CN116402346A - Risk early warning method, system, equipment and storage medium based on urban pipe network - Google Patents

Risk early warning method, system, equipment and storage medium based on urban pipe network Download PDF

Info

Publication number
CN116402346A
CN116402346A CN202310352091.4A CN202310352091A CN116402346A CN 116402346 A CN116402346 A CN 116402346A CN 202310352091 A CN202310352091 A CN 202310352091A CN 116402346 A CN116402346 A CN 116402346A
Authority
CN
China
Prior art keywords
pipe network
alarm
state data
data
drainage pipe
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202310352091.4A
Other languages
Chinese (zh)
Inventor
尤勇敏
请求不公布姓名
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Jiuling Shanghai Intelligent Technology Co ltd
Original Assignee
Jiuling Shanghai Intelligent Technology Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Jiuling Shanghai Intelligent Technology Co ltd filed Critical Jiuling Shanghai Intelligent Technology Co ltd
Priority to CN202310352091.4A priority Critical patent/CN116402346A/en
Publication of CN116402346A publication Critical patent/CN116402346A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
    • F17D—PIPE-LINE SYSTEMS; PIPE-LINES
    • F17D5/00—Protection or supervision of installations
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01W—METEOROLOGY
    • G01W1/00—Meteorology
    • G01W1/14—Rainfall or precipitation gauges
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/10—Office automation; Time management
    • G06Q10/103—Workflow collaboration or project management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Strategic Management (AREA)
  • Theoretical Computer Science (AREA)
  • Tourism & Hospitality (AREA)
  • Economics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Marketing (AREA)
  • General Business, Economics & Management (AREA)
  • Data Mining & Analysis (AREA)
  • Quality & Reliability (AREA)
  • Operations Research (AREA)
  • Environmental & Geological Engineering (AREA)
  • Educational Administration (AREA)
  • Development Economics (AREA)
  • General Engineering & Computer Science (AREA)
  • Evolutionary Biology (AREA)
  • Mechanical Engineering (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Hydrology & Water Resources (AREA)
  • Game Theory and Decision Science (AREA)
  • Atmospheric Sciences (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Ecology (AREA)
  • Environmental Sciences (AREA)
  • Alarm Systems (AREA)

Abstract

The invention provides a risk early warning method, a system, equipment and a storage medium based on an urban pipe network, which comprises the following steps: based on basic information of a drainage pipe network, collecting data parameters, and establishing a BIM model; detecting data through an internet of things sensor, collecting state data of a drainage pipe network and correlating the state data to a BIM model in real time, wherein the state data comprises: operational environmental data, weather environmental data; extracting state data of the drainage pipe network when the historical alarm event occurs according to the time period when the historical alarm event occurs, and extracting the state data of the drainage pipe network as an alarm condition according to the historical alarm event, and associating the alarm condition to the BIM; and judging whether to send out an alarm or not according to the alarm condition in the BIM and the state data of the current drainage pipe network. The technical scheme of the invention can effectively preprocess emergencies and faults in time.

