CN115452739A

CN115452739A - Water quality thermal pollution detection method and device, electronic equipment and storage medium

Info

Publication number: CN115452739A
Application number: CN202110632456.XA
Authority: CN
Inventors: 不公告发明人
Original assignee: Quantaeye Beijing Technology Co ltd
Current assignee: Quantaeye Beijing Technology Co ltd
Priority date: 2021-06-07
Filing date: 2021-06-07
Publication date: 2022-12-09

Abstract

The disclosure relates to a method and a device for detecting water quality thermal pollution, electronic equipment and a storage medium, wherein the method comprises the following steps: measuring the water temperature measured value of a preset water area in real time; determining a predicted water temperature value of a preset water area according to the climate information and the time information when the water temperature measured value is determined; and determining the thermal pollution judgment information of the preset water area according to the water temperature measured value and the water temperature predicted value. According to the water quality thermal pollution detection method disclosed by the embodiment of the disclosure, the water temperature can be detected in real time, the real-time property of detection is improved, whether the predetermined water area is subjected to thermal pollution or not can be determined through the water temperature measured value and the water temperature predicted value, the water quality thermal pollution can be monitored in real time, and a foundation is provided for finding the thermal pollution in time and performing targeted prevention and treatment.

Description

Water quality thermal pollution detection method and device, electronic equipment and storage medium

Technical Field

The present disclosure relates to the field of computer technologies, and in particular, to a method and an apparatus for detecting thermal pollution in water, an electronic device, and a storage medium.

Background

Global temperature generally tends to rise, on one hand, the greenhouse effect caused by carbon dioxide emission is generated, on the other hand, waste heat discharged in industrial production and life directly brings thermal pollution to the environment, for example, production waste water discharged from petroleum, chemical industry, paper making and other factories contains a large amount of waste heat, and after the waste heat is discharged into ground water, the water temperature can be raised. The abnormal change of the water environment temperature caused by human activities can bring great harm to the ecological environment, species survival and human health.

However, at present, on-line monitoring systems based on river and lake water environments at home and abroad mainly monitor pollution aiming at pollutants, and ignore the damage of thermal pollution to the water environment quality and the timely disposal of the damage.

Disclosure of Invention

The disclosure provides a method and a device for detecting thermal pollution of water quality, electronic equipment and a storage medium.

According to an aspect of the present disclosure, there is provided a method for detecting thermal pollution of water quality, comprising: measuring the water temperature measured value of a preset water area in real time; according to the climate information and the time information when the water temperature measurement value is determined, determining a water temperature prediction value of the preset water area; and determining the thermal pollution judgment information of the preset water area according to the water temperature measurement value and the water temperature prediction value.

In one possible implementation manner, the determining the predicted water temperature value of the predetermined water area according to the climate information and the time information when the water temperature measurement value is determined includes: inputting the lowest air temperature and the time information of the current day into a water temperature prediction model to obtain the water temperature prediction value, wherein the water temperature prediction model comprises a day cycle characteristic item of the water temperature and a season cycle characteristic item of the water temperature, the day cycle characteristic item represents the relation between a water temperature index and the time when the water temperature index is measured, and the season cycle characteristic item represents the relation between climate information and the water temperature index.

In one possible implementation, the method further includes: determining a day cycle characteristic item of water temperature according to a plurality of water temperature indexes acquired in a preset water area in a first time period; according to the climate information in the second time period and a plurality of water temperature indexes acquired in a preset water area, determining a seasonal period characteristic item of the water temperature; and determining the water temperature prediction model according to the day cycle characteristic item and the season cycle characteristic item.

In a possible implementation manner, determining a day cycle characteristic item of the water temperature according to a plurality of water temperature indexes acquired in a predetermined water area in a first time period includes: and carrying out Fourier fitting processing on the plurality of water temperature indexes obtained in the first time period and the time when the water temperature indexes are obtained, so as to obtain the daily cycle characteristic item.

