CN114839340A - Water quality biological activity detection method and device, electronic equipment and storage medium - Google Patents
Water quality biological activity detection method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The disclosure relates to a water quality bioactivity detection method and device, electronic equipment and a storage medium. The method comprises the steps of determining a plurality of water quality index sequences of a preset water area in a first time period according to water quality information of the preset water area; and determining the water quality biological activity index according to the plurality of water quality index sequences and the pre-trained water quality detection network. According to the water quality bioactivity detection method disclosed by the embodiment of the disclosure, a plurality of water quality index sequences of a preset water area can be measured in real time and high frequency, so that the data in the water quality index sequences can reflect the current microbial activity in the water body in real time. Furthermore, the water quality index sequence is a plurality of water quality indexes measured in a time period, and the fluctuation condition of biological activity can be reflected through the fluctuation of the water quality indexes, so that the condition of microorganisms in the water body can be more accurately reflected, and bases are provided for water quality monitoring, water bloom prediction and the like.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a method and an apparatus for detecting biological activity of water, an electronic device, and a storage medium.
Background
Biological activity in water is one of the important indicators for water quality detection, however, unlike other water quality indicators, microorganisms in water are active, the biological activity of the microorganism may change all the time, so that it is difficult to accurately reflect the current activity of the microorganism in the water body by a method with a low detection frequency such as assay, etc., and in addition, the activity of the microorganism (for example, respiration, photosynthesis, etc.) may cause the water quality indexes such as dissolved oxygen concentration index, pH index, conductivity index, chemical oxygen demand index, biochemical oxygen demand ammonia nitrogen index, nitro nitrogen index, nitroso nitrogen index, total phosphorus index, etc. to change, so that the change of the water quality indexes may reflect the activity of the microorganism in the water, and the static data obtained by single detection is difficult to reflect the fluctuation condition of the biological activity index, so the activity of microorganisms in the water body is difficult to reflect.
Disclosure of Invention
The disclosure provides a water quality bioactivity detection method and device, electronic equipment and a storage medium.
According to an aspect of the present disclosure, there is provided a water quality bioactivity detection method, the method comprising: determining a plurality of water quality index sequences of a predetermined water area in a first time period according to water quality information of the predetermined water area, wherein the water quality index sequences comprise the same water quality indexes obtained at a plurality of moments in the first time period; and determining a water quality biological activity index according to the plurality of water quality index sequences and a pre-trained water quality detection network, wherein the water quality biological activity index comprises at least one of chlorophyll concentration and algae density in a preset water area.
In one possible implementation manner, determining the water quality bioactivity index according to the plurality of water quality index sequences and the pre-trained water quality detection network includes: determining at least one biological activity identification parameter according to the plurality of water quality index sequences; and inputting the biological activity identification parameters into the water quality detection network to obtain the water quality biological activity indexes.
In one possible implementation, the biological activity identification parameter includes at least one of a mean value, a standard deviation, a peak height, a number of peaks, a time to peak, and a rate of change of each water quality indicator sequence.
In one possible implementation, the method further includes: determining a plurality of sample water quality index sequences of the water body samples in a second time period according to the water quality information of the water body samples; determining sample activity indexes according to the plurality of sample water quality index sequences and the water quality detection network; determining the network loss of the water quality detection network according to the sample activity index and the labeling information of the water body sample, wherein the labeling information comprises a biological activity index of the water body sample obtained through chemical inspection; and training the water quality detection network according to the network loss.
In one possible implementation, the plurality of water quality indicator sequences includes a dissolved oxygen concentration indicator sequence, a pH indicator sequence, a conductivity indicator sequence, a chemical oxygen demand indicator sequence, a biochemical oxygen demand indicator sequence, an ammonia nitrogen indicator sequence, a nitronitrogen indicator sequence, a nitrosonitrogen indicator sequence, a total nitrogen indicator sequence, and a total phosphorus indicator sequence.
According to an aspect of the present disclosure, there is provided a water quality bioactivity detection apparatus, the apparatus comprising: the system comprises a sequence obtaining module, a water quality control module and a water quality control module, wherein the sequence obtaining module is used for determining a plurality of water quality index sequences of a preset water area in a first time period according to water quality information of the preset water area, and the water quality index sequences comprise the same water quality indexes obtained at a plurality of moments in the first time period; and the index obtaining module is used for determining a water quality bioactivity index according to the plurality of water quality index sequences and the pre-trained water quality detection network, wherein the water quality bioactivity index comprises at least one of chlorophyll concentration and algae density in a preset water area.
In one possible implementation manner, the index obtaining module is further configured to: determining at least one biological activity identification parameter according to the plurality of water quality index sequences; and inputting the biological activity identification parameters into the water quality detection network to obtain the water quality biological activity indexes.
