Smart water management method and system based on big data

Abstract

A smart water management method and system based on big data. The system comprises sensor assemblies, a server, and a management terminal; each water pipe of a water pipe network is provided with a plurality of detection points; the number of the sensor assemblies is consistent with the number of the detection points, and the sensor assemblies are in one-to-one correspondence with the detection points; the sensor assemblies are arranged at the corresponding detection points; a same water pipe is provided with at least two detection points, and every two adjacent detection points are at least spaced by a preset distance. According to the method and system, the flow value of each section of a water pipe can be acquired in real time on the basis of a liquid flow sensor, whether the water pipe leaks or not is then monitored in real time on the basis of the real-time flow value, a leakage point of the leaked water pipe is accurately located, and the location of the leakage point is sent to the management terminal, thereby solving the problem in existing solutions that leakage of a water pipe cannot be found in time.

Description

A smart water management method and system based on big data Technical Field

[0001] The present invention relates to the technical field of water management, and in particular to a smart water management method and system based on big data. Background Art

[0002] With the continuous acceleration of urbanization, the construction of urban infrastructure is also developing continuously. People's demand for water safety and environmental protection of sewage treatment is constantly increasing, which has led to a sharp increase in the number of water treatment equipment. The normal operation of a large number of water treatment equipment requires a large number of professionals to conduct timely inspection, maintenance and repair.

[0003] At present, when solving outdoor water pipe leakage, the water system can only determine whether there is a water pipe leakage by visually observing obvious water leakage, that is, it is unable to detect the water pipe leakage problem in time, which leads to serious waste of water resources.

[0004] Summary of the Invention

[0005] The main purpose of the present invention is to provide a smart water management method and system based on big data, aiming to solve the problem that existing solutions cannot detect water pipe leaks in a timely manner.

[0006] The technical solution proposed by the present invention is:

[0007] A big data-based smart water management method is applied to a big data-based smart water management system; the system includes a sensor assembly, a server, and a management terminal; each water pipe in a water pipe network is provided with multiple detection points; the number of sensor assemblies matches the number of detection points, and the sensor assemblies and detection points are provided in a one-to-one correspondence; at least two detection points are provided on the same water pipe, and two adjacent detection points are separated by at least a preset distance; the sensor assembly includes a liquid flow sensor and a wireless communication module; the liquid flow sensor is used to detect the real-time flow value of the water pipe; the liquid flow sensor is communicatively connected to the server via the wireless communication module; the method includes:

[0008] The server obtains the real-time flow value corresponding to each detection point through the sensor component;

[0009] The server marks two adjacent detection points on the same water pipe that are not connected to other water pipes as the first target group;

[0010] The server determines whether there is a first target group that meets a first condition, wherein the first condition is that an absolute value of a difference between real-time flow values ​​of two detection points in the first target group at the same time is greater than a first preset value;

[0011] If so, the server determines that the water pipe section corresponding to the first target group that meets the first condition has a leak;

[0012] The server sends the location of the water pipe section corresponding to the first target group that meets the first condition to the management terminal.

[0013] Preferably, the server obtains the real-time flow value corresponding to each detection point through the sensor component, and then further includes:

[0014] The server marks the water pipe with the downstream branch as the upper pipe, and marks the detection point of the upper pipe closest to the downstream branch as the upper point;

[0015] The server marks the downstream branch corresponding to the upper-level pipe as the lower-level pipe, and marks the detection point of the lower-level pipe close to the upper-level point as the lower-level point;

[0016] The server marks the upper level point and all the lower level points adjacent to the upper level point as the second target group;

[0017] The server determines whether there is a second target group that meets a second condition, wherein the second condition is that the absolute value of the difference between the sum of the real-time flow values ​​corresponding to all subordinate points in the same second target group and the real-time flow value corresponding to the superior point is greater than a first preset value;

[0018] If so, the server determines that a leak occurs in the water pipe segment between the second target group that meets the second condition;

[0019] The server sends the locations of the water pipe sections between the second target groups that meet the second condition to the management terminal.

