Water Quality Thermal Pollution Detection Method and Device, Electronic Device, and Storage Medium

Abstract

The present disclosure relates to a method and device for detecting water quality thermal pollution, an electronic device, and a storage medium. The method includes: measuring the water temperature measurement value of a predetermined water area in real time; determining the water temperature prediction value of the predetermined water area according to the climate information and time information when determining the water temperature measurement value; and determining the thermal pollution discrimination information of the predetermined water area according to the water temperature measurement value and the water temperature prediction value. According to the water quality thermal pollution detection method of the embodiments of the present disclosure, the water temperature can be detected in real time, the real-time performance of detection is improved, and it can be determined whether the predetermined water area is thermally polluted through the water temperature measurement value and the water temperature prediction value, so that the water quality thermal pollution can be monitored in real time, providing a basis for timely discovering thermal pollution and carrying out targeted prevention and control.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technologies, and in particular, to a method and apparatus for detecting water quality thermal pollution, an electronic device, and a storage medium. Background Art

[0002] The overall global temperature shows an upward trend. On the one hand, it comes from the greenhouse effect caused by carbon dioxide emissions. On the other hand, the waste heat discharged in industrial production and daily life directly brings environmental thermal pollution. For example, the industrial wastewater discharged from factories such as petroleum, chemical, and paper-making contains a large amount of waste heat. After this waste heat is discharged into surface water bodies, it can raise the water temperature. The abnormal change of the water environment temperature caused by human activities may bring great harm to the ecological environment, species survival, and human health.

[0003] However, so far, the online monitoring systems based on river and lake water environments at home and abroad mainly focus on pollution monitoring of pollutants, ignoring the damage of thermal pollution to water environment quality and the timely disposal of the damage. Summary of the Invention

[0004] The present disclosure provides a method and apparatus for detecting water quality thermal pollution, an electronic device, and a storage medium.

[0005] According to one aspect of the present disclosure, a method for detecting water quality thermal pollution is provided, including: measuring a water temperature measurement value of a predetermined water area in real time; determining a water temperature prediction value of the predetermined water area according to climate information and time information when determining the water temperature measurement value; and determining thermal pollution discrimination information of the predetermined water area according to the water temperature measurement value and the water temperature prediction value.

[0006] In a possible implementation manner, the climate information includes the lowest temperature on the day when the water temperature measurement value is determined. Determining the water temperature prediction value of the predetermined water area according to climate information and time information when determining the water temperature measurement value includes: inputting the lowest temperature on the day and the time information into a water temperature prediction model to obtain the water temperature prediction value, where the water temperature prediction model includes a daily cycle feature item of water temperature and a seasonal cycle feature item of water temperature, the daily cycle feature item represents the relationship between the water temperature index and the moment when the water temperature index is measured, and the seasonal cycle feature item represents the relationship between climate information and the water temperature index.

[0007] In a possible implementation manner, the method further includes: determining a daily cycle feature item of water temperature according to a plurality of water temperature indexes obtained in a predetermined water area within a first time period; determining a seasonal cycle feature item of water temperature according to climate information within a second time period and a plurality of water temperature indexes obtained in the predetermined water area; and determining the water temperature prediction model according to the daily cycle feature item and the seasonal cycle feature item.

[0008] In a possible implementation, based on a plurality of water temperature indicators obtained in a predetermined water area during a first time period, a daily cycle characteristic item of the water temperature is determined, including: performing Fourier fitting processing on the plurality of water temperature indicators obtained during the first time period and the moments when the water temperature indicators are obtained, to obtain the daily cycle characteristic item.

[0009] In a possible implementation, based on the climate information during a second time period and a plurality of water temperature indicators obtained in a predetermined water area, a seasonal cycle characteristic item of the water temperature is determined, including: performing regression analysis on the plurality of water temperature indicators obtained during the second time period and the lowest air temperature on the day when the water temperature indicators are obtained, to obtain the seasonal cycle characteristic item.

[0010] In a possible implementation, based on the daily cycle characteristic item and the seasonal cycle characteristic item, the water temperature prediction model is determined, including: performing a summation process on the daily cycle characteristic item and the seasonal cycle characteristic item, to obtain the water temperature prediction model.

[0011] In a possible implementation, based on the water temperature measurement value and the water temperature prediction value, the heat pollution discrimination information of the predetermined water area is determined, including: determining the difference between the water temperature measurement value and the water temperature prediction value; in the case where the difference is greater than or equal to a preset threshold, determining the heat pollution discrimination information as water temperature anomaly.

[0012] According to one aspect of the present disclosure, a water quality heat pollution detection device is provided, including: a measurement module for real-time measuring the water temperature measurement value of a predetermined water area; a prediction module for determining the water temperature prediction value of the predetermined water area according to the climate information and time information when the water temperature measurement value is determined; and a discrimination module for determining the heat pollution discrimination information of the predetermined water area according to the water temperature measurement value and the water temperature prediction value.

[0013] In a possible implementation, the climate information includes the lowest air temperature on the day when the water temperature measurement value is determined, and the prediction module may include: inputting the lowest air temperature on the day and the time information into a water temperature prediction model to obtain the water temperature prediction value, where the water temperature prediction model includes a daily cycle characteristic item of the water temperature and a seasonal cycle characteristic item of the water temperature, the daily cycle characteristic item represents the relationship between the water temperature indicator and the moment when the water temperature indicator is measured, and the seasonal cycle characteristic item represents the relationship between the climate information and the water temperature indicator.

