Method, device, electronic device and storage medium for determining channel hopper flow rate

Abstract

The present invention relates to the technical field of irrigation canal water flow measurement, and particularly to a method, device, electronic device and storage medium for determining the flow rate at the head of a canal. The method of the present invention first obtains a plurality of monitoring data sets of branch canals; then determines a plurality of standard flow rate arrays according to the branch canal flow rate estimation model and the plurality of monitoring data sets of branch canals; then determines a plurality of roughness coefficients according to a plurality of cross-sectional areas, a plurality of total flow rates and the plurality of standard flow rate arrays; finally, determines the flow rate at the head of the canal according to the roughness coefficient, the cross-sectional area, the second monitoring data set of the branch canal and the branch canal flow rate estimation model. The embodiment of the present invention estimates the standard flow rate through a plurality of monitoring data sets of branch canals, and then determines the roughness coefficient of each branch canal through the estimation of the total flow rate and the standard flow rate. Since the roughness coefficient is an important factor affecting flow measurement and is also the most difficult factor to determine, after obtaining a relatively accurate roughness coefficient, a relatively accurate measured flow rate can be obtained, and the measurement result is more accurate and reliable.

Description

Technical Field

[0001] The present invention relates to the technical field of irrigation canal water flow measurement, and particularly relates to a method, device, electronic device and storage medium for determining the flow rate at the head of a canal section. Background Art

[0002] A canal system consists of a series of open canal sections and pumping stations. The open canal sections usually include a main canal that diverts water from a river, multiple branch canals that divert water from the main canal, and multiple distributary canals that divert water from the branch canals; the position where the distributary canal is connected to the branch canal is called the head of the canal section. A head gate is provided at the head of the canal section, and a water retaining gate is provided at the downstream position of the head of the canal section on the branch canal. The distributary canal supplies water to farmers for irrigating farmland, and usually measures the total flow rate at the head of the canal section according to a certain predetermined water supply flow rate and usage time.

[0003] However, in the actual water supply process, the situation where the actual water supply flow rate does not match the predetermined water supply flow rate often occurs, resulting in a large measurement error of irrigation water use, reducing the water use experience of farmers, and generating water use contradictions.

[0004] The water supply stability formed by the traditional irrigation operation control method is poor, and the flow rate at the head of the canal section is greatly affected by the water level height of the water flow in the upper-level canal. When the water depth of the main canal changes, it affects the water discharge flow rate at the opened head of the canal section.

[0005] Based on this, it is necessary to develop and design a method for determining the flow rate at the head of a canal section. Summary of the Invention

[0006] Embodiments of the present invention provide a method, device, electronic device and storage medium for determining the flow rate at the head of a canal section, which are used to solve the problem of inaccurate measurement of the flow rate at the head of a canal section in the prior art.

[0007] In a first aspect, an embodiment of the present invention provides a method for determining the flow rate at the head of a canal section, including:

[0008] Obtaining a plurality of distributary canal monitoring data sets, where each distributary canal monitoring data set includes a plurality of first distributary canal monitoring arrays, and each first distributary canal monitoring array corresponds to a distributary canal;

[0009] Determining a plurality of standard flow rate arrays according to a distributary canal flow rate estimation model and the plurality of distributary canal monitoring data sets, where each flow rate array is obtained according to a distributary canal monitoring data set, and the standard flow rate array includes a plurality of standard flow rate estimations;

[0010] Determining a plurality of roughness coefficients according to a plurality of cross-sectional areas, a plurality of total flow rates, and the plurality of standard flow rate arrays, where each distributary canal corresponds to a roughness coefficient and a cross-sectional area, and the total flow rate is the total water flow rate of a plurality of distributary canals;

[0011] Determine the channel head discharge according to the roughness coefficient, cross-sectional area of the flowing water, the monitoring array of the second branch canal, and the branch canal flow estimation model.

[0012] In a possible implementation manner, the obtaining of multiple branch canal monitoring data sets includes:

[0013] Each branch canal monitoring data set is obtained through the following steps respectively:

[0014] Randomly adjust the heights of multiple head gates.

[0015] For each branch canal, construct the multiple water level heights of the branch canal into a water level monitoring array in the order of the preset water level positions, and add it to the monitoring array queue.

[0016] Determine the water level stability coefficients of multiple branch canals according to multiple monitoring array queues.

[0017] If the water level stability coefficients of the multiple branch canals are all lower than the fluctuation threshold, then construct the water level monitoring array taken from the end of the multiple monitoring array queues into a branch canal monitoring data set.

[0018] Otherwise, jump to the step of constructing the multiple water level heights of the branch canal into a water level monitoring array in the order of the preset water level positions.