In one possible implementation, the apparatus further includes: the training module is used for determining a plurality of sample water quality index sequences of the water body samples in the second time period according to the water quality information of the water body samples; determining sample activity indexes according to the plurality of sample water quality index sequences and the water quality detection network; determining the network loss of the water quality detection network according to the sample activity index and the labeling information of the water body sample, wherein the labeling information comprises a biological activity index of the water body sample obtained through chemical inspection; and training the water quality detection network according to the network loss.
In one possible implementation, the biological activity identification parameter includes at least one of a mean value, a standard deviation, a peak height, a number of peaks, a time to peak, and a rate of change of each water quality indicator sequence.
In one possible implementation, the plurality of water quality indicator sequences include a dissolved oxygen concentration indicator sequence, a pH indicator sequence, a conductivity indicator sequence, a chemical oxygen demand indicator sequence, a biochemical oxygen demand indicator sequence, an ammonia nitrogen indicator sequence, a nitronitrogen indicator sequence, a nitrosonitrogen indicator sequence, a total nitrogen indicator sequence, and a total phosphorus indicator sequence.
According to an aspect of the present disclosure, there is provided an electronic device including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to: the water quality biological activity detection method is implemented.
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 water quality bioactivity detection method.
According to the water quality biological activity detection method disclosed by the embodiment of the disclosure, a plurality of water quality index sequences of a preset water area can be measured in real time at high frequency, so that the data in the water quality index sequences can reflect the current microbial activity in the water body in real time. Furthermore, the water quality index sequence is a plurality of water quality indexes measured in a time period, the fluctuation condition of the biological activity can be reflected through the fluctuation of the water quality indexes, the condition of microorganisms in the water body can be further accurately reflected, and a basis is provided for water quality monitoring, water bloom prediction and the like.
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 present disclosure and, together with the description, serve to explain the principles of the disclosure.
FIG. 1 shows a flow diagram of a water quality bioactivity detection method according to an embodiment of the present disclosure;
FIG. 2 shows a schematic diagram of an application of a water quality bioactivity detection method according to an embodiment of the present disclosure;
FIG. 3 shows a block diagram of a water quality bioactivity detection 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 illustrates a block diagram of an electronic device according to an embodiment of the disclosure.
Detailed Description
Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
The word "exemplary" is used exclusively 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" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, 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.
Furthermore, in the following detailed description, numerous specific details are set forth 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 that are well known to those skilled in the art have not been described in detail so as not to obscure the present disclosure.
Fig. 1 shows a flow chart of a water quality bioactivity detection method according to an embodiment of the present disclosure, as shown in fig. 1, the method includes:
in step S11, determining a plurality of water quality index sequences of a predetermined water area in a first time period according to water quality information of the predetermined water area, wherein the water quality index sequences comprise one water quality index obtained at a plurality of moments in the first time period;
in step S12, a water quality bioactivity indicator is determined according to the plurality of water quality indicator sequences and the pre-trained water quality detection network, wherein the water quality bioactivity indicator comprises at least one of chlorophyll concentration and algae density in a predetermined water area.
According to the water quality bioactivity detection method disclosed by the embodiment of the disclosure, a plurality of water quality index sequences of a preset water area can be measured in real time and high frequency, so that the data in the water quality index sequences can reflect the current microbial activity in the water body in real time. Furthermore, the water quality index sequence is a plurality of water quality indexes measured in a time period, and the fluctuation condition of biological activity can be reflected through the fluctuation of the water quality indexes, so that the condition of microorganisms in the water body can be more accurately reflected, and bases are provided for water quality monitoring, water bloom prediction and the like.
In an example, water quality information of a predetermined water area may be measured by a micro-spectral sensor, such as a quantum spectral sensor. The quantum dot spectroscopic sensor can measure incident light (e.g., light after light is transmitted or scattered through a water sample in a predetermined region) based on physical and optical properties of the nanocrystals to obtain spectral information of the incident light. For example, a quantum dot spectroscopic sensor can include a quantum dot spectroscopic probe that can include therein a nanocrystal chip made of a plurality of nanocrystals, the nanocrystal chip containing an arrangement of the plurality of nanocrystals (e.g., an array of nanocrystals), wherein each nanocrystal has different light absorption characteristics, and wherein different types of semiconductor nanocrystals, e.g., can be of different materials, sizes, etc., such that the nanocrystal chip can modulate response to wavelengths within a wider range of wavelengths to obtain modulated spectral information of incident light within the wider range of wavelengths. The spectral information of one or more specific wavelengths can be obtained by modulating and responding to the wavelengths in one or more specific wavelength ranges through a specific semiconductor nanocrystal chip according to actual needs.