[0014] In a possible implementation, the device further includes: a daily cycle module, configured to determine the daily cycle characteristic items of the water temperature according to a plurality of water temperature indicators obtained in a predetermined water area within a first time period; a seasonal cycle module, configured to determine the seasonal cycle characteristic items of the water temperature according to the climate information within a second time period and a plurality of water temperature indicators obtained in the predetermined water area; a water temperature prediction module, configured to determine the water temperature prediction model according to the daily cycle characteristic items and the seasonal cycle characteristic items.

[0015] In a possible implementation, the daily cycle module is further configured to: perform Fourier fitting processing on the plurality of water temperature indicators obtained within the first time period and the moments when the water temperature indicators are obtained, to obtain the daily cycle characteristic items.

[0016] In a possible implementation, the seasonal cycle module is further configured to: perform regression analysis on the plurality of water temperature indicators obtained within the second time period and the lowest air temperature on the day when the water temperature indicators are obtained, to obtain the seasonal cycle characteristic items.

[0017] In a possible implementation, the water temperature prediction module is further configured to: perform a summation process on the daily cycle characteristic items and the seasonal cycle characteristic items, to obtain the water temperature prediction model.

[0018] In a possible implementation, the discrimination module is further configured to: determine the difference between the water temperature measurement value and the water temperature prediction value; in the case where the difference is greater than or equal to a preset threshold, determine the thermal pollution discrimination information as water temperature anomaly.

[0019] 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: execute the above-mentioned water quality thermal pollution detection method.

[0020] According to an aspect of the present disclosure, there is provided a computer-readable storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the above-mentioned water quality thermal pollution detection method is implemented.

[0021] The water quality thermal pollution detection method according to the embodiments of the present disclosure can detect the water temperature in real time, improve the real-time performance of detection, and can determine whether the predetermined water area is thermally polluted through the water temperature measurement value and the water temperature prediction value, and can monitor the water quality thermal pollution in real time, providing a basis for timely discovering thermal pollution and carrying out targeted prevention and control.

[0022] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and do not limit the present disclosure.

[0023] Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Description of the Drawings

[0024] The accompanying drawings herein are incorporated into and constitute a part of this specification, which illustrate embodiments consistent with the present disclosure and, together with the specification, are used to explain the technical solutions of the present disclosure.

[0025] Figure 1 A flowchart showing a method for detecting water quality thermal pollution according to an embodiment of the present disclosure;

[0026] Figure 2 A schematic diagram showing water temperature indicators according to an embodiment of the present disclosure;

[0027] Figure 3 A schematic diagram showing the relationship between the daily average water temperature and the lowest air temperature;

[0028] Figure 4 A schematic diagram showing the application of a method for detecting water quality thermal pollution according to an embodiment of the present disclosure;

[0029] Figure 5 A block diagram showing a device for detecting water quality thermal pollution according to an embodiment of the present disclosure;

[0030] Figure 6 A block diagram showing an electronic device according to an embodiment of the present disclosure;

[0031] Figure 7 A block diagram showing an electronic device according to an embodiment of the present disclosure. Detailed Embodiments

[0032] Various exemplary embodiments, features, and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the drawings denote elements having the same or similar functions. Although various aspects of the embodiments are shown in the drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0033] The term "exemplary" used herein means "serving as an example, embodiment, or illustration". Any embodiment described as "exemplary" herein is not necessarily to be construed as superior to or better than other embodiments.

[0034] The term "and / or" herein is merely a description of the associated relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: 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 can represent including any one or more elements selected from the set composed of A, B, and C.

[0035] In addition, for a better illustration of the present disclosure, numerous specific details are provided in the following detailed implementation manners. Those skilled in the art should understand that the present disclosure can also be implemented without certain specific details. In some instances, methods, means, elements, and circuits well-known to those skilled in the art are not described in detail so as to highlight the gist of the present disclosure.

[0036] Figure 1 A flowchart showing a water quality thermal pollution detection method according to an embodiment of the present disclosure is as Figure 1 shown, and the method includes:

[0037] In step S11, the water temperature measurement value of a predetermined water area is measured in real time;

[0038] In step S12, according to the climate information and time information when determining the water temperature measurement value, the water temperature prediction value of the predetermined water area is determined;

[0039] In step S13, according to the water temperature measurement value and the water temperature prediction value, the thermal pollution discrimination information of the predetermined water area is determined.

[0040] The water quality thermal pollution detection method according to an embodiment of the present disclosure can detect the water temperature in real time, improve the real-time performance of detection, and can determine whether the predetermined water area is thermally polluted through the water temperature measurement value and the water temperature prediction value, and can monitor the water quality thermal pollution in real time, providing a basis for timely discovering thermal pollution and carrying out targeted prevention and control.

[0041] In an example, a temperature measuring device can be used to measure the water temperature measurement value of a predetermined water area. For example, a thermometer, an infrared thermometer, a thermal imager, etc. can be used. The present disclosure does not limit the devices used for measuring the water temperature. In an example, the thermometer can directly obtain the water temperature measurement value by contacting the water quality of the predetermined water area. The infrared thermometer and the thermal imager, etc. can indirectly obtain the water temperature measurement value based on the infrared light radiated by the water quality of the predetermined water area.

[0042] In an example, a micro spectrometer such as a quantum dot spectrometer can also be used to measure the water temperature measurement value of a predetermined water area. Moreover, by measuring the spectral information of the water quality of the predetermined water area, other information can also be obtained. For example, the contents of various substances in the water body can be combined with the water temperature condition to further judge the water quality pollution condition. The present disclosure does not limit the application manner of the quantum dot spectrometer. Further, the water temperature and various water quality indicators can be obtained simultaneously through the quantum dot spectrometer, and there is no need to set up multiple instruments, which can save the detection cost.

