CN109902394B

CN109902394B - Network modeling method based on user interval information

Info

Publication number: CN109902394B
Application number: CN201910160357.9A
Authority: CN
Inventors: 陈龙雨
Original assignee: Individual
Current assignee: Individual
Priority date: 2019-03-04
Filing date: 2019-03-04
Publication date: 2020-05-05
Anticipated expiration: 2039-03-04
Also published as: CN109902394A

Abstract

The invention provides a network modeling method based on user interval information, which is used for quickly associating user information into a network by building a connection relation between a user and equipment. The invention solves the technical problem that the subsequent manual input of the user information is needed in the network modeling, greatly reduces the workload of manually adding the user information and improves the efficiency of establishing the network simulation model.

Description

Network modeling method based on user interval information

Technical Field

The invention relates to a network modeling technology, in particular to a network modeling method based on user interval information.

Background

With the development of computer operation, network communication, machine vision and other technologies, device data collection is performed in a network through image devices, a device model is established through network communication, and smart city network modeling is realized through a computer program, which is one of the subjects that are rapidly developed in recent years. Currently, the interest in network modeling has evolved from single object modeling to large-scale scene modeling of networks, and the prior art has almost all focused on modeling of the planning design of urban buildings and roads. However, there are still certain difficulties with a complete network modeling, especially with regard to automatically associating end-user information to a network.

The domestic patent 1 with the patent application number of CN201810131136.4 and the name of "a modeling analysis method based on geographic objects" discloses a modeling analysis method based on geographic objects, which collects the position information of ground nodes through a mobile client, classifies equipment according to a predetermined rule, displays the node positions on an electronic map and establishes a network model of the equipment. When the power failure range is analyzed, the topological connection relation of each node is analyzed through the change of the connection and disconnection state in the network, the power supply node equipment of the power supply side transformer substation is analyzed, and the power failure range is analyzed through the topological relation of upper and lower target nodes. However, the topological connection of the nodes only analyzes the power supply range between the devices, the user influence range cannot be directly obtained in the modeling system, and the user information needs to be manually input into the modeling system in advance in the power failure user range. It follows that domestic patent 1 fails to complete a complete network modeling involving user information correlation.

The device network modeling method is characterized in that the device network modeling method comprises a domestic patent 2 with a patent application number of CN201810863931.2 and a domestic patent 3 with a patent application number of CN201810863942.0 and a name of a device network modeling method based on drawings, wherein the domestic patent 2 and the patent application number of CN201810863942.0 are both named as a device network modeling method based on independent spaces, the two patents are used for modeling a network in a special closed environment or by using special tools such as municipal drawings, however, the device modeling is carried out on the two domestic patents under the ground and on paper respectively, the final connection between user information and pipe network equipment is not realized, and the subsequent manual input of the user information is still needed. The three patents quickly finish the establishment of the equipment simulation network by identifying the existing equipment, but in real life, the user information controlled by the terminal user equipment is not intuitive and can be collected, and in the existing intelligent city modeling scheme, the user influence range caused by fault equipment cannot be visually judged, the fault reason cannot be timely informed and the like due to the fact that the manual input of the user information is required subsequently, so that the manual workload is huge, the modeling efficiency is low.

Disclosure of Invention

In order to solve the technical problem, the invention provides a network modeling method based on user interval information, which automatically associates the user information into a network modeling system and quickly identifies the influence range of equipment on a user, and comprises the following steps:

step S100: acquiring user data, and uploading the user data to a cloud electronic map, wherein the user data comprises a boundary range of a position where a user is located;

step S200: identifying the user data, and identifying the user type according to the user data;

step S300: the cloud electronic map establishes a user model according to the user attribute; wherein the user attributes comprise the user type, user location, user boundary;

step S400: acquiring equipment data, uploading the equipment data to a cloud electronic map, and establishing an equipment model according to equipment attributes; wherein the device attributes comprise a device type and a device location.