[0019] Among them, the determining of the water level stability coefficients of multiple branch canals according to multiple monitoring array queues includes:

[0020] For each branch canal, determine the water level stability coefficient according to the first formula, where the first formula is:

[0021]

[0022] In the formula, is the water level stability coefficient, is the th data of the th array in the monitoring array queue, is the th data of the th array in the monitoring array queue, is the total number of data in the water level monitoring array, is the total number of arrays in the monitoring array queue.

[0023] In a possible implementation manner, the branch canal flow estimation model includes multiple flow probability models, and each probability model corresponds to a model flow. The determining of multiple standard flow arrays according to the branch canal flow estimation model and the multiple branch canal monitoring data sets includes:

[0024] For each branch canal monitoring data set, perform the following steps respectively:

[0025] Traverse and take out the first canal monitoring array from the canal monitoring dataset as the array to be input;

[0026] Input the array to be input into multiple flow probability models respectively to obtain multiple probability estimates;

[0027] Determine the standard flow estimate according to the multiple probability estimates and the model flows of the multiple flow probability models, and add the standard flow estimate to the standard flow array;

[0028] If the traversal of the canal monitoring dataset is not completed, jump to the step of traversing and taking out the first canal monitoring array from the canal monitoring dataset as the array to be input;

[0029] Among them,

[0030] The determining the standard flow estimate according to the multiple probability estimates and the model flows of the multiple flow probability models includes:

[0031] Determine the standard flow estimate according to the second formula, where the second formula is:

[0032]

[0033] In the formula, is the standard flow estimate, is the th probability estimate, is the model flow corresponding to the th flow probability model, is the total number of flow probability models.

[0034] In a possible implementation manner, the construction process of the flow probability model includes:

[0035] Obtain multiple sample sets, where the sample set includes multiple sample canal monitoring arrays, the Euclidean distances between the multiple sample canal monitoring arrays in the sample set and the center of the sample set are less than the distance threshold, and each sample canal monitoring array corresponds to a sample flow;

[0036] Calculate the probabilities of the target samples appearing in each sample set respectively to obtain multiple sample probabilities, where the target sample is a sample canal monitoring array with a deviation value from the model flow of the flow probability model less than the threshold;

[0037] Substitute the multiple sample probabilities and the centers of the multiple sample sets into the basic probability equation to obtain the first system of equations;

[0038] Determine the multiple coefficients in the basic probability model according to the first system of equations;

[0039] Construct a flow probability model based on the multiple coefficients and the probability basic equation;

[0040] Among them, the probability basic equation is:

[0041]

[0042] In the formula, is the sample probability, is the natural constant, is the weighted independent variable, is the th first coefficient, is the th sample set center, is the bias constant.

[0043] In a possible implementation manner, the determining of the multiple roughness coefficients according to the multiple cross-sectional areas, the multiple total flows, and the multiple standard flow arrays includes:

[0044] Construct the multiple cross-sectional areas into a cross-sectional area matrix;

[0045] Construct the multiple total flows into a total flow column vector;

[0046] Construct the multiple standard flow arrays into a standard flow matrix;

[0047] According to the relationship between the cross-sectional area, the roughness coefficient, and the standard flow array and the total flow, construct the cross-sectional area matrix, the total flow column vector, and the standard flow matrix into a second equation set regarding the multiple roughness coefficients;

[0048] Solve the second equation set to determine the multiple roughness coefficients.

[0049] In a possible implementation manner, the second equation set is:

[0050]

[0051] In the formula, is the cross-sectional area matrix, is the standard flow matrix, is the roughness coefficient column vector, is the total flow column vector, is the th cross-sectional area of the th bucket opening for the th time, is the th standard flow of the th standard flow array, is the roughness coefficient of the th bucket opening, for the The total flow corresponding to a standard flow array.

[0052] In a possible implementation, determining the channel head discharge according to the roughness coefficient, the cross-sectional area of the water flow, the second monitoring array of the secondary canal, and the secondary canal discharge estimation model includes:

[0053] Inputting the second monitoring array of the secondary canal into the secondary canal discharge estimation model, and determining the standard flow according to the output of the secondary canal discharge estimation model;

[0054] Taking the product of the standard flow, the roughness coefficient, and the cross-sectional area of the water flow as the channel head discharge.

