CN116757441A - Sewage flow automatic monitoring and early warning method and system based on artificial intelligence - Google Patents
Sewage flow automatic monitoring and early warning method and system based on artificial intelligence Download PDFInfo
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Abstract
The invention relates to the technical field of sewage management, in particular to an artificial intelligence-based sewage flow automatic monitoring and early warning method and system, comprising a construction layer, a monitoring layer and an analysis layer; the invention can complete construction of a sewage pipeline model by acquiring data in the electronic drawing through input of the sewage pipeline electronic drawing, further analyze the relevance among the sewage pipelines based on the sewage pipeline model, and monitor the flow of the sewage in the sewage pipeline in a liquid flow rate sensor mode, so that the flow monitoring of the sewage pipeline is more accurate and effective.
Description
Technical Field
The invention relates to the technical field of sewage management, in particular to an artificial intelligence-based sewage flow automatic monitoring and early warning method and system.
Background
The sewage pipeline system consists of pipeline for collecting and conveying city sewage and its auxiliary structure, and the pipeline is small to large, distributed like river and dendritic, and is usually flowed from high to low by means of water level difference at two ends of pipeline, and the interior of pipeline is not under pressure, i.e. flowed by means of gravity.
The invention patent with application number 202210244797.4 discloses a sewage monitoring system which is characterized by comprising a data acquisition module, a preliminary analysis processing module, a comprehensive analysis processing module and an information release module; the data acquisition module is used for acquiring data to be analyzed and sending the data to be analyzed to the preliminary analysis processing module; the data to be analyzed comprises sewage data, precipitation data, water supply data and pipe network position data; the primary analysis processing module is used for receiving the data to be analyzed sent by the data acquisition module, analyzing and processing the data to be analyzed based on an edge gateway, determining a primary analysis result, and sending the data to be analyzed and the primary analysis result to the comprehensive analysis processing module: the preliminary analysis result comprises at least one of basic infiltration amount, inflow infiltration amount, whether a pipe network reaches the standard, sewage flow variation statistics, predicted pump station parameters and sewage concentration; the comprehensive analysis processing module is used for receiving the data to be analyzed and the preliminary analysis result sent by the preliminary analysis processing module, analyzing the data to be analyzed and the preliminary analysis result based on the comprehensive analysis platform to obtain a comprehensive analysis result, and sending the comprehensive analysis result to the information release module; the comprehensive analysis platform comprises at least one of a geographic information system data platform, a facility equipment management platform, a comprehensive monitoring and early warning platform, a comprehensive operation management platform and an auxiliary analysis decision platform.
The application aims at solving the problems: "to the problem that urban drainage pipeline exists, such as leak, jam etc., often need wait for a large amount of sewage to walk on ground, rely on resident's reflection of making a call to know, reaction rate is slow and the rush repair degree of difficulty is big. And, the control of urban drainage pipeline needs to carry out parameter adjustment according to expert analysis, can appear certain hysteresis, is difficult to in time dredge sewage. And, the phenomenon of stealing and arranging is difficult to find and locate in time for enterprises, and the problem of' is solved.
However, the existing sewage flow monitoring technology is not innovated for a long time, and is mainly monitored by a flow sensor at present, so that the current sewage pipeline flow monitoring data can still be obtained only by the flow sensor, and the accuracy and the reliability are relatively single without better reference conditions.
Disclosure of Invention
Aiming at the defects existing in the prior art, the invention provides an artificial intelligence-based automatic sewage flow monitoring and early warning method and system, which solve the problems that the existing sewage flow monitoring and early warning technology is not innovated for a long time, and the current monitoring mainly adopts a flow sensor to monitor, so that the current sewage pipeline flow monitoring data can still be obtained only through the flow sensor, and the accuracy and the reliability are relatively single and no better reference comparison condition exists.
In order to achieve the above purpose, the invention is realized by the following technical scheme:
in a first aspect, an artificial intelligence-based automatic sewage flow monitoring and early warning system comprises a construction layer, a monitoring layer and an analysis layer;
the method comprises the steps that an electronic drawing built by an underground sewage pipeline is input through a building layer, the building layer builds an underground sewage pipeline model based on information acquired from the electronic drawing, pipeline association analysis is further carried out by applying the underground sewage pipeline model, a monitoring layer receives the underground sewage pipeline model built in the building layer and pipeline association analysis results, flow monitoring is carried out on the sewage pipeline in real time, the analysis layer receives sewage pipeline flow monitoring data in the monitoring layer in real time, whether a warning situation exists in the sewage pipeline is analyzed based on the sewage pipeline flow monitoring data, and when the warning situation exists, the warning situation is output corresponding to the sewage pipeline;
the monitoring layer comprises a monitoring module and a metering module, wherein the monitoring module is used for monitoring real-time transmission sewage parameters in the sewage pipeline, the metering module is used for receiving the real-time transmission sewage parameters in the sewage pipeline monitored by the monitoring module, the real-time transmission sewage flow cross-section area in the sewage pipeline is calculated by using the sewage parameters, and the calculation formula is as follows:
wherein: r is the radius of the sewage pipeline; m is a collection of liquid flow rate sensors in the monitoring module; n is the central angle number of the region formed by the coordinates corresponding to the non-running sub-liquid flow rate sensor in the liquid flow rate sensor; r is (r) 1 、r 2 、...、r x-1 、r x The radius of the region is formed by coordinates corresponding to the neutron liquid flow velocity sensor in the liquid flow velocity sensor;
wherein { r 1 、r 2 、...、r x-1 、r x Any one of the items is not less than 0.