[0020] Preferably, the server determines that a water pipe segment between the second target groups that meets the second condition has a leak, and then further includes:

[0021] The server marks the first target group that meets the second condition as the first group to be analyzed;

[0022] The server obtains flow rate data corresponding to each second preset time period of each day in the first preset time period in which no leakage accident occurred in the past for the first group to be analyzed, and obtains the historical average flow rate value of the second preset time period corresponding to each subordinate point in the first group to be analyzed at the current moment based on the flow rate data;

[0023] The server obtains the standard flow rate drop difference corresponding to each subordinate point in the first group to be analyzed based on the historical average flow rate value;

[0024] The server determines whether the leakage point is located between the upper point of the first group to be analyzed and the intersection of the upper pipe and the nearest lower pipe based on the real-time flow value corresponding to each lower point in the first group to be analyzed, the corresponding historical average flow value, and the corresponding standard flow drop difference.

[0025] Preferably, the server obtains the standard flow rate drop difference corresponding to each subordinate point in the first group to be analyzed based on the historical average flow rate value using the following calculation formula:

[0026] Where C B,i is the standard flow rate drop difference corresponding to the i-th subordinate point in the first group to be analyzed, i is a positive integer less than or equal to N, and N is the number of all subordinate points in the first group to be analyzed; L L,i L is the historical average flow value of the second preset time period corresponding to the i-th subordinate point in the first group to be analyzed at the current moment; S,S is the real-time flow value corresponding to the upper point in the first group to be analyzed; L S,i is the real-time flow value corresponding to the i-th subordinate point in the first group to be analyzed.

[0027] Preferably, the server determines whether the leakage point is located between the upper point in the first group to be analyzed and the intersection of the upper pipe and the nearest lower pipe based on the real-time flow value, the corresponding historical average flow value, and the corresponding standard flow drop difference corresponding to each lower point in the first group to be analyzed, including:

[0028] The server marks the difference between the real-time flow value of each subordinate point in the first group to be analyzed and the historical average flow value of the second preset time period corresponding to the current moment as the actual difference;

[0029] The server calculates the absolute value of the difference between the standard flow rate drop difference and the actual difference corresponding to each subordinate point in the first group to be analyzed, and marks it as the deviation value. The calculation formula of the deviation value is: P C,i =|(L L,i -L S,i )-C B,i |,

[0030] Where, P C,i is the deviation value corresponding to the i-th subordinate point in the first group to be analyzed;

[0031] The server determines whether each deviation value is less than a second preset value;

[0032] If so, the server determines that the leak point is located between the upper point of the first group to be analyzed and the intersection of the upper pipe and the nearest lower pipe;

[0033] If not, the server determines that the leakage point is not located between the upper point of the first to-be-analyzed group and the intersection of the upper pipe and the nearest lower pipe.

[0034] Preferably, the server determines that the leakage point is not located between the upper point of the first group to be analyzed and the intersection of the upper pipe and the nearest lower pipe, and then further includes:

[0035] The server marks the lower-level points in the first group to be analyzed whose actual difference is less than the second preset value as safe points;

[0036] The server marks all subordinate points except the safe points in the first group to be analyzed as target points;

[0037] The server determines that a leak occurs between the intersection of the water pipe corresponding to the target point and the adjacent upstream pipe and the target point.

[0038] Preferably, the sensor assembly further includes a sound intensity sensor communicatively connected to the wireless communication module; the server determines that a water pipe section corresponding to the first target group meeting the first condition has a leak, and then further includes:

[0039] The server marks the first target group that meets the first condition as the second group to be analyzed;

[0040] The server obtains an average value of the real-time sound intensity values ​​collected by the sound intensity sensors of the two detection points in the second group to be analyzed within a third preset time period from the current moment, and marks the average value group;

[0041] The server marks the downstream detection point in the second to-be-analyzed group as a reference point;

[0042] The server obtains the distance value between the estimated leakage point and the reference point based on the average value group.

[0043] Preferably, the server obtains the distance value between the estimated leakage point and the reference point based on the average value group using the following calculation formula:

[0044] Where L2 is the distance between the estimated leakage point and the reference point, in meters; L1 is the distance between the two detection points in the second group to be analyzed, in meters; q1 is the average value corresponding to the upstream detection point in the average value group; and q2 is the average value corresponding to the reference point.