In a possible implementation manner, determining a seasonal period characteristic item of the water temperature according to the climate information in the second time period and a plurality of water temperature indexes acquired in a predetermined water area includes: and performing regression analysis on a plurality of water temperature indexes obtained in the second time period and the lowest air temperature of the day on which the water temperature indexes are obtained, and obtaining the seasonal period characteristic item.

In one possible implementation, determining the water temperature prediction model according to the day cycle characteristic item and the season cycle characteristic item includes: and summing the daily cycle characteristic items and the seasonal cycle characteristic items to obtain the water temperature prediction model.

In one possible implementation manner, the determining thermal pollution discrimination information of the predetermined water area according to the water temperature measured value and the water temperature predicted value includes: determining a difference between the water temperature measurement and the water temperature prediction; and determining the thermal pollution judgment information as abnormal water temperature under the condition that the difference value is greater than or equal to a preset threshold value.

According to an aspect of the present disclosure, there is provided a water quality thermal pollution detection apparatus, including: the measuring module is used for measuring the water temperature measured value of a preset water area in real time; the prediction module is used for determining a predicted water temperature value of the preset water area according to the climate information and the time information when the water temperature measured value is determined; and the judging module is used for determining the thermal pollution judging information of the preset water area according to the water temperature measured value and the water temperature predicted value.

In one possible implementation, the climate information includes a minimum air temperature that is determined on the day of the water temperature measurement, and the prediction module may include: inputting the lowest air temperature of the current day and the time information into a water temperature prediction model to obtain the water temperature prediction value, wherein the water temperature prediction model comprises a day cycle characteristic item of the water temperature and a season cycle characteristic item of the water temperature, the day cycle characteristic item represents the relation between a water temperature index and the time when the water temperature index is measured, and the season cycle characteristic item represents the relation between climate information and the water temperature index.

In one possible implementation, the apparatus further includes: the water temperature control system comprises a day cycle module, a water temperature sensor and a control module, wherein the day cycle module is used for determining a day cycle characteristic item of water temperature according to a plurality of water temperature indexes acquired in a predetermined water area in a first time period; the seasonal period module is used for determining seasonal period characteristic items of the water temperature according to the climate information in the second time period and a plurality of water temperature indexes acquired in a preset water area; and the water temperature prediction module is used for determining the water temperature prediction model according to the daily cycle characteristic item and the seasonal cycle characteristic item.

In one possible implementation, the daily cycle module is further configured to: and carrying out Fourier fitting processing on the plurality of water temperature indexes obtained in the first time period and the time when the water temperature indexes are obtained to obtain the day cycle characteristic item.

In one possible implementation, the seasonal period module is further to: and performing regression analysis on a plurality of water temperature indexes obtained in the second time period and the lowest air temperature of the day on which the water temperature indexes are obtained, and obtaining the seasonal period characteristic item.

In one possible implementation, the water temperature prediction module is further configured to: and summing the daily cycle characteristic items and the seasonal cycle characteristic items to obtain the water temperature prediction model.

In one possible implementation manner, the determination module is further configured to: determining a difference between the water temperature measurement value and the water temperature prediction value; and determining the thermal pollution judgment information as abnormal water temperature under the condition that the difference value is greater than or equal to a preset threshold value.

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 thermal pollution 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 thermal pollution detection method.

According to the water quality thermal pollution detection method disclosed by the embodiment of the disclosure, the water temperature can be detected in real time, the real-time property of detection is improved, whether the predetermined water area is thermally polluted or not can be determined through the water temperature measured value and the water temperature predicted value, the water quality thermal pollution can be monitored in real time, and a foundation is provided for finding the thermal pollution in time and performing targeted prevention and treatment.

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 chart of a water quality thermal pollution detection method according to an embodiment of the present disclosure;

FIG. 2 shows a schematic diagram of a water temperature indicator according to an embodiment of the present disclosure;

FIG. 3 is a schematic diagram illustrating the relationship between the daily average water temperature and the lowest air temperature;

FIG. 4 shows a schematic diagram of an application of a water quality thermal pollution detection method according to an embodiment of the present disclosure;

FIG. 5 shows a block diagram of a water quality thermal pollution detection device according to an embodiment of the present disclosure;

FIG. 6 shows a block diagram of an electronic device according to an embodiment of the disclosure;

fig. 7 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 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 relationship describing an associated object, and means that there may be three relationships, for example, a and/or B, which 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, and 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 thermal pollution detection method according to an embodiment of the present disclosure, as shown in fig. 1, the method includes:

in step S11, measuring the water temperature measured value of a preset water area in real time;

in step S12, according to the climate information and the time information when the water temperature measurement value is determined, determining a predicted water temperature value of the predetermined water area;

in step S13, thermal pollution determination information of the predetermined water area is determined based on the water temperature measurement value and the water temperature prediction value.

