CN112214717B - Method for estimating catching depth by utilizing sonar data - Google Patents

Method for estimating catching depth by utilizing sonar data Download PDF

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CN112214717B
CN112214717B CN202011148342.XA CN202011148342A CN112214717B CN 112214717 B CN112214717 B CN 112214717B CN 202011148342 A CN202011148342 A CN 202011148342A CN 112214717 B CN112214717 B CN 112214717B
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CN112214717A (en
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张胜茂
王书献
戴阳
王鲁民
王永进
邹国华
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Shanghai Junding Fishery Technology Co ltd
East China Sea Fishery Research Institute Chinese Academy of Fishery Sciences
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Abstract

The invention relates to a method for estimating catching depth by utilizing sonar data, which comprises the following steps of: acquiring underwater acoustic data of a sea area by using sonar equipment, analyzing the underwater acoustic data, extracting effective information, and calculating target intensity of the sea area at each depth; determining noise data in the underwater acoustic data according to target intensity corresponding to the target fishery resources and the target intensity at each depth of the sea area, and filtering the noise data to obtain effective data; counting the target resource amount of each depth in the effective data; and according to the counted target resource amount of each depth, obtaining a mapping relation between the resource amount and the depth value, and estimating the optimal fishing depth according to the mapping relation. The invention can quickly estimate the optimal fishing depth of the target fishery resource.

Description

Method for estimating catching depth by utilizing sonar data
Technical Field
The invention relates to the field of application of underwater acoustic data in fishery, in particular to a method for estimating catching depth by utilizing sonar data.
Background
Acoustics belongs to wave mechanics in physics. The main research objects in the acoustic field include sound waves, ultrasonic waves, infrasonic waves and the like. Since the 70 s of the 20 th century, the acoustic evaluation of fishery resources began to be promoted internationally. As an application of acoustics in the ocean, water acoustics has been widely used for fishery undersea work (fishery resource detection, fishery resource evaluation, and the like) at present. However, the related inventions and researches can only study the vertical distribution state of a specific organism in a certain sea area at a given time, and are difficult to be used in the actual fishing environment. In the traditional fishing depth estimation method, software is used for analyzing the output file, and the data distribution condition is observed by naked eyes so as to make a decision. The traditional method has great uncertainty in counting the amount of target submarine resources, and firstly, because the data volume of sonar equipment metadata is large, the structure is complex, the observation and judgment by naked eyes are difficult, and the accuracy is not high; secondly, because the distribution condition of the marine organisms is variable, if the fishing is manually decided each time, a large amount of time is consumed.
Disclosure of Invention
The invention aims to solve the technical problem of providing a method for estimating the fishing depth by utilizing sonar data, and quickly estimating the optimal fishing depth of target fishery resources.
The technical scheme adopted by the invention for solving the technical problems is as follows: the provided method for estimating the catching depth by utilizing sonar data comprises the following steps:
(1) acquiring underwater acoustic data of a sea area by using sonar equipment, analyzing the underwater acoustic data, extracting effective information, and calculating target intensity of the sea area at each depth;
(2) determining noise data in the underwater acoustic data according to target intensity corresponding to the target fishery resources and the target intensity at each depth of the sea area, and filtering the noise data to obtain effective data;
(3) counting the target resource amount of each depth in the effective data;
(4) and according to the counted target resource amount of each depth, obtaining a mapping relation between the resource amount and the depth value, and estimating the optimal fishing depth according to the mapping relation.
When the hydroacoustic data is analyzed in the step (1), all the subdata are divided into binary files and XML files according to a file coding mode, and the subdata are analyzed one by one according to the types of file headers.
Passing Ts ═ Pr +40log in the step (1)10R+2αR-10log10(Pt2G0 2λ2/16π2) Calculating the target intensity of the sea area, wherein Pr is the received power, R is the correction range, alpha is the absorption coefficient, Pt is the transmission power, G0λ is the wavelength for the sensor peak gain.
The step (3) further includes displaying the statistical target resource amount at each depth in a graphical manner.