[0043] In an example, the quantum dot spectrometer may include a quantum dot spectroscopy probe. The quantum dot spectroscopy probe may measure incident light (for example, light after passing through a water sample in a predetermined area and being transmitted or scattered) based on the physical and optical properties of nanocrystals to obtain spectral information of the incident light, and the spectral information may represent the water quality information of the water area. For example, the quantum dot spectroscopy probe may include a nanocrystal chip made of a variety of nanocrystals. The nanocrystal chip contains a certain arrangement of a variety of nanocrystals (for example, a nanocrystal array). Among them, each nanocrystal has different light absorption characteristics or emission characteristics. Different types of semiconductor nanocrystals, for example, can be of different materials, sizes, etc., so that the nanocrystal chip can modulate the response to wavelengths in a relatively wide wavelength range to obtain the spectrum of the incident light adjusted in a relatively wide wavelength range.

[0044] In a possible implementation, the light after being transmitted or scattered by water may be affected by substances in the water (such as suspended solids, pollutants, etc.), so as to obtain specific spectral information. The quantum dot spectroscopy probe can obtain this spectral information in real time and determine the water quality index represented by this spectral information. For example, by the absorption intensity of water samples for light of different wavelengths, spectral information of light in different frequency bands can be obtained, and the water quality index can be calculated through this spectral information. In the example, the water quality index includes chemical oxygen demand (COD), turbidity, permanganate index, total suspended solids, biological oxygen demand, total organic carbon, sulfate content, chloride content, dissolved iron content, dissolved manganese content, dissolved copper content, dissolved 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, volatile phenol content, fecal coliform group content, sulfide content, etc. The water temperature can also be measured according to the infrared spectrum in the spectral information. Or, the quantum dot spectroscopy probe can infer the water quality index through a neural network. For example, the spectral information can be input into the neural network, and the neural network can infer the concentrations of various substances (water quality indexes). The present disclosure does not limit the method of determining the water quality index. The present disclosure does not limit the working principle of the quantum dot spectroscopy probe.

[0045] In the example, the quantum dot spectral probe can determine water quality indicators based on the light absorption characteristics of various substances contained in water. For example, the light intensity of light at a specific wavelength can be analyzed through spectral information, and then the concentration of substances corresponding to the light in the specific wavelength range (water quality indicators) can be obtained. Measuring indicators with the quantum dot spectral probe can achieve on-line, in-situ, high-frequency, and real-time measurement. When detecting water quality indicators, the spectral information of the light passing through a predetermined water area can be detected by the quantum dot spectral probe, and then the water quality indicators can be quickly calculated based on the spectral information to obtain water quality indicators with strong real-time performance. Compared with the process of bringing water quality back to the laboratory for testing, detecting with the quantum dot spectral probe has better real-time performance (that is, the detected water quality indicators are the current water quality indicators, while the time required for laboratory testing is relatively long, and during the period of waiting for the test results, the water quality indicators in the predetermined water area may have changed). By setting the quantum dot spectral probe in a preset water area and measuring multiple times within a certain period, a water quality indicator sequence of the above two water quality indicators in this water area can be obtained. The water quality indicators in the water quality indicator sequence are the water quality indicators obtained at multiple moments at the same location, so they have consistency and comparability and can be used to observe the change law of water quality indicators over a period of time to judge water pollution. For example, the measurement frequency of the quantum dot spectral probe can reach 3 - 60 minutes per time, preferably 5 - 30 minutes per time, particularly preferably 8 - 20 minutes per time, and most preferably 10 - 15 minutes per time. The measurement frequency is much higher than that of bringing water samples back to the laboratory for testing, and the quantum dot spectral probe can be set at a fixed position in the predetermined water area to ensure the consistency of water samples. When bringing water samples back to the laboratory for testing, it is difficult to ensure that the sampling is exactly at the same location for two measurements, and due to the low measurement frequency and the long interval between two measurements, even if it is possible to ensure that the sampling is exactly at the same location for two measurements, due to the fluidity of water, the water quality at this location may have changed significantly during the long interval, making it difficult to ensure the consistency of measurement and the comparability of measurement results.

[0046] In the example, in addition to the above water quality indicators, the infrared spectrum of the water quality in the predetermined water area can also be detected by the quantum dot spectral probe to determine the water temperature measurement value of the water quality. If the water temperature measurement value is abnormal, the pollution status can also be determined through the above water quality indicators to further determine the reason for the abnormal water temperature or the reason for water quality thermal pollution.

[0047] In a possible implementation manner, when measuring the water temperature, the quantum dot spectrometer can not contact the water and realize the measurement of the water temperature based on the spectral information of the reflected light of the water, which can reduce the corrosion of the instrument and extend the service life of the instrument.

[0048] In a possible implementation, in step S11, the water temperature measurement value of a predetermined water area can be measured, and the measurement time can be recorded. In the example, the water temperature of the predetermined water area can be measured multiple times, and the times of multiple measurements can be recorded.

[0049] In a possible implementation, after obtaining the water temperature measurement value, it can be determined whether the water temperature measurement value is abnormal. For example, the water temperature measurement value can be compared with the water temperature prediction value, and the water temperature prediction value can be the normal water temperature of the predetermined water area, that is, the normal water temperature that the predetermined water area should reach when the water temperature measurement value is obtained. If the difference between the water temperature measurement value and the normal water temperature is large, it can indicate that the predetermined water area is thermally polluted. Otherwise, it can indicate that the predetermined water area is not thermally polluted.

[0050] In a possible implementation, in step S12, the water temperature prediction value of the predetermined water area can be determined, that is, estimate the normal water temperature that the predetermined water area should reach when the water temperature measurement value is obtained. In the example, the normal water temperature can be estimated based on the climate information and time information when the water temperature measurement value is obtained.

