CN118735234B - Wisdom delivery management system - Google Patents
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- CN118735234B CN118735234B CN202411230582.2A CN202411230582A CN118735234B CN 118735234 B CN118735234 B CN 118735234B CN 202411230582 A CN202411230582 A CN 202411230582A CN 118735234 B CN118735234 B CN 118735234B
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- 238000007726 management method Methods 0.000 title claims description 14
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 237
- 238000005192 partition Methods 0.000 claims abstract description 43
- 230000002159 abnormal effect Effects 0.000 claims abstract description 42
- 238000004458 analytical method Methods 0.000 claims abstract description 22
- 230000000694 effects Effects 0.000 claims abstract description 12
- 230000001788 irregular Effects 0.000 claims description 51
- 238000000034 method Methods 0.000 claims description 10
- 230000005856 abnormality Effects 0.000 claims description 7
- 238000004457 water analysis Methods 0.000 claims description 6
- 238000004519 manufacturing process Methods 0.000 description 7
- 230000008859 change Effects 0.000 description 4
- 238000001514 detection method Methods 0.000 description 4
- 238000005406 washing Methods 0.000 description 4
- 241000251468 Actinopterygii Species 0.000 description 2
- 238000003287 bathing Methods 0.000 description 2
- 230000003796 beauty Effects 0.000 description 2
- 235000013361 beverage Nutrition 0.000 description 2
- 238000010411 cooking Methods 0.000 description 2
- 230000007423 decrease Effects 0.000 description 2
- 235000012054 meals Nutrition 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 239000008213 purified water Substances 0.000 description 2
- 230000009182 swimming Effects 0.000 description 2
- 230000002411 adverse Effects 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- 235000012206 bottled water Nutrition 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 239000003651 drinking water Substances 0.000 description 1
- 238000004880 explosion Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
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Abstract
The invention relates to the technical field of urban water supply, in particular to an intelligent water supply management system. Comprises a data acquisition module and a data acquisition module, the system is used for acquiring the pipe network flow acquired by the front-end acquisition equipment; the partition identification module is used for identifying the single daily water consumption of each partition according to the preset DMA partition management and the pipe network flow of each partition; the fluctuation identification module is preset with the user types of all the subareas, and generates a normal fluctuation interval of the daily water consumption according to the user types; the abnormal identification module is used for marking the subarea as an abnormal subarea when the single daily water consumption of the subarea is outside a normal fluctuation interval; the time period setting module is used for identifying a minimum flow period according to the pipe network flow of each time period of the abnormal partition; and the user analysis module is used for analyzing the activity period of each user according to the pipe network flow of the abnormal partition. The estimation error at the time of night minimum flow identification can be reduced.
Description
Technical Field
The invention relates to the technical field of urban water supply, in particular to an intelligent water supply management system.
Background
Water supply networks are an important component of modern urban construction, which takes on the important task of delivering potable water to thousands of households. However, the interference of objective factors such as pipeline aging, natural disasters, uneven ground subsidence and the like can lead to the failure of water supply system components, such as pipeline rupture, pipe explosion, leakage and the like. The water supply network is failed to generate a plurality of adverse effects, so that the waste of water resources is caused, the trouble is generated to the normal production and life of people, and the damage to public safety caused by road surface collapse is also caused when serious. Therefore, the water supply enterprises need to monitor and identify the pipeline leakage condition;
In the prior art, a common method for identifying leakage is a night minimum flow method, the leakage is estimated by analyzing night flow, the consumption of water by most users at night is usually between 2:00 and 4:00 a day in the early morning when the users stop consuming water, the consumption of water by the users is minimum in the period, the main flow in the water supply system is from leakage, and the efficiency of the water supply system is estimated by statistically analyzing the data to estimate the leakage.
However, this requirement requires a small number of people to use water at night, and in practice, there are users who use much water at night, such as bath centers, hotel hotels, night-night stores, and some residents. The more users are used, the greater the error in the estimation of leakage. Therefore, how to reduce the leakage estimation error is a problem to be solved.