In the step (4), a basic algorithm is adopted to estimate the optimal fishing depth according to the mapping relationship, wherein the basic algorithm specifically comprises the following steps: finding out the depth corresponding to the maximum value of the target resource amount, wherein if the depth value mapped by the maximum value of the target resource amount is unique, the depth value mapped by the maximum value of the target resource amount is the finally determined optimal fishing depth; if the depth value mapped by the maximum value of the target resource is not unique, calculating the depth difference of the depth values mapped by the maximum values of two adjacent target resources, determining a depth difference dictionary, finding out the minimum value of the depth difference from the depth difference dictionary, and if the minimum value of the depth difference is unique, taking the depth value corresponding to the minimum value of the depth difference as the optimal fishing depth; and if the minimum depth difference value is not unique, calculating the depth difference values of the minimum depth difference values and the maximum target resource amount one by one to form a new depth difference dictionary, judging by using the new depth difference dictionary until only one depth value is left finally, and taking the depth value as the optimal fishing depth.
When the depth value mapped by the maximum value of the target resource is unique, calculating an extension interval, counting depth data corresponding to the target resource amount in the extension interval, constructing a depth difference dictionary, if the number of the depth values corresponding to the minimum depth difference in the depth difference dictionary is more than 3, updating the depth difference dictionary in a manner of updating the depth difference dictionary in the basic algorithm, and otherwise, calculating the prediction factors of the 1-3 depths; wherein the extension interval is
Figure GDA0003004921700000021
NpingIndicates the number of pings, V, input into the sonar equipmentmaxActual peaks representing target resource amounts in the statistically valid data; the prediction factor is calculated in the manner of
Figure GDA0003004921700000022
∑CtargetRepresents the sum of the target resource amounts at that depth, and Δ d represents the depth difference.
Advantageous effects
Due to the adoption of the technical scheme, compared with the prior art, the invention has the following advantages and positive effects: the invention can quickly estimate the optimal fishing depth of a certain fishery resource in a sea area according to the on-site data acquisition and dynamic target setting mode without the limitation of factors such as sea areas, fishery resource types and the like, thereby improving the fishery fishing efficiency and reducing the fishing cost.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic view of a sea area according to an embodiment of the present invention;
FIG. 3 is a form of a statistical map generated in an embodiment of the present invention;
FIG. 4 is another form of a statistical map generated in an embodiment of the present invention;
FIG. 5 is a flow chart of a base algorithm in an embodiment of the present invention;
FIG. 6 is a schematic diagram of a hypothetical scenario in an embodiment of the present invention.
Detailed Description
The invention will be further illustrated with reference to the following specific examples. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Further, it should be understood that various changes or modifications of the present invention may be made by those skilled in the art after reading the teaching of the present invention, and such equivalents may fall within the scope of the present invention as defined in the appended claims.
The embodiment of the invention relates to a method for estimating catching depth by utilizing sonar data, which comprises the following steps as shown in figure 1: acquiring underwater acoustic data of a sea area by using sonar equipment, analyzing the underwater acoustic data, extracting effective information, and calculating target intensity of the sea area at each depth; determining noise data in the underwater acoustic data according to target intensity corresponding to the target fishery resources and the target intensity at each depth of the sea area, and filtering the noise data to obtain effective data; counting the target resource amount of each depth in the effective data; and according to the counted target resource amount of each depth, obtaining a mapping relation between the resource amount and the depth value, and estimating the optimal fishing depth according to the mapping relation.
The invention is further illustrated by the following specific example.
1. Water acoustic data is acquired.
Sonar equipment is used to acquire underwater acoustic data under a certain sea area as shown in fig. 2. The sonar equipment adopted in the embodiment is a water acoustic instrument EK80 scientific echo sounder, and the working frequency of the equipment is 38kHz, 70kHz and 130 kHz. Wherein the working frequency of the test data is 38 kHz.
2. Targeting fishery resources (fishing objects)
The embodiment determines krill as the target resource, consults relevant documents, and configures the target intensity range to be-69.5 dB to-40.8 dB, namely determines-69.5 dB to-40.8 dB as the effective range.
