CN116951328A - Intelligent drainage pipeline operation monitoring system based on big data - Google Patents
Intelligent drainage pipeline operation monitoring system based on big data Download PDFInfo
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- 238000013527 convolutional neural network Methods 0.000 claims description 22
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Classifications
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17D—PIPE-LINE SYSTEMS; PIPE-LINES
- F17D5/00—Protection or supervision of installations
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17D—PIPE-LINE SYSTEMS; PIPE-LINES
- F17D5/00—Protection or supervision of installations
- F17D5/02—Preventing, monitoring, or locating loss
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/2433—Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
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- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
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Abstract
The application discloses a drainage pipeline operation intelligent monitoring system based on big data, which comprises the steps of collecting and integrating compliance values of preset safe water flow speed and density in a drainage pipeline according to the pressure of the inner wall and the outer wall of the drainage pipeline when different water flow rates are in the drainage pipeline, so as to obtain an integrated graph of the water flow speed and the density of the reference drainage pipeline in unit time; taking the integrated graph of the reference drainage pipeline water flow speed and the density unit time as reference comparison data, monitoring the pressure change of the inner wall and the outer wall of the pipeline by utilizing a preset multi-scale convolution neural network on the received fixed position water flow speed and the density, judging whether the fixed position water flow speed and the density are abnormal water flow speed and density, and sending out a pipeline abnormal signal aiming at the abnormal water flow speed and the abnormal water flow density; the application also provides a drainage pipeline monitoring system which adopts different models and algorithms to intelligently process drainage pipeline data, thereby improving the efficiency of drainage pipeline monitoring.
Description
Technical Field
The application relates to the field of drainage pipeline monitoring, in particular to a drainage pipeline operation intelligent monitoring system based on big data.
Background
The urban drainage pipe network system is a complex and huge network system, and due to the reasons of pipeline aging, overlarge long-term pressure and the like, the underground drainage pipeline is broken, and the regular detection and disease evaluation of the drainage pipeline are effective measures for avoiding accidents. Pipeline inspection is mainly used to investigate the internal defect condition of a pipeline to determine whether or not the pipeline should be repaired and how to repair the pipeline.
The detection of the drainage pipeline is divided into two main types, namely the functional detection of the drainage pipeline and the structural detection of the drainage pipeline. At present, the detection data of the drainage pipeline are mainly stored in the form of data such as video, pictures, attribute tables and the like, and are generally analyzed in a manual auxiliary computer mode, so that the functional defects and structural defects of the drainage pipeline are evaluated.
Because the underground drainage pipeline belongs to underground hidden engineering, the defect of the underground drainage pipeline is not easy to be found by operators, and meanwhile, if the underground drainage pipeline cannot be monitored in time, holes can be formed around the pipeline when leakage is serious, and once the pipeline is damaged by structures such as cracks or the like or the bearing capacity of the pipeline is insufficient, collapse and the like can occur, so that loss is caused or safety accidents can occur. Therefore, in order to ensure the quality of the drainage pipeline, various detection technologies are required to be comprehensively applied, the detection of the drainage pipeline is enhanced, various drainage pipeline problems are reduced, and the normal operation of the drainage pipeline system is ensured.
In the prior art, the application of CN201510802059.7 of a second building limited company of China bureau of China discloses a leakage positioning system of an urban underground drainage pipeline, wherein a waterproof layer is arranged outside the drainage pipeline, a humidity sensor is arranged outside the waterproof layer, and leakage of the drainage pipeline is judged when the humidity detected by the humidity sensor is greater than a threshold value. The application patent of CN201610088117.9 proposes a drain line leakage detection method, wherein an inner insulating layer, a conductive layer and an outer insulating layer are arranged outside a drain line, the conductive layer is connected through a leakage detection device, the resistance of the drain line is detected, and when the resistance exceeds a threshold value, the drain line leakage is judged. The prior art needs to arrange a detection layer outside the drainage pipeline, and the installation and construction process is complex and easy to damage.
Disclosure of Invention
The present application proposes a big data based intelligent monitoring system and system for the operation of a drain line to solve the above mentioned drawbacks of the prior art.
The application provides a drainage pipeline operation intelligent monitoring system based on big data, which comprises the following steps:
step A1: acquiring and integrating compliance values of preset safe water flow speed and density in the drainage pipeline according to the pressure of the inner wall and the outer wall of the drainage pipeline at different water flow rates, and carrying out curve plotting on the preset safe water flow speed and density according to the integrated compliance values of the preset safe water flow speed and density to obtain a standard drainage pipeline water flow speed and density unit time integrated graph;
step A2: taking the integrated graph of the reference drainage pipeline water flow speed and the density unit time as reference comparison data, monitoring the pressure change of the inner wall and the outer wall of the pipeline by utilizing a preset multi-scale convolution neural network on the received fixed position water flow speed and the density, judging whether the fixed position water flow speed and the density are abnormal water flow speed and density, and sending out a pipeline abnormal signal aiming at the abnormal water flow speed and the abnormal water flow density;
step A3: judging whether the abnormal water flow speed and the abnormal water flow density are misjudged by using a preset multi-scale convolutional neural network, if so, eliminating the abnormal signal of the pipeline and executing the step A4, and if not, executing the step A5;
step A4: constructing a drainage pipeline safety information center, judging whether the fixed position water flow speed and the density belong to the safety drainage pipeline water flow speed and the density by a background of an intelligent monitoring system, if so, adding the fixed position water flow speed and the density into the drainage pipeline safety information center, and if not, executing the step A5;
step A5: and placing the abnormal water flow speed and the abnormal water flow density into an intelligent monitoring system for self-adaptive pipeline inner and outer wall pressure change response and pipeline inner and outer wall pressure change maintenance strategy generation, extracting non-compliance data of the abnormal water flow speed and the abnormal water flow density, and constructing a critical node information center of a dangerous source according to the non-compliance data.
