An iterative prediction method for water temperature in water diversion channels

Abstract

The present invention provides an iterative prediction method for water temperature in a water diversion channel. By systematically analyzing various influencing factors of water temperature, scientifically selecting historical water temperature and weather forecast data as parameter factors of the model, and using regression analysis method to establish the model, after model correction, inspection and evaluation, an iterative water temperature prediction model is constructed, and the model is used to predict the water temperature of the channel in winter. The advantages are: the establishment method is scientific and efficient, and the short-term prediction of the water temperature of the channel in winter is achieved by combining regression analysis and iterative prediction. The minimum temperature and temperature extreme difference parameter data selected by the model are directly obtained using weather forecasts, which are highly practical and can provide accurate information for ice forecasting. Because the parameters used are simple and easy to obtain, the interference of parameter changes on the prediction results during the prediction process is reduced, and the prediction efficiency is improved. The three correction coefficients in the prediction model provided by the present invention can be continuously calibrated as the amount of data increases, so that the model prediction accuracy is higher and the prediction results are more accurate.

Description

Technical Field

[0001] The invention relates to the field of water temperature prediction, and in particular to a method for iteratively predicting water temperature in a water diversion channel. Background Art

[0002] Water temperature is an important thermal factor in the development of channel ice conditions, affecting the evolution of channel ice conditions. When the water temperature drops to 0°C in winter, further cooling causes the water body to be in a supercooled state and freeze; in spring, the water temperature rises, and the ice layer gradually disintegrates and completely melts. Therefore, the change of water temperature in winter plays a vital role in the development of channel ice conditions. By accurately predicting the water temperature and predicting the development of ice conditions in advance, the safe operation of channel water delivery in winter can be guaranteed.

[0003] At present, industry technicians are committed to conducting winter water temperature change analysis and prediction research to obtain the evolution law of ice conditions and prevent the impact of ice damage on projects. Usually, theoretical research or artificial intelligence methods are adopted, which involve complicated theories and multiple parameters that are difficult to determine, so that the practical value is not high or the prediction accuracy is not enough. Therefore, it has become an urgent problem to propose a simple and practical water temperature prediction model that can make high-precision predictions of channel winter water temperature to better serve ice condition forecasting. Summary of the invention

[0004] The present invention provides an iterative prediction method for water temperature in a water diversion channel, which is used to solve the problem that the existing water temperature prediction method involves complicated theories, resulting in a complex water temperature prediction model, and the problem that multiple parameters participate in the prediction, resulting in the prediction result being easily affected by the change of a certain parameter, resulting in inaccurate prediction.

[0005] In order to achieve the above-mentioned purpose, the technical solution of the present invention provides a method for iteratively predicting water temperature in a water diversion channel, comprising: obtaining the heat difference after the water body moves from an initial section to a target section; obtaining a heat loss expression of the water body according to the heat difference, wherein the heat loss expression includes water surface temperature, section temperature, and average air temperature;

[0006] Construct an iterative water temperature prediction model based on the heat loss expression:

[0007] in, is the predicted water temperature of the water section on day n+i; is the measured water temperature of the water section on the nth day; The lowest temperature of the weather forecast day n+i; is the extreme temperature difference of the weather forecast day n+i; i is a natural number.

[0008] As a preferred embodiment of the above technical solution, preferably, the heat difference after the water body moves from the initial section to the target section is obtained according to the section temperature: Where c is the specific heat capacity of water, J / (kg·℃); ρ is the density of water, kg / m 3 ; v is the water velocity, m / s; b is the water surface width of the flow section, m; h is the water depth of the flow section, m; is the temperature of the water body at the initial movement section on the nth day, ℃; is the temperature of the target section when the water reaches it on the n+i day, ℃.

[0009] As a preferred embodiment of the above technical solution, preferably, only the heat exchange between the water body and the atmosphere is considered, and before obtaining the heat difference, the contact heat exchange value Q of the water body surface is obtained. α , evaporation heat exchange value Q β And the radiation heat dissipation value Qy:

[0010]

[0011]

[0012]

[0013] α is the contact heat transfer coefficient, W / m 2 ; is the average water surface temperature, °C; is the average temperature, ℃; A is the contact / evaporation area between water and air, m 2 ; β is the evaporation coefficient, mm / (d×hPa); γ w is the latent heat of vaporization of water, J / kg; ρ is the density of water, kg / m 3 ;P s is the vapor pressure corresponding to the water surface temperature, hPa; P α is the air vapor pressure at 1.5m above the water surface, hPa; A is the evaporation area of ​​the water body, m 2 ; ε is the water surface radiation coefficient, 0.97; σ is the Stafan-Boltzman constant, 5.67×10 -8 W / (m 2 ℃ 4 ).

