CN116433948A - Sediment type identification method and device, electronic equipment and storage medium - Google Patents
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Abstract
The disclosure relates to a sediment type identification method and device, electronic equipment and storage medium. The method comprises the following steps: acquiring spectrum information of a water body containing sediment; determining the particle size distribution of sediment in the water body according to the spectrum information; and determining the type and the sand content of the sediment contained in the water body according to the particle size distribution. According to the sediment type identification method disclosed by the embodiment of the invention, the particle size distribution of sediment can be determined through the spectral information of the water body so as to determine the sediment content of the sediment and the class of the sediment, the measurement of the sediment content can be refined, and the sediment type identification method can be used in the fields of class judgment of the sediment, tracing of the sediment and the like, thereby providing a more sufficient basis for treating pollution discharge, water loss and soil erosion.
Description
Technical Field
The disclosure relates to the technical field of environmental protection, and in particular relates to a sediment type identification method and device, electronic equipment and a storage medium.
Background
The sand content of a water body is an important water quality index, and in the related art, only the total sand content in the water body can be measured, more specific information of the sediment is difficult to refine, for example, the distribution condition of the sediment with various particle sizes cannot be determined, and therefore, the type of the sediment cannot be determined. The basis for treating the phenomena of pollution discharge, water loss, soil erosion and the like is very limited.
CN113588601a provides an automatic silt amount monitor and system, and the automatic silt amount monitor can be arranged on surface runoff to realize real-time monitoring of liquid level data, flow velocity data and turbidity data of the surface runoff, so as to determine runoff data and sand content data of the surface runoff in real time, and realize real-time and accurate monitoring of water and soil loss. But it can only provide sand content data and cannot provide further information.
Disclosure of Invention
Aiming at the problems in the prior art, the disclosure provides a sediment type identification method and device, electronic equipment and storage medium.
According to an aspect of the present disclosure, there is provided a silt type identification method, the method comprising: acquiring spectrum information of a water body containing sediment; determining the particle size distribution of the sediment in the water body according to the spectrum information; and determining the type and the sand content of the sediment contained in the water body according to the particle size distribution.
In a possible implementation manner, the spectrum information includes meter scattering spectrum information of scattered light in a case that the water body is irradiated by a light source of a preset spectrum.
In one possible implementation manner, determining the particle size distribution of the sediment contained in the water body according to the spectrum information includes: determining scattering amplitudes of a plurality of scattering directions according to the rice scattering spectrum information; and determining the particle size distribution of the sediment according to the scattering amplitudes of the scattering directions.
In one possible implementation manner, determining the particle size distribution of the sediment according to the scattering amplitude of each wave band includes: determining a ratio of scattering amplitudes in each scattering direction based on the scattering amplitudes in the plurality of scattering directions; and determining the particle size distribution according to the ratio of scattering amplitudes of the scattering directions.
In one possible implementation, determining the type of sediment and the sediment content contained in the water body according to the particle size distribution includes: and carrying out integral treatment on the particle size distribution to determine the sand content of the sediment in the water body.
In one possible implementation, the method further includes: determining the chromaticity of the water body according to the spectrum information; and determining the type and the sand content of the sediment contained in the water body according to the particle size distribution, wherein the method comprises the following steps of: and determining the type and the sand content of the sediment contained in the water body according to the particle size distribution and the chromaticity of the water body.
In one possible implementation, determining the type of sediment contained in the body of water is implemented by a neural network, the method further comprising: inputting the particle size distribution of a water body sample into the neural network for processing, and determining the prediction type of sediment contained in the water body sample; determining the network loss of the neural network according to the prediction type and the labeling information of the water body sample; training the neural network based on the network loss.
According to an aspect of the present disclosure, there is provided a silt type recognition apparatus, the apparatus comprising: the spectrum information acquisition module is used for acquiring spectrum information of the water body containing the sediment; the distribution determining module is used for determining the particle size distribution of the sediment in the water body according to the spectrum information; and the result determining module is used for determining the type and the sand content of the sediment contained in the water body according to the particle size distribution.
In a possible implementation manner, the spectrum information includes meter scattering spectrum information of scattered light in a case that the water body is irradiated by a light source of a preset spectrum.
In one possible implementation, the distribution determining module is further configured to: determining scattering amplitudes of a plurality of scattering directions according to the rice scattering spectrum information; and determining the particle size distribution of the sediment according to the scattering amplitudes of the scattering directions.