[0045] Preferably, the system further includes solenoid valves having the same number as the detection points; the solenoid valves correspond to the detection points one-to-one; the solenoid valves are arranged near the corresponding detection points and are located downstream of the flow sensors corresponding to the corresponding detection points; the solenoid valves are used to control the on-off of the water pipes; the server marks the first target group that meets the first condition as the second group to be analyzed, and then further includes:

[0046] The server closes the solenoid valves corresponding to the two detection points in the second group to be analyzed;

[0047] The server marks the solenoid valve corresponding to the upstream detection point in the second to-be-analyzed group as the target valve;

[0048] The server controls the target valve to open and marks the opening time as the starting time;

[0049] The server obtains the time from the start time to the time when the real-time sound intensity value collected by the sound intensity sensor corresponding to the downstream detection point in the second to-be-analyzed group reaches the maximum value, and marks it as the path time;

[0050] The server calculates the distance between the estimated leakage point and the downstream detection point in the second group to be analyzed based on the path length, water flow velocity, and the distance between the two detection points in the second group to be analyzed:

[0051] Where L2 is the distance between the estimated leakage point and the downstream detection point in the second group to be analyzed; S1 is the water flow velocity, in m / s; S2 is the speed of sound propagation in the water pipe, in m / s; L1 is the distance between the two detection points in the second group to be analyzed, in meters; and t is the path time, in seconds.

[0052] The present invention also proposes a smart water management system based on big data, which is applied to any of the smart water management methods based on big data; the system includes a sensor component, a server and a management terminal; each water pipe in the water network is provided with multiple detection points; the number of sensor components is consistent with the number of detection points, and the sensor components correspond to the detection points one-to-one; the sensor components are set at the corresponding detection points; the same water pipe is provided with at least two detection points, and the two adjacent detection points are at least separated by a preset distance; the sensor component includes a liquid flow sensor and a wireless communication module; the liquid flow sensor is used to detect the real-time flow value of the water pipe; the liquid flow sensor is connected to the server through the wireless communication module.

[0053] The above technical solution can achieve the following beneficial effects:

[0054] The big data-based smart water management method proposed in the present invention can obtain the flow values ​​of each section of the water pipe in real time based on the liquid flow sensor, and then monitor whether the water pipe is leaking in real time based on the real-time flow value, and accurately locate the leakage point of the leaking water pipe, and send the location of the leakage point to the management terminal; thereby solving the problem that the existing solution cannot detect water pipe leakage in time. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0056] FIG1 is a flow chart of a first embodiment of a smart water management method based on big data proposed by the present invention. DETAILED DESCRIPTION

[0057] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0058] The present invention proposes a smart water management method and system based on big data.

[0059] As shown in FIG1 , in a first embodiment of a big data-based smart water management method proposed by the present invention, the method is applied to a big data-based smart water management system; the system includes a sensor assembly, a server, and a management terminal; each water pipe in the water pipe network is provided with multiple detection points; the number of sensor assemblies is consistent with the number of detection points, and the sensor assemblies and detection points are provided in a one-to-one correspondence; at least two detection points are provided on the same water pipe, and two adjacent detection points are separated by at least a preset distance (e.g., 10 m); the sensor assembly includes a liquid flow sensor and a wireless communication module; the liquid flow sensor is used to detect the real-time flow value of the water pipe; the liquid flow sensor is communicatively connected to the server via the wireless communication module; this embodiment includes the following steps:

[0060] Step S110: The server obtains the real-time flow value corresponding to each detection point through the sensor component.

[0061] Step S120: The server marks two adjacent detection points on the same water pipe without being connected to other water pipes as a first target group.

[0062] Step S130: The server determines whether there is a first target group that meets the first condition, wherein the first condition is: the absolute value of the difference between the real-time flow values ​​of the two detection points in the first target group at the same time is greater than the first preset value (the first preset value here corresponds to the flow of the leakage point, for example 5L / s).

[0063] If so, step S140 is executed: the server determines that the water pipe section corresponding to the first target group that meets the first condition has a leak.

[0064] Specifically, if the absolute value of the difference between the real-time flow rates at two detection points at the same time is greater than a first preset value, it indicates that there is a significant deviation between the flow rate at the downstream detection point and the flow rate at the upstream detection point, indicating a leak between the two detection points. In other words, the water pipe section corresponding to the first target group that meets the first condition has a leak.

[0065] Step S150: The server sends the location of the water pipe section corresponding to the first target group that meets the first condition to the management terminal.

[0066] The big data-based smart water management method proposed in the present invention can obtain the flow values ​​of each section of the water pipe in real time based on the liquid flow sensor, and then monitor whether the water pipe is leaking in real time based on the real-time flow value, and accurately locate the leakage point of the leaking water pipe, and send the location of the leakage point to the management terminal; thereby solving the problem that the existing solution cannot detect water pipe leakage in time.