The system operates by classifying and setting abnormal water flow speed and density, transmitting basic pure pressure difference data in an off-line state, forming an independent identification library, wherein the independent identification library is in the off-line state, mainly extracting corresponding information from the independent identification library during comparison each time, classifying the abnormal water flow speed and density according to the flow steps, classifying according to false alarm data, drainage line safety information data and dangerous source key node information data by extracting a form of pressure difference data compliance value, and independently forming a data set.
In a specific embodiment, the compliance value includes: the flatness of the inner wall and the outer wall of the pipeline, the fluctuation value of the water flow speed and the density per unit time and the pressure value at the water inlet and outlet of the pipeline.
In a specific embodiment, the curve plotting the preset safe water flow speed and the density according to the integrated compliance value of the preset safe water flow speed and the density to obtain a reference integrated graph of the water flow speed and the density of the drainage pipeline in unit time specifically includes:
constructing a periodic variation equation by utilizing the compliance value of the preset safe water flow speed and the preset density, carrying out variation calculation on the preset safe water flow speed and the preset density according to unit time, and storing the periodic variation equation as an integrated graph of the reference water flow speed and the preset density of the drainage pipeline in unit time;
the periodic variation equation has the expression:
wherein ,lambda represents the water flow speed and density change function, J represents the number of unit time, J represents the total number of unit time, gc represents the water flow speed and density change artificial factor matrix, du represents the water flow speed and density change natural factor matrix, xi and ω represent the artificial factor and natural factor weight respectively, and%>Compliance value representing water flow rate and density, +.>Indicating the range of compliance fluctuation of the water flow velocity and density.
And constructing the reference drainage pipeline water flow speed and density unit time integrated graph and the periodic change equation into a standard water flow speed and density integrated comparison table.
In a specific embodiment, the construction of the drainage line safety information center is directly set according to the access of the intelligent monitoring system background, and the water flow speed and density data of the drainage line safety information center are calculated according to the drainage flow rate change.
In a specific embodiment, the step A2 specifically includes:
constructing a multi-scale convolutional neural network calculation unit according to the reference contrast data and unit time;
collecting pressure difference data of the inner wall and the outer wall of the drainage pipeline, and carrying out safety analysis on the drainage pipeline;
checking inner and outer wall pressure difference data of the fixed position water flow speed and density by using a corresponding characteristic matching algorithm, and judging problems including data jump and acquisition errors in the inner and outer wall pressure difference data;
and checking the inner and outer wall pressure difference data by using a preset multi-scale convolutional neural network, judging that the inner and outer wall pressure difference data is abnormal water flow speed and density when the information in the inner and outer wall pressure difference data is matched with the preset parameters in the multi-scale convolutional neural network, sending out a pipeline abnormal signal, and transmitting the pipeline abnormal signal to a man-machine interaction interface of an intelligent monitoring system for display.
In a specific embodiment, the multi-scale convolutional neural network specifically includes: and identifying the data size, fluctuation condition, fluctuation reason and drainage pipeline risk of the inner and outer wall pressure difference data of the water flow speed and the density at the fixed position according to a preset rule.
In a specific embodiment, the step A3 specifically includes:
and judging the influence factors of the water flow speed and the density at the fixed position monitored by the preset rule, comparing key node information with the integrated graph of the water flow speed and the density unit time of the reference drainage pipeline, and carrying out drainage pipeline risk identification, and judging whether the abnormal water flow speed and the abnormal water flow density are misjudgment according to the maximum value which is set in advance and is used for judging the water flow speed and the density of the safety drainage pipeline.
In a specific embodiment, the step A4 specifically includes:
setting a data processing period, timing according to the period, and reminding an intelligent monitoring system to process the water flow speed and the water flow density at the fixed position;
the intelligent monitoring system carries out real-time remote processing on the water flow speed and the density at the fixed position in the time range, and when the intelligent monitoring system processes the water flow speed and the density at the fixed position into the water flow speed and the density of the safe drainage pipeline, the compliance value of the water flow speed and the density at the fixed position is extracted and recorded in the safety information center of the drainage pipeline;
if the compliance value of the water flow speed and the density of the fixed position received subsequently exists in the drainage pipeline safety information center, the intelligent monitoring system is not reminded to process the water flow speed and the density of the fixed position;
and if the timing is finished, the intelligent monitoring system is not used for processing the water flow speed and the water flow density at the fixed position, and the step A5 is executed.
The alarm information formed by the drainage pipeline safety information center is timing setting, and the alarm operation is active operation of the intelligent monitoring system.