In an example, a temperature measuring device may be used to measure a water temperature measurement value of a predetermined water area, for example, a thermometer, an infrared thermometer, a thermal imager, etc. may be used, and the present disclosure does not limit the device used to measure the water temperature. In an example, the thermometer may directly obtain a water temperature measurement by contacting the water quality of a predetermined body of water. The infrared thermometer, the thermal imager and the like can indirectly obtain a water temperature measurement value based on infrared light radiated from the water quality of a predetermined water area.

In an example, a micro spectrometer such as a quantum dot spectrometer may also be used to measure the water temperature measurement value of the predetermined water area, and by measuring the spectrum information of the water quality of the predetermined water area, other information may also be obtained, for example, the content of various substances in the water body, so as to further determine the condition of water quality pollution by combining the condition of the water temperature. Furthermore, water temperature and various water quality indexes can be obtained simultaneously through the quantum dot spectrometer, various instruments do not need to be arranged, and detection cost can be saved.

In an example, a quantum dot spectrometer may include a quantum dot spectrometer probe that may measure incident light (e.g., light that is transmitted or scattered through a water sample in a predetermined area) based on physical and optical properties of the nanocrystals to obtain spectral information of the incident light, which may represent water quality information of the water area. For example, a quantum dot spectroscopy probe may include a nanocrystal chip made from a plurality of nanocrystals, the nanocrystal chip containing an arrangement of nanocrystals (e.g., an array of nanocrystals), wherein each nanocrystal has a different light absorption or emission characteristic, and wherein different types of semiconductor nanocrystals, for example, may be of different materials, sizes, etc., such that the nanocrystal chip may be responsive to modulation of wavelengths over a wider range of wavelengths to obtain a spectrum tailored to incident light over a wider range of wavelengths.

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 probe can obtain the spectrum information in real time and determine the water quality index 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 an example, the water quality indicator includes water quality Chemical Oxygen Demand (COD), turbidity, 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, fluoride content, selenium content, total arsenic content, total mercury content, total cadmium content, chromium content, total lead content, total cyanide content, volatile phenol content, coliform group content, sulfide content, and the like. The water temperature can also be measured according to the infrared spectrum in the spectral information. Alternatively, the quantum dot spectroscopic probe may estimate the water quality index through 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 present disclosure is not limited as to the manner in which the water quality indicator is determined. The working principle of the quantum dot spectrum probe is not limited by the disclosure.

In an example, the quantum dot spectrum probe can determine a water quality index by the absorption characteristics of various substances contained in water to light, for example, the light intensity of light with a specific wavelength can be analyzed by spectrum information, and the concentration of the substance (water quality index) corresponding to the light with the specific wavelength range can be obtained. The on-line, in-situ, high-frequency and real-time measurement can be realized by measuring the indexes through the quantum dot spectrum probe. When detecting the water quality index, accessible quantum dot spectral probe detects the spectral information of the light through predetermined waters, and then can calculate the water quality index fast based on spectral information, in order to obtain the water quality index that the real-time is stronger, compare in the chemical examination process of bringing water quality back to the laboratory, it has better real-time to detect through quantum dot spectral probe (namely, the water quality index that detects out is current water quality index promptly, and the laboratory test required time is longer, in the time quantum of waiting for the chemical examination result, the water quality index of predetermined waters may have changed). The quantum dot spectrum probe arranged in the preset water area is used for measuring for multiple times in a certain time period, so that the water quality index sequences of the two water quality indexes of the water area can be obtained, and the water quality indexes in the water quality index sequences are water quality indexes obtained at multiple moments in the same place, so that the water quality index sequences have 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 measuring frequency of the quantum dot spectrum probe 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 measuring frequency is far higher than the frequency of bringing the water body back to a laboratory test, and the quantum dot spectrum probe 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 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, but 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 measurement and the comparability of measuring result.