In one possible implementation, the distribution determining module is further configured to: determining a ratio of scattering amplitudes in each scattering direction based on the scattering amplitudes in the plurality of scattering directions; and determining the particle size distribution according to the ratio of scattering amplitudes of the scattering directions.
In one possible implementation, the result determining module is further configured to: and carrying out integral treatment on the particle size distribution to determine the sand content of the sediment in the water body.
In one possible implementation, the apparatus further includes: the chromaticity determining module is used for determining chromaticity of the water body according to the spectrum information; wherein the result determination module is further to: and determining the type and the sand content of the sediment contained in the water body according to the particle size distribution and the chromaticity of the water body.
In one possible implementation, determining the type of sediment contained in the body of water is implemented by a neural network, the apparatus further comprising: the training module is used for inputting the particle size distribution of the water body sample into the neural network for processing and determining the prediction type of sediment contained in the water body sample; determining the network loss of the neural network according to the prediction type and the labeling information of the water body sample; training the neural network based on the network loss.
According to an aspect of the present disclosure, there is provided an electronic apparatus including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to: and executing the sediment type identification method.
According to an aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the above-described silt type identification method.
According to the sediment type identification method disclosed by the embodiment of the invention, the particle size distribution of sediment can be determined through the spectral information of the water body so as to determine the sediment content of the sediment and the class of the sediment, the measurement of the sediment content can be refined, and the sediment type identification method can be used in the fields of class judgment of the sediment, tracing of the sediment and the like, thereby providing a more sufficient basis for treating pollution discharge, water loss and soil erosion.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the technical aspects of the disclosure.
FIG. 1 shows a flow chart of a silt type identification method according to an embodiment of the present disclosure;
FIG. 2 shows a schematic application of a silt type identification method according to an embodiment of the present disclosure;
FIG. 3 shows a schematic application diagram of a silt type recognition apparatus according to an embodiment of the present disclosure;
FIG. 4 shows a block diagram of an electronic device according to an embodiment of the disclosure;
fig. 5 shows a block diagram of an electronic device according to an embodiment of the disclosure.
Detailed Description
Various exemplary embodiments, features and aspects of the disclosure will be described in detail below with reference to the drawings. In the drawings, like reference numbers indicate identical or functionally similar elements. Although various aspects of the embodiments are illustrated in the accompanying drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
The word "exemplary" is used herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.
The term "and/or" is herein merely an association relationship describing an associated object, meaning that there may be three relationships, e.g., a and/or B, may represent: a exists alone, A and B exist together, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, for example, including at least one of A, B, C, and may mean including any one or more elements selected from the group consisting of A, B and C.
In addition, numerous specific details are set forth in the following detailed description in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements, and circuits well known to those skilled in the art have not been described in detail in order not to obscure the present disclosure.
Fig. 1 shows a flowchart of a silt type identification method according to an embodiment of the present disclosure, as shown in fig. 1, the method including:
in step S11, spectrum information of a water body containing sediment is obtained;
in step S12, determining a particle size distribution of sediment contained in the water body according to the spectral information;
in step S13, the type of sediment and the sediment content contained in the water body are determined according to the particle size distribution.
According to the sediment type identification method disclosed by the embodiment of the invention, the particle size distribution of sediment can be determined through the spectral information of the water body so as to determine the sediment content of the sediment and the class of the sediment, the measurement of the sediment content can be refined, and the sediment type identification method can be used in the fields of class judgment of the sediment, tracing of the sediment and the like, thereby providing a more sufficient basis for treating pollution discharge, water loss and soil erosion.
In one possible implementation, the reasons for the sediment in the water body may be various, for example, sediment in the land enters the water due to water loss and soil erosion, sediment in the water bottom rolls to cause the sediment in the water body, and pollutants artificially discharged into the water body contain the sediment and the like. Therefore, detecting the sediment content of the water body in a general way can only detect the concentration of sediment in the water, cannot judge the type of the sediment, cannot determine the source of the sediment, cannot distinguish the reason for the sediment in the water body, and cannot provide more powerful basis for treating pollution discharge and water and soil loss.