[0067] In a second embodiment of a smart water management method based on big data proposed by the present invention, based on the first embodiment, step S110 further includes the following steps:

[0068] Step S210: The server marks the water pipe with the downstream branch as the upper-level pipe, and marks the detection point of the upper-level pipe closest to the downstream branch as the upper-level point.

[0069] Step S220: the server marks the downstream branch corresponding to the upper-level pipe as a lower-level pipe, and marks the detection point of the lower-level pipe close to the upper-level point as a lower-level point.

[0070] Step S230: The server marks the upper-level point and all the lower-level points adjacent to the upper-level point as a second target group.

[0071] Specifically, for example, in this embodiment, the upper tube (tube A) is connected to three lower tubes (tubes B, C, and D), the upper point of tube A is point A, the lower point of tube B is point B, the lower point of tube C is point C, and the lower point of tube D is point D.

[0072] Then the second target group includes point A (upper level point), point B (lower level point), point C (lower level point) and point D (lower level point).

[0073] Step S240: The server determines whether there is a second target group that meets the second condition, wherein the second condition is: the absolute value of the difference between the sum of the real-time flow values ​​corresponding to all subordinate points in the same second target group and the real-time flow value corresponding to the superior point is greater than the first preset value (for example, 0.5L / s).

[0074] If so, step S250 is executed: the server determines that a water pipe segment between the second target groups that meets the second condition has a leak.

[0075] Specifically, if the absolute value of the difference between the sum of the real-time flow values ​​at points B, C, and D and the real-time flow value at point A is greater than a first preset value, it indicates that there is a leakage point in the water pipe section between the second target group that meets the second condition (i.e., between points A and B, or between points A and C, or between points A and D).

[0076] Step S260: The server sends the locations of the water pipe segments between the second target groups that meet the second condition to the management terminal.

[0077] In a third embodiment of a smart water management method based on big data proposed by the present invention, based on the second embodiment, step S250 further includes the following steps:

[0078] Step S310: The server marks the first target group that meets the second condition as the first group to be analyzed.

[0079] Step S320: The server obtains the flow value data corresponding to each second preset time period (for example, hourly period) of each day within the first preset time period (for example, 1 year) in which no leakage accident occurred in the past for the first group to be analyzed, and obtains the historical average flow value of the second preset time period corresponding to each subordinate point in the first group to be analyzed at the current moment based on the flow value data.

[0080] Specifically, the flow rate data corresponding to each hour of each day in the first group to be analyzed is obtained for the past year without a leak incident. This allows the historical average flow rate value for each subordinate point in the first group to be analyzed at the current time (e.g., 1:20 p.m.) for the second preset time period. Because municipal water use has a stable regularity, the historical average flow rate value is the estimated flow rate value for the two detection points in the first group to be analyzed if there were no leaks.

[0081] Step S340: The server obtains the standard flow rate drop difference corresponding to each subordinate point in the first group to be analyzed based on the historical average flow rate value.

[0082] Step S350: The server determines whether the leakage point is located between the upper point of the first group to be analyzed and the intersection of the upper pipe and the nearest lower pipe based on the real-time flow value corresponding to each lower point in the first group to be analyzed, the corresponding historical average flow value, and the corresponding standard flow drop difference.

[0083] Specifically, through the above technical solution, it is possible to determine whether the leakage point is located between the upper point of the first group to be analyzed and the intersection of the upper pipe and the nearest lower pipe.

[0084] In a fourth embodiment of a smart water management method based on big data proposed by the present invention, based on the third embodiment, the server obtains the calculation formula for the standard flow rate drop difference corresponding to each subordinate point in the first group to be analyzed based on the historical average flow rate value:

[0085] Where C B,i is the standard flow rate drop difference corresponding to the i-th subordinate point in the first group to be analyzed, i is a positive integer less than or equal to N, and N is the number of all subordinate points in the first group to be analyzed (when the leakage point is located in pipe A, the real-time flow rates of the downstream pipes B, C, and D will all be lower than their respective historical average flow rates; the standard flow rate drop difference here is the standard value by which the real-time flow rates of the downstream pipes B, C, and D will be reduced when the leakage point is located in pipe A; for example, in this embodiment, the subordinate point of pipe A is the first subordinate point, the subordinate point of pipe B is the second subordinate point, and the subordinate point of pipe C is the third subordinate point; in this embodiment, C B,1 is the standard flow rate drop difference corresponding to the first subordinate point in the first group to be analyzed, C B,2 is the standard flow rate drop difference corresponding to the second lower point in the first group to be analyzed, C B,3 is the standard flow rate drop difference corresponding to the third lower point in the first group to be analyzed); L L,i L is the historical average flow value of the second preset time period corresponding to the i-th subordinate point in the first group to be analyzed at the current moment; S,S is the real-time flow value corresponding to the upper point in the first group to be analyzed; L S,i is the real-time flow value corresponding to the i-th subordinate point in the first group to be analyzed.