Further, the construction layer comprises a loading module, a construction module and a sniffing module, wherein the loading module is used for inputting an electronic map built by the underground sewage pipeline, the modeling module is used for receiving the electronic map input by the loading module, acquiring construction specification parameters and position information of the underground sewage pipeline in the electronic map, further completing construction of an underground sewage pipeline model based on the construction specification parameters and the position information of the sewage pipeline, and the sniffing module is used for traversing and reading the position information of the underground sewage pipeline acquired by the modeling module in the electronic map and carrying out relevance analysis on the sewage pipeline based on the position information of the underground sewage pipeline;
each piece of sewage pipeline position information at least comprises two groups of position coordinates, the modeling module is used for performing traversal searching on the acquired underground sewage pipeline position information, capturing the same position coordinates in the position information of each sewage pipeline, further determining a sewage pipeline distribution area based on the position coordinates contained in the position information of the sewage pipeline, judging the sewage pipeline which does not capture the same position coordinates based on the sewage pipeline distribution area, and discarding the position information of the sewage pipeline which does not capture the same position coordinates and is not located at the boundary of the sewage pipeline distribution area.
Furthermore, when the modeling module performs the operation of searching the same position coordinates on the position information of the underground sewage pipeline, the operation of searching the same item is completed by adopting the following formula:
wherein: the name (a, b) is a judgment index, and the name (a, b) is more than 0; n is a collection of sewer line location information;the x-axis corresponding value of the position coordinates of the ith group in the sewage pipeline position information a; />The y-axis corresponding value of the ith group of position coordinates in the sewage pipeline position information a; />The z-axis corresponding value of the ith group of position coordinates in the sewage pipeline position information a; />The corresponding value of the x-axis of the position coordinates of the ith group in the position information b of the sewage pipeline; />The y-axis corresponding value of the ith group of position coordinates in the sewage pipeline position information b; />The z-axis corresponding value of the ith group of position coordinates in the sewage pipeline position information b; n is n 0 For limiting the coefficient, n is defined when the position coordinates of the sewage pipeline position information are three-dimensional coordinates 0 When the value is 3 and the position coordinate of the sewage pipeline position information is two-dimensional coordinate, n 0 The value is 2;
when the sum (a, b) =1, it is determined that the two groups of sewer line position information are the Same items, otherwise, the two groups of sewer line position information are different items, and the modeling module performs the searching operation of the Same position coordinates on the underground sewer line position information, that is, the association analysis of the sewer line in the building layer.
Still further, the monitoring module is integrated by liquid flow rate sensor, liquid flow rate sensor is provided with a plurality of groups, liquid flow rate sensor is annular equidistance and deploys in the sewer line is inside, every group liquid flow rate sensor is integrated by a plurality of groups of sub-liquid flow rate sensor, a plurality of groups of sub-liquid flow rate sensor is annular equidistance form and installs in liquid flow rate sensor's surface.
Still further, the sub-liquid flow rate sensors on each set of said liquid flow rate sensors are each configured with corresponding two-dimensional coordinates based on the cross section of the sewer line.
Furthermore, a system end user in the monitoring module manually sets a monitoring period, the metering module synchronously operates according to the monitoring period set in the monitoring module, and after the metering module operates and calculates a real-time transmission sewage flow section in the sewage pipeline, the real-time transmission sewage flow section data in the sewage pipeline is further based on the monitoring period and the calculated real-time transmission sewage flow section data, the real-time transmission sewage change situation in the sewage pipeline is estimated, and an estimation formula is as follows:
wherein: k is a set of monitoring periods; s is the cross-sectional area of the sewer line, s=pi r 2 ；Calculating the calculated sewage flow cross-sectional area for the third last group of monitoring periods; />Calculating the calculated sewage flow cross-sectional area for the penultimate group of monitoring periods; />Calculating the calculated sewage flow cross-sectional area for the last one group of monitoring periods;
wherein, the sewage transmission change situation T obtained by the above formula is inversely proportional to the sewage flow of the sewage pipeline.