In one possible implementation manner, for the above problems, more detailed detection (including particle size distribution of sediment in water and sediment content in different particle size distributions) can be performed by measuring spectral information of the water body, so as to determine the content and type of sediment in the water body, thereby providing more sufficient basis for treating pollution discharge and water and soil loss.
In one possible implementation, in step S11, spectral information of the silt containing body of water may be measured using a spectrometer, such as a quantum dot spectral sensor.
In an example, water quality information of a predetermined water area may be measured by a micro-spectrum sensor, such as a quantum spectrum sensor. The quantum dot spectral sensor may measure incident light (e.g., light after the light is transmitted or scattered through a water sample in a predetermined area) based on physical and optical characteristics of the nanocrystals to obtain spectral information of the incident light. For example, a quantum dot spectroscopic sensor may include a quantum dot spectroscopic sensor that may include a nanocrystal chip made of a plurality of nanocrystals, the nanocrystal chip comprising an array of a plurality of nanocrystals (e.g., an array of nanocrystals), wherein each nanocrystal has different light absorption characteristics, and different types of semiconductor nanocrystals, e.g., may be of different materials, sizes, etc., such that the nanocrystal chip may modulate response to wavelengths over a broad wavelength range to obtain spectral information tuned for incident light over the broad wavelength range. The spectral information of one or more specific wavelengths can also be obtained by modulating the response of the specific semiconductor nanocrystal chip to the wavelength in the specific wavelength range according to the actual needs.
In one possible implementation, the scattered light of the light through the water may be affected by substances in the water (e.g., suspended matter, contaminants, etc.), thereby obtaining specific spectral information. The quantum dot spectrum sensor can obtain the spectrum information in real time and determine the particle size information of the sediment represented by the spectrum information, for example, the type of the sediment contained in the water body and the sediment content can be determined. Alternatively, the quantum dot spectroscopic sensor may infer the type and sand content of the sediment through a neural network, for example, spectroscopic information may be input into the neural network, which may infer the type and sand content of the sediment. The present disclosure does not limit the method of processing the spectral information. The present disclosure does not limit the working principle of the quantum dot spectrum sensor.
In an example, the quantum dot spectrum sensor may determine a specific index (for example, a particle size distribution of silt contained in water) according to scattering characteristics of light by various substances contained in water, for example, may analyze light intensities of light rays of specific wavelengths in respective scattering directions through spectrum information, and thus obtain an index corresponding to the light rays of the specific wavelength range. The measurement index of the quantum dot spectrum sensor can be used for realizing online, in-situ, high-frequency and real-time measurement. When detecting specific indexes, the quantum dot spectrum sensor can be used for detecting the spectrum information of the light rays of the preset water area, and then the specific indexes can be rapidly calculated based on the spectrum information so as to obtain the specific indexes with stronger instantaneity. The measuring frequency of the quantum dot spectrum sensor can reach 3-60 min/time, preferably 5-30 min/time, particularly preferably 8-20 min/time, most preferably 10-15 min/time, the measuring frequency is far higher than the frequency of bringing the water body back to laboratory test, and the quantum dot spectrum sensor can be arranged at a fixed position of a preset water area, so that the consistency of the water body sample can be ensured. And the water body is brought back to a laboratory to perform an assay, so that the water body is difficult to ensure that the water body is completely sampled at the same place during the two measurements, and because the measurement frequency is low, the interval time between the two measurements is long, even if the water body can be completely sampled at the same place during the two measurements, the water quality at the place can have great change in the long interval time due to the fluidity of the water, and the consistency of the measurement and the comparability of the measurement result are difficult to ensure.
In one possible implementation, the spectral information may include a variety of information, such as a reflection spectrum of reflected light, a transmission spectrum of transmitted light, a scattering spectrum of scattered light, and so forth. In an example, the spectral information includes meter scattering spectral information of scattered light in the case of a body of water illuminated by a light source (e.g., natural light, a visible light source of 380-700nm, a broad spectrum light source of 235-780nm, a near infrared light source, a combination of visible and near infrared light sources, etc., for example, 700-2500 nm, further 750-1500nm, preferably 780-1200nm, or 780-1100 nm). The intensity of the light scattered by the light may be different in various directions as it passes through particles (e.g., silt particles) contained in the body of water. Based on the above, the reflection directions and the light intensities of the silt particles with different particle diameters are different, so that the rice scattering spectrum information of the water body under the irradiation of the light source can be measured to determine the light intensities of a plurality of scattering directions, further determine the content of the silt with corresponding particle diameters, and further determine the particle size distribution. Further, the type of silt may be determined based on the particle size distribution.
