CN108960049B

CN108960049B - Method and device for identifying high back fruit zone of long oil and gas pipeline and storage medium

Info

Publication number: CN108960049B
Application number: CN201810513064.XA
Authority: CN
Inventors: 韩文超; 刘亮; 高海康; 周利剑; 贾韶辉; 郭磊; 欧新伟; 徐杰; 吴官生; 杨宝龙; 任武; 张新建; 吴志强; 朱峰
Original assignee: Petrochina Co Ltd
Current assignee: Petrochina Co Ltd
Priority date: 2018-05-25
Filing date: 2018-05-25
Publication date: 2021-01-29
Anticipated expiration: 2038-05-25
Also published as: CN108960049A

Abstract

The invention discloses a method and a device for identifying a high-back fruit area of a long oil and gas pipeline and a storage medium, and belongs to the technical field of risk evaluation of the long oil and gas pipeline. The method comprises the following steps: the method comprises the steps of obtaining a target remote sensing image formed by a pipeline area with the center line of a target pipeline as a symmetry axis and a first distance threshold as a symmetry radius, identifying a plurality of ground object categories in the target remote sensing image through a first classification identification model to determine areas where the ground object categories are located, determining ground object attribute information of the area where each ground object category in the ground object categories is located according to geographic information of the pipeline area in the target remote sensing image, and identifying a high-back fruit area of the target pipeline from the pipeline area in the image areas. The invention can determine the high-consequence area of the target pipeline by automatically identifying the target remote sensing image covering the target pipeline, thereby improving the identification efficiency and accuracy of the high-consequence area of the long oil and gas pipeline.

Description

Method and device for identifying high back fruit zone of long oil and gas pipeline and storage medium

Technical Field

The invention relates to the technical field of risk evaluation of long oil and gas pipelines, in particular to a method and a device for identifying a high fruit region of a long oil and gas pipeline and a storage medium.

Background

The long oil and gas transmission pipeline is a long-distance oil and gas transmission pipeline with the length of more than 50 kilometers, and the high consequence area of the long oil and gas transmission pipeline is an area where the leakage of the pipeline existing around the long oil and gas transmission pipeline can seriously endanger the public safety or cause great environmental damage. With the continuous development of social economy, population and environmental resources around the established long oil and gas pipeline can change at any time, so that new high-back fruit areas such as personnel intensive places or environment sensitive places are easily formed around the long oil and gas pipeline, and in practical application, if risk control measures are not taken for the places in time, once an oil and gas leakage event of the long oil and gas pipeline occurs, adverse effects and even casualties can be caused. Therefore, the high-consequence areas of the long oil and gas pipelines need to be identified timely and accurately so as to reduce the adverse effects of the operation of the long oil and gas pipelines on the social public and living environments.

At present, manual field inspection is a main means for identifying the high fruit area of a long oil and gas pipeline. Specifically, technicians can manually patrol the periphery of the long oil and gas pipeline along the pipeline direction, segment the long oil and gas pipeline by taking the length of 2000 meters as a standard in patrol, and manually visually observe the environmental state of the periphery of each segment of the oil and gas pipeline within a certain range respectively so as to count the distribution conditions of personnel, buildings, water systems, vegetation, land, wetlands and roads in the periphery of each segment of the oil and gas pipeline within a certain range. And then estimating the number of personnel and buildings in the peripheral area of each section of oil and gas pipeline according to the statistical result of each section of oil and gas pipeline, and determining whether the peripheral area of each section of long oil and gas pipeline is a high back fruit area or not according to the number of personnel and buildings in the peripheral area of each section of oil and gas pipeline, thereby identifying the high back fruit area of the whole long oil and gas pipeline.

In the process of implementing the invention, the inventor finds that the prior art has at least the following problems:

because the long oil and gas pipeline is long, the surrounding environment of the pipeline is complex, and manual field inspection is often limited by geographical environment, climate environment and traffic, the workload for identifying the high fruit area is large, the working efficiency is low, the identification process is subjectively influenced, and the result is not accurate enough.