[0014] As a preferred embodiment of the above technical solution, preferably, the heat loss expression of the water body is obtained according to the heat difference:

[0015]

[0016] According to the wave-temperature ratio, we can get:

[0017]

[0018] Then the heat loss expression is obtained:

[0019] in, f(δ) is the heat change including the part of heat in Qy.

[0020] As a preferred embodiment of the above technical solution, it is preferable to construct an average temperature expression with the minimum temperature and the temperature extreme difference as parameter factors: is the average air temperature during the water temperature prediction period, is the minimum temperature of the forecast day, The extreme temperature range for the forecast day.

[0021] As a preferred embodiment of the above technical solution, it is preferable to construct a water surface temperature expression with the initial cross-sectional temperature of the water body and the target cross-sectional temperature as parameter factors: is the average temperature of the water surface during the water movement, is the cross-sectional temperature of the water body on day n, is the cross-sectional temperature of the water body on the n+ith day.

[0022] As a preferred embodiment of the above technical solution, preferably, the water body heat balance expression is constructed according to the heat difference from moving to the target section, the heat loss expression, the average air temperature expression and the water surface temperature expression:

[0023]

[0024] The formula includes four parameter factors: the water section temperature on the nth day, the water section temperature on the n+ith day, the minimum temperature on the n+ith day, and the temperature extreme difference on the n+ith day.

[0025] As a preferred embodiment of the above technical solution, preferably, a water temperature prediction iterative equation is constructed by using regression analysis combined with a water body heat balance expression with the measured cross-section water temperature on the nth day, the lowest temperature and the temperature extreme difference on the n+ith day of the weather forecast as parameter factors:

[0026]

[0027] As a preferred embodiment of the above technical solution, it is better to obtain three correction coefficients: obtain the predicted water temperature will change by an average of x when the measured water temperature changes by one unit under the condition that the minimum temperature and the temperature extreme difference remain unchanged; obtain the predicted water temperature will change by an average of y when the minimum temperature changes by one unit under the condition that the measured water temperature and the temperature extreme difference remain unchanged; obtain the predicted water temperature will change by an average of z when the temperature extreme difference changes by one unit under the condition that the measured water temperature and the minimum temperature remain unchanged; the values ​​of x, y, and z tend to stabilize with the increase of data capacity.

[0028] As a preferred embodiment of the above technical solution, preferably, the parameter factor of the actually measured water temperature on the nth day is specifically an iterative variable, which can realize the iterative prediction of the water temperature.

[0029] The technical solution of the present invention provides an iterative prediction method for water temperature in a water diversion channel, which obtains the heat difference after the water body moves from the initial section to the target section, and obtains the heat loss expression of the water body according to the heat difference. An iterative water temperature prediction model is constructed according to the heat loss expression, and the water temperature is predicted using this model.

[0030] The advantages of the present invention are: the iterative water temperature prediction method is scientific and efficient, and a combination of regression analysis and iterative prediction is used to achieve short-term prediction of channel water temperature in winter. The minimum temperature and temperature extreme difference parameter data selected by this model can be directly obtained using weather forecast data, which is highly practical and can provide accurate information for ice conditions forecasting. Since the parameters used are simple and easy to obtain, the interference of parameter changes on the prediction results during the prediction process is reduced, thereby improving the prediction efficiency. The three corrected coefficients in the prediction model provided by the present invention can be continuously calibrated as the amount of data increases, so that the model prediction accuracy is higher and the prediction results are more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0032] Figure 1 A flowchart of a model building process provided by an embodiment of the present invention.

[0033] Figure 2 This is a time variation trend chart of the measured water temperature and the predicted water temperature of the winter water temperature prediction model established for a certain channel.

[0034] Figure 3 This is an evaluation result diagram of a regression analysis of the predicted value and measured value of the water temperature in a certain channel using the water temperature prediction model provided by the present invention.

[0035] Figure 4 Based on Figure 3 Residual scatter plot of the residual data obtained from the model shown in .