Step S500: and presetting a connection rule between the equipment and the user, and completing network modeling according to the attributes of the equipment model and the user model.

Preferably, the step S100 further includes the steps of:

step S101: acquiring a user boundary range through a digital map; and/or the presence of a gas in the gas,

step S102: performing image segmentation on the aerial image to obtain a user boundary range;

preferably, the step S200 further includes the steps of:

step S201: and capturing user name keywords, matching the user name keywords with a preset keyword library, and identifying and classifying the user types according to matching results.

Preferably, the step S400 further includes the steps of:

step S401: through the mobile terminal with the GPS function, equipment data collection along the line direction is carried out along two sides of the road, and the collected data are uploaded to the cloud electronic map.

Preferably, the device data is acquired by using a static image and/or a dynamic video acquired by an image acquisition unit.

Preferably, the step S400 further includes the steps of:

step S402: and directly acquiring the equipment data through the digital map, and uploading the geographical position and the equipment type of the equipment data to the cloud electronic map.

In order to solve the above technical problem, the present invention further provides a method for identifying an influence range of a faulty device, which includes a network modeling of a device and a user, where the network modeling adopts the above described network modeling method, and further includes the following steps:

step S1000: receiving a fault equipment number;

step S2000: locating the faulty device in a network model;

step S3000: an influence range is identified, which is displayed by a marker.

Preferably, in the step S3000, the method further includes the steps of:

step S3001: the range of influence caused by different malfunctioning devices is displayed by the corresponding indicia.

In order to solve the technical problem, the invention adopts another technical scheme as follows: a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the above-mentioned method.

Therefore, according to the network modeling method provided by the invention, the user information is obtained, the user modeling is carried out on the cloud electronic map, when the user equipment is to be acquired, the cloud electronic map is updated in real time, the complete modeling of the equipment and the user is completed, the step of manually inputting user data is reduced, the problem that the seen and invisible municipal equipment are connected to the user information is solved, the user name of the land on the electronic map is associated with the municipal equipment, the user can be quickly and automatically associated into the simulation network, and the effect of reducing the labor cost is achieved.

According to the technical scheme provided by the invention, the invention has the following beneficial effects:

1. the user data is identified and input in advance, and the subsequent step of manually inputting the user data is reduced;

2. the user boundary range is obtained through a digital map and aerial photography data, and manual input is not needed;

3. the user data comprises user boundaries, and the influence range is rapidly identified;

4. meanwhile, image recognition and digital map functions are adopted to acquire equipment and user information, so that the data acquisition can be met in any environment;

5. the type is quickly judged by carrying out matching travel data identification on the keyword library;

6. displaying the fault range and equipment through the corresponding marks, and rapidly and visually judging the fault influence;

7. predefining a connection rule between a user and equipment, and automatically completing intelligent network modeling;

8. the method is suitable for connection of the pipe network equipment and users in any municipal engineering, and meets the requirements of different occasions;

9. the intelligent city model can be used in a city modeling method.

Therefore, the invention quickly accesses the user information into the peripheral equipment network, realizes the automatic completion of the establishment of the complete intelligent network simulation network, and simultaneously provides a data basis for technicians to quickly judge the equipment fault range.

Drawings

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention:

FIG. 1 is a schematic diagram of a network modeling process of the present invention;

FIG. 2 is a schematic diagram of obtaining a user interval range through a digital map;

FIG. 3 is a schematic diagram of a user interval range obtained by an image algorithm;

FIG. 4 is a schematic diagram of the network modeling effect of the present invention;

FIG. 5 is a diagram illustrating the modeling effect of the device and the user according to the present invention.

Detailed Description

For a fuller understanding of the technical content of the present invention, reference should be made to the following detailed description taken together with the accompanying drawings.