In one possible implementation, when measuring the average particle size of the sediment in the water body, the spectrum information includes scattering spectrum information of the scattered light in the case that the water body is irradiated by a light source with a preset spectrum. Step S12 may include: determining scattering amplitudes of a plurality of scattering directions according to the rice scattering spectrum information; and determining the particle size distribution of the sediment according to the scattering amplitudes of the scattering directions.
In one possible implementation manner, the light source with a preset spectrum may be a broad spectrum light source, the broad spectrum light source may emit composite light with multiple wavebands, after the composite light is subjected to the rice scattering effect of the water body containing the silt, the rice scattering spectrum information may be detected by the quantum dot spectrum sensor pair, so as to determine scattering amplitudes in multiple scattering directions, and further obtain a ratio of the intensity of the emergent light in each scattering direction to the intensity of the incident light.
In an example, the content of the silt particles with different particle sizes is different, and the scattering effect on the light is different, for example, if the content of the silt particles with a certain size is high, the scattering effect on the light can be stronger, and in the collected rice scattering spectrum information, the scattering amplitude in a specific scattering direction is larger, that is, the light intensity of the light in the scattering direction is larger than the light intensity of the incident light. The sediment particle content of the particle size of the other size is lower, and the scattering effect of the light is weaker, so that in the meter scattering spectrum information, the scattering amplitude in the corresponding scattering direction is smaller, that is, the light intensity of the light in the scattering direction is smaller than the light intensity of the incident light.
In one possible implementation manner, in the meter scattering spectrum information, scattering amplitudes in multiple scattering directions may be measured, and since the content of the silt particles with different particle diameters corresponds to the scattering amplitudes in each scattering direction, the distribution situation of the silt particles with various particle diameters, that is, the particle diameter distribution, may be determined based on the above correspondence.
In one possible implementation manner, determining the particle size distribution of the sediment according to the scattering amplitude of each wave band includes: determining a ratio of scattering amplitudes in each scattering direction based on the scattering amplitudes in the plurality of scattering directions; and determining the particle size distribution according to the ratio of scattering amplitudes of the scattering directions.
In an example, a ratio of scattering amplitudes in each scattering direction may be determined, and a ratio of contents of the various particle sizes of the silt particles may be determined based on the ratio, thereby determining a distribution of the various particle sizes of the silt particles. For example, if the content of the sediment particles of various particle diameters is proportional to the scattering amplitude in each scattering direction, the ratio of the content of the sediment particles of various particle diameters can be determined as the ratio of the scattering amplitudes in each scattering direction, and the particle size distribution can be determined. For example, if the content of the sediment particles of various particle diameters is proportional to the square of the scattering amplitude in each scattering direction, the ratio of the content of the sediment particles of various particle diameters is determined as the ratio of the squares of the scattering amplitudes in each scattering direction, and the particle size distribution can be determined. In an example, the particle size distribution may represent not only the ratio of the contents of the various particle sizes of the sediment particles, but also the contents of the sediment particles of various particle sizes, and the contents of the sediment particles of the particle sizes may be determined according to the scattering amplitude of a certain particle size, and further the contents of the sediment particles of other particle sizes may be determined according to the ratio of the contents. The present disclosure is not limited to the particular manner in which the particle size distribution is determined.
By the method, the scattering amplitudes in a plurality of scattering directions can be measured based on the rice scattering spectrum information, so that the particle size distribution of sediment particles in the water body can be determined, and the traditional method is not required: the quality of the residue after the equivalent per cubic meter water body is dried is measured in a drying and weighing mode, the water body is tested to obtain the sand content, the detection efficiency is improved, and the instantaneity and the accuracy of the detection data are also improved.
In one possible implementation, in step S13, the type of sediment and the amount of sediment in the body of water may be determined by the particle size distribution of the sediment.
In one possible implementation, the particle size distribution of the sediment may represent the ratio of the sediment content of each particle size, and may also represent the sediment particle content of each particle size. The sand content of the sediment in the body of water, i.e. the total content of all sediment contained in the body of water, can be determined based on the particle size distribution. Step S13 may include: and carrying out integral treatment on the particle size distribution to determine the sand content of the sediment in the water body.