In a specific embodiment, the step A5 specifically includes:
the method comprises the steps of carrying out decision tree model analysis and classification on all abnormal water flow speeds and densities to obtain if a plurality of decision tree branches are dried, and comparing the compliance value of each decision tree branch with the compliance value stored in the dangerous source key node information center;
if the same compliance value is compared, the corresponding abnormal water flow speed and density are directly fed back to the intelligent monitoring system to carry out self-adaptive pipeline inner and outer wall pressure change response and pipeline inner and outer wall pressure change maintenance strategy generation;
if the same compliance value is not compared, judging that the corresponding abnormal water flow speed and density are the novel water flow speed and density with pressure change of the inner wall and the outer wall of the pipeline, performing characteristic extraction according to pressure difference data of the abnormal water flow speed and the density, storing the extracted compliance value into the critical node information center of the dangerous source, and performing self-adaptive pipeline inner wall and outer wall pressure change response and pipeline inner wall and outer wall pressure change maintenance strategy generation by a synchronous response intelligent monitoring system;
generating similar pressure change alarms of the inner wall and the outer wall of the pipeline for abnormal water flow speeds and densities belonging to the same decision tree branch, and dividing the abnormal water flow speeds and densities belonging to the same decision tree branch into the same type of water flow speed and density data according to the compliance value.
The application divides the alarms with the same compliance value, thereby further reducing the quantity of alarm information.
The application provides a drainage pipeline operation intelligent monitoring system based on big data, which comprises:
reference drain line water flow rate and density integration unit: the method comprises the steps of configuring inner wall and outer wall pressures according to different water flow rates in a drainage pipeline, collecting and integrating compliance values of preset safe water flow rate and density in the drainage pipeline, and carrying out curve plotting on the preset safe water flow rate and density according to the integrated compliance values of the preset safe water flow rate and density to obtain a standard drainage pipeline water flow rate and density unit time integrated graph;
abnormal water flow speed and density identifying unit: the method comprises the steps of configuring an integrated graph of the reference drainage pipeline water flow speed and density in unit time as reference comparison data, monitoring the pressure change of the inner wall and the outer wall of a pipeline for the received fixed position water flow speed and density by utilizing a preset multi-scale convolution neural network, judging whether the fixed position water flow speed and density are abnormal water flow speed and density, and sending out a pipeline abnormal signal aiming at the abnormal water flow speed and density;
abnormality judgment supervision unit: the system comprises a water discharge pipeline safety information identification monitoring unit, a risk source key node information center establishing unit, a water discharge pipeline safety information identification monitoring unit, a risk source key node information center establishing unit and a water discharge pipeline safety information identification monitoring unit, wherein the water discharge pipeline safety information identification monitoring unit is configured to utilize a preset multi-scale convolutional neural network to judge whether the abnormal water flow speed and the abnormal water flow density are misjudged;
the drainage line safety information identification monitoring unit: the intelligent monitoring system background judges whether the fixed position water flow speed and density belong to the safe water flow speed and density of the water drainage pipeline, if so, the fixed position water flow speed and density are added into the safe water drainage pipeline information center, and if not, a dangerous source key node information center building unit is executed;
the dangerous source key node information center building unit: the system is configured to put the abnormal water flow speed and the abnormal water flow density into an intelligent monitoring system for self-adaptive pipeline inner and outer wall pressure change response and pipeline inner and outer wall pressure change maintenance strategy generation, extract the non-compliance data of the abnormal water flow speed and the abnormal water flow density, and construct a critical node information center of a dangerous source according to the non-compliance data.
The beneficial effects are that:
the application provides a drainage pipeline operation intelligent monitoring system based on big data, which is used for monitoring the water flow speed and density of a drainage pipeline to construct a drainage pipeline safety information center and a dangerous source key node information center, carrying out data size, fluctuation condition, fluctuation reason and drainage pipeline risk identification on the inner and outer wall pressure difference data of the water flow speed and density at fixed positions according to preset rules, and effectively monitoring the drainage pipeline through different dimensions.
Drawings
The accompanying drawings are included to provide a further understanding of the embodiments and are incorporated in and constitute a part of this specification. The drawings illustrate embodiments and together with the description serve to explain the principles of the application. Many of the intended advantages of other embodiments and embodiments will be readily appreciated as they become better understood by reference to the following detailed description. Other features, objects and advantages of the present application will become more apparent upon reading of the detailed description of non-limiting embodiments, made with reference to the accompanying drawings in which:
FIG. 1 is a first flowchart of the operation of the system of the present application;
FIG. 2 is a second flowchart of the operation of the system of the present application;
FIG. 3 is a third flowchart of the operation of the system of the present application;
FIG. 4 is a fourth flowchart of the operation of the system of the present application;
FIG. 5 is a diagram of the system unit components of the present application.
Detailed Description
The application is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application. It should be noted that, for convenience of description, only the portions related to the present application are shown in the drawings.
It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other. The application will be described in detail below with reference to the drawings in connection with embodiments.
Big data based drain line operation intelligent monitoring system according to one embodiment of the present application, as shown in fig. 1, is a flow chart of the operation of the big data based drain line operation intelligent monitoring system according to an embodiment of the present application. The system operation comprises the following steps:
step A1: and acquiring and integrating compliance values of preset safe water flow speed and density in the drainage pipeline according to the pressure of the inner wall and the outer wall of the drainage pipeline at different water flow rates, and carrying out curve plotting on the preset safe water flow speed and density according to the integrated compliance values of the preset safe water flow speed and density to obtain a standard drainage pipeline water flow speed and density unit time integrated graph.
