Self-adaptive sea fog distribution calculation method based on dynamic programming
Through dynamic programming adaptive segmentation and Fernald inversion methods, the problem of low detection accuracy of sea fog distribution is solved, and high-precision sea fog distribution monitoring is achieved, which is suitable for complex marine environments.
Patent Information
- Application Number
- CN202411704379.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-26
- Publication Date
- 2025-03-14
AI Technical Summary
In the prior art, the detection accuracy of sea fog distribution is poor, and due to the complex marine environment, it is difficult to effectively monitor the non-uniform distribution of sea fog.
The adaptive sea fog distribution calculation method based on dynamic programming is adopted, and the extinction coefficient and Fernald backward inversion method are calculated through signal preprocessing, dynamic programming segmentation, and slope methods to obtain the overall distribution of sea fog.
It improves the accuracy and accuracy of sea fog distribution calculation, can flexibly adapt to different sea fog monitoring scenarios, reduces noise interference, and provides more comprehensive sea fog distribution information.
Smart Images
Figure CN119620108A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of laser radar detection technology, and in particular to an adaptive sea fog distribution calculation method based on dynamic programming. Background Art
[0002] Sea fog refers to a type of fog located on the sea surface or in coastal areas under the influence of the ocean and ocean currents. It can generally be divided into four types: advection fog, radiation fog, mixed fog and mixed fog. Sea fog can cause a sharp drop in visibility in local areas, posing a serious threat to maritime traffic and shipping safety. Therefore, high-precision, high-temporal and high-spatial resolution sea fog monitoring is of great significance to shipping safety and maritime activities.
[0003] At present, LiDAR has begun to be used in the field of sea fog monitoring. LiDAR can continuously monitor sea fog with a radius of several kilometers by emitting laser pulses to interact with the detection target, receiving the echo signal and performing inversion. In recent years, LiDAR technology has been widely used in visibility monitoring and aerosol measurement. Although it has performed well in atmospheric monitoring, its application in sea fog distribution monitoring is still in its early stages. The inversion theory of horizontal visibility has not yet formed a systematic calculation method. Common extinction inversion methods include the Collis slope method, the Klett method and the Fernald method. The formation and dissipation of sea fog are affected by many factors, including temperature, humidity, wind speed and ocean currents. The distribution of sea fog is usually uneven, which poses new challenges to the inversion of horizontal visibility. Summary of the invention
[0004] The technical problem to be solved by the present invention is: in order to solve the technical problem in the prior art that the sea fog distribution detection accuracy is poor due to the influence of the complex marine environment, the present invention provides an adaptive sea fog distribution calculation method based on dynamic programming.
[0005] The technical solution adopted by the present invention to solve the technical problem is: an adaptive sea fog distribution calculation method based on dynamic programming, comprising the following steps: Step 1: Collect sea fog visibility signal data, pre-process the signal to obtain a distance correction signal, then perform logarithmic transformation on the distance correction signal to obtain D(x), and perform signal adaptive segmentation according to the distribution of D(x) through an optimized dynamic programming algorithm; Step 2, calculating the average extinction coefficient in each segmented area by the slope method; Step 3, taking the average extinction coefficient calculated within each segment range as the extinction of the reference point, and obtaining the extinction value of each point within the acquisition range by Fernald backward inversion method; Step 4: Based on the visibility equation, convert the visibility according to the extinction coefficient to obtain the sea fog distribution.
[0006] Furthermore, in the step one, the preprocessing process is: according to the visibility lidar equation, the signal is subjected to background subtraction processing and the distance correction signal of the lidar is calculated.
[0007] Furthermore, in step 1, the distance correction signal of the laser radar is described as: ,in, , P(z) is the visibility lidar equation, , and are the backscattering coefficients of aerosol and atmospheric molecules, respectively, , and are the extinction coefficients of aerosols (fog droplets) and atmospheric molecules respectively, and C is the radar system constant.
[0008] Furthermore, the relationship between the visibility signal and the extinction coefficient is constructed. ,in Distance correction signal The logarithmic expression of .
[0009] Furthermore, in the step 1, the signal is first segmented using a global optimization algorithm, and then Fernandez inversion is performed based on the segmentation results.
[0010] Furthermore, the signal segmentation process is specifically as follows: construct a state transfer equation for the segmented error function, use the data matrix of the error function as a lookup table, fill the dp table through the state transfer equation, find the optimal segmentation point from the dp table, and trace back through the recorded segments table to find the starting point of each segment.
[0011] Furthermore, the state transfer equation of the segmented error function is: ,in, represents the minimum total error of the first i data points divided into j segments, Represents the fitting error from the k+1th point to the i-th point.
[0012] Furthermore, in step 2, the distance and Sequence, firstly, the abnormal outliers in the current segment are eliminated based on the 3-sigma criterion, and then the slope of the current segment range is calculated using the least squares method.