Disclosure of Invention

The embodiment of the invention provides a method and a device for identifying a high-posterior fruit zone of a long oil and gas pipeline and a storage medium, which can be used for solving the problems of inaccurate result and low efficiency of manual identification of the high-consequence zone. The technical scheme is as follows:

in a first aspect, a method for identifying a high fruit zone of a long oil and gas pipeline is provided, the method comprising:

acquiring a target remote sensing image, wherein the target remote sensing image comprises a pipeline area which takes a central line of a target pipeline as a symmetry axis and a first distance threshold value as a symmetry radius, the first distance threshold value is greater than the outer diameter of the target pipeline, and the target pipeline is a long oil and gas pipeline to be researched;

identifying a plurality of ground feature classes in the target remote sensing image through a first classification identification model to determine areas where the ground feature classes are located, wherein the first classification identification model is obtained through training according to sample images of the ground feature classes;

determining the feature attribute information of the region where each feature type in the plurality of feature types is located according to the geographic information of the pipeline region in the target remote sensing image;

and identifying a high back fruit area of the target pipeline from pipeline areas in a plurality of image areas according to the feature attribute information of the areas where the feature types are located, wherein the image areas are obtained by dividing the target remote sensing image along the pipeline direction of the target pipeline by taking a second distance threshold as an interval.

Optionally, the identifying, according to the feature attribute information of the region where the plurality of feature types are located, the high-consequence region of the target pipeline from the pipeline regions in the plurality of image regions includes:

determining the region grade of the pipeline region in each image region in the plurality of image regions according to the feature attribute information of the region in which the plurality of feature types are located, wherein the region grade is used for indicating the population concentration degree of the pipeline region in each image region;

and identifying a high-back fruit area of the target pipeline from the pipeline areas in the plurality of image areas according to the feature attribute information of the areas where the plurality of feature types are located and the area grade of the pipeline area in each image area in the plurality of image areas.

Optionally, the determining, according to the feature attribute information of the region where the plurality of feature types are located, the region level of the pipeline region in each of the plurality of image regions includes:

determining the ith image area included by the target remote sensing image according to the initial position of the pipeline included by the ith-1 image area included by the target remote sensing image, the second distance threshold and the third distance threshold;

when i is 1, the starting position of the pipeline included in the i-th image region coincides with the starting position of the pipeline included in the i-th image region, and the starting positions of the pipelines are the starting positions of the target pipeline in the target remote sensing image;

determining feature attribute information of the plurality of feature types in the ith image area from feature attribute information of areas where the plurality of feature types are located;

determining an initial region grade of a pipeline region in the ith image region according to the feature attribute information of the plurality of feature types in the ith image region;

determining at least one image area which is coincident with the ith image area from a plurality of image areas included in the target remote sensing image, and determining the maximum initial area grade in the initial area grades of the at least one image area as the area grade of the ith image area.

Optionally, the determining an initial region level of a pipeline region in the ith image region according to the feature attribute information of the plurality of feature classes located in the ith image region includes:

determining a single-storey building area and a low-storey building area included in the ith image area according to the feature attribute information of the plurality of feature types in the ith image area, wherein the low-storey building is a building with a floor larger than 1 and smaller than or equal to a floor threshold value;

determining that the initial region grade of the ith image area is one grade when the first image area comprises a single-storey house area smaller than a first area threshold value, the low-storey house area smaller than a second area threshold value and the ith image area does not comprise suburban population centralization places, urban areas and transportation hubs according to the feature attribute information of the plurality of feature categories in the ith image area;

determining an initial area level of the ith image area to be two-level when the ith image area includes a flat-room area greater than or equal to the first area threshold and less than a third area threshold, includes a low-rise building area greater than or equal to the second area threshold and less than a fourth area threshold, and determines that the ith image area does not include suburban concentrated sites, urban areas and transportation hubs according to the feature attribute information of the plurality of feature classes located in the ith image area, the third area threshold being greater than the first area threshold, the fourth area threshold being greater than the second area threshold;

when the ith image area comprises a single-story floor area which is larger than or equal to the third area threshold value, or comprises a low-rise floor area which is larger than or equal to the fourth area threshold value, or determines that the ith image area comprises a suburban population concentration place according to the feature attribute information of the plurality of feature categories positioned in the ith image area, determining that the initial area grade of the ith image area is three levels;

determining that the initial region level of the ith image region is four levels when the ith image region is determined to include an urban area or a transportation hub according to the feature attribute information of the plurality of feature classes located in the ith image region.