In one possible implementation, the type of sediment may be determined by the particle size distribution, that is, according to the content of sediment particles with various particle sizes and the proportion of each other, to determine what sediment is contained in the water body, for example, sediment flushed from land into the water body, or sediment rolled up from the water bottom, which is not limited by the present disclosure. In an example, the type of silt may be determined from a priori experience. For example, the particle size of the sediment at the bottom of the water is uniform after long-term flushing of the water flow, i.e. the particle size distribution can be concentrated. As another example, sediment washed into the body of water on land may contain relatively complex conditions, such as dust, dirt, quartz sand, even small particle stones, etc., and the particle size distribution may be relatively diffuse. The type of sediment can be determined accordingly, for example, whether sediment is water bottom rolling sediment or land flushing sediment into the water. The above types are merely examples, and the present disclosure does not limit the types of silt and the judgment standards.
In one possible implementation, the particle size distribution may be in the form of a distribution spectrum, and the distribution spectrum may include content information and proportion information of silt particles with various particle sizes. The distribution spectrum of the particle size distribution may be subjected to integral processing, so that the sand content of the sediment contained in the water body may be obtained, for example, the whole distribution spectrum may be subjected to score processing, so that the total sand content of all the sediment is obtained, and/or the distribution spectrums of the particle sizes of the various distribution features may be respectively integrated based on the correspondence between the particle size distribution and the sediment type, so that the sand content of the sediment of various types is obtained.
In one possible implementation, the particle sizes of the silt particles of different types may be similar, for example, the particle sizes of the clay particles may be close to those of the finer quartz sand particles, so that other water quality information can be further measured on the basis of the particle sizes of the silt particles to provide a richer classification basis.
In an example, the chromaticity of a body of water may be determined. For example, the chromaticity of the different types of silt particles is different, for example, the chromaticity of the soil particles is different from that of the quartz sand particles under natural light irradiation, and thus the chromaticity of the water body containing the soil particles and the quartz sand particles is different. Therefore, chromaticity can be used as a classification basis, and classification accuracy is further improved. The method further comprises the steps of: determining the chromaticity of the water body according to the spectrum information; step S13 may include: and determining the type and the sand content of the sediment contained in the water body according to the particle size distribution and the chromaticity of the water body.
In an example, from the spectral information, the chromaticity of the body of water may be determined. Because the types of the silt particles contained in the water body are different, the absorption effect and the transmission effect of the water body on the light emitted by the light source are different. For example, the earth particles have a strong absorption of light in one band and a weak absorption of light in another band, and thus, can transmit light in another band. The wavelength bands of light transmitted by different types of particles are different, so that the chromaticity of the light is different, the spectral information of the light transmitted by the water body can be detected, the chromaticity of the light is further determined, and the chromaticity is determined as the chromaticity of the water body. Based on the chromaticity, the type of the sediment can be comprehensively determined with the particle size distribution of the sediment. Further, the sand content may be obtained based on a particle size distribution, which is not described in detail herein.
In one possible implementation manner, the determining the type of the sediment contained in the water body is implemented through a neural network, that is, the trained neural network is utilized to process the particle size distribution to classify the sediment contained in the water body, thereby determining the type of the sediment.
In one possible implementation, the neural network that determines the sand content may be trained to improve the accuracy of the neural network. The method further comprises the steps of: inputting the particle size distribution of a water body sample into the neural network for processing, and determining the prediction type of sediment contained in the water body sample; determining the network loss of the neural network according to the prediction type and the labeling information of the water body sample; training the neural network based on the network loss.
In an example, a body of water of known type containing sediment may be used as a sample, and known type information may be used as labeling information for the body of water sample, through which the neural network is trained.
In an example, the meter scattering spectrum information of the water body sample can be detected by a quantum dot spectrum sensor, and the particle size distribution of the sediment in the water body sample can be determined based on the meter scattering spectrum information. Further, the above particle size distribution may be input into a neural network, and the neural network may output a predicted type of the water sample, i.e., a type of sediment predicted by the neural network after calculation.
In an example, the type of sediment predicted by the neural network may be compared to the known sediment types (labeled information) described above to determine the network loss of the neural network. Such as cross entropy, relative entropy, etc. The present disclosure does not limit the way in which network losses are calculated.