Further, the analysis layer comprises a storage module, a prediction module and an alarm module, wherein the storage module is used for receiving the real-time transmission sewage flow cross-section area in the sewage pipeline calculated in the metering module, storing the real-time transmission sewage flow cross-section area in the sewage pipeline, the prediction module is used for setting a judgment area and judgment logic, carrying out alarm condition prediction on the sewage flow cross-section area data stored in the storage module based on the judgment area and the judgment logic, and the alarm module is used for receiving the operation result of the prediction module, triggering the operation through the operation result of the prediction module and triggering the output operation of the sewage pipeline with alarm condition;
the judging area set in the predicting module consists of sewage flowing section area data stored in a plurality of groups of storage modules, the sewage flowing section area data used in the judging area are set to be continuous, when judging logic set in the predicting module presents increasing trend for three groups of continuous sewage flowing section area data in the judging area, the predicting module predicts that the warning situation exists, otherwise, the warning situation does not exist, when the predicting module predicts that the warning situation exists, the warning module is triggered to operate, the warning module acquires the corresponding sewage pipeline based on the judging of the used sewage flowing section area data by the predicting module, and the acquired sewage pipeline is used as an output target to be output.
Furthermore, in the stage of constructing the underground sewage pipeline model, when the underground sewage pipeline model is applied to execute pipeline association analysis, the system end edits and sets the sewage sources of all sewage pipelines synchronously;
when the warning module outputs the sewage pipes, the sewage pipe sewage sources corresponding to the sewage pipes output by the warning module are synchronously output based on the underground sewage pipe model and the sewage source of each sewage pipe.
Furthermore, the loading module is connected with the modeling module and the sniffing module through the dielectric property, the sniffing module is connected with the monitoring module through the dielectric property, the monitoring module is connected with the metering module through the dielectric property, the metering module is connected with the storage module through the dielectric property, and the storage module is connected with the prediction module and the alarm module through the dielectric property.
In a second aspect, an artificial intelligence-based automatic sewage flow monitoring and early warning method includes the following steps:
step 1: uploading a sewage pipeline electronic drawing, acquiring sewage pipeline construction specification parameters and position information from the sewage pipeline electronic drawing, and constructing a sewage pipeline model based on the sewage pipeline construction specification parameters and the position information;
step 11: manually binding the corresponding sewage sources of each pipeline in the sewage pipeline model;
step 2: disposing a liquid flow rate sensor in the interior of the sewage pipeline, and carrying out real-time monitoring on the sewage conveyed in the interior of the sewage pipeline in real time based on the liquid flow rate sensor;
step 3: according to the real-time monitoring data of the liquid flow rate sensor, the sewage flow cross-section area is obtained, and the real-time sewage transmission change situation in the sewage pipeline is estimated based on the sewage flow cross-section area in the sewage pipeline;
step 4: according to the real-time monitoring data of the liquid flow rate sensor, the sewage flow cross-section area is obtained, and the real-time sewage transmission change situation in the sewage pipeline is estimated based on the sewage flow cross-section area in the sewage pipeline;
step 41: setting a judging threshold value, and judging whether the sewage pipeline has a warning condition or not based on the comparison of the real-time transmission sewage change situation in the sewage pipeline and the judging threshold value;
step 5: and storing the obtained sewage flow cross-sectional area, and predicting that the sewage flow cross-sectional area data with the alarm condition corresponds to the sewage pipeline according to the stored sewage flow cross-sectional area data.
Compared with the prior art, the technical proposal provided by the invention has the following advantages that
The beneficial effects are that:
1. the invention provides an artificial intelligence-based automatic sewage flow monitoring and early warning system, which can complete the construction of a sewage pipeline model by inputting an electronic drawing of the sewage pipeline and acquiring data in the electronic drawing, further analyze the relevance of the sewage pipelines based on the sewage pipeline model, and monitor the flow of the sewage transmitted in the sewage pipeline by adopting a liquid flow rate sensor so that the flow monitoring of the sewage pipeline is more accurate and effective.
2. In the system operation process, in the sewage pipeline model construction stage, the position information of the sewage pipeline in the sewage pipeline construction drawing can be subjected to the check and reconstruction operation, so that on one hand, the stable construction of the sewage pipeline model is maintained, and on the other hand, repeated and useless data in the input electronic drawing are removed, the purpose of reducing the accuracy required in the sewage pipeline construction electronic drawing input stage is achieved, and further convenience is brought to a user in the uploading sewage pipeline construction electronic drawing stage.
3. In the running process of the system, the real-time sewage transmission change situation in the sewage pipeline can be evaluated based on the real-time sewage transmission flow cross-section area in the sewage pipeline, so that another sewage flow monitoring logic is provided for the system, the robustness of the system is better, and the functionality is more comprehensive.
