to determine multiple types of inferred statistical features of waves based on the wave spectrum model;

[0119] The observation statistical feature determination unit 403 is used to obtain wave observation data of a specific sea area within a certain period of time, and determine multiple types of observation statistical features of waves based on the wave observation data;

[0120] The longer period wave determination unit 404 is used to determine whether the wave observation data is a longer period wave based on multiple classification statistical features and multiple classification statistical features.

[0121] The longer period wave determination device 400 provided in the above embodiment can implement the technical solution described in the above embodiment of the longer period wave determination method. The specific implementation principles of the above modules or units can refer to the corresponding contents in the above embodiment of the longer period wave determination method, which will not be repeated here.

[0122] like Figure 5 As shown, the present invention also provides a relatively long period wave determination device 500. The relatively long period wave determination device 500 includes a processor 501, a memory 502 and a display 503. Figure 5 Only some of the components of the longer period wave determination apparatus 500 are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0123] In some embodiments, the processor 501 may be a central processing unit (CPU), a microprocessor or other data processing chip, used to run program codes or process data stored in the memory 502, such as the longer period wave determination method of the present invention.

[0124] In some embodiments of the present invention, processor 501 may be a single server or a server group. The server group may be centralized or distributed. In some embodiments, processor 501 may be local or remote. In some embodiments, processor 501 may be implemented in a cloud platform. In one embodiment, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-cloud, etc., or any combination thereof.

[0125] In some embodiments, the memory 502 may be an internal storage unit of the longer period wave determination device 500, such as a hard disk or memory of the longer period wave determination device 500. In other embodiments, the memory 502 may also be an external storage device of the longer period wave determination device 500, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the longer period wave determination device 500.

[0126] Furthermore, the memory 502 may include both an internal storage unit and an external storage device of the longer period wave determination device 500. The memory 502 is used to store application software installed in the longer period wave determination device 500 and various data.

[0127] In some embodiments, the display 503 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display 503 is used to display information of the longer period wave determination device 500 and to display a visual user interface. The components 501-503 of the longer period wave determination device 500 communicate with each other via a system bus.

[0128] In some embodiments of the present invention, when the processor 501 executes the longer period wave determination program in the memory 502, the following steps may be implemented:

[0129] Construct wave spectrum model based on measured data in specific sea areas;

[0130] Determine the multi-classification statistical characteristics of waves based on the wave spectrum model;

[0131] Obtain wave observation data in a specific sea area within a certain period of time, and determine multiple types of observation statistical characteristics of waves based on the wave observation data;

[0132] Based on multi-class inference statistical characteristics and multi-class observation statistical characteristics, it is determined whether the wave observation data is a longer period wave.

[0133] It should be understood that: when the processor 501 executes the longer period wave determination program in the memory 502, in addition to the above functions, other functions can also be implemented. For details, please refer to the description of the corresponding method embodiment above.

[0134] Furthermore, the embodiment of the present invention does not specifically limit the type of the longer period wave determination device 500 mentioned, and the longer period wave determination device 500 may be a portable longer period wave determination device such as a mobile phone, a tablet computer, a personal digital assistant (PDA), a wearable device, a laptop computer, etc. Exemplary embodiments of portable longer period wave determination devices include but are not limited to portable longer period wave determination devices equipped with IOS, Android, Microsoft or other operating systems. The above-mentioned portable longer period wave determination device may also be other portable longer period wave determination devices. It should also be understood that in some other embodiments of the present invention, the longer period wave determination device 500 may not be a portable longer period wave determination device, but a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0135] Correspondingly, an embodiment of the present invention also provides a computer-readable storage medium, which is used to store computer-readable programs or instructions. When the program or instructions are executed by a processor, it can implement the steps or functions of the longer period wave determination method provided in the above-mentioned method embodiments.