In one possible implementation, the light transmitted or scattered through the water may be affected by substances in the water (e.g., suspended matter, contaminants, etc.) to obtain specific spectral information. The quantum dot spectrum sensor can obtain the spectrum information in real time and determine the water quality index information represented by the spectrum information. For example, the spectral information of light in different frequency bands can be obtained by the absorption intensity of the water sample to light with different wavelengths, and the water quality index can be calculated through the spectral information. In examples, the water quality indicators include water quality Chemical Oxygen Demand (COD), biochemical Oxygen Demand, turbidity, pH, conductivity, dissolved Oxygen concentration, permanganate index, total suspended matter, biological Oxygen Demand, total organic carbon, sulfate content, chloride content, soluble iron content, soluble manganese content, soluble copper content, soluble zinc content, nitrate content, nitrite content, total nitrogen content, ammonia nitrogen content, nitro nitrogen content, nitroso nitrogen content, total phosphorus content, fluoride content, selenium content, total arsenic content, total mercury content, total cadmium content, chromium content, total lead content, total cyanide, volatile phenol content, coliform group content, sulfide content, and the like. The water temperature can also be determined from the infrared spectrum in the spectral information. The working principle of the quantum dot spectrum sensor is not limited by the disclosure. In an example, the quantum dot spectrum sensor may determine a water quality index from absorption characteristics of various substances contained in water to light, for example, the light intensity of light of a specific wavelength may be analyzed by spectral information, and the concentration of a substance (water quality index) corresponding to the light of the specific wavelength range may be obtained. Alternatively, the quantum dot spectroscopic sensor may estimate the water quality index by a neural network, for example, spectral information may be input to the neural network, and the neural network may estimate the concentration of each substance (water quality index). The manner in which the water quality indicator is determined is not limited by this disclosure.
In an example, the quantum dot spectrum sensor may determine a water quality index from absorption characteristics of various substances contained in water to light, for example, the light intensity of light of a specific wavelength may be analyzed by spectral information, and the concentration of a substance (water quality index) corresponding to the light of the specific wavelength range may be obtained. The on-line, in-situ, high-frequency and real-time measurement can be realized by measuring indexes through the quantum dot spectral sensor. When detecting the water quality index, accessible quantum dot spectral sensor detects the spectral information of the light through predetermined waters, and then can obtain the water quality index fast based on spectral information, compare in the chemical examination process of bringing water quality back to the laboratory, detect through quantum dot spectral sensor and have better real-time (promptly, the water quality index that detects out is current water quality index, and laboratory chemical examination required time is longer, waits for the time quantum of chemical examination result, the water quality index of predetermined waters may have changed). The water quality index sequence containing the water quality indexes in the water area can be obtained by measuring the water quality indexes in a certain time period through the quantum dot spectrum sensor arranged in the preset water area, and the water quality indexes in the water quality index sequence are water quality indexes obtained at multiple moments in the same place, so that the water quality index sequence has consistency and comparability and can be used for observing the change rule of the water quality indexes in a period of time to judge water pollution. For example, the measurement frequency of the quantum dot spectrum sensor can reach 3-60 min/time, preferably 5-30 min/time, particularly preferably 8-20 min/time, and most preferably 10-15 min/time, the measurement frequency is far higher than the frequency of bringing the water body back to a laboratory test, and the quantum dot spectrum sensor can be arranged at a fixed position of a predetermined water area, so that the consistency of the water body sample can be ensured. And bring the water body back to the laboratory to carry out the assay, then be difficult to guarantee to take a sample in the same place totally when measuring twice, and because the measuring frequency is lower, the interval time is longer between two measurements, even can guarantee to take a sample in the same place totally when measuring twice, because the mobility of water, the quality of water in this place probably has taken place great change in longer interval time, is difficult to guarantee the uniformity of measuring and the comparability of measuring result.
In one possible implementation, the water quality indicators in the water may be periodically changed under the influence of respiration and/or photosynthesis activities of microorganisms (e.g., algae, plankton, etc.) in the water, for example, during the day, photosynthesis has a large influence on the water quality indicators, which may cause some water quality indicators to rise, and during the night, respiration has a large influence on the water quality indicators, which may cause the water quality indicators to fall, while other water quality indicators rise.
For example, in the daytime, due to the sunny weather, the photosynthesis of microorganisms is stronger than the respiration, and inorganic salts containing nitrogen and phosphorus and dissolved carbon dioxide are converted into organic substances while oxygen is released. The water quality indexes such as the concentration of dissolved oxygen, the pH value, the chemical oxygen demand and the like in the water body are increased, and the conductivity is reduced. On the contrary, at night, sunlight is insufficient, so that photosynthesis of microorganisms is weaker than respiration, the microorganisms absorb oxygen and decompose organic matters into carbon dioxide and water, the conductivity in the water body rises, and water quality indexes such as dissolved oxygen concentration, pH value and chemical oxygen demand are reduced.