Optionally, the identifying, according to the feature attribute information of the region where the plurality of feature categories are located and the region level of the pipeline region in each of the plurality of image regions, the high-consequence region of the target pipeline from the pipeline regions in the plurality of image regions includes:

when the target pipeline is a long oil pipeline, for any image area A in the plurality of image areas, if the image area A meets a first identification condition, determining that the pipeline area in the image area A is a high posterior fruit area of the target pipeline, wherein the first identification condition means that the area grade of the pipeline area in the image area A is three or four, or an environment-sensitive place or a road without a transportation junction exists in the image area A, or a bungalow area included in the image area A is larger than a fifth area threshold, a low-rise building area included in the image area A is larger than a sixth area threshold, and the pipeline area in the image area A belongs to a village or a township, and the environment-sensitive place includes a natural protection area and a water source;

when the target pipeline is a long gas transmission pipeline, for any image area A in the plurality of image areas, if the image area A meets a second identification condition, determining that the pipeline area in the image area A is a high posterior fruit area of the target pipeline, wherein the second identification condition means that the area grade of the pipeline area in the image area A is three-level or four-level, or a centralized population place or a flammable and explosive place exists in the image area A.

Optionally, the determining that the pipe region in the image region a is the high-consequence region of the target pipe if the image region a satisfies the first recognition condition includes:

if a road without a transportation junction exists in the image area A, determining that a pipeline area in the image area A is a first-level high back fruit area of the target pipeline;

if the area grade of the pipeline area in the image area A is three levels, or a natural protection area exists in the image area A, or the flat-room area included in the image area A is larger than the fifth area threshold, the low-rise building area included in the image area A is larger than the sixth area threshold, and the pipeline area in the image area A belongs to a village or a village, determining that the pipeline area in the image area A is a second-level high back fruit area of the target pipeline;

if the area grade of the pipeline area in the image area A is four levels, or a water source exists in the image area A, determining that the pipeline area in the image area A is a three-level high back fruit area of the target pipeline.

Optionally, the determining that the pipe region in the image region a is the high-consequence region of the target pipe if the image region a satisfies the second recognition condition includes:

if a place with concentrated population exists in the image area A and the potential influence radius of the pipeline included in the image area A is smaller than or equal to a fourth distance threshold, determining that the pipeline area in the image area A is a first-level high posterior fruit area of the target pipeline, wherein the potential influence radius is determined according to the outer diameter of the pipeline included in the image area A and the maximum allowable operation pressure;

if a place with concentrated population exists in the image area A and the potential influence radius of the pipeline included in the image area A is larger than a fourth distance threshold, or the area grade of the pipeline area in the image area A is three-level, or a flammable and explosive place exists in the image area A, determining that the pipeline area in the image area A is a second-level high back-fruit area of the target pipeline;

and if the area grade of the pipeline area in the image area A is four, determining that the pipeline area in the image area A is a three-level high back fruit area of the target pipeline.

Optionally, the acquiring a target remote sensing image includes:

acquiring center line position information of the target pipeline and boundary position information of a pipeline area of the target pipeline, wherein the boundary position information is used for indicating the pipeline area which takes the center line of the target pipeline as a symmetry axis and the first distance threshold value as a symmetry radius;

acquiring a plurality of initial remote sensing images according to the central line position information, wherein each initial remote sensing image covers a partial pipeline area of the target pipeline;

splicing the multiple initial remote sensing images according to the central line position information to obtain spliced remote sensing images, wherein the spliced remote sensing images cover all pipeline areas of the target pipeline;

and cutting the spliced remote sensing image according to the boundary position information to obtain the target remote sensing image.

Optionally, before identifying, by the first classification identification model, the plurality of surface feature classes in the target remote sensing image, the method further includes:

acquiring sample images of the plurality of ground object categories;

performing feature extraction on the sample images of the multiple ground feature types to obtain geometric texture features and spectral features of the sample images of the multiple ground feature types;

and training a second classification recognition model according to the geometric texture features and the spectral features of the sample images of the multiple ground feature types to obtain the first classification recognition model, wherein the second classification recognition model is a classification recognition model to be trained and used for recognizing the ground feature types.