[0055] In a second aspect, an embodiment of the present invention provides a device for determining the channel head discharge, which is used to implement the method for determining the channel head discharge described in the first aspect or any possible implementation manner of the first aspect above. The device for determining the channel head discharge includes:

[0056] A monitoring data acquisition module, configured to acquire a plurality of secondary canal monitoring data sets, where each secondary canal monitoring data set includes a plurality of first monitoring arrays of the secondary canal, and each first monitoring array of the secondary canal corresponds to a secondary canal;

[0057] A standard flow estimation module, configured to determine a plurality of standard flow arrays according to the secondary canal discharge estimation model and the plurality of secondary canal monitoring data sets, where each flow array is obtained according to a secondary canal monitoring data set, and the standard flow array includes a plurality of standard flow estimations;

[0058] A roughness coefficient determination module, configured to determine a plurality of roughness coefficients according to a plurality of cross-sectional areas of the water flow, a plurality of total flows, and the plurality of standard flow arrays, where each secondary canal corresponds to a roughness coefficient and a cross-sectional area of the water flow, and the total flow is the total water flow of the plurality of secondary canals;

[0059] And,

[0060] A head discharge determination module, configured to determine the channel head discharge according to the roughness coefficient, the cross-sectional area of the water flow, the second monitoring array of the secondary canal, and the secondary canal discharge estimation model.

[0061] In a third aspect, an embodiment of the present invention provides an electronic device, including a memory and a processor. A computer program that can run on the processor is stored in the memory. When the processor executes the computer program, the steps of the method described in the first aspect or any possible implementation manner of the first aspect above are implemented.

[0062] Fourthly, an embodiment of the present invention provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the method described in the first aspect or any possible implementation manner of the first aspect above are implemented.

[0063] The beneficial effects of the embodiment of the present invention compared with the prior art are as follows:

[0064] An embodiment of the present invention discloses a method for determining the flow rate at the channel hopper opening. First, a plurality of monitoring data sets of the ditches are obtained, where each monitoring data set of the ditches includes a plurality of first monitoring arrays of the ditches, and each first monitoring array of the ditches corresponds to a ditch. Then, a plurality of standard flow rate arrays are determined according to the ditch flow rate estimation model and the plurality of monitoring data sets of the ditches. Each flow rate array is obtained according to a monitoring data set of the ditches, and the standard flow rate array includes a plurality of standard flow rate estimations. Then, according to a plurality of cross-sectional areas, a plurality of total flow rates, and the plurality of standard flow rate arrays, a plurality of roughness coefficients are determined. Each ditch corresponds to a roughness coefficient and a cross-sectional area, and the total flow rate is the total water flow rate of the plurality of ditches. Finally, according to the roughness coefficient, the cross-sectional area, the second monitoring array of the ditch, and the ditch flow rate estimation model, the flow rate at the channel hopper opening is determined. In the embodiment of the present invention, the standard flow rate is estimated through a plurality of monitoring data sets of the ditches, and then the roughness coefficient of each ditch is determined through the total flow rate and the standard flow rate estimation. Since the roughness coefficient is an important factor affecting the flow rate measurement and is also the most difficult factor to determine, after obtaining a relatively accurate roughness coefficient, a relatively accurate measured flow rate can be obtained, and the measurement result is more accurate and reliable. Description of the Drawings

[0065] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0066] Figure 1 is a flowchart of the method for determining the flow rate at the channel hopper opening provided by the embodiment of the present invention;

[0067] Figure 2 is a schematic diagram of the acquisition process of the standard flow rate estimation provided by the embodiment of the present invention;

[0068] Figure 3 is a functional block diagram of the device for determining the flow rate at the channel hopper opening provided by the embodiment of the present invention;

[0069] Figure 4 is a functional block diagram of the electronic device provided by the embodiment of the present invention. Detailed Embodiments

[0070] In the following description, specific details such as specific system architectures and technologies are presented for purposes of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, those skilled in the art should understand that the present invention can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, and methods are omitted to avoid unnecessary details from obscuring the description of the present invention.

[0071] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will illustrate through specific embodiments in conjunction with the accompanying drawings.

[0072] The following provides a detailed description of the embodiments of the present invention. These embodiments are implemented on the premise of the technical solutions of the present invention, and detailed implementation manners and specific operation processes are given. However, the protection scope of the present invention is not limited to the following embodiments.

[0073] Figure 1 It is a flowchart of the method for determining the channel bucket mouth flow rate provided for the embodiments of the present invention.

[0074] As Figure 1 shown, it shows the implementation flowchart of the method for determining the channel bucket mouth flow rate provided for the embodiments of the present invention, which is described in detail as follows:

[0075] In step 101, multiple monitoring data sets of the bucket channels are obtained, where each monitoring data set of the bucket channels includes multiple first monitoring arrays of the bucket channels, and each first monitoring array of the bucket channels corresponds to a bucket channel.

