A method and device for segmenting the flow characteristic curve of secondary water supply

Abstract

The present invention provides a method for segmenting the flow characteristic curve of secondary water supply, a device, and a method for selecting a water pump unit based on the segmentation method. The segmentation method includes: a data acquisition step of obtaining the water usage data of the secondary water supply system and constructing a time-series flow characteristic curve of the water usage data; an optimal segmentation step of using an optimal segmentation algorithm to divide the time-series flow characteristic curve into k characteristic segments and determining an optimal segmentation scheme. The optimal segmentation step further includes: a first division step of dividing the time-series flow characteristic curve using the optimal segmentation algorithm; a second division step of using the maximum cumulative deviation value V of the minimum segment length L as a measurement criterion and comparing it with the cumulative deviation value v of each characteristic segment. If the cumulative deviation value v of a certain characteristic segment is greater than the maximum cumulative deviation value V, then this characteristic segment is separately extracted and further divided using the optimal segmentation algorithm. L‑max as a measurement criterion l for comparison with the cumulative deviation value v of each characteristic segment l If the cumulative deviation value v of a certain characteristic segment is greater than the maximum cumulative deviation value V L‑max , then this characteristic segment is separately extracted and further divided using the optimal segmentation algorithm.

Description

Technical Field

[0001] The present invention relates to the technical field of flow analysis of secondary water supply systems, in particular to a method and device for segmenting the flow characteristic curve of secondary water supply, and a method for selecting a water pump unit based on the segmentation of the flow characteristic curve of secondary water supply. Background Art

[0002] In a secondary water supply system, it is necessary to predict the water supply volume and control the operation of water supply equipment according to the predicted data.

[0003] The water supply flow of the secondary pumping station of the waterworks or the secondary water supply flow of the building has obvious periodic variation rules and has a variety of different flow operation conditions. However, due to the lack of a method for quantitatively analyzing its segmentation characteristics, in many projects, a certain fixed time period is directly defined as the time period of the maximum flow rate during the day or the time period of the small flow rate during the night, and only this is used as the basis for judging and controlling "day-night segmented water supply", which no longer meets the requirements of energy conservation. Summary of the Invention

[0004] In view of the deficiencies in the prior art, the present invention provides a method and device for segmenting the flow characteristic curve of secondary water supply, which effectively segments the characteristic curve based on the optimal segmentation algorithm, and can be used to improve the design parameters of the secondary water supply system, guide the selection of pump sets, and optimize the control of system operation, etc.

[0005] To achieve the above object, on the one hand, the present invention provides a method for segmenting the flow characteristic curve of secondary water supply, including the following steps: a data acquisition step of obtaining the water consumption data of the secondary water supply system and constructing a time-series flow characteristic curve of the water consumption data; an optimal segmentation step of using the optimal segmentation algorithm to divide the time-series flow characteristic curve into k characteristic segments and determining the optimal segmentation scheme, where k≥2 and k is a positive integer; the optimal segmentation step further includes: a first division step of dividing the time-series flow characteristic curve using the optimal segmentation algorithm; a second division step of using the maximum cumulative deviation value V of the fluctuations at the minimum segmentation length L L-max as a measurement standard, comparing it with the cumulative deviation value v of the fluctuations of each of the characteristic segments l if the cumulative deviation value v of the fluctuations of a certain characteristic segment l is greater than the maximum cumulative deviation value V of the fluctuations L-max , then this characteristic segment is separately extracted and further divided using the optimal segmentation algorithm.

[0006] The minimum segmentation length L is jointly determined by the minimum segmentation duration T required for the operation control of the secondary water supply system and the acquisition interval duration e of the water consumption data, as follows:[[]] where the minimum segmentation duration T is set according to the water supply equipment of the secondary water supply system.

[0007] The water supply device is a water pump, and the minimum segmentation duration T is as follows:

[0008] T = T1 + T2 + T3

[0009] Where: T1 is the starting duration of the water pump from receiving the pump starting signal to normal operation; T2 is the duration required for the frequency conversion acceleration and deceleration process of the water pump; T3 is the duration of stable operation of the water pump.

[0010] The second division step further includes: comparing the duration of the feature segment with 2eL. If the duration of the feature segment is less than 2eL, the feature segment will not be further divided.

[0011] The optimal segmentation step further includes: adopting an improved silhouette coefficient S k Ensure that the minimum segmentation length is L. When S k = -1, there is a situation where the minimum segmentation length of the feature segment is less than L, and at this time, the iterative calculation will no longer be performed; the improved silhouette coefficient S k Uses the absolute value of the difference between two points to replace the average distance, and compares the target feature segment with its adjacent feature segments.

[0012] The improved silhouette coefficient S k Is as follows:

[0013]

[0014] Where: S k Represents the average value of the silhouette coefficients of the k feature segments, and -1 ≤ S k ≤ 1; s i Represents the silhouette coefficient of each water consumption data, as follows:

[0015]

[0016] Where: i = 1, 2, 3... n, representing each sample point of the water consumption data; a i Represents the average absolute value of the difference between the i-th sample point and other sample points within the feature segment where the sample point is located, and b i Represents the minimum value of the average absolute value of the difference between the i-th sample point and all sample points in the adjacent segment.

[0017] The calculation process of a i Is as follows:

[0018]

[0019] Where, i = 1, 2, 3... n; the water consumption data is a flow rate set {y1, y2,..., yn}, y t represents the flow rate at time t in the water consumption data {y1, y2, …, y n}; The water consumption data {y1, y2, …, y n} is divided into k segments (k ≥ 2), then there are k - 1 water consumption data segmentation points; i l represents the position where the water consumption data segmentation point is located, l = 1, 2 ……, k - 1, and i0 = 1, i k -1 = n.

[0020] The said b i The calculation process is as follows:

[0021]

[0022] where i = 1, 2, 3 …… n; The water consumption data is a flow rate set {y1, y2,..., yn}, yt represents the flow rate at time t in the water consumption data {y1, y2,..., y n}; The water consumption data {y1, y2,..., y n} is divided into k segments (k ≥ 2), then there are k - 1 water consumption data segmentation points; i l represents the position where the water consumption data segmentation point is located, l = 1, 2 ……, k - 1, and i0 = 1, i k -1 = n.