Description

Risk early warning method, system, equipment and storage medium based on urban pipe network
Technical Field
The invention relates to the technical field of urban pipe network operation and maintenance, in particular to a risk early warning method, system, equipment and storage medium based on an urban pipe network.
Background
The urban pipe network is a life line for urban operation, and has the characteristic of confidentiality because the pipe network is laid at the bottom of the ground, and when faults or emergency situations occur, the urban pipe network is difficult to process and has long processing time.
Therefore, it is very important to accurately and real-time early warn the risk of the urban pipe network, and the occurrence of emergency can be avoided or the processing time can be saved.
Disclosure of Invention
The invention provides a risk early warning method, a system, equipment and a storage medium based on an urban pipe network, which are used for solving the problems that in the prior art, when faults or sudden emergency occur, the problems are difficult to process and the processing time is long.
In order to solve the technical problems, the invention is realized by the following technical scheme:
according to a first aspect of the present invention, there is provided a risk early warning method based on an urban network, comprising:
building a BIM model: based on basic information of a drainage pipe network, collecting data parameters, and establishing a BIM model;
and (3) collecting state data: detecting data through an Internet of things sensor, collecting state data of the drainage pipe network, and correlating the state data to the BIM in real time; wherein the status data comprises: operational environmental data, weather environmental data;
alarm condition association: extracting state data of the drainage pipe network when the historical alarm event occurs according to the time period when the historical alarm event occurs, and extracting the state data of the drainage pipe network as an alarm condition according to the historical alarm event, wherein the state data is associated to the BIM;
and (3) alarm judgment: and judging whether to send out an alarm according to the alarm condition of the drainage pipe network in the BIM and by combining the current state data of the drainage pipe network.
Preferably, the method further comprises the steps of between the state data acquisition and the alarm judgment or after the alarm judgment:
and calculating the limit drainage amount of the drainage pipe network in real time through the BIM model, and sending out risk early warning if the predicted rainfall amount in the meteorological environment data is close to or equal to the limit drainage amount.
Preferably, the method further comprises the steps of between the state data acquisition and the alarm judgment or after the alarm judgment:
adjusting the acquisition frequency of the Internet of things sensor according to the meteorological environment data; the method specifically comprises the following steps:
when the meteorological environment data are extreme weather, the acquisition frequency of the sensor of the Internet of things is improved; and the extreme weather is that the forecast rainfall is larger than a first preset value.
Preferably, the extracting the state data of the drainage pipe network according to the historical alarm event as the alarm condition specifically includes:
classifying the historical alarm events;
and according to the classification of the historical alarm events, carrying out alarm condition statistics, and calculating the average value of the alarm conditions of a plurality of events of the same type as the alarm condition of the event.
Preferably, the alarm condition of the drainage pipe network includes: and the state data of the drainage pipe network exceeds an alarm threshold value and/or the state data of the drainage pipe network changes abnormally.
Preferably, between the alarm condition association and the alarm judgment, further comprises: archiving state data of the historical alarm event and event related information to generate a historical event processing table; wherein the event related information includes: one or more of event cause, event phenomenon, event location, processing method, processing result;
extracting features of the historical event processing table, taking the state data as an identification parameter, and establishing a plan library;
and generating an associated plan form by taking the event related information as a refinement item.
Preferably, the alarm judgment specifically includes: and establishing association between the state data of the drainage pipe network and the plan form to generate an auxiliary suggestion plan.
Preferably, the associating the state data of the drainage pipe network with the plan form, and generating the auxiliary suggestion plan specifically includes:
the state data in the pre-plan form is used as an identifier, and the current state data is compared;
calculating the abnormal frequency of the state data in the preset table, and improving the comparison frequency of the state data aiming at the region with the abnormal frequency exceeding a second preset value;
identifying the region for improving the comparison frequency in the BIM model, and associating the region with the BIM model of the corresponding region;
when the current state data accords with the alarm condition in the pre-plan form, alarming the area, generating an abnormal event and processing the abnormal event;
an auxiliary suggestion protocol is generated.
Preferably, the auxiliary suggestion scheme includes: the position, the state data range, the reason and the processing method of the abnormal event.
Preferably, the generating the auxiliary suggestion scheme further comprises:
and optimizing the plan form according to the processing result of the abnormal event.
According to a second aspect of the present invention, there is provided a risk early warning system based on urban network, comprising:
the BIM model building unit is used for collecting data parameters based on basic information of the drainage pipe network and building a BIM model;
the system comprises an Internet of things sensor, a BIM model and a water drainage pipe network, wherein the Internet of things sensor is used for detecting data, collecting state data of the water drainage pipe network and relating the state data to the BIM model in real time; wherein the status data comprises: operational environmental data, weather environmental data;
the historical alarm event association unit is used for extracting the state data of the drainage pipe network when the historical alarm event occurs according to the time period when the historical alarm event occurs, extracting the state data of the drainage pipe network according to the historical alarm event as an alarm condition, and associating the alarm condition to the BIM;
and the alarm judging unit is used for judging whether to send an alarm or not according to the alarm condition of the drainage pipe network in the BIM and the current state data of the drainage pipe network.
According to a third aspect of the present invention, there is provided an electronic device comprising:
a processor;
and a memory for storing processor-executable instructions;
wherein the processor implements the steps of the method described above by executing the executable instructions.
According to a fourth aspect of the present invention there is provided a storage medium having stored thereon a computer program which when executed by a processor performs the steps of the method described above.
According to the risk early warning method, system, equipment and storage medium based on the urban pipe network, provided by the invention, the state of the drainage pipe network is comprehensively evaluated through the operation environment data and the meteorological environment data, and the emergency and the fault are effectively preprocessed in time.
In an alternative aspect of the present invention, the method further includes: the reporting frequency of the sensor of the Internet of things is adjusted according to the meteorological environment data, so that the problem can be found more timely.
In an alternative aspect of the present invention, the method further includes: and generating a historical event processing table, which is convenient to check and reference.
In an alternative aspect of the present invention, the method further includes: and an auxiliary proposal plan is generated, so that the alarm event can be more conveniently processed.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.
FIG. 1 is a flow chart of a risk early warning method based on an urban network according to an embodiment of the invention;
FIG. 2 is a flow chart of generating an auxiliary suggestion scheme in accordance with an embodiment of the present invention;
FIG. 3 is a block diagram of a risk early warning system based on an urban network according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of an electronic device according to an embodiment of the invention;
an 11-BIM model building unit,
a sensor of the 12-internet of things,
13-a history alert event association unit,
14-an alarm judging unit;
a 21-a processor configured to process the data,
a 22-an internal bus line, which is connected to the internal bus,
a 23-network interface (a-l-a-c),
a 24-memory for storing the data of the memory,
25-memory.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. 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.
In the description of the present specification, it should be understood that the directions or positional relationships indicated by the terms "upper", "lower surface", "upper surface", etc. are based on the directions or positional relationships shown in the drawings, are merely for convenience of description and simplification of description, and do not indicate or imply that the apparatus or element to be referred to must have a specific direction, be configured and operated in a specific direction, and thus should not be construed as limiting the present invention.
In the description of the present specification, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature.
In the description of the present invention, the meaning of "plurality" means a plurality, for example, two, three, four, etc., unless explicitly specified otherwise.
In the description of the present invention, unless explicitly stated and limited otherwise, the term "coupled" and the like should be construed broadly, and may be, for example, fixedly coupled, detachably coupled, or integrally formed; may be mechanically connected, may be electrically connected or may communicate with each other; can be directly connected or indirectly connected through an intermediate medium, and can be communicated with the inside of two elements or the interaction relationship of the two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
The technical scheme of the invention is described in detail below by specific examples. The following embodiments may be combined with each other, and some embodiments may not be repeated for the same or similar concepts or processes.
In one embodiment, a risk early warning method based on an urban pipe network is provided, which includes:
s101: based on basic information of a drainage pipe network, collecting data parameters, and establishing a BIM model; the basic information of the drainage pipe network can comprise the material, the size, the type, the position and the like of the pipeline;