[0051] In the example, the normal water temperature that the predetermined water area should reach can be estimated by measuring the water temperature at other monitoring points. For example, among other monitoring points, monitoring points with similar air temperatures to the predetermined water area (for example, the temperature difference is less than set values such as 1°C, 2°C, etc.) can be selected, and the water temperatures measured at these monitoring points can be obtained, and the average value of the water temperatures at these measurement points can be determined to estimate the normal water temperature that the predetermined water area should reach. However, there may be a problem that other measurement points are also thermally polluted, resulting in inaccurate estimated values. Or due to water body flow, although the air temperatures are similar, the water temperature differences are large, etc., which may also lead to inaccurate estimated values.

[0052] In the example, the normal water temperature that the predetermined water area should reach can also be speculated based on various factors such as climate and time. The heat received by the predetermined water area at different times of the day is different. For example, at noon, the water receives a large amount of solar thermal radiation and can receive heat conducted from the ground or air, etc., resulting in a higher water temperature. In the morning or evening, the water receives less solar thermal radiation, and the air and ground temperatures are low, and they cannot conduct much heat to the water either. Therefore, the water temperature is low.

[0053] In the example, the water temperature of the predetermined water area can also be affected by climate information. For example, in summer, the water temperature is high, and in winter, the water temperature is low. Or, on sunny days, the water temperature is high, and on rainy days, the water temperature is low.

[0054] In summary, the water temperature of the predetermined water area can be affected by the time of day (i.e., time information) and climate information. Therefore, the normal water temperature of the predetermined water area, that is, the water temperature prediction value, can be estimated by obtaining the time information and climate information when the water temperature measurement value is obtained.

[0055] In the example, the normal water temperature, i.e., the predicted water temperature value, can be predicted by a water temperature prediction model. The water temperature prediction model can be a neural network model, a regression model, etc. The present disclosure does not limit the type of the water temperature prediction model. The time information and climate information when the water temperature measurement value is obtained can be input into the water temperature prediction model to determine the predicted water temperature value.

[0056] In the example, since the influence of climate information on water temperature needs to be measured over a relatively long period of time. For example, it is necessary to measure the influence of climate information on water temperature over one year or multiple years, that is, to determine the relationship between the climate and water temperature in each season over multiple years. In the example, the relationship between the daily air temperature and the daily average value of water temperature can be determined. The daily air temperature can represent the daily climate information (for example, the air temperature is relatively low in winter and relatively high in summer), and the daily average value of water temperature can represent the water temperature. The relationship between the daily air temperature and the daily average value of water temperature can be used to represent the relationship between climate and water temperature. Further, according to actual empirical data, the linear correlation coefficient between the daily minimum air temperature and the daily average value of water temperature is relatively high (linear correlation coefficient ≥ 0.97). Therefore, the daily minimum air temperature can be used to predict the water temperature.

[0057] In a possible implementation manner, the climate information includes the minimum air temperature on the day when the water temperature measurement value is determined. Step S12 may include: inputting the minimum air temperature on the day and the time information into the water temperature prediction model to obtain the predicted water temperature value, where the water temperature prediction model includes a daily cycle feature term of water temperature and a seasonal cycle feature term of water temperature. The daily cycle feature term represents the relationship between the water temperature index and the moment when the water temperature index is measured, and the seasonal cycle feature term represents the relationship between the climate information and the water temperature index.

[0058] In the example, when determining the predicted water temperature value, the daily minimum air temperature and the time information when the water temperature measurement value is obtained can be input into the water temperature prediction model, and the water temperature prediction model can output the predicted water temperature value. The daily minimum air temperature can be used to represent the climate information and is used to estimate the seasonal cycle feature term. The time information is used to estimate the daily cycle feature term, that is, the water temperature shows a periodic change law at different moments of a day. The normal water temperature at that moment can be predicted according to the moment when the water temperature measurement value is obtained and the above law.

[0059] In a possible implementation manner, the method for establishing a water temperature prediction model may include: determining the daily cycle feature term of water temperature according to a plurality of water temperature indexes obtained in a predetermined water area within a first time period; determining the seasonal cycle feature term of water temperature according to the climate information within a second time period and a plurality of water temperature indexes obtained in the predetermined water area; and determining the water temperature prediction model according to the daily cycle feature term and the seasonal cycle feature term.

[0060] Figure 2 A schematic diagram showing the water temperature index according to an embodiment of the present disclosure. AsFigure 2 As shown, the first time period may include multiple days, and the water temperature index may be measured at multiple moments within the multiple days to obtain a water temperature index sequence \(\{(t_1,y_1),(t_2,y_2),(t_3,y_3),\cdots,(t_m,y_m),\cdots\}\), where \(m\) is any positive integer, \(t_m\) is the \(m\)-th moment, and \(y_m\) is the water temperature index measured at the \(m\)-th moment. The water temperature index may present a periodic change rule. For example, the water temperature rises at noon and drops in the morning and evening, etc. (As shown in m ,y m ),\cdots\}, where \(m\) is any positive integer, \(t_m\) is the \(m\)-th moment, and \(y_m\) is the water temperature index measured at the \(m\)-th moment. The water temperature index may present a periodic change rule. For example, the water temperature rises at noon and drops in the morning and evening, etc. (As shown in m is the \(m\)-th moment, and \(y_m\) is the water temperature index measured at the \(m\)-th moment. The water temperature index may present a periodic change rule. For example, the water temperature rises at noon and drops in the morning and evening, etc. (As shown in m ), where \(m\) is any positive integer, \(t_m\) is the \(m\)-th moment, and \(y_m\) is the water temperature index measured at the \(m\)-th moment. The water temperature index may present a periodic change rule. For example, the water temperature rises at noon and drops in the morning and evening, etc. (As shown in Figure 2 ), due to temperature changes, the water temperature shows periodic changes. However, when the climatic conditions do not change significantly, the water temperature may fluctuate around a certain temperature value (such as 25 °C). Since the water temperature shows a periodic change rule, therefore, the daily cycle characteristic term can be determined by Fourier fitting, that is, the relationship between the water temperature index and the moment when the water temperature index is measured.