The periodic variation may reflect the activity of microorganisms in the water. For example, if the microbial activity is higher, the influence on the water quality index is larger, for example, the fluctuation of the water quality index is more intense, the change range is larger, and the like, whereas if the microbial activity is lower, the influence on the water quality index is smaller. Therefore, the activity of microorganisms in water can be reflected by the fluctuation of the water quality index. Further, since the activity of the microorganism is related to an index such as chlorophyll concentration or algal density, the activity of the microorganism in water can be reflected by mapping the fluctuation (for example, the amplitude of fluctuation) of the water quality index with the index such as chlorophyll concentration or algal density. And further can be used in the fields of water quality monitoring, water bloom prediction and the like.
In an example, the plurality of water quality indicator sequences includes a dissolved oxygen concentration indicator sequence, a pH indicator sequence, a conductivity indicator sequence, a chemical oxygen demand indicator sequence, a biochemical oxygen demand indicator sequence, an ammonia nitrogen indicator sequence, a nitronitrogen indicator sequence, a nitrosonitrogen indicator sequence, a total nitrogen indicator sequence, a total phosphorus indicator sequence, and the like. The present disclosure does not limit the type of water quality index.
In one possible implementation, in step S11, an in-situ water quality monitoring device may be installed in a predetermined water area, and the device may be equipped with, for example, a micro-spectral sensor, a dissolved oxygen probe, a pH measurement probe, and the like, and in an example, two or more of the above probes may be integrated into one water quality device. The micro spectral sensor, such as a quantum dot spectral sensor, can obtain absorption spectrum information of a predetermined water area, and can determine water quality indexes such as Chemical Oxygen Demand (COD) through the spectrum information. Further, the dissolved oxygen content can be obtained by a dissolved oxygen probe, the principle of which is to measure the content of dissolved oxygen in water by a method known in the art, such as fluorescence quenching. The pH value of the preset water area can be measured by a pH value measuring probe, and the principle can be that the pH value of the water body is calculated by the conductivity of the water body. The working principle of the water quality detection equipment is not limited by the disclosure.
In another example, the water quality monitoring device may also only comprise one probe, namely, a quantum dot spectrum probe, and the multiple water quality indexes can be calculated through the spectrum information obtained by the quantum dot spectrum probe. The present disclosure does not limit the structure of the quantum dot spectroscopic sensor.
In an example, a plurality of water quality indicators may be measured in the above manner during the first period of time to obtain a sequence of water quality indicators. For example, a plurality of COD indicators measured by a quantum dot spectrum sensor can be expressed as { (t) 1,1 ,x 1,1 ),(t 1,2 ,x 1,2 ),(t 1,3 ,x 1,3 ),…,(t 1,n ,x 1,n ) Where n is a positive integer, t 1,i To obtain the ith (i is a positive integer, and i is less than or equal to n) COD index x 1,i The time of day. Similarly, a dissolved oxygen concentration indicator sequence, a pH indicator sequence, a conductivity indicator sequence, and the like may also be determined.
In one possible implementation, in step S12, the microbial activity index may be determined by the water quality index sequence described above. In an example, the water quality index sequence may be processed through a water quality detection network to obtain an activity index of the microorganism. In an example, the data characteristics of the water quality index sequence can be extracted and processed through a water quality detection network. For example, the data features are data features capable of reflecting fluctuation conditions of the water quality index sequence, such as peak heights, peak time differences, change rates and the like, and the data features are not limited by the disclosure.
In one possible implementation, step S12 may include: determining at least one biological activity identification parameter according to the plurality of water quality index sequences; and inputting the biological activity identification parameters into the water quality detection network to obtain the water quality biological activity indexes.
In one possible implementation, the biological activity identification parameter includes at least one of a mean value, a standard deviation, a peak height, a number of peaks, a time to peak, and a rate of change of each water quality indicator sequence.
In an example, the dissolved oxygen concentration index sequence, the pH index sequence, the conductivity index sequence, and the chemical oxygen demand index sequence mean, standard deviation, peak height, peak number, peak arrival time, and rate of change may be determined separately to determine the fluctuation of each index sequence.