[0086] In a fifth embodiment of a smart water management method based on big data proposed by the present invention, based on the fourth embodiment, step S350 includes the following steps:

[0087] Step S510: The server marks the difference between the real-time flow value of each subordinate point in the first group to be analyzed and the historical average flow value of the second preset time period corresponding to the current moment as the actual difference.

[0088] Step S520: The server calculates the absolute value of the difference between the standard flow rate drop difference and the actual difference corresponding to each subordinate point in the first group to be analyzed, and marks it as a deviation value. The calculation formula of the deviation value is: C,i =|(L L,i -L S,i )-C B,i |,

[0089] Where, P C,iis the deviation value corresponding to the i-th subordinate point in the first group to be analyzed.

[0090] Step S530: The server determines whether each deviation value is smaller than a second preset value (eg, 0.05 L / s).

[0091] If yes, step S540 is executed: the server determines that the leakage point is located between the upper point of the first group to be analyzed and the intersection of the upper pipe and the nearest lower pipe.

[0092] Specifically, if the deviation values ​​are all less than the second preset value, it means that the actual difference is close to the standard flow drop difference, which means that all downstream branches have experienced flow drops, which is consistent with the situation where the leakage point is located in pipe A. The leakage point is then directly determined to be between the upper point of the first group to be analyzed and the intersection of the upper pipe and the nearest lower pipe.

[0093] If not, step S550 is executed: the server determines that the leakage point is not located between the upper point of the first to-be-analyzed group and the intersection of the upper pipe and the nearest lower pipe.

[0094] Specifically, if the deviation values ​​are not all less than the second preset value, it means that not all downstream branches have experienced a flow drop, and the leakage point is not located between the upper point of the first group to be analyzed and the intersection of the upper pipe and the nearest lower pipe, but may be located in each branch.

[0095] In a sixth embodiment of a smart water management method based on big data proposed by the present invention, based on the fifth embodiment, step S550 further includes the following steps:

[0096] Step S610: The server marks the lower-level points in the first group to be analyzed whose actual difference values ​​are less than the second preset value as safe points.

[0097] Specifically, if the actual difference is smaller than the second preset value, it indicates that the flow rate value of the branch pipe is not affected and reduced, and therefore the branch pipe does not leak.

[0098] Step S620: The server marks all subordinate points in the first to-be-analyzed group except the safety points as target points.

[0099] Specifically, that is to say, the branch pipes other than the safety pipe are leaking water pipes, so the subordinate points other than the safety point in the first group to be analyzed are marked as target points.

[0100] Step S630: The server determines whether a leak occurs between the intersection point between the water pipe corresponding to the target point and the adjacent upstream pipe and the target point.

[0101] Specifically, the target point is a lower-level point corresponding to the branch pipe where the leak occurs. The server determines that a leak occurs between the intersection of the water pipe corresponding to the target point and the adjacent upper-level pipe and the target point.

[0102] In a seventh embodiment of a smart water management method based on big data proposed by the present invention, based on the first embodiment, the sensor assembly further includes a sound intensity sensor communicatively connected to the wireless communication module; step S140, followed by the following steps:

[0103] Step S710: The server marks the first target group that meets the first condition as the second group to be analyzed.

[0104] Specifically, leakage occurs between two detection points of the second group to be analyzed.

[0105] Step S720: The server obtains the average value of the real-time sound intensity values ​​collected by the sound intensity sensors of the two detection points in the second group to be analyzed within a third preset time period (eg, 1 minute) from the current moment, and marks it as an average value group.

[0106] Step S730: The server marks the downstream detection point in the second to-be-analyzed group as a reference point.

[0107] Step S740: The server obtains the distance value between the estimated leakage point and the reference point based on the average value group.

[0108] Specifically, since water leakage at the leakage point will generate a sound signal that is greater than the normal water flow, the distance value between the leakage point and the reference point can be calculated based on the sound intensity values ​​collected by the sound intensity sensors corresponding to the two detection points.

[0109] In an eighth embodiment of a smart water management method based on big data proposed by the present invention, based on the seventh embodiment, the server obtains a calculation formula for the distance value between the estimated leakage point and the reference point based on the average value group:

[0110] Where L2 is the distance between the estimated leakage point and the reference point, in meters; L1 is the distance between the two detection points in the second group to be analyzed, in meters; q1 is the average value corresponding to the upstream detection point in the average value group; and q2 is the average value corresponding to the reference point.

[0111] Specifically, since the sound intensity is inversely proportional to the square of the propagation distance, the distance between the estimated leakage point and the reference point can be calculated based on the above formula.

[0112] In a ninth embodiment of a smart water management method based on big data proposed by the present invention, based on the seventh embodiment, the system further includes solenoid valves having the same number as the detection points; the solenoid valves correspond to the detection points one-to-one; the solenoid valves are disposed near the corresponding detection points and are located downstream of the flow sensors corresponding to the corresponding detection points; the solenoid valves are used to control the on / off of the water pipes; step S710 is followed by the following steps:

[0113] Step S910: The server closes the solenoid valves corresponding to the two detection points in the second to-be-analyzed group.

[0114] Specifically, this prevents new water from flowing into the space between the two detection points, and ultimately, most of the remaining water between the two detection points will flow out through the leak point.

[0115] Step S920: The server marks the solenoid valve corresponding to the upstream detection point in the second to-be-analyzed group as a target valve.

[0116] Step S930: The server controls the target valve to open, and marks the opening time as the starting time.

[0117] Specifically, when the water between the two detection points no longer flows out from the leakage point (at this time there is a gap in the water pipe space between the two detection points), the server controls the target valve to open and marks the opening time as the starting time.

[0118] Step S940: The server obtains the time from the start time to the time when the real-time sound intensity value collected by the sound intensity sensor corresponding to the downstream detection point in the second group to be analyzed reaches the maximum value, and marks it as the path time.

[0119] Specifically, after the target valve is opened, the upstream water will first flow through the target valve, then through the leakage point, and then reach the solenoid valve corresponding to the downstream detection point in the second group to be analyzed, where the sound intensity generated when the water flows through the leakage point is the largest; then, after obtaining the time from the starting moment to the time when the real-time sound intensity value collected by the sound intensity sensor corresponding to the downstream detection point in the second group to be analyzed reaches the maximum value, the distance value between the estimated leakage point and the downstream detection point in the second group to be analyzed can be calculated in combination with the water flow velocity and the distance value between the two detection points in the second group to be analyzed.

[0120] Step S950: The server calculates the distance between the estimated leakage point and the downstream detection point in the second group to be analyzed based on the passage time, the water flow velocity, and the distance between the two detection points in the second group to be analyzed:

[0121] Where L2 is the distance between the estimated leakage point and the downstream detection point in the second group to be analyzed; S1 is the water flow velocity, in m / s; S2 is the speed of sound propagation in the water pipe, in m / s; L1 is the distance between the two detection points in the second group to be analyzed, in meters; and t is the path time, in seconds.

[0122] In the tenth embodiment of the smart water management method based on big data proposed by the present invention, based on the ninth embodiment, step S710 further includes the following steps:

[0123] Step S1010: The server closes the solenoid valve corresponding to the upstream detection point in the second analysis group.

[0124] Specifically, the solenoid valve is closed in time to avoid water leakage and waste.

[0125] In the eleventh embodiment of the smart water management method based on big data proposed by the present invention, based on the seventh embodiment, step S710 further includes the following steps:

[0126] Step S1110: The server obtains the difference in real-time flow values ​​of two detection points in the second group to be analyzed at each moment in a fourth preset time period (eg, 10 minutes) in the future, and marks it as a difference set.

[0127] Step S1120: The server determines whether the difference set has an increasing trend.

[0128] If so, step S110 is executed: the server marks the water pipe section corresponding to the second to-be-analyzed group as a priority maintenance section.

[0129] Specifically, if the difference set has an increasing trend, it means that the leakage point is expanding and the situation is more urgent, so the water pipe section corresponding to the second group to be analyzed is marked as a priority maintenance section.

