CN116307673A - Underground pipe network interaction risk early warning system for urban lifeline - Google Patents
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Abstract
The embodiment of the invention discloses an underground pipe network interaction risk early warning system for an urban lifeline, which relates to the field of urban lifeline operation monitoring and can improve the efficiency and the timely degree of personnel supervision of underground pipe network interaction safety. The invention comprises the following steps: the auxiliary analysis terminal is accessed to a front end sensor of the area to be detected through a mobile communication network; the auxiliary analysis terminal determines grade information according to the acquired front-end sensor information and sends the grade information to the data processing center; the data processing center establishes a risk interaction prediction model, determines the underground pipe network interaction risk level according to the level information reported by the auxiliary analysis terminal, and reports the underground pipe network interaction risk level to the dispatching monitoring center; and the dispatching monitoring center determines a risk treatment scheme aiming at the area to be detected according to the reported underground pipe network interaction risk level, and sends the risk treatment scheme to the auxiliary analysis terminal.
Description
Technical Field
The invention relates to the field of urban lifeline operation monitoring, in particular to an underground pipe network interactive risk early warning system for an urban lifeline.
Background
With the rapid promotion of urban China, population, resources, industry and other factors are further concentrated towards cities, the urban scale is rapidly enlarged, and urban operation systems are also increasingly complicated. Against increasingly heavy urban operation pressure, urban life lines such as fuel gas, water drainage, water supply, third party construction and the like are continuously subjected to reconstruction, extension and new construction, the types of infrastructures are continuously increased, the quantity is continuously increased, and the scale is continuously increased. Particularly, with the wide application of information technologies such as big data, cloud computing, internet of things, artificial intelligence and the like in urban lifelines, urban lifelines with huge scale such as gas, water supply, water discharge, third party construction and the like are interwoven together, so that a complex network system of mutual association, mutual dependence, mutual influence and interaction is formed. Therefore, no matter the equipment of the life line system is failed or the operation is performed by human errors, no matter the natural disasters such as typhoons, floods or earthquakes are sudden, or the urban safety accidents such as gas fires and explosions are caused, the faults caused to the life line system can be quickly spread through a complex network and generate cascading effect, namely, the problems with a small degree can possibly lead to paralysis of a large-area system through butterfly effect, so that the complex risks with the characteristics of high uncertainty, surging, crossing, serious hazard and the like are induced. However, in clear contrast to the severe risk situation, the monitoring and early warning means of most cities in China are relatively lagged, and the problems of light prevention of heavy rescue, light early warning of heavy planning and the like widely exist. The construction of a comprehensive, efficient and accurate multidimensional safety monitoring and intelligent prediction early warning system becomes urgent for improving the precise management of the safety operation of the urban lifeline.
The existing urban operation monitoring system cannot fully combine underground pipe network risk big data to carry out comprehensive analysis, and is difficult to realize optimal matching of risk categories, potential scales and emergency strategies mainly depending on experience of management staff and an offline scheduling scheme in monitoring operation and decision, so that the problems that risk identification is inaccurate before risk occurs and the risk cannot be treated in time after risk occurs are caused, and safety accidents are easily expanded, and even casualties are caused.
Disclosure of Invention
The embodiment of the invention provides an underground pipe network interaction risk early warning system for an urban life line, which can combine underground pipe network risk big data to carry out comprehensive analysis, provide support for information and treatment schemes for personnel in the aspects of monitoring operation, decision making and the like, and improve the efficiency and the timely degree of personnel supervision of underground pipe network interaction safety.
In order to achieve the above purpose, the embodiment of the present invention adopts the following technical scheme:
the system comprises an auxiliary analysis terminal (1), a data processing center (2) and a dispatching monitoring center (3); the auxiliary analysis terminal (1) is accessed to a front end sensor of the area to be detected through a mobile communication network; the auxiliary analysis terminal (1) determines grade information according to the collected front-end sensor information and sends the grade information to the data processing center (2), wherein the grade information comprises the following components: the gas pipe network of the region to be detected comprises a combustible gas concentration grade, a liquid level grade and a drainage flow grade of the drainage pipe network, and a pressure grade and a water supply flow grade of the water supply pipe network; the data processing center (2) establishes a risk interaction prediction model, and the data processing center (2) determines the underground pipe network interaction risk level according to the level information reported by the auxiliary analysis terminal (1) and reports the underground pipe network interaction risk level to the dispatching monitoring center (3); and the dispatching monitoring center (3) determines a risk treatment scheme aiming at the region to be detected according to the reported underground pipe network interaction risk level, and sends the risk treatment scheme to the auxiliary analysis terminal (1).