In an example, the neural network may be trained in a direction that minimizes network loss based on the network loss described above. For example, the network loss can be counter-propagated by a gradient descent method to adjust network parameters of the neural network, thereby gradually improving the accuracy of the neural network.
In an example, the training steps described above may be iteratively performed until a training condition is met. For example, the training condition is satisfied when the number of iterations reaches a predetermined number, or the training condition is satisfied when the accuracy of the neural network reaches a predetermined accuracy level. The present disclosure does not limit the training conditions.
According to the sediment type identification method disclosed by the embodiment of the invention, the particle size distribution of sediment can be determined through the spectral information of the water body, the sediment content and the sediment type of the sediment can be determined through the neural network, the measurement of the sediment content can be refined, and the specific type of the sediment can be determined. The method can be used in the fields of sediment type judgment, sediment tracing and the like, thereby providing more sufficient basis for treating pollution discharge and water and soil loss.
Fig. 2 illustrates an application schematic diagram of a sediment type identification method according to an embodiment of the present disclosure, as illustrated in fig. 2, a quantum dot spectrum sensor may be disposed at a water body to be detected, and the water body may be detected.
In one possible implementation manner, the water body containing the sediment disperses the light rays emitted by the natural light and other broad spectrum light sources to form scattered light, and the scattered light can be detected by the sub-point spectrum sensor to determine the meter scattering spectrum information of the scattered light, so that the scattering amplitude of each scattering direction is determined based on the meter scattering spectrum information. Further, a ratio of scattering amplitudes in the respective scattering directions may be determined based on the scattering amplitudes, to determine a particle size distribution of the various particle size of the sediment contained in the water body by the ratio.
In one possible implementation, the particle size distribution may be integrated to obtain the sand content of the sediment contained in the body of water. Further, the types of the sediment can be classified through the neural network, for example, the neural network is used for processing the particle size distribution to judge the types of the sediment, for example, the sediment from land flushing to water, the sediment rolled under the water, the sediment artificially discharged, and the like.
In one possible implementation, the type of sediment and the particle size distribution may provide a basis for environmental remediation, for example, the type of sediment may be determined to be sediment flushed into water on land by the particle size distribution, and the water loss phenomenon may be determined to be serious if the sediment content in the water is determined to be high. If the type of the sediment is determined to be the sediment with water bottom rolling through the grain size distribution, the phenomena of water and soil loss and the like cannot be indicated. Therefore, by the sediment type identification method, sediment can be traced, the reason for the sediment in the water body can be determined, and a basis is provided for treating pollution and/or water and soil loss and other phenomena. The application field of the sediment type identification method is not limited by the disclosure.
Fig. 3 shows a schematic application diagram of a silt type recognition apparatus according to an embodiment of the present disclosure, as shown in fig. 3, the apparatus includes: the spectrum information acquisition module 11 is used for acquiring spectrum information of the water body containing the sediment; a distribution determining module 12, configured to determine a particle size distribution of sediment contained in the water body according to the spectral information; and the result determining module 13 is used for determining the type and the sand content of the sediment contained in the water body according to the particle size distribution.
In a possible implementation manner, the spectrum information includes meter scattering spectrum information of scattered light in a case that the water body is irradiated by a light source of a preset spectrum.
In one possible implementation, the distribution determining module is further configured to: determining scattering amplitudes of a plurality of scattering directions according to the rice scattering spectrum information; and determining the particle size distribution of the sediment according to the scattering amplitudes of the scattering directions.
In one possible implementation, the distribution determining module is further configured to: determining a ratio of scattering amplitudes in each scattering direction based on the scattering amplitudes in the plurality of scattering directions; and determining the particle size distribution according to the ratio of scattering amplitudes of the scattering directions.
In one possible implementation, the result determining module is further configured to: and carrying out integral treatment on the particle size distribution to determine the sand content of the sediment in the water body.
In one possible implementation, the apparatus further includes: the chromaticity determining module is used for determining chromaticity of the water body according to the spectrum information; wherein the result determination module is further to: and determining the type and the sand content of the sediment contained in the water body according to the particle size distribution and the chromaticity of the water body.
In one possible implementation, determining the type of sediment contained in the body of water is implemented by a neural network, the apparatus further comprising: the training module is used for inputting the particle size distribution of the water body sample into the neural network for processing and determining the prediction type of sediment contained in the water body sample; determining the network loss of the neural network according to the prediction type and the labeling information of the water body sample; training the neural network based on the network loss.
