Method for converting electronic chart ENC data into SENC data

Abstract

The invention discloses a method for converting electronic chart ENC data into SENC data. The method comprises the following steps: creating an actual sampling time interval model of an electronic chart ENC; obtaining a sea chart sheet and a sea chart precision value of the required SENC data and a sea chart precision value of the ENC; calculating an actual sampling time interval of the ENC under the SENC data precision; performing data sampling on the same sea chart sheet of the ENC by using the Shannon theorem to obtain a sampling data sequence X of the ENC; traversing the X to judge the similarity between the current data and the preorder data, updating data parameters and counter parameter values, and recording the position source of the data in the ENC map sheet by using an index; counting and recording a key value pair sequence of the data counter; and transmitting the key value pair sequence of the data counter to the SENC, and outputting the key value pair sequence at the corresponding position of the same mapsheet of the SENC according to the index to finish data conversion. The conversion efficiency can be improved, and the system space is saved.

Description

Technical Field

[0001] The present invention belongs to the field of electronic chart data processing, and particularly relates to a method for converting Electronic Navigational Chart (ENC) data into System Electronic Navigational Chart (SENC) data. Background Art

[0002] As an officially released standardized electronic chart, ENC (Electronic Navigational Chart) provides authoritative, comprehensive, and updated data support. In addition to being widely used in intelligent navigation, maritime and port management, with the development of informatization, the application fields of electronic charts and their application systems almost cover all industries with the ocean or even inland rivers as the activity space, including: marine fishery operations, offshore engineering construction, marine military, marine environmental investigation and protection, etc. Due to the comprehensiveness and compatibility of ENC, it has become the basic data source for various navigation systems. However, due to data format issues, ENC is not the most efficient method for storing, operating, or preparing data. Therefore, researchers designed SENC (System Electronic Navigational Chart) based on the usage requirements of operating systems and performance standards. As the internal optimized data format of a specific navigation system, SENC realizes more efficient display and operation functions through real-time processing and dynamic update of ENC data, ensuring the safety and efficiency of navigation.

[0003] The existing conversion of Electronic Navigational Chart (ENC) data into System Electronic Navigational Chart (SENC) data mainly involves reading data such as points, lines, and surfaces in the original ENC data source and reconstructing them. After projecting and converting the reconstructed points, lines, and surfaces into screen coordinates, the chart is drawn and displayed on the display terminal. However, the reconstruction and conversion processes are time-consuming, inefficient, and require a large amount of storage space. Therefore, a method for efficiently converting ENC data into SENC data has become the research direction. Summary of the Invention

[0004] In order to solve the deficiencies of the existing technology, the present invention provides a method for converting ENC data into SENC data. Based on the established actual sampling time interval model of the ENC, according to Shannon's theorem, chart data with a large quantity, large scale, high complexity, and high repetition degree of image elements is collected without distortion and classified and statistically compressed. When compressing the ENC data information, an index is established according to the position information, and during decompression of the SENC chart data, it is restored according to the position, ensuring the correctness of the image when displayed on SENC, improving the conversion efficiency, and saving system space.

[0005] To achieve the above object, a method for converting electronic chart ENC data into SENC data according to an embodiment of the present invention includes the following steps:

[0006] S1. Create an actual sampling time interval model for the electronic chart ENC:

[0007]

[0008] Where T 实际 is the actual sampling time interval of the electronic 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 chart ENC, and F is the Shannon theorem sampling frequency under the accuracy of the electronic chart ENC;

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

[0010] of the electronic chart ENC;

[0011] S3. Calculate the actual sampling time interval T 实际 of the electronic chart ENC under the SENC data accuracy according to the obtained data and the created actual sampling time interval model of the electronic chart ENC;

[0012] S4. According to the calculated actual sampling time interval T 实际 , on the same chart sheet E SENC of the ENC, use the Shannon theorem to perform data sampling to obtain the sampling data sequence X = {χ(1), χ(2), χ(3), …, χ(t) …, χ(n)} of the ENC;

[0013] S5. Initialize the current data parameter curr val and counter parameter count of the sampling data sequence X;

[0014] S6. Traverse the sampling data sequence X of the ENC, judge the similarities and differences between the current data and the previous data, continuously update the values of the parameters curr_val and count, and record the position source of the data in the ENC chart sheet with an index when updating;

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

[0016] Further, in the model of step S1, the Shannon theorem sampling frequency F at the ENC accuracy is:

[0017] F = k × f max (7);

[0018] where f max is the highest frequency of the ENC chart data signal, and k is a constant greater than or equal to 2.