[0023] The cumulative deviation value of the fluctuation v l is as follows:

[0024] v l = max(v l，t ) - min(v l，t )

[0025] In the formula: v l，t is the cumulative value of the fluctuation of the water consumption data at time t, where y t represents the water consumption at time t in the water consumption data {y1, y2,..., y n}; represents the average value of the characteristic segment where the water consumption is located; i l ≤ t < i l+1 , l = 1, 2 ……, k - 1, and i0 = 1, i k -1 = n.

[0026] The maximum cumulative deviation value of the fluctuation V L-maxIt is: after determining the minimum segmentation length L, calculate the first cumulative deviation of fluctuations corresponding to L water usage data adjacent to each water usage time t, and then compare the maximum value obtained from these first cumulative deviations of fluctuations, where L ≤ t ≤ n and t is an integer.

[0027] On the other hand, the present invention also provides a segmentation device for the secondary water supply flow characteristic curve, including a processor, and the processor is used to execute the following program modules stored in the memory: a data acquisition module, which is used to obtain the water usage data of the secondary water supply system and construct the time-series flow characteristic curve of the water usage data; an optimal segmentation module, which is used to divide the time-series flow characteristic curve into k characteristic segments by using the optimal segmentation algorithm and determine the optimal segmentation scheme, where k ≥ 2 and k is a positive integer; the optimal segmentation module further includes: a first division unit, which is used to divide the time-series flow characteristic curve by using the optimal segmentation algorithm; a second division unit, which uses the maximum cumulative deviation of fluctuations V L-max as a measurement standard, and compares it with the cumulative deviation of fluctuations v l of each of the characteristic segments. If the cumulative deviation of fluctuations v l of a certain characteristic segment is greater than the maximum cumulative deviation of fluctuations V L-max , then extract this characteristic segment separately and further divide this characteristic segment by using the optimal segmentation algorithm.

[0028] On the other hand, the present invention also provides a method for selecting a water pump unit based on the segmentation of the secondary water supply flow characteristic curve, including: determining the optimal segmentation scheme by using the above-mentioned segmentation method of the secondary water supply flow characteristic curve; a water pump unit selection step, according to the optimal segmentation scheme, combining the performance parameters of the water pump unit to select a water pump unit that matches the optimal segmentation scheme.

[0029] The number of water pumps running simultaneously in the water pump unit does not exceed 3.

[0030] The water pump unit is 4 water pumps, and the water supply flow ratio of the 4 water pumps is 1:1:0.5:0.09.

[0031] In another embodiment, the water pump unit is 4 water pumps, the 4 water pumps include 3 main pumps and 1 small-flow pump, the flow rates of the 3 main pumps are 50%, 50%, and 25% of the designed flow rate of the secondary water supply system respectively, the flow rate of 1 small-flow pump is 5% of the designed flow rate of the secondary water supply system, and the total flow rate of the 4 water pumps is 1.3 times the designed flow rate of the secondary water supply system.

[0032] From the above solutions, the advantages of the present invention are as follows:

[0033] The method of the present invention effectively divides the flow characteristic curve of secondary water supply through an optimal segmentation algorithm, uses the first segmentation to identify the "macro" change trend in the water supply flow, and uses the second segmentation to identify the "micro" change trend in the water supply flow. It can be used to improve the design parameters of the secondary water supply system, guide the selection of pump sets, and optimize the control of system operation, etc.

[0034] The present invention proposes concepts such as the minimum segmentation length and the cumulative deviation value of water consumption fluctuations according to the control requirements of the operation of the secondary water supply system, making the segmentation result more scientific and detailed, and capable of effectively identifying the specific moment when the secondary water supply system changes.

[0035] In the method for selecting a water pump unit provided by the present invention, the water supply capacity of the working water pump unit not only meets the maximum design flow of the system, but also can simultaneously meet the operating conditions in the small flow section at night, so as to ensure that the water supply pump set can operate in the high-efficiency section for a long time. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 is a flowchart of the segmentation method for the secondary water supply flow characteristic curve of the present invention;

[0037] Figure 2 is another flowchart of the segmentation method for the secondary water supply flow characteristic curve of the present invention;

[0038] Figure 3 is a graph of the monitoring results (L / s) of the instantaneous water flow for building water supply in the specific application scenario of the present invention;

[0039] Figure 4 is for the present invention using the Fisher optimal segmentation algorithm for Figure 3 segmentation result graph;

[0040] Figure 5 is a graph of the change amplitude of water pump supply in the specific application scenario of the present invention;

[0041] Figure 6 is a schematic structural diagram of the segmentation device for the secondary water supply flow characteristic curve of the present invention;

[0042] Among them, reference numerals:

[0043] 1 - Segmentation method for the secondary water supply flow characteristic curve;

[0044] 2 - Segmentation device for the secondary water supply flow characteristic curve;

[0045] 20 - Processor;

[0046] 21 - Memory;

[0047] 210 - Data acquisition module;

[0048] 211 - Optimal segmentation module;

[0049] 2110 - First division unit;

[0050] 2111 - Second division unit;

[0051] S10, S20, S200, S201 - Steps. Detailed implementation manners

[0052] The technical solution of the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments to further understand the purpose, solution and efficacy of the present invention, but it is not intended to limit the protection scope of the appended claims of the present invention.

[0053] References in the specification to "embodiment", "another embodiment", "the present embodiment", etc. mean that the described embodiment may include specific features, structures or characteristics, but not every embodiment must include these specific features, structures or characteristics. In addition, such expressions do not refer to the same embodiment. Further, when combining specific features, structures or characteristics in an embodiment, whether or not there is an explicit description, it has been shown that combining such features, structures or characteristics into other embodiments is within the knowledge of those skilled in the art.

[0054] In the specification and subsequent claims, certain terms are used to refer to specific components or parts. Those of ordinary skill in the art should understand that a technical user or manufacturer may use different nouns or terms to refer to the same component or part. The specification and claims do not use the difference in name as a way to distinguish components or parts, but use the difference in function of components or parts as the criterion for distinction. The terms "including" and "comprising" mentioned throughout the specification and claims are open-ended terms and should be interpreted as "including but not limited to". In addition, the term "connection" herein includes any direct and indirect electrical connection means. Indirect electrical connection means include connection through other devices.

[0055] It should be noted that in the description of the present invention, the orientation or positional relationship or parameters indicated by the terms "lateral", "longitudinal", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and "about", or "approximately", "substantially", "about" and so on are all based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description content, and does not indicate or imply that the device or element referred to must have a specific orientation, a specific size or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the present invention.