It will be appreciated that the above-mentioned method embodiments of the present disclosure may be combined with each other to form a combined embodiment without departing from the principle logic, and are limited to the description of the present disclosure.
In addition, the disclosure further provides a silt type recognition device, an electronic device, a computer readable storage medium and a program, and the above can be used for implementing any silt type recognition method provided by the disclosure, and corresponding technical schemes and descriptions and corresponding records referring to method parts are not repeated.
It will be appreciated by those skilled in the art that in the above-described method of the specific embodiments, the written order of steps is not meant to imply a strict order of execution but rather should be construed according to the function and possibly inherent logic of the steps.
In some embodiments, a function or a module included in an apparatus provided by the embodiments of the present disclosure may be used to perform a method described in the foregoing method embodiments, and a specific implementation thereof may refer to the description of the foregoing method embodiments, which is not repeated herein for brevity
The disclosed embodiments also provide a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the above-described method. The computer readable storage medium may be a non-volatile computer readable storage medium.
The embodiment of the disclosure also provides an electronic device, which comprises: a processor; a memory for storing processor-executable instructions; wherein the processor is configured as the method described above.
The electronic device may be provided as a terminal, server or other form of device.
Fig. 4 is a block diagram of an electronic device 800, according to an example embodiment. For example, electronic device 800 may be a mobile phone, computer, digital broadcast terminal, messaging device, game console, tablet device, medical device, exercise device, personal digital assistant, or the like.
Referring to fig. 4, the electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power component 806, a multimedia component 808, an audio component 810, an input/output (I/O) interface 812, a sensor component 814, and a communication component 816.
The processing component 802 generally controls overall operation of the electronic device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to perform all or part of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interactions between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.
The memory 804 is configured to store various types of data to support operations at the electronic device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 804 may be implemented by any type or combination of volatile or nonvolatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.
The power supply component 806 provides power to the various components of the electronic device 800. The power components 806 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for the electronic device 800.
The multimedia component 808 includes a screen between the electronic device 800 and the user that provides an output interface. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may sense not only the boundary of a touch or slide action, but also the duration and pressure associated with the touch or slide operation. In an example, the screen may provide a function of interaction between the electronic device and the user, for example, the user may input information through the screen, for example, input a particle size distribution of sediment, or input a number of a sensor (for example, the number of the sensor is in one-to-one correspondence with a water area, and the number of the sensor is input, so that the particle size distribution detected by the sensor in the corresponding water area may be obtained), and the screen may output the sediment content and the sediment type of the water area to the user, for example, display information of the sediment content, the sediment type, and the like. In the tracing work, a map and a water flow diagram can be displayed, and the position information of water and soil loss or pollution discharge can be predicted. The present disclosure does not limit the interactive contents of the screen.
In some embodiments, the multimedia component 808 includes a front camera and/or a rear camera. When the electronic device 800 is in an operational mode, such as a shooting mode or a video mode, the front camera and/or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have focal length and optical zoom capabilities.
The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the electronic device 800 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may be further stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 further includes a speaker for outputting audio signals.
The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be a keyboard, click wheel, buttons, etc. These buttons may include, but are not limited to: homepage button, volume button, start button, and lock button.
The sensor assembly 814 includes one or more sensors for providing status assessment of various aspects of the electronic device 800. For example, the sensor assembly 814 may detect an on/off state of the electronic device 800, a relative positioning of the components, such as a display and keypad of the electronic device 800, the sensor assembly 814 may also detect a change in position of the electronic device 800 or a component of the electronic device 800, the presence or absence of a user's contact with the electronic device 800, an orientation or acceleration/deceleration of the electronic device 800, and a change in temperature of the electronic device 800. The sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscopic sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 816 is configured to facilitate communication between the electronic device 800 and other devices, either wired or wireless. The electronic device 800 may access a wireless network based on a communication standard, such as WiFi,2G, or 3G, or a combination thereof. In one exemplary embodiment, the communication component 816 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, ultra Wideband (UWB) technology, bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the electronic device 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic elements for executing the methods described above.
In an exemplary embodiment, a non-transitory computer readable storage medium is also provided, such as memory 804 including computer program instructions executable by processor 820 of electronic device 800 to perform the above-described methods.