Example 1

Referring to fig. 1-5, the present embodiment is a network modeling method based on user interval information, and the network modeling method is used to add a user interval range into network modeling. The modeling method comprises the following steps:

at present, there are many methods for obtaining user data, such as obtaining user data in various ways such as aboveground, underground, drawings and the like as described in the domestic invention patents 1 to 3, and directly obtaining user data by a plurality of domestic digital map providers. According to the method, the data base is established for network modeling by acquiring the user data and importing the user data into the cloud electronic map.

The invention acquires the boundary range of the position of the user and adopts the following steps:

step S101: acquiring a user boundary range through a digital map;

as shown in fig. 2, the digital map provider not only obtains the location of the user, but also obtains the user boundary range, which is beneficial to analyzing the influence of the device on the user range in the subsequent network modeling, thereby assisting technicians to quickly judge the influence range caused by the device failure.

as shown in fig. 3, the unmanned aerial vehicle is used for aerial photography in the area range, the obtained aerial image is used for obtaining the boundary range by using an image segmentation algorithm, and a data basis is established for network modeling.

taking power supply of power equipment as an example, the user types need to be identified and classified before modeling according to different power requirements. As shown in fig. 4, the map has different user types, such as "a university" and "a building", and in practical cases, the electricity consumers further include "a certain cell", "a certain mall", "a certain factory", and the like. According to the electricity utilization rule, the electricity utilization requirements of different user types are different, but general user data only provides specific user names and cannot identify and classify corresponding users.

The invention adopts the following steps for identifying and classifying the user types:

step S201: and capturing the user name keywords by taking the user name keywords as characteristics, matching the user name keywords with a preset keyword library, and identifying and classifying the user types according to matching results.

Generally, the names of users are various, users of power equipment are taken as examples and comprise factories, shops, cells, substations and the like, keywords in the names of the users are captured to be matched with a preset keyword library, and the matching is fed back through a calculation program, so that the modeling system can identify the types of the corresponding users, and a foundation is provided for the modeling system to connect adjacent power equipment according to different types. In addition, the users of the same type include various keyword features, such as, for example, a house type, "a certain cell", "a certain garden", "a certain house", and the like, which relate to a special name of a developer for a certain house parcel, so synonyms, near synonyms, and special name extensions are periodically performed on the keyword features in the keyword library to ensure the accuracy of type identification.

Step S300: the cloud electronic map establishes a user model according to the user attribute; the user attributes comprise a user type, a user position and a user boundary.

As shown in fig. 4, in order to show one example of the user model on the cloud electronic map, where the geographic location of Y1 represents a first cell, the location of Y1' represents a second cell, the location of Y3 represents a shop, the location of Y4 represents a factory, and the boundary range of each user can be obtained through step S101 or step S102, and the boundary range is represented by a dotted line. In addition, since it is known that the first cell and the second cell belong to the same residential user type in step S201, they are represented by the same type Y1. As can be seen, fig. 5 establishes a user model including user attributes on the cloud electronic map, where the user is located, the type, and the boundary range.

The device data acquisition means at least comprises two modes:

step S401, equipment data acquisition is carried out along a road through a mobile terminal with a GPS function, the equipment data are static images and/or dynamic videos acquired by an image acquisition unit, and the equipment data are uploaded to a cloud electronic map;

and S402, directly acquiring the equipment data through the digital map, and uploading the geographic position and the equipment type of the equipment data to the cloud electronic map.

The step S401 can rapidly collect the devices on both sides of the road, and position the device position according to the GPS function, and acquire the device type by acquiring and recognizing the device appearance feature, the nameplate data, and the like, and has the disadvantages that the device needs to be collected by a worker or a machine along the road direction, and the data such as images and videos needs to be recognized by subsequent image processing software, whether the collected images and videos are clear will affect the recognition of the device type, and whether the device and user information positioning is accurate during the collection is still limited by the network signal, the GPS module algorithm, and the like. Step S402 can quickly obtain the device information according to the mark of the digital map, and has a disadvantage that accurate and comprehensive data cannot be obtained, for example, the device data is not recorded in the electronic map or the digital map is not updated in time. Therefore, the technical personnel in the field can adopt a single or combined means to acquire data according to actual occasions so as to meet the requirement of network modeling data acquisition. In addition, those skilled in the art can also collect data according to other existing means, such as collecting device data and user data from underground, enclosed space and drawing as shown in domestic patents 2 and 3, and is not limited to the above-mentioned collecting means.

