Method and system for demonstrating performance of flow measurement equipment by utilizing flow balance method

Abstract

The invention relates to a method and a system for demonstrating the performance of flow measurement equipment by using a flow balance method. The method comprises the following steps: S1, establishing a loss model by using historical data, demonstrating a measurement result of equipment, and continuing to execute if a data breakthrough rate is qualified; otherwise, judging that the equipment is unreliable, and ending execution; s2, analyzing the equipment; firstly, based on a loss model, a logic value of a flow loss value is calculated through an AI algorithm; then, comparing the logic value with a downstream measured value to judge whether the equipment is reliable or not; if the downstream measured value is in the logic value interval corresponding to the logic value, the equipment judges to be reliable, and execution is continued; if the downstream measured value is not in the logic value interval, the equipment is not reliable, and execution is ended; s3, performing data extraction on the upstream and downstream observation stations, and performing cross validation according to a comparison method and a threshold method to judge the reliability of the equipment; and when the equipment is judged to be reliable, continuing to execute.

Description

Technical Field

[0001] The invention relates to a method and a system for demonstrating the performance of flow measuring equipment by utilizing a flow balance method. Background Art

[0002] In modern water management, closed artificial canals are widely used because they effectively reduce evaporation, prevent pollution, and improve water delivery efficiency. However, achieving accurate flow measurement in complex and changing environmental conditions to ensure an accurate and objective reflection of on-site flow conditions has become a pressing issue. Furthermore, because large flow measurements are required, accurate calibration is not readily available. Ensuring the reliability of measurement equipment is a pressing technical challenge.

[0003] Existing flow measurement equipment basically determines the reliability of the equipment based on the status of the equipment itself and its related technical indicators. However, if the reliability of the equipment is judged solely based on the equipment status itself, without standard data to judge, it is impossible to prove the performance of the flow measurement equipment. Summary of the Invention

[0004] The technical problem to be solved by the present invention is generally to provide a method and system for demonstrating the performance of flow measuring equipment using the flow balance method. In the absence of third-party detection means and measurement point calibration, the result values ​​measured by the flow measuring equipment can be used to reversely demonstrate the reliability of the measurement data of the flow measuring equipment.

[0005] In order to solve the above problems, the technical solution adopted by the present invention is:

[0006] A method for demonstrating the performance of a flow measuring device using a flow balance method comprises the following steps:

[0007] Step S1: Use historical data to establish a loss model and verify the device's own measurement results. If the data breakthrough rate is qualified, continue the execution; otherwise, determine that the device is unreliable and terminate the execution;

[0008] Step S2: Analyze the device. First, use the AI ​​algorithm to calculate the logical value S2_L of the flow loss value based on the loss model. Then, compare the logical value S2_L with the downstream measured value to determine whether the device is reliable.

[0009] If the downstream measured value is within the logical value interval corresponding to the logical value S2_L, the device is judged to be reliable and execution continues; if the downstream measured value is not within the logical value interval, the device is judged to be unreliable and execution ends;

[0010] Step S3: extract data from upstream and downstream stations, and perform cross-validation to determine the reliability of the equipment using the comparison method and threshold method; if the equipment is determined to be reliable, continue the process; otherwise, terminate the process.

[0011] Furthermore, the method is applied to an artificial main canal;

[0012] In step S1, first, the change process of water level or flow rate shows a linear positive correlation;

[0013] Then, based on historical data, the stability of the positive correlation is judged. Among them, the historical data adopts historical measurement data, and the measurement data includes the measured water level or the measured flow rate;

[0014] Secondly, the mutability of the measurement data in the set time period Z is calculated randomly, and it is judged whether the measurement data in the time period Z is reliable according to the mutation rate;

[0015] When the mutability is greater than the set threshold K, it is determined that the measurement data does not meet the set stability index, the measurement data in the time period Z belongs to unreliable, and it is marked as unreliable measurement data, and the execution ends;

[0016] When the mutability is not greater than the set threshold K, it is determined that the measurement data reaches the set stability index, and the measurement data in the time period Z is marked as reliable measurement data;

[0017] Among them, the stability index means that the measurement data A and B are collected successively in the time period Z, and the absolute value of the growth rate is abs(A - B) / A. The absolute value of the growth rate abs(A - B) / A is compared with the set growth rate. When the absolute value of the growth rate ≤ the set growth rate, it is determined that it meets the stability index;