[0076] In some embodiments, the obtaining of multiple monitoring data sets of the bucket channels includes:

[0077] Each monitoring data set of the bucket channels is obtained through the following steps respectively:

[0078] Randomly adjust the heights of multiple bucket gateways;

[0079] For each bucket channel, construct multiple water level heights of the bucket channel into a water level monitoring array in the order of the preset water level positions, and add it to the monitoring array queue;

[0080] Determine the water level stability coefficients of multiple bucket channels according to multiple monitoring array queues;

[0081] If the water level stability coefficients of the multiple bucket channels are all lower than the fluctuation threshold, construct the water level monitoring array taken from the end of the multiple monitoring array queues into a monitoring data set of the bucket channels;

[0082] Otherwise, jump to the step of constructing multiple water level heights of the bucket channel into a water level monitoring array in the order of the preset water level positions;

[0083] Among them, determining the water level stability coefficients of multiple ditches according to multiple monitoring array queues includes:

[0084] For each ditch, determine the water level stability coefficient according to the first formula, where the first formula is:

[0085]

[0086] In the formula, is the water level stability coefficient, is the th data of the th array in the monitoring array queue, is the th data of the th array in the monitoring array queue, is the total number of data in the water level monitoring array, is the total number of arrays in the monitoring array queue.

[0087] Exemplarily, in the embodiment of the present invention, multiple water level depth sensors are provided in the ditch. Relying on the multiple water level sensors in the ditch, the water level height information of the ditch is obtained. The water level height information collected at the same moment is constructed into an array, and the arrays at the same moment are grouped into a ditch monitoring data set. Through multiple ditch data sets and the flow rates of the branch canals corresponding to multiple ditches (the ditches obtain water sources through the branch canals), the roughness coefficient of each ditch can be determined. After the roughness coefficient is determined, the flow rate of the ditch can be determined according to the water level monitoring data.

[0088] To implement the above concept, in the construction of the ditch monitoring data set in the embodiment of the present invention, by verifying the stability of the water level gradient of each ditch, on the premise that the water levels of all ditches are stable, water level monitoring data is obtained to construct the ditch monitoring data set.

[0089] Specifically, first randomly adjust the heights of multiple ditch gates. Then, for each ditch, construct the multiple water level heights of the ditch into a water level monitoring array in the order of the preset water level monitoring point positions and add it to the monitoring array queue. Then determine the water level stability coefficients of multiple ditches according to multiple monitoring array queues. If the water level stability coefficients of multiple ditches are all lower than the fluctuation threshold, construct the water level monitoring array taken out from the end of multiple monitoring array queues into a ditch monitoring data set. Otherwise, repeat the above steps of collecting the water level monitoring array. In this way, the obtained ditch monitoring data set is the water level monitoring data when the water level gradients of all ditches are stable, providing data guarantee for determining the roughness coefficient of the ditch.

[0090] Regarding the water level stability coefficient, the embodiment of the present invention applies the following formula for determination:

[0091]

[0092] In the formula, is the water level stability coefficient, is the th data of the th array in the monitoring array queue, is the th data of the th array in the monitoring array queue, is the total number of data in the water level monitoring array, is the total number of arrays in the monitoring array queue.

[0093] In step 102, multiple standard flow arrays are determined according to the canal flow estimation model and the multiple canal monitoring data sets, wherein each flow array is obtained according to a canal monitoring data set, and the standard flow array includes multiple standard flow estimations.

[0094] In some embodiments, the canal flow estimation model includes multiple flow probability models, each probability model corresponding to a model flow rate. The determining of multiple standard flow arrays according to the canal flow estimation model and the multiple canal monitoring data sets includes:

[0095] For each canal monitoring data set, the following steps are respectively executed:

[0096] Traversingly take out the first canal monitoring array from the canal monitoring data set as the to-be-input array;

[0097] Input the to-be-input array into multiple flow probability models respectively to obtain multiple probability estimations;

[0098] Determine the standard flow estimation according to the multiple probability estimations and the model flow rates of the multiple flow probability models, and add the standard flow estimation to the standard flow array;

[0099] If the traversal of the canal monitoring data set is not completed, jump to the step of traversingly taking out the first canal monitoring array from the canal monitoring data set as the to-be-input array;

[0100] Wherein,

[0101] The determining of the standard flow estimation according to the multiple probability estimations and the model flow rates of the multiple flow probability models includes:

[0102] Determine the standard flow estimation according to the second formula, wherein the second formula is:

[0103]

[0104] In the formula, For standard flow estimation, For the th probability estimate, For the model flow corresponding to the th flow probability model,

[0105] In some embodiments, the construction process of the flow probability model includes:

[0106] Obtain a plurality of sample sets, where each sample set includes a plurality of sample irrigation ditch monitoring arrays, and the Euclidean distance between the plurality of sample irrigation ditch monitoring arrays in the sample set and the center of the sample set is less than a distance threshold, and each sample irrigation ditch monitoring array corresponds to a sample flow;

[0107] Calculate the probability of the target sample appearing in each sample set respectively to obtain a plurality of sample probabilities, where the target sample is a sample irrigation ditch monitoring array with a deviation value from the model flow of the flow probability model less than a threshold;

[0108] Substitute the plurality of sample probabilities and the centers of the plurality of sample sets into the probability basic equation to obtain a first set of equations;

[0109] Determine a plurality of coefficients in the probability basic model according to the first set of equations;

[0110] Construct a flow probability model according to the plurality of coefficients and the probability basic equation;

[0111] Wherein, the probability basic equation is:

[0112]

[0113] In the formula, is the sample probability, is the natural constant, is the weighted independent variable, is the th first coefficient, is the th sample set center, is the bias constant.