In a specific embodiment, the compliance value includes: the flatness of the inner wall and the outer wall of the pipeline, the fluctuation value of the water flow speed and the density per unit time and the pressure value at the water inlet and outlet of the pipeline.
In a specific embodiment, the curve plotting the preset safe water flow speed and the density according to the integrated compliance value of the preset safe water flow speed and the density to obtain a reference integrated graph of the water flow speed and the density of the drainage pipeline in unit time specifically includes:
constructing a periodic variation equation by utilizing the compliance value of the preset safe water flow speed and the preset density, carrying out variation calculation on the preset safe water flow speed and the preset density according to unit time, and storing the periodic variation equation as an integrated graph of the reference water flow speed and the preset density of the drainage pipeline in unit time;
the periodic variation equation has the expression:
wherein ,lambda represents the water flow speed and density change function, J represents the number of unit time, J represents the total number of unit time, gc represents the water flow speed and density change artificial factor matrix, du represents the water flow speed and density change natural factor matrix, xi and ω represent the artificial factor and natural factor weight respectively, and%>Compliance value representing water flow rate and density, +.>Indicating the range of compliance fluctuation of the water flow velocity and density.
And constructing the reference drainage pipeline water flow speed and density unit time integrated graph and the periodic change equation into a standard water flow speed and density integrated comparison table.
Step A2: and taking the integrated graph of the reference drainage pipeline water flow speed and the density unit time as reference comparison data, monitoring the pressure change of the inner wall and the outer wall of the pipeline by utilizing a preset multi-scale convolution neural network for the received fixed position water flow speed and the density, judging whether the fixed position water flow speed and the density are abnormal water flow speed and density, and sending out a pipeline abnormal signal aiming at the abnormal water flow speed and the abnormal water flow density.
As shown in fig. 2, in a specific embodiment, the step A2 specifically includes:
constructing a multi-scale convolutional neural network calculation unit according to the reference contrast data and unit time;
collecting pressure difference data of the inner wall and the outer wall of the drainage pipeline, and carrying out safety analysis on the drainage pipeline;
checking inner and outer wall pressure difference data of the fixed position water flow speed and density by using a corresponding characteristic matching algorithm, and judging problems including data jump and acquisition errors in the inner and outer wall pressure difference data;
and checking the inner and outer wall pressure difference data by using a preset multi-scale convolutional neural network, judging that the inner and outer wall pressure difference data is abnormal water flow speed and density when the information in the inner and outer wall pressure difference data is matched with the preset parameters in the multi-scale convolutional neural network, sending out a pipeline abnormal signal, and transmitting the pipeline abnormal signal to a man-machine interaction interface of an intelligent monitoring system for display.
A schematic diagram of a multi-scale convolutional neural network computing unit according to a specific embodiment of the present application, as shown in the figure, the multi-scale convolutional neural network computing unit includes:
the drain line carries out the security analysis unit: collecting pressure difference data of the inner wall and the outer wall of the drainage pipeline, and carrying out safety analysis on the drainage pipeline;
a data processing unit: the unit uses a corresponding characteristic matching algorithm to check the pressure difference data of the inner wall and the outer wall, and discovers the behavior of the original data, such as data jump, acquisition error and the like, and the pressure difference data is transmitted to the rule setting unit after being preprocessed;
rule setting unit: the unit is a core unit of the multi-scale convolutional neural network; when the differential pressure data is sent from the preprocessor, the rule setting unit checks the differential pressure data by using a preset rule, and once the information in the differential pressure data is found to be matched with a certain rule, the alarm output unit is notified;
alarm output unit: the multi-scale convolutional neural network data checked by the rule setting unit needs to be output in a certain mode, if a certain rule in the rule setting unit is matched, an alarm is sent out, and the alarm information is transmitted to the man-machine interaction interface of the intelligent monitoring system for display through a protocol command.
In a specific embodiment, the multi-scale convolutional neural network specifically includes: and identifying the data size, fluctuation condition, fluctuation reason and drainage pipeline risk of the inner and outer wall pressure difference data of the water flow speed and the density at the fixed position according to a preset rule.
In a specific embodiment, according to the data size, the fluctuation condition, the fluctuation reason and the drainage line risk identification of the preset rule, the method specifically comprises the following steps:
(1) Firstly judging the influence factors of the water flow speed and the density at the fixed position monitored by a preset rule, comparing key node information with a reference water flow speed and density unit time integrated graph, identifying the risk of the water drainage pipeline, further increasing the percentage upper line of the maximum value under the condition that the maximum value is exceeded according to the maximum value set for the water flow speed and the density of the safety water drainage pipeline, and determining whether the water flow speed and the density at the fixed position are misjudged based on 3 percent setting, if not, directly entering the next step;
(2) According to the alarm output unit, an alarm message is sent out, the alarm message is a yellow alarm message, the time is aged according to the set time, the real-time remote processing time of the intelligent monitoring system is given, if the intelligent monitoring system processes the current speed and the density of the safe drainage pipeline in real time, the compliance value of the pressure difference data at the fixed position is extracted and recorded in the safety information center of the drainage pipeline, the follow-up reminding is not carried out, and if the intelligent monitoring system processes the current after the time is finished, the next step is carried out;
(3) After analyzing all abnormal pipeline inner and outer wall pressure change water flow speed and density decision tree models, comparing the water flow speed and density decision tree models with compliance values stored in a critical node information center of a dangerous source, directly feeding back an intelligent monitoring system to generate self-adaptive pipeline inner and outer wall pressure change response and pipeline inner and outer wall pressure change maintenance strategies under the condition that the compliance values are the same, judging that the novel pipeline inner and outer wall pressure change water flow speed and density are monitored when corresponding compliance values are not available in the critical node information center of the dangerous source, firstly carrying out feature extraction according to fixed position pressure difference data, storing the extracted compliance values into the critical node information center of the dangerous source, synchronously responding to the intelligent monitoring system to generate self-adaptive pipeline inner and outer wall pressure change maintenance strategies, and finally forming similar pipeline inner and outer wall pressure change alarms only according to items analyzed by the decision tree models and dividing the similar pipeline inner and outer wall pressure change alarms into the same types according to the compliance values.