[0036] Figure 5 Based on Figure 3 The residual distribution histogram obtained from the residual data of the model shown in . DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] Figure 1 A schematic diagram of a flow chart provided for an embodiment of the present invention, such as Figure 1 As shown:

[0039] Ignore the heat exchange between water and the earth, and only consider the heat exchange between water and the atmosphere:

[0040] Step 101: Calculate the contact heat exchange value of the water surface.

[0041]

[0042] Where α is the contact heat transfer coefficient, W / m 2 ; is the average water surface temperature, °C; is the average temperature, ℃; A is the contact / evaporation area between water and air, m 2 .

[0043] Step 102: Calculate the evaporation heat exchange value of the water body.

[0044] Q β =1.157×10 -8 βγ w ρ(P s -P α )A (2)

[0045] Where, β is the evaporation coefficient, mm / (d×hPa); γ w is the latent heat of vaporization of water, J / kg; ρ is the density of water, kg / m 3 ;P s is the vapor pressure corresponding to the water surface temperature, hPa; P α is the air vapor pressure at 1.5m above the water surface, hPa; A is the evaporation area of ​​the water body, m 2 .

[0046] Step 103: Calculate the radiation heat dissipation value of the water body.

[0047]

[0048] Among them, Q γis the radiation heat dissipation value, ε is the water surface radiation coefficient, 0.97; σ is the Stafan-Boltzman constant, 5.67×10 -8 W / (m 2 ℃ 4 ).

[0049] Step 104: Obtain the heat difference of the water body during the movement process according to the cross-section temperature.

[0050] Specifically, the heat difference ΔQ after the water body moves from the initial section to the target section is obtained according to the measured section temperature.

[0051]

[0052] Where c is the specific heat capacity of water, J / (kg·℃); ρ is the density of water, kg / m 3 ; v is the water velocity, m / s; b is the water surface width of the flow section, m; h is the water depth of the flow section, m; is the temperature of the water body at the initial movement section on the nth day, ℃; is the temperature of the target section when the water reaches it on the n+i day, ℃.

[0053] Step 105: Construct an expression for heat loss of the water body.

[0054] Specifically, the heat loss generated by water during movement is the heat exchange between the water and the atmosphere, including contact heat exchange, evaporative heat exchange and radiation heat dissipation.

[0055] So we can get, ΔQ = Q α +Q β +Q γ (5)

[0056] Because, Bowenbi:

[0057]

[0058] Among them, C2 is the empirical coefficient; is the average water surface temperature, °C; is the average temperature, ℃; P s ″ is the vapor pressure corresponding to the water temperature on the water surface, hPa; P α is the air vapor pressure at 1.5m above the water surface, hPa; P is the atmospheric pressure, hPa.

[0059] Further we get:

[0060] Thus, the heat loss expression is obtained:

[0061] in, f(δ) is the sum of Q γ The caloric changes in the middle part of the calories.

[0062] Step 106: Construct an average temperature expression.

[0063]

[0064] in, is the average air temperature during the water temperature prediction period, is the minimum temperature of the forecast day, The extreme temperature range for the forecast day.

[0065] Since the forecast parameter commonly used in daily weather forecasts is air temperature, forecast data for other meteorological parameters (such as solar radiation, wind speed and direction, atmospheric pressure, relative humidity, etc.) are usually difficult to obtain. The heat exchange between the atmosphere and the water body affects the change in water temperature. The main reason is that the change in solar radiation intensity causes changes in a series of other factors. The direct cause of water temperature changes in winter is the dominant role of meteorological factors, and the direct cause of meteorological changes is solar radiation intensity. Due to the day and night phenomena caused by the rotation of the earth, solar radiation intensity becomes a discontinuous function in the mathematical sense. At night, the intensity of solar radiation remains at a very low level (close to 0), but during this period, both air and water temperatures have relatively large changes, and are not strongly correlated or regular with the intensity of solar radiation.

[0066] After mathematical and statistical analysis of influencing factors such as water temperature, air temperature, solar radiation intensity, ground temperature, wind speed, air pressure, relative humidity, water depth, flow velocity, and flow, it was found that air temperature and ground temperature have a significant impact on the change of water temperature. Among them, air temperature is affected by solar radiation and ground temperature. At the same time, in daily weather forecasts, the daily maximum temperature and daily minimum temperature are conventional forecast parameters, and the daily average temperature can be expressed by the daily maximum temperature and daily minimum temperature. At the same time, considering that this model is mainly used for winter water temperature prediction, the impact of the minimum temperature and the cooling range on water temperature are key factors. Therefore, the "minimum temperature" and "temperature extremes" (the difference between the maximum temperature and the minimum temperature) are used as the main parameters of the model, which can clearly reflect the intensity of winter cold. The influence of solar radiation and ground temperature is also indirectly considered.