In an example, the mean may represent an average of each water quality indicator sequence, e.g., an arithmetic average, a geometric average, etc., and the present disclosure does not limit the type of the mean. In the example, the standard deviation can represent the dispersion degree of the data in each water quality index sequence and can also represent the intensity of the fluctuation of the water quality index. In an example, the peak height may represent a maximum amplitude of the water quality indicator change, e.g., a difference between a maximum value of the water quality indicator and an average value of the water quality indicator. In an example, the number of peaks may indicate the number of times the water quality indicator fluctuates and may also indicate the frequency of the fluctuation of the water quality indicator. In an example, the time to peak may represent a duration between a time at which the water quality indicator fluctuates from the beginning and a time at which the peak is reached. In the example, the change rate may be a ratio between the value of the peak and the time to peak, and may indicate the speed of the fluctuation of the water quality index, and may also indicate the intensity of the fluctuation of the water quality index.
In one possible implementation, the above parameters may be related to fluctuations of the water quality indicator, and thus may be related to a water quality biological activity indicator (e.g., chlorophyll concentration or algae density, etc.), but there may also be a possibility that the water quality biological activity indicator is not significantly related, and an effective parameter, i.e., a biological activity identification parameter, may be selected from the above parameters. For example, it can be determined whether each of the above parameters has a statistical correlation with the water quality bioactivity index by a statistical method in the related art, and a parameter with a higher correlation (e.g., higher than a certain statistical threshold) is determined as the bioactivity identification parameter. The manner in which the correlations are determined is not limited by this disclosure.
In one possible implementation manner, one or more of the bioactivity identification parameters determined in the above manner can be input into a water quality detection network for detection to determine the water quality bioactivity index. In an example, the above-mentioned process of determining the water quality bioactivity indicator may be implemented by a water quality detection network. The water quality detection network may be a deep learning neural network. The biological activity identification parameters can be processed through a water quality detection network to obtain biological activity indexes such as chlorophyll concentration or algae density and the like so as to reflect biological activity in water quality.
In an example, the peak heights of the dissolved oxygen concentration index sequence can be input into a water quality detection network to obtain the chlorophyll concentration. Or inputting the mean values, standard deviations and peak heights of the dissolved oxygen concentration index sequence, the pH value index sequence, the conductivity index sequence and the chemical oxygen demand index sequence into a water quality detection network to obtain the chlorophyll concentration and the algae density. The present disclosure does not limit the types of input parameters and output results of the water quality detection network.
In an example, the water quality bioactivity indicator may also be determined by other data analysis models, for example, a relationship between the bioactivity identification parameter (sample) and the water quality bioactivity indicator (sample) may be determined by a regression model, and the regression coefficient may be solved. After the regression coefficient is obtained, the biological activity identification parameter of the water quality in the predetermined water area can be substituted into the regression equation corresponding to the regression coefficient to solve the biological activity index of the water quality. The present disclosure does not limit the class of models.
In one possible implementation, the water quality detection network may be a deep learning neural network, and the present disclosure does not limit the type of the water quality detection network. The water quality detection network may be trained prior to detection using the water quality detection network. The method further comprises the following steps: determining a plurality of sample water quality index sequences of the water body samples in a second time period according to the water quality information of the water body samples; determining sample activity indexes according to the plurality of sample water quality index sequences and the water quality detection network; determining the network loss of the water quality detection network according to the sample activity index and the labeling information of the water body sample, wherein the labeling information comprises a biological activity index of the water body sample obtained through chemical inspection; and training the water quality detection network according to the network loss.
In a possible implementation manner, the water sample is a water body with a known biological activity index, for example, a water body with a chlorophyll concentration measured by an assay or the like, and the known biological activity index can be used as labeling information to train a water quality detection network.
In a possible implementation manner, a plurality of sample water quality index sequences, such as a dissolved oxygen concentration index sequence, a pH index sequence, a conductivity index sequence, and a chemical oxygen demand index sequence, in the second time period may be detected by a water quality detection device, such as a quantum dot spectrum sensor, and a biological activity identification parameter (such as a mean value, a standard deviation, a peak height, a peak number, and the like) of the water quality index sequence is calculated, and the obtained one or more biological activity identification parameters are input into the water quality detection network. The water quality detection network can output the activity index of the sample.
In one possible implementation, the sample activity index may be compared to known biological activity indexes (labeling information) to determine network loss of the water quality detection network. For example, network loss such as cross entropy and mutual information, the present disclosure does not limit the determination manner of the network loss.
In one possible implementation, the water quality detection network may be trained on network losses, for example, the network losses may be propagated back through a gradient descent method to adjust parameters of the water quality detection network such that the network losses of the water quality detection network are minimized. Furthermore, the precision of the water quality detection network can be improved through multiple iterations, and training is completed when the training conditions are met. For example, when the iteration number reaches a predetermined number, the training is completed, or when the accuracy of the water quality detection network reaches a preset condition, the training is completed, and the trained water quality detection network is obtained.