Step S500: and presetting a connection rule between the equipment and the user, and finishing network modeling by the equipment model and the user model according to the connection rule.

Taking power grid equipment as an example, please refer to fig. 5, switch equipment E1, E2 and a transformer E3 are collected along an electric wire E0, except for special equipment, special power supply and other conditions, a conventional connection rule is generally adopted, that is, residential areas Y1, Y1', shop Y3 and factory Y4 are taken as examples, factory Y4 belongs to a special transformer user, and the power source of the factory Y4 is from an adjacent switch E1; shop Y3 belongs to 220/380V powered small users, whose power comes from the adjacent common transformer E3; the transformers of the residential areas Y1 and Y1 'have different use cases from the transformer of the factory Y4, the transformers of the residential areas Y1 and Y1' are in the interior of the residential areas, the power supplies of the transformers are from adjacent switches, namely the transformers in the residential area Y1 are connected with the adjacent switches E1, and the residential area Y2 is connected with the adjacent switches E2.

As shown in fig. 5, when collecting the device data through step S401, a technician in the art collects the device data along the electric wire E0, when the technician or machine arrives at the switching device E1 along the road, records the position of the switching device E1, uploads the still image or the dynamic video of the switching device E1 to the cloud electronic map, identifies the type of the device E1, i.e., the switching device, by an image recognition algorithm, connects users in the vicinity of the switching device E1, i.e., the residential community Y1 and the factory Y4, according to a predetermined connection rule, and then quickly identifies the boundary range affected by the switching device E1 on the residential community Y1 and the factory Y4, e.g., the area a1 in fig. 5, so that the technician or machine continues to go along the electric wire E0, connects the residential community Y1' with the switching device E2, connects the shop Y3 with the public transformer E3, and once the connection between the device and the user is completed, i.e., the device is immediately connected to the cloud electronic map, The user and the influence range of the user are modeled, and technicians or machines are connected with the cloud map through the intelligent terminals, so that the influence range of the equipment is obtained, and the network modeling system capable of modeling while walking is completed. It should be noted that, in the present invention, the device type may also be directly identified in the mobile terminal, and the device type and the device location are uploaded to the cloud electronic map as device data, which is only an adaptive modification made to the embodiment by those skilled in the art.

Therefore, the equipment and the user are quickly connected by combining the preset connection rule, the influence range of the equipment on the user is directly known in network modeling, and technicians can conveniently and quickly judge the influence range of the equipment fault on the user.

The preset connection rule of the power equipment is only a conventional connection rule in power grid modeling, a person skilled in the art can adaptively modify the connection rule according to specific conditions, and in addition, according to a specific application environment, the preset connection rule can be preset aiming at the connection rules of other municipal pipe network equipment, such as tap water, sewage pipe network equipment, natural gas network equipment and the like, so that different pipe network equipment is connected with user information.

Example 2

The embodiment is a method for identifying an influence range of a fault device, which includes network modeling of a device and a user, wherein the network modeling adopts the network modeling method, and further includes the following steps:

step S1000: receiving a fault equipment number;

step S2000: locating the faulty device in a network model;

step S3000: an influence range is identified, which is displayed by a marker.

As shown in fig. 5, a network model is formed on the cloud electronic map, wherein users affected by the device E1 are a residential cell Y1 and a factory Y4, user ranges of the residential cell Y1 and the factory Y4 are displayed by an area a1, and so on, a user range of a residential cell Y1' affected by the device E2 is displayed by an area a2, and a section range of a shop Y3 affected by the public transformer E3 is indicated by an area A3.