[0067] Step 107: Construct a water surface temperature expression.

[0068]

[0069] in, is the average temperature of the water surface during the water movement, is the cross-sectional temperature of the water body on day n, is the cross-sectional temperature of the water body on the n+ith day.

[0070] Since the change of negative accumulated temperature has a huge impact on water temperature and ice conditions, it is a very important meteorological factor used to indicate the intensity and duration of winter cold. In constructing a water temperature prediction model, the negative accumulated temperature factor needs to be taken into account. The current water temperature is the result of the combined influence of various meteorological factors, hydraulic factors, and negative accumulated temperature factors. Therefore, taking the historical measured water temperature as another important parameter factor to construct the water surface temperature expression and introduce it into the model can directly reflect the combined effect of all influencing factors on the water temperature.

[0071] Step 108: Construct a water body heat balance expression.

[0072] Specifically, the water body heat balance expression is constructed according to the heat difference expression and the heat loss expression in step 104-step 105:

[0073]

[0074] According to formula (1) to formula (6), it can be seen that the change of water temperature is affected by multiple factors, including meteorological factors (air temperature, solar radiation intensity, ground temperature, wind speed, wind direction, air pressure, relative humidity) and hydraulic conditions (water depth, flow rate, flow), etc. These factors all play different roles in the change of channel water temperature. However, during the movement of water bodies, these factors are functions that change with time and space, so the heat balance equation (Formula 11) can only be solved by numerical methods.

[0075] According to the average air temperature expression and water surface temperature expression in step 106-step 107, the water body heat balance expression is expressed as:

[0076]

[0077] Formula (12) contains four parameter factors: the water section temperature on the nth day, the water section temperature on the n+ith day, the minimum temperature on the n+ith day, and the temperature extreme difference on the n+ith day.

[0078] Step 109: construct a water temperature iterative prediction model.

[0079]

[0080] In this step, the regression analysis method is combined with the above-mentioned water body heat balance expression to construct a water temperature prediction iterative equation with the measured section water temperature on the nth day, the lowest temperature and the temperature range on the n+ith day of the weather forecast as parameter factors.

[0081] in, is the predicted water temperature of the water section on day n+i; is the measured water temperature of the water section on the nth day; The lowest temperature of the weather forecast day n+i; is the temperature extreme difference of the weather forecast day n+i; i is a natural number. In order to ensure the accuracy of the model, usually i≤14, but for model prediction, i can be any natural number, which means that the water temperature of any future day can be calculated based on the weather forecast. Therefore, i≤14 is a general condition to meet the accuracy requirements of weather forecasts, not a condition for the model to be applicable. The measured water temperature on the nth day (equivalent to inches) ) is the iterative variable in this model, and this iterative variable is used to make a short-term forecast of the water temperature in the future winter. Specifically, the water temperature forecast model is used to predict the water temperature value on the n+ith day, and this value is used as the "measured water temperature" of the water temperature forecast model in the next forecast period. Parameters are used to predict the water temperature value of the next cycle. And so on, the predicted water temperature is continuously used as the old value to recursively deduced the new value, thus realizing iterative prediction of water temperature.

[0082] Specifically, the prediction cycle of the water temperature iterative prediction method is now explained by taking n as 1 and i as 6 as an example: According to formula (13), in principle, the water temperature within 7 days can be predicted. The measured water temperature of the water body section in the past 6 days, the lowest temperature and the temperature range of the 7-day weather forecast are used as parameter factors, and it is further assumed that the water temperature prediction values ​​from the 2nd to the 7th day are T2, T3, ..., T7. The next cycle is predicted based on the water temperature from the 2nd to the 7th day, and the water temperature from the 8th to the 13th day can be predicted: the water temperature prediction values ​​T2, T3, ..., T7 from the 2nd to the 7th day are used as the measured water temperature, and the lowest temperature and the temperature range of the 15-day weather forecast from the 8th to the 13th day are used as parameter factors, thereby obtaining the water temperature prediction values ​​from the 8th to the 13th day.

[0083] The iterative prediction method provided by the present invention removes the influence of time variables, can continuously use the predicted water temperature as the old value to recursively deduced the new value, realizes the iterative prediction of the water temperature, achieves the effect of efficient prediction, and improves the prediction accuracy.

[0084] Step 110: Correct each coefficient in the water temperature prediction model.

[0085] The x, y, and z coefficients in the model are corrected using the supplementary and extended data.

[0086] Get the predicted water temperature will change by an average of x if the measured water temperature changes by one unit while the minimum temperature and the temperature range remain unchanged; get the predicted water temperature will change by an average of y if the minimum temperature changes by one unit while the measured water temperature and the temperature range remain unchanged; get the predicted water temperature will change by an average of z if the temperature range changes by one unit while the measured water temperature and the minimum temperature remain unchanged.

[0087] As the data volume increases, the values ​​of x, y, and z become asymptotically unbiased, asymptotically efficient, and consistent.

[0088] The above model has a clear physical meaning. The water temperature forecast value is affected by the historical water temperature. In fact, it includes the intensity of the recent low temperature and the historical changes. It indirectly reflects the meteorological and hydraulic factors, as well as the comprehensive impact of negative accumulated temperature on the water temperature. The other two parameters, "minimum temperature" and "temperature extremes", respectively reflect the cold intensity and temperature change range on the forecast day. The three prediction parameters of the model in step 109 are meteorological data and operation data that are easier to obtain in daily life. As long as the water temperature observation value of a certain part is obtained and combined with the weather forecast data, the water temperature can be efficiently predicted, especially when a cold wave comes. This prediction result has a high practical value for operation scheduling.

[0089] Figure 2 This is a time-varying trend chart of the measured water temperature and the predicted water temperature of the winter water temperature prediction model established for a certain channel. The model was established using the water temperature observation data of the four winters from 2016 to 2020, and the water temperature prediction results for 2016 to 2017, 2017 to 2018, 2018 to 2019, and 2019 to 2020 were calculated using the prediction model, and then the consistency of the time-varying trend of the water temperature observations and predictions was analyzed. Figure 2 The analysis results of two randomly selected years (2016-2017 and 2018-2019) are as follows: it can be seen that the predicted values ​​are close to the observed values, and the model can capture the trend changes of each measured water temperature (increase or decrease in water temperature) in a timely manner; and for the transition point position of each changing trend, the predicted values ​​are also in good agreement with the observed values.

[0090] Figure 3 This is an evaluation result diagram of the regression analysis of the predicted value and measured value of the water temperature in a certain channel using the water temperature prediction model provided by the present invention. A scatter plot is drawn using the observed values ​​of the channel water temperature in the four winters from 2016 to 2020, and the predicted values ​​based on the observed values ​​and the water temperature iteration model provided by the present invention. A regression analysis is performed on the predicted water temperature values ​​and the measured water temperature values ​​to evaluate the model's sensitivity to capturing data change trends: It can be seen that the regression effect of the water temperature results predicted by the water temperature prediction model and the actual observed water temperature values ​​is very significant, the observed water temperature values ​​and the predicted values ​​change linearly, and the relationship curve y=1.0026x-0.0084 is fitted between the two, and the correlation between the predicted value and the actual value is The trends of the two are extremely consistent.

[0091] pass Figure 2 and Figure 3 The analysis shows that the prediction model provided by the present invention can more sensitively capture the changing trend of data.

[0092] Figure 4 and Figure 5 This is an example of testing the scatter plot and distribution diagram of the model residuals. Figure 4 and 5 The shown is based on Figure 3 The results shown in the figure verify the rationality of the model and evaluate the prediction accuracy of the model by analyzing the distribution of the residuals of the predicted values ​​and observed values ​​of the channel water temperature in the four winters from 2016 to 2020. It is concluded that no matter how the water temperature observation value changes, the residuals fluctuate around y=0 and are distributed in a horizontal band; the residual distribution histogram is high in the middle and low on both sides, which satisfies the normal distribution; the root mean square error (RMSE) of the model is 0.225; the frequency of residual values ​​distributed in the interval of [-0.25, 0.25] is 81.7%; the frequency of residual values ​​distributed in the interval of [-0.30, 0.30] is 86.2%; the frequency of residual values ​​distributed in the interval of [-0.50, 0.50] is 97.4%.

[0093] pass Figure 4 and Figure 5 The analysis proves that the prediction model provided by the present invention is reasonable and the prediction effect is accurate.