[0013] Furthermore, the slope of the jth segment is ,in, Indicates the distance between the i-th point in the current calculation segment and the emission point. Indicates distance The value of the point, represents the slope of the jth segment, the average extinction within each segment .
[0014] Furthermore, in step 3, based on the calculated average extinction of each segment, the average extinction is used as the reference extinction within the range of segment j, and the backscattering coefficient of each point in the current segment is calculated using the Fernald back integration method. ,in is the lidar ratio of aerosols and atmospheric molecules, where the lidar ratio of atmospheric molecules can be calculated using Rayleigh scattering theory .
[0015] Compared with the prior art, the present invention has the following beneficial effects: This method uses an optimized dynamic programming method to adaptively calculate the overall distribution of sea fog. Dynamic programming can find the global optimal solution among all possible segmentation schemes through recursion and storage of solutions to subproblems, and can flexibly adapt to different constraints and make adjustments according to actual needs to adapt to different sea fog monitoring scenarios. In addition, by reasonably designing the error function, the influence of noise on the sea fog distribution calculation can be reduced to a large extent, thereby improving the accuracy of the calculation results.
[0016] Specifically, the invention takes into account the change in the overall signal slope over the entire detection distance, and divides the sea fog into segments so that the sea fog is evenly distributed in each segment, thereby obtaining the overall distribution of the sea fog within the collection range.
[0017] The present invention can find the global optimal solution among all possible segmentation schemes by using dynamic programming in the process of signal segmentation through recursion and storage of sub-problem solutions. This means that no matter how the data is distributed, dynamic programming can ensure that the segmentation scheme found has the smallest overall error, thereby accurately reflecting the actual distribution of sea fog and improving the inversion accuracy.
[0018] Furthermore, dynamic programming can flexibly adapt to different constraints. For example, an upper limit on the number of segments can be set, or a regularization term can be introduced to balance the number of segments and the fitting error. This flexibility enables dynamic programming to be adjusted according to actual needs, thus adapting to different sea fog monitoring scenarios.
[0019] The segmented processing method and error minimization principle in the invention make it robust to noise in the data. By reasonably designing the error function, the influence of noise on the calculation of sea fog distribution can be reduced to a large extent, thereby improving the accuracy of the calculation results. BRIEF DESCRIPTION OF THE DRAWINGS
[0020] The present invention is further described below in conjunction with the accompanying drawings and embodiments.
[0021] Figure 1 It is a schematic diagram of an implementation flow of a method for calculating adaptive sea fog distribution based on dynamic programming of the present invention; Figure 2 It is a schematic diagram of the implementation process of dynamic signal segmentation in the present invention; Figure 3 This is a schematic diagram of signal segmentation when the sea fog is distributed relatively evenly within the detection range of the lidar and the visibility is basically consistent within the detectable range; Figure 4 This is a schematic diagram of the signal segmentation where the sea fog quickly turns from light fog to thick fog within the detection range of the lidar and the signal attenuates rapidly.
[0022] Figure 5 This is a signal segmentation diagram showing the gradual transformation of sea fog from high visibility to medium visibility within the lidar detection range. DETAILED DESCRIPTION
[0023] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.
[0024] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "multiple" means two or more.
[0025] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.
[0026] like Figures 1 to 5 As shown, it is the best embodiment of the present invention. The adaptive sea fog distribution calculation method based on dynamic programming in this embodiment includes the following steps: Step 1: Signal acquisition and segmentation processing.
[0027] In this step, the sea fog visibility signal data is collected, the signal is preprocessed to obtain the distance correction signal, and then the distance correction signal is logarithmically transformed to obtain D(x). According to the distribution of D(x), the signal is adaptively segmented through an optimized dynamic programming algorithm to ensure the uniform distribution of sea fog within each segment, thereby determining the overall distribution of sea fog.
[0028] S101 builds the relationship between the acquisition signal and extinction The extinction coefficient is generally considered to be an important parameter for describing the attenuation of light intensity in the atmosphere. The presence of sea fog will cause the extinction coefficient to increase significantly. If the atmosphere is uniform, the extinction coefficient is considered to be a constant, and the corresponding radar signal will show linear attenuation. Therefore, the overall signal is segmented according to the slope change, and the slope change at different positions is calculated to obtain the overall distribution of the sea fog. The automatic signal segmentation of the present invention is realized based on an optimized dynamic programming algorithm, and its calculation process is as follows: According to the signal segmentation theory, the visibility lidar equation can be expressed by equation (1): (1) Among them, z is the collected sea fog distance signal, is the average power of the laser pulse, is the system constant of the lidar system, is the overlapping factor of the system, is the speed of light, is the pulse width of the laser pulse, is the effective area of the receiving telescope, and are the backscattering coefficients of aerosols (fog droplets) and atmospheric molecules, and are the extinction coefficients of aerosols (droplets) and atmospheric molecules, respectively, and z' is the integrated variable of distance.