According to the water quality bioactivity detection method disclosed by the embodiment of the disclosure, a plurality of water quality index sequences of a preset water area can be measured in real time and high frequency, so that the data in the water quality index sequences can reflect the current microbial activity in the water body in real time. Furthermore, the water quality index sequence is a plurality of water quality indexes measured in a time period, the fluctuation condition of the water quality indexes can be reflected through the biological activity identification parameters so as to more accurately reflect the biological activity of microorganisms in the water body, and the mapping relation between the biological activity identification parameters and the water quality biological activity indexes such as chlorophyll concentration, algae density and the like can be obtained through the water quality detection network so as to more accurately represent the conditions of the microorganisms in the water body.
Fig. 2 is a schematic diagram illustrating an application of the water quality bioactivity detection method according to an embodiment of the present disclosure, and as shown in fig. 2, a quantum dot spectroscopic sensor may be disposed in a predetermined water area, and the water quality detection probe, such as a quantum dot spectroscopic probe, a dissolved oxygen probe, a pH meter, and the like, is integrated in the sensor, and a dissolved oxygen concentration indicator sequence, a pH indicator sequence, a conductivity indicator sequence, and a chemical oxygen demand indicator sequence in a first time period are respectively detected.
In one possible implementation, to better reflect the activity of microorganisms in the body of water, a biological activity identification parameter, such as the mean, standard deviation, peak height, number of peaks, time to peak, and rate of change of each water quality index sequence, may be extracted from the A.R. Pat. No.
In a possible implementation manner, the biological activity identification parameter may be input into the water quality detection network to obtain a biological activity index capable of accurately reflecting the activity of the microorganism in the water body, for example, indices such as chlorophyll concentration and algae density may be obtained.
In a possible implementation manner, the water quality biological activity detection method can accurately reflect the activity of microorganisms in the water body through fluctuation conditions of various water quality indexes, and provide a basis for predicting the water bloom and other phenomena, for example, the water bloom condition or the water bloom trend can be determined. The application field of the water quality bioactivity detection method is not limited by the disclosure.
Fig. 3 shows a block diagram of a water quality bioactivity detection apparatus according to an embodiment of the present disclosure, as shown in fig. 3, the apparatus includes: the sequence obtaining module 11 is configured to determine, according to water quality information of a predetermined water area, a plurality of water quality indicator sequences of the predetermined water area in a first time period, where the water quality indicator sequences include the same water quality indicators obtained at a plurality of times in the first time period; and an index obtaining module 12, configured to determine a water quality bioactivity index according to the plurality of water quality index sequences and the pre-trained water quality detection network, where the water quality bioactivity index includes at least one of chlorophyll concentration and algae density in a predetermined water area.
In one possible implementation manner, the index obtaining module is further configured to: determining at least one biological activity identification parameter according to the plurality of water quality index sequences; and inputting the biological activity identification parameters into the water quality detection network to obtain the water quality biological activity indexes.
In one possible implementation, the apparatus further includes: the training module is used for determining a plurality of sample water quality index sequences of the water body samples in the second time period according to the water quality information of the water body samples; determining sample activity indexes according to the plurality of sample water quality index sequences and the water quality detection network; determining the network loss of the water quality detection network according to the sample activity index and the labeling information of the water body sample, wherein the labeling information comprises a biological activity index of the water body sample obtained through chemical inspection; and training the water quality detection network according to the network loss.
In one possible implementation, the biological activity identification parameter includes at least one of a mean value, a standard deviation, a peak height, a number of peaks, a time to peak, and a rate of change of each water quality indicator sequence.
In one possible implementation, the plurality of water quality indicator sequences includes a dissolved oxygen concentration indicator sequence, a pH indicator sequence, a conductivity indicator sequence, a chemical oxygen demand indicator sequence, a biochemical oxygen demand indicator sequence, an ammonia nitrogen indicator sequence, a nitronitrogen indicator sequence, a nitrosonitrogen indicator sequence, a total nitrogen indicator sequence, and a total phosphorus indicator sequence.
It is understood that the above-mentioned method embodiments of the present disclosure can be combined with each other to form a combined embodiment without departing from the logic of the principle, which is limited by the space, and the detailed description of the present disclosure is omitted.
In addition, the disclosure also provides a water quality bioactivity detection device, an electronic device, a computer readable storage medium and a program, which can be used for realizing any water quality bioactivity detection method provided by the disclosure, and the corresponding technical scheme and description and corresponding records in the method section are referred to, and are not described again.
It will be understood by those skilled in the art that in the method of the present invention, the order of writing the steps does not imply a strict order of execution and any limitations on the implementation, and the specific order of execution of the steps should be determined by their function and possible inherent logic.