[0114] Exemplarily, the embodiment of the present invention estimates the flow of each irrigation ditch through a flow estimation model (standard flow estimation of the irrigation ditch).

[0115] Such as Figure 2As shown, the figure shows the schematic diagram of the standard flow estimation acquisition process. The flow estimation model of the embodiment of the present invention includes a plurality of flow probability models 202, and each probability model 202 corresponds to a model flow 203. For each branch canal, the branch canal monitoring array 201 is traversed and taken out from the branch canal monitoring dataset and input into a plurality of flow probability models 202 respectively to obtain a plurality of probability estimates 204. According to the plurality of probability estimates 204 and the model flows 203 of the plurality of flow probability models, the standard flow estimate 205 of the branch canal is determined, and the standard flow estimate 205 of the branch canal is added to the standard flow array. By repeating the above steps, the standard flow array is obtained. That is to say, each branch canal monitoring dataset corresponds to a standard flow array.

[0116] Specifically, in terms of determining the standard flow estimate 205 of the branch canal according to the plurality of probability estimates 204 and the model flows 203 of the plurality of flow probability models, the embodiment of the present invention uses the following formula:

[0117]

[0118] In the formula, is the standard flow estimate, is the th probability estimate, is the model flow corresponding to the th flow probability model, is the total number of flow probability models.

[0119] In terms of the construction of the flow probability model, the embodiment of the present invention is constructed according to a plurality of sample sets. The sample set includes a plurality of sample branch canal monitoring arrays. The Euclidean distance between the plurality of sample branch canal monitoring arrays in the sample set and the sample set center is less than the distance threshold, and each sample branch canal monitoring array corresponds to a sample flow. After obtaining a plurality of sample sets, first calculate the probability of the target sample appearing in each sample set respectively to obtain a plurality of sample probabilities, where the target sample is a sample branch canal monitoring array with a deviation value from the model flow of the flow probability model less than the threshold; then substitute the plurality of sample probabilities and the centers of the plurality of sample sets into the probability basic equation to obtain the first equation set; finally, determine a plurality of coefficients in the probability basic model according to the first equation set, and construct a flow probability model according to the plurality of coefficients and the probability basic equation.

[0120] In an application scenario, the probability basic equation is:

[0121]

[0122] In the formula, is the sample probability, is the natural constant, is the weighted independent variable, is the a first coefficient, is the center of the sample set, is the bias constant.

[0123] In step 103, according to multiple cross-sectional areas, multiple total flows, and the multiple standard flow rate arrays, multiple roughness coefficients are determined, where each branch canal corresponds to a roughness coefficient and a cross-sectional area, and the total flow is the total water flow of multiple branch canals.

[0124] In some embodiments, the determining multiple roughness coefficients according to multiple cross-sectional areas, multiple total flows, and the multiple standard flow rate arrays includes:

[0125] Construct the multiple cross-sectional areas into a cross-sectional area matrix;

[0126] Construct the multiple total flows into a total flow column vector;

[0127] Construct the multiple standard flow rate arrays into a standard flow rate matrix;

[0128] According to the relationship between the cross-sectional area, the roughness coefficient, the standard flow rate array, and the total flow, construct the cross-sectional area matrix, the total flow column vector, and the standard flow rate matrix into a second system of equations for the multiple roughness coefficients;

[0129] Solve the second system of equations to determine multiple roughness coefficients.

[0130] In some embodiments, the second system of equations is:

[0131]

[0132] In the formula, is the cross-sectional area matrix, is the standard flow rate matrix, is the roughness coefficient column vector, is the total flow column vector, is the th cross-sectional area of the th mouth of the th standard flow, is the roughness coefficient of the th mouth of the canal,

[0133] Exemplarily, after obtaining multiple standard flow arrays in the embodiments of the present invention, the roughness coefficient can be determined according to the cross-sectional area of the bucket opening of each branch canal (in one application scenario, since the shape of the bucket opening is fixed, the cross-sectional area of the branch canal can be determined according to the water level height of the bucket opening) and the total flow rate (in one application scenario, since the branch canal obtains water from the main canal, the flow rate of the main canal is the total flow rate of multiple branch canals).