4. The invention provides an artificial intelligence-based automatic sewage flow monitoring and early warning method, which can effectively maintain the stability of system operation by further executing steps in the method, and further provides a logic architecture for outputting results in the technical scheme in the executing process of the steps of the method, so that the output process of the results in the technical scheme is more stable.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It is evident that the drawings in the following description are only some embodiments of the present invention and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
FIG. 1 is a schematic diagram of an artificial intelligence-based automatic sewage flow monitoring and early warning system;
FIG. 2 is a schematic flow chart of an artificial intelligence-based automatic sewage flow monitoring and early warning method;
FIG. 3 is a conceptual illustration of a sewer model according to the invention;
FIG. 4 is a schematic diagram showing a deployment and distribution state of a liquid flow rate sensor in a sewage pipeline according to the present invention;
FIG. 5 is a schematic diagram of a neutron liquid flow sensor deployment profile of the present invention;
FIG. 6 is a schematic view of the cross-sectional area of the wastewater flow in the present invention;
reference numerals in the drawings represent respectively: 1. a sewage conduit; 2. a liquid flow rate sensor; 3. a sub-liquid flow rate sensor.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention is further described below with reference to examples.
Example 1
An artificial intelligence-based automatic sewage flow monitoring and early warning system in the embodiment, as shown in fig. 1, comprises a construction layer, a monitoring layer and an analysis layer;
the method comprises the steps that an electronic drawing built by an underground sewage pipeline is input through a building layer, the building layer builds an underground sewage pipeline model based on information acquired from the electronic drawing, pipeline association analysis is further carried out by applying the underground sewage pipeline model, a monitoring layer receives the underground sewage pipeline model built in the building layer and pipeline association analysis results, flow monitoring is carried out on the sewage pipeline in real time, the analysis layer receives sewage pipeline flow monitoring data in the monitoring layer in real time, whether a warning situation exists in the sewage pipeline is analyzed based on the sewage pipeline flow monitoring data, and when the warning situation exists, the warning situation is output corresponding to the sewage pipeline;
the monitoring layer comprises a monitoring module and a metering module, wherein the monitoring module is used for monitoring real-time transmission sewage parameters in the sewage pipeline, the metering module is used for receiving the real-time transmission sewage parameters in the sewage pipeline monitored by the monitoring module, the real-time transmission sewage flow cross-section area in the sewage pipeline is calculated by using the sewage parameters, and the calculation formula is as follows:
wherein: r is the radius of the sewage pipeline; m is a collection of liquid flow rate sensors in the monitoring module; n is the central angle number of the region formed by the coordinates corresponding to the non-running sub-liquid flow rate sensor in the liquid flow rate sensor; r is (r) 1 、r 2 、...、r x-1 、r x The radius of the region is formed by coordinates corresponding to the neutron liquid flow velocity sensor in the liquid flow velocity sensor;
wherein { r 1 、r 2 、...、r x-1 、r x Any one of the items is not less than 0;
the construction layer comprises a loading module, a construction module and a sniffing module, wherein the loading module is used for inputting an electronic map built by the underground sewage pipeline, the modeling module is used for receiving the electronic map input by the loading module, acquiring construction specification parameters and position information of the underground sewage pipeline in the electronic map, further completing construction of an underground sewage pipeline model based on the construction specification parameters and the position information of the sewage pipeline, and the sniffing module is used for traversing and reading the position information of the underground sewage pipeline acquired by the modeling module in the electronic map and carrying out relevance analysis on the sewage pipeline based on the position information of the underground sewage pipeline;
each piece of sewage pipeline position information at least comprises two groups of position coordinates, the modeling module is used for performing traversal searching on the acquired underground sewage pipeline position information, capturing the same position coordinates in the position information of each sewage pipeline, further determining a sewage pipeline distribution area based on the position coordinates contained in the position information of the sewage pipeline, judging the sewage pipeline which does not capture the same position coordinates based on the sewage pipeline distribution area, and discarding the position information of the sewage pipeline which does not capture the same position coordinates and is not positioned at the boundary of the sewage pipeline distribution area;
the analysis layer comprises a storage module, a prediction module and an alarm module, wherein the storage module is used for receiving the real-time transmission sewage flow cross-section area in the sewage pipeline calculated in the metering module, storing the real-time transmission sewage flow cross-section area in the sewage pipeline, the prediction module is used for setting a judgment area and judgment logic, carrying out alarm condition prediction on the sewage flow cross-section area data stored in the storage module based on the judgment area and the judgment logic, and the alarm module is used for receiving the operation result of the prediction module, triggering the operation through the operation result of the prediction module and triggering the output operation of the sewage pipeline with alarm condition;
the method comprises the steps that a judging area set in a predicting module consists of sewage flow cross-section area data stored in a plurality of groups of storage modules, the sewage flow cross-section area data used in the judging area are set to be continuous, when judging logic set in the predicting module presents an increasing trend for three groups of continuous sewage flow cross-section area data in the judging area, a predicting result of the predicting module predicts that a warning situation exists, otherwise, the warning situation does not exist, when the predicting result of the predicting module predicts that the warning situation exists, the warning module is triggered to operate, and the warning module acquires corresponding sewage pipelines based on the judging of the used sewage flow cross-section area data and takes the acquired sewage pipelines as output targets to output;
the loading module is connected with the modeling module and the sniffing module through medium electrical property, the sniffing module is connected with the monitoring module through medium electrical property, the monitoring module is connected with the metering module through medium electrical property, the metering module is connected with the storage module through medium electrical property, and the storage module is connected with the prediction module and the alarm module through medium electrical property.
