CN115146851A - Artificial intelligence-based water supply pipe network optimization method - Google Patents
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Abstract
The invention discloses an artificial intelligence-based water supply network optimization method, which comprises the steps of installing a plurality of pressure sensors in a water supply pipeline, acquiring pressure data of the water supply pipeline with different sections by using the pressure sensors, calculating data acquisition point coefficients of the water supply pipeline sensors needing noise reduction, calculating distribution threshold values of the data acquisition point coefficients of the water supply pipeline sensors, establishing a water supply pipeline pressure change function, describing the pressure distribution of the water supply pipeline by using the function, calculating the probability of the change of the water supply pipeline pressure data along with the change of pipeline leakage positions and counting the number of positions of jump of the water supply pipeline pressure, counting the number of positions of no jump of the water supply pipeline pressure, establishing a water supply pressure selection function, and calculating the required pressure value between the nodes of the water supply pipeline.
Description
Technical Field
The invention relates to the field of water supply and algorithms, in particular to an artificial intelligence-based water supply network optimization method.
Background
The water supply network pressure monitoring system is set by taking on-line monitoring equipment as a core, applying the intelligent Internet of things technology and passing through special analysis software. The comprehensive monitoring management system is used for rapidly collecting, storing, processing, inquiring, reporting and early warning monitoring information such as pressure, flow and the like of a tap water pipeline in real time.
The water supply network pressure monitoring system is suitable for a water supply enterprise remote monitoring water supply network, and a worker can remotely monitor the pressure and flow conditions of the water supply network in the whole city at a water supply dispatching center. Scientifically commands the start and stop of water supply equipment of each water plant, ensures the balance of water supply pressure and stable flow, and timely discovers and predicts the occurrence of pipe explosion accidents.
However, various problems are encountered in the current water supply process, such as: the pressure is insufficient, so that the water yield is low; the pressure is too high, and the condition of pipe explosion is easily caused.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides an artificial intelligence-based water supply pipe network optimization method.
The technical scheme adopted by the invention is that the method comprises the following steps:
s1, installing a plurality of pressure sensors on a water supply pipeline, acquiring pressure data of the water supply pipeline of different sections by using the pressure sensors, and calculating data acquisition point coefficients of the water supply pipeline sensors needing noise reduction;
s2, calculating a distribution threshold value of a coefficient of a data acquisition point of a sensor of the water supply pipe;
s3, establishing a function of pressure change of the water supply pipe, and describing the pressure distribution of the water supply pipeline by using the function;
s4, calculating the probability that the pressure data of the water supply pipe changes along with the change of the leakage position of the pipeline and counting the number of positions where the pressure of the water supply pipe jumps;
s5, counting the number of positions where the pressure of the water supply pipe does not jump;
s6, establishing a water supply pressure selection function;
s7, calculating a pressure value required between the nodes of the water supply pipeline and the nodes;
and S8, adjusting the water supply pressure by the water supply center according to the calculation result of the pressure data.
Further, the acquisition point coefficient is expressed as:
wherein, B is x (a) Data acquisition points a and B of the pressure sensor B at x position of the water supply pipe represent radius of the water supply pipe at the data acquisition points, mu represents data transmission rate of the pressure sensor in the water supply pipe, eta represents transmission power of data transmission channel of the pressure sensor in the water supply pipe, A xy And the coefficient of a data acquisition point of the water supply pipe pressure sensor needing noise reduction is shown, and x and y represent the abscissa and the ordinate of the position of the data acquisition point.
Further, the distribution threshold value of the coefficient of the data acquisition point of the water supply pipe sensor is calculated by the expression:
wherein c represents the distribution threshold value of the coefficient of the data acquisition points of the water supply pipe sensor, g ' represents the distance between the data acquisition points, d ' represents the data transmission efficiency, c ' represents the sending rate of the data by the water supply pipe sensor, c ' represents the receiving rate of the data by the water supply center, e ' represents the value of the decentralization coefficient of the data acquisition points, and f ' represents the path length of the data d ' in the data transmission of the water supply pipe sensor.