[0061] In a possible implementation manner, according to multiple water temperature indexes obtained in a predetermined water area within the first time period, determining the daily cycle characteristic term of the water temperature includes: performing Fourier fitting processing on the multiple water temperature indexes obtained within the first time period and the moment when the water temperature index is obtained to obtain the daily cycle characteristic term.

[0062] In the example, the daily cycle characteristic term can be determined according to the following formula (1):

[0063]

[0064] where \(t\) is the moment when the water temperature index is obtained, \(N\) is the number of days included in the first time period, \(P\) is the minimum positive period. In the example, the minimum positive period may be 1 day, and the present disclosure does not limit the minimum positive period. \(n\) is the date when the water temperature index is obtained, and \(c_m\) n is the Fourier coefficient. \(s(t)\) is the daily cycle characteristic term, that is, the moment when the water temperature measurement value is obtained can be input into the daily cycle characteristic term, and the result output by the daily cycle characteristic term is the normal water temperature predicted based on that moment.

[0065] In a possible implementation manner, the seasonal cycle characteristic term can be determined, that is, it represents the rule of the water temperature changing with the climate change. According to the climate information within the second time period and multiple water temperature indexes obtained in the predetermined water area, determining the seasonal cycle characteristic term of the water temperature includes: performing regression analysis on the multiple water temperature indexes obtained within the second time period and the lowest temperature on the day when the water temperature index is obtained to obtain the seasonal cycle characteristic term.

[0066] In the example, the second time period may include multiple years, and there is a high correlation coefficient between the daily minimum temperature and the daily average water temperature over each day of the multiple years. Climate information can be represented by the minimum temperature, and the water temperature index can be represented by the daily average water temperature. Moreover, the relationship between the daily minimum temperature and the daily average water temperature, that is, the seasonal cycle characteristic term, can be determined.

[0067] Figure 3 A schematic diagram showing the relationship between the daily average water temperature and the minimum temperature is shown, as Figure 3 shown. There is a high correlation coefficient between the daily minimum temperature and the daily average water temperature. Regression analysis can be performed on the daily minimum temperature and the daily average water temperature to obtain the seasonal cycle characteristic term. In the example, the seasonal cycle characteristic term can be determined by the following formula (2):

[0068] g(t) = kT env,daily_min + b (2)

[0069] where k is the regression coefficient, b is the intercept, T env,daily_min is the daily minimum temperature, and g(t) is the seasonal cycle characteristic term. That is, the climate information (i.e., the daily minimum temperature) for obtaining the water temperature measurement value can be input into the seasonal cycle characteristic term, and the result output by the seasonal cycle characteristic term is the normal water temperature based on this minimum temperature.

[0070] In one possible implementation, after determining the daily cycle characteristic term and the seasonal cycle characteristic term, a water temperature prediction model can be obtained using the daily cycle characteristic term and the seasonal cycle characteristic term. In the example, determining the water temperature prediction model according to the daily cycle characteristic term and the seasonal cycle characteristic term may include: performing a summation process on the daily cycle characteristic term and the seasonal cycle characteristic term to obtain the water temperature prediction model.

[0071] In the example, the water temperature prediction model can be determined by the following formula (3):

[0072]

[0073] where is the water temperature prediction value.

[0074] In the example, the water temperature prediction model can refer to climate information and time information to obtain the normal water temperature, that is, the water temperature prediction value. The above two items can be summed up to obtain the normal water temperature that should be reached in a predetermined water area at the moment of obtaining the water temperature measurement value under the daily minimum temperature, that is, the water temperature prediction value. For example, the moment of obtaining the water temperature measurement value is 15:00 in the afternoon, the season of the day is summer, and the daily minimum temperature is 28°C. The time information (15:00) and the climate information (28°C) can be input into the water temperature prediction model to obtain the water temperature prediction value, that is, the normal water temperature that should be reached in the predetermined water area at 15:00 in the afternoon under the condition that the daily minimum temperature is 28°C.

[0075] In this way, the water temperature prediction model can refer to time information and climate information, fully consider multiple factors affecting the water temperature, and improve the accuracy of the water temperature prediction value.

[0076] In the example, the factors affecting the water temperature prediction value may further include the presence or absence of inflowing water. In the case where the predetermined water area is the ocean, the factors affecting the water temperature prediction value may further include tides, etc. The above factors may also be added to the water temperature prediction model, and the present disclosure does not limit the factors affecting the water temperature prediction value.

[0077] In a possible implementation manner, in step S13, the water temperature measurement value and the water temperature prediction value may be compared. Among them, the water temperature prediction value is the normal temperature that the predetermined water area should reach, and the water temperature measurement value is the measured temperature. If the deviation between the water temperature measurement value and the water temperature prediction value is large, the water temperature at this moment is abnormal, and a thermal pollution event may occur.

[0078] In a possible implementation manner, step S13 may include: determining the difference between the water temperature measurement value and the water temperature prediction value; in the case where the difference is greater than or equal to a preset threshold, determining the thermal pollution discrimination information as abnormal water temperature.