[0056] The present invention provides a method for segmenting the flow characteristic curve of secondary water supply, which solves the problem in the prior art that the lack of a method for quantitatively analyzing the segmentation characteristics does not meet the energy-saving requirements.

[0057] To solve the above problems, the general idea of the technical solution adopted by the present invention is as follows: A flowmeter is set on the main outlet pipe of the water supply system, and the timing data is read at a frequency of 2 flow data per second, and the timing flow characteristic curve is drawn. The Fisher optimal segmentation algorithm is used to divide the timing flow characteristic curve multiple times to determine the optimal segmentation scheme.

[0058] Embodiment 1:

[0059] Figure 1 and Figure 2 is a flowchart of a method for segmenting the flow characteristic curve of secondary water supply provided by Embodiment 1 of the present invention (hereinafter referred to as Method 1). This Method 1 includes the following steps:

[0060] S10: Data acquisition step, obtaining the water usage data of the secondary water supply system, and constructing the timing flow characteristic curve of the water usage data;

[0061] S20: Optimal segmentation step, using the optimal segmentation algorithm to segment the timing flow characteristic curve into k characteristic segments, and determining the optimal segmentation scheme.

[0062] In this embodiment, the optimal segmentation step S20 further includes:

[0063] S200: First segmentation step, using the optimal segmentation algorithm to segment the timing flow characteristic curve;

[0064] S201: Second segmentation step, taking the maximum cumulative deviation of fluctuations V L-max under the minimum segmentation length L as the measurement standard, and comparing it with the cumulative deviation of fluctuations v l of each of the characteristic segments. If the cumulative deviation of fluctuations v l > V L-max of a certain characteristic segment, then this characteristic segment is separately extracted, and the optimal segmentation algorithm is used to further segment this characteristic segment.

[0065] In step S10:

[0066] Read the flow rate of the secondary water supply system at a rate of no less than 2 data per second. The time-series flow rate curves at different intervals such as 1 s, 1 min, and 5 min can be obtained by merging the water consumption data in the same time period. The data is processed with a cycle of 24 hours a day, weekdays, weekends, and holidays. Through the study of water use events, it is found that the change characteristics of water use data are not only significantly discrete but also show a certain degree of aggregation. Among them, industrial water use is affected by production processes, while the changes in domestic water use are specific reflections of water use events and are affected by both natural and human factors. For a single water use event, affected by natural factors such as region and climate and human factors such as user age, gender, and living habits, the number of uses, water use duration, and occurrence time of each water use device and sanitary ware in a day are random. Through investigation and research, it is found that the average water use duration of men using washbasins is generally 8.8 s, that of women is about 13.8 s, and the shortest water use duration is only 2 s; the pre-flush of the induction urinal is about 3 s, and the post-flush is about 8 s; the water inlet duration of the flush toilet is jointly determined by the water supply pressure and the water tank volume, generally about 10 s; the average water use duration of residential showers is about 760 s. There are many factors affecting the occurrence of water use events and they are mutually coupled, and the probability distributions of water use events in different buildings are completely different. On the time coordinate, the combination forms of water use events occurring on different dates, at different times, and different are complex and changeable. The water use data of the building will fluctuate greatly at each moment. The smaller the interval of water use data collection, the more refined the building water use law it reflects, and the richer the water use event information it contains.

[0067] Therefore, the change characteristics of the second flow rate of the urban water supply system and the building water supply system are similar. To scientifically, clearly, and reasonably divide the operating conditions of the secondary water supply system, it is necessary to deeply explore the time-series characteristic flow rate curve of the secondary water supply. The water use data adopted in the present invention is the second flow rate of the building water supply.

[0068] In step S20:

[0069] The optimal segmentation algorithm is the Fisher optimal segmentation algorithm. Without changing the time arrangement order of the water use data, the Fisher optimal segmentation algorithm determines the segmentation scheme of the water use data by defining the class diameter and taking the minimum sum of squared deviations of each segment after segmentation as the objective function, as shown in Equation (1).

[0070]

[0071] In the formula: D(i l ,i l+1 -1) is the class diameter, that is, the sum of squared deviations, representing the degree of difference of the same-class samples, The larger the sum of squared deviations of the sample is, the higher the degree of dispersion of the data is; the water consumption data {y1, y2, …, y n} is divided into k segments (k ≥ 2), then there are k - 1 water consumption data segmentation points; i l represents the position where the water consumption data segmentation point is located, l = 1, 2 ……, k - 1, and i0 = 1, i k - 1 = n.

[0072] Since the Fisher optimal segmentation algorithm takes the minimum sum of squared deviations of each segment as the objective function, it can only identify and extract the "macro" change trend of the building water supply instantaneous flow rate, while the "micro" change trend is ignored.

[0073] Therefore, the optimal segmentation step S20 of the present invention further includes a first segmentation step S200 and a second segmentation step S201. Specifically, first, the Fisher optimal segmentation algorithm is used to perform "macro" segmentation on the characteristic curve of the building water supply instantaneous flow rate, that is, the first segmentation step S200; then, with the maximum cumulative deviation value of fluctuations V L-max under the minimum segment length L as the measurement standard, it is compared with the cumulative deviation value of fluctuations v k of each water consumption characteristic segment. After identifying and extracting the water consumption characteristic segments with existing "micro" change trends, the dynamic Fisher optimal segmentation algorithm is used to further segment the water consumption characteristic segments with existing "micro" change trends, that is, the second segmentation step S201, and finally the "macro" and "micro" change trends are identified and segmented to obtain the optimal segmentation scheme of the characteristic curve of the building water supply instantaneous flow rate.

[0074] In step S201:

[0075] The minimum segment length L of the building water supply instantaneous flow rate is jointly determined by the minimum segment duration T of the water consumption characteristic segments (that is, k characteristic segments) and the acquisition interval duration e of the building water supply instantaneous flow rate, as shown in formula (2).

[0076]

[0077] In the formula: T is the minimum segment duration of the water consumption characteristic segments, min; e is the acquisition interval duration of the building water supply instantaneous flow rate, min.