In this embodiment, the loading module operates an electronic map built by inputting an underground sewage pipeline, the modeling module synchronously receives the electronic map input in the loading module, acquires construction specification parameters and position information of the underground sewage pipeline in the electronic map, further completes construction of an underground sewage pipeline model based on the construction specification parameters and the position information of the sewage pipeline, the sniffer module operates and traverses and reads the position information of the underground sewage pipeline acquired by the modeling module in the electronic map, performs relevance analysis on the sewage pipeline based on the position information of the underground sewage pipeline, monitors real-time transmission sewage parameters in the sewage pipeline by the monitoring module, the metering module receives real-time transmission sewage parameters in the sewage pipeline monitored by the monitoring module in real time, calculates real-time transmission sewage flow cross-section area in the sewage pipeline by using the sewage parameters, finally receives the real-time transmission sewage flow cross-section area in the sewage pipeline calculated by the metering module by the storage module, stores the real-time transmission sewage flow cross-section area in the sewage pipeline, sets a judgment area and judgment logic based on the judgment area and judgment logic, performs warning prediction on the sewage flow cross-section area data stored in the storage module, and then receives warning result of the prediction module operation result by the warning module, and triggers output warning operation of the sewage existence condition of the sewage pipeline operation trigger operation result by the prediction module operation;
through the formula calculation, the area of the sewage flow cross section in the sewage pipeline can be calculated, and necessary data support is provided for the module operation in the result output stage in the system;
referring to fig. 3 to 5, fig. 3 shows a sewer line model, in which each section of pipeline node is marked with black dots to indicate the existence position of the same item in the position information of the sewer line, and fig. 4 and 5 show the liquid flow rate sensor 2 and the sub-liquid flow rate sensor 3 in the sewer line 1 for easier understanding of the implementation of the technical scheme;
referring to fig. 6, the area required by the equation for calculating the sewage flow cross-sectional area is a blank area other than the same type of shadow, and the shadow is obtained by the operation states of the liquid flow rate sensor 2 and the sub-liquid flow rate sensor 3 and their corresponding coordinates.
Example two
On the aspect of implementation, on the basis of embodiment 1, this embodiment further specifically describes, with reference to fig. 1, an artificial intelligence-based automatic sewage flow monitoring and early warning system in embodiment 1:
when the modeling module performs searching operation of the same position coordinates on the position information of the underground sewage pipeline, the following operation of searching the same item is adopted, and the formula is as follows:
wherein: the name (a, b) is a judgment index, and the name (a, b) is more than 0; n is a collection of sewer line location information;the x-axis corresponding value of the position coordinates of the ith group in the sewage pipeline position information a; />The y-axis corresponding value of the ith group of position coordinates in the sewage pipeline position information a; />The z-axis corresponding value of the ith group of position coordinates in the sewage pipeline position information a; />The corresponding value of the x-axis of the position coordinates of the ith group in the position information b of the sewage pipeline; />The y-axis corresponding value of the ith group of position coordinates in the sewage pipeline position information b; />The z-axis corresponding value of the ith group of position coordinates in the sewage pipeline position information b; n is n 0 For limiting the coefficient, n is defined when the position coordinates of the sewage pipeline position information are three-dimensional coordinates 0 When the value is 3 and the position coordinate of the sewage pipeline position information is two-dimensional coordinate, n 0 The value is 2;
when the name (a, b) =1, it is determined that the two groups of sewer line position information are the Same items, otherwise, the two groups of sewer line position information are different items, and the modeling module performs the searching operation of the Same position coordinates on the underground sewer line position information, that is, the association analysis of the sewer line in the construction layer.
Through the formula calculation, the same item search is carried out on the position coordinates used in the modeling module when the sewage pipeline model is constructed, so that useless position coordinates are eliminated, and the sewage pipeline model can be stably constructed.
As shown in fig. 1, the monitoring module is integrated by liquid flow rate sensors, the liquid flow rate sensors are provided with a plurality of groups, the liquid flow rate sensors are distributed in the sewage pipeline at equal intervals in an annular shape, each group of liquid flow rate sensors is integrated by a plurality of groups of sub-liquid flow rate sensors, and the plurality of groups of sub-liquid flow rate sensors are mounted on the surface of the liquid flow rate sensors at equal intervals in an annular shape.
The distribution and arrangement of the liquid flow rate sensors and the sub-liquid flow rate sensors bring a limiting effect.
As shown in fig. 1, the sub-liquid flow rate sensors on each set of liquid flow rate sensors are each configured with corresponding two-dimensional coordinates based on the cross section of the sewer line.
The above arrangement provides a precondition for the calculation of the flow cross-sectional area of the real-time transfer sewage in the sewage conduit.