Fig. 5 is a block diagram illustrating an electronic device 1900 according to an example embodiment. For example, electronic device 1900 may be provided as a server. Referring to FIG. 5, electronic device 1900 includes a processing component 1922 that further includes one or more processors and memory resources represented by memory 1932 for storing instructions, such as application programs, that can be executed by processing component 1922. The application programs stored in memory 1932 may include one or more modules each corresponding to a set of instructions. Further, processing component 1922 is configured to execute instructions to perform the methods described above.
The electronic device 1900 may also include a power component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input/output (I/O) interface 1958. The electronic device 1900 may operate an operating system based on a memory 1932, such as Windows Server TM ，Mac OS XTM ，Unix TM ,Linux TM ，FreeBSD TM Or the like.
In an exemplary embodiment, a non-transitory computer readable storage medium is also provided, such as memory 1932, including computer program instructions executable by processing component 1922 of electronic device 1900 to perform the methods described above.
The present disclosure may be a system, method, and/or computer program product. The computer program product may include a computer readable storage medium having computer readable program instructions embodied thereon for causing a processor to implement aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: portable computer disks, hard disks, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disk read-only memory (CD-ROM), digital Versatile Disks (DVD), memory sticks, floppy disks, mechanical coding devices, punch cards or in-groove structures such as punch cards or grooves having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.
The computer readable program instructions described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium in the respective computing/processing device.
Computer program instructions for performing the operations of the present disclosure can be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, c++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present disclosure are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information of computer readable program instructions, which can execute the computer readable program instructions.
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable medium having the instructions stored therein includes an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The foregoing description of the embodiments of the present disclosure has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or the improvement of technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (10)
1. A method for identifying a sediment type, the method comprising:
acquiring spectrum information of a water body containing sediment;
determining the particle size distribution of the sediment in the water body according to the spectrum information;
and determining the type and the sand content of the sediment contained in the water body according to the particle size distribution.
2. The method of claim 1, wherein the spectral information comprises rice scattering spectral information of scattered light in the case of a body of water illuminated by a light source.
3. The method according to claim 2, wherein determining the particle size distribution of sediment contained in the body of water based on the spectral information comprises:
Determining scattering amplitudes of a plurality of scattering directions according to the rice scattering spectrum information;
and determining the particle size distribution of the sediment according to the scattering amplitudes of the scattering directions.
4. A method according to claim 3, wherein determining the particle size distribution of the silt from the scattering amplitudes of the bands comprises:
determining a ratio of scattering amplitudes in each scattering direction based on the scattering amplitudes in the plurality of scattering directions;
and determining the particle size distribution according to the ratio of scattering amplitudes of the scattering directions.
5. The method of claim 1, wherein determining the type of sediment and the amount of sediment contained in the body of water from the particle size distribution comprises:
and carrying out integral treatment on the particle size distribution to determine the sand content of the sediment in the water body.
6. The method according to claim 1, wherein the method further comprises:
determining the chromaticity of the water body according to the spectrum information;
and determining the type and the sand content of the sediment contained in the water body according to the particle size distribution, wherein the method comprises the following steps of:
and determining the type and the sand content of the sediment contained in the water body according to the particle size distribution and the chromaticity of the water body.
7. The method according to claim 1, wherein determining the type of sediment contained in the body of water is accomplished by means of a neural network,
the method further comprises the steps of:
inputting the particle size distribution of a water body sample into the neural network for processing, and determining the prediction type of sediment contained in the water body sample;
determining the network loss of the neural network according to the prediction type and the labeling information of the water body sample;
training the neural network based on the network loss.
8. A silt type recognition apparatus, said apparatus comprising:
the spectrum information acquisition module is used for acquiring spectrum information of the water body containing the sediment;
the distribution determining module is used for determining the particle size distribution of the sediment in the water body according to the spectrum information;
and the result determining module is used for determining the type and the sand content of the sediment contained in the water body according to the particle size distribution.
9. An electronic device, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to: performing the method of any one of claims 1 to 7.
10. A computer readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the method of any of claims 1 to 7.
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| CN117233055B (en) * | 2023-11-14 | 2024-02-09 | 芯视界（北京）科技有限公司 | Sediment content measuring method and device, electronic equipment and storage medium |
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