In a second aspect, there is provided an apparatus for identifying a high fruit zone of a long oil and gas pipeline, the apparatus comprising:

the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a target remote sensing image, the target remote sensing image comprises a pipeline area which takes a central line of a target pipeline as a symmetry axis and a first distance threshold value as a symmetry radius, the first distance threshold value is larger than the outer diameter of the target pipeline, and the target pipeline is a long oil and gas pipeline to be researched;

the first determination module is used for identifying a plurality of ground feature types in the target remote sensing image through a first classification identification model so as to determine the areas where the ground feature types are located, and the first classification identification model is obtained by training according to sample images of the ground feature types;

the second determination module is used for determining the feature attribute information of the region where each feature type in the plurality of feature types is located according to the geographic information of the pipeline region in the target remote sensing image;

and the identification module is used for identifying the high back fruit area of the target pipeline from the pipeline areas in the plurality of image areas according to the feature attribute information of the areas where the plurality of feature types are located, wherein the plurality of image areas are obtained by dividing the target remote sensing image along the pipeline direction of the target pipeline by taking a second distance threshold value as an interval.

Optionally, the identification module comprises:

the first determining unit is used for determining the area grade of the pipeline area in each image area in the plurality of image areas according to the feature attribute information of the area where the plurality of feature types are located, and the area grade is used for indicating the population concentration degree of the pipeline area in each image area;

and the first identification unit is used for identifying the high back fruit area of the target pipeline from the pipeline areas in the plurality of image areas according to the feature attribute information of the areas where the plurality of feature types are located and the area grade of the pipeline area in each image area in the plurality of image areas.

Optionally, the first determining unit includes:

the first determining subunit is used for determining the ith image area included by the target remote sensing image according to the starting position of the pipeline included by the (i-1) th image area included by the target remote sensing image, the second distance threshold and the third distance threshold;

a second determining subunit, configured to determine, from the feature attribute information of the region where the plurality of feature types are located, feature attribute information of the plurality of feature types located in the ith image region;

a third determining subunit, configured to determine, according to the feature attribute information of the plurality of feature types located in the ith image area, an initial region level of the pipeline area within the ith image area;

a fourth determining subunit, configured to determine, from a plurality of image regions included in the target remote sensing image, at least one image region that coincides with the ith image region, and determine, as a region rank of the ith image region, a maximum initial region rank in initial region ranks of the at least one image region.

Optionally, the third determining subunit is specifically configured to:

Optionally, the first identification unit includes:

a first identifying subunit, configured to, when the target pipeline is a long oil pipeline, determine, for any image area a in the plurality of image areas, if the image area a satisfies a first identifying condition, that a pipeline area in the image area a is a high posterior fruit area of the target pipeline, where the first identifying condition is that an area grade of the pipeline area in the image area a is three or four, or that a road in the image area a exists in an environmentally sensitive place or a non-traffic junction, or that a bungalow area included in the image area a is greater than a fifth area threshold, a low-rise building area included in the image area a is greater than a sixth area threshold, and the pipeline area in the image area a belongs to a rural area or a township, where the environmentally sensitive place includes a natural protection area and a water source;

a second identifying subunit, configured to, when the target pipeline is a long gas transmission pipeline, determine, for any image area a of the multiple image areas, that a pipeline area in the image area a is a high posterior fruit area of the target pipeline if the image area a satisfies a second identifying condition, where the second identifying condition is that the area grade of the pipeline area in the image area a is three or four, or that a population concentration place or a flammable and explosive place exists in the image area a.

Optionally, the first identifier unit is specifically configured to:

Optionally, the second identifier unit is specifically configured to:

Optionally, the first obtaining module includes:

a first obtaining unit, configured to obtain center line position information of the target pipeline and boundary position information of a pipeline region of the target pipeline, where the boundary position information is used to indicate a pipeline region in which a center line of the target pipeline is used as a symmetry axis and the first distance threshold is used as a symmetry radius;

the second acquisition unit is used for acquiring a plurality of initial remote sensing images according to the central line position information, and each initial remote sensing image covers a part of the pipeline area of the target pipeline;

the first processing unit is used for splicing the multiple initial remote sensing images according to the central line position information to obtain spliced remote sensing images, and the spliced remote sensing images cover all pipeline areas of the target pipeline;

and the second processing unit is used for cutting the spliced remote sensing image according to the boundary position information to obtain the target remote sensing image.