Step A3: and (3) judging whether the abnormal water flow speed and the abnormal water flow density are misjudged by using a preset multi-scale convolutional neural network, if so, eliminating the abnormal signal of the pipeline and executing the step A4, and if not, executing the step A5.
In a specific embodiment, the step A3 specifically includes:
and judging the influence factors of the water flow speed and the density at the fixed position monitored by the preset rule, comparing key node information with the integrated graph of the water flow speed and the density unit time of the reference drainage pipeline, and carrying out drainage pipeline risk identification, and judging whether the abnormal water flow speed and the abnormal water flow density are misjudgment according to the maximum value which is set in advance and is used for judging the water flow speed and the density of the safety drainage pipeline.
Step A4: and (3) constructing a drainage pipeline safety information center, judging whether the fixed position water flow speed and the density belong to the safety drainage pipeline water flow speed and the density by a background of the intelligent monitoring system, if so, adding the fixed position water flow speed and the density into the drainage pipeline safety information center, and if not, executing the step (A5).
In a specific embodiment, the construction of the drainage line safety information center is directly set according to the access of the intelligent monitoring system background, and the water flow speed and density data of the drainage line safety information center are calculated according to the drainage flow rate change.
As shown in fig. 3, in a specific embodiment, the step A4 specifically includes:
setting a data processing period, timing according to the period, and reminding an intelligent monitoring system to process the water flow speed and the water flow density at the fixed position;
the intelligent monitoring system carries out real-time remote processing on the water flow speed and the density at the fixed position in the time range, and when the intelligent monitoring system processes the water flow speed and the density at the fixed position into the water flow speed and the density of the safe drainage pipeline, the compliance value of the water flow speed and the density at the fixed position is extracted and recorded in the safety information center of the drainage pipeline;
if the compliance value of the water flow speed and the density of the fixed position received subsequently exists in the drainage pipeline safety information center, the intelligent monitoring system is not reminded to process the water flow speed and the density of the fixed position;
and if the timing is finished, the intelligent monitoring system is not used for processing the water flow speed and the water flow density at the fixed position, and the step A5 is executed.
Step A5: and placing the abnormal water flow speed and the abnormal water flow density into an intelligent monitoring system for self-adaptive pipeline inner and outer wall pressure change response and pipeline inner and outer wall pressure change maintenance strategy generation, extracting non-compliance data of the abnormal water flow speed and the abnormal water flow density, and constructing a critical node information center of a dangerous source according to the non-compliance data.
As shown in fig. 4, in a specific embodiment, the step A5 specifically includes:
the method comprises the steps of carrying out decision tree model analysis and classification on all abnormal water flow speeds and densities to obtain if a plurality of decision tree branches are dried, and comparing the compliance value of each decision tree branch with the compliance value stored in the dangerous source key node information center;
if the same compliance value is compared, the corresponding abnormal water flow speed and density are directly fed back to the intelligent monitoring system to carry out self-adaptive pipeline inner and outer wall pressure change response and pipeline inner and outer wall pressure change maintenance strategy generation;
if the same compliance value is not compared, judging that the corresponding abnormal water flow speed and density are the novel water flow speed and density with pressure change of the inner wall and the outer wall of the pipeline, performing characteristic extraction according to pressure difference data of the abnormal water flow speed and the density, storing the extracted compliance value into the critical node information center of the dangerous source, and performing self-adaptive pipeline inner wall and outer wall pressure change response and pipeline inner wall and outer wall pressure change maintenance strategy generation by a synchronous response intelligent monitoring system;
generating similar pressure change alarms of the inner wall and the outer wall of the pipeline for abnormal water flow speeds and densities belonging to the same decision tree branch, and dividing the abnormal water flow speeds and densities belonging to the same decision tree branch into the same type of water flow speed and density data according to the compliance value.
As shown in FIG. 5, a framework diagram of a big data based drain line operation intelligent monitoring system is provided in accordance with one embodiment of the present application. The system comprises a reference drainage pipeline water flow speed and density integration unit, an abnormal water flow speed and density identification unit, an abnormality judgment and supervision unit, a drainage pipeline safety information identification and supervision unit and a dangerous source key node information center establishment unit.