The front end sensor device comprises at least: a gas sensor disposed on the gas pipeline, a liquid sensor disposed on the water drain pipeline, and a pressure sensor disposed on the water supply pipeline; wherein, the gas sensor of deployment on the gas pipeline includes: a combustible gas concentration monitor; a liquid sensor deployed on a drain line, comprising: a level gauge and a flow meter; a pressure sensor deployed on a water supply line, comprising: a pressure instrument and a flow instrument; the auxiliary analysis terminal (1) at least comprises a gas pipe network auxiliary analysis module (1-1), a drainage pipe network auxiliary analysis module (1-2) and a water supply pipe network auxiliary analysis module (1-4); the gas pipe network auxiliary analysis module (1-1) is used for detecting the concentration level of the combustible gas of the gas pipe network; the auxiliary analysis module (1-2) of the drainage pipe network is used for detecting the liquid level grade and the drainage flow grade of the drainage pipe network; the water supply network auxiliary analysis module (1-4) is used for detecting the pressure level and the water supply flow level of the water supply network.
The auxiliary analysis terminal (1) further comprises an engineering excavation analysis module (1-3), wherein the engineering excavation analysis module (1-3) is connected with the earth surface displacement monitoring equipment; the rank information further includes: and the earth surface displacement level is determined by the engineering excavation analysis module (1-3) according to earth surface displacement data acquired by the earth surface displacement monitoring equipment.
A data processing center (2), comprising: a predictive modeling module (2-3) and an early warning analysis module (2-4); the prediction modeling module (2-3) is used for building and maintaining a risk interaction prediction model; and the early warning analysis module (2-4) is used for inputting the grade information into the risk interaction prediction model, outputting the underground pipe network interaction risk grade, and reporting the underground pipe network interaction risk grade to the dispatching monitoring center (3).
A data processing center (2), further comprising: the data acquisition module (2-1) and the data characteristic multidimensional analysis module (2-2); the auxiliary analysis terminal (1) is also used for uploading the original data acquired by the front-end sensor to the big data acquisition module (2-1) of the data processing center (2); the big data acquisition module (2-1) is also used for preprocessing the original data uploaded by the auxiliary analysis terminal (1), and the preprocessing comprises the following steps: sequentially extracting, cleaning and loading the original data; and the data characteristic multidimensional analysis module (2-2) is used for carrying out space-time distribution characteristic analysis on the original data output by the big data acquisition module (2-1).
The spatiotemporal distribution characterization includes: adding a tag to the preprocessed raw data, wherein the added tag comprises: time tags, space tags and attribute tags; the time tag represents a time stamp at which the original data was acquired; the space tag represents the position of a front-end sensor for collecting original data; the attribute tag represents the state of a front-end sensor for collecting original data; and clustering the preprocessed original data according to the added labels.
The information recorded by the attribute tag at least comprises: IP address information of the front-end sensor; and/or, a maintenance time of the front-end sensor for the last scheduled maintenance; and/or at least one of a personnel number, a team number, and a unit number responsible for front-end sensor maintenance; and/or the manufacturing time and lifetime of the front end sensor.
Clustering the preprocessed raw data, including: clustering the collected original data in the same time interval; clustering the collected original data in the same geographic range; clustering the original data with the labels with the same attribute partially; and establishing the association relation of the original data according to the clustering result.
The risk interaction prediction model comprises:
R＝∑Xij (1-1)
Xij＝Fij*Sij (1-2)
wherein Xij is the score of each auxiliary analysis module index, i=1, 2,3,4; j=1, 2, … …, n, n is the number of threshold levels set for the corresponding i-th auxiliary analysis module, fij is the value of the auxiliary analysis module, sij is the influence factor coefficient of the auxiliary analysis module, and R is the numerical value reflecting the interaction risk degree of the underground pipe network.