Disclosure of Invention
The technical problem to be solved by the invention is to provide an intelligent water supply management system which can reduce estimation errors during night minimum flow identification.
The basic scheme provided by the invention is as follows: the intelligent water supply management system comprises front-end acquisition equipment and a server, wherein the server comprises a data acquisition module, a partition identification module, a fluctuation identification module, an abnormality identification module, a time period setting module and a time period analysis module;
The data acquisition module is used for acquiring the pipe network flow acquired by the front-end acquisition equipment;
the partition identification module is used for identifying the single daily water consumption of each partition according to the preset DMA partition management and the pipe network flow of each partition;
the fluctuation identification module is preset with the user types of all the subareas, and generates a normal fluctuation interval of the daily water consumption according to the user types;
the abnormal identification module is used for marking the subarea as an abnormal subarea when the single daily water consumption of the subarea is outside a normal fluctuation interval;
the time period setting module is used for identifying a minimum flow period according to the pipe network flow of each time period of the abnormal partition;
the user analysis module is used for analyzing the activity period of each user according to the pipe network flow of the abnormal partition;
And the period analysis module is used for identifying the number of active users in the minimum flow period of the abnormal partition, and analyzing whether the abnormal partition has leakage or not according to the pipe network flow of the minimum flow period of the abnormal partition and the user type of the active users in the minimum flow period when the number of active users is higher than a preset threshold value.
The principle and the advantages of the invention are as follows: the pipe network flow is collected by front-end collection equipment, the front-end collection equipment is specifically a flowmeter, and the front-end collection equipment is arranged at key nodes such as each main pipe and each branch pipe and monitors the flow condition of the whole pipe network. And the single daily water consumption of each area is counted independently through a preset DMA partition (independent metering area). And setting a normal fluctuation interval for each partition independently according to the user type and the water consumption of the user in each area. When the water consumption of a certain subarea exceeds the normal fluctuation range through the monitoring of the pipe network flow, judging that the water consumption is abnormal in the subarea, and identifying the minimum flow period according to the regional pipe network flow. Since the leakage amount is unchanged and the water consumption of a user is minimum when the pipe network is leaked, whether leakage exists is judged by the mode. Thereafter, the active user during the time period, i.e., the user still using water during the time period, is identified, and specifically by collecting meter readings for each user, meter collection can be individually set for each user's household water pipe. And according to the user types of the active users, analyzing whether the abnormal area has leakage or not.
Compared with the prior art, when the leakage is analyzed in the minimum flow period, the current active user is introduced as a variable, and the water consumption is different according to the different types of the active user, so that the change of the actual water consumption is different, and the leakage estimation error can be reduced when the leakage judgment is estimated.
Further, the fluctuation recognition module comprises a type recognition module, a rule recognition module and a water analysis module;
The type identification module is used for respectively identifying the user types of all users in the subareas, wherein the user types comprise resident domestic water, non-resident water and special water;
The system comprises a rule recognition module, a rule recognition module and a control module, wherein the rule recognition module is used for dividing each user into regular users and irregular users according to the single daily water consumption condition of the users in a preset period, giving a water fluctuation factor M to the users, wherein when the single daily water consumption of the users is more than the upward fluctuation times, the value of M is [0, 1], the more the downward fluctuation times are, the more M is close to 0, and when the single daily water consumption of the users is more than the downward fluctuation times, the value of M is [1,2], the more the upward fluctuation times are, the more M is close to 2;
And a water analysis module:
The method comprises the steps of obtaining average daily water consumption A of a regular user in a preset period, and setting the basic water consumption to be A according to the water consumption;
The method comprises the steps of obtaining average daily water consumption B of each irregular user in a preset period, and setting a fluctuation upper limit according to the water consumption;
wherein, For the upper bound of fluctuation, x is the irregular number of users,For the i-th irregular user to average the water usage per day in a preset period,Presetting a water fluctuation factor in a period for an ith irregular user;
when the water consumption per day exceeds A+P, the area is judged to be an abnormal area.