Further, the water supply pipe pressure variation function has the expression:
wherein h is i The function of the change of the pressure of the water supply pipe is represented, and p represents the number of the pipeline sections for supplying high-pressure waterQ represents a pipe distance for high pressure water supply, z represents the number of pipe segments for low pressure water supply, F' represents a pipe distance for low pressure water supply, L represents a water supply pipe sensor data transmission distance, o i Weight coefficient, r, representing the distance of water supply from a water supply pipe for high pressure i And weight coefficients representing the low-pressure water supply distance of the water supply pipe, wherein the weight coefficients respectively correspond to the channel condition of the high-pressure data and the channel condition of the low-pressure data of the water supply pipe sensor.
Further, the probability of the change of the water supply pipe pressure data along with the change of the leakage position of the pipeline is expressed as follows:
s(t)＝1-uw，t∈z
wherein s (t) represents the probability of the change of the water supply pipe pressure data with the change of the pipeline leakage position, z represents all leakage points of the water supply pipeline, t represents any one of all leakage points, u represents the density value of the water supply pipe pressure data, and w represents a leakage factor;
further, the number of positions where the pressure of the water supply pipe jumps is expressed as follows:
wherein, P represents the position number of the pressure jump of the water supply pipe, xi represents the jump coefficient, lambda represents the distance between the sensors of the water supply pipe, and Q represents the mean value of the pressure change of the water supply pipe in unit time.
Further, the number of positions where the pressure of the water supply pipe does not jump is expressed as:
wherein Y represents the number of positions where the pressure of the water supply pipe does not jump, ξ 1 A data factor representing the water supply line, and n represents the number of sensors that are smooth to pass water supply pressure data.
Further, the water supply pressure selection function has the expression:
wherein H Yn (l, v) represents a water supply pressure selection function, l represents a water supply distance, H wz Indicating the number of nodes in the pipeline requiring staged pressurisation, λ jc Number of nodes, λ, representing high voltage kc Number of nodes representing low pressure, w real-time pressure in water supply pipe, w 0 Represents the average pressure in the water supply line;
further, the maximum value of the pressure which can be borne by the water supply pipeline is as follows:
where M represents the maximum pressure that the water supply line can withstand.
Further, the pressure value required between the nodes of the water supply pipeline is expressed as follows:
H Dn (l)＝lH g
wherein H Dn (l) Indicating the pressure value, H, required between the nodes of the water supply pipe g The pressure value lost in the pipe is indicated, and l represents the water supply distance.
Has the advantages that:
the invention provides an artificial intelligence-based water supply network optimization method, which comprises the steps of collecting pressure data of water supply pipelines of different sections by using a pressure sensor, then calculating a data collection point coefficient of the water supply pipeline sensor needing noise reduction and a distribution threshold value of the data collection point coefficient, establishing a water supply pipeline pressure change function, counting the position number of jumping and unsent jumping of the water supply pipeline pressure and the like.
Drawings
FIG. 1 is a flow chart of the overall steps of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments can be combined with each other without conflict, and the present application will be further described in detail with reference to the drawings and specific embodiments.
As shown in fig. 1, a water supply network optimization method based on artificial intelligence includes:
s1, installing a plurality of pressure sensors on a water supply pipeline, acquiring pressure data of the water supply pipeline of different sections by using the pressure sensors, and calculating data acquisition point coefficients of the water supply pipeline sensors needing noise reduction;
the invention adopts an HY-P300 pressure sensor to monitor the pressure of a water supply pipeline, supports standard analog quantity output of 4-20mA, 0-10V, 0-5V, 1-5V, 485 and the like, adopts connecting threads of M20X 1.5, M14X 1.5, G1/4, G1/2 and the like, adopts a power supply: DC24V (9 to 36V), output: 4-20mA;1-5V;0-10V; RS485, precision: the 0.3 level and the 0.5 level are selectable, and the range is as follows: 0.1 to 60MPa (range of span cannot be lower than 10 KPa), pressure type: gauge pressure, absolute pressure, sealing pressure, compensation temperature: -10 ℃ to 70 ℃, operating temperature: -20 ℃ to 85 ℃, medium temperature: -209 ℃ -85 ℃, storage temperature: -40 ℃ to 85 ℃, zero temperature drift: 1.5% FS/10C, sensitivity temperature drift: 1.5% FS/10 ℃, overload pressure: 150% FS, long term stability ±. 0.2% FS/year, response time: s100ms ≦ (up to 90% FS), insulation: 100M Ω,25VDC, protection level: IP65, load resistance: (U-8.5V)/0.02A, U = supply voltage.