[0078] The minimum segment duration T is set according to the water supply equipment (such as water pumps) of the secondary water supply system. In this embodiment, the minimum segment duration T is mainly related to the operation control level of the water pump, and generally should not be less than 15 minutes. Operating for less than 15 minutes is uneconomical, and the maximum operating time is determined according to the water use characteristics. During the process of achieving two-way balance between water use and water supply in the building water supply system, the water pump unit needs to complete the start command to start working and output flow, and also needs to complete the adjustment command to adjust the water output flow of the water pump after receiving the signal from the pressure sensor. Therefore, within the minimum segment duration T, the secondary water supply system needs to complete the start-up and adjustment processes of the water pump and be able to maintain stable operation for a long period of time, as shown in Equation (3).

[0079] T = T1 + T2 + T3 (3)

[0080] Where: T1 is the start-up duration of the water pump from receiving the pump start signal to normal operation, in minutes; T2 is the duration required for the frequency conversion acceleration and deceleration process of the water pump, in minutes; T3 is the duration of stable operation of the water pump, in minutes. The minimum segment duration T of the building water supply second flow rate not only ensures that the water pump can complete the operation control command within the specified time, but also ensures that the water pump can operate stably for a long period of time.

[0081] Therefore, the minimum segment length L not only meets the requirements of stable operation of the secondary water supply system, avoids the impact on the system caused by the power frequency start of the water pump, and extends the service life of the water pump. Moreover, this value can ensure the stable operation of the water pump for a long period of time, providing an operation control basis for realizing the stable operation of the water pump in the high-efficiency section.

[0082] To ensure that the optimal segmentation scheme meets the setting requirements of the minimum segment length L, the present invention improves the silhouette coefficient method so that the final segmentation scheme can meet the requirements of segmenting according to water use characteristics. The silhouette coefficient method combines the cohesion and separation degree as a measure of the clustering effect. In ordered clustering, there is a fixed order among the segmented water use characteristic segments, and it is not necessary to compare the target characteristic segment with all other characteristic segments when calculating the inter-class difference. The improved silhouette coefficient method uses the absolute value of the difference between two points instead of the average distance, and compares the target special stage with its adjacent special stage to determine the optimal segmentation scheme of the building water supply second flow rate, and its calculation process is shown in Equation (4).

[0083]

[0084] Where: S k represents the average value of the improved silhouette coefficient when the number of segmentation segments is k, and the value of the improved silhouette coefficient S k varies between -1 and 1, that is, -1 ≤ S k ≤ 1, S kThe larger the value, the better the scheme with the segmentation number of k; s i It is denoted as the improved silhouette coefficient for each water consumption data, and its calculation process is shown in Equation (5).

[0085]

[0086] In the formula: a i represents the average absolute value of the difference between the i-th object and all other objects within the segment, and its calculation process is shown in Equation (6); b i represents the minimum value of the average absolute value of the difference between the i-th object and all objects in the adjacent segment, and its calculation process is shown in Equation (7).

[0087]

[0088] Among them, i = 1, 2, 3... n; the water consumption data is the flow rate set {y1, y2,..., y n}, y t represents the flow rate at the t-th moment in the water consumption data {y1, y2,..., y n}; the water consumption data {y1, y2,..., y n} is divided into k segments (k ≥ 2), then there are k - 1 water consumption data segmentation points; i l represents the position where the water consumption data segmentation point is located, l = 1, 2,..., k - 1, and i0 = 1, i k -1 = n.

[0089] When the number of data points in a certain feature segment is less than L, the improved silhouette coefficient S k of this segmentation scheme is defined as -1 as a penalty coefficient. When S k = -1, there is a situation where the minimum segmentation length of the feature segment is less than L, and at this time, the iterative calculation is no longer performed.

[0090] Since the Fisher optimal segmentation algorithm starts from the total sum of squared deviations of the data as a whole, it is difficult to effectively identify the "microscopic" change trend existing locally in the building water supply second flow rate. To effectively identify whether there is a microscopic change trend in each water consumption feature segment, the cumulative deviation of fluctuations is proposed for determination. First, the water consumption fluctuations of each feature segment are extracted, as shown in Equation (8).

[0091]

[0092] In the formula, y t represents the water consumption at the t-th moment in the building water supply second flow rate {y1, y2,..., y n}; It represents the average value of all water consumption amounts in the water consumption characteristic segment where the water consumption amount is located. There are significant differences in the water consumption fluctuations in different water consumption characteristic stages. The water consumption fluctuations in the water consumption characteristic segments with "microscopic" change trends cannot be evenly distributed around the value of 0. To measure the unevenness of the fluctuations in different water consumption characteristic stages and effectively identify the segments with microscopic change trends, the cumulative deviation value v of the fluctuations in this characteristic segment is defined l , as shown in Equation (9).

[0093] v l = max(v l,t ) - min(v l,t ) (9)

[0094] In the formula: v l,t is the cumulative value of the water consumption fluctuation at time t, where i l ≤ t < i l+1 , l = 1, 2 ……, k - 1, and i0 = 1, i k - 1 = n. The larger the cumulative deviation value v l of the fluctuations, the more uneven the change in the water consumption fluctuations within this water consumption characteristic segment.

[0095] Meanwhile, there is a relationship between the maximum cumulative deviation value V L-max of the fluctuations and the minimum segment length L. If the minimum segment length allowed for the water consumption characteristic segment is L, that is, the adjacent L building water supply second flow rates {y t-L , y t-L+1 , …, y t} (where L ≤ t ≤ n and t is an integer) are divided into a water consumption characteristic segment. After determining the minimum segment length, without segmenting the water consumption data, the first cumulative deviation value v t corresponding to each water consumption moment when the adjacent L building water supply second flow rates are divided into a water consumption characteristic segment can be calculated according to Equation (8) and Equation (9). There are a total of L first cumulative deviation values v t of the water consumption data. Then, by comparing these first cumulative deviation values v t , the maximum cumulative deviation value V L-max is obtained. By comparing the maximum cumulative deviation value V L-max with the cumulative deviation values v l of each characteristic segment after segmentation, a conclusion can be drawn on whether there is a microscopic change trend in the segmentation scheme. Specifically, when the minimum segment length is set to L, if the cumulative deviation v l of a certain water consumption characteristic segment > V L-max, it indicates that there is a microscopic change trend in this feature segment, and then this water usage feature segment is extracted separately. First, compare the duration of this water usage feature segment with 2eL. If the duration of this water usage feature segment is less than 2eL, then according to the minimum segmentation requirement, this water usage feature segment cannot be further segmented.

[0096] Only when this water usage feature segment simultaneously satisfies the cumulative deviation of fluctuations v l >V L-max and the duration is greater than 2eL, this water usage feature segment is extracted and further segmented until S k =-1 appears, which indicates that in any segmentation scheme of the water usage feature segment at this time, there must be a situation where the minimum segmentation length is less than L, and then the iterative calculation is no longer performed at this time.

[0097] To facilitate the understanding of the above embodiments, the following will take a specific application scenario of the above embodiments as an example for illustration:

[0098] Embodiment 2:

[0099] The water usage data for verifying this application scenario is the measured building water supply instantaneous flow rate of a certain residential community. There are 3 multi-story residential buildings in this community, the highest building floor is 7 floors, the occupancy rate of the community is high, the total number of residential households is 70, and the total number of residents is approximately 245. During the monitoring period, the highest daily domestic water usage in this community occurred on January 21, 2023 (New Year's Eve, the 30th day of the 12th lunar month), and the domestic water usage on that day was 32.28m 3 . The acquisition interval of the water usage data is only 1 minute, and the number of data collected in a day is 1440 in total. Along with the shortening of the acquisition interval, the amount of water usage data collected increases exponentially, and at the same time, the fluctuation characteristics of the water usage data are more obvious, which increases the difficulty of analyzing the water usage change trend, as Figure 3 shown.

[0100] Before formally performing the segmentation calculation, it is also necessary to modify the loop judgment criterion and the two iterative judgment parameters of the improved silhouette coefficient of the dynamic Fisher optimal segmentation algorithm according to the data characteristics and the minimum segmentation requirements to ensure that the segmentation result meets the requirements for the stable operation of the secondary water supply equipment.

[0101] Investigation and research on the operating parameters of the water pumps in this community found that: during the adjustment process of the water pumps, when the water pumps are just started, the system parameters change significantly. The automatic start-up time from receiving the pump start signal to the normal operation of the water pumps is generally 2 minutes; during the inertial convergence and stabilization process, the acceleration and deceleration times of the water pump frequency conversion are generally set to 50Hz / 5 - 10s. To ensure the stability of the secondary water supply system, avoid the impact on the system caused by the industrial frequency start-up of the water pumps and extend the service life of the water pumps. Considering the operating control characteristics of the water pumps and the demand for stable water supply in the secondary water supply system, it is more reasonable that the minimum segmentation duration T of the water use characteristic section is 15 minutes.

[0102] The minimum continuous duration T of the water use characteristic section is 15 minutes, and the acquisition interval e of the building water supply second flow rate is 1 minute. According to formula (2), the minimum segmentation length L of the building water supply second flow rate can be calculated as 15, that is, at least 15 adjacent building water supply second flow rates are allowed to be divided into one section.

[0103] According to formula (8) and formula (9), when the minimum segmentation length L of the building water supply second flow rate is 15, the maximum value of the fluctuation cumulative deviation value v t corresponding to each water use moment is 127.33L, so the loop determination basis of the dynamic Fisher optimal segmentation algorithm is V 15-max = 127.33L.

[0104] Calculated by the segmentation method 1 of the secondary water supply flow characteristic curve provided by the present invention, the segmentation results of the building water supply second flow rate are as Figure 4 shown.

[0105] Specifically, the relevant parameters such as the start and end times of each water use characteristic section, the stage average water consumption, and the cumulative deviation values corresponding to each stage are shown in Table 1.

[0106] Table 1 Duration and cumulative deviation values of each water use characteristic section segmented based on the Fisher optimal segmentation algorithm

[0107]

[0108]

[0109] Such as Figure 4, the segmentation results of the building water supply second flow rate by the Fisher optimal segmentation algorithm show that the water use characteristic segment with the longest duration occurs from 1:00 to 4:28, with a duration of 208 minutes. During this water use characteristic stage, the community residents are in a resting state, and the water use appliances and equipment are basically rarely used. Moreover, the average hourly water use volume in this characteristic segment is the smallest, only 110.77 L / h. The water use characteristic segment with the shortest duration occurs from 10:43 to 10:58, with a duration of only 15 minutes. The average hourly water use volume in this water use characteristic segment is significantly higher than that of the two adjacent water use characteristic segments, and the difference in the average hourly water use volume between the two adjacent water use characteristic segments is not large. This phenomenon indicates that the present invention can effectively identify the "microscopic" change trend in water use data. The water use characteristic segment with the largest average hourly water use volume in the stage occurs from 19:43 to 21:59, with a duration of 136 minutes. At the same time, due to the high degree of dispersion of the water use data in this characteristic segment, this segment cannot be further segmented, and the cumulative deviation value of fluctuations in this characteristic segment reaches 365.38 L.

[0110] The number of segmented segments of water use data is related to the change characteristics of the building water supply second flow rate itself, and there is no strict regulation. Therefore, the present invention proposes concepts such as the minimum segmentation length and the cumulative deviation value of water use fluctuations according to the control requirements of the secondary water supply equipment operation, making the segmentation results more scientific and detailed, capable of effectively identifying the specific moments when the building water supply second flow rate changes, and reflecting the rising, peak, and falling trend characteristics of the small flow rate segment at night and each water use peak period during the day. The building water supply second flow rate segmentation method proposed by the present invention provides a scientific and reasonable data mining tool for in-depth research and analysis of the change characteristics of the building water supply second flow rate and the optimal operation control of the secondary water supply system.

[0111] Example 3:

[0112] When selecting pumps in the design of a water supply system, in the prior art, the pump combination is often based on the maximum design flow rate of the water supply system. In actual projects, there are multiple parallel operation schemes for pumps such as one in use and one standby, two in use and one standby, three in use and one standby, four in use and one standby, etc. There are various combination methods for flow matching, such as 50% + 50%, 40% + 40% + 20%, 10% + 30% + 30% + 30%, 25% + 25% + 25% + 25%, etc. This method often cannot effectively take into account the small flow rate operation condition at night, resulting in the water supply system running in a non-efficient section for a long time.

[0113] In the actual application scenario of Embodiment 2, the present invention provides a method for selecting a water pump unit based on the segmentation method 1 of the secondary water supply flow characteristic curve. As shown in Table 1, according to the segmentation result (i.e., the optimal segmentation scheme) and the performance parameters of the water pump, a water pump unit is selected to make the water pump operate in the high-efficiency section. In this embodiment, the water pump unit consists of 4 pumps, namely A + 2B + C, where: A is a small-flow pump with a water supply flow rate of 110 L / h, 1 unit; B is a large-flow pump with a water supply flow rate of 1250 L / h, 2 units; C is a medium-flow pump with a water supply flow rate of 650 L / h, 1 unit. For example: when the water consumption is relatively small, only the small-flow pump A can be turned on; when the water consumption increases, the medium-flow pump C or the large-flow pump B can be selected, or any combination of the three water pumps, but the number of water pumps running simultaneously does not exceed 3 units. In this way, most of the secondary water supply system operates in the high-efficiency section, and the secondary water supply system is economical and reasonable. The variation ranges of water supply by different water pumps are also different, as Figure 5 shown.

[0114] Specifically: Combining the water pump operation characteristic curve Q-H, determine the high-efficiency section Q1~Q3 of the water pump operation, where the water pump operation efficiency η2 at the Q2 operating point is the highest. Select a water pump whose water pump design operating point Q2 coincides with the average water consumption of the water use characteristic segmentation, so as to ensure that the water pump can operate within the high-efficiency section Q1~Q3.

[0115] In this embodiment, the water supply characteristic curve is segmented and analyzed by the segmentation method 1 of the secondary water supply flow characteristic curve, which provides a decision-making basis for the selection of water pumps in the small-flow section at night. Specifically, the water supply flow rate ratio of the 4 water pumps in the water pump unit can be selected according to the ratio relationship criterion of 1:1:0.5:0.09. Exemplarily, the water pump unit consists of 3 main pumps with 50%, 50%, and 25% of the maximum design flow rate of the secondary water supply system, and 1 small-flow pump with 5% of the maximum design flow rate, and the number of water pumps put into operation throughout the day does not exceed 3 units. At this time, the total flow rate of the water pump unit is 1.3 times the design flow rate of the secondary water supply system.

[0116] The advantage of this embodiment is that the water supply capacity of the working water pump unit not only meets the maximum design flow rate of the secondary water supply system, but also can simultaneously meet the operating conditions in the small-flow section at night, thus ensuring that the water supply pump group can operate in the high-efficiency section for a long time.

[0117] The following is a device embodiment corresponding to the above method embodiment. This embodiment can be implemented in cooperation with the above embodiment. The relevant technical details mentioned in the above embodiment are still valid in this embodiment. To avoid repetition, they will not be elaborated here. Correspondingly, the relevant technical details mentioned in this embodiment can also be applied to the above embodiment.

[0118] Embodiment 4:

[0119] Embodiment 4 of the present invention further provides a segmentation device 2 for the flow characteristic curve of secondary water supply, including a processor 20, and the processor 20 is used to execute the following program modules stored in a memory 21:

[0120] A data acquisition module 210, configured to obtain water usage data of a secondary water supply system and construct a time-series flow characteristic curve of the water usage data;

[0121] An optimal segmentation module 211, configured to divide the time-series flow characteristic curve into k characteristic segments by using an optimal segmentation algorithm and determine an optimal segmentation scheme, where k≥2 and k is a positive integer.

[0122] In this embodiment, the optimal segmentation module 211 further includes: a first division unit 2110, configured to divide the time-series flow characteristic curve by using the optimal segmentation algorithm; a second division unit 2111, configured to use the maximum cumulative deviation from the mean value V L-max under the minimum segmentation length L as a measurement criterion, and compare it with the cumulative deviation from the mean value v l of each of the characteristic segments. If the cumulative deviation from the mean value v l of a certain characteristic segment is greater than the maximum cumulative deviation from the mean value V L-max , then extract this characteristic segment separately and further divide this characteristic segment by using the optimal segmentation algorithm.

[0123] The data acquisition module 210 is configured to: read the flow rate of the secondary water supply system at no less than 2 data per second, and form a time-series flow curve through time-series flow processing, and process the data with 24 hours a day, working days per week, rest on Saturdays and Sundays, and holidays as a cycle. The water usage data adopted in this embodiment is the building water supply second flow rate.

[0124] The optimal segmentation algorithm in the optimal segmentation module 211 is the Fisher optimal segmentation algorithm. Without changing the time arrangement order of the water usage data, the Fisher optimal segmentation algorithm determines the segmentation scheme of the water usage data by defining the class diameter and using the minimum sum of squared deviations of each segment after segmentation as the objective function, as shown in Equation (1).

[0125] Since the Fisher optimal segmentation algorithm uses the minimum sum of squared deviations of each segment as the objective function, it can only identify and extract the "macro" change trend of the building water supply second flow rate, while ignoring the "micro" change trend.

[0126] Therefore, the optimal segmentation module 211 of this embodiment further includes a first partitioning unit 2110 and a second partitioning unit 2111. Specifically, the first partitioning unit 2110 is used to perform a "macroscopic" partitioning on the characteristic curve of the building water supply instantaneous flow rate by using the Fisher optimal segmentation algorithm; the second partitioning unit 2111 is used to take the maximum cumulative deviation value V of the fluctuations at the minimum segment length L as the measurement standard, compare it with the cumulative deviation value v of the fluctuations of each water use characteristic segment, identify and extract the water use characteristic segments with the existing "microscopic" change trend, and then use the dynamic Fisher optimal segmentation algorithm to further partition the water use characteristic segments with the "microscopic" change trend. Finally, the "macroscopic" and "microscopic" change trends are identified and partitioned to obtain the optimal segmentation scheme of the building water supply instantaneous flow rate characteristic curve. L-max As the measurement standard, compare it with the cumulative deviation value v of the fluctuations of each water use characteristic segment l to identify and extract the water use characteristic segments with the existing "microscopic" change trend, and then use the dynamic Fisher optimal segmentation algorithm to further partition the water use characteristic segments with the "microscopic" change trend. Finally, the "macroscopic" and "microscopic" change trends are identified and partitioned to obtain the optimal segmentation scheme of the building water supply instantaneous flow rate characteristic curve.

[0127] In the second partitioning unit 2111:

[0128] The minimum segment length L of the building water supply instantaneous flow rate is jointly determined by the minimum segment duration T of the water use characteristic segments (i.e., k characteristic segments) and the acquisition interval duration e of the building water supply instantaneous flow rate, as shown in Equation (2).