Example III
On the aspect of implementation, on the basis of embodiment 1, this embodiment further specifically describes, with reference to fig. 1, an artificial intelligence-based automatic sewage flow monitoring and early warning system in embodiment 1:
the system end user in the monitoring module manually sets a monitoring period, the metering module synchronously operates according to the monitoring period set in the monitoring module, the metering module operates to calculate the real-time transmission sewage flow section in the sewage pipeline, and then further based on the monitoring period and the calculated real-time transmission sewage flow section data in the sewage pipeline, the real-time transmission sewage change situation in the sewage pipeline is estimated, and an estimation formula is as follows:
wherein: k is a set of monitoring periods; s is the cross-sectional area of the sewer line, s=pi r 2 ；Calculating the calculated sewage flow cross-sectional area for the third last group of monitoring periods; />Calculating the calculated sewage flow cross-sectional area for the penultimate group of monitoring periods; />Calculating the calculated sewage flow cross-sectional area for the last one group of monitoring periods;
wherein, the sewage transmission change situation T obtained by the above formula is inversely proportional to the sewage flow of the sewage pipeline.
By the calculation of the formula, further data reference is provided for monitoring the sewage pipeline flow in the system, and therefore, the comprehensiveness of the system function can be improved to a certain extent.
As shown in fig. 1, in the stage of constructing the underground sewage pipeline model, when the underground sewage pipeline model is applied to execute pipeline association analysis, the system end edits and sets the sewage sources of each sewage pipeline synchronously;
when the warning module outputs the sewage pipes, the sewage pipe sewage sources corresponding to the sewage pipes output by the warning module are synchronously output based on the underground sewage pipe model and the sewage source of each sewage pipe.
Through above-mentioned setting, can in time acquire the sewage source end when sewer line is monitored and is had huge burden, and then with the control to sewer source in the sewer line, further maintained sewer line's operation, avoid sewer line to appear damaging because of huge burden.
Example IV
On the aspect of implementation, on the basis of embodiment 1, this embodiment further specifically describes an artificial intelligence-based automatic sewage flow monitoring and early warning system in embodiment 1 with reference to fig. 2:
an artificial intelligence-based automatic sewage flow monitoring and early warning method comprises the following steps:
step 1: uploading a sewage pipeline electronic drawing, acquiring sewage pipeline construction specification parameters and position information from the sewage pipeline electronic drawing, and constructing a sewage pipeline model based on the sewage pipeline construction specification parameters and the position information;
step 11: manually binding the corresponding sewage sources of each pipeline in the sewage pipeline model;
step 2: disposing a liquid flow rate sensor in the interior of the sewage pipeline, and carrying out real-time monitoring on the sewage conveyed in the interior of the sewage pipeline in real time based on the liquid flow rate sensor;
step 3: according to the real-time monitoring data of the liquid flow rate sensor, the sewage flow cross-section area is obtained, and the real-time sewage transmission change situation in the sewage pipeline is estimated based on the sewage flow cross-section area in the sewage pipeline;
step 4: according to the real-time monitoring data of the liquid flow rate sensor, the sewage flow cross-section area is obtained, and the real-time sewage transmission change situation in the sewage pipeline is estimated based on the sewage flow cross-section area in the sewage pipeline;
step 41: setting a judging threshold value, and judging whether the sewage pipeline has a warning condition or not based on the comparison of the real-time transmission sewage change situation in the sewage pipeline and the judging threshold value;
step 5: and storing the obtained sewage flow cross-sectional area, and predicting that the sewage flow cross-sectional area data with the alarm condition corresponds to the sewage pipeline according to the stored sewage flow cross-sectional area data.
In summary, in the system in the above embodiment, through inputting the electronic drawing of the sewage pipeline, data is acquired from the electronic drawing to complete construction of a sewage pipeline model, and correlations between the sewage pipelines are further analyzed based on the sewage pipeline model, and meanwhile, the flow rate of the sewage transported in the sewage pipeline is monitored by adopting a liquid flow rate sensor, so that the flow rate monitoring of the sewage pipeline is more accurate and effective; meanwhile, in the operation process of the system, in the construction stage of the sewage pipeline model, the position information of the sewage pipeline in the construction drawing of the sewage pipeline can be subjected to the check and reconstruction operation, so that on one hand, the stable construction of the sewage pipeline model is maintained, and on the other hand, repeated and useless data in the input electronic drawing are removed, the purpose of reducing the accuracy required in the input stage of the construction electronic drawing of the sewage pipeline is achieved, and further convenience is brought to a user in the stage of uploading the construction electronic drawing of the sewage pipeline; in addition, in the running process of the system, the real-time transmission sewage change situation in the sewage pipeline can be evaluated based on the real-time transmission sewage flow cross-section area in the sewage pipeline, so that another sewage flow monitoring logic is provided for the system, the robustness of the system is better, and the functionality is more comprehensive; on the other hand, the method provided by the embodiment can effectively maintain the stability of the system operation, and further provides a logic architecture of the output result of the technical scheme in the step execution process of the method, so that the output process of the result in the technical scheme is more stable.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. An artificial intelligence-based sewage flow automatic monitoring and early warning system is characterized by comprising a construction layer, a monitoring layer and an analysis layer;