[0018] When the absolute value of the growth rate > the set growth rate, it is considered that there is a mutation in the measurement data A and B, and it is determined that the stability index cannot be reached;

[0019] When there are T + 1 pieces of measurement data in the time period Z, the total number of growth rates is T, and the number of mutations is C. The mutation rate is calculated as the mutation rate Pc = C / T; the threshold of the predefined normal range is Y. When Pc < Y, it is considered that the data in the time period Z is reliable; otherwise, there is a mutation;

[0020] Secondly, based on the reliable measurement data, it is stored as historical data and a loss model is established;

[0021] The loss model is a neural network model. The loss model is trained using an AI algorithm. The calibration results of the upstream and downstream measurement stations for each operation and maintenance are used as the training set. When calibrating each time, the measured values of flow rate, water level, and flow velocity are measured at the upstream and downstream measurement stations, and the measured acquisition parameters of the upstream measurement station are used as reliable parameters to adjust the neural network algorithm and calculate the loss value to ensure that the measured values of flow rate, water level, and flow velocity at the downstream measurement station meet the upper and lower fluctuation ranges of the corresponding flow rate, water level, and flow velocity setting logic value S2_L, so as to establish a reliable model;

[0022] The acquisition parameters also include time, distance between upstream and downstream measuring stations, temperature, channel type and / or weather factors.

[0023] Furthermore, in step S2, the reliability of the equipment performance is determined by using the loss model using the flow balance relationship;

[0024] First, input the acquisition parameters; use the acquisition parameters as input values ​​of the loss model;

[0025] Then, the loss value is calculated through the loss model to estimate the flow situation at the downstream measuring station; and then a judgment is made based on the result value of the downstream measurement;

[0026] When calculating the loss value, the downstream logical flow, water level, and flow rate are simulated by inputting the acquisition parameters; the upper and lower fluctuation ranges are compared with the downstream measured parameters; when the downstream measured parameters are within the upper and lower fluctuation ranges, the equipment is determined to be reliable.

[0027] Furthermore, in S3, cross-validation is performed using other upstream and downstream measurement points;

[0028] First, randomly select at least three upstream and downstream stations; the at least three stations include upstream station A, midstream station B, and downstream station C, which are set in sequence according to the flow direction.

[0029] Secondly, when the measured flow from station A to station B changes, then the flow at station B ≠ the flow at station A; the flow at station C ≠ the flow at station A, and the flow at station B is synchronized with the flow at station C.

[0030] When the flow rate of measuring station C is out of sync with the flow rate of measuring station B, the equipment performance is determined to be unreliable; among them, the logical flow rate of measuring station A is Aq, the logical flow rate of measuring station B is Bq, and the logical flow rate of measuring station C is Cq;

[0031] The measured flow deviation of the interval measuring stations is calculated, that is, the difference between the measured flow of measuring station A and the measured flow of measuring station C, which is defined as the upstream and downstream measurement value deviation S3_AC;

[0032] Secondly, the range of change of the logical flow is preset; taking the logical flow Aq of measuring station A as the benchmark, the floating interval of the logical flow Bq of measuring station B is calculated. When the measured flow change of measuring station B does not meet the floating interval of measuring station B, it is determined that there is a flow imbalance, otherwise it is balanced; taking the logical flow Bq of measuring station B as the benchmark, the flow floating interval of the logical flow Cq of measuring station C is calculated. When the measured flow change of measuring station C does not meet the floating interval of measuring station C, it is determined that there is a flow imbalance, otherwise it is balanced;

[0033] Afterwards, the floating range of the logical flow Cq of measuring station C is calculated based on the logical flow Aq of measuring station A. When the measured flow change of measuring station C does not meet the floating range of measuring station C, it is determined that there is flow imbalance, otherwise it is balanced.

[0034] Furthermore, in step S2, if the distance between the upstream and downstream stations is greater than the set distance, the weather factor is taken into consideration and the weather parameters of the upstream and downstream stations are input into the loss model;

[0035] Weather parameters include air humidity, temperature and / or wind speed.