The underground pipe network interaction risk level comprises: when R is more than 75, the corresponding grade IV represents an extremely high risk grade; when R is more than 50 and less than or equal to 75, the corresponding grade III represents a high risk grade; when R is more than 30 and less than or equal to 50, the corresponding grade II represents a moderate risk grade; when R is less than or equal to 30, the corresponding grade I represents a low risk grade.
The embodiment of the invention provides the following components. . . In the method and the device,
the gas, water drainage and water supply risk alarm information and the third party construction risk data in the underground pipe network, which are acquired by the wireless communication network module, can be used for establishing and maintaining a risk interaction model; identifying and managing category and grade of the modeling risk data; and comprehensively analyzing risk characteristics, making an optimal treatment strategy according to the risk interaction type, the grade and the like, and eliminating potential risk hazard and real-time linkage treatment through closed-loop scheduling of a scheduling monitoring center so as to ensure the operation safety of the urban underground pipe network.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a system architecture according to an embodiment of the present invention;
fig. 2 and fig. 3 are schematic diagrams of specific examples provided in the embodiments of the present invention.
Detailed Description
The present invention will be described in further detail below with reference to the drawings and detailed description for the purpose of better understanding of the technical solution of the present invention to those skilled in the art. Embodiments of the present invention will hereinafter be described in detail, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary only for explaining the present invention and are not to be construed as limiting the present invention. As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless expressly stated otherwise, as understood by those skilled in the art. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items. It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The embodiment of the invention provides an underground pipe network interactive risk early warning system for an urban lifeline, as shown in fig. 1, comprising: the system comprises an auxiliary analysis terminal (1), a data processing center (2) and a dispatching monitoring center (3);
the auxiliary analysis terminal (1) is accessed to a front end sensor of the area to be detected through a mobile communication network;
the auxiliary analysis terminal (1) determines grade information according to the collected front-end sensor information and sends the grade information to the data processing center (2), wherein the grade information comprises the following components: the gas pipe network of the region to be detected comprises a combustible gas concentration grade, a liquid level grade and a drainage flow grade of the drainage pipe network, and a pressure grade and a water supply flow grade of the water supply pipe network;
the data processing center (2) establishes a risk interaction prediction model, and the data processing center (2) determines the underground pipe network interaction risk level according to the level information reported by the auxiliary analysis terminal (1) and reports the underground pipe network interaction risk level to the dispatching monitoring center (3);
and the dispatching monitoring center (3) determines a risk treatment scheme aiming at the region to be detected according to the reported underground pipe network interaction risk level, and sends the risk treatment scheme to the auxiliary analysis terminal (1). Specifically, the auxiliary analysis terminal (1) can establish connection with the data processing center (2) through the wireless communication module (1-5), and transmit the collected original data to the data processing center (2) in real time.
In this embodiment, the front end sensor apparatus includes at least: a gas sensor disposed on the gas pipeline, a liquid sensor disposed on the water drain pipeline, and a pressure sensor disposed on the water supply pipeline;
wherein, the gas sensor of deployment on the gas pipeline includes: a combustible gas concentration monitor; a liquid sensor deployed on a drain line, comprising: a level gauge and a flow meter; a pressure sensor deployed on a water supply line, comprising: a pressure instrument and a flow instrument;
the auxiliary analysis terminal (1) at least comprises a gas pipe network auxiliary analysis module (1-1), a drainage pipe network auxiliary analysis module (1-2) and a water supply pipe network auxiliary analysis module (1-4);
the gas pipe network auxiliary analysis module (1-1) is used for detecting the concentration level of the combustible gas of the gas pipe network; the auxiliary analysis module (1-2) of the drainage pipe network is used for detecting the liquid level grade and the drainage flow grade of the drainage pipe network; the water supply network auxiliary analysis module (1-4) is used for detecting the pressure level and the water supply flow level of the water supply network.
Further optionally, the auxiliary analysis terminal (1) further comprises an engineering excavation analysis module (1-3), wherein the engineering excavation analysis module (1-3) is connected with the ground displacement monitoring equipment; the rank information further includes: and the earth surface displacement level is determined by the engineering excavation analysis module (1-3) according to earth surface displacement data acquired by the earth surface displacement monitoring equipment.
In practical application, as shown in fig. 2, the auxiliary analysis module of the gas pipe network monitors the concentration of the gas through the gas concentration monitor, sets concentration threshold values and classifies the gas according to grades so as to implement different grade coping strategies. The gas pipe network database and the corresponding analysis software system can be established in the data processing center so as to accumulate the acquired data and prepare for more complex background analysis work; the drainage pipe network auxiliary analysis module monitors liquid level and flow through a rain gauge, a liquid level gauge and a flow meter, sets liquid level and flow threshold values, classifies the liquid level and the flow threshold values according to grades, carries out different grade coping strategies, and establishes a drainage pipe network database and an analysis software system; the engineering excavation state is monitored through the vehicle-mounted monitoring terminal and the earth surface displacement monitoring equipment, displacement threshold values are set, classification is carried out according to grades, and different grade coping strategies are implemented. The engineering excavation database can be built in the data processing center so as to accumulate the acquired data and prepare for more complex background analysis work; the auxiliary analysis module of the water supply network monitors the real-time pressure and flow of the water supply network through a pressure instrument and a flow instrument, sets pressure and flow thresholds, classifies according to grades, and implements different grades of coping strategies so as to establish a water supply network database in a data processing center, thereby being convenient for accumulating collected data and preparing for more complex background analysis work, and comprises the following steps: in the subsequent more complex background analysis, characteristic research can be carried out on the risk source data, a data mining algorithm is adopted to construct a high-efficiency accurate risk characteristic extraction model, the space-time characteristics of the risk source data are analyzed, and finally urban life line risk characteristic information is obtained; according to the space-time distribution characteristics of the risk source data, designing an urban lifeline underground pipe network risk identification algorithm, identifying risk source information and distinguishing urban lifeline underground pipe network interaction risk characteristics; according to the interactive risk feature connotation of the underground pipe network of the urban lifeline, extracting a multidimensional analysis and feature index formula for establishing data characteristics, and establishing an interactive risk prediction model; and analyzing the interaction risk diffusion influence factors of the underground pipe network of the urban lifeline by combining the interaction risk characteristics and the interaction risk prediction model algorithm of the underground pipe network of the urban lifeline, and carrying out result analysis statistics. The present embodiment provides sufficient data preparation for the analysis process described above.
In this embodiment, a data processing center (2) includes: a predictive modeling module (2-3) and an early warning analysis module (2-4);
the prediction modeling module (2-3) is used for building and maintaining a risk interaction prediction model;
and the early warning analysis module (2-4) is used for inputting the grade information into the risk interaction prediction model, outputting the underground pipe network interaction risk grade, and reporting the underground pipe network interaction risk grade to the dispatching monitoring center (3).
Specifically, the data processing center (2) further comprises: the data acquisition module (2-1) and the data characteristic multidimensional analysis module (2-2); the auxiliary analysis terminal (1) is also used for uploading the original data acquired by the front-end sensor to the big data acquisition module (2-1) of the data processing center (2); the big data acquisition module (2-1) is also used for preprocessing the original data uploaded by the auxiliary analysis terminal (1), and the preprocessing comprises the following steps: sequentially extracting, cleaning and loading the original data; and the data characteristic multidimensional analysis module (2-2) is used for carrying out space-time distribution characteristic analysis on the original data output by the big data acquisition module (2-1). For example: as shown in fig. 3, the ETL tool may be adopted in this embodiment to set the requirement based on the thematic threshold, where data extraction refers to that the content of the data source is selected according to the threshold level; the data cleaning refers to filtering and cleaning useless data and incomplete data, and converting different types of data and different granularities of data; data loading refers to the final loading of the extracted and cleaned data into the topical data warehouse. And obtaining data which accords with the threshold setting range of the auxiliary analysis terminal after preprocessing.