Selecting a water supply pipe pressure sensor data communication node, decomposing water supply pipe pressure sensor communication node data to obtain a node coefficient value, determining a threshold value of the water supply pipe pressure sensor data node according to the node coefficient value, and after carrying out noise reduction processing on the threshold value of the node coefficient, controlling the establishment of a water supply pipe pressure sensor communication transmission channel, clearing interference obstacles of the water supply pipe pressure sensor communication channel, and laying a foundation for water supply pressure algorithm control output.
S2, calculating a distribution threshold value of a data acquisition point coefficient of a sensor of the water supply pipe;
the distribution threshold is a description of the pressures at different nodes of the water supply pipeline, because the pressure distributed to each node is different for a plurality of times during the water supply process, the distribution threshold indicates the maximum pressure which can be borne by the node, and the size of the distribution threshold is related to the distance between the data acquisition points, the value of the decentralization coefficient of the data acquisition points and the length of the path in data transmission.
S3, establishing a function of pressure change of the water supply pipe, and describing the pressure distribution of the water supply pipeline by using the function;
the pressure variation function of the water supply pipe is related to the number of pipeline sections for high-pressure water supply, the distance of the high-pressure water supply pipeline, the number of pipeline sections for low-pressure water supply, the distance of the low-pressure water supply pipeline, the data transmission distance of a sensor of the water supply pipe and the like, long-distance water supply is not a constant pressurization process and needs pressurization and pressurization to be performed alternately, if the constant pressurization process is the constant pressurization process, great pressure is caused to the water supply pipeline, and even the pipeline is broken artificially.
S4, calculating the probability that the pressure data of the water supply pipe changes along with the change of the leakage position of the pipeline and counting the number of positions at which the pressure of the water supply pipe jumps;
the probability that the pressure data of the water supply pipe changes along with the change of the leakage position of the pipeline is related to the number of leakage points, the density value of the pressure data of the water supply pipe and a leakage factor, the number of the positions where the pressure of the water supply pipe jumps is related to a jump coefficient, the distance between sensors of the water supply pipe and the mean value of the pressure change of the water supply pipe in unit time;
s5, counting the number of positions where the pressure of the water supply pipe does not jump;
the number of the positions where the pressure of the water supply pipe does not jump is related to the data factor of the water supply pipeline and the number of the sensors for smooth passing of the water supply pressure data, the number of the positions where the pressure of the water supply pipe does not jump represents the normal operation of the water supply pipe, and the more the number of the positions where the pressure of the water supply pipe does not jump is, the better the operation of the water supply pipe is, and the less the leakage condition is.
S6, establishing a water supply pressure selection function;
the establishment of the water supply pressure selection function is beneficial to the water supply center to adjust the water supply pressure in time, and the water supply center can utilize the function to realize automatic control on the water supply pressure, thereby ensuring the stability of water supply.
S7, calculating a pressure value required between the nodes of the water supply pipeline and the nodes;
when going out the leakage between water supply pipe node and the node, obvious change can appear in pressure, help in time finding the leakage point on the one hand through calculating the pressure value that needs between water supply pipe node and the node, on the other hand helps grasping the pressure demand between node and the node in real time, and the pressure value of this pressure value and loss in the pipeline is relevant with the water supply distance.
And S8, adjusting the water supply pressure by the water supply center according to the calculation result of the pressure data.
The adjustment of the water supply center to the pressure can be manually adjusted, and a corresponding threshold value can also be set, and when the pressure in the pipeline is greater than or less than the corresponding threshold value, the automatic adjustment is carried out by using a computer.
The coefficient of the data acquisition point is expressed as:
wherein, B x (a) Data acquisition points a and B of the pressure sensor B at x position of the water supply pipe represent radius of the water supply pipe at the data acquisition points, mu represents data transmission rate of the pressure sensor in the water supply pipe, eta represents transmission power of data transmission channel of the pressure sensor in the water supply pipe, A xy And the coefficient of a data acquisition point of the water supply pipe pressure sensor needing noise reduction is shown, and x and y represent the abscissa and the ordinate of the position of the data acquisition point.
Calculating the distribution threshold value of the coefficient of the data acquisition point of the water supply pipe sensor, wherein the expression is as follows:
wherein c represents the distribution threshold value of the coefficient of the data acquisition points of the water supply pipe sensor, g ' represents the distance between the data acquisition points, d ' represents the data transmission efficiency, c ' represents the sending rate of the data by the water supply pipe sensor, c ' represents the receiving rate of the data by the water supply center, e ' represents the value of the decentralization coefficient of the data acquisition points, and f ' represents the path length of the data d ' in the data transmission of the water supply pipe sensor.
The water supply pipe pressure change function has the expression:
wherein h is i A function representing a variation in pressure of a water supply pipe, p a number of stages of a pipe for supplying water at high pressure, q a distance of the pipe for supplying water at high pressure, z a number of stages of the pipe for supplying water at low pressure, F' a distance of the pipe for supplying water at low pressure, L a data transmission distance of a sensor of the water supply pipe, and o i Weight coefficient r representing the distance of water supply pipes for supplying water under high pressure i And weight coefficients representing the low-pressure water supply distance of the water supply pipe, wherein the weight coefficients respectively correspond to the channel condition of the high-pressure data and the channel condition of the low-pressure data of the water supply pipe sensor.
The probability that the water supply pipe pressure data changes along with the change of the leakage position of the pipeline is expressed as follows:
s(t)＝1-uw，t∈z
wherein s (t) represents the probability of the change of the water supply pipe pressure data with the change of the pipeline leakage position, z represents all leakage points of the water supply pipeline, t represents any one of all leakage points, u represents the density value of the water supply pipe pressure data, and w represents a leakage factor;
the quantity of the positions where the pressure of the water supply pipe jumps is expressed as follows:
wherein, P represents the position number of the pressure jump of the water supply pipe, xi represents the jump coefficient, lambda represents the distance between the sensors of the water supply pipe, and Q represents the mean value of the pressure change of the water supply pipe in unit time.
The number of positions where the pressure of the water supply pipe does not jump is expressed as follows:
wherein Y represents the number of positions where the pressure of the water supply pipe does not jump, ξ 1 And n represents the number of sensors for smooth passing of water supply pressure data.
A supply water pressure selection function, the expression being:
wherein H Yn (l, v) represents a water supply pressure selection function, l represents a water supply distance, H wz Indicating the number of nodes in the pipeline requiring staged pressurisation, λ jc Number of nodes, λ, representing high voltage kc Indicating the number of nodes of low pressure, w indicating the real-time pressure in the water supply pipe, w 0 Represents the average pressure in the water supply line;
the maximum value of the pressure which can be borne by the water supply pipeline is as follows:
where M represents the maximum pressure that the water supply line can withstand.
The pressure value that needs between water supply pipe node and node, the expression is:
H Dn (l)＝lH g
wherein H Dn (l) Indicating the pressure value, H, required between the nodes of the water supply pipe g The pressure value lost in the pipe is represented, and l represents the water supply distance.
The invention provides an artificial intelligence-based water supply network optimization method, which comprises the steps of collecting pressure data of water supply pipelines of different sections by using a pressure sensor, then calculating a data collection point coefficient of the water supply pipeline sensor needing noise reduction and a distribution threshold value of the data collection point coefficient, establishing a water supply pipeline pressure change function, counting the position number of jumping and unsent jumping of the water supply pipeline pressure and the like.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various equivalent changes, modifications, substitutions and alterations can be made herein without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.
Claims (8)
1. A water supply network optimization method based on artificial intelligence is characterized by comprising the following steps:
s1, installing a plurality of pressure sensors on a water supply pipeline, acquiring pressure data of the water supply pipeline of different sections by using the pressure sensors, and calculating data acquisition point coefficients of the water supply pipeline sensors needing noise reduction;
s2, calculating a distribution threshold value of a data acquisition point coefficient of a sensor of the water supply pipe;
s3, establishing a function of pressure change of the water supply pipe, and describing the pressure distribution of the water supply pipeline by using the function;
s4, calculating the probability that the pressure data of the water supply pipe changes along with the change of the leakage position of the pipeline and counting the number of positions at which the pressure of the water supply pipe jumps;
step S5: counting the number of positions where the pressure of the water supply pipe does not jump;
s6, establishing a water supply pressure selection function;
s7, calculating a pressure value required between the nodes of the water supply pipeline and the nodes;
and S8, adjusting the water supply pressure by the water supply center according to the calculation result of the pressure data.
2. The water supply network optimization method based on artificial intelligence, as set forth in claim 1, wherein the collection point coefficients are expressed as:
wherein, B x (a) Data acquisition points a and B of the pressure sensor B at x position of the water supply pipe represent radius of the water supply pipe at the data acquisition points, mu represents data transmission rate of the pressure sensor in the water supply pipe, eta represents transmission power of data transmission channel of the pressure sensor in the water supply pipe, A xy And the coefficient of a data acquisition point of the water supply pipe pressure sensor needing noise reduction is shown, and x and y represent the abscissa and the ordinate of the position of the data acquisition point.
3. The artificial intelligence based water supply network optimization method as claimed in claim 1, wherein the distribution threshold of the coefficient of the data acquisition points of the water supply pipe sensors is calculated by the expression:
wherein c represents the distribution threshold value of the coefficient of the data acquisition points of the water supply pipe sensor, g ' represents the distance between the data acquisition points, d ' represents the data transmission efficiency, c ' represents the sending rate of the data by the water supply pipe sensor, c ' represents the receiving rate of the data by the water supply center, e ' represents the value of the decentralization coefficient of the data acquisition points, and f ' represents the path length of the data d ' in the data transmission of the water supply pipe sensor.
4. The artificial intelligence based water supply network optimization method of claim 1, wherein the water supply pipe pressure variation function is expressed as:
wherein h is i A function representing a variation in pressure of a water supply pipe, p represents the number of stages of a pipe for supplying water at high pressure, q represents a distance of the pipe for supplying water at high pressure, z represents the number of stages of the pipe for supplying water at low pressure, F' represents a distance of the pipe for supplying water at low pressure, L represents a data transmission distance of a water supply pipe sensor, and o i Weight coefficient r representing the distance of water supply pipes for supplying water under high pressure i And weight coefficients representing the low-pressure water supply distance of the water supply pipe, wherein the weight coefficients respectively correspond to the channel condition of the high-pressure data and the channel condition of the low-pressure data of the water supply pipe sensor.
5. The artificial intelligence based water supply network optimization method of claim 1, wherein the probability that the water supply pipe pressure data changes with the change of the leakage position of the pipeline is expressed as:
s(t)＝1-uw，t∈z
wherein s (t) represents the probability of the change of the water supply pipe pressure data with the change of the pipeline leakage position, z represents all leakage points of the water supply pipeline, t represents any one of all leakage points, u represents the density value of the water supply pipe pressure data, and w represents a leakage factor;
the number of the positions of the pressure jump of the water supply pipe is expressed as follows:
wherein, P represents the position number of the pressure jump of the water supply pipe, xi represents the jump coefficient, lambda represents the distance between the sensors of the water supply pipe, and Q represents the mean value of the pressure change of the water supply pipe in unit time.
6. The artificial intelligence based water supply network optimization method of claim 1, wherein the number of positions where no jump occurs in the pressure of the water supply pipe is expressed as:
wherein Y represents the number of positions where the pressure of the water supply pipe does not jump, ξ 1 A data factor representing the water supply line, and n represents the number of sensors that are smooth to pass water supply pressure data.
7. The artificial intelligence based water supply network optimization method of claim 1, wherein the water supply pressure selection function has an expression:
wherein H Yn (l, v) represents a water supply pressure selection function, l represents a water supply distance, H wz Indicating the number of nodes in the pipeline requiring staged pressurisation, λ jc Number of nodes, λ, representing high voltage kc Number of nodes representing low pressure, w real-time pressure in water supply pipe, w 0 Represents the average pressure in the water supply line;
the maximum value of the pressure which can be borne by the water supply pipeline is as follows:
where M represents the maximum pressure that the water supply line can withstand.
8. The artificial intelligence based water supply network optimization method of claim 1, wherein the pressure values required between the water supply pipeline nodes are expressed as:
H Dn (l)＝lH g
wherein H Dn (l) Indicating the pressure value, H, required between the nodes of the water supply pipe g The pressure value lost in the pipe is represented, and l represents the water supply distance.
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Application publication date: 20221004 |