s102: through thing networking sensor detection data, gather drainage pipe network's state data, state data includes: the operation environment data and the weather environment data are associated to the BIM in real time; the operational environment data may include: humidity, temperature, gas concentration, water flow, etc.;
s103: extracting state data of the drainage pipe network when the historical alarm event occurs according to the time period when the historical alarm event occurs, and extracting the state data of the drainage pipe network as an alarm condition according to the historical alarm event, and associating the alarm condition to the BIM; the alarm condition includes: operating environment data alarm conditions, meteorological environment data alarm conditions;
s104: and judging whether to give an alarm according to the alarm condition in the BIM model by combining the current drainage pipe network state data, and giving an alarm when the alarm condition is met.
In the above embodiment, the steps are not performed in the above sequence, for example: s102 is performed all the time, and may be performed before or simultaneously with S101; after S104, the process may return to S103, and the event in which the alarm occurs in S104 may be archived to the historical alarm event.
In an embodiment, the first alarm of the historical alarm event may be determined according to a preset alarm condition, or the historical alarm event may be an event that has occurred. Each subsequent alarm event may be archived as a historical alarm event and associated with the BIM model.
In one embodiment, the method may further include: and calculating the limit drainage amount of the drainage pipe network in real time through a BIM model, and if the predicted rainfall amount in the meteorological environment data is close to or equal to or exceeds the limit drainage amount, giving out risk early warning, paying attention to the drainage amount and paying attention to accumulated water.
In one embodiment, the method may further include: calculating water flow according to the forecast rainfall in the meteorological environment data, and when the calculated water flow exceeds the historical water flow risk threshold of the drainage pipe network, sending out risk early warning.
It should be noted that, the risk early warning judging modes are all one type of risk early warning modes, and different risk early warning modes can be distinguished by one or more modes of different early warning names, different early warning sounds, different colors of early warning lamps and the like.
In one embodiment, between S102 and S104 or after S104 further includes: and adjusting the acquisition frequency of the sensor of the Internet of things according to the meteorological environment data.
In an embodiment, adjusting the acquisition frequency of the sensor of the internet of things according to the meteorological environment data specifically includes: when the meteorological environment data are extreme weather, the acquisition frequency of the sensor of the Internet of things is improved, and the area of the extreme weather is monitored in a key area. Wherein, extreme weather is that the forecast rainfall is greater than a first preset value, such as: the forecast may be rainy and take a long time.
In one embodiment, extracting the state data of the drainage pipe network as the alarm condition according to the historical alarm event specifically includes:
categorizing historical alert events, such as: the system can be classified according to the material and the size of the pipeline, and can also be classified according to the type of the alarm event;
and according to the classification of the historical alarm events, carrying out alarm condition statistics, and calculating the average value of the alarm conditions of a plurality of events of the same type as the alarm condition of the event.
In one embodiment, between S103 and S104 further includes: archiving state data of the historical alarm event and event related information to generate a historical event processing table; wherein the event related information includes: one or more of event cause, event phenomenon, event location, processing method, processing result;
extracting features of the historical event processing table, taking the state data as an identification parameter, and establishing a plan library; and generating an associated plan form by taking the event related information as a refined item.
In one embodiment, the alarm condition of the drain network may include: the state data of the drainage pipe network exceeds the alarm threshold value and/or the state data of the drainage pipe network changes abnormally. Specifically, the alarm condition may be an alarm threshold, and when the state data of the drainage pipe network exceeds the alarm threshold (for example, is higher than or lower than the alarm threshold), an alarm is sent; the alarm condition can also be abnormal change, and when the operation environment data of the drainage pipe network is abnormal (for example, the operation environment data rapidly rises or falls within 1-10 minutes, the rapid rise or fall can be 60-degree upward or downward in the change curve), the alarm is sent.
In addition, after the alarm is sent out, a survey task can be generated, and an abnormal survey link is entered. Generating a survey task according to the received abnormal operation environment data, and surveying the place where the abnormal operation environment data is generated; forming the survey results into a survey report, which may include: operation environment data, meteorological environment data, event reasons, event positions, event phenomena, pipe network position parameters, processing personnel, processing modes and processing results; and archiving the investigation report into the historical event processing table.