[0036] Further, after step S3, perform the following steps:

[0037] Step S4: Perform a comprehensive judgment on step S2 and step S3 (take a weight ratio according to the actual situation) and calculate the comprehensive index R;

[0038] Step S5: The comprehensive index R of step S4 is judged. If it meets the set requirements, the device is judged to be reliable. If it does not meet the requirements, the device is judged to be unreliable.

[0039] Further, in step S4, a comprehensive index R is obtained based on the logic value S2_L of step S2 and the actual upstream and downstream measurement value deviation S3_AC of step S3;

[0040] Comprehensive index R = S2_L*α+S3_A*β;

[0041] Among them, α is the weight of S2_L; β is the weight of S3_AC; α and β are set according to experience.

[0042] Further, in step S5, a threshold value of the comprehensive index R is set;

[0043] If the comprehensive index R is within the set threshold range, the equipment is deemed reliable.

[0044] Furthermore, a system for demonstrating the performance of flow measurement equipment using the flow balance method includes a loss model established by the above method, which is set in a waterway;

[0045] The loss model includes measuring stations placed upstream and downstream in the waterway;

[0046] A flow meter, a water level meter and / or a thermometer are provided at the measuring station.

[0047] The existing technology generally performs inspections through on-site calibration, that is, calibrating a result data manually or with other equipment, and then comparing it with the result data of the flow measuring equipment. This method requires calibration every time, and each calibration costs a lot of financial and material resources. However, the flow balance verification method of the present invention can avoid third-party verification. It only needs to analyze and compare data to achieve the verification of equipment reliability. It has the advantages of simplicity, convenience, wide application range, and high timeliness. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a schematic flow chart of step 1 of the present invention.

[0049] Figure 2 It is a schematic flow chart of step 2 of the present invention.

[0050] Figure 3 It is a schematic flow chart of step three of the present invention.

[0051] Figure 4 It is a schematic diagram of the overall process of the present invention. DETAILED DESCRIPTION

[0052] Example 1, as Figure 1 ,Step 1, use historical data to establish a loss model to ,determine the reliability of the equipment itself;

[0053] First, the characteristic of water flow that changes steadily is utilized, which is especially evident in artificial canals. That is, the water level is positively correlated and / or the flow is positively correlated, and the water level does not experience sudden jumps. The change process of water level or flow is linear and positively correlated.

[0054] Then, the stability of the positive correlation is determined based on historical data, where the historical data uses historical measurement data, including measured water levels or measured flow rates. Secondly, the mutation rate of the measurement data in a set time period Z is randomly calculated, and the reliability of the measurement data in time period Z is determined based on the mutation rate.

[0055] When the mutation rate is greater than the set threshold K, the measurement data is deemed to not meet the set stability index, the measurement data of the time period Z is unreliable, marked as unreliable measurement data, and the execution ends; when the mutation rate is not greater than the set threshold K, the measurement data is deemed to meet the set stability index, and the measurement data of the time period Z is marked as reliable measurement data;

[0056] Let’s illustrate the following definitions with examples:

[0057] Definition of stability index: In the collected measurement data, the absolute value of the growth rate between two temporally adjacent water level data is ≤50% or the absolute value of the growth rate between two temporally adjacent flow data is ≤50%; the data collection frequency for time period Z is set to one data point per minute;

[0058] Among them, assume that two measurement data A and B are collected successively; then the absolute value of the growth rate is abs(AB) / A;

[0059] Definition of data mutation: If the growth rate of two adjacent measurement data A and B is greater than 50%, it is considered that the measurement data A and B have mutations and cannot meet the stability index;

[0060] Determine whether the data for period Z is reliable: For example, if period Z is one day, and one day equals 1440 minutes, there are 1440 measurement data points. If a growth rate is calculated for two adjacent measurement data points, the result is 1439 growth rates. Assuming the number of data mutations is 200, the final mutation rate is: 200 / 1439 = 13.9%. If the normal range is defined as less than 5%, that is, the threshold is Y = 5%, then the data for that one day is unreliable.

[0061] Calculation formula: In time period Z, the total growth rate is T, the number of mutations is C, and the mutation rate Pc = C / T. When Pc < 5%, the data in time period Z is normal, otherwise it is a mutation.