In this embodiment, the spatiotemporal distribution characteristic analysis includes: adding a label to the preprocessed original data; and clustering the preprocessed original data according to the added labels. Wherein the added tag comprises: time tags, space tags and attribute tags; the time tag represents a time stamp at which the original data was acquired; the space tag represents the position of a front-end sensor for collecting original data; the attribute tags represent the status of the front-end sensor that collected the raw data. For example: features of three aspects of inherent time, space and attribute of data can be overlapped to present multidimensional, semantic and space-time dynamic association, so that space-time data comprise association relations of objects, processes, events in the aspects of space, time, semantic and the like, the original data and association relations of clusters can reflect the running state of an underground pipe network, and engineering personnel and technicians can estimate the running state of the underground pipe network according to the data and association relations.
Optionally, the information recorded by the attribute tag at least includes: IP address information of the front-end sensor; and/or, a maintenance time of the front-end sensor for the last scheduled maintenance; and/or at least one of a personnel number, a team number, and a unit number responsible for front-end sensor maintenance; and/or the manufacturing time and lifetime of the front end sensor.
Clustering the preprocessed raw data, including: clustering the collected original data in the same time interval; clustering the collected original data in the same geographic range; clustering the original data with the labels with the same attribute partially; and establishing the association relation of the original data according to the clustering result. The predictive modeling module (2-3) may update the risk interaction predictive model with the results of the spatio-temporal distribution characteristic analysis.
For example: increasing or decreasing the influence factor coefficient according to the clustering result, for example: increasing/decreasing the importance of a certain period of time, such as the peak period/valley period of water consumption, and correspondingly increasing/decreasing the influence factor coefficient; increasing/decreasing the importance of a certain geographical range, for example, for an old urban area with construction age above 20 years, or a new urban area with construction age below 5 years, the influence factor coefficient can be correspondingly increased/decreased; for another example, the impact factor coefficient is increased/decreased according to the manufacturing time and the service life of the front end sensor.
In a preferred embodiment, the risk interaction prediction model includes:
R＝∑Xij (1-1)
Xij＝Fij*Sij (1-2)
wherein Xij is the score of each auxiliary analysis module index, i=1, 2,3,4; j=1, 2, … …, n, n is the number of threshold levels set for the corresponding i-th auxiliary analysis module, fij is the value of the auxiliary analysis module, sij is the influence factor coefficient of the auxiliary analysis module, and R is the numerical value reflecting the interaction risk degree of the underground pipe network.
Further, after the R value is calculated, the interactive risk grade value of the underground pipe network can be obtained. For example: the interactive risk level of the underground pipe network is shown in table 1, which comprises the following steps: when R is more than 75, the corresponding grade IV represents an extremely high risk grade; when R is more than 50 and less than or equal to 75, the corresponding grade III represents a high risk grade; when R is more than 30 and less than or equal to 50, the corresponding grade II represents a moderate risk grade; when R is less than or equal to 30, the corresponding grade I represents a low risk grade.
TABLE 1
| Risk level | R |
| Grade IV (extremely high risk) | R＞75 |
| Grade III (high risk) | 50<R≤75 |
| Grade II (moderate risk) | 30<R≤50 |
| Grade I (Low risk) | R≤30 |
The scheduling monitoring center (3) dynamically plans a scheduling scheme according to the pre-interaction risk R value, determines a scheduling scheme strategy corresponding to the risk levels I, II, III and IV, realizes the optimal matching of the risk types, the number and the scheduling strategy, and simultaneously realizes the functions of early warning of daily, daily and monthly risk levels, category data statistics, scheduling scheme strategy data storage, treatment result management and the like. For example: the system comprises a water supply pipe network, a rain gauge, a liquid level meter, a flowmeter, a ground surface displacement monitoring device, a pressure instrument, a flow instrument, a wireless communication module and a wireless communication module.
The risk monitoring of the underground pipe network in the current urban life line is simpler and more lagged, has singleness and passivity, performs space-time distribution characteristic division on the risk data of the multi-pipe network scene to obtain prediction risk and linkage treatment, and grasps various data through a wireless communication module according to a database provided by a big data module. In practical application, the method is realized by establishing a modularized software design framework for the underground pipe network risk interaction analysis early warning system, wherein the design framework comprises risk data storage, risk data statistics analysis and risk treatment result management, and a dispatching monitoring center designs a dispatching scheme for dynamic planning according to a dispatching demand prediction model, so that the optimal matching of risk category, quantity and dispatching strategy is realized, and simultaneously, the functions of data statistics, dispatching data storage, treatment result management and the like are realized.