[0130] The present invention also proposes a smart water management system based on big data, which is applied to any of the smart water management methods based on big data; the system includes a sensor component, a server and a management terminal; each water pipe in the water network is provided with multiple detection points; the number of sensor components is consistent with the number of detection points, and the sensor components correspond to the detection points one-to-one; the sensor components are set at the corresponding detection points; the same water pipe is provided with at least two detection points, and the two adjacent detection points are at least separated by a preset distance; the sensor component includes a liquid flow sensor and a wireless communication module; the liquid flow sensor is used to detect the real-time flow value of the water pipe; the liquid flow sensor is connected to the server through the wireless communication module.

[0131] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0132] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better embodiment. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0133] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are protected by the present invention.

Claims

1. A smart water management method based on big data, characterized in that: Applicable to a smart water management system based on big data; the system includes a sensor component, a server and a management terminal; each water pipe of the water pipe network is provided with a plurality of detection points; the number of sensor components is consistent with the number of detection points, and the sensor components and the detection points are provided in a one-to-one correspondence; at least two detection points are provided on the same water pipe, and two adjacent detection points are at least separated by a preset distance; the sensor component includes a liquid flow sensor and a wireless communication module; Liquid flow sensor is used to detect the real-time flow value of water pipe; The liquid flow sensor is connected to the server through a wireless communication module; the method includes: The server obtains the real-time flow value corresponding to each detection point through the sensor component; The server marks two adjacent detection points on the same water pipe that are not connected to other water pipes as the first target group; The server determines whether there is a first target group that meets the first condition, wherein the first condition is: the absolute value of the difference between the real-time flow values ​​of two detection points in the first target group at the same time is greater than a first preset value; If so, the server determines that the water pipe section corresponding to the first target group that meets the first condition has a leak; The server sends the location of the water pipe section corresponding to the first target group that meets the first condition to the management terminal.

2. According to the big data-based smart water management method of claim 1, it is characterized in that: The server obtains the real-time flow value corresponding to each detection point through the sensor component, and then further includes: The server marks the water pipe with the downstream branch as the upper pipe, and marks the detection point of the upper pipe closest to the downstream branch as the upper point; The server marks the downstream branch corresponding to the upper-level pipe as the lower-level pipe, and marks the detection point of the lower-level pipe close to the upper-level point as the lower-level point; The server marks the upper level point and all the lower level points adjacent to the upper level point as the second target group; The server determines whether there is a second target group that meets the second condition, wherein the second condition is that the absolute value of the difference between the sum of the real-time flow values ​​corresponding to all subordinate points in the same second target group and the real-time flow value corresponding to the superior point is greater than the first preset value; If so, the server determines that a water pipe segment between the second target group that satisfies the second condition has a leak; The server sends the locations of the water pipe sections between the second target groups that meet the second condition to the management terminal.

3. The smart water management method based on big data according to claim 2 is characterized in that: The server determines that a water pipe segment between the second target groups that meets the second condition has a leak, and then further includes: The server marks the first target group that meets the second condition as the first group to be analyzed; The server obtains the flow value data corresponding to each second preset time period of each day in the first preset time period when no leakage accident occurred in the past for the first group to be analyzed, and obtains the historical average flow value of the second preset time period corresponding to each subordinate point in the first group to be analyzed at the current time based on the flow value data; The server obtains the standard flow rate drop difference corresponding to each subordinate point in the first group to be analyzed based on the historical average flow rate value; The server determines whether the leakage point is located between the upper point of the first group to be analyzed and the intersection of the upper pipe and the nearest lower pipe based on the real-time flow value corresponding to each lower point in the first group to be analyzed, the corresponding historical average flow value, and the corresponding standard flow drop difference.

4. The smart water management method based on big data according to claim 3 is characterized in that: The server obtains the calculation formula of the standard flow rate drop difference corresponding to each subordinate point in the first group to be analyzed based on the historical average flow rate value: In the formula, C B,i is the standard flow rate drop difference corresponding to the i-th subordinate point in the first group to be analyzed, i is a positive integer less than or equal to N, and N is the number of all subordinate points in the first group to be analyzed; L L,i is the historical average flow value of the second preset time period corresponding to the i-th subordinate point in the first group to be analyzed at the current moment; L S,S is the real-time flow value corresponding to the upper point in the first group to be analyzed; L S,i is the real-time flow value corresponding to the i-th subordinate point in the first group to be analyzed.