In one embodiment, according to the alarm condition in the BIM model, in combination with the current state data of the drainage pipe network, determining whether to send out the alarm specifically further includes: and (3) associating the state data of the drainage pipe network with the plan form to generate an auxiliary suggestion plan.
In one embodiment, referring to fig. 2, associating the state data of the drainage pipe network with the plan form, the generating the auxiliary suggestion plan specifically includes:
s201: the state data in the plan form is used as an identifier, and the current state data is compared;
s202: calculating the abnormal frequency of the state data in the plan form, and improving the comparison frequency of the state data aiming at the region with the abnormal frequency exceeding a second preset value; the second preset value is how large, the frequency is improved, and different designs can be carried out according to different conditions;
s203: identifying the region with the increased comparison frequency in the BIM model, and associating the region with the BIM model of the corresponding region;
s204: when the current state data accords with an alarm condition (which can be an alarm threshold value and/or abnormal change), alarming the area, generating an abnormal event, and processing the abnormal event;
s205: generating the auxiliary suggestion protocol may include: the location, status data range, cause, processing method, etc. of the abnormal event.
In one embodiment, the generating of the auxiliary suggestion scheme further comprises: and optimizing the plan form according to the processing result of the abnormal event. The method specifically comprises the following steps: the reasons which appear for many times are marked, so that the retrieval probability is improved; and meanwhile, according to the processing result, extracting or calculating loss data (namely, state data of an abnormal event) according to the abnormal parameters, and relating the loss data to the BIM model to update the BIM model. The loss data is extracted or calculated according to the abnormal parameters, specifically: searching abnormal events through abnormal parameters, checking associated loss data in the abnormal event data, and taking the average of the abnormal event data as the loss data if the abnormal event data are a plurality of abnormal data of the same type.
In an embodiment, there is also provided a risk early warning system based on an urban network, including: BIM model establishment unit 1, thing networking sensor 2, history alarm incident association unit 3 and warning judgement unit 4 please refer to FIG. 3.
The BIM model building unit is used for collecting data parameters based on basic information of the drainage pipe network and building a BIM model; the sensor of the internet of things is used for detecting data, collecting state data of a drainage pipe network and correlating the state data to a BIM model in real time, wherein the state data comprises: operational environmental data, weather environmental data; the historical alarm event association unit is used for extracting the state data of the drainage pipe network when the historical alarm event occurs according to the time period when the historical alarm event occurs, extracting the state data of the drainage pipe network according to the historical alarm event as an alarm condition, and associating the alarm condition to the BIM; the alarm judging unit is used for judging whether to send out an alarm or not according to the alarm condition in the BIM and the current state data.
In an embodiment, an electronic device is further provided, please refer to fig. 4. At the hardware level the device comprises a processor 21, an internal bus 22, a network interface 23, a memory 24 and a storage 25, possibly of course also the hardware required for other services. One or more embodiments of the invention may be implemented on a software basis, such as by the processor 21 reading a corresponding computer program from the memory 25 into the memory 24 and then running. Of course, in addition to software implementation, one or more embodiments of the present invention do not exclude other implementation, such as a logic device or a combination of software and hardware, etc., that is, the execution subject of the following process flows is not limited to each logic unit, but may also be hardware or a logic device.
The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. A typical implementation device is a computer, which may be in the form of a personal computer, laptop computer, cellular telephone, camera phone, smart phone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or a combination of any of these devices.
In a typical configuration, a computer includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, read only compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic disk storage, quantum memory, graphene-based storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by the computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
The foregoing describes certain embodiments of the present invention. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In the description of the present specification, the descriptions of the terms "one embodiment," "an embodiment," "a particular implementation," "an example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
It should be understood that while the terms first, second, third, etc. may be used in one or more embodiments of the invention to describe various information, such information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of one or more embodiments of the invention. The word "if" as used herein may be interpreted as "at … …" or "at … …" or "responsive to a determination", depending on the context.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; 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 or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.