The gas, water drainage and water supply risk alarm information and the third party construction risk data in the underground pipe network, which are acquired by the wireless communication network module, can be used for establishing and maintaining a risk interaction model; identifying and managing category and grade of the modeling risk data; and comprehensively analyzing risk characteristics, making an optimal treatment strategy according to the risk interaction type, the grade and the like, and eliminating potential risk hazard and real-time linkage treatment through closed-loop scheduling of a scheduling monitoring center so as to ensure the operation safety of the urban underground pipe network.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for the apparatus embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments for relevant points. The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any changes or substitutions easily contemplated by those skilled in the art within the scope of the present invention should be included in the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Claims (10)
1. An underground pipe network interactive risk early warning system for an urban lifeline, which is characterized by comprising: the system comprises an auxiliary analysis terminal (1), a data processing center (2) and a dispatching monitoring center (3);
the auxiliary analysis terminal (1) is accessed to a front end sensor of the area to be detected through a mobile communication network;
the auxiliary analysis terminal (1) determines grade information according to the collected front-end sensor information and sends the grade information to the data processing center (2), wherein the grade information comprises the following components: the gas pipe network of the region to be detected comprises a combustible gas concentration grade, a liquid level grade and a drainage flow grade of the drainage pipe network, and a pressure grade and a water supply flow grade of the water supply pipe network;
the data processing center (2) establishes a risk interaction prediction model, and the data processing center (2) determines the underground pipe network interaction risk level according to the level information reported by the auxiliary analysis terminal (1) and reports the underground pipe network interaction risk level to the dispatching monitoring center (3);
and the dispatching monitoring center (3) determines a risk treatment scheme aiming at the region to be detected according to the reported underground pipe network interaction risk level, and sends the risk treatment scheme to the auxiliary analysis terminal (1).
2. The underground utility grid interactive risk early warning system for an urban lifeline of claim 1, wherein the front-end sensor apparatus comprises at least: a gas sensor disposed on the gas pipeline, a liquid sensor disposed on the water drain pipeline, and a pressure sensor disposed on the water supply pipeline;
wherein, the gas sensor of deployment on the gas pipeline includes: a combustible gas concentration monitor; a liquid sensor deployed on a drain line, comprising: a level gauge and a flow meter; a pressure sensor deployed on a water supply line, comprising: a pressure instrument and a flow instrument;
the auxiliary analysis terminal (1) at least comprises a gas pipe network auxiliary analysis module (1-1), a drainage pipe network auxiliary analysis module (1-2) and a water supply pipe network auxiliary analysis module (1-4);
the gas pipe network auxiliary analysis module (1-1) is used for detecting the concentration level of the combustible gas of the gas pipe network; the auxiliary analysis module (1-2) of the drainage pipe network is used for detecting the liquid level grade and the drainage flow grade of the drainage pipe network; the water supply network auxiliary analysis module (1-4) is used for detecting the pressure level and the water supply flow level of the water supply network.
3. The underground pipe network interactive risk early warning system for the urban lifeline according to claim 1 or 2, further comprising an engineering excavation analysis module (1-3) in the auxiliary analysis terminal (1), wherein the engineering excavation analysis module (1-3) is connected with the earth surface displacement monitoring equipment;
the rank information further includes: and the earth surface displacement level is determined by the engineering excavation analysis module (1-3) according to earth surface displacement data acquired by the earth surface displacement monitoring equipment.
4. The underground pipe network interactive risk early warning system for an urban lifeline according to claim 1, characterized by a data processing center (2) comprising: a predictive modeling module (2-3) and an early warning analysis module (2-4);
the prediction modeling module (2-3) is used for building and maintaining a risk interaction prediction model;
and the early warning analysis module (2-4) is used for inputting the grade information into the risk interaction prediction model, outputting the underground pipe network interaction risk grade, and reporting the underground pipe network interaction risk grade to the dispatching monitoring center (3).