[0029] According to formula (1), the distance correction signal of the laser radar can be simplified as: (2) in, , , , C is the radar system constant, taking the natural logarithm of both ends, we get: (3) Distance correction signal The logarithmic expression of , and assuming a uniform atmosphere, then As a constant, we can get , taking the derivatives of both sides, we can get: (4) Thus, the relationship between the collected signal and extinction is established. Further, the visibility can be calculated based on the collected extinction coefficient. The atmospheric extinction coefficient is obtained according to formula (4): In order to ensure the rationality of the atmospheric homogeneity assumption, we need to reasonably segment the signal to ensure that the attenuation rate of each signal segment is consistent, so as to accurately extract the extinction parameters of each signal segment. This segmentation processing can not only reduce the noise interference in the signal, but also improve the accuracy of the extinction coefficient calculation.
[0030] S102 signal adaptive segmentation The goal of this step is to divide the signal into several segments so that each segment can be fitted with a linear model, thereby confirming that the sea fog is evenly distributed in each distance segment and ultimately ensuring that the overall fitting error is minimized. Therefore, the signal segmentation problem can be transformed into a dynamic programming problem to solve, which can effectively handle optimal substructures and overlapping subproblems and ensure global optimality. The core of dynamic programming is to decompose the problem into subproblems.
[0031] For the piecewise regression problem, the subproblem can be defined as: given the first i points of the data and at most j segments, find the segmentation solution that minimizes the error.
[0032] The state transfer equation of the piecewise error function can be defined as: (5) in, represents the minimum total error of the first i data points, divided into j segments, Represents the fitting error from the k+1th point to the ith point. The data matrix constructed by formula (5) is a lookup table, the dp table. The dp table is filled by the above state transition equation. Finally, dp[n-1][max_segments] is the minimum error of the first n points divided into max_segments segments. Find the optimal segmentation point from the dp table, and trace back through the recorded segments table to find the starting point of each segment. The overall signal segmentation process is as follows: Figure 2 shown.
[0033] It should be noted that the dynamic programming algorithm in this application is used for adaptive segmentation without considering the uniformity of the collected signal. During the segmentation process, a global optimization algorithm is used to segment the signal before inversion, and then Fernandez inversion is performed based on the segmentation results, thereby improving the accuracy of the adaptive segmentation.
[0034] In other embodiments, adaptive segmentation of the signal may also be achieved through machine learning algorithms such as greedy algorithms, linear regression, Bayesian models, and deep learning algorithms.
[0035] Step 2: accurately calculate the average extinction coefficient in each segmented area using the slope method.
[0036] According to the coordinates of the segmentation points, the distance and Sequence, firstly, remove the abnormal outliers in the current segment based on the 3-sigma criterion, and then calculate the slope of the current segment range according to the least squares method. The specific calculation formula is as follows: (6) in, Indicates the distance between the i-th point in the current calculation segment and the emission point. Indicates distance The value of the point, represents the slope of the jth segment.
[0037] According to formula (4), the average extinction within each segment can be calculated : (7) Step 3: The average extinction coefficient calculated within each segment range is used as the extinction of the reference point, and the extinction value of each point within the acquisition range is obtained by the Fernald backward inversion method.
[0038] Based on the calculated average extinction of each segment, it is used as the reference extinction within the range of segment j, and the Fernald back-integration method is used to calculate the backscattering coefficient of each point in the current segment. The formula is as follows: (8) in , respectively, are the lidar ratios of aerosols and atmospheric molecules. The lidar ratio of atmospheric molecules can be calculated using Rayleigh scattering theory. The laser radar ratio of aerosol (fog droplet) is closely related to the scale factor, complex refractive index and particle shape. For the convenience of calculation, the commonly used assumption is that its laser radar ratio is a known constant. After obtaining the aerosol backscattering coefficient profile, it can be converted into an aerosol (fog droplet) extinction coefficient through the laser radar ratio. .
[0039] Step 4: The visibility value within the detection range of the visibility lidar can be obtained through the horizontal visibility equation. The visibility calculation result can be obtained according to the conversion formula between the extinction coefficient and visibility as follows: (9) like Figure 3-5 As shown in the figure, signals under different sea fog distributions were selected respectively, and the optimized dynamic programming algorithm was used to segment the signals. The segmentation results clearly showed the overall distribution of sea fog. The overall changes of signals belonging to the same segmented area were relatively consistent. The larger the slope of the signal in different segments, the faster the signal attenuation in this area and the higher the sea fog concentration, which provided sufficient theoretical basis for the subsequent visibility inversion.
[0040] In summary, the present invention takes into account the change in the overall signal slope over the entire detection distance, and divides the detection range into segments so that the sea fog is evenly distributed in each segment, thereby obtaining the overall distribution of the sea fog within the acquisition range.
[0041] The present invention can find the global optimal solution among all possible segmentation schemes by using dynamic programming in the process of signal segmentation through recursion and storage of sub-problem solutions. This means that no matter how the data is distributed, dynamic programming can ensure that the segmentation scheme found has the smallest overall error, thereby accurately reflecting the actual distribution of sea fog and improving the inversion accuracy.
[0042] Furthermore, dynamic programming can flexibly adapt to different constraints. For example, an upper limit on the number of segments can be set, or a regularization term can be introduced to balance the number of segments and the fitting error. This flexibility enables dynamic programming to be adjusted according to actual needs, thus adapting to different sea fog monitoring scenarios.
[0043] The segmented processing method and error minimization principle in the invention make it robust to noise in the data. By reasonably designing the error function, the influence of noise on the calculation of sea fog distribution can be reduced to a large extent, thereby improving the accuracy of the calculation results.
[0044] The above description is based on the ideal embodiment of the present invention. Through the above description, relevant personnel can make various changes and modifications without departing from the technical concept of the present invention. The technical scope of the present invention is not limited to the content of the specification, and its technical scope must be determined according to the scope of the claims.
Claims
1. An adaptive sea fog distribution calculation method based on dynamic programming, characterized in that: The following steps are involved: Step 1: Collect sea fog visibility signal data, pre-process the signal to obtain a distance correction signal, then perform logarithmic transformation on the distance correction signal to obtain D(x), and perform signal adaptive segmentation according to the distribution of D(x) through an optimized dynamic programming algorithm; Step 2, calculating the average extinction coefficient in each segmented area by the slope method; Step 3, taking the average extinction coefficient calculated within each segment range as the extinction of the reference point, and obtaining the extinction value of each point within the acquisition range by Fernald backward inversion method; Step 4: Based on the visibility equation, convert the visibility according to the extinction coefficient to obtain the sea fog distribution.
2. The adaptive sea fog distribution calculation method based on dynamic programming according to claim 1 is characterized in that: In the step 1, the preprocessing process is: according to the visibility lidar equation, the signal is processed to remove the background and the distance correction signal of the lidar is calculated.
3. The adaptive sea fog distribution calculation method based on dynamic programming according to claim 2 is characterized in that: In step 1, according to the visibility lidar equation, the distance correction signal of the lidar can be described as ,in, , P(z) is the visibility lidar equation, , and are the backscattering coefficients of aerosol and atmospheric molecules, respectively, , and are the extinction coefficients of aerosols (fog droplets) and atmospheric molecules respectively, and C is the radar system constant.
4. The adaptive sea fog distribution calculation method based on dynamic programming according to claim 3 is characterized in that: In step 1, assuming that the sea fog is evenly distributed, the relationship between the visibility signal and the extinction coefficient is constructed according to the lidar equation, and the extinction coefficient can be obtained. ,in Distance correction signal The logarithmic expression of .
5. The adaptive sea fog distribution calculation method based on dynamic programming according to claim 1 is characterized in that: In the step 1, the signal is first segmented using a global optimization algorithm, and then Fernandez inversion is performed based on the segmentation results.
6. The adaptive sea fog distribution calculation method based on dynamic programming according to claim 5 is characterized in that: The signal segmentation process is as follows: construct the state transfer equation of the segmented error function, use the data matrix of the error function as the lookup table, fill the dp table through the state transfer equation, find the optimal segmentation point from the dp table, and trace back through the recorded segments table to find the starting point of each segment.
7. The adaptive sea fog distribution calculation method based on dynamic programming according to claim 6 is characterized in that: The state transfer equation of the segmented error function is: ,in, represents the minimum total error of the first i data points divided into j segments, Represents the fitting error from the k+1th point to the i-th point.
8. The adaptive sea fog distribution calculation method based on dynamic programming according to claim 1 is characterized in that: In the second step, the distance and Sequence, firstly, the abnormal outliers in the current segment are eliminated based on the 3-sigma criterion, and then the slope of the current segment range is calculated using the least squares method.
9. The adaptive sea fog distribution calculation method based on dynamic programming according to claim 8 is characterized in that: The slope of the jth segment is ,in, Indicates the distance between the i-th point in the current calculation segment and the emission point. Indicates distance The value of the point, represents the slope of the jth segment, the average extinction within each segment .
10. The adaptive sea fog distribution calculation method based on dynamic programming according to claim 4 is characterized in that: In step 3, based on the calculated average extinction of each segment, the average extinction is used as the reference extinction within the range of segment j, and the backscattering coefficient of each point in the current segment is calculated using the Fernald back integration method. ,in is the lidar ratio of aerosols and atmospheric molecules, where the lidar ratio of atmospheric molecules can be calculated using Rayleigh scattering theory .