[0129] The minimum segment duration T is set according to the water supply equipment (such as water pumps) of the secondary water supply system. In this embodiment, the minimum segment duration T is mainly related to the operation control level of the water pumps. Generally, it should not be less than 15 minutes. Operating for less than 15 minutes is uneconomical, and the maximum operating time is determined according to the water use characteristics. During the process of achieving the two-way balance of water use and water supply in the building water supply system, the water pump unit needs to complete the start command to start working and output flow, and also needs to complete the adjustment command for adjusting the water output flow after receiving the signal from the pressure sensor. Therefore, within the minimum segment duration T, the secondary water supply system needs to complete the start-up and adjustment processes of the water pumps and be able to operate stably for a long period of time, as shown in Equation (3). The minimum segment duration T of the building water supply instantaneous flow rate not only ensures that the water pumps can complete the operation control commands within the specified time, but also ensures that the water pumps can operate smoothly for a long period of time.

[0130] Therefore, the minimum segment length L not only meets the requirements of the stable operation of the secondary water supply system, avoids the impact on the system caused by the industrial frequency start of the water pumps, and prolongs the service life of the water pumps. Moreover, this value can ensure the stable operation of the water pumps for a long period of time, providing an operation control basis for the stable operation of the water pumps in the high-efficiency section.

[0131] To ensure that the optimal segmentation scheme meets the set requirement of the minimum segmentation length L, the present invention improves the silhouette coefficient method, so that the final segmentation scheme can meet the requirements of water use characteristic segmentation. The silhouette coefficient method combines the cohesion and separation degree as a measure of the clustering effect. In ordered clustering, there is a fixed order among the segmented water use characteristic segments, and when calculating the inter-class difference, it is not necessary to compare the target characteristic segment with all other characteristic segments. The improved silhouette coefficient method uses the absolute value of the difference between two points instead of the average distance, and compares the target special stage with its adjacent special stage to determine the optimal segmentation scheme of the building water supply second flow rate, and its calculation process is shown in Equations (4) to (7).

[0132] When the number of data points in a certain characteristic segment is less than L, the improved silhouette coefficient S of this segmentation scheme is defined k as -1, which is used as a penalty coefficient. When S k = -1, there is a situation where the minimum segmentation length of the characteristic segment is less than L, and at this time, the iterative calculation is no longer performed.

[0133] Since the Fisher optimal segmentation algorithm starts from the whole of the data and is difficult to effectively identify the "microscopic" change trend existing locally in the building water supply second flow rate. To effectively identify whether there is a microscopic change trend in each water use characteristic segment, the cumulative deviation of fluctuations is proposed for determination. First, the water use fluctuations of each characteristic segment are extracted, as shown in Equation (8). There are large differences in the water use fluctuations of different water use characteristic stages, and the water use fluctuations of the water use characteristic segments with "microscopic" change trends cannot be evenly distributed around the value of 0. To measure the unevenness of the fluctuations of different water use characteristic stages and effectively identify the sections with microscopic change trends, the cumulative deviation of fluctuations v of this characteristic segment is defined l , as shown in Equation (9). The larger the cumulative deviation of fluctuations, the more uneven the water use fluctuations within this water use characteristic segment.

[0134] At the same time, there is a relationship between the maximum cumulative deviation of fluctuations V L-max and the minimum segmentation length L. If the minimum segmentation length of the water use characteristic segment is allowed to be L, that is, the adjacent L building water supply second flow rates {y t-L , y t-L+1 , …, y t} (where L ≤ t ≤ n and t is an integer) are divided into a water use characteristic segment. After determining the minimum segmentation length, without segmenting the water use data, then according to Equations (8) and (9), the first cumulative deviation of fluctuations v t corresponding to each water use moment when the adjacent L building water supply second flow rates are divided into a water use characteristic segment can be calculated. There are a total of L first cumulative deviations of fluctuations v t of the L water use data, and then these first cumulative deviations of fluctuations v are comparedt Obtain the maximum cumulative deviation value of fluctuations V L-max , and compare the maximum cumulative deviation value of fluctuations V L-max with the cumulative deviation values of fluctuations v of each characteristic segment after segmentation l to draw a conclusion on whether there is a micro-change trend in the segmentation scheme.

[0135] Specifically, when setting the minimum segmentation length as L, if the cumulative deviation of fluctuations v of a certain water consumption characteristic segment l > V L-max , it indicates that there is a micro-change trend in this characteristic segment, and then this water consumption characteristic segment is extracted separately. First, compare the duration of the water consumption characteristic segment with 2eL. If the duration of this water consumption characteristic segment is less than 2eL, then according to the minimum segmentation requirement, this water consumption characteristic segment cannot be further segmented.

[0136] Only when this water consumption characteristic segment simultaneously satisfies the cumulative deviation of fluctuations v l > V L-max and the duration is greater than 2eL, this water consumption characteristic segment is extracted and further segmented until S k = -1, which indicates that in any segmentation scheme of the water consumption characteristic segment at this time, there must be a situation where the minimum segmentation length is less than L, and then the iterative calculation is no longer performed at this time.

[0137] To sum up, the present invention stipulates the length of the minimum segmentation according to the control requirements of the operation of the secondary water supply equipment. The segmentation result is more scientific and detailed, can effectively identify the specific moment when the building water supply second flow rate changes, and reflects the rising, peak and falling trend characteristics of the small flow rate segment at night and each water consumption peak period during the day. The segmentation method proposed by the present invention provides a scientific and reasonable data mining tool for realizing in-depth research and analysis of the change characteristics of the building water supply second flow rate and the optimal operation control of the secondary water supply system.

[0138] In addition, the water pump unit selection method of the segmentation method 1 based on the secondary water supply flow characteristic curve provided by the present invention can make the water supply capacity of the working water pump unit not only meet the maximum design flow rate of the system, but also be able to meet the operating conditions of the small flow rate segment at night, so as to ensure that the water supply pump group can operate in the high-efficiency section for a long time.

[0139] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are only illustrative and not restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make many forms without departing from the purpose of the present invention and the scope protected by the claims, and all belong to the protection scope of the present invention.

Claims

1. A segmentation method for a secondary water supply flow characteristic curve, characterized in that: The following steps are involved: A data collection step, obtaining water use data of the secondary water supply system, and constructing a time series flow characteristic curve of the water use data; An optimal segmentation step, using an optimal segmentation algorithm to divide the time series flow characteristic curve into k characteristic segments, and determining an optimal segmentation scheme, wherein k ≥ 2 and is a positive integer; The optimal segmentation step further comprises: The first division step is to divide the time series flow characteristic curve using an optimal segmentation algorithm; The second division step is to use the maximum fluctuation cumulative deviation value V under the minimum segment length L L-max As a measure, the cumulative deviation value v from the fluctuation of each characteristic segment is l For comparison, if the cumulative deviation value v of a certain characteristic segment l Greater than the maximum fluctuation cumulative deviation value V L-max , then the feature segment is extracted separately, and the feature segment is further divided using the optimal segmentation algorithm.

2. The method according to claim 1, characterized in that The minimum segment length L is determined by the minimum segment duration T and the collection interval duration e of the water use data, as shown in the following formula: Among them, the minimum segment time T is set according to the water supply equipment of the secondary water supply system.

3. The method according to claim 2, characterized in that The water supply equipment is a water pump, and the minimum segment duration T is as follows: T=T1+T2+T3 Where: T1 is the pump start time from receiving the pump start signal to the normal operation of the pump; T2 is the time required for the water pump's frequency conversion acceleration and deceleration process; T3 is the time it takes for the water pump to operate stably.

4. The method according to claim 2, characterized in that: The second dividing step further comprises: The duration of the feature segment is compared with 2eL. If the duration of the feature segment is less than 2eL, the feature segment is not further divided.

5. The method according to claim 2, characterized in that: The optimal segmentation step further comprises: Using the improved silhouette coefficient S k To ensure that the minimum segment length is L, when S k = -1, the minimum segment length of the feature segment is less than L, and no iterative calculation is performed at this time; The improved silhouette coefficient S k The absolute value of the difference between two points is used instead of the average distance to compare the target feature segment with its adjacent feature segments.

6. The method according to claim 5, characterized in that The improved silhouette coefficient S k As follows: Where: S k represents the average value of the silhouette coefficients of the k feature segments, and -1≤S k ≤1; s i It is expressed as the silhouette coefficient of each water use data, as follows: Where: i = 1, 2, 3...n, represents each sample point of the water use data; a i represents the average absolute value of the difference between the i-th sample point and other sample points in the feature segment where the sample point is located, b i Indicates the minimum value of the average absolute value of the difference between the i-th sample point and all sample points in the adjacent segment.

7. The method according to claim 6, characterized in that The a i The calculation process is as follows: Wherein, i = 1, 2, 3...n; the water consumption data is a flow set {y1, y2,..., y n },y t Represents the water use data {y1, y2, ..., y n } at time t; the water consumption data {y1,y2,…,y n } is divided into k segments (k ≥ 2), which contains k-1 water use data segmentation points; i l Indicates the location of the water use data segmentation point, l = 1, 2..., k-1, and i0 = 1, i k -1=n.

8. The method according to claim 6, characterized in that The b i The calculation process is as follows: Wherein, i = 1, 2, 3...n; the water consumption data is a flow set {y1, y2,..., y n },y t Represents the water use data {y1, y2, ..., y n } at time t; the water consumption data {y1,y2,…,y n } is divided into k segments (k ≥ 2), which contains k-1 water use data segmentation points; i l Indicates the location of the water use data segmentation point, l = 1, 2..., k-1, and i0 = 1, i k -1=n.

9. The method according to claim 8, characterized in that The fluctuation cumulative deviation value v l As follows: v l =max(v l,t )-min(v l,t ) Where: v l,t is the accumulated value of the water consumption data fluctuation at time t, where y t Represents water consumption data {y1,y2,…,y n }The water consumption at time t; Indicates the average value of the characteristic segment where the water consumption is located; i l ≤t≤i l+1 -1, l=1,2……,k-1, and i0=1, i k -1=n.

10. The method according to claim 9, characterized in that The maximum fluctuation cumulative deviation value V L-max After determining the minimum segment length L, calculate the first fluctuation cumulative deviation values ​​corresponding to L adjacent water use data at each water use moment, and compare the maximum values ​​obtained by these first fluctuation cumulative deviation values.

11. A segmentation device for a secondary water supply flow characteristic curve, characterized in that: The system comprises a processor configured to execute the following program modules stored in a memory: A data acquisition module, used to obtain water consumption data of the secondary water supply system and construct a time series flow characteristic curve of the water consumption data; An optimal segmentation module, used to divide the time series flow characteristic curve into k characteristic segments using an optimal segmentation algorithm, and determine an optimal segmentation scheme, wherein k ≥ 2 and is a positive integer; The optimal segmentation module further comprises: A first segmentation unit, configured to segment the time series flow characteristic curve using an optimal segmentation algorithm; The second division unit is used to accumulate the deviation value V with the maximum fluctuation under the minimum segment length L. L-max As a measure, the cumulative deviation value v from the fluctuation of each characteristic segment is l For comparison, if the cumulative deviation value v of a certain characteristic segment l Greater than the maximum fluctuation cumulative deviation value V L-max , then the feature segment is extracted separately, and the feature segment is further divided using the optimal segmentation algorithm.

12. A method for selecting a pump unit based on segmented secondary water supply flow characteristic curve, characterized in that: include: Determine the optimal segmentation scheme by using the segmentation method of the secondary water supply flow characteristic curve according to any one of claims 1 to 10; The water pump unit selection step is to select a water pump unit that matches the optimal segmentation scheme according to the optimal segmentation scheme and in combination with the performance parameters of the water pump unit.

13. The method according to claim 12, characterized in that The number of water pumps running simultaneously in the water pump unit shall not exceed 3.

14. The method according to claim 12, characterized in that The water pump unit consists of 4 water pumps, and the water supply flow ratio of the 4 water pumps is 1:1:0.5:0.

09.

15. The method according to claim 12, characterized in that The water pump unit consists of 4 water pumps, including 3 main pumps and 1 small-flow pump. The flow rates of the 3 main pumps are 50%, 50% and 25% of the design flow rate of the secondary water supply system respectively, and the flow rate of the 1 small-flow pump is 5% of the design flow rate of the secondary water supply system. The total flow rate of the water pump unit is 1.3 times the design flow rate of the secondary water supply system.

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