5. The underground pipe network interactive risk early warning system for an urban lifeline according to claim 4, further comprising a data processing center (2): the data acquisition module (2-1) and the data characteristic multidimensional analysis module (2-2);
the auxiliary analysis terminal (1) is also used for uploading the original data acquired by the front-end sensor to the big data acquisition module (2-1) of the data processing center (2);
the big data acquisition module (2-1) is also used for preprocessing the original data uploaded by the auxiliary analysis terminal (1), and the preprocessing comprises the following steps: sequentially extracting, cleaning and loading the original data;
and the data characteristic multidimensional analysis module (2-2) is used for carrying out space-time distribution characteristic analysis on the original data output by the big data acquisition module (2-1).
6. The underground pipe network interactive risk early warning system for an urban lifeline of claim 5, wherein the spatiotemporal distribution characteristic analysis comprises:
adding a tag to the preprocessed raw data, wherein the added tag comprises: time tags, space tags and attribute tags; the time tag represents a time stamp at which the original data was acquired; the space tag represents the position of a front-end sensor for collecting original data; the attribute tag represents the state of a front-end sensor for collecting original data;
and clustering the preprocessed original data according to the added labels.
7. The underground utility network interactive risk early warning system for an urban lifeline of claim 6, wherein the information recorded by the attribute tags comprises at least:
IP address information of the front-end sensor;
and/or, a maintenance time of the front-end sensor for the last scheduled maintenance;
and/or at least one of a personnel number, a team number, and a unit number responsible for front-end sensor maintenance;
and/or the manufacturing time and lifetime of the front end sensor.
8. The underground utility network interactive risk early warning system for an urban lifeline of claim 6, wherein clustering the preprocessed raw data comprises:
clustering the collected original data in the same time interval;
clustering the collected original data in the same geographic range;
clustering the original data with the labels with the same attribute partially;
and establishing the association relation of the original data according to the clustering result.
9. The underground pipe network interactive risk early warning system for an urban lifeline of claim 1, wherein the risk interaction prediction model comprises:
R＝∑Xij (1-1)
Xij＝Fij*Sij (1-2)
wherein Xij is the score of each auxiliary analysis module index, i=1, 2,3,4; j=1, 2, … …, n, n is the number of threshold levels set for the corresponding i-th auxiliary analysis module, fij is the value of the auxiliary analysis module, sij is the influence factor coefficient of the auxiliary analysis module, and R is the numerical value reflecting the interaction risk degree of the underground pipe network.
10. The underground pipe network interactive risk early warning system for an urban lifeline of claim 9, wherein the underground pipe network interactive risk level comprises:
when R is more than 75, the corresponding grade IV represents an extremely high risk grade;
when R is more than 50 and less than or equal to 75, the corresponding grade III represents a high risk grade;
when R is more than 30 and less than or equal to 50, the corresponding grade II represents a moderate risk grade;
when R is less than or equal to 30, the corresponding grade I represents a low risk grade.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117371668A (en) * | 2023-12-06 | 2024-01-09 | 北京晨豪科技有限公司 | Urban pipeline flow allocation optimization method based on visual view and network flow |
| CN117370818A (en) * | 2023-12-05 | 2024-01-09 | 四川发展环境科学技术研究院有限公司 | Intelligent diagnosis method and intelligent environment-friendly system for water supply and drainage pipe network based on artificial intelligence |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117370818A (en) * | 2023-12-05 | 2024-01-09 | 四川发展环境科学技术研究院有限公司 | Intelligent diagnosis method and intelligent environment-friendly system for water supply and drainage pipe network based on artificial intelligence |
| CN117370818B (en) * | 2023-12-05 | 2024-02-09 | 四川发展环境科学技术研究院有限公司 | Intelligent diagnosis method and intelligent environment-friendly system for water supply and drainage pipe network based on artificial intelligence |
| CN117371668A (en) * | 2023-12-06 | 2024-01-09 | 北京晨豪科技有限公司 | Urban pipeline flow allocation optimization method based on visual view and network flow |
| CN117371668B (en) * | 2023-12-06 | 2024-02-09 | 北京晨豪科技有限公司 | Urban pipeline flow allocation optimization method based on visual view and network flow |
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