[0134] Specifically, construct the multiple cross-sectional areas into a cross-sectional area matrix, construct the multiple total flow rates into a total flow rate column vector (each total flow rate corresponds to a time node, that is, corresponds to a monitoring data set of a branch canal), construct the multiple standard flow arrays into a standard flow matrix, and construct a row vector, the column vector, and the matrix into a second system of equations regarding multiple roughness coefficients according to the relationship between the cross-sectional area, the roughness coefficient, the standard flow array, and the total flow rate. By solving the second system of equations, multiple roughness coefficients are determined. In one application scenario, the second system of equations is:

[0135]

[0136] In the formula, is the cross-sectional area matrix, is the standard flow matrix, is the roughness coefficient column vector, is the total flow rate column vector, is the th cross-sectional area of the rd bucket opening, is the th standard flow of the th standard flow array, is the roughness coefficient of the rd bucket opening, is the th total flow rate corresponding to the standard flow array.

[0137] In step 104, the flow rate of the bucket opening of the canal is determined according to the roughness coefficient, the cross-sectional area, the second monitoring data set of the branch canal, and the flow rate estimation model of the branch canal.

[0138] In some embodiments, the determining the flow rate of the bucket opening of the canal according to the roughness coefficient, the cross-sectional area, the second monitoring data set of the branch canal, and the flow rate estimation model of the branch canal includes:

[0139] Input the second monitoring data set of the branch canal into the flow rate estimation model of the branch canal, and determine the standard flow rate according to the output of the flow rate estimation model of the branch canal;

[0140] Take the product of the standard flow rate, the roughness coefficient, and the cross-sectional area as the flow rate of the bucket opening of the canal.

[0141] Exemplarily, after the roughness coefficient is determined, the channel bucket flow rate can be determined according to the roughness coefficient, the cross-sectional area of the bucket opening, and the standard flow rate. Specifically, for each branch canal, multiple water level monitoring information thereof is constructed into an array and input into multiple flow probability models to obtain multiple probability values. Then, according to the multiple probability values and the flows of the multiple flow probability models, the standard flow rate is determined. The product of the standard flow rate, the roughness coefficient, and the cross-sectional area of the bucket opening is the channel bucket flow rate.

[0142] In the implementation manner of the method for determining the channel bucket flow rate of the present invention, first, multiple branch canal monitoring data sets are obtained. Among them, the branch canal monitoring data set includes multiple first branch canal monitoring arrays, and each first branch canal monitoring array corresponds to a branch canal. Then, according to the branch canal flow estimation model and the multiple branch canal monitoring data sets, multiple standard flow rate arrays are determined. Among them, each flow rate array is obtained according to a branch canal monitoring data set, and the standard flow rate array includes multiple standard flow rate estimations. Then, according to multiple cross-sectional areas, multiple total flow rates, and the multiple standard flow rate arrays, multiple roughness coefficients are determined. Among them, each branch canal corresponds to a roughness coefficient and a cross-sectional area, and the total flow rate is the total water flow rate of multiple branch canals. Finally, according to the roughness coefficient, the cross-sectional area, the second branch canal monitoring array, and the branch canal flow estimation model, the channel bucket flow rate is determined. In the implementation manner of the present invention, the standard flow rate is estimated through multiple branch canal monitoring data sets, and then the roughness coefficient of each branch canal is determined through the total flow rate and the standard flow rate estimation. Since the roughness coefficient is an important factor affecting flow measurement and is also the most difficult factor to determine, after obtaining a relatively accurate roughness coefficient, a relatively accurate measured flow rate can be obtained, and the measurement result is more accurate and reliable.

[0143] It should be understood that the magnitudes of the sequence numbers of the steps in the above implementation manners do not mean the order of execution. The order of execution of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the implementation manner of the present invention.

[0144] The following is the device implementation manner of the present invention. For the details not described in detail therein, reference may be made to the corresponding method implementation manner above.

[0145] Figure 3 is the functional block diagram of the device for determining the channel bucket flow rate provided by the implementation manner of the present invention. Referring to Figure 3 , the device for determining the channel bucket flow rate includes: a monitoring data acquisition module 301, a standard flow rate estimation module 302, a roughness coefficient determination module 303, and a bucket opening flow rate determination module 304, where:

[0146] The monitoring data acquisition module 301 is configured to acquire multiple branch canal monitoring data sets. Among them, the branch canal monitoring data set includes multiple first branch canal monitoring arrays, and each first branch canal monitoring array corresponds to a branch canal;

[0147] A standard flow estimation module 302 is configured to determine a plurality of standard flow arrays according to a canal flow estimation model and the plurality of canal monitoring data sets, wherein each flow array is obtained according to a canal monitoring data set, and the standard flow array includes a plurality of standard flow estimations;

[0148] A roughness coefficient determination module 303 is configured to determine a plurality of roughness coefficients according to a plurality of cross-sectional areas, a plurality of total flows, and the plurality of standard flow arrays, wherein each canal corresponds to a roughness coefficient and a cross-sectional area, and the total flow is the total water flow of the plurality of canals;

[0149] A canal outlet flow determination module 304 is configured to determine the canal outlet flow according to the roughness coefficient, the cross-sectional area, the second canal monitoring array, and the canal flow estimation model.