In a specific embodiment, the reference drain line water flow speed and density integration unit is configured to collect and integrate compliance values of preset safe water flow speed and density in the drain line according to inner wall and outer wall pressures of different water flow rates in the drain line, and perform curve plotting on the preset safe water flow speed and density according to the integrated compliance values of the preset safe water flow speed and density to obtain a reference drain line water flow speed and density unit time integration graph;
the abnormal water flow speed and density identification unit is configured to take the integrated graph of the reference water flow speed and density of the drainage pipeline in unit time as reference comparison data, monitor the pressure change of the inner wall and the outer wall of the pipeline on the received water flow speed and density of the fixed position by utilizing a preset multi-scale convolution neural network, judge whether the water flow speed and density of the fixed position are abnormal water flow speed and density, and send out a pipeline abnormal signal aiming at the abnormal water flow speed and density;
the abnormal judgment and supervision unit is configured to judge whether the abnormal water flow speed and the abnormal water flow density are misjudged by using a preset multi-scale convolutional neural network, if so, the abnormal signals of the pipeline are eliminated, the drainage pipeline safety information identification and supervision unit is executed, and if not, the dangerous source key node information center establishment unit is executed;
the drainage pipeline safety information identification monitoring unit is configured to construct a drainage pipeline safety information center, the intelligent monitoring system background judges whether the fixed position water flow speed and density belong to the safety drainage pipeline water flow speed and density, if so, the fixed position water flow speed and density are added into the drainage pipeline safety information center, and if not, the dangerous source key node information center building unit is executed;
the dangerous source key node information center establishing unit is configured to put the abnormal water flow speed and the abnormal water flow density into an intelligent monitoring system for self-adaptive pipeline inner and outer wall pressure change response and pipeline inner and outer wall pressure change maintenance strategy generation, extract non-compliance data of the abnormal water flow speed and the abnormal water flow density, and construct a dangerous source key node information center according to the non-compliance data.
The system collects and integrates the compliance value of preset safe water flow speed and density in the drainage pipeline according to the pressure of the inner wall and the outer wall of the drainage pipeline at different water flow rates, and obtains an integrated graph of the water flow speed and the density of the reference drainage pipeline in unit time; taking the integrated graph of the reference drainage pipeline water flow speed and the density unit time as reference comparison data, monitoring the pressure change of the inner wall and the outer wall of the pipeline by utilizing a preset multi-scale convolution neural network on the received fixed position water flow speed and the density, judging whether the fixed position water flow speed and the density are abnormal water flow speed and density, and sending out a pipeline abnormal signal aiming at the abnormal water flow speed and the abnormal water flow density; the system monitors the water flow speed and the density of the drainage pipeline, constructs a drainage pipeline safety information center and a dangerous source key node information center, carries out data size, fluctuation condition, fluctuation reason and drainage pipeline risk identification on the inner wall pressure difference data and the outer wall pressure difference data of the water flow speed and the density at fixed positions according to preset rules, effectively monitors the drainage pipeline through different dimensions, intelligently processes the drainage pipeline data by adopting different models and algorithms, is simple to realize, overcomes the defect that a detection layer is required to be arranged outside the drainage pipeline in the prior art, is complex in installation and construction process and is easy to damage, and improves the monitoring efficiency of the drainage pipeline.
In the description of the present application, it should be noted that, unless explicitly specified and limited otherwise, the terms "disposed," "mounted," "connected," and "fixed" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art in a specific case.
Although embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various equivalent changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the application, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. Big data-based intelligent monitoring system for drain line operation, characterized in that the system operation comprises:
step A1: acquiring and integrating compliance values of preset safe water flow speed and density in the drainage pipeline according to the pressure of the inner wall and the outer wall of the drainage pipeline at different water flow rates, and carrying out curve plotting on the preset safe water flow speed and density according to the integrated compliance values of the preset safe water flow speed and density to obtain a standard drainage pipeline water flow speed and density unit time integrated graph;
step A2: taking the integrated graph of the reference drainage pipeline water flow speed and the density unit time as reference comparison data, monitoring the pressure change of the inner wall and the outer wall of the pipeline by utilizing a preset multi-scale convolution neural network on the received fixed position water flow speed and the density, judging whether the fixed position water flow speed and the density are abnormal water flow speed and density, and sending out a pipeline abnormal signal aiming at the abnormal water flow speed and the abnormal water flow density;
step A3: judging whether the abnormal water flow speed and the abnormal water flow density are misjudgment or not by using a preset multi-scale convolutional neural network;
step A4: if yes, eliminating the abnormal signal of the pipeline and constructing a drainage pipeline safety information center, judging whether the fixed position water flow speed and the fixed position water flow density belong to the safety drainage pipeline water flow speed and the fixed position water flow density by an intelligent monitoring system background, and if yes, adding the fixed position water flow speed and the fixed position water flow density into the drainage pipeline safety information center;
step A5: if not, the abnormal water flow speed and the abnormal water flow density are placed into an intelligent monitoring system for self-adaptive pipeline inner and outer wall pressure change response and pipeline inner and outer wall pressure change maintenance strategy generation, non-compliance data of the abnormal water flow speed and the abnormal water flow density are extracted, and a critical node information center of a dangerous source is constructed according to the non-compliance data.
2. The big data based intelligent monitoring system of drain line operation of claim 1, wherein the compliance value comprises: the flatness of the inner wall and the outer wall of the pipeline, the fluctuation value of the water flow speed and the density per unit time and the pressure value at the water inlet and outlet of the pipeline.