[0019] Further, the steps for obtaining the highest frequency f of the ENC chart data signal max include:

[0020] P1. Extract the characteristics of the ENC chart data signal;

[0021] P2. Apply the Fourier transform to the extracted signal characteristics to identify the frequency components therein;

[0022] P3. Calculate the power spectrum, generate the power spectral density PSD, and determine f by finding the frequency point with the maximum power max .

[0023] Further, the Fourier transform in step P2 converts the time-domain signal x(t s ) into the frequency-domain signal X(f);

[0024] The calculation expression using the discrete Fourier transform is:

[0025]

[0026] where x[n] is the discrete-time sequence of the signal, n is the number of samples of the signal, f is the continuous frequency variable, j is the imaginary unit, and e -j2πft is a complex exponential function.

[0027] Further, the characteristics of the ENC chart data signal in step P1 include waterways, coastlines, water depths, navigation aids, and obstacles.

[0028] Further, in the ENC sampling data sequence X = {χ(1), χ(2), x(3), …, x(t) …, x(n)} in step S4:

[0029]

[0030] where x(t) is the value of each electronic chart ENC data sampling point, which can be completely represented by its sampling value at t = nT 实际 in actuality, t is the time from the start of sampling, and T 实际 is the actual sampling time interval of the electronic chart ENC, and n is the total number of samples.

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

[0032] Further, in step S7, the data counter key-value pair sequence Y is:

[0033]

[0034] where 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.

[0035] The beneficial effects of the present invention are as follows:

[0036] 1. By creating an actual sampling time interval model for the electronic chart ENC, the present invention dynamically adjusts the amount of data M SENC collected according to the display accuracy actually required by the SENC, realizes the adaptive extraction of the amount of data, and realizes the conversion and transmission of small amounts of data while meeting the usage requirements, thereby improving the efficiency of converting electronic chart ENC data into SENC data;

[0037] 2. By using the Shannon theorem, the present invention realizes the lossless sampling of the electronic chart ENC data, and processes the sampled data into key-value pairs by using the situation that a large number of repeated value data are included in the chart data. By traversing the sequence of data collection to count the data key-value pairs, a large reduction in the amount of data is achieved, and based on the indexing method for recording the data positions, the data is decompressed and the data is restored at the SENC end, achieving a data accuracy rate of over 99%. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a flowchart of a method for converting electronic chart ENC data into SENC data according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] To make the objectives, technical solutions, and advantages of the present invention clearer, the following further explains with reference to the drawings and embodiments.

[0040] It should be noted that in this article, relational terms such as first and second are only used 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 term "comprises", "comprising", or any other variation thereof is intended to cover a non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or device.

[0041] As shown in the attached Figure 1 figure, the present invention provides a method for converting electronic chart ENC data into SENC data, which includes the following steps:

[0042] S1. Create an actual sampling time interval model for the electronic chart ENC:

[0043]

[0044] Among them, T 实际 is the actual sampling time interval of the electronic 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 chart ENC, and F is the Shannon theorem sampling frequency under the ENC accuracy; specifically as follows:

[0045] The Shannon theorem (or Shannon-Nyquist sampling theorem) is an important concept in information theory. This theorem describes the sampling conditions of discrete signals to ensure that continuous signals can be completely reconstructed from the sampling points.

[0046] The Shannon theorem states that: if the highest frequency of a signal is f max , then this signal can be completely reconstructed by sampling at a frequency of at least 2f max Hz. Assume that x(t) is a band-limited signal (its spectrum X(f) is 0 when the absolute value frequency f exceeds f max ), then x(t) can be completely represented by its sampling values at t = nT.

[0047] According to the Shannon theorem, the ENC sampling frequency F that maintains the ENC accuracy must be at least twice the highest frequency f max of the data signal. This is the minimum sampling frequency, that is: F ≥ 2f max .

[0048] According to the sampling frequency F, calculate the sampling time interval T ENC for normally maintaining the ENC accuracy:

[0049] Normal sampling process: According to the above sampling time interval T ENC , regularly extract acquisition sample points from the ENC data to obtain undistorted ENC data.