Claims (12)

1. The risk early warning method based on the urban pipe network is characterized by comprising the following steps of:
building a BIM model: based on basic information of a drainage pipe network, collecting data parameters, and establishing a BIM model;
and (3) collecting state data: detecting data through an Internet of things sensor, collecting state data of the drainage pipe network, and correlating the state data to the BIM in real time; wherein the status data comprises: operational environmental data, weather environmental data;
alarm condition association: extracting state data of the drainage pipe network when the historical alarm event occurs according to the time period when the historical alarm event occurs, and extracting the state data of the drainage pipe network as an alarm condition according to the historical alarm event, wherein the state data is associated to the BIM;
and (3) alarm judgment: and judging whether to send out an alarm according to the alarm condition of the drainage pipe network in the BIM and by combining the current state data of the drainage pipe network.
2. The urban pipe network-based risk early warning method according to claim 1, wherein the step of collecting the status data and the alarm judgment or after the alarm judgment further comprises:
and calculating the limit drainage amount of the drainage pipe network in real time through the BIM model, and sending out risk early warning if the predicted rainfall amount in the meteorological environment data is close to or equal to the limit drainage amount.
3. The urban pipe network-based risk early warning method according to claim 1, wherein the step of collecting the status data and the alarm judgment or after the alarm judgment further comprises:
adjusting the acquisition frequency of the Internet of things sensor according to the meteorological environment data; the method specifically comprises the following steps:
when the meteorological environment data are extreme weather, the acquisition frequency of the sensor of the Internet of things is improved; and the extreme weather is that the forecast rainfall is larger than a first preset value.
4. The risk early warning method based on the urban network according to claim 1, wherein the extracting the state data of the drainage network according to the historical alarm event as the alarm condition specifically comprises:
classifying the historical alarm events;
and according to the classification of the historical alarm events, carrying out alarm condition statistics, and calculating the average value of the alarm conditions of a plurality of events of the same type as the alarm condition of the event.
5. The urban pipe network-based risk early warning method according to claim 1, wherein the warning condition of the drainage pipe network comprises: and the state data of the drainage pipe network exceeds an alarm threshold value and/or the state data of the drainage pipe network changes abnormally.
6. The urban pipe network-based risk early warning method according to any one of claims 1 to 5, wherein between the alarm condition association and the alarm judgment, further comprises: archiving state data of the historical alarm event and event related information to generate a historical event processing table; wherein the event related information includes: one or more of event cause, event phenomenon, event location, processing method, processing result;
extracting features of the historical event processing table, taking the state data as an identification parameter, and establishing a plan library;
and generating an associated plan form by taking the event related information as a refinement item.
7. The risk early warning method based on the urban network according to claim 6, wherein the warning judgment specifically comprises: and establishing association between the state data of the drainage pipe network and the plan form to generate an auxiliary suggestion plan.
8. The risk early warning method based on the urban network according to claim 7, wherein the associating the state data of the drainage network with the plan form, the generating of the auxiliary suggestion plan specifically comprises:
the state data in the pre-plan form is used as an identifier, and the current state data is compared;
calculating the abnormal frequency of the state data in the preset table, and improving the comparison frequency of the state data aiming at the region with the abnormal frequency exceeding a second preset value;
identifying the region for improving the comparison frequency in the BIM model, and associating the region with the BIM model of the corresponding region;
when the current state data accords with the alarm condition in the pre-plan form, alarming the area, generating an abnormal event and processing the abnormal event;
an auxiliary suggestion protocol is generated.
9. The risk early warning method based on urban network according to claim 8, wherein the generating the auxiliary suggestion scheme further comprises:
and optimizing the plan form according to the processing result of the abnormal event.
10. Risk early warning system based on city pipe network, characterized by comprising:
the BIM model building unit is used for collecting data parameters based on basic information of the drainage pipe network and building a BIM model;
the system comprises an Internet of things sensor, a BIM model and a water drainage pipe network, wherein the Internet of things sensor is used for detecting data, collecting state data of the water drainage pipe network and relating the state data to the BIM model in real time; wherein the status data comprises: operational environmental data, weather environmental data;
the historical alarm event association unit is used for extracting the state data of the drainage pipe network when the historical alarm event occurs according to the time period when the historical alarm event occurs, extracting the state data of the drainage pipe network according to the historical alarm event as an alarm condition, and associating the alarm condition to the BIM;
and the alarm judging unit is used for judging whether to send an alarm or not according to the alarm condition of the drainage pipe network in the BIM and the current state data of the drainage pipe network.
11. An electronic device, comprising:
a processor;
and a memory for storing processor-executable instructions;
wherein the processor implements the steps of the method of any of claims 1-9 by executing the executable instructions.
12. A computer-readable storage medium, characterized in that a computer program is stored thereon, which computer program, when being executed by a processor, realizes the steps in the method of any of claims 1-9.
CN202310352091.4A 2023-04-04 2023-04-04 Risk early warning method, system, equipment and storage medium based on urban pipe network Pending CN116402346A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310352091.4A CN116402346A (en) 2023-04-04 2023-04-04 Risk early warning method, system, equipment and storage medium based on urban pipe network