5. The smart water management method based on big data according to claim 4 is characterized in that: The server determines whether the leakage point is located between the upper point in the first group to be analyzed and the intersection of the upper pipe and the nearest lower pipe based on the real-time flow value corresponding to each lower point in the first group to be analyzed, the corresponding historical average flow value, and the corresponding standard flow drop difference, including: The server marks the difference between the real-time flow value of each subordinate point in the first group to be analyzed and the historical average flow value of the second preset time period corresponding to the current moment as the actual difference; The server calculates the absolute value of the difference between the standard flow rate drop difference and the actual difference corresponding to each subordinate point in the first group to be analyzed, and marks it as a deviation value, wherein the calculation formula of the deviation value is: P C,i =|(L L,i -L S,i )-C B,i |, Where P C,i is the deviation value corresponding to the i-th subordinate point in the first group to be analyzed; The server determines whether each deviation value is less than a second preset value; If so, the server determines that the leak point is located between the upper point of the first group to be analyzed and the intersection of the upper pipe and the nearest lower pipe; If not, the server determines that the leakage point is not located between the upper point of the first group to be analyzed and the intersection of the upper pipe and the nearest lower pipe.

6. The smart water management method based on big data according to claim 5 is characterized in that: The server determines that the leakage point is not located between the upper point of the first group to be analyzed and the intersection of the upper pipe and the nearest lower pipe, and then further includes: The server marks the lower-level points in the first group to be analyzed whose actual difference is less than the second preset value as safe points; The server marks other subordinate points in the first group to be analyzed except the safe points as target points; The server determines that a leak occurs between the intersection point between the water pipe corresponding to the target point and the adjacent upper pipe and the target point.

7. The smart water management method based on big data according to claim 1 is characterized in that: The sensor assembly further includes a sound intensity sensor in communication with the wireless communication module; the server determines that a water pipe section corresponding to the first target group satisfying the first condition has a leak, and then further includes: The server marks the first target group that meets the first condition as the second group to be analyzed; The server obtains the average value of the real-time sound intensity values ​​collected by the sound intensity sensors of the two detection points in the second group to be analyzed within the third preset time period in the future from the current moment, and marks it as average Value Group; The server marks the downstream detection point in the second to-be-analyzed group as a reference point; The server obtains the distance value between the estimated leakage point and the reference point based on the average value group.

8. The smart water management method based on big data according to claim 7 is characterized in that: The server obtains the distance value between the estimated leakage point and the reference point based on the average value group by the following calculation formula: Wherein, L2 is the distance between the estimated leakage point and the reference point, in meters; L1 is the distance between the two detection points in the second group to be analyzed, in meters; q1 is the average value corresponding to the upstream detection point in the average value group; q2 is the average value corresponding to the reference point.

9. The smart water management method based on big data according to claim 7 is characterized in that: The system further comprises solenoid valves having the same number as the detection points; the solenoid valves correspond to the detection points one by one; the solenoid valves are arranged close to the corresponding detection points, and the solenoid valves are located downstream of the flow sensors corresponding to the corresponding detection points; The solenoid valve is used to control the on and off of the water pipe; the server marks the first target group that meets the first condition as the second group to be analyzed, and then further includes: The server closes the solenoid valves corresponding to the two detection points in the second group to be analyzed; The server marks the solenoid valve corresponding to the upstream detection point in the second to-be-analyzed group as the target valve; The server controls the target valve to open and marks the opening time as the starting time; The server obtains the time from the start time to the time when the real-time sound intensity value collected by the sound intensity sensor corresponding to the downstream detection point in the second to-be-analyzed group reaches the maximum value, and marks it as the path time; The server calculates the distance between the estimated leakage point and the downstream detection point in the second group to be analyzed based on the passage time, water flow speed and the distance between the two detection points in the second group to be analyzed: Where, L2 is the distance between the estimated leakage point and the downstream detection point in the second group to be analyzed; S1 is the water flow velocity, in m / s; S2 is the propagation speed of sound on the water pipe, in m / s; L1 is the distance between the two detection points in the second group to be analyzed, in m; t is the passage time, in s。 10. A smart water management system based on big data, characterized in that: A smart water management method based on big data applied to any one of claims 1-9; the system includes a sensor component, a server and a management terminal; each water pipe of the water pipe network is provided with a plurality of detection points; the number of sensor components is consistent with the number of detection points, and the sensor components correspond to the detection points one by one; the sensor components are arranged at corresponding detection points; at least two detection points are arranged on the same water pipe, and two adjacent detection points are at least separated by a preset distance; the sensor component includes a liquid flow sensor and a wireless communication module; the liquid flow sensor is used to detect the real-time flow value of the water pipe; the liquid flow sensor is connected to the server through the wireless communication module.

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