The user types include domestic water for residents, domestic water for non-residents, and special water. The resident water is water for daily life of ordinary residents, and the non-resident water includes industrial process water and administrative and utility water, such as army, financial supply business, municipal water, consumption water, enterprise process water, etc. The special water comprises bathing, massaging, entertainment, water park, automobile washing, body building, beauty and hairdressing, business hall, purified water, beverage production, travel hotel, restaurant, slaughterhouse, fish pond, etc. According to the single daily water consumption condition of each user in a preset period, for example, the single daily water consumption of each user in the past 30 days, the users are divided into regular users and irregular users, wherein the regular users refer to users with similar single daily water consumption, and most of the users are resident water consumption users. Irregular users refer to users with larger variation of water consumption in a single day, and the water consumption varies according to various factors, for example, in bath, the water consumption in a single day varies according to different numbers of guests. And setting a fluctuation factor M for the irregular user according to the single daily water consumption condition of the irregular user. When the number of upward fluctuation times of the water consumption per day of the user is larger, the value of M is [0,1 ], and when the number of upward fluctuation times of the water consumption per day of the user is larger, the value of M is [1,2], and the number of upward fluctuation times is larger, and is closer to 2. Specifically, when the single daily water consumption of the user has more downward fluctuation times, the actual water consumption of the user is more frequently compared with the previous day, and the actual water consumption of the user can be increased in a certain day, so that the situation mostly belongs to a recreational place, the water consumption is increased during the weekend, the water consumption is reduced during the working day, and the possibility that the average water consumption is smaller in comparison with the period of the user after the actual water consumption changes is larger and the value is smaller than 1. If the number of upward and downward waves is close, for example, the water consumption per day is large and the water consumption per day is small. Most of the situations belong to the situations of fishponds, swimming pipes and the like, and when water is changed every other day, after the actual water consumption of the type of users changes, the average water consumption in the period of the users is closer to that of the users, so that the value of the fluctuation factor M is close to 1. If the number of upward fluctuation times is large, the number of times of water consumption increase in the area is large, and the situation mostly belongs to enterprise production, municipal water and the like, the water consumption increases in working days, and the water consumption decreases in weekends. By setting the fluctuation factor for each irregular user, then setting the normal fluctuation range of each area.
Firstly, aiming at the regular users, the average value of the daily water of the regular users is calculated because the water consumption of the regular users is stable. And multiplying the average value of the single-day water consumption of each irregular user in the period by a fluctuation factor, wherein when the fluctuation factor is smaller than 1, the calculated single-day water consumption is lower than the average water consumption, and when the fluctuation factor is larger than 1, the calculated single-day water consumption is higher than the average water consumption. The estimated water consumption of each irregular user is added to be the upper limit of fluctuation water consumption, and because in the scheme, leakage is monitored, when leakage occurs, the generated water consumption is inevitably higher than the water consumption under normal conditions, so that only the fluctuation upper limit is considered, and when the actual water consumption exceeds A+P, the situation that the water consumption actually generated in the area exceeds the normal water consumption is judged to be an abnormal area.
Further, the abnormality identification module is further configured to determine the area as an abnormal area when a number of days for which the single daily water consumption continuously exceeds a number of a+p exceeds a preset number of days threshold.
Since the water consumption of irregular users is estimated, through continuous multi-day detection, when the water consumption of the area detected by continuous multi-day detection is abnormal, the area is judged to have leakage.
Further, the period analysis module comprises a period identification module and a leakage analysis module;
the time period identification module is used for identifying the water consumption of each time period in the abnormal partition according to the pipe network flow and identifying the minimum flow time period according to the water consumption of each time period;
And the leakage analysis module is used for generating an identification error according to the pipe network flow in the minimum flow period and the historical data in the same period in the period, and judging whether leakage exists or not according to the identification error.
And comparing and analyzing the flow in the minimum period and the flow in the historical period to judge whether the flow generates large change or not, so as to judge whether leakage exists or not.