3. Analyzing and reconstructing data
The EK80 metadata file is complicated in structure, but can be divided into two types, XML data file and binary data file from the data format point of view. XML data describes configuration information, environmental information, etc.; the binary information describes fine data such as motion data, sample data, and the like. The original data is divided according to the purpose, and comprises configuration XML data, binary filter data, environment XML data, NEMA data packets, channel sample data and other data packets. The channel sample data is the object of the key analysis and research in this step. The channel sample data comprises a parameter data packet and a sample binary data packet, and asynchronous NEMA text data packets, annotation text data packets, motion data packets and other auxiliary description data packets exist between the two packets.
The sample data for each channel and Ping is the core content of the EK80 raw data file. The partial data mainly comprises two parts of parameter XML data and sample binary data. The parameter XML packet contains some parameter information (unlike the configuration XML packet, the parameter information may change in the next Ping). Its header type is XML 0. These parameter information include channel ID, sampling interval, start frequency, end frequency, etc.; the sample binary data records the sample information obtained by Ping this time. The header type is RAW 3. The sizes of the data are not consistent and are mainly influenced by the Datatype parameter. The sample binary data contains various information such as channel identification code, sample number, and sample value (see table 1).
TABLE 1 sample binary data field
Name of field Description of the invention
DgHeader Using binary data packets of the type RAW3
ChannelID[128] Channel identification code
Datatype Data type
Spare[2] Unused fields, reserved for extension
Offset First number of samples
Count Total number of samples
Samples The received sample value is typically a plurality of complex numbers
The EK80 raw data was separately parsed by MATLAB development tool, depending on the header type. The data file is first read in binary form. The header type is obtained with ReadDatagraheader () function. If the header type is XML0, the packet may be a configuration XML packet, an environment XML packet, a parameter XML packet, etc. Analyzing the Name of the data packet to finally confirm the type of the data packet; if the header type of the data packet is FIL1, the data packet is a filtering binary data packet; if the file header type of the data packet is NME0 or TAG0, the data packet is a character data packet; if the file header type of the data packet is MRU0, the data packet is a motion data packet; if the packet header type is RAW3, the packet is a sample binary packet. And according to the data file header type classification, calling corresponding analysis functions in sequence.
Using a target intensity model Ts ═ Pr +40log10R+2αR-10log10(Pt2G0 2λ2/16π2) Calculating the target intensity of the sea area, wherein R is the correction range, Pr is the received power, alpha is the absorption coefficient, Pt is the transmitted power, G0λ is the wavelength for the sensor peak gain. And after the target intensity is calculated, writing the depth value, the target intensity value and the belonging ping number into a CSV file, and waiting for further processing.
4. Statistical data
And (4) counting the target resource amount at each depth according to the accuracy of 0.1 meter by using the data acquired in the step (3). And traversing the CSV file by using a python program, if the target intensity is within a set target intensity interval, automatically increasing the accumulated value at the position of the corresponding depth value of the record, and otherwise, skipping.
5. Generating a statistical map
And displaying the effective data distribution in the form of scattered dots. In fig. 3 and 4, the abscissa indicates the depth, and the ordinate indicates the number of records corresponding to the set target intensity range at the depth. In fig. 4, the color depth indicates the number of different samples, and the darker the color, the larger the number.
After the target fishery resource amount under each depth is obtained through statistics of a statistical scheme, an algorithm needs to be determined to evaluate the optimal fishing depth. Whether a certain depth is suitable as the optimal fishing depth is judged, and two factors are mainly considered in the embodiment. First, a target amount of resources at that depth; second, the amount of target resources within a certain depth neighborhood of the depth. Ideally, the fishing depth obtained by the algorithm is the maximum amount of target fishery resources, so the factor is a core factor which must be considered when designing the algorithm. However, from the test data, there are extreme cases of partial data: target resource amount at the position close to the maximum value of part of target resource amount (the maximum value point is not unique) is poor in performance and is not suitable for being used as the finally determined fishing depth, so that a second factor is introduced as a supplement. Based on the analysis, two optimal catching depth algorithms are designed.