[0050] Suppose we want to sample a data signal with a frequency range (i.e., signal bandwidth) from 20 Hz to 20,000 Hz. When calculating how to sample according to the required precision, consider: According to the Shannon theorem, the sampling frequency f must be at least twice the highest frequency of the signal (20,000 Hz × 2 = 40,000 Hz), which is the minimum sampling frequency. Suppose after calculation, it is required to collect data with a time interval T ENC = 10 s, then there will be 40,000 x 10 = 400,000 data points. There is a linear relationship between the sampling time interval and the amount of data obtained by sampling, that is:

[0051] T 实际 / T ENC = M SENC / 400,000 (1)

[0052] Because among them, 400,000 is actually the amount of undistorted ENC data M ENC under T ENC ; that is, equation (1) is actually:

[0053] T 实际 / T ENC = M SENC / M ENC (2)

[0054] For the chart sheet E ENC and accuracy P ENC of the electronic navigation chart ENC data, the size M ENC of the electronic navigation chart ENC data can be expressed as: Also because of the above that is, equation (2) is:

[0055] After the derivation of equation (3), it is:

[0056]

[0057] For the chart sheet E SENC and accuracy P SENC of the system electronic navigation chart SENC data, the size M SENC of the system electronic navigation chart SENC data can also be expressed as: That is, equation (4) is:

[0058]

[0059] Because in the present invention, the data conversion of the same chart sheet is adopted, that is, the chart sheet E SENC = the chart sheet E ENC ; then equation (5) is:

[0060]

[0061] Among them, T 实际 is the actual sampling time interval of the electronic navigational chart ENC, and P SENC is the chart accuracy value of the system electronic navigational chart SENC, and P ENC is the chart accuracy value of the electronic navigational chart ENC, and F is the Shannon theorem sampling frequency under the ENC accuracy.

[0062] Thus, the actual sampling time interval model of the created electronic navigational chart ENC is obtained. Applying this model, T 实际 can adaptively change based on the data accuracy requirements displayed by the system electronic navigational chart SENC, sample according to the actual sampling time interval of the electronic navigational chart ENC, and achieve the adaptive extraction of the chart data volume on the ENC.

[0063] As described above, the size M of the electronic navigational chart ENC data ENC can be expressed as: The size M of the system electronic navigational chart SENC data SENC can be expressed as:

[0064] However, since the display accuracy P of the system electronic navigational chart SENC SENC is generally lower than P ENC (which is equivalent to different scales of the system electronic navigational chart SENC and the electronic navigational chart ENC), therefore, the size of the collected data volume M can be dynamically adjusted according to the display accuracy actually required by the SENC SENC to achieve the adaptive extraction of the data volume, and to achieve small data volume data conversion and transmission while meeting the usage requirements, thereby improving the efficiency of converting electronic navigational chart ENC data to SENC data.

[0065] S2. Obtain the chart sheet E of the system electronic navigational chart SENC data required SENC , the chart accuracy value P SENC and the chart accuracy value P of the electronic navigational chart ENC ENC ;

[0066] S3. According to the obtained data and the actual sampling time interval model of the created electronic navigational chart ENC, calculate the actual sampling time interval T of the electronic navigational chart ENC under the SENC data accuracy 实际 ;

[0067] According to the actual sampling time interval model of the electronic navigational chart ENC:

[0068]

[0069] Among them, T 实际 is the actual sampling time interval of the electronic navigational chart ENC, and PSENC is the chart accuracy value of the system electronic navigational chart SENC, P ENC is the chart accuracy value of the electronic navigational chart ENC, and F is the Shannon theorem sampling frequency under the ENC accuracy.

[0070] According to step S2, the chart accuracy value P of the system electronic navigational chart SENC data SENC and the chart accuracy value P of the electronic navigational chart ENC ENC are known data obtained. The following describes the method for solving the Shannon theorem sampling frequency F under the ENC accuracy:

[0071] In the present invention, the Shannon theorem sampling frequency F under the chart accuracy (scale is generally 1:1) of the electronic navigational chart ENC is:

[0072] F = k * f max (7);

[0073] wherein, f max is the highest frequency of the chart data signal of the electronic navigational chart ENC, and k is a constant greater than or equal to 2.

[0074] Furthermore, the steps for obtaining the highest frequency f of the chart data signal of the electronic navigational chart ENC max include:

[0075] P1. Extract the characteristics of the chart data signal of the electronic navigational chart ENC;

[0076] The characteristics of the chart data signal of the electronic navigational chart ENC include waterways, coastline, water depth, navigation aids, obstacles, etc.

[0077] P2. Apply Fourier transform to the extracted characteristics of the chart data signal of the electronic navigational chart ENC to identify the frequency components therein;

[0078] Use the scientific computing library of Python (such as NumPy) to perform Fourier transform. Fourier transform can convert the time-domain signal x(t s ) into the frequency-domain signal X(f). The calculation expression for using the discrete Fourier transform is:

[0079]

[0080] wherein, x[n] is the discrete time series of the signal, n is the number of samples of the signal, f is the continuous frequency variable, j is the imaginary unit, and e -j2πft is a complex exponential function.

[0081] In practice, the fast Fourier transform (FFT) is usually used to efficiently calculate the Fourier transform.

[0082] P3. Calculate the power spectrum;

[0083] 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:

[0084] P(f) = |X(f)| 2 (9);

[0085] where |X(f)| is the signal amplitude after Fourier transform.

[0086] P4. Calculate the power spectral density PSD;

[0087] The power spectral density (PSD) represents the power distribution within a unit frequency bandwidth, and it is the normalization of the power spectrum P(f) with respect to frequency. The commonly used formula for the power spectral density is:

[0088]

[0089] where: B is the frequency bandwidth (or the resolution of spectral analysis).

[0090] In actual calculation, the PSD can be estimated by averaging the power spectrum in the frequency domain.

[0091] P5. Find the highest frequency f max ;

[0092] On the plotted PSD graph, the frequency corresponding to the peak is the highest frequency f max . Determine the highest frequency f by finding the frequency point with the maximum power max .

[0093] Find the highest frequency f max After that, determine the value of the constant k (k ≥ 2), and calculate the Shannon theorem sampling frequency F for maintaining the accuracy of the electronic navigational chart ENC through the formula (7) F = k * f max Then, through the formula (6) According to the chart accuracy value P of the required system electronic navigational chart SENC data obtained in step S2 SENC and the chart accuracy value P of the electronic navigational chart ENC ENC , calculate the actual sampling time interval T of the electronic navigational chart ENC under the SENC data accuracy 实际 .

[0094] S4. According to the calculated actual sampling time interval T 实际 , on the same chart sheet E of the ENC SENC , use the Shannon theorem for data sampling to obtain the sampling data sequence X = {χ(1), χ(2), x(3), …, x(t) …, x(n)} of the ENC;

[0095] The ENC sampling data sequence of the electronic chart X = {x(1), x(2), χ(3), …, χ(t) …, χ(n)}, where, according to the Shannon theorem:

[0096]

[0097] where, the value of each ENC data point of the electronic chart is χ(t), which can be completely represented by its sampling value at t = nT. t is the time from the start of sampling, and T 实际 is the actual sampling time interval on the ENC.

[0098] S5. Initialize the current data parameter curr val and the counter parameter count of the sampling data sequence X;

[0099] S6. Traverse the sampling data sequence X of the ENC, judge the similarities and differences between the current data and the previous data, continuously update the values of the parameters curr_val and count, and record the position source of the data in the ENC chart sheet with an index when updating;

[0100] Since the chart data of both ENC and SENC contain a large amount of complex image information, but there are actually a large number of duplicate data when they are stored, the present invention adopts sampling based on the Shannon theorem and RLE to collect and compress the chart data with a large quantity, large scale and high complexity (here only some characteristic elements between images have high complexity, and the rest of most image elements have high repetition degree). In addition, an index is established when compressing the chart data information, so that the originally disordered data becomes ordered, so that it can be decompressed in order, ensuring the correctness of the image when it is displayed on the SENC.

[0101] The factors for judging the similarities and differences between the current data and the previous data include transparency, grayscale, hue, saturation and brightness.

[0102] Traverse the sampling data sequence X of the ENC, judge the similarities and differences between the current data and the previous data according to the above factors including transparency, grayscale, hue, saturation and brightness, continuously update the values of the parameters curr_val and count, and record the position source of the data in the ENC chart sheet with an index when updating;

[0103] That is to say, the ENC sampling data sequence X = {χ(1), χ(2), χ(3), …, x(n)…, x(n)} is divided into multiple classes. Each class contains data points with the same factors such as transparency, grayscale, hue, saturation, and brightness. Then, curr_val is equivalent to the representative data of this class, count records the total number of data in this class, and an index is used to record the original position source of each data in the ENC map sheet for subsequent recovery.

[0104] S7. Statistically record the data counter key-value pair sequence Y;

[0105]

[0106] 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.

[0107] Taking the sampling embodiment of a certain map sheet as an example, the data counter key-value pair sequence Y may be:

[0108] Among them to are respectively a representative data point, and 20, 35…213 record the total number of data for each representative data point.

[0109] S8. After transmitting the data counter key-value pair sequence Y to the SENC, output the data sequence Y at the original position of the same map sheet in the SENC according to the index to complete the data conversion.