[0136] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware (such as a processor, a controller, etc.) through a computer program, and the computer program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0137] The above is a detailed introduction to a method, device, equipment and storage medium for determining a longer period wave provided by the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for determining a relatively long period wave, characterized in that: include: Construct wave spectrum model based on measured data in specific sea areas; Determine the multi-classification statistical characteristics of waves based on the wave spectrum model; Acquire wave observation data of the specific sea area within a certain period of time, and determine multiple types of observation statistical characteristics of the waves based on the wave observation data; Determining whether the wave observation data is a relatively long period wave based on the multiple types of inferred statistical features and the multiple types of observed statistical features; The determining whether the wave observation data is a long period wave based on the multiple categories of inference statistical features and the multiple categories of observation statistical features comprises: Determine whether a difference between a characteristic value of the observed statistical feature and a characteristic value of the inferred statistical feature is less than a preset difference; If it is less than, the wave observation data is a longer period wave, if not, the wave observation data is not a longer period wave; The derived statistical features include derived wave height statistical features and derived period statistical features. The derived wave height statistical features include a first proportional coefficient between the average wave height and the zero-order moment of the spectrum, a second proportional coefficient between the root mean square wave height and the zero-order moment of the spectrum, a third proportional coefficient between the effective wave height and the zero-order moment of the spectrum, and a fourth proportional coefficient between the 1 / 10 large wave average wave height and the zero-order moment of the spectrum; the derived period statistical features include a first period proportional coefficient between the average period and the zero first-order spectrum period, a second period proportional coefficient between the average period and the zero second-order spectrum period, a third period proportional coefficient between the spectrum peak period and the effective wave height period, and a fourth period proportional coefficient between the spectrum peak period and the average period.

2. The method for determining a longer period wave according to claim 1, characterized in that: The wave spectrum model is: In the formula, is the wave spectral density function; Wave frequency; is the effective wave height; is the spectrum peak period; is the peak enhancement factor.

3. The method for determining a longer period wave according to claim 1, characterized in that: After determining whether the wave observation data is a long-period wave based on the multiple-category inference statistical features and the multiple-category observation statistical features, the method further includes: Determining multiple types of wave group characteristics based on the wave observation data; Determining group parameters of the wave observation data based on the multiple types of wave group characteristics; When the group parameter is greater than a threshold parameter, it is determined that the verification result of the wave observation data is a longer period wave.

4. The method for determining a longer period wave according to claim 3, characterized in that: The multiple types of wave group characteristics include wave group height characteristics and wave group length characteristics. The wave group height characteristics include GF parameters and GFH parameters. The wave group length characteristics include GLF parameters, average company length parameters and average total company length parameters.

5. The method for determining a longer period wave according to claim 4, characterized in that: The determining of the group parameters of the wave observation data based on the multiple types of wave group characteristics comprises: A weighted sum of the GF parameter, the GFH parameter, the GLF parameter, the average company length parameter and the average total company length parameter is calculated based on preset weights, and the weighted sum is used as the group parameter.

6. A device for determining a relatively long period wave, characterized in that: include: A wave spectrum model building unit, used to build a wave spectrum model based on measured data of a specific sea area; An inferred statistical feature determination unit, used for determining multiple types of inferred statistical features of waves based on the wave spectrum model; An observation statistical feature determination unit, used for acquiring wave observation data of the specific sea area within a certain period of time, and determining multiple types of observation statistical features of the waves based on the wave observation data; A longer period wave determination unit, used to determine whether the wave observation data is a longer period wave based on the multiple categories of inference statistical features and the multiple categories of observation statistical features; The determining whether the wave observation data is a long period wave based on the multiple categories of inference statistical features and the multiple categories of observation statistical features comprises: Determine whether a difference between a characteristic value of the observed statistical feature and a characteristic value of the inferred statistical feature is less than a preset difference; If it is less than, the wave observation data is a longer period wave, if not, the wave observation data is not a longer period wave; The derived statistical features include derived wave height statistical features and derived period statistical features. The derived wave height statistical features include a first proportional coefficient between the average wave height and the zero-order moment of the spectrum, a second proportional coefficient between the root mean square wave height and the zero-order moment of the spectrum, a third proportional coefficient between the effective wave height and the zero-order moment of the spectrum, and a fourth proportional coefficient between the 1 / 10 large wave average wave height and the zero-order moment of the spectrum; the derived period statistical features include a first period proportional coefficient between the average period and the zero first-order spectrum period, a second period proportional coefficient between the average period and the zero second-order spectrum period, a third period proportional coefficient between the spectrum peak period and the effective wave height period, and a fourth period proportional coefficient between the spectrum peak period and the average period.

7. A device for determining a relatively long period wave, characterized in that: comprising a memory and a processor, wherein: The memory is used to store programs; The processor is coupled to the memory and is used to execute the program stored in the memory to implement the steps in the method for determining a longer period wave as described in any one of claims 1 to 5 above.

8. A computer-readable storage medium, characterized in that: Used to store computer-readable programs or instructions, which, when executed by a processor, can implement the steps of the method for determining a longer period wave as described in any one of claims 1 to 5.

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