[0079] In the example, a preset threshold ε of the water temperature deviation may be set, and the difference between the water temperature measurement value y(t) and the water temperature prediction value is determined. For example, the absolute value of the difference If If then it can be considered that the deviation between the water temperature measurement value and the normal water temperature is within a reasonable range, and there is no thermal pollution event. If then it can be considered that the deviation between the water temperature measurement value and the normal water temperature is large, exceeding the normal range, and there may be a thermal pollution event. The water temperature measurement value y(t) may be the measurement value at any moment, or the average value of multiple measurements, or the maximum value of multiple measurements, etc. The present disclosure does not limit the water temperature measurement value.

[0080] According to the water quality thermal pollution detection method of the embodiments of the present disclosure, the water temperature can be detected in real time, improving the real-time performance of the detection, and it can be determined whether the predetermined water area is thermally polluted through the water temperature prediction model. The water temperature prediction model can fully consider multiple factors affecting the water temperature and improve the accuracy of the water temperature prediction value. Further, the method can monitor the water quality thermal pollution in real time, providing a basis for timely discovering thermal pollution and carrying out targeted prevention and control.

[0081] Figure 4 Show an application schematic diagram of the water quality thermal pollution detection method according to the embodiments of the present disclosure, as Figure 4As shown, a quantum dot spectrometer can be set in a predetermined water area to measure the water temperature. The quantum dot spectrometer can obtain real-time and high-frequency water temperature measurement values through spectral line information in the infrared band.

[0082] In a possible implementation, when determining whether there is a water quality thermal pollution event at a certain moment, the daily minimum temperature and the time information when the water temperature measurement value is obtained can be input into the water temperature prediction model, and the water temperature prediction value can be obtained.

[0083] In a possible implementation, the water temperature prediction model can include a seasonal cycle feature term and a daily cycle feature term. The daily cycle feature term represents the relationship between the water temperature index and the moment when the measured water temperature index is obtained, and the seasonal cycle feature term represents the relationship between the climate information and the water temperature index.

[0084] In a possible implementation, the daily minimum temperature can represent the climate information. The seasonal cycle feature term can process the daily minimum temperature to obtain the predicted water temperature under this temperature condition. The daily cycle feature term can process the time information to obtain the predicted water temperature at this moment. The above two predicted water temperatures can be summed to obtain the water temperature prediction value, that is, the water temperature that the predetermined water area should reach under this temperature condition at this moment.

[0085] In a possible implementation, whether a water quality thermal pollution event occurs can be determined by the deviation between the water temperature measurement value and the water temperature prediction value. A preset threshold of the water temperature deviation can be set, and the difference between the water temperature measurement value and the water temperature prediction value can be determined. If this difference is greater than or equal to the preset threshold, it can be considered that the deviation between the water temperature measurement value and the normal water temperature is large, exceeding the normal range, and there may be a thermal pollution event. Otherwise, it can be considered that the deviation between the water temperature measurement value and the normal water temperature is within a reasonable range and there is no thermal pollution event.

[0086] Furthermore, if there is a thermal pollution event, other water quality indicators can also be measured by the quantum dot spectrometer to further determine the specific situation of water quality pollution. The present disclosure does not limit the usage method of the quantum dot spectrometer.

[0087] Figure 5 The block diagram of the water quality thermal pollution detection device according to an embodiment of the present disclosure is shown, as Figure 5 As shown, the device includes: a measurement module 11 for measuring the water temperature measurement value of a predetermined water area in real time; a prediction module 12 for determining the water temperature prediction value of the predetermined water area according to the climate information and time information when determining the water temperature measurement value; a discrimination module 13 for determining the thermal pollution discrimination information of the predetermined water area according to the water temperature measurement value and the water temperature prediction value.

[0088] In a possible implementation, the climate information includes the lowest temperature on the day when the water temperature measurement value is determined. The prediction module may include: inputting the lowest temperature on the day and the time information into a water temperature prediction model to obtain the water temperature prediction value. Wherein, the water temperature prediction model includes a daily cycle feature term of the water temperature and a seasonal cycle feature term of the water temperature. The daily cycle feature term represents the relationship between the water temperature index and the moment when the water temperature index is measured, and the seasonal cycle feature term represents the relationship between the climate information and the water temperature index.

[0089] In a possible implementation, the device further includes: a daily cycle module for determining the daily cycle feature term of the water temperature according to a plurality of water temperature indexes obtained in a predetermined water area within a first time period; a seasonal cycle module for determining the seasonal cycle feature term of the water temperature according to the climate information within a second time period and a plurality of water temperature indexes obtained in the predetermined water area; a water temperature prediction module for determining the water temperature prediction model according to the daily cycle feature term and the seasonal cycle feature term.

[0090] In a possible implementation, the daily cycle module is further configured to: perform Fourier fitting processing on the plurality of water temperature indexes obtained within the first time period and the moment when the water temperature indexes are obtained to obtain the daily cycle feature term.

[0091] In a possible implementation, the seasonal cycle module is further configured to: perform regression analysis on the plurality of water temperature indexes obtained within the second time period and the lowest temperature on the day when the water temperature indexes are obtained to obtain the seasonal cycle feature term.

[0092] In a possible implementation, the water temperature prediction module is further configured to: perform a summation process on the daily cycle feature term and the seasonal cycle feature term to obtain the water temperature prediction model.

[0093] In a possible implementation, the discrimination module is further configured to: determine the difference between the water temperature measurement value and the water temperature prediction value; in the case where the difference is greater than or equal to a preset threshold, determine the thermal pollution discrimination information as abnormal water temperature.