[0110] After transmitting the data counter key-value pair sequence Y to the SENC, according to each data counter key-value pair in the sequence, output the data sequence Y at the corresponding position of the same map sheet in the SENC according to the index to complete the data conversion.

[0111] During actual output, taking the first data counter key-value pair in the sequence as an example, there is an index record that matches this data counter key-value pair and is used to record the original position source of each data in the ENC map sheet. When outputting the data, only according to the position recorded by the index, the data points are respectively restored at 20 corresponding positions. And so on, until all the data points of the data counter key-value pairs in the output sequence Y are output, and the complete map sheet data is restored to complete the data conversion.

[0112] According to the data storage format of the Electronic Navigational Chart (ENC) and the System Electronic Navigational Chart (SENC), as well as the display accuracy of the SENC, the present invention designs the data format during the conversion of ENC to SENC, thereby reducing the overall size of the data and achieving the purpose of data compression.

[0113] Taking the chart data extracted from actual navigating ships as example data, the above data method is used to process the data from ENC, and then the data packet is output at the SENC end, with the following technical effects:

[0114] The original chart sheet data was approximately 312 GB in size. After counting and recording the sequence of data counter key-value pairs Y, the size was approximately 43 GB, and the compression efficiency was approximately 86.2%.

[0115] The chart data packet of the data counter key-value pair sequence Y is output at the SENC end. After the output is completed, when the chart data is opened, there is no obvious distortion. After measurement, the data accuracy rate reaches more than 99%.

[0116] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

[0117] The above describes the present invention and its implementation manners. This description is not restrictive. What is shown in the drawings is only one of the implementation manners of the present invention, and the actual structure is not limited thereto. Generally speaking, if those of ordinary skill in the art are inspired by it and, without departing from the purpose of the present invention, design similar structural manners and embodiments without creative efforts, they shall fall within the protection scope of the present invention.

Claims

1. A method for converting electronic chart ENC data to SENC data, characterized in that: It includes the steps of: S1. Create a model for the actual sampling time interval of the electronic chart ENC: Among them, T 实际 is the actual sampling time interval of the electronic 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 chart ENC, and F is the Shannon theorem sampling frequency under the accuracy of the electronic chart ENC; S2. Obtain the chart sheet E of the required system electronic chart SENC data SENC , chart accuracy value P SENC and the chart accuracy value P of the electronic chart ENC ENC ; S3. Calculate the actual sampling time interval T of the electronic nautical chart ENC under the SENC data accuracy based on the actual sampling time interval model of the electronic nautical chart ENC created by acquiring the data. 实际 ; S4, according to the calculated actual sampling time interval T 实际 , on the same chart sheet E of ENC SENC On the basis of the above, Shannon’s theorem is used to perform data sampling, and the sampling data sequence of ENC is obtained as X = {χ(1), χ(2), χ(3), …, χ(t) …, χ(n)}; S5. Initialize the current data parameter curr of the sampled data sequence X val and counter parameter count; S6, traverse the sampling data sequence X of ENC, determine the similarities and differences between the current data and the previous data, continuously update the values ​​of the parameters curr_val and count, and use the index to record the location source of the data in the ENC map during the update; S7, counting and recording the data counter key-value pair sequence Y; S8. After transmitting the data counter key-value pair sequence Y to SENC, output the data sequence Y at the corresponding position of the same map sheet of SENC according to the index, thus completing 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 maximum 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 into 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 therein; P3, calculate the power spectrum, generate the power spectrum density PSD, and determine f by finding the frequency point with maximum power max .

4. The method for converting electronic nautical chart 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[n] is the discrete time series of the signal, n is the number of samples of the signal, f is the continuous frequency variable, j is the imaginary unit, and e -j2πft is a complex exponential function.

5. The method for converting electronic nautical chart ENC data to 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 electronic nautical chart ENC data to SENC data according to claim 1, characterized in that: In step S4, the ENC sampling data sequence X={χ(1),χ(2),x(3),…,x(t)…,χ(n)}: Where x(t) is the value of each electronic chart ENC data sampling point, which can be obtained from its value at t = nT 实际 The actual sampling value is fully expressed, t is the time from the start of sampling, T 实际 is the actual sampling time interval of the electronic nautical chart ENC, and n is the total number of samples.

7. The method for converting electronic nautical chart 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 data and the previous data include transparency, grayscale, hue, saturation and brightness.

8. The method for converting electronic nautical chart ENC data to SENC data according to claim 1, characterized in that: The data counter key-value pair sequence Y in step S7 is: Among them, curr val 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.

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