In some embodiments, functions of or modules included in the apparatus provided in the embodiments of the present disclosure may be used to execute the method described in the above method embodiments, and specific implementation thereof may refer to the description of the above method embodiments, and for brevity, will not be described again here.
Embodiments of the present disclosure also provide a computer-readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the above-mentioned method. The computer readable storage medium may be a non-volatile computer readable storage medium.
An embodiment of the present disclosure further provides an electronic device, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured as the above method.
The electronic device may be provided as a terminal, server, or other form of device.
Fig. 4 is a block diagram illustrating an electronic device 800 in accordance with an example embodiment. For example, the electronic device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, or the like terminal.
Referring to fig. 4, electronic device 800 may include one or more of the following components: processing component 802, memory 804, power component 806, multimedia component 808, audio component 810, input/output (I/O) interface 812, sensor component 814, and 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 components 802 may include one or more processors 820 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interaction 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 non-volatile 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 disks.
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 supplies, and other components associated with generating, managing, and distributing power for the electronic device 800.
The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and a user. 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 an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the electronic device 800 is in an operation mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.
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 further be stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 also 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 keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.
The sensor assembly 814 includes one or more sensors for providing various aspects of state assessment for the electronic device 800. For example, the sensor assembly 814 may detect an open/closed state of the electronic device 800, the relative positioning of components, such as a display and keypad of the electronic device 800, the sensor assembly 814 may also detect a change in the position of the electronic device 800 or a component of the electronic device 800, the presence or absence of user contact with the electronic device 800, orientation or acceleration/deceleration of the electronic device 800, and a change in the temperature of the electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object 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 gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. 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 an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an 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, micro-controllers, microprocessors or other electronic components for performing the above-described methods.
In an exemplary embodiment, a non-transitory computer-readable storage medium, such as the memory 804, is also provided that includes computer program instructions executable by the processor 820 of the 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, the electronic device 1900 may be provided as a server. Referring to fig. 5, electronic device 1900 includes a processing component 1922 further including one or more processors and memory resources, represented by memory 1932, for storing instructions, e.g., applications, executable by processing component 1922. The application programs stored in memory 1932 may include one or more modules that each correspond to a set of instructions. Further, the processing component 1922 is configured to execute instructions to perform the above-described method.
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 based on an operating system stored in memory 1932, such as Windows Server, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, or the like.
In an exemplary embodiment, a non-transitory computer readable storage medium, such as the memory 1932, is also provided that includes computer program instructions executable by the processing component 1922 of the electronic device 1900 to perform the above-described methods.
The present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present disclosure.
The computer-readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory 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: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be interpreted as a transitory signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or an electrical signal transmitted through an electrical wire.
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 via 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 transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter 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.
The computer program instructions for carrying out operations of the present disclosure may be assembler 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 execute 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 type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
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 storing the instructions comprises 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 flowchart 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.
Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or improvements to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (10)
1. A water quality bioactivity detection method is characterized by comprising the following steps:
determining a plurality of water quality index sequences of a predetermined water area in a first time period according to water quality information of the predetermined water area, wherein the water quality index sequences comprise the same water quality indexes obtained at a plurality of moments in the first time period;
and determining a water quality biological activity index according to the plurality of water quality index sequences and a pre-trained water quality detection network, wherein the water quality biological activity index comprises at least one of chlorophyll concentration and algae density in a preset water area.
2. The method of claim 1, wherein determining a water quality bioactivity indicator based on the plurality of water quality indicator sequences and a pre-trained water quality detection network comprises:
determining at least one biological activity identification parameter according to the plurality of water quality index sequences;
and inputting the biological activity identification parameters into the water quality detection network to obtain the water quality biological activity indexes.
3. The method of claim 2, wherein the bioactivity identification parameters comprise at least one of a mean, a standard deviation, a peak height, a number of peaks, a time to peak, and a rate of change of each water quality indicator sequence.
4. The method of claim 1, further comprising:
determining a plurality of sample water quality index sequences of the water body samples in a second time period according to the water quality information of the water body samples;
determining sample activity indexes according to the plurality of sample water quality index sequences and the water quality detection network;
determining the network loss of the water quality detection network according to the sample activity index and the labeling information of the water body sample, wherein the labeling information comprises a biological activity index of the water body sample obtained through chemical inspection;
and training the water quality detection network according to the network loss.
5. The method of claim 1, wherein the plurality of water quality indicator sequences comprises a dissolved oxygen concentration indicator sequence, a pH indicator sequence, a conductivity indicator sequence, a chemical oxygen demand indicator sequence, a biochemical oxygen demand indicator sequence, an ammonia nitrogen indicator sequence, a nitronitrogen indicator sequence, a nitroso nitrogen indicator sequence, a total nitrogen indicator sequence, and a total phosphorus indicator sequence.