[0094] It can be understood that, without violating the principle logic, the above-mentioned various method embodiments mentioned in the present disclosure can be combined with each other to form a combined embodiment. Due to space limitations, the present disclosure will not elaborate further.

[0095] In addition, the present disclosure also provides a water quality thermal pollution detection device, an electronic device, a computer-readable storage medium, and a program, all of which can be used to implement any water quality thermal pollution detection method provided by the present disclosure. For the corresponding technical solutions and descriptions, refer to the corresponding records in the method part and will not be elaborated further.

[0096] Those skilled in the art can understand that in the above methods of the specific embodiments, the writing order of each step does not mean a strict execution order and does not impose any limitation on the implementation process. The specific execution order of each step should be determined according to its function and possible internal logic.

[0097] In some embodiments, the functions or modules included in the device provided by the embodiments of the present disclosure can be used to execute the methods described in the above method embodiments. The specific implementation can refer to the description of the above method embodiments. For the sake of brevity, it will not be repeated here.

[0098] The embodiments of the present disclosure also propose a computer-readable storage medium, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the above methods are implemented. The computer-readable storage medium can be a non-volatile computer-readable storage medium.

[0099] The embodiments of the present disclosure also propose an electronic device, including: a processor; a memory for storing processor-executable instructions; wherein, the processor is configured to perform the above methods.

[0100] The electronic device can be provided as a terminal, a server or other forms of devices.

[0101] Figure 6 It is a block diagram of an electronic device 800 shown according to an exemplary embodiment. For example, the electronic device 800 can 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 and other terminals.

[0102] Referring to Figure 6 , the electronic device 800 can include one or more of the following components: a processing component 802, a memory 804, a power component 806, a multimedia component 808, an audio component 810, an input / output (I / O) interface 812, a sensor component 814, and a communication component 816.

[0103] The processing component 802 generally controls the overall operation of the electronic device 800, such as operations associated with display, telephone call, data communication, camera operation and recording operation. The processing component 802 can include one or more processors 820 to execute instructions to complete all or part of the steps of the above methods. In addition, the processing component 802 can include one or more modules to facilitate the interaction between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate the interaction between the multimedia component 808 and the processing component 802.

[0104] The memory 804 is configured to store various types of data to support the operation of the electronic device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phone book data, messages, pictures, videos, and the like. The memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, 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, a magnetic disk, or an optical disk.

[0105] The power supply component 806 provides power to the various components of the electronic device 800. The power supply component 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.

[0106] The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the 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 can be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors can not only sense the boundaries of the touch or swipe actions but also detect the duration and pressure associated with the touch or swipe operation. In some embodiments, the multimedia component 808 includes a front camera and / or a rear camera. When the electronic device 800 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera can receive external multimedia data. Each of the front camera and the rear camera can be a fixed optical lens system or have a focal length and optical zoom capabilities.

[0107] The audio component 810 is configured to output and / or input audio signals. For example, the audio component 810 includes a microphone (MIC) that is configured to receive external audio signals when the electronic device 800 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals can be further stored in the memory 804 or transmitted via the communication component 816. In some embodiments, the audio component 810 further includes a speaker for outputting audio signals.

[0108] The I / O interface 812 provides an interface between the processing component 802 and a peripheral interface module, which can be a keyboard, a click wheel, buttons, etc. These buttons can include, but are not limited to: a home button, a volume button, a power-on button, and a lock button.

[0109] The sensor assembly 814 includes one or more sensors for providing an assessment of various aspects of the status of the electronic device 800. For example, the sensor assembly 814 can detect the on / off state of the electronic device 800, the relative positioning of components, such as the display and keypad of the electronic device 800. The sensor assembly 814 can 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, the orientation or acceleration / deceleration of the electronic device 800, and a change in the temperature of the electronic device 800. The sensor assembly 814 can include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 814 can 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 can also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0110] The communication component 816 is configured to facilitate communication between the electronic device 800 and other devices in a wired or wireless manner. The electronic device 800 can access a wireless network based on communication standards, 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 communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0111] In an exemplary embodiment, the electronic device 800 can be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components for performing the above-described methods.

[0112] In an exemplary embodiment, a non-transitory computer-readable storage medium is also provided, such as a memory 804 including computer program instructions, which can be executed by a processor 820 of the electronic device 800 to complete the above-described methods.

[0113] Figure 7 is a block diagram of an electronic device 1900 shown in accordance with an exemplary embodiment. For example, the electronic device 1900 can be provided as a server. Referring to Figure 7, the electronic device 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by a memory 1932 for storing instructions executable by the processing component 1922, such as application programs. The application programs stored in the memory 1932 may include one or more modules each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute instructions to perform the above method.

[0114] 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 the memory 1932, such as Windows Server TM , Mac OS XTM , Unix TM , Linux TM , FreeBSD TM or the like.

[0115] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as the memory 1932 including computer program instructions, and the above computer program instructions can be executed by the processing component 1922 of the electronic device 1900 to complete the above method.

[0116] The present disclosure may be a system, a method, and / or a computer program product. The computer program product may include a computer-readable storage medium having thereon computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0117] A computer-readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. A computer-readable storage medium may be, for example—but not limited to—an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include: a portable computer disk, 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 mechanically encoded device such as a punched card or raised structures in grooves storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage medium as used herein is not construed as an instantaneous 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., an optical pulse through an optical fiber cable), or an electrical signal transmitted through a wire.

[0118] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to respective computing / processing devices, or downloaded 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 a copper transmission cable, an optical fiber transmission, a wireless transmission, a router, a firewall, a switch, a gateway computer, and / or an edge server. A 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 each computing / processing device.