[0150] Figure 4 It is a functional block diagram of an electronic device provided by an embodiment of the present invention. As Figure 4 shown, the electronic device 4 of this embodiment includes: a processor 400 and a memory 401, and a computer program 402 that can run on the processor 400 is stored in the memory 401. When the processor 400 executes the computer program 402, the steps in the above-mentioned various canal outlet flow determination methods and embodiments are implemented, for example Figure 1 the steps 101 to 104 shown.

[0151] Exemplarily, the computer program 402 can be divided into one or more modules / units, and the one or more modules / units are stored in the memory 401 and executed by the processor 400 to complete the present invention.

[0152] The electronic device 4 can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The electronic device 4 may include, but is not limited to, a processor 400 and a memory 401. Those skilled in the art can understand that Figure 4 it is only an example of the electronic device 4 and does not constitute a limitation on the electronic device 4. It may include more or fewer components than shown in the figure, or combine some components, or different components. For example, the electronic device 4 may further include input / output devices, network access devices, a bus, etc.

[0153] The so-called processor 400 may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0154] The memory 401 may be an internal storage unit of the electronic device 4, such as the hard disk or memory of the electronic device 4. The memory 401 may also be an external storage device of the electronic device 4, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc. equipped on the electronic device 4. Further, the memory 401 may also include both the internal storage unit and the external storage device of the electronic device 4. The memory 401 is used to store the computer program 402 and other programs and data required by the electronic device 4. The memory 401 may also be used to temporarily store data that has been output or is to be output.

[0155] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the above-mentioned division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiments can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of this application. The specific working processes of the units and modules in the above system can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated here.

[0156] In the above embodiments, the descriptions of the various embodiments have their own emphases. For parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0157] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0158] In the embodiments provided by the present invention, it should be understood that the disclosed device / electronic device and method can be implemented in other ways. For example, the device / electronic device embodiments described above are merely illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical or other forms.

[0159] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0160] In addition, the functional units in each embodiment of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0161] When the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such understanding, to implement all or part of the processes in the above-described embodiments of the method of the present invention, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described various method and apparatus embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0162] The above-described 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 foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for determining the flow rate at a channel outlet, characterized in that: include: Acquire a plurality of ditches monitoring data sets, wherein the ditches monitoring data sets include a plurality of first ditches monitoring arrays, each of the first ditches monitoring arrays corresponding to a ditches; Determining a plurality of standard flow arrays according to the ditches flow estimation model and the plurality of ditches monitoring data sets, wherein each flow array is obtained according to a ditches monitoring data set, and the standard flow array includes a plurality of standard flow estimates; Determine a plurality of roughnesses according to a plurality of water flow cross-sectional areas, a plurality of total flow rates and the plurality of standard flow arrays, wherein each ditches corresponds to a roughness and a water flow cross-sectional area, and the total flow rate is the total water flow rate of the plurality of ditches; Determine the channel hopper flow rate based on the roughness, water flow cross-sectional area, the second hopper monitoring array and the hopper flow estimation model; Wherein, the determining of a plurality of roughnesses according to a plurality of water flow cross-sectional areas, a plurality of total flow rates and a plurality of standard flow rate arrays comprises: constructing the plurality of water flow cross-sectional areas into a water flow area matrix; constructing the plurality of total flows into a total flow column vector; constructing the plurality of standard flow arrays into a standard flow matrix; According to the relationship between the water flow cross-sectional area, the roughness, the standard flow array and the total flow, the water flow area matrix, the total flow column vector and the standard flow matrix are constructed into a second set of equations about the multiple roughnesses, wherein the second set of equations is: In the formula, is the water area matrix, is the standard flow matrix, is the roughness column vector, is the total flow column vector, For the Doukoudi The cross-sectional area of ​​water flow, For the The first of the standard flow arrays Standard flow rate, For the The roughness of the mouth of the bucket, For the The total flow corresponding to the standard flow array; The second set of equations is solved to determine a plurality of roughnesses.