6. Performing a base algorithm
Traversing the analyzed data, finding out the maximum value of the target resource quantity, and recovering the mapping relation between the target resource quantity and the depth. If the maximum value of the target resource quantity is unique, the basic algorithm is ended, and the depth value mapped by the maximum value of the target resource quantity is the finally determined optimal fishing depth (in the algorithm, the priority of the direct target resource quantity is highest, and the priority of the target resource quantity in the depth neighborhood is second); and if the maximum value of the target resource quantity is not unique, calculating the depth difference of the depths mapped by the two adjacent maximum points. And determining a depth difference dictionary, wherein key in the depth difference dictionary is a depth value, and value is a depth difference value between the current depth and the next depth for obtaining the maximum target resource amount. Solving the minimum value of the depth difference, if the minimum value is unique, taking the depth value corresponding to the minimum value as the optimal fishing depth, and ending the basic algorithm; and if the minimum value is not unique, calculating the depth difference value with the minimum depth difference and the maximum target resource amount one by one to form a new depth difference dictionary. And taking the new dictionary as an input, and circularly calling until only one depth value is left finally. The depth value is the optimal fishing depth and the basic algorithm is finished. This basic algorithm process is shown in fig. 5.
In this embodiment, a depth value corresponding to the maximum resource amount is found by using a python program. In this set of test samples, the maximum resource size is not unique, and the maximum resource size 737 is both 249.7 meters and 254.9 meters. And (4) calculating a depth difference dictionary according to the setting in the basic algorithm, and circularly traversing the program to finally obtain the optimal fishing depth of 249.7 meters.
7. Performing an extension algorithm
The basic algorithm can theoretically calculate the optimal fishing depth, but the algorithm is greatly influenced by the peak value of the target resource amount in the execution process and cannot reflect the 'best' in the real state. Assume that such a data state exists: as shown in fig. 6, in the region (r), the point a has the peak value of the target resource amount, but the target resource amount is small at other points (B, C, D, E, etc.) in the depth neighborhood of the point a. However, in the region, although no point has the peak of the target resource amount in the entire section, the target resource amounts corresponding to the points in the region are all close to the peak. In actual production, the fishing significance of the region II is obviously higher than that of the region I.
Based on the above analysis, the biggest disadvantage of the basic algorithm is that it over-emphasizes the peak value of the maximum resource amount, and ignores the points where the large amount of resource is close to the peak value. Therefore, the extension algorithm proposes an "extend maximum resource amount" interval. That is, the selection range of the maximum resource amount is expanded, and as long as the target resource amount mapped by a certain point is in the expanded maximum resource amount interval (instead of requiring to obtain a peak value), the point depth can be added into the candidate optimal fishing depth.
Therefore, the embodiment proposes an extended algorithm based on the basic algorithm, and the extended algorithm specifically includes: and calculating an extension interval, and counting depth data corresponding to the target resource amount in the extension interval. And constructing a depth difference dictionary (key is a depth value, and value is a depth difference between the current depth and the depth of the next target resource amount in the extension interval). If the number of the depth values corresponding to the minimum depth difference is larger than 3, updating the depth difference dictionary; otherwise, the prediction factors of the 1-3 depths are calculated.
The embodiment determines the calculation model for expanding the maximum resource amount as
Figure GDA0003004921700000061
Wherein N ispingNumber of pings (up to 2 target resources detected per ping) representing incoming EK80 data, VmaxRepresenting the actual peak of the target amount of resources in the statistical data.
Besides expanding the selection interval, the idea of the expansion algorithm at the final data analysis is also different. In the extended algorithm, if the length of the depth difference dictionary executed circularly is larger than 3, a new depth difference dictionary is continuously calculated and executed circularly (the same as the circulation in the basic algorithm); if the dictionary length is less than 3, calculating depth prediction factors one by one
Figure GDA0003004921700000062
Wherein, sigma CtargetRepresents the sum of the target resource amounts at that depth, and Δ d represents the depth difference. The exponential model amplifies the difference of each data point, and brings convenience to final decision. Finally, a depth prediction coefficient is calculated according to the depth prediction factor obtained by calculation
Figure GDA0003004921700000063
The depth finally obtained by the expansion algorithm can be 1-3, and the recommendation coefficients P of the depth values can be obtained, and a user can comprehensively judge according to the coefficients and the statistical chart.