[0062] Secondly, based on reliable measurement data, store it as historical data and establish a loss model;

[0063] The loss model is a neural network (NN) model. Loss model training uses an AI algorithm, using the calibration results of upstream and downstream stations during each operation and maintenance as the training set. During each calibration, the upstream and downstream stations measure the actual values ​​of flow, water level, and flow velocity. Using the measured acquisition parameters of the upstream station as reliable parameters, the NN algorithm is adjusted to calculate the loss value to ensure that the measured values ​​of flow, water level, and flow velocity at the downstream station meet the upper and lower fluctuation range requirements of the set logical value S2_L for the corresponding flow, water level, and flow velocity. The upper and lower fluctuation range of the logical value S2_L can be ±20%.

[0064] Example 2, as Figure 2 ,Step 2, using the flow balance relationship and the loss model to ,judge the reliability of the equipment performance;

[0065] First, input the acquisition parameters; use the acquisition parameters as input values ​​of the loss model;

[0066] The collection parameters include physical elements and collected data information such as time, upstream flow, upstream flow velocity, upstream water level, distance between upstream and downstream measuring stations, temperature, channel type and weather factors;

[0067] Then, the loss value is calculated through the loss model to estimate the downstream flow situation; and then a judgment is made based on the result value of the downstream measurement;

[0068] When calculating the loss value, the downstream logical flow, water level, and flow rate are simulated by inputting the acquisition parameters. For example, the logical flow L = 8m 3 / s, the judgment interval is defined as ±20%, the downstream flow should be in [6.4, 9.6], and this interval is compared with the downstream measured flow. Assuming that the downstream measured flow Qc = 7, then the sign requirement is met and the equipment is reliable.

[0069] Example 3, as Figure 3 ,Step 3, cross-validate using other upstream and downstream measurement points;

[0070] Randomly select three upstream and downstream measuring stations: measuring station A, measuring station B, and measuring station C, where measuring station B is in the middle. Using the flow balance principle, if there is new water injected or released from measuring station A to measuring station B, then the flow rate of measuring station B must be greater than or less than the flow rate of measuring station A, and the value of measuring station C must also be greater than or less than the flow rate of measuring station A. Measuring station C and measuring station B are synchronized. If measuring station C and measuring station B are out of sync, then there must be a problem, and the unreliability of equipment performance can be determined.

[0071] The comparison here is based on the upstream measuring station, and the range comparison is performed. The normal variation range set by the system can be -10%. If the measured variation exceeds the normal variation range value, then there may be flow imbalance; then, it is necessary to compare the two downstream measuring stations according to the above method to confirm.

[0072] For example: upstream station A, its logical flow: Aq 10m 3 / s, then the logical flow rate of the downstream measuring station B: Bq belongs to the interval [9,10]. Assuming Bq = 9, then the flow rate of the downstream measuring station C of measuring station B should be Cq in the interval [8.1, 9]. After obtaining the theoretical interval, we can use the measured value for comparison.

[0073] Secondly, a cross-judgment is performed on any two groups of upstream and downstream measuring stations (for example, measuring stations A and C; measuring stations B and C; measuring stations A and B) to determine whether the equipment is reliable.

[0074] The measured flow deviation of the interval measuring stations is calculated, that is, the difference between the measured flow of measuring station A and the measured flow of measuring station C, which is defined as the upstream and downstream measurement value deviation S3_AC.

[0075] Example 4, as Figure 1-4 , combined with Examples 1-3, as a whole explanation,

[0076] Step S1: Use the steps in Example 1 to verify the measurement results of the device itself. If it fails, there is no need to proceed to the following steps and the device is directly determined to be unreliable. If the data breakthrough rate is qualified, continue the process.

[0077] Step S2: Analyze the device using the steps of Example 2; calculate the logical flow loss value using the AI ​​algorithm based on the loss model; and then compare it with the actual measured value downstream to determine whether the device is reliable;

[0078] If the downstream measured value is within the logical value range, the device is considered reliable and execution continues; if the downstream measured value is not within the logical value range, the device is considered unreliable and execution ends;

[0079] If the upstream and downstream distances are far, local weather issues need to be considered. The weather conditions of the two locations need to be added to the loss model, and then judgments are made based on the range of calculated results and the measured values.

[0080] Step S3: Using the steps of Example 3, extract data from upstream and downstream measurement stations, and perform cross-validation to determine the reliability of the equipment using the comparison method and threshold method;

[0081] Step S4: Perform a comprehensive judgment on step S2 and step S3 (take a weight ratio according to the actual situation) and calculate the comprehensive index R;

[0082] Step S5: The comprehensive index R of step S4 is judged. If it meets the requirements, the equipment is judged to be reliable. If it does not meet the requirements, the equipment is judged to be unreliable.

[0083] In step S4, a comprehensive index R is obtained based on the logic value S2_L of step S2 and the actual upstream and downstream measurement value deviation S3_AC of step S3;

[0084] Comprehensive index R = S2_L*α+S3_A*β;

[0085] Among them, α is the weight of S2_L; β is the weight of S3_AC; α and β are set according to experience.

[0086] In step S5, a threshold value of the comprehensive index R is set;

[0087] If the comprehensive index R is within the set threshold range, the equipment is deemed reliable.

[0088] The present invention is fully described for a clearer disclosure, and the prior art is not listed one by one.

[0089] Finally, it should be noted that the above embodiments are intended only to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may be modified or some of the technical features thereof may be replaced with equivalents. It is obvious for those skilled in the art to combine multiple technical solutions of the present invention. However, these modifications or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. Any technical content not fully described in this invention is well-known technology.

Claims

1. A method for demonstrating the performance of flow measurement equipment using the flow balance method, characterized in that: including the following steps; Step S1: Establish a loss model using historical data to verify the measurement results of the device itself. If the data breakthrough rate is qualified, continue; otherwise, determine that the device is unreliable and end the execution; Step S2: Analyze the device. First, based on the loss model, calculate the logical value S2_L of the flow loss value through the AI algorithm. Then, compare the logical value S2_L with the measured value downstream to determine whether the device is reliable; If the measured value downstream is within the logical value interval corresponding to the logical value S2_L, the device is judged to be reliable and continue; if the measured value downstream is not within the logical value interval, the device is unreliable and end the execution; Step S3: Extract data from the upstream and downstream measurement stations, and perform cross-validation according to the comparison method and threshold method to determine the reliability of the device; when it is determined that the device is reliable, continue; otherwise, end the execution.

2. The method for demonstrating the performance of flow measurement equipment using the flow balance method according to claim 1, characterized in that: The method is applied in an artificial main canal; In step S1, first, the change process of water level or flow is in a linearly positive correlation; Then, judge the stability of the positive correlation relationship based on historical data, where the historical data uses historical measurement data, and the measurement data includes the measured water level or the measured flow; Secondly, randomly calculate the mutability of the measurement data in the set time period Z, and judge whether the measurement data in the time period Z is reliable according to the mutation rate; When the mutability is greater than the set threshold K, it is determined that the measurement data does not meet the set stability index, the measurement data in the time period Z belongs to unreliable, marked as unreliable measurement data, and end the execution; When the mutability is not greater than the set threshold K, it is determined that the measurement data reaches the set stability index, and the measurement data in the time period Z is marked as reliable measurement data; Among them, the stability index means that in the time period Z, the measurement data A and B are collected successively, and the absolute value of the growth rate is abs(A - B) / A. Compare the absolute value of the growth rate abs(A - B) / A with the set growth rate. When the absolute value of the growth rate ≤ the set growth rate, it is determined to meet the stability index; When the absolute value of the growth rate > the set growth rate, it is considered that there is a mutation in the measurement data A and B, and it is determined that the stability index is not reached; When there are T + 1 pieces of measurement data in the time period Z, the total number of growth rates is T, and the number of mutations is C. Calculate the mutation rate as the mutation rate Pc = C / T. The threshold of the predefined normal range is Y. When Pc < Y, it is considered that the data in the time period Z is reliable; otherwise, there is a mutation; Secondly, store the reliable measurement data as historical data and establish a loss model; The loss model is a neural network model. The loss model is trained using the AI algorithm, and the calibration results of the upstream and downstream measurement stations for each operation and maintenance are used as the training set. When calibrating each time, the measured values of flow, water level, and flow velocity are measured at the upstream and downstream measurement stations. The measured acquisition parameters of the upstream measurement station are used as reliable parameters, and the neural network algorithm is adjusted to calculate the loss value to ensure that the measured values of flow, water level, and flow velocity at the downstream measurement station satisfy the upper and lower fluctuation intervals of the corresponding flow, water level, and flow velocity setting logical value S2_L, so as to establish a reliable model; The acquisition parameters also include time, distance between upstream and downstream measurement stations, temperature, channel type, and / or weather elements.

3. The method for demonstrating the performance of flow measurement equipment using the flow balance method according to claim 2, characterized in that: In step S2, the reliability of the equipment performance is determined by using the loss model using the flow balance relationship; First, input the acquisition parameters; use the acquisition parameters as input values ​​of the loss model; Then, the loss value is calculated through the loss model to estimate the flow situation at the downstream measuring station; and then a judgment is made based on the result value of the downstream measurement; When calculating the loss value, the downstream logical flow, water level, and flow rate are simulated by inputting the acquisition parameters; the upper and lower fluctuation ranges are compared with the downstream measured parameters; when the downstream measured parameters are within the upper and lower fluctuation ranges, the equipment is determined to be reliable.

4. The method for demonstrating the performance of flow measurement equipment using the flow balance method according to claim 3, characterized in that: In S3, cross-validation is performed using other upstream and downstream measurement points; First, randomly select at least three upstream and downstream stations; the at least three stations include upstream station A, midstream station B, and downstream station C, which are set in sequence according to the flow direction. Secondly, when the measured flow from station A to station B changes, then the flow at station B ≠ the flow at station A; the flow at station C ≠ the flow at station A, and the flow at station B is synchronized with the flow at station C. When the flow rate of measuring station C is out of sync with the flow rate of measuring station B, the equipment performance is determined to be unreliable; among them, the logical flow rate of measuring station A is Aq, the logical flow rate of measuring station B is Bq, and the logical flow rate of measuring station C is Cq; The measured flow deviation of the interval measuring stations is calculated, that is, the difference between the measured flow of measuring station A and the measured flow of measuring station C, which is defined as the upstream and downstream measurement value deviation S3_AC; Secondly, the range of change of the logical flow is preset; taking the logical flow Aq of measuring station A as the benchmark, the floating interval of the logical flow Bq of measuring station B is calculated. When the measured flow change of measuring station B does not meet the floating interval of measuring station B, it is determined that there is a flow imbalance, otherwise it is balanced; taking the logical flow Bq of measuring station B as the benchmark, the flow floating interval of the logical flow Cq of measuring station C is calculated. When the measured flow change of measuring station C does not meet the floating interval of measuring station C, it is determined that there is a flow imbalance, otherwise it is balanced; Afterwards, the floating range of the logical flow Cq of measuring station C is calculated based on the logical flow Aq of measuring station A. When the measured flow change of measuring station C does not meet the floating range of measuring station C, it is determined that there is flow imbalance, otherwise it is balanced.

5. The method for demonstrating the performance of flow measuring equipment using the flow balance method according to claim 4, characterized in that: In step S2, if the distance between the upstream and downstream stations is greater than the set distance, the weather factor is taken into consideration and the weather parameters of the upstream and downstream stations are input into the loss model; Weather parameters include air humidity, temperature and / or wind speed.

6. The method for demonstrating the performance of flow measuring equipment using the flow balance method according to claim 5, characterized in that: After step S3, perform the following steps: Step S4: Perform comprehensive judgment on step S2 and step S3, and calculate the comprehensive index R; Step S5: The comprehensive index R of step S4 is judged. If it meets the set requirements, the device is judged to be reliable. If it does not meet the requirements, the device is judged to be unreliable.

7. The method for demonstrating the performance of flow measuring equipment using the flow balance method according to claim 6, characterized in that: In step S4, a comprehensive index R is obtained based on the logic value S2_L of step S2 and the actual upstream and downstream measurement value deviation S3_AC of step S3; Comprehensive index R = S2_L*α+S3_A*β; Among them, α is the weight of S2_L; β is the weight of S3_AC; α and β are set according to experience.

8. The method for demonstrating the performance of flow measurement equipment using the flow balance method according to claim 6, characterized in that: In step S5, a threshold value of the comprehensive index R is set; If the comprehensive index R is within the set threshold range, the equipment is deemed reliable.

9. A system for demonstrating the performance of flow measurement equipment using the flow balance method, characterized by: A loss model comprising the method of any one of claims 1 to 8, arranged in a waterway; The loss model includes measuring stations placed upstream and downstream in the waterway; A flow meter, a water level meter and / or a thermometer are provided at the measuring station.

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