2. The method for determining the channel hopper flow rate according to claim 1, characterized in that: The obtaining of multiple ditches monitoring data sets includes: Each ditch monitoring data set is obtained through the following steps: Randomly adjust the height of multiple bucket gates; For each ditch, multiple water level heights of the ditch are constructed into a water level monitoring array according to the order of preset water level positions, and added to the monitoring array queue; Determine the water level stability coefficients of multiple ditches based on multiple monitoring array arrays; If the water level stability coefficients of the plurality of ditches are all lower than the fluctuation threshold, constructing the water level monitoring arrays taken from the end of the plurality of monitoring array queues into a ditches monitoring data set; Otherwise, jump to the step of constructing a water level monitoring array of the multiple water level heights of the ditch according to the order of the preset water level positions; The method of determining the water level stability coefficients of multiple ditches according to multiple monitoring array queues includes: For each ditch, the water level stability coefficient is determined according to the first formula, wherein the first formula is: In the formula, is the water level stability coefficient, To monitor the array queue The first data, To monitor the array queue The first data, is the total number of data in the water level monitoring array, To monitor the total number of arrays in the array queue.

3. The method for determining the channel hopper flow rate according to claim 1, characterized in that: The ditches flow estimation model includes a plurality of flow probability models, each probability model corresponds to a model flow, and the plurality of standard flow arrays are determined according to the ditches flow estimation model and the plurality of ditches monitoring data sets, including: For each canal monitoring data set, perform the following steps: traversally extracting a first ditch monitoring array from the ditch monitoring data set as an array to be input; Inputting the array to be input into multiple traffic probability models respectively to obtain multiple probability estimates; Determining a standard flow estimate based on the multiple probability estimates and the model flows of the multiple flow probability models, and adding the standard flow estimate to a standard flow array; If the traversal of the ditch monitoring data set is not completed, jump to the step of traversing and taking out the first ditch monitoring array from the ditch monitoring data set as the array to be input; in, The determining of a standard flow estimate according to the plurality of probability estimates and the model flows of the plurality of flow probability models comprises: The standard flow estimate is determined according to a second formula, wherein the second formula is: In the formula, is the standard flow estimate, For the A probability estimate, For the The model traffic corresponding to the traffic probability model is is the total number of traffic probability models.

4. The method for determining the channel hopper flow rate according to claim 3, characterized in that: The construction process of the traffic probability model includes: Obtain multiple sample sets, wherein the sample set includes multiple sample ditches monitoring arrays, the Euclidean distances between the multiple sample ditches monitoring arrays in the sample set and the center of the sample set are less than a distance threshold, and each sample ditches monitoring array corresponds to a sample flow; The probability of the target sample appearing in each sample set is calculated respectively to obtain multiple sample probabilities, wherein the target sample is a sample ditches monitoring array whose model flow deviation value from the flow probability model is less than a threshold value; Substituting the multiple sample probabilities and the multiple sample set centers into the probability basic equation to obtain a first set of equations; Determining a plurality of coefficients in the probability basic model according to the first set of equations; Constructing a flow probability model according to the plurality of coefficients and the probability basic equation; Wherein, the probability basic equation is: In the formula, is the sample probability, is a natural constant, is the weighted independent variable, For the The first coefficient, For the The sample set center, is the bias constant.

5. The method for determining the channel hopper flow rate according to any one of claims 1 to 4, characterized in that: The method of determining the channel hopper flow rate according to the roughness, the water flow cross-sectional area, the second hopper monitoring array and the hopper flow rate estimation model includes: Inputting the second ditch monitoring array into the ditch flow estimation model, and determining the standard flow according to the output of the ditch flow estimation model; The product of the standard flow rate, the roughness and the water flow cross-sectional area is taken as the channel bucket flow rate.

6. A device for determining the flow rate at the channel mouth, characterized in that: Used to implement the channel bucket flow determination method according to any one of claims 1 to 5, the channel bucket flow determination device comprises: A monitoring data acquisition module, used to acquire a plurality of ditches monitoring data sets, wherein the ditches monitoring data sets include a plurality of first ditches monitoring arrays, each of which corresponds to a ditches; A standard flow estimation module, configured to determine a plurality of standard flow arrays according to the ditches flow estimation model and the plurality of ditches monitoring data sets, wherein each flow array is obtained according to a ditches monitoring data set, and the standard flow array includes a plurality of standard flow estimates; A roughness determination module, used to determine a plurality of roughnesses according to a plurality of water flow cross-sectional areas, a plurality of total flow rates and the plurality of standard flow arrays, wherein each ditches corresponds to a roughness and a water flow cross-sectional area, and the total flow rate is a total water flow rate of the plurality of ditches; as well as, The bucket mouth flow determination module is used to determine the channel bucket mouth flow according to the roughness, water flow cross-sectional area, the second bucket channel monitoring array and the bucket channel flow estimation model.

7. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

Images