Further, the leakage analysis module comprises an error identification module and a leakage judgment module;
The error identification module is used for respectively acquiring the pipe network flow when the current period is the active user in the history data of each irregular user, and calculating and identifying errors according to the pipe network flow:
Wherein W is an identification error, In the history data of irregular users, the current time period is used as the average value of the pipe network flow of active users, n is the number of times that the irregular users use as the active users in the history data of the period, and m is the period number of days;
Based on the identification errors, a total identification error is calculated,
Wherein x is the number of irregular users;
A leakage judging module for judging when the difference between the water consumption of the period and the average water consumption of the period exceeds And judging that the region has leakage.
During identification, because the water consumption of regular users and the water consumption period are relatively fixed, such as washing, cooking water and the like in the fixed period, the error mainly depends on irregular users, such as guests, hotels and meal points, and the water consumption can be different when the guests arrive at different times according to different numbers of guests. The average value of each irregular user in the current period is calculated, the fluctuation factor is used for judging that the fluctuation of the irregular user in the period is downward fluctuation or upward fluctuation, the number of times n of the period is introduced as the number of the activity, the more the number of days of occurrence is, the higher the activity frequency of the user in the period is, the more stable the activity is, and the smaller the identification error caused by the user in the period is. And finally, adding the identification errors of all irregular users to obtain a total identification error, and judging that leakage exists when the water consumption in the period and the average water consumption in the previous period exceed the total identification error.
Drawings
FIG. 1 is a logic diagram of an intelligent water supply management system according to an embodiment of the present invention.
Detailed Description
The following is a further detailed description of the embodiments:
an example is substantially as shown in figure 1:
the intelligent water supply management system comprises front-end acquisition equipment and a server, wherein the server comprises a data acquisition module, a partition identification module, a fluctuation identification module, an abnormality identification module, a time period setting module and a time period analysis module;
The data acquisition module is used for acquiring the pipe network flow acquired by the front-end acquisition equipment;
the partition identification module is used for identifying the single daily water consumption of each partition according to the preset DMA partition management and the pipe network flow of each partition;
the fluctuation identification module is preset with the user types of all the subareas, and generates a normal fluctuation interval of the daily water consumption according to the user types;
the abnormal identification module is used for marking the subarea as an abnormal subarea when the single daily water consumption of the subarea is outside a normal fluctuation interval;
the time period setting module is used for identifying a minimum flow period according to the pipe network flow of each time period of the abnormal partition;
the user analysis module is used for analyzing the activity period of each user according to the pipe network flow of the abnormal partition;
And the period analysis module is used for identifying the number of active users in the minimum flow period of the abnormal partition, and analyzing whether the abnormal partition has leakage or not according to the pipe network flow of the minimum flow period of the abnormal partition and the user type of the active users in the minimum flow period when the number of active users is higher than a preset threshold value.
The pipe network flow is collected by front-end collection equipment, the front-end collection equipment is specifically a flowmeter, and the front-end collection equipment is arranged at key nodes such as each main pipe and each branch pipe and monitors the flow condition of the whole pipe network. And the single daily water consumption of each area is counted independently through a preset DMA partition (independent metering area). And setting a normal fluctuation interval for each partition independently according to the user type and the water consumption of the user in each area. When the water consumption of a certain subarea exceeds the normal fluctuation range through the monitoring of the pipe network flow, judging that the water consumption is abnormal in the subarea, and identifying the minimum flow period according to the regional pipe network flow. Since the leakage amount is unchanged and the water consumption of a user is minimum when the pipe network is leaked, whether leakage exists is judged by the mode. Thereafter, the active user during the time period, i.e., the user still using water during the time period, is identified, and specifically by collecting meter readings for each user, meter collection can be individually set for each user's household water pipe. And according to the user types of the active users, analyzing whether the abnormal area has leakage or not.
In the application, when the leakage is analyzed in the minimum flow period, the current active user is introduced as a variable, and the water consumption is different according to the different types of the active user, so that the change generated to the actual water consumption is different, and the leakage estimation error can be reduced when the leakage judgment is estimated.
The fluctuation recognition module comprises a type recognition module, a rule recognition module and a water analysis module;
The type identification module is used for respectively identifying the user types of all users in the subareas, wherein the user types comprise resident domestic water, non-resident water and special water;
The system comprises a rule recognition module, a rule recognition module and a control module, wherein the rule recognition module is used for dividing each user into a regular user and an irregular user according to the single-day water consumption condition of the user in a preset period, giving a water fluctuation factor M to the users, wherein when the single-day water consumption of the user has more downward fluctuation times, the value of M is [0,1 ], the more the downward fluctuation times are, the more M is close to 0, and when the single-day water consumption of the user has more upward fluctuation times, the value of M is [1,2], and the more the upward fluctuation times are, the more M is close to 2;
And a water analysis module:
The method comprises the steps of obtaining average daily water consumption A of a regular user in a preset period, and setting the basic water consumption to be A according to the water consumption;
The method comprises the steps of obtaining average daily water consumption B of each irregular user in a preset period, and setting a fluctuation upper limit according to the water consumption;
wherein, For the upper bound of fluctuation, x is the irregular number of users,For the i-th irregular user to average the water usage per day in a preset period,Presetting a water fluctuation factor in a period for an ith irregular user;
when the water consumption per day exceeds A+P, the area is judged to be an abnormal area.
The user types include domestic water for residents, domestic water for non-residents, and special water. The resident water is water for daily life of ordinary residents, and the non-resident water includes industrial process water and administrative and utility water, such as army, financial supply business, municipal water, consumption water, enterprise process water, etc. The special water comprises bathing, massaging, entertainment, water park, automobile washing, body building, beauty and hairdressing, business hall, purified water, beverage production, travel hotel, restaurant, slaughterhouse, fish pond, etc. According to the single daily water consumption condition of each user in a preset period, for example, the single daily water consumption of each user in the past 30 days, the users are divided into regular users and irregular users, wherein the regular users refer to users with similar single daily water consumption, and most of the users are resident water consumption users. Irregular users refer to users with larger variation of water consumption in a single day, and the water consumption varies according to various factors, for example, in bath, the water consumption in a single day varies according to different numbers of guests. And setting a fluctuation factor M for the irregular user according to the single daily water consumption condition of the irregular user. When the number of upward fluctuation times of the water consumption per day of the user is larger, the value of M is [0,1 ], and when the number of upward fluctuation times of the water consumption per day of the user is larger, the value of M is [1,2], and the number of upward fluctuation times is larger, and is closer to 2. Specifically, when the single daily water consumption of the user has more downward fluctuation times, the actual water consumption of the user is more frequently compared with the previous day, and the actual water consumption of the user can be increased in a certain day, so that the situation mostly belongs to a recreational place, the water consumption is increased during the weekend, the water consumption is reduced during the working day, and the possibility that the average water consumption is smaller in comparison with the period of the user after the actual water consumption changes is larger and the value is smaller than 1. If the number of upward and downward waves is close, for example, the water consumption per day is large and the water consumption per day is small. Most of the situations belong to the situations of fishponds, swimming pipes and the like, and when water is changed every other day, after the actual water consumption of the type of users changes, the average water consumption in the period of the users is closer to that of the users, so that the value of the fluctuation factor M is close to 1. If the number of upward fluctuation times is large, the number of times of water consumption increase in the area is large, and the situation mostly belongs to enterprise production, municipal water and the like, the water consumption increases in working days, and the water consumption decreases in weekends. By setting the fluctuation factor for each irregular user, then setting the normal fluctuation range of each area.
Firstly, aiming at the regular users, the average value of the daily water of the regular users is calculated because the water consumption of the regular users is stable. And multiplying the average value of the single-day water consumption of each irregular user in the period by a fluctuation factor, wherein when the fluctuation factor is smaller than 1, the calculated single-day water consumption is lower than the average water consumption, and when the fluctuation factor is larger than 1, the calculated single-day water consumption is higher than the average water consumption. The estimated water consumption of each irregular user is added to be the upper limit of fluctuation water consumption, and because in the scheme, leakage is monitored, when leakage occurs, the generated water consumption is inevitably higher than the water consumption under normal conditions, so that only the fluctuation upper limit is considered, and when the actual water consumption exceeds A+P, the situation that the water consumption actually generated in the area exceeds the normal water consumption is judged to be an abnormal area.