When equipment has a fault, such as the fault of the public transformer E3, the cloud electronic map receives a signal from the fault of the public transformer E3, the fault position of the public transformer E3 is located in the network model, and meanwhile, the influence range of an affected shop Y3 user is displayed through a mark, wherein the mark can be represented in various ways, such as a red flashing mark and the like, so that the judgment of a worker on the fault influence range is enhanced.

Wherein, for the identification of the influence range, the method further comprises the following steps:

As shown in fig. 5, the electric wire E0 is provided with a switch E1, a switch E2 and a common transformer E3, and power is transmitted along the line, when the switch E1 fails or in two cases, (1) only affects the user residential area Y1 and the factory Y4; (2) the power transmission is disconnected from E1, causing power switch E2, common transformer E3 to be affected, and in turn affecting residential Y1 'and store Y3, whereby it can be seen that the rapid provision of equipment failure information by different markings will facilitate the user's knowledge of the power failure.

For example, when the common transformer E3 fails, the above-mentioned A3 region indicates the influence range with a first mark, and the influence range data is pushed to the power failure system, the system automatically informs the users in the influence range according to the range data and informs specific failure equipment, if the switch E1 fails and affects the switch E2 and the common transformer E3, all affected areas including the above-mentioned a3 area will be indicated with a second label different from the first label, and pushes the second tagged reach data to the power failure system, which automatically notifies all reach users and specific failed devices based on the reach data, whereby, for the same store Y3, the specific fault identification can quickly identify the fault reason caused by the fault equipment through different marks such as the first mark, the second mark and the like, and the system automatically pushes the fault reason, so that the fault identification efficiency is improved.

The technical solutions provided by the embodiments of the present invention are described in detail above, and the principles and embodiments of the present invention are explained herein by using specific examples, and the descriptions of the embodiments are only used to help understanding the principles of the embodiments of the present invention; meanwhile, for a person skilled in the art, according to the embodiments of the present invention, there may be variations in the specific implementation manners and application ranges, and in summary, the content of the present description should not be construed as a limitation to the present invention.

Claims

1. A network modeling method based on user interval information is characterized by comprising the following steps:

step S400: acquiring equipment data, uploading the equipment data to a cloud electronic map, and establishing an equipment model according to equipment attributes; wherein the device attributes comprise a device type and a device location;

step S500: presetting a connection rule between equipment and a user, wherein the equipment model and the user model complete network modeling according to the connection rule;

in step S100, the method further includes the steps of: step S101: acquiring a user boundary range through a digital map;

in step S100, the method further includes the steps of: step S102: and carrying out image segmentation on the aerial image to obtain a user boundary range.

2. The network modeling method of claim 1, wherein: in step S200, the method further includes the following steps:

3. The network modeling method of claim 1, wherein: in step S400, the method further includes the following steps:

and S401, acquiring equipment data along a road through a mobile terminal with a GPS function, and uploading the equipment data to a cloud electronic map.

4. A network modeling method as defined in claim 3, wherein: the device data is static images and/or dynamic videos acquired by an image acquisition unit.

5. The network modeling method of claim 1, wherein: in step S400, the method further includes the following steps:

and S402, directly acquiring equipment data through a digital map, and uploading the geographic position and the equipment type of the equipment data to a cloud electronic map.

6. A method for identifying the range of influence of a faulty device, comprising a network modeling of the device and the user, said network modeling using the network modeling method according to claims 1-5, further comprising the steps of:

step S1000: receiving a fault equipment number;

step S2000: locating the faulty device in a network model;

step S3000: an influence range is identified, which is displayed by a marker.

7. The identification method of claim 6, wherein: in step S3000, the method further includes:

8. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 5.