Optionally, the apparatus further comprises:

the second acquisition module is used for acquiring sample images of the plurality of ground object types;

the extraction module is used for extracting the characteristics of the sample images of the plurality of ground feature types to obtain the geometric texture characteristics and the spectral characteristics of the sample images of the plurality of ground feature types;

and the training module is used for training a second classification recognition model according to the sample images of the multiple ground feature types and the geometric texture characteristics and the spectral characteristics of the sample images of the multiple ground feature types to obtain the first classification recognition model, wherein the second classification recognition model is a classification recognition model to be trained and used for recognizing the ground feature types.

In a third aspect, there is provided an apparatus for identifying a high fruit zone of a long oil and gas pipeline, the apparatus comprising:

a processor and a memory for storing processor-executable instructions;

wherein the processor is configured to perform the method for identifying a high fruit bearing zone of any one of the long oil and gas pipelines provided by the first aspect.

In a fourth aspect, a computer-readable storage medium is provided, in which a computer program is stored, and the computer program, when executed by a processor, implements the method for identifying a high fruit zone of any one of the long oil and gas pipelines provided in the first aspect.

The technical scheme provided by the embodiment of the invention at least has the following beneficial effects: in the embodiment of the invention, for a long oil and gas pipeline to be researched, namely a target pipeline, a target remote sensing image consisting of pipeline areas with the center line of the target pipeline as a symmetry axis and a first distance threshold as a symmetry radius can be obtained, a plurality of ground feature categories in the target remote sensing image are identified through a first classification identification model to determine the areas where the ground feature categories are located, ground feature attribute information of the area where each ground feature category in the ground feature categories is located is determined according to geographic information of the pipeline areas in the target remote sensing image, and finally, a high fruit area of the target pipeline is identified from the pipeline areas in the image areas according to the ground feature attribute information of the areas where the ground feature categories are located. That is, in the embodiment of the present invention, a target remote sensing image covering a pipeline region of a target pipeline may be obtained, and an image recognition algorithm is used to automatically recognize the target remote sensing image to determine a high consequence region of the target pipeline, so that compared with a method for manually recognizing the high consequence region in the related art, recognition efficiency and accuracy are improved.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

FIG. 1 is a schematic diagram of a high consequence area identification system provided by an embodiment of the present invention;

FIG. 2 is a schematic flow chart of a method for identifying a high fruit zone of a long oil and gas pipeline according to an embodiment of the present invention;

FIG. 3 is a schematic flow chart of another method for identifying a high fruit zone of a long oil and gas pipeline according to an embodiment of the present invention;

FIG. 4 is a schematic structural diagram of an identification device for a high fruit zone of a long oil and gas pipeline according to an embodiment of the present invention;

fig. 5 is a schematic structural diagram of a terminal 500 according to an embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

Before explaining the embodiments of the present invention in detail, terms, application scenarios and system architectures related to the embodiments of the present invention are explained separately.

First, terms related to embodiments of the present invention will be described.

Class of ground object

The ground object refers to a general term of various objects (such as mountains, forests, buildings and the like) and non-objects (such as provinces, counties and the like) on the ground, and generally refers to relatively fixed objects on the earth surface. The land feature categories are obtained by classifying land features, for example, the land feature categories may include buildings, water systems, vegetation, land, roads, and the like.

Surface feature attribute information

The feature attribute information is used to indicate specific attributes of the features, and may be information indicating functions and purposes thereof, for example, and may be used to classify each feature in detail. For example, the attribute information of the building may be a residential area, a hospital, a school, a business area, an industrial area, a kindergarten, an old home, a shopping mall, or the like.

Classification recognition model

The classification recognition model is a model that can recognize a plurality of surface feature types in the remote sensing image, and specifically may be a CNN (Convolutional Neural Networks) model or a RNN (Recurrent Neural Networks) model.

Radius of potential influence

The potentially affected radius of a gas pipeline is the radius of the area where the peripheral public safety and property may be significantly affected in the event of a failure of the gas pipeline, determined by the outside diameter of the gas pipeline and the maximum allowable operating pressure.