3. The big data-based intelligent monitoring system for drain line operation according to claim 1, wherein the curve-plotting the preset safe water flow speed and the density according to the integrated compliance value of the preset safe water flow speed and the density to obtain a reference drain line water flow speed and density unit time integrated graph, specifically comprises:
constructing a periodic variation equation by utilizing the compliance value of the preset safe water flow speed and the preset density, carrying out variation calculation on the preset safe water flow speed and the preset density according to unit time, and storing the periodic variation equation as an integrated graph of the reference water flow speed and the preset density of the drainage pipeline in unit time;
the periodic variation equation has the expression:
wherein ,lambda represents the water flow speed and density change function, J represents the number of unit time, J represents the total number of unit time, gc represents the water flow speed and density change artificial factor matrix, du represents the water flow speed and density change natural factor matrix, xi and ω represent the artificial factor and natural factor weight respectively, and%>Compliance value representing water flow rate and density, +.>A compliance value fluctuation range representing the water flow speed and the density;
and constructing the reference drainage pipeline water flow speed and density unit time integrated graph and the periodic change equation into a standard water flow speed and density integrated comparison table.
4. The intelligent monitoring system for the operation of a drain line based on big data according to claim 1, wherein the construction of the drain line safety information center is directly set according to the access of the background of the intelligent monitoring system, and the water flow speed and the density data of the drain line safety information center are calculated according to the change of the drain flow.
5. The intelligent monitoring system for drain line operation based on big data according to claim 1, wherein the step A2 specifically comprises:
constructing a multi-scale convolutional neural network calculation unit according to the reference contrast data and unit time;
collecting pressure difference data of the inner wall and the outer wall of the drainage pipeline, and carrying out safety analysis on the drainage pipeline;
checking inner and outer wall pressure difference data of the fixed position water flow speed and density by using a corresponding characteristic matching algorithm, and judging problems including data jump and acquisition errors in the inner and outer wall pressure difference data;
and checking the inner and outer wall pressure difference data by using a preset multi-scale convolutional neural network, judging that the inner and outer wall pressure difference data is abnormal water flow speed and density when the information in the inner and outer wall pressure difference data is matched with the preset parameters in the multi-scale convolutional neural network, sending out a pipeline abnormal signal, and transmitting the pipeline abnormal signal to a man-machine interaction interface of an intelligent monitoring system for display.
6. The big data based drainage line operation intelligent monitoring system of claim 1, wherein the multi-scale convolutional neural network specifically comprises: and identifying the data size, fluctuation condition, fluctuation reason and drainage pipeline risk of the inner and outer wall pressure difference data of the water flow speed and the density at the fixed position according to a preset rule.
7. The intelligent big data based drainage line operation monitoring system of claim 6, wherein the step A3 specifically comprises:
and judging the influence factors of the water flow speed and the density at the fixed position monitored by the preset rule, comparing key node information with the integrated graph of the water flow speed and the density unit time of the reference drainage pipeline, and carrying out drainage pipeline risk identification, and judging whether the abnormal water flow speed and the abnormal water flow density are misjudgment according to the maximum value which is set in advance and is used for judging the water flow speed and the density of the safety drainage pipeline.
8. The intelligent big data based drainage line operation monitoring system of claim 6, wherein the step A4 specifically comprises:
setting a data processing period, timing according to the period, and reminding an intelligent monitoring system to process the water flow speed and the water flow density at the fixed position;
the intelligent monitoring system carries out real-time remote processing on the water flow speed and the density at the fixed position in the time range, and when the intelligent monitoring system processes the water flow speed and the density at the fixed position into the water flow speed and the density of the safe drainage pipeline, the compliance value of the water flow speed and the density at the fixed position is extracted and recorded in the safety information center of the drainage pipeline;
if the compliance value of the water flow speed and the density of the fixed position received subsequently exists in the drainage pipeline safety information center, the intelligent monitoring system is not reminded to process the water flow speed and the density of the fixed position;
and if the timing is finished, the intelligent monitoring system is not used for processing the water flow speed and the water flow density at the fixed position, and the step A5 is executed.
9. The intelligent big data based drainage line operation monitoring system of claim 6, wherein the step A5 specifically comprises:
the method comprises the steps of carrying out decision tree model analysis and classification on all abnormal water flow speeds and densities to obtain if a plurality of decision tree branches are dried, and comparing the compliance value of each decision tree branch with the compliance value stored in the dangerous source key node information center;
if the same compliance value is compared, the corresponding abnormal water flow speed and density are directly fed back to the intelligent monitoring system to carry out self-adaptive pipeline inner and outer wall pressure change response and pipeline inner and outer wall pressure change maintenance strategy generation;
if the same compliance value is not compared, judging that the corresponding abnormal water flow speed and density are the novel water flow speed and density with pressure change of the inner wall and the outer wall of the pipeline, performing characteristic extraction according to pressure difference data of the abnormal water flow speed and the density, storing the extracted compliance value into the critical node information center of the dangerous source, and performing self-adaptive pipeline inner wall and outer wall pressure change response and pipeline inner wall and outer wall pressure change maintenance strategy generation by a synchronous response intelligent monitoring system;
generating similar pressure change alarms of the inner wall and the outer wall of the pipeline for abnormal water flow speeds and densities belonging to the same decision tree branch, and dividing the abnormal water flow speeds and densities belonging to the same decision tree branch into the same type of water flow speed and density data according to the compliance value.