the method comprises the steps that an electronic drawing built by an underground sewage pipeline is input through a building layer, the building layer builds an underground sewage pipeline model based on information acquired from the electronic drawing, pipeline association analysis is further carried out by applying the underground sewage pipeline model, a monitoring layer receives the underground sewage pipeline model built in the building layer and pipeline association analysis results, flow monitoring is carried out on the sewage pipeline in real time, the analysis layer receives sewage pipeline flow monitoring data in the monitoring layer in real time, whether a warning situation exists in the sewage pipeline is analyzed based on the sewage pipeline flow monitoring data, and when the warning situation exists, the warning situation is output corresponding to the sewage pipeline;
the monitoring layer comprises a monitoring module and a metering module, wherein the monitoring module is used for monitoring real-time transmission sewage parameters in the sewage pipeline, the metering module is used for receiving the real-time transmission sewage parameters in the sewage pipeline monitored by the monitoring module, the real-time transmission sewage flow cross-section area in the sewage pipeline is calculated by using the sewage parameters, and the calculation formula is as follows:
wherein: r is the radius of the sewage pipeline; m is a collection of liquid flow rate sensors in the monitoring module; n is the central angle number of the region formed by the coordinates corresponding to the non-running sub-liquid flow rate sensor in the liquid flow rate sensor; r is (r) 1 、r 2 、...、r x-1 、r x The radius of the region is formed by coordinates corresponding to the neutron liquid flow velocity sensor in the liquid flow velocity sensor;
wherein { r 1 、r 2 、...、r x-1 、r x Any one of the items is not less than 0.
2. The automatic sewage flow monitoring and early warning system based on artificial intelligence according to claim 1, wherein the construction layer comprises a loading module, a construction module and a sniffing module, the loading module is used for inputting an electronic map built by the underground sewage pipeline, the modeling module is used for receiving the electronic map input by the loading module, acquiring construction specification parameters and position information of the underground sewage pipeline in the electronic map, further completing construction of an underground sewage pipeline model based on the construction specification parameters and the position information of the sewage pipeline, and the sniffing module is used for traversing and reading the position information of the underground sewage pipeline acquired by the modeling module in the electronic map and carrying out correlation analysis on the sewage pipeline based on the position information of the underground sewage pipeline;
each piece of sewage pipeline position information at least comprises two groups of position coordinates, the modeling module is used for performing traversal searching on the acquired underground sewage pipeline position information, capturing the same position coordinates in the position information of each sewage pipeline, further determining a sewage pipeline distribution area based on the position coordinates contained in the position information of the sewage pipeline, judging the sewage pipeline which does not capture the same position coordinates based on the sewage pipeline distribution area, and discarding the position information of the sewage pipeline which does not capture the same position coordinates and is not located at the boundary of the sewage pipeline distribution area.
3. The automatic sewage flow monitoring and early warning system based on artificial intelligence according to claim 2, wherein when the modeling module performs the operation of searching for the position information of the underground sewage pipeline with the same position coordinates, the operation of searching for the same term is completed by adopting the following formula:
wherein: the name (a, b) is a judgment index, and the name (a, b) is more than 0; n is a collection of sewer line location information;the x-axis corresponding value of the position coordinates of the ith group in the sewage pipeline position information a; />The y-axis corresponding value of the ith group of position coordinates in the sewage pipeline position information a; />The z-axis corresponding value of the ith group of position coordinates in the sewage pipeline position information a; x is x bi The corresponding value of the x-axis of the position coordinates of the ith group in the position information b of the sewage pipeline; />The y-axis corresponding value of the ith group of position coordinates in the sewage pipeline position information b; />The z-axis corresponding value of the ith group of position coordinates in the sewage pipeline position information b; n is n 0 For limiting the coefficient, n is defined when the position coordinates of the sewage pipeline position information are three-dimensional coordinates 0 When the value is 3 and the position coordinate of the sewage pipeline position information is two-dimensional coordinate, n 0 The value is 2;
when the sum (a, b) =1, it is determined that the two groups of sewer line position information are the Same items, otherwise, the two groups of sewer line position information are different items, and the modeling module performs the searching operation of the Same position coordinates on the underground sewer line position information, that is, the association analysis of the sewer line in the building layer.
4. The automatic sewage flow monitoring and early warning system based on artificial intelligence according to claim 1, wherein the monitoring module is integrated by liquid flow rate sensors, the liquid flow rate sensors are provided with a plurality of groups, the liquid flow rate sensors are distributed in the sewage pipeline at equal intervals in an annular shape, each group of the liquid flow rate sensors is integrated by a plurality of groups of sub-liquid flow rate sensors, and the sub-liquid flow rate sensors are mounted on the surface of the liquid flow rate sensors at equal intervals in an annular shape.
5. The automatic sewage flow monitoring and early warning system based on artificial intelligence according to claim 4, wherein the sub-liquid flow rate sensors on each group of the liquid flow rate sensors are configured with corresponding two-dimensional coordinates based on the section of the sewage pipeline.