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310352091.4A CN116402346A (en) 2023-04-04 2023-04-04 Risk early warning method, system, equipment and storage medium based on urban pipe network

Publications (1)

Publication Number Publication Date
CN116402346A true CN116402346A (en) 2023-07-07

Family

ID=87013730

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310352091.4A Pending CN116402346A (en) 2023-04-04 2023-04-04 Risk early warning method, system, equipment and storage medium based on urban pipe network

Country Status (1)

Country Link
CN (1) CN116402346A (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117436700A (en) * 2023-11-14 2024-01-23 山东和同信息科技股份有限公司 BIM-based new energy engineering data management system and method
CN118518157A (en) * 2024-04-18 2024-08-20 知码芯(长春)科技有限公司 Sensor module intelligent monitoring system and method based on Internet of things

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117436700A (en) * 2023-11-14 2024-01-23 山东和同信息科技股份有限公司 BIM-based new energy engineering data management system and method
CN117436700B (en) * 2023-11-14 2024-04-12 山东和同信息科技股份有限公司 BIM-based new energy engineering data management system and method
CN118518157A (en) * 2024-04-18 2024-08-20 知码芯(长春)科技有限公司 Sensor module intelligent monitoring system and method based on Internet of things

Similar Documents

Publication Publication Date Title
CN116402346A (en) Risk early warning method, system, equipment and storage medium based on urban pipe network
CN111275333B (en) Pollution data processing method and device
CN109656793A (en) A kind of information system performance stereoscopic monitoring method based on multi-source heterogeneous data fusion
CN111917877A (en) Data processing method and device for Internet of things equipment, electronic equipment and storage medium
CN106253977B (en) The method of adjustment and optical module of LOS alarm decision threshold
CN117809439A (en) River discharge abnormality early warning system based on multiple environmental factors
CN111504417A (en) Power distribution station room water level early warning system and method
CN116524691A (en) Hidden danger warning method and device for power transmission line, storage medium and computer equipment
CN116645013A (en) Urban ecological toughness assessment and early warning method and device and related equipment
CN115038088A (en) Intelligent network security detection early warning system and method
CN113079047B (en) Alarm processing method and device
CN117610732A (en) Monitoring management method and monitoring management system for electric power energy storage reservoir
CN114138601A (en) Service alarm method, device, equipment and storage medium
CN117852860A (en) Work early warning system and method for overflow equipment under condition of large water overflow dam of hydropower station
CN116151665A (en) Urban pipe network service cycle prediction method and device, processor and electronic equipment
CN114047693B (en) Self-adaptive prevention method and system for fire disaster of automobile battery facing charging process
CN113884889B (en) Method and device for battery safety pre-warning, storage medium and electronic equipment
CN111381567A (en) Safety detection system and method for industrial control system
Sylvia et al. Analysis of IoT big weather data for early flood forecasting system
CN117033913A (en) Abnormality detection method and device based on power equipment portrait, and storage medium
CN118506555B (en) Environment early warning method, device and medium based on multi-tunnel linkage
CN111447086A (en) Service processing method and device and electronic equipment
CN118295346B (en) Equipment management method and system based on vibration detector
CN117873838B (en) Method and system for monitoring ambient temperature of telecommunication equipment
CHOWDARY et al. Design and development of novel flood detection system using IoT

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination