A method for converting electronic nautical chart ENC data to SENC data

Abstract

The present invention discloses a method for converting electronic nautical chart (ENC) data to electronic nautical chart (SENC) data, comprising: creating an actual sampling interval model for the electronic nautical chart (ENC); obtaining a chart sheet, a chart accuracy value, and an ENC chart accuracy value for the required SENC data; calculating the actual sampling interval for the ENC at the SENC data accuracy; sampling data on the same chart sheet as the ENC using Shannon's theorem to obtain an ENC sampling data sequence X; traversing X to determine similarities and differences between current data and previous data, updating data parameters and counter parameter values, and recording the location source of the data in the ENC sheet using an index; counting and recording a sequence of data counter key-value pairs; transmitting the data counter key-value pair sequence to the SENC, and outputting the sequence of data counter key-value pairs at a corresponding position on the same SENC sheet according to the index, thereby completing data conversion. The present invention can improve conversion efficiency and save system space.

Description

Technical Field

[0001] The present invention belongs to the field of electronic nautical chart data processing, and in particular relates to a method for converting electronic nautical chart ENC data into SENC data. Background Art

[0002] As an officially released standardized electronic nautical chart, ENC (Electronic Navigational Chart) provides authoritative, comprehensive, and up-to-date data support. In addition to its widespread application in intelligent navigation, maritime affairs, and port and shipping management, with the advancement of informatization, ENC and its application systems have expanded into nearly every industry operating at sea, even in inland waterways, including marine fisheries, offshore engineering and construction, marine military operations, and marine environmental surveys and protection. Due to its comprehensiveness and compatibility, ENC has become a fundamental data source for various navigation systems. However, due to data format issues, ENC is not the most efficient method for storing, manipulating, and preparing data. Therefore, researchers designed SENC (System Electronic Navigational Chart), based on the requirements of operating systems and performance standards. SENC, as an internally optimized data format for specific navigation systems, enables more efficient display and operation through real-time processing and dynamic updates of ENC data, ensuring safe and efficient navigation.

[0003] Existing methods for converting electronic nautical chart (ENC) data to system electronic nautical chart (SENC) data primarily involve reading and reconstructing the point, line, and surface data from the electronic nautical chart source data. These reconstructed points, lines, and surfaces are then projected and converted to screen coordinates before being drawn and displayed on a display terminal. However, this reconstruction and conversion process is time-consuming, inefficient, and requires significant storage space. Therefore, developing an efficient method for converting electronic nautical chart (ENC) data to SENC data has become a research priority. Summary of the Invention

[0004] In order to address the deficiencies in the prior art, the present invention provides a method for converting electronic nautical chart (ENC) data into SENC data. Based on the actual sampling time interval model of the electronic nautical chart (ENC), and in accordance with Shannon's theorem, the method collects and compresses the chart data, which is large in quantity, large in scale, highly complex, and with a high degree of image element repetition, without distortion. The data is then classified and statistically compressed. The electronic nautical chart (ENC) data information is indexed according to position information during compression, and the data is restored according to position during decompression of the SENC chart data. This ensures the correctness of the image when displayed on the SENC, improves conversion efficiency, and saves system space.

[0005] To achieve the above-mentioned object, a method for converting electronic nautical chart (ENC) data to SENC data according to one embodiment of the present invention comprises the following steps:

[0006] S1. Create a model for the actual sampling interval of the electronic nautical chart (ENC):

[0007]

[0008] Among them, T 实际 is the actual sampling time interval of the electronic nautical chart ENC, P SENC is the chart accuracy value of the system electronic chart SENC, P ENC is the chart accuracy value of the electronic nautical chart ENC, and F is the Shannon theorem sampling frequency under the accuracy of the electronic nautical chart ENC;

[0009] S2. Obtain the chart sheet E of the required system electronic chart SENC data SENC , chart accuracy value P of system electronic chart SENC data SENC and the chart accuracy value P of the electronic chart ENC ENC ;

[0010] S3, according to the obtained P SENC 、P ENC and the actual sampling time interval model of the electronic nautical chart ENC created to calculate P SENC The actual sampling time interval T of the electronic nautical chart ENC with the highest accuracy 实际 ;

[0011] S4. According to T 实际 and the chart sheet E of the required system electronic chart SENC SENC , on the same chart sheet E of ENC SENC On the other hand, Shannon’s theorem is used to perform data sampling, and the sampling data sequence of ENC is obtained as X = {x(1), x(2), x(3), …, x(t) …, x(n)};

[0012] Where x(t) is the value of the t-th electronic chart ENC data sampling point, t takes a value between 1 and n, and n is the total number of samples;

[0013] S5. Initialize the current chart data parameter curr of the sampled data sequence X val and a counter parameter count corresponding to the chart data parameter;

[0014] S6. Traverse the ENC sampling data sequence X, determine the similarities and differences between the current chart data and the previous chart data, and continuously update the parameter curr val and count values, and uses the index to record the location source of the data in the ENC map when updating;

[0015] S7. Count and record the data counter key-value pair sequence Y;

[0016]

[0017] Among them, curr val is the chart data parameter, count is the counter parameter corresponding to the chart data parameter, and m is the total number of data counter key-value pairs;

[0018] S8, after transmitting the data counter key-value pair sequence Y to SENC, the system electronic chart SENC is mapped to the chart sheet E according to the index. SENC The corresponding position outputs the data sequence Y to complete the data conversion.

[0019] Furthermore, in the step S1 model, the Shannon theorem sampling frequency F under ENC accuracy is:

[0020] F=k×f max (7);

[0021] Among them, f max is the highest frequency of the ENC chart data signal, and k is a constant greater than or equal to 2.

[0022] Furthermore, the highest frequency f of the ENC chart data signal is obtained. max The steps include:

[0023] P1. Extract ENC chart data signal features;

[0024] P2. Apply Fourier transform to the extracted signal features to identify the frequency components;

[0025] P3, calculate the power spectrum, generate the power spectrum density PSD, and determine f by finding the frequency point of maximum power max .

[0026] Furthermore, the Fourier transform in step P2 transforms the time domain signal x(t s ) is converted into a frequency domain signal X(f);

[0027] The calculation expression using discrete Fourier transform is:

[0028]

[0029] Where x[n1] is the discrete time series of the signal, n1 is the number of samples of the signal, f is the continuous frequency variable, j is the imaginary unit, is a complex exponential function.

[0030] Furthermore, the signal characteristics of the ENC chart data in step P1 include waterways, coastlines, water depths, navigation marks and obstacles.

[0031] Furthermore, in step S6, the factors for determining the similarities and differences between the current nautical chart data and the previous nautical chart data include transparency, grayscale, hue, saturation and brightness.

[0032] The beneficial effects of the present invention are:

[0033] 1. The present invention creates an actual sampling time interval model for the electronic nautical chart (ENC) and dynamically adjusts the amount of collected data M according to the display accuracy actually required by SENC. SENC The size of the data can be adjusted to achieve adaptive extraction of data volume, and small data volume conversion and transmission can be achieved while meeting usage requirements, thereby improving the efficiency of converting electronic nautical chart ENC data to SENC data;

[0034] 2. The present invention uses Shannon's theorem to achieve distortion-free sampling of electronic nautical chart (ENC) data. Taking advantage of the fact that chart data contains a large amount of repeated values, the sampled data is processed using key-value pairs. By traversing the key-value pairs of the sequence statistics of data collection, the data volume is significantly compressed. Based on an indexing method that records the data location, the data is decompressed and placed in its original position on the SENC side, achieving a data accuracy rate of over 99%. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 The present invention is a flowchart of a method for converting electronic nautical chart ENC data into SENC data. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solutions and advantages of the present invention more clear, further description is given below with reference to the accompanying drawings and embodiments.

[0037] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0038] As attached Figure 1 As shown, the present invention provides a method for converting electronic nautical chart ENC data to SENC data, which includes the steps of:

[0039] S1. Create a model for the actual sampling interval of the electronic nautical chart (ENC):

[0040]

[0041] Among them, T 实际 is the actual sampling time interval of the electronic nautical chart ENC, P SENC is the chart accuracy value of the system electronic chart SENC, P ENC is the chart accuracy value of the electronic nautical chart ENC, and F is the Shannon theorem sampling frequency under the ENC accuracy; the details are as follows:

[0042] Shannon's theorem (or Shannon-Nyquist sampling theorem) is an important concept in information theory, which describes the sampling conditions of discrete signals to ensure that the continuous signal can be completely reconstructed from the sampled points.

[0043] Shannon's theorem states that if the highest frequency of a signal is f max , then this signal can be passed through a max Hertz frequency sampling is completely reconstructed. Assume that x(t) is a band-limited signal (its spectrum X(f) has an absolute value at frequencies f exceeding f max is 0), then x(t) can be completely represented by its sample value at t=wT.

[0044] According to Shannon's theorem, the ENC sampling frequency F to maintain ENC accuracy must be at least the highest frequency f of the data signal. max Twice, this is the minimum sampling frequency, that is: F ≥ 2f max .

[0045] According to the sampling frequency F, calculate the sampling time interval T that maintains the ENC accuracy normally ENC :

[0046] Normal sampling process: According to the above sampling time interval T ENC ,By regularly extracting sample points from the ENC data, undistorted ENC data can be obtained.

[0047] Suppose you want to sample a data signal whose frequency range (i.e., signal bandwidth) is 20Hz to 20,000Hz. When calculating how to sample with the required accuracy, consider: According to Shannon's theorem, the sampling frequency f must be at least twice the highest frequency of the signal (20,000Hz×2=40,000hz). This is the minimum sampling frequency. Suppose after calculation, the time interval T is required. ENC = 10s to collect data, then the number of data points is 40000 x 10 = 400000. There is a linear relationship between the sampling time interval and the amount of data obtained by sampling, that is:

[0048] T 实际 / T ENC =MSENC / 400000 (1)

[0049] Because 400000 is actually T ENC The amount of ENC data without distortion M ENC ; That is, formula (1) is actually:

[0050] T 实际 / T ENC =M SENC / M ENC (2)

[0051] For the ENC data, the chart sheet E ENC And the precision P ENC For example, the data size of electronic chart ENC is M ENC It can be expressed as: Because of the above That is, formula (2) is:

[0052] Formula (3) is derived as follows:

[0053]

[0054] For the chart sheet E of the system electronic nautical chart SENC data SENC And the precision P SENC For example, the system electronic chart SENC data size is M SENC It can also be expressed as: That is, formula (4) is:

[0055]

[0056] Because the data conversion of the same map size is adopted in the present invention, that is, the map size E SENC = Map size E ENC ; then formula (5) is:

[0057]

[0058] Among them, T 实际 is the actual sampling time interval of the electronic nautical chart ENC, P SENC is the chart accuracy value of the system electronic chart SENC, P ENC is the chart accuracy value of the electronic nautical chart ENC, and F is the Shannon theorem sampling frequency under the ENC accuracy.

[0059] Thus, the actual sampling time interval model of the electronic nautical chart ENC is obtained. Applying this model, T 实际It can adaptively change based on the data accuracy requirements displayed by the system electronic nautical chart SENC, perform sampling according to the actual sampling time interval of the electronic nautical chart ENC, and realize the adaptive extraction of the chart data volume on the ENC.

[0060] As mentioned above, the data size of the electronic chart ENC is M ENC It can be expressed as: System electronic chart SENC data size M SENC It can be expressed as:

[0061] However, due to the display accuracy of the system electronic chart SENC P SENC Generally lower than P ENC (It is equivalent to the different scales of the system electronic chart SENC and the electronic chart ENC). Therefore, the amount of data M collected can be dynamically adjusted according to the actual display accuracy required by SENC. SENC The size of the data can be adjusted to achieve adaptive extraction of data volume, and small data volume conversion and transmission can be achieved while meeting usage requirements, thereby improving the efficiency of converting electronic nautical chart ENC data to SENC data.

[0062] S2. Obtain the chart sheet E of the required system electronic chart SENC data SENC , chart accuracy value P of system electronic chart SENC data SENC and the chart accuracy value P of the electronic chart ENC ENC ;

[0063] S3, according to the obtained P SENC 、P ENC and the actual sampling time interval model of the electronic nautical chart ENC created to calculate P SENC The actual sampling time interval T of the electronic nautical chart ENC with the highest accuracy 实际 ;

[0064] According to the actual sampling time interval model of the electronic nautical chart ENC:

[0065]

[0066] Among them, T 实际 is the actual sampling time interval of the electronic nautical chart ENC, P SENC is the chart accuracy value of the system electronic chart SENC, P ENC is the chart accuracy value of the electronic nautical chart ENC, and F is the Shannon theorem sampling frequency under the ENC accuracy.

[0067] According to step S2, the chart accuracy value P of the system electronic chart SENC data SENC and the chart accuracy value P of the electronic chart ENC ENCGiven the acquired data, the following describes how to solve the Shannon theorem sampling frequency F under ENC accuracy:

[0068] In the present invention, the Shannon theorem sampling frequency F while maintaining the accuracy of the electronic nautical chart ENC (the scale is generally 1:1) is:

[0069] F=k*f max (7);

[0070] Among them, f max is the maximum frequency of the electronic chart ENC chart data signal, and k is a constant greater than or equal to 2.

[0071] Furthermore, the highest frequency f of the electronic chart ENC chart data signal is obtained. max The steps include:

[0072] P1. Extract the signal characteristics of ENC chart data;

[0073] The chart data signal characteristics of the electronic nautical chart ENC include waterways, coastlines, water depths, navigation marks, obstacles, etc.

[0074] P2. Apply Fourier transform to the extracted signal features of the electronic chart ENC data to identify the frequency components therein;

[0075] Use Python's scientific computing library (such as NumPy) to perform Fourier transform, which can transform the time domain signal x(t s ) is converted into a frequency domain signal X(f), and the calculation expression using discrete Fourier transform is:

[0076]

[0077] Where x[n1] is the discrete time series of the signal, n1 is the number of samples of the signal, f is the continuous frequency variable, j is the imaginary unit, is a complex exponential function.

[0078] In practice, the Fast Fourier Transform (FFT) is often used to efficiently calculate the Fourier transform.

[0079] P3, calculate the power spectrum;

[0080] The power spectrum is the intensity of each frequency component in the frequency domain, usually the square of the signal amplitude. The formula for calculating the power spectrum is:

[0081] P(f)=|X(f)| 2 (9);

[0082] Where |X(f)| is the signal amplitude after Fourier transformation.

[0083] P4. Calculate the power spectral density PSD;

[0084] Power spectral density (PSD) represents the power distribution within a unit frequency bandwidth. It is the normalized power spectrum P(f) with respect to frequency. The commonly used power spectral density formula is:

[0085]

[0086] Where: B is the frequency bandwidth (or resolution of spectrum analysis).

[0087] In actual calculation, the PSD can be estimated by averaging the power spectrum in the frequency domain.

[0088] P5. Find the highest frequency f max ;

[0089] On the drawn PSD graph, the frequency corresponding to the peak is the highest frequency f max The highest frequency f is determined by finding the frequency point of maximum power max .

[0090] Find the highest frequency f max After that, determine the value of constant k (k≥2), and use formula (7) F=k*f max The Shannon theorem sampling frequency F is calculated to maintain the accuracy of the electronic chart ENC chart. Then, through formula (6) The chart accuracy value P of the required system electronic chart SENC data obtained in step S2 SENC and the chart accuracy value P of the electronic chart ENC ENC , calculate the actual sampling time interval T of the electronic nautical chart ENC under the accuracy of SENC data 实际 .

[0091] S4. According to T 实际 and the chart sheet E of the required system electronic chart SENC data SENC , on the same chart sheet E of ENC SENC On the other hand, Shannon’s theorem is used to perform data sampling, and the sampling data sequence of ENC is obtained as X = {x(1), x(2), x(3), …, x(t) …, x(n)};

[0092] The electronic chart ENC sampling data sequence X = {x(1), x(2), x(3), …, x(t) …, x(n)}, where, according to Shannon’s theorem:

[0093]

[0094] The value of each ENC data point is x(t), which can be obtained from its value at t=wT实际 The sampling value at is fully represented, t is the time from the start of sampling, T 实际 is the actual sampling time interval on the ENC.

[0095] S5. Initialize the current chart data parameter curr of the sampled data sequence X val and a counter parameter count corresponding to the chart data parameter;

[0096] S6. Traverse the ENC sampling data sequence X, determine the similarities and differences between the current chart data and the previous chart data, and continuously update the parameter curr val and count values, and uses the index to record the location source of the data in the ENC map when updating;

[0097] Since both ENC and SENC chart data contain a large amount of complex image information, but there is actually a large amount of duplicate data when they are stored, the present invention uses sampling based on Shannon's theorem and RLE to collect and compress chart data that is large in number, large in scale, and highly complex (here, only certain characteristic elements between images are complex, and most other image elements are highly duplicated). In addition, the chart data information is indexed during compression, so that the originally disordered data becomes orderly, so that it can be decompressed in order, ensuring the accuracy of the image when displayed on SENC.

[0098] Factors used to determine the similarities and differences between current data and previous data include transparency, grayscale, hue, saturation, and brightness.

[0099] Traverse the ENC sampling data sequence X, and judge the similarities and differences between the current data and the previous data based on the above factors including transparency, grayscale, hue, saturation and brightness, and continuously update the parameter curr val and count values, and uses the index to record the location source of the data in the ENC map when updating;

[0100] That is to say, the ENC sampling data sequence X = {x(1), x(2), x(3), ..., x(t) ..., x(n)} is divided into multiple classes, each of which contains data points with the same transparency, grayscale, hue, saturation and brightness, and then curr val It is equivalent to the representative data of this class. Count records the total number of data in this class, and uses an index to record the original location of each data in the ENC map for subsequent recovery.

[0101] S7. Count and record the data counter key-value pair sequence Y;

[0102]

[0103] Among them, curr val is the chart data parameter, count is the counter parameter corresponding to the chart data parameter, and m is the total number of data counter key-value pairs.

[0104] Taking the sampling embodiment of a certain image as an example, the data counter key-value pair sequence Y may be:

[0105] Among them to are respectively a representative data point, and 20, 35, …, 213 record the total number of data for each representative data point.

[0106] S8, after transmitting the data counter key-value pair sequence Y to SENC, the system electronic chart SENC is mapped to the chart sheet E according to the index. SENC The corresponding position outputs the data sequence Y to complete the data conversion.

[0107] After the data counter key-value pair sequence Y is transmitted to SENC, the data sequence Y is output at the corresponding position of the same map sheet in SENC according to the index for each data counter key-value pair in the sequence, completing the data conversion.

[0108] When actually outputting, the first data counter key-value pair in the sequence is used For example, there is an index record that matches the key-value pair of the data counter, which is used to record the original location of each data in the ENC map. When the data is output, it is only necessary to convert the data point into the original location according to the index record. It is sufficient to restore at 20 corresponding positions respectively, and so on, until all data points of the data counter key-value pairs in the output sequence Y are restored, the complete map data is restored, and the data conversion is completed.

[0109] The present invention designs a data format for converting ENC to SENC based on the data storage formats of the electronic nautical chart ENC and the system electronic nautical chart SENC and the display accuracy of SENC, thereby reducing the overall size of the data and achieving the purpose of data compression.

[0110] Using chart data extracted from an actual sailing ship as example data, the above data method is used to process the data from the ENC, and then the data packet is output on the SENC side, achieving the following technical effects:

[0111] The original chart sheet data is about 312 GB in size. After counting and recording the data counter key-value pair sequence Y, the size is about 43 GB, and the compression efficiency is about 86.2%.

[0112] The chart data packet of the data counter key-value pair sequence Y is output on the SENC end. After the output is completed, there is no obvious distortion when the chart data is opened. After measurement, the data accuracy reaches more than 99%.

[0113] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

[0114] The present invention and its embodiments are described above. This description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if a person skilled in the art is inspired by this and, without departing from the purpose of the present invention, designs structures and embodiments similar to this technical solution without inventiveness, they shall fall within the scope of protection of the present invention.

Claims

1. A method for converting electronic nautical chart ENC data to SENC data, characterized in that: It includes the steps of: S1. Create a model for the actual sampling interval of the electronic nautical chart (ENC): Among them, T 实际 is the actual sampling time interval of the electronic nautical chart ENC, P SENC is the chart accuracy value of the system electronic chart SENC, P ENC is the chart accuracy value of the electronic nautical chart ENC, and F is the Shannon theorem sampling frequency under the accuracy of the electronic nautical chart ENC; S2. Obtain the chart sheet E of the required system electronic chart SENC data SENC , chart accuracy value P of system electronic chart SENC data SENC and the chart accuracy value P of the electronic chart ENC ENC ; S3, according to the obtained P SENC 、P ENC and the actual sampling time interval model of the electronic nautical chart ENC created to calculate P SENC The actual sampling time interval T of the electronic nautical chart ENC with the highest accuracy 实际 ; S4. According to T 实际 and the chart sheet E of the required system electronic chart SENC SENC , on the same chart sheet E of ENC SENC On the other hand, Shannon’s theorem is used to perform data sampling, and the sampling data sequence of ENC is obtained as X = {x(1), x(2), x(3), …, x(t) …, x(n)}; Where x(t) is the value of the t-th electronic chart ENC data sampling point, t takes a value between 1 and n, and n is the total number of samples; S5. Initialize the current chart data parameter curr of the sampled data sequence X val and a counter parameter count corresponding to the chart data parameter; S6. Traverse the ENC sampling data sequence X, determine the similarities and differences between the current chart data and the previous chart data, and continuously update the parameter curr val and count values, and uses the index to record the location source of the chart data in the ENC sheet when updating; S7. Count and record the data counter key-value pair sequence Y; Among them, curr val is the chart data parameter, count is the counter parameter corresponding to the chart data parameter, and m is the total number of data counter key-value pairs; S8, after transmitting the data counter key-value pair sequence Y to SENC, the system electronic chart SENC is mapped to the chart sheet E according to the index. SENC The corresponding position outputs the data sequence Y to complete the data conversion.

2. The method for converting electronic nautical chart ENC data to SENC data according to claim 1, characterized in that: In the step S1 model, the Shannon theorem sampling frequency F under ENC accuracy is: F=k×f max (7); Among them, f max is the highest frequency of the ENC chart data signal, and k is a constant greater than or equal to 2.

3. The method for converting ENC data to SENC data according to claim 2, characterized in that: Get the highest frequency f of ENC chart data signal max The steps include: P1. Extract ENC chart data signal features; P2. Apply Fourier transform to the extracted signal features to identify the frequency components; P3, calculate the power spectrum, generate the power spectrum density PSD, and determine f by finding the frequency point of maximum power max .

4. The method for converting ENC data to SENC data according to claim 3, characterized in that: The Fourier transform in step P2 transforms the time domain signal x(t s ) is converted into a frequency domain signal X(f); The calculation expression using discrete Fourier transform is: Where x[n1] is the discrete time series of the signal, n1 is the number of samples of the signal, f is the continuous frequency variable, j is the imaginary unit, is a complex exponential function.

5. The method for converting ENC data into SENC data according to claim 3, characterized in that: The signal characteristics of the ENC chart data in step P1 include waterways, coastlines, water depths, navigation marks and obstacles.

6. The method for converting ENC data to SENC data according to claim 1, characterized in that: In step S6, the factors for determining the similarities and differences between the current chart data and the previous chart data include transparency, grayscale, hue, saturation and brightness.

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