6. A water quality bioactivity detection device, characterized in that the device comprises:
the system comprises a sequence obtaining module, a water quality control module and a water quality control module, wherein the sequence obtaining module is used for determining a plurality of water quality index sequences of a preset water area in a first time period according to water quality information of the preset water area, and the water quality index sequences comprise the same water quality indexes obtained at a plurality of moments in the first time period;
and the index obtaining module is used for determining a water quality bioactivity index according to the plurality of water quality index sequences and the pre-trained water quality detection network, wherein the water quality bioactivity index comprises at least one of chlorophyll concentration and algae density in a preset water area.
7. The apparatus of claim 6, wherein the indicator obtaining module is further configured to:
determining at least one biological activity identification parameter according to the plurality of water quality index sequences;
and inputting the biological activity identification parameters into the water quality detection network to obtain the water quality biological activity indexes.
8. The apparatus of claim 6, further comprising:
the training module is used for determining a plurality of sample water quality index sequences of the water body samples in the second time period according to the water quality information of the water body samples;
determining sample activity indexes according to the plurality of sample water quality index sequences and the water quality detection network;
determining the network loss of the water quality detection network according to the sample activity index and the labeling information of the water body sample, wherein the labeling information comprises a biological activity index of the water body sample obtained through chemical inspection;
and training the water quality detection network according to the network loss.
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 5.
10. A computer readable storage medium having computer program instructions stored thereon, which when executed by a processor implement the method of any one of claims 1 to 5.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101158674A (en) * | 2007-11-15 | 2008-04-09 | 天津市市政工程设计研究院 | Method for predicting chlorophyll a concentration in water based on BP nerval net |
| CN107664682A (en) * | 2016-07-30 | 2018-02-06 | 复凌科技（上海）有限公司 | A kind of water quality hard measurement Forecasting Methodology of ammonia nitrogen |
| CN111368923A (en) * | 2020-03-05 | 2020-07-03 | 上海商汤智能科技有限公司 | Neural network training method and device, electronic equipment and storage medium |
| CN112990614A (en) * | 2021-05-21 | 2021-06-18 | 芯视界（北京）科技有限公司 | Method and device for detecting water bloom, electronic equipment and storage medium |
| CN113077019A (en) * | 2021-06-07 | 2021-07-06 | 芯视界（北京）科技有限公司 | Pollution type identification method and device and storage medium |
| CN113159275A (en) * | 2021-03-05 | 2021-07-23 | 深圳市商汤科技有限公司 | Network training method, image processing method, device, equipment and storage medium |
| CN113433086A (en) * | 2021-06-28 | 2021-09-24 | 淮阴工学院 | Method for predicting water quality COD (chemical oxygen demand) by combining fuzzy neural network with spectrophotometry |
| CN114118397A (en) * | 2020-08-31 | 2022-03-01 | 上海商汤智能科技有限公司 | Neural network method and apparatus, electronic device, and storage medium |
-
2022
- 2022-04-27 CN CN202210457195.7A patent/CN114839340A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101158674A (en) * | 2007-11-15 | 2008-04-09 | 天津市市政工程设计研究院 | Method for predicting chlorophyll a concentration in water based on BP nerval net |
| CN107664682A (en) * | 2016-07-30 | 2018-02-06 | 复凌科技（上海）有限公司 | A kind of water quality hard measurement Forecasting Methodology of ammonia nitrogen |
| CN111368923A (en) * | 2020-03-05 | 2020-07-03 | 上海商汤智能科技有限公司 | Neural network training method and device, electronic equipment and storage medium |
| CN114118397A (en) * | 2020-08-31 | 2022-03-01 | 上海商汤智能科技有限公司 | Neural network method and apparatus, electronic device, and storage medium |
| CN113159275A (en) * | 2021-03-05 | 2021-07-23 | 深圳市商汤科技有限公司 | Network training method, image processing method, device, equipment and storage medium |
| CN112990614A (en) * | 2021-05-21 | 2021-06-18 | 芯视界（北京）科技有限公司 | Method and device for detecting water bloom, electronic equipment and storage medium |
| CN113077019A (en) * | 2021-06-07 | 2021-07-06 | 芯视界（北京）科技有限公司 | Pollution type identification method and device and storage medium |
| CN113433086A (en) * | 2021-06-28 | 2021-09-24 | 淮阴工学院 | Method for predicting water quality COD (chemical oxygen demand) by combining fuzzy neural network with spectrophotometry |
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