The abnormality identification module is further configured to determine the area as an abnormal area when a number of days in which the single daily water consumption continuously exceeds a number of a+p exceeds a preset number of days threshold.
Since the water consumption of irregular users is estimated, through continuous multi-day detection, when the water consumption of the area detected by continuous multi-day detection is abnormal, the area is judged to have leakage.
The time period analysis module comprises a time period identification module and a leakage analysis module;
the time period identification module is used for identifying the water consumption of each time period in the abnormal partition according to the pipe network flow and identifying the minimum flow time period according to the water consumption of each time period;
And the leakage analysis module is used for generating an identification error according to the pipe network flow in the minimum flow period and the historical data in the same period in the period, and judging whether leakage exists or not according to the identification error.
And comparing and analyzing the flow in the minimum period and the flow in the historical period to judge whether the flow generates large change or not, so as to judge whether leakage exists or not.
The leakage analysis module comprises an error identification module and a leakage judgment module;
The error identification module is used for respectively acquiring the pipe network flow when the current period is the active user in the history data of each irregular user, and calculating and identifying errors according to the pipe network flow:
Wherein W is an identification error, In the history data of irregular users, the current time period is used as the average value of the pipe network flow of active users, n is the number of times that the irregular users use as the active users in the history data of the period, and m is the period number of days;
Based on the identification errors, a total identification error is calculated,
Wherein x is the number of irregular users;
A leakage judging module for judging when the difference between the water consumption of the period and the average water consumption of the period exceeds And judging that the region has leakage.
During identification, because the water consumption of regular users and the water consumption period are relatively fixed, such as washing, cooking water and the like in the fixed period, the error mainly depends on irregular users, such as guests, hotels and meal points, and the water consumption can be different when the guests arrive at different times according to different numbers of guests. The average value of each irregular user in the current period is calculated, the fluctuation factor is used for judging that the fluctuation of the irregular user in the period is downward fluctuation or upward fluctuation, the number of times n of the period is introduced as the number of the activity, the more the number of days of occurrence is, the higher the activity frequency of the user in the period is, the more stable the activity is, and the smaller the identification error caused by the user in the period is. And finally, adding the identification errors of all irregular users to obtain a total identification error, and judging that leakage exists when the water consumption in the period and the average water consumption in the previous period exceed the total identification error.
The foregoing is merely exemplary of the present application, and specific structures and features well known in the art will not be described in detail herein, so that those skilled in the art will be aware of all the prior art to which the present application pertains, and will be able to ascertain the general knowledge of the technical field in the application or prior art, and will not be able to ascertain the general knowledge of the technical field in the prior art, without using the prior art, to practice the present application, with the aid of the present application, to ascertain the general knowledge of the same general knowledge of the technical field in general purpose. It should be noted that modifications and improvements can be made by those skilled in the art without departing from the structure of the present application, and these should also be considered as the scope of the present application, which does not affect the effect of the implementation of the present application and the utility of the patent. The protection scope of the present application is subject to the content of the claims, and the description of the specific embodiments and the like in the specification can be used for explaining the content of the claims.