In an example, in addition to the water quality indicator, an infrared spectrum of the water quality of the predetermined water area may be detected by a quantum dot spectroscopic probe to determine a water temperature measurement of the water quality. If the water temperature measuring value is abnormal, the pollution condition can be determined through the water quality index so as to further determine the reason of the abnormal water temperature or the reason of the thermal pollution of the water quality.

In a possible implementation mode, the quantum dot spectrometer can be not in contact with water when measuring the water temperature, the water temperature is measured based on spectral information of reflected light of the water, corrosion to an instrument can be reduced, and the service life of the instrument is prolonged.

In one possible implementation, in step S11, a water temperature measurement value of a predetermined water area may be measured, and the time of the measurement may be recorded. In an example, the water temperature of a predetermined body of water may be measured multiple times and the times of the multiple measurements recorded.

In one possible implementation, after obtaining the water temperature measurement, it may be determined whether the water temperature measurement is abnormal. For example, the water temperature measurement may be compared to a water temperature prediction value, which may be a normal water temperature of the predetermined water area, i.e., a normal water temperature that the predetermined water area should reach when the water temperature measurement is obtained, and may indicate that the predetermined water area is thermally contaminated if the water temperature measurement differs significantly from the normal water temperature, and may otherwise indicate that the predetermined water area is not thermally contaminated.

In one possible implementation, in step S12, a predicted value of the water temperature of the predetermined water area may be determined, i.e., a normal water temperature that the predetermined water area should reach when the measured value of the water temperature is obtained is estimated. In an example, the normal water temperature may be estimated from climate information and time information when the water temperature measurement is obtained.

In an example, the normal water temperature that a predetermined body of water should reach may be estimated by measuring the water temperature at other monitored locations. For example, the monitoring points with the temperature close to the temperature of the predetermined water area (for example, the temperature difference is less than a set value of 1 ℃,2 ℃ and the like) may be selected from the other monitoring points, the water temperatures measured by the monitoring points may be obtained, and the average value of the water temperatures of the measuring points may be determined to estimate the normal water temperature to be reached by the predetermined water area. However, the method has the problem that other measurement points are also thermally polluted, so that the estimation value is inaccurate. Or the temperature difference is large although the temperature is close due to the flow of the water body, and the like, thereby causing the problem of inaccurate estimation value.

In an example, the normal water temperature of the predetermined water area can be estimated according to various factors such as climate and time. Predetermined waters receive different amounts of heat at different times of the day, for example, at midday, the water receives greater heat radiation from the sun, and may receive heat conducted from the ground or the air, etc., resulting in higher water temperatures. In the morning or evening, the water is subjected to less heat radiation from the sun and the air and ground temperatures are lower, and it is not possible to conduct more heat to the water, so the water temperature is lower.

In an example, the water temperature of the predetermined water area may also be affected by climate information, e.g. the water temperature is higher in summer and lower in winter. Or, in sunny days, the water temperature is higher, and in rainy days, the water temperature is lower.

In summary, the water temperature of the predetermined water area may be affected by the time of day (i.e., time information) and the climate information, and thus, the normal water temperature of the predetermined water area, i.e., the predicted water temperature value, may be estimated by the time information when the water temperature measurement value is obtained and the climate information.

In an example, the normal water temperature, i.e., the water temperature prediction value, may be predicted by a water temperature prediction model. The water temperature prediction model may be a neural network model, a regression model, etc., and the present disclosure does not limit the type of the water temperature prediction model. The time information and climate information at the time the water temperature measurement is obtained may be input into the water temperature prediction model to determine the predicted water temperature value.

In an example, the influence of the climate information on the water temperature is measured over a longer period of time, for example, over one or more years, i.e. the relation between the climate and the water temperature is determined for each season of the years. In an example, a relationship between daily air temperature and a daily average of water temperature may be determined, the daily air temperature may represent daily climate information (e.g., air temperature is low during winter and air temperature is high during summer), the daily average of water temperature may represent water temperature, and the relationship between climate and water temperature may be represented by a relationship between daily air temperature and daily average of water temperature. Furthermore, according to actual experience data, the linear correlation coefficient of the daily minimum air temperature and the daily average value of the water temperature is high (the linear correlation coefficient is more than or equal to 0.97), so that the daily minimum air temperature can be used for predicting the water temperature.

In one possible implementation, the climate information includes a lowest air temperature of the water temperature measurement value on the current day, and step S12 may include: inputting the lowest air temperature and the time information of the current day into a water temperature prediction model to obtain the water temperature prediction value, wherein the water temperature prediction model comprises a day cycle characteristic item of the water temperature and a season cycle characteristic item of the water temperature, the day cycle characteristic item represents the relation between a water temperature index and the time when the water temperature index is measured, and the season cycle characteristic item represents the relation between climate information and the water temperature index.

In an example, in determining the predicted value of water temperature, the daily minimum air temperature and the time information at which the measured value of water temperature was obtained may be input into a water temperature prediction model, which may output a predicted value of water temperature. The day lowest air temperature can be used for representing climate information and estimating seasonal period characteristic items. The time information is used for estimating a day cycle characteristic item, namely a rule that the water temperature shows periodic change at different moments of the day, and the normal water temperature at the moment can be predicted according to the moment of obtaining the water temperature measurement value and the rule.

In one possible implementation, the method of modeling the water temperature prediction may include: determining a day cycle characteristic item of water temperature according to a plurality of water temperature indexes acquired in a preset water area in a first time period; according to the climate information in the second time period and a plurality of water temperature indexes acquired in a preset water area, determining a seasonal period characteristic item of the water temperature; and determining the water temperature prediction model according to the day cycle characteristic item and the season cycle characteristic item.

FIG. 2 shows a schematic diagram of a water temperature indicator according to an embodiment of the present disclosure. As shown in FIG. 2, the first time period may include multiple days, and the water temperature indicator may be measured at multiple times of the multiple days to obtain a water temperature indicator sequence { (t) ₁ ,y ₁ ),(t ₂ ,y ₂ ),(t ₃ ,y ₃ ),…,(t _m ,y _m ) 8230, wherein m is any positive integer, and t _m Is the m-th time, y _m The water temperature index measured at the mth moment. The water temperature indicator may exhibit a periodic pattern of changes, such as an increase in water temperature at noon, a decrease in water temperature in the morning and evening, etc. (as shown in fig. 2, the water temperature exhibits a periodic change due to temperature changes, but may fluctuate around a certain temperature value (e.g., 25 ℃) without significant changes in climate conditions). Since the water temperature shows a periodic change rule, the characteristic term of the daily period, namely the water temperature index and the measured water temperature index, can be determined by Fourier fittingThe relationship between the target time instants.

In one possible implementation manner, determining a characteristic item of a daily cycle of the water temperature according to a plurality of water temperature indexes acquired in a predetermined water area in a first time period includes: and carrying out Fourier fitting processing on the plurality of water temperature indexes obtained in the first time period and the time when the water temperature indexes are obtained to obtain the day cycle characteristic item.

In an example, the day cycle characteristic term can be determined according to the following equation (1):

wherein t is a time when the water temperature index is obtained, N is a number of days included in the first time period, and P is a minimum positive cycle, in an example, the minimum positive cycle may be 1 day, and the minimum positive cycle is not limited by the present disclosure. n is the date when the water temperature index is obtained, c _n Are fourier coefficients. s (t) is a day cycle characteristic item, namely, the time when the water temperature measurement value is obtained can be input into the day cycle characteristic item, and the output result of the day cycle characteristic item is the normal water temperature predicted based on the time.

In one possible implementation, a seasonal period characteristic term may be determined, i.e., a law representing the variation of water temperature with climate. According to the climate information in the second time period and a plurality of water temperature indexes acquired in a preset water area, determining a seasonal period characteristic item of the water temperature, wherein the seasonal period characteristic item comprises the following steps: and performing regression analysis on a plurality of water temperature indexes obtained in the second time period and the lowest air temperature on the day of obtaining the water temperature indexes to obtain the seasonal period characteristic item.

In an example, the second period may include a plurality of years, the lowest air temperature of each day of the plurality of years has a high correlation coefficient with the water temperature daily average value, the climate information may be represented by the lowest air temperature, the water temperature index may be represented by the water temperature daily average value, and a relationship between the daily lowest air temperature and the water temperature daily average value, that is, a season cycle characteristic item may be determined.

Fig. 3 is a schematic diagram illustrating a relationship between the daily average value of water temperature and the lowest value of air temperature, and as shown in fig. 3, the lowest air temperature and the daily average value of water temperature of each day have a high correlation coefficient, and the lowest air temperature and the daily average value of water temperature of each day can be subjected to regression analysis processing to obtain a seasonal period feature term. In an example, the seasonal period characteristic term may be determined by the following equation (2):

g(t)＝kT _{env,daily_min} +b (2)

where k is the regression coefficient, b is the intercept, T _{env,daily_min} The daily minimum air temperature is g (t), the seasonal period characteristic item is g (t), namely, the climate information (namely, the daily minimum air temperature) for obtaining the water temperature measurement value can be input into the seasonal period characteristic item, and the output result of the seasonal period characteristic item is the normal water temperature based on the minimum air temperature.

In one possible implementation, after determining the day cycle characteristic item and the season cycle characteristic item, the water temperature prediction model may be obtained using the day cycle characteristic item and the season cycle characteristic item. In an example, determining the water temperature prediction model based on the day cycle characteristic term and the season cycle characteristic term may include: and summing the daily cycle characteristic items and the seasonal cycle characteristic items to obtain the water temperature prediction model.

In an example, the water temperature prediction model may be determined by the following equation (3):

wherein,

the predicted value of the water temperature is obtained.

In an example, the water temperature prediction model may obtain the normal water temperature, i.e., a predicted water temperature value, with reference to the climate information and the time information, and may sum the two to obtain the normal water temperature, i.e., a predicted water temperature value, that the predetermined water area should reach at the time of obtaining the measured water temperature value at the lowest temperature of the day. For example, the time when the water temperature measurement value is obtained is 15 pm, the season of the day is summer, and the minimum temperature of the day is 28 ℃, the time information (15 00) and the climate information (28 ℃) may be input into the water temperature prediction model to obtain a predicted value of the water temperature, that is, the normal water temperature that the predetermined water area should reach at 15 pm under the condition that the minimum temperature of the day is 28 ℃.

By the mode, the water temperature prediction model can refer to the time information and the climate information, multiple factors influencing the water temperature can be fully considered, and the accuracy of the water temperature prediction value is improved.

In an example, the factors affecting the predicted value of the water temperature may further include the presence or absence of an influx of water, and in the case where the predetermined body of water is an ocean, the factors affecting the predicted value of the water temperature may further include a tide, etc. The factors can also be added to the water temperature prediction model, and the factors influencing the water temperature prediction value are not limited by the disclosure.

In one possible implementation, in step S13, the water temperature measured value and the water temperature predicted value are compared, wherein the water temperature predicted value is the normal temperature to be reached by the predetermined water area, and the water temperature measured value is the measured temperature, and if the deviation between the water temperature measured value and the water temperature predicted value is large, the water temperature at the moment is abnormal, and the thermal pollution event may occur.

In one possible implementation, step S13 may include: determining a difference between the water temperature measurement value and the water temperature prediction value; and determining the thermal pollution judgment information as abnormal water temperature under the condition that the difference value is greater than or equal to a preset threshold value.

In an example, a preset threshold ε of water temperature deviation may be set and a water temperature measurement y (t) and a predicted water temperature value determined

The difference between, e.g. the absolute value of, the difference

If it is not

The deviation between the measured water temperature and the normal water temperature is considered to be within a reasonable range, and no thermal pollution existsAn event of staining, if

The deviation of the water temperature measurement from the normal water temperature may be considered large, outside of the normal range, and a thermal contamination event may be present. The water temperature measurement value y (t) may be a measurement value at any time, or may be an average value of multiple measurements, or a maximum value of multiple measurements, and the like, and the water temperature measurement value is not limited by the disclosure.

According to the water quality thermal pollution detection method disclosed by the embodiment of the disclosure, the water temperature can be detected in real time, the detection real-time performance is improved, whether the predetermined water area is thermally polluted or not can be determined through the water temperature prediction model, a plurality of factors influencing the water temperature can be fully considered through the water temperature prediction model, and the accuracy of the water temperature prediction value is improved. Furthermore, the method can monitor the water quality thermal pollution in real time, and provides a foundation for timely discovering the thermal pollution and performing targeted prevention and treatment.

Fig. 4 is a schematic diagram illustrating an application of the method for detecting thermal pollution of water quality according to the embodiment of the present disclosure, and as shown in fig. 4, a quantum dot spectrometer may be disposed in a predetermined water area to measure water temperature, and the quantum dot spectrometer may obtain a water temperature measurement value in real time and at a high frequency through spectral line information in an infrared frequency band.

In one possible implementation, when determining whether a thermal pollution event of water quality exists at a certain time, the lowest air temperature of the day and the time information when the water temperature measurement value is obtained can be input into the water temperature prediction model, and the predicted water temperature value can be obtained.

In one possible implementation, the water temperature prediction model may include a seasonal period feature term and a daily period feature term. The day cycle characteristic item represents the relationship between the water temperature index and the time when the water temperature index is measured, and the season cycle characteristic item represents the relationship between the climate information and the water temperature index.

In one possible implementation, the current day minimum temperature may represent climate information, and the seasonal period feature may process the current day minimum temperature to obtain a predicted water temperature under the climate condition. The day cycle characteristic item can process the time information to obtain the predicted water temperature at the moment. The two predicted water temperatures may be summed to obtain a predicted water temperature value, i.e., the water temperature that the predetermined water area should reach at that time under the air temperature condition.

In one possible implementation, whether a water quality thermal pollution event has occurred may be determined by a deviation between the water temperature measurement and the water temperature prediction. A preset threshold for the deviation of the water temperature may be set, a difference between the measured value of the water temperature and the predicted value of the water temperature may be determined, and if the difference is greater than or equal to the preset threshold, it may be considered that the deviation between the measured value of the water temperature and the normal water temperature is large, and out of the normal range, there may be a thermal pollution event. Otherwise, the deviation between the measured water temperature and the normal water temperature can be considered to be in a reasonable range, and no thermal pollution event exists.

Further, if a thermal pollution event exists, other water quality indexes can be measured by the quantum dot spectrometer so as to further determine the specific condition of water quality pollution. The present disclosure is not limited to methods of using quantum dot spectrometers.

Fig. 5 shows a block diagram of a water quality thermal pollution detection apparatus according to an embodiment of the present disclosure, as shown in fig. 5, the apparatus includes: the measuring module 11 is used for measuring the water temperature measured value of a preset water area in real time; the prediction module 12 is used for determining a predicted water temperature value of the predetermined water area according to the climate information and the time information when the water temperature measured value is determined; and the judging module 13 is configured to determine the thermal pollution judging information of the predetermined water area according to the water temperature measured value and the water temperature predicted value.

In one possible implementation, the apparatus further includes: the water temperature control system comprises a daily period module, a water temperature control module and a water temperature control module, wherein the daily period module is used for determining a daily period characteristic item of water temperature according to a plurality of water temperature indexes acquired in a preset water area in a first time period; the seasonal period module is used for determining seasonal period characteristic items of the water temperature according to the climate information in the second time period and a plurality of water temperature indexes acquired in a preset water area; and the water temperature prediction module is used for determining the water temperature prediction model according to the day cycle characteristic item and the season cycle characteristic item.