[0119] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine - related instructions, microcode, firmware instructions, state - setting data, or source code or object code written in any combination of one or more programming languages, including object - oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as the "C" language or similar programming languages. The computer - readable program instructions may be executed entirely on the user's computer, partially on the user's computer, executed as a stand - alone software package, partially on the user's computer and partially on a remote computer, or entirely on a 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 may be connected to an external computer (e.g., through the Internet using an Internet service provider). In some embodiments, by using the state information of the computer - readable program instructions to customize an electronic circuit, such as a programmable logic circuit, a field - programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit can execute the computer - readable program instructions to implement various aspects of the present disclosure.

[0120] 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 should 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.

[0121] These computer - readable program instructions can be provided to a processor of a general - purpose computer, a special - purpose computer, or other programmable data - processing apparatus to produce a machine, such that the instructions, when executed by the processor of the computer or other programmable data - processing apparatus, create a means for implementing the functions / acts specified in one or more blocks of the flowchart and / or block diagram. These computer - readable program instructions can also be stored in a computer - readable storage medium, which causes a computer, a programmable data - processing apparatus, and / or other devices to operate in a particular manner, so that the computer - readable medium storing the instructions includes a manufacture that includes instructions for implementing various aspects of the functions / acts specified in one or more blocks of the flowchart and / or block diagram.

[0122] 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 data processing apparatus, or other devices to produce a computer-implemented process such that the instructions executed on the computer, other programmable data processing apparatus, or other devices implement the functions / acts specified in one or more boxes of the flowchart and / or block diagram.

[0123] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a portion of an instruction, which contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two consecutive blocks may in fact be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending upon the functionality involved. It should also be noted that each block of the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

[0124] The embodiments of the present disclosure have been described above. The above description is exemplary, not exhaustive, and is 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 choice of terms used herein is intended to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the market, or to enable other ordinary skilled persons in the art to understand the embodiments disclosed herein.

Claims

1. A method for detecting thermal pollution of water quality, characterized in that, Including: Real-time measurement of the water temperature measurement value of a predetermined water area; Determining the predicted water temperature value of the predetermined water area according to the climate information and time information when determining the water temperature measurement value; Determining the thermal pollution discrimination information of the predetermined water area according to the water temperature measurement value and the predicted water temperature value; Wherein, the climate information includes the lowest temperature on the day when the water temperature measurement value is determined, The determining the predicted water temperature value of the predetermined water area according to the climate information and time information when determining the water temperature measurement value includes: Inputting the lowest temperature on the day and the time information into a water temperature prediction model to obtain the predicted water temperature value, wherein the water temperature prediction model includes a daily cycle feature term of the water temperature and a seasonal cycle feature term of the water temperature, the daily cycle feature term represents the relationship between the water temperature index and the moment when the water temperature index is measured, and the seasonal cycle feature term represents the relationship between the climate information and the water temperature index.

2. The method according to claim 1, wherein The method further includes: Determining the daily cycle feature term of the water temperature according to multiple water temperature indexes obtained in a predetermined water area within a first time period; Determining the seasonal cycle feature term of the water temperature according to the climate information within a second time period and multiple water temperature indexes obtained in the predetermined water area; Determining the water temperature prediction model according to the daily cycle feature term and the seasonal cycle feature term.

3. The method according to claim 2, wherein Determining the daily cycle feature term of the water temperature according to multiple water temperature indexes obtained in a predetermined water area within a first time period includes: Performing Fourier fitting processing on the multiple water temperature indexes obtained within the first time period and the moment when the water temperature indexes are obtained to obtain the daily cycle feature term.

4. The method according to claim 2, wherein Determining the seasonal cycle feature term of the water temperature according to the climate information within a second time period and multiple water temperature indexes obtained in the predetermined water area includes: Performing regression analysis on the multiple water temperature indexes obtained within the second time period and the lowest temperature on the day when the water temperature indexes are obtained to obtain the seasonal cycle feature term.

5. The method according to claim 2, characterized in that, Determining the water temperature prediction model according to the daily cycle feature term and the seasonal cycle feature term includes: Performing a summation process on the daily cycle feature term and the seasonal cycle feature term to obtain the water temperature prediction model.

6. The method according to claim 1, wherein Determining the thermal pollution discrimination information of the predetermined water area according to the water temperature measurement value and the predicted water temperature value includes: Determining the difference between the water temperature measurement value and the predicted water temperature value; When the difference is greater than or equal to a preset threshold, determining the thermal pollution discrimination information as abnormal water temperature.

7. A water quality thermal pollution detection device, characterized in that, Including: A measurement module for real-time measurement of the water temperature measurement value of a predetermined water area; A prediction module for determining the predicted water temperature value of the predetermined water area according to the climate information and time information when determining the water temperature measurement value; A discrimination module for determining the thermal pollution discrimination information of the predetermined water area according to the water temperature measurement value and the predicted water temperature value; Wherein, the climate information includes the lowest temperature on the day when the water temperature measurement value is determined, The determining the predicted water temperature value of the predetermined water area according to the climate information and time information when determining the water temperature measurement value includes: Input the lowest temperature of the day and the time information into a water temperature prediction model to obtain the water temperature prediction value, where the water temperature prediction model includes a daily cycle feature term of the water temperature and a seasonal cycle feature term of the water temperature. The daily cycle feature term represents the relationship between the water temperature index and the moment when the water temperature index is measured, and the seasonal cycle feature term represents the relationship between the climate information and the water temperature index.

8. An electronic device, characterized in that, Comprising: A processor; A memory for storing processor-executable instructions; Wherein, the processor is configured to execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, the method according to any one of claims 1 to 6 is implemented.

Images