Claims (4)
1. An wisdom delivery management system, its characterized in that: the system comprises front-end acquisition equipment and a server, wherein the server comprises a data acquisition module, a partition identification module, a fluctuation identification module, an abnormality identification module, a time period setting module and a time period analysis module;
The data acquisition module is used for acquiring the pipe network flow acquired by the front-end acquisition equipment;
the partition identification module is used for identifying the single daily water consumption of each partition according to the preset DMA partition management and the pipe network flow of each partition;
the fluctuation identification module is preset with the user types of all the subareas, and generates a normal fluctuation interval of the daily water consumption according to the user types;
the abnormal identification module is used for marking the subarea as an abnormal subarea when the single daily water consumption of the subarea is outside a normal fluctuation interval;
the time period setting module is used for identifying a minimum flow period according to the pipe network flow of each time period of the abnormal partition;
the user analysis module is used for analyzing the activity period of each user according to the pipe network flow of the abnormal partition;
The time period analysis module is used for identifying the number of active users in the minimum flow time period of the abnormal partition, and analyzing whether the abnormal partition has leakage or not according to the pipe network flow of the minimum flow time period of the abnormal partition and the user type of the active users in the minimum flow time period when the number of active users is higher than a preset threshold;
The fluctuation recognition module comprises a type recognition module, a rule recognition module and a water analysis module;
The type identification module is used for respectively identifying the user types of all users in the subareas, wherein the user types comprise resident domestic water, non-resident water and special water;
The system comprises a rule recognition module, a rule recognition module and a control module, wherein the rule recognition module is used for dividing each user into regular users and irregular users according to the single daily water consumption condition of the users in a preset period, giving a water fluctuation factor M to the users, wherein when the single daily water consumption of the users is more than the upward fluctuation times, the value of M is [0, 1], the more the downward fluctuation times are, the more M is close to 0, and when the single daily water consumption of the users is more than the downward fluctuation times, the value of M is [1,2], the more the upward fluctuation times are, the more M is close to 2;
And a water analysis module:
The method comprises the steps of obtaining average daily water consumption A of a regular user in a preset period, and setting the basic water consumption to be A according to the water consumption;
The method comprises the steps of obtaining average daily water consumption B of each irregular user in a preset period, and setting a fluctuation upper limit according to the water consumption;
wherein, For the upper bound of fluctuation, x is the irregular number of users,For the i-th irregular user to average the water usage per day in a preset period,Presetting a water fluctuation factor in a period for an ith irregular user;
the abnormality identification module is used for judging that the area is an abnormal area when the single daily water consumption exceeds A+P.
2. The intelligent water management system of claim 1, wherein: the abnormality identification module is further configured to determine the area as an abnormal area when a number of days in which the single daily water consumption continuously exceeds a number of a+p exceeds a preset number of days threshold.
3. An intelligent water management system according to claim 2, wherein: the time period analysis module comprises a time period identification module and a leakage analysis module;
the time period identification module is used for identifying the water consumption of each time period in the abnormal partition according to the pipe network flow and identifying the minimum flow time period according to the water consumption of each time period;
And the leakage analysis module is used for generating an identification error according to the pipe network flow in the minimum flow period and the historical data in the same period in the period, and judging whether leakage exists or not according to the identification error.
4. A smart water management system as claimed in claim 3, wherein: the leakage analysis module comprises an error identification module and a leakage judgment module;
The error identification module is used for respectively acquiring the pipe network flow when the current period is the active user in the history data of each irregular user, and calculating and identifying errors according to the pipe network flow:
Wherein W is an identification error, In the history data of irregular users, the current time period is used as the average value of the pipe network flow of active users, n is the number of times that the irregular users use as the active users in the history data of the period, and m is the period number of days;
Based on the identification errors, a total identification error is calculated,
Wherein x is the number of irregular users;
A leakage judging module for judging when the difference between the water consumption of the period and the average water consumption of the period exceeds And judging that the region has leakage.
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| AU2017294523A1 (en) * | 2016-07-08 | 2019-01-24 | Suez International | An improved system for estimating water flows at the boundaries of a sub-network of a water distribution network |
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| CN112594555A (en) * | 2020-12-07 | 2021-04-02 | 熊猫智慧水务有限公司 | Water-saving space assessment method based on pipeline leakage and tail end abnormity |
| CN113588179B (en) * | 2021-06-24 | 2023-11-21 | 武汉众智鸿图科技有限公司 | Water supply network leakage detection method and system |
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| CN118504185B (en) * | 2024-07-16 | 2024-09-17 | 广东工业大学 | Intelligent control method and device for water supply pipe network leakage |
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| AU2017294523A1 (en) * | 2016-07-08 | 2019-01-24 | Suez International | An improved system for estimating water flows at the boundaries of a sub-network of a water distribution network |
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