Next, an application scenario related to the embodiment of the present invention is described.

The safe operation of the long oil and gas pipeline is not only related to the performance assessment of a pipeline operator, but also related to the safety of lives and properties of people around the pipeline, and with the rapid development of social economy, the expansion of the central area of a city can change the original non-high consequence area of the long oil and gas pipeline into a high back effect area, so that the high consequence area of the long oil and gas pipeline needs to be identified regularly to reduce the adverse effect of the operation of the long oil and gas pipeline on the social public and the living environment.

Finally, a system architecture according to an embodiment of the present invention is described.

The identification method of the high fruit bearing area of the long oil and gas pipeline provided by the embodiment of the invention can be applied to an identification system of the high result area, fig. 1 is a schematic diagram of the identification system of the high result area provided by the embodiment of the invention, and as shown in fig. 1, the identification system can comprise a database server 10, a position information display and analysis module 20, a remote sensing image preprocessing module 30, a remote sensing image classification module 40, a geographic information module 50, a high result area identification module 60 and a data acquisition management module 70. The modules and the database server 10 can perform data interaction, and the modules can be connected through a network.

The database server 10 is configured to store data results, data indexes, documents, and other contents of other modules, for example, location information of the location information presentation and analysis module 20, a preprocessing result of the remote sensing image preprocessing module 30, a classification result of the remote sensing image classification module 40, or an identification result of the high consequence region identification module 60 may be stored.

The position information display and analysis module 20 is configured to store, display, or analyze a centerline position of the target pipeline, and may use the centerline position of the target pipeline as a basis for obtaining a remote sensing image, and may further define and manage a boundary range of a peripheral vector of the pipeline.

And the remote sensing image preprocessing module 30 is used for preprocessing the remote sensing image, including geometric correction, registration, fusion, mosaic, cutting and the like of the remote sensing image.

And the remote sensing image classification module 40 is used for identifying a plurality of ground object classes in the remote sensing image so as to realize classification of the ground objects in the remote sensing image.

The Geographic Information module 50 is configured to improve the classification result of the remote sensing image classification module 40 and identify the feature attribute Information of each feature type, and may specifically be configured with auxiliary Information such as a GIS (Geographic Information System), an electronic map, a thematic map, political planning data, environmental ecological data, or population data.

And the high consequence area identification module 60 is used for identifying, displaying and managing the high consequence area of the target pipeline.

And the data acquisition management module 70 is configured to acquire centerline position information of the target pipeline and acquire a peripheral vector boundary range of the target pipeline.

It should be noted that the above identification system is only an exemplary system provided by the embodiment of the present invention, and in practical applications, the above identification system may further include more or fewer modules, or some of the modules may be replaced by other modules, as long as the identification method of the high fruit zone of the long oil and gas pipeline provided by the embodiment of the present invention can be implemented. Further, the modules included in the identification system may be concentrated in one terminal, or may be concentrated in multiple terminals, and then, in the following embodiment of fig. 3, taking an example that all the modules included in the identification system are concentrated in one terminal, a detailed description will be given of the identification method for a high fruit zone of a long oil and gas pipeline according to an embodiment of the present invention.

Fig. 2 is a schematic flow chart of a method for identifying a high fruit bearing zone of a long oil and gas pipeline according to an embodiment of the present invention, where the method may be applied to a terminal, and the terminal may be a mobile phone, a tablet computer, a computer, or the like. Referring to fig. 2, the method comprises the steps of:

step 201: and acquiring a target remote sensing image, wherein the target remote sensing image comprises a pipeline area which takes the central line of a target pipeline as a symmetry axis and a first distance threshold value as a symmetry radius, the first distance threshold value is greater than the outer diameter of the target pipeline, and the target pipeline is a long oil and gas pipeline to be researched.

Step 202: and identifying a plurality of ground feature classes in the target remote sensing image through a first classification identification model to determine the areas where the ground feature classes are located, wherein the first classification identification model is obtained by training according to sample images of the ground feature classes.

Step 203: and determining the feature attribute information of the region where each feature type in the plurality of feature types is located according to the geographic information of the pipeline region in the target remote sensing image.