[0094] The prediction model parameter factors provided by the present invention are simple to select and easy to obtain, and the data does not require complex calculations, which is convenient for computer programming. The prediction accuracy is high, and it can predict short-term water temperature changes, which helps to predict the development of ice conditions in advance and ensure normal water delivery and safe operation of channels in winter.

[0095] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for iterative prediction of water temperature in a water diversion channel, characterized in that: The method comprises: Considering only the heat exchange between water and atmosphere, before obtaining the heat difference, obtain the contact heat exchange value Q of the water surface α , evaporation heat exchange value Q β and the radiation heat dissipation value Q γ : Q β =1.157×10 -8 bg w ρ(P s ″-P α )A; α is the contact heat transfer coefficient, W / m 2 ; is the average water surface temperature, °C; is the average temperature, ℃; A is the contact / evaporation area between water and air, m 2 ; β is the evaporation coefficient, mm / (d×hPa); γ w is the latent heat of vaporization of water, J / kg; ρ is the density of water, kg / m3; P s ″ is the vapor pressure corresponding to the water temperature on the water surface, hPa; P α is the air vapor pressure at 1.5m above the water surface, hPa; A is the evaporation area of ​​the water body, m 2 ; ε is the water surface radiation coefficient, 0.97; σ is the Stafan-Boltzman constant, 5.67×10 -8 W / (m 2 ℃ 4 ); The heat difference after the water body moves from the initial section to the target section is obtained. Specifically, the heat difference after the water body moves from the initial section to the target section is obtained according to the section temperature: Where c is the specific heat capacity of water, J / (kg·℃); ρ is the density of water, kg / m 3 ; v is the water velocity, m / s; b is the water surface width of the flow section, m; h is the water depth of the flow section, m; is the temperature of the water body at the initial movement section on the nth day, ℃; is the temperature of the target section when the water body moves on the n+ith day, ℃; The heat loss expression of the water body is obtained according to the heat difference, and the heat loss expression includes the water surface temperature, the cross-section temperature, and the average air temperature. Specifically: According to the wave-temperature ratio, we get: Then the heat loss expression is obtained: in, f(δ) is the sum of Q γ Caloric changes in the middle portion of the calories; An iterative water temperature prediction model is constructed according to the heat loss expression, including: Construct an average temperature expression with minimum temperature and temperature range as parameter factors: is the average air temperature during the water temperature prediction period, is the minimum temperature of the forecast day, The extreme temperature range for the forecast day; Construct a water surface temperature expression with the initial section temperature and target section temperature of the water body as parameter factors: is the average temperature of the water surface during the water movement, is the cross-sectional temperature of the water body on day n, is the cross-sectional temperature of the water body on day n+i; The water body heat balance expression is constructed based on the heat difference from moving to the target section, the heat loss expression, the average air temperature expression and the water surface temperature expression: The formula includes four parameter factors: the water section temperature on the nth day, the water section temperature on the n+ith day, the minimum temperature on the n+ith day, and the temperature extreme difference on the n+ith day; Thus we get the prediction model: in, is the predicted water temperature of the water section on day n+i; is the measured water temperature of the water section on the nth day; The lowest temperature of the weather forecast day n+i; is the extreme temperature difference of the weather forecast day n+i; i is a natural number.

2. The iterative prediction method for water temperature in a water diversion channel according to claim 1, characterized in that: The regression analysis method is combined with the water body heat balance expression to construct a water temperature prediction iterative equation with the measured cross-section water temperature on the nth day, the lowest temperature and the temperature extreme difference on the n+ith day of the weather forecast as parameter factors:

3. The iterative prediction method for water temperature in a water diversion channel according to claim 1, characterized in that: Also included is obtaining the three correction coefficients: Get the predicted water temperature change of x on average when the measured water temperature changes by one unit under the condition that the minimum temperature and the temperature range remain unchanged; Get the predicted water temperature will change by y on average when the lowest temperature changes by one unit under the condition that the measured water temperature and the temperature extreme difference remain unchanged; Under the condition that the measured water temperature and the minimum air temperature remain unchanged, the predicted water temperature will change by an average of z if the temperature range changes by one unit; The values ​​of x, y, and z tend to be stable as the data capacity increases.

4. The iterative prediction method for water temperature in a water diversion channel according to claim 1, characterized in that: The parameter factor of the measured water temperature on the nth day is specifically an iterative variable to achieve iterative prediction of the water temperature.

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