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 thermal pollution detection device, an electronic device, a computer readable storage medium and a program, which can be used for realizing any water quality thermal pollution 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 of skill in the art that in the above method of the present embodiment, the order of writing the steps does not imply a strict order of execution and does not impose any limitations on the implementation, as the order of execution of the steps should be determined by their function and possibly 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 foregoing method embodiments, and for specific implementation, reference may be made to the description of the foregoing method embodiments, and for brevity, details are not described here again

Embodiments of the present disclosure also provide a computer-readable storage medium, on which computer program instructions are stored, and when executed by a processor, the computer program instructions implement the above 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. 6 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. 6, 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 signal 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 broadcast signals 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. 7 is a block diagram illustrating an electronic device 1900 in accordance with an example embodiment. For example, the electronic device 1900 may be provided as a server. Referring to fig. 7, 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, that are 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, such as Windows Server, stored in memory 1932 ^TM ，Mac OS ^XTM ，Unix ^TM ,Linux ^TM ，FreeBSD ^TM 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 the 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 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 latter scenario, 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.

The foregoing description of the embodiments of the present disclosure has been presented for purposes of illustration and description, but 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 described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for detecting thermal pollution of water quality is characterized by comprising the following steps:

measuring the water temperature measurement value of a preset water area in real time;

according to the climate information and the time information when the water temperature measurement value is determined, determining a water temperature prediction value of the preset water area;

and determining the thermal pollution judgment information of the preset water area according to the water temperature measurement value and the water temperature prediction value.

2. The method of claim 1, wherein the climate information includes a lowest air temperature that determines the water temperature measurement value for the day,

according to the climate information and the time information when the water temperature measurement value is determined, a predicted water temperature value of the preset water area is determined, and the method comprises the following steps:

inputting the lowest air temperature and the time information of the current day into a water temperature prediction model to obtain the water temperature prediction value, wherein the water temperature prediction model comprises a day cycle characteristic item of the water temperature and a season cycle characteristic item of the water temperature, the day cycle characteristic item represents the relation between a water temperature index and the time when the water temperature index is measured, and the season cycle characteristic item represents the relation between climate information and the water temperature index.

3. The method of claim 2, further comprising:

determining a day cycle characteristic item of water temperature according to a plurality of water temperature indexes acquired in a predetermined water area within a first time period;

determining a seasonal period characteristic item of the water temperature according to the climate information in the second time period and a plurality of water temperature indexes acquired in a preset water area;

and determining the water temperature prediction model according to the day cycle characteristic item and the season cycle characteristic item.

4. The method of claim 3, wherein determining the characteristic day cycle of the water temperature based on a plurality of water temperature indicators taken over a predetermined area of water during a first time period comprises:

and carrying out Fourier fitting processing on the plurality of water temperature indexes obtained in the first time period and the time when the water temperature indexes are obtained to obtain the day cycle characteristic item.

5. The method of claim 3, wherein determining the seasonal characteristic of the water temperature based on the climate information for the second time period and a plurality of water temperature indicators obtained for a predetermined body of water comprises:

and performing regression analysis on a plurality of water temperature indexes obtained in the second time period and the lowest air temperature of the day on which the water temperature indexes are obtained, and obtaining the seasonal period characteristic item.

6. The method of claim 3, wherein determining the water temperature prediction model from the daily cycle feature and the seasonal cycle feature comprises:

and summing the day cycle characteristic items and the season cycle characteristic items to obtain the water temperature prediction model.

7. The method of claim 1, wherein determining thermal pollution discrimination information for the predetermined body of water based on the water temperature measurement and the water temperature prediction comprises:

determining a difference between the water temperature measurement value and the water temperature prediction value;

and determining the thermal pollution judgment information as abnormal water temperature under the condition that the difference value is greater than or equal to a preset threshold value.

8. A water quality thermal pollution detection device is characterized by comprising:

the measuring module is used for measuring the water temperature measured value of a preset water area in real time;

the prediction module is used for determining a predicted water temperature value of the preset water area according to the climate information and the time information when the water temperature measured value is determined;

and the judging module is used for determining the thermal pollution judging information of the preset water area according to the water temperature measured value and the water temperature predicted value.

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 computer program instructions stored thereon, which when executed by a processor implement the method of any one of claims 1 to 7.