6. The automatic sewage flow monitoring and early warning system based on artificial intelligence according to claim 1, wherein a system end user in the monitoring module manually sets a monitoring period, the metering module synchronously operates according to the monitoring period set in the monitoring module, and after the metering module operates to calculate a real-time transmission sewage flow section in the sewage pipeline, the metering module further evaluates the real-time transmission sewage change situation in the sewage pipeline based on the monitoring period and the calculated real-time transmission sewage flow section data in the sewage pipeline, and the evaluation formula is as follows:
wherein: k is a set of monitoring periods; s is the cross-sectional area of the sewer line, s=pi r 2 ；Calculating the calculated sewage flow cross-sectional area for the third last group of monitoring periods; />Calculating the calculated sewage flow cross-sectional area for the penultimate group of monitoring periods; />Calculating the calculated sewage flow cross-sectional area for the last one group of monitoring periods;
wherein, the sewage transmission change situation T obtained by the above formula is inversely proportional to the sewage flow of the sewage pipeline.
7. The automatic sewage flow monitoring and early warning system based on artificial intelligence according to claim 1, wherein the analysis layer comprises a storage module, a prediction module and an alarm module, the storage module is used for receiving the real-time transmission sewage flow cross-sectional area in the sewage pipeline calculated in the metering module, storing the real-time transmission sewage flow cross-sectional area in the sewage pipeline, the prediction module is used for setting a judgment area and judgment logic, carrying out alarm condition prediction on the sewage flow cross-sectional area data stored in the storage module based on the judgment area and the judgment logic, the alarm module is used for receiving the operation result of the prediction module, and triggering the output operation of the sewage pipeline with alarm condition through the operation result triggering operation of the prediction module;
the judging area set in the predicting module consists of sewage flowing section area data stored in a plurality of groups of storage modules, the sewage flowing section area data used in the judging area are set to be continuous, when judging logic set in the predicting module presents increasing trend for three groups of continuous sewage flowing section area data in the judging area, the predicting module predicts that the warning situation exists, otherwise, the warning situation does not exist, when the predicting module predicts that the warning situation exists, the warning module is triggered to operate, the warning module acquires the corresponding sewage pipeline based on the judging of the used sewage flowing section area data by the predicting module, and the acquired sewage pipeline is used as an output target to be output.
8. The automatic sewage flow monitoring and early warning system based on artificial intelligence according to claim 1 or 7, wherein the underground sewage pipeline model construction stage is characterized in that when pipeline association analysis is executed by applying the underground sewage pipeline model, the sewage sources of all sewage pipelines are edited and set by a system end synchronously;
when the warning module outputs the sewage pipes, the sewage pipe sewage sources corresponding to the sewage pipes output by the warning module are synchronously output based on the underground sewage pipe model and the sewage source of each sewage pipe.
9. The automatic sewage flow monitoring and early warning system based on artificial intelligence according to claim 2, wherein the loading module is connected with the modeling module and the sniffing module through medium electrical property, the sniffing module is connected with the monitoring module through medium electrical property, the monitoring module is connected with the metering module through medium electrical property, the metering module is connected with the storage module through medium electrical property, and the storage module is connected with the prediction module and the warning module through medium electrical property.
10. An artificial intelligence based automatic sewage flow monitoring and early warning method, which is an implementation method of the automatic sewage flow monitoring and early warning system based on the artificial intelligence as set forth in any one of claims 1 to 9, and is characterized by comprising the following steps:
step 1: uploading a sewage pipeline electronic drawing, acquiring sewage pipeline construction specification parameters and position information from the sewage pipeline electronic drawing, and constructing a sewage pipeline model based on the sewage pipeline construction specification parameters and the position information;
step 11: manually binding the corresponding sewage sources of each pipeline in the sewage pipeline model;
step 2: disposing a liquid flow rate sensor in the interior of the sewage pipeline, and carrying out real-time monitoring on the sewage conveyed in the interior of the sewage pipeline in real time based on the liquid flow rate sensor;
step 3: according to the real-time monitoring data of the liquid flow rate sensor, the sewage flow cross-section area is obtained, and the real-time sewage transmission change situation in the sewage pipeline is estimated based on the sewage flow cross-section area in the sewage pipeline;
step 4: according to the real-time monitoring data of the liquid flow rate sensor, the sewage flow cross-section area is obtained, and the real-time sewage transmission change situation in the sewage pipeline is estimated based on the sewage flow cross-section area in the sewage pipeline;
step 41: setting a judging threshold value, and judging whether the sewage pipeline has a warning condition or not based on the comparison of the real-time transmission sewage change situation in the sewage pipeline and the judging threshold value;
step 5: and storing the obtained sewage flow cross-sectional area, and predicting that the sewage flow cross-sectional area data with the alarm condition corresponds to the sewage pipeline according to the stored sewage flow cross-sectional area data.
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