In this embodiment, the extension interval is calculated to be 590.9-737 according to the above formula, 761 samples corresponding to the extension interval are total, and the depth between two adjacent samples is calculated to form a depth difference dictionary. And calculating that the minimum depth difference is 0.1 and the corresponding depth value is not unique, updating the depth difference dictionary according to the depth difference of 0.1, and performing circular traversal. Finally 187.9, 249.9 and 280.9 depth values are obtained, and the coefficients are 0.33, 0.35 and 0.32 respectively.
The invention can quickly estimate the optimal fishing depth of a certain fishery resource in a sea area according to the modes of data acquisition in the field and dynamic target setting without the limitation of factors such as sea areas, fishery resource types and the like, thereby improving the fishery fishing efficiency and reducing the fishing cost.

Claims (5)

1. A method for estimating catching depth by utilizing sonar data is characterized by comprising the following steps:
(1) acquiring underwater acoustic data of a sea area by using sonar equipment, analyzing the underwater acoustic data, extracting effective information, and calculating target intensity of the sea area at each depth;
(2) determining noise data in the underwater acoustic data according to target intensity corresponding to the target fishery resources and the target intensity at each depth of the sea area, and filtering the noise data to obtain effective data;
(3) counting the target resource amount of each depth in the effective data;
(4) according to the counted target resource amount of each depth, obtaining a mapping relation between the resource amount and the depth value, and estimating the optimal fishing depth according to the mapping relation; and estimating the optimal fishing depth by adopting a basic algorithm according to the mapping relation, wherein the basic algorithm specifically comprises the following steps: finding out the depth corresponding to the maximum value of the target resource amount, wherein if the depth value mapped by the maximum value of the target resource amount is unique, the depth value mapped by the maximum value of the target resource amount is the finally determined optimal fishing depth; if the depth value mapped by the maximum value of the target resource is not unique, calculating the depth difference of the depth values mapped by the maximum values of two adjacent target resources, determining a depth difference dictionary, finding out the minimum value of the depth difference from the depth difference dictionary, and if the minimum value of the depth difference is unique, taking the depth value corresponding to the minimum value of the depth difference as the optimal fishing depth; and if the minimum depth difference value is not unique, calculating the depth difference values of the minimum depth difference values and the maximum target resource amount one by one to form a new depth difference dictionary, judging by using the new depth difference dictionary until only one depth value is left finally, and taking the depth value as the optimal fishing depth.
2. The sonar data capturing depth estimation method according to claim 1, wherein when the hydroacoustic data is parsed in step (1), all the sub-data are divided into a binary file and an XML file according to a file coding method, and the sub-data are parsed one by one according to a type of a file header.
3. The sonar data capturing depth estimation method according to claim 1, wherein the capturing depth in step (1) is estimated by Ts ═ Pr +40log10R+2αR-10log10(Pt2G0 2λ2/16π2) Calculating the target intensity of the sea area, wherein Pr is the received power, R is the correction range, alpha is the absorption coefficient, Pt is the transmission power, G0λ is the wavelength for the sensor peak gain.
4. The method for estimating depth of fishing using sonar data according to claim 1, wherein the step (3) further includes graphically displaying the statistical target resource amount at each depth.
5. The method for estimating the capturing depth by using the sonar data according to claim 1, wherein when the depth value mapped by the maximum value of the target resource is unique, an extended interval is calculated, the depth data corresponding to the amount of the target resource in the extended interval is counted, a depth difference dictionary is constructed, if the number of the depth values corresponding to the minimum depth difference in the depth difference dictionary is greater than 3, the depth difference dictionary is updated in a manner of updating the depth difference dictionary in the basic algorithm, and if not, the prediction factors of the 1-3 depth values are calculated; wherein the extension interval is
Figure FDA0003004921690000021
NpingIndicates the number of pings, V, input into the sonar equipmentmaxActual peaks representing target resource amounts in the statistically valid data; the prediction factor is calculated in the manner of
Figure FDA0003004921690000022
∑CtargetRepresents the sum of the target resource amounts at that depth, and Δ d represents the depth difference.
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