10. The big data based intelligent monitoring system for drain line operation of any of claims 1-9, the system comprising:
the standard drainage pipeline water flow speed and density integration unit is used for collecting and integrating compliance values of preset safe water flow speed and density in the drainage pipeline according to the pressure of the inner wall and the outer wall when different water flow rates are in the drainage pipeline, and carrying out curve plotting on the preset safe water flow speed and density according to the compliance values of the preset safe water flow speed and density obtained through integration to obtain a standard drainage pipeline water flow speed and density unit time integration graph;
the abnormal water flow speed and density identification unit is used for taking the integrated graph of the reference water flow speed and density unit time as reference comparison data, monitoring the pressure change of the inner wall and the outer wall of the pipeline according to the received water flow speed and density at the fixed position by utilizing a preset multi-scale convolution neural network, judging whether the water flow speed and density at the fixed position are abnormal water flow speed and density, and sending out a pipeline abnormal signal according to the abnormal water flow speed and density;
the abnormal judgment and supervision unit is used for judging whether the abnormal water flow speed and the abnormal water flow density are misjudged by utilizing a preset multi-scale convolutional neural network, if so, eliminating the abnormal signal of the pipeline and executing the drainage pipeline safety information identification and supervision unit, and if not, executing the dangerous source key node information center establishment unit;
the intelligent monitoring system background judges whether the fixed position water flow speed and density belong to the safe water flow speed and density of the water discharge pipeline, if so, the fixed position water flow speed and density are added into the safe water discharge pipeline information center, and if not, the dangerous source key node information center building unit is executed;
the system comprises a dangerous source key node information center establishing unit, a dangerous source key node information center establishing unit and a dangerous source key node information center establishing unit, wherein the dangerous source key node information center establishing unit is used for placing abnormal water flow speed and abnormal water flow density into an intelligent monitoring system for self-adaptive pipeline inner and outer wall pressure change response and pipeline inner and outer wall pressure change maintenance strategy generation, extracting non-compliance data of the abnormal water flow speed and abnormal water flow density and establishing the dangerous source key node information center according to the non-compliance data.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117686921A (en) * | 2024-02-02 | 2024-03-12 | 江苏林洋亿纬储能科技有限公司 | Method and system for detecting short circuit in battery and computing device |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014046122A1 (en) * | 2012-09-18 | 2014-03-27 | 日本電気株式会社 | Leakage analysis system, measurement terminal, leakage analysis apparatus, and leakage detection method |
| CN105065917A (en) * | 2015-08-24 | 2015-11-18 | 苏交科集团股份有限公司 | Urban drainage network online detection method |
| US20210192388A1 (en) * | 2019-12-23 | 2021-06-24 | Saudi Arabian Oil Company | Pipeline sensor integration for product mapping |
| CN116010857A (en) * | 2023-03-24 | 2023-04-25 | 山东联合能源管道输送有限公司 | Intelligent management and control system for port crude oil |
| US20230213370A1 (en) * | 2023-02-13 | 2023-07-06 | Chengdu Qinchuan Iot Technology Co., Ltd. | Methods and internet of things (iot) systems for predicting gas transmission loss of smart gas |
| CN116432866A (en) * | 2023-06-08 | 2023-07-14 | 青岛沃泰水技术设备制造有限公司 | Urban intelligent drainage pipeline safety management method and system based on Internet of things |
| CN116452191A (en) * | 2023-04-20 | 2023-07-18 | 重庆水利电力职业技术学院 | Intelligent rural water supply data collection and analysis system and method based on big data |
-
2023
- 2023-07-21 CN CN202310898507.2A patent/CN116951328B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014046122A1 (en) * | 2012-09-18 | 2014-03-27 | 日本電気株式会社 | Leakage analysis system, measurement terminal, leakage analysis apparatus, and leakage detection method |
| CN105065917A (en) * | 2015-08-24 | 2015-11-18 | 苏交科集团股份有限公司 | Urban drainage network online detection method |
| US20210192388A1 (en) * | 2019-12-23 | 2021-06-24 | Saudi Arabian Oil Company | Pipeline sensor integration for product mapping |
| US20230213370A1 (en) * | 2023-02-13 | 2023-07-06 | Chengdu Qinchuan Iot Technology Co., Ltd. | Methods and internet of things (iot) systems for predicting gas transmission loss of smart gas |
| CN116010857A (en) * | 2023-03-24 | 2023-04-25 | 山东联合能源管道输送有限公司 | Intelligent management and control system for port crude oil |
| CN116452191A (en) * | 2023-04-20 | 2023-07-18 | 重庆水利电力职业技术学院 | Intelligent rural water supply data collection and analysis system and method based on big data |
| CN116432866A (en) * | 2023-06-08 | 2023-07-14 | 青岛沃泰水技术设备制造有限公司 | Urban intelligent drainage pipeline safety management method and system based on Internet of things |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117686921A (en) * | 2024-02-02 | 2024-03-12 | 江苏林洋亿纬储能科技有限公司 | Method and system for detecting short circuit in battery and computing device |
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