Step 204: and identifying a high back fruit area of the target pipeline from pipeline areas in the plurality of image areas according to the feature attribute information of the areas where the plurality of feature types are located, wherein the plurality of image areas are obtained by dividing the target remote sensing image along the pipeline direction of the target pipeline by taking the second distance threshold value as an interval.

In the embodiment of the invention, for a long oil and gas pipeline to be researched, namely a target pipeline, a target remote sensing image consisting of pipeline areas with the center line of the target pipeline as a symmetry axis and a first distance threshold as a symmetry radius can be obtained, a plurality of ground feature categories in the target remote sensing image are identified through a first classification identification model to determine the areas where the ground feature categories are located, ground feature attribute information of the area where each ground feature category in the ground feature categories is located is determined according to geographic information of the pipeline areas in the target remote sensing image, and finally, a high fruit area of the target pipeline is identified from the pipeline areas in the image areas according to the ground feature attribute information of the areas where the ground feature categories are located. That is, in the embodiment of the present invention, a target remote sensing image covering a pipeline region of a target pipeline may be obtained, and an image recognition algorithm is used to automatically recognize the target remote sensing image to determine a high consequence region of the target pipeline, so that compared with a method for manually recognizing the high consequence region in the related art, recognition efficiency and accuracy are improved.

Optionally, identifying a high-consequence region of the target pipeline from the pipeline regions in the plurality of image regions according to the feature attribute information of the region where the plurality of feature types are located includes:

and identifying a high back fruit area of the target pipeline from the pipeline areas in the plurality of image areas according to the feature attribute information of the areas where the plurality of feature types are located and the area grade of the pipeline area in each image area in the plurality of image areas.

Optionally, determining, according to the feature attribute information of the region where the plurality of feature types are located, a region level of the pipe region in each of the plurality of image regions, includes:

determining an ith image area included by the target remote sensing image according to the initial position of the pipeline included by the (i-1) th image area included by the target remote sensing image, the second distance threshold and the third distance threshold;

when i is 1, the starting position of the pipeline included in the i-1 th image area coincides with the starting position of the pipeline included in the i-1 th image area, and the starting positions of the target pipeline in the target remote sensing image;

determining feature attribute information of a plurality of feature types in the ith image area from feature attribute information of areas where the feature types are located;

determining an initial region grade of a pipeline region in the ith image region according to the feature attribute information of a plurality of feature types in the ith image region;

and determining at least one image area which is overlapped with the ith image area from a plurality of image areas included in the target remote sensing image, and determining the maximum initial area grade in the initial area grades of the at least one image area as the area grade of the ith image area.

Optionally, determining an initial region rank of the pipeline region within the ith image region according to the feature attribute information of the plurality of feature categories located in the ith image region, includes:

determining a single-storey building area and a low-storey building area included in an ith image area according to the feature attribute information of a plurality of feature types in the ith image area, wherein the low-storey building is a building with a floor larger than 1 and smaller than or equal to a floor threshold;

when the first image area comprises a single-storey house area smaller than a first area threshold value, the low-storey building area smaller than a second area threshold value and the ith image area does not comprise suburban population concentration places, urban areas and transportation hubs according to the feature attribute information of a plurality of feature types in the ith image area, determining that the initial region grade of the ith image area is one level;

when the flat-house area included in the ith image area is larger than or equal to a first area threshold value and smaller than a third area threshold value, the included low-rise building area is larger than or equal to a second area threshold value and smaller than a fourth area threshold value, and the ith image area does not comprise suburban population concentration places, urban areas and traffic hubs according to the feature attribute information of a plurality of feature types located in the ith image area, determining that the initial area level of the ith image area is two levels, wherein the third area threshold value is larger than the first area threshold value, and the fourth area threshold value is larger than the second area threshold value;

when the first image area comprises a first area threshold value, or comprises a second area threshold value, or comprises a first floor building area, or comprises a second floor building area, or comprises a third floor building area, or comprises a fourth floor building area, or determines that the first image area comprises a suburban population concentration place according to the feature attribute information of a plurality of feature types in the first image area, determining that the initial region grade of the first image area is three levels;

when the ith image area is determined to comprise a downtown area or a traffic junction according to the feature attribute information of a plurality of feature types in the ith image area, determining that the initial region level of the ith image area is four levels.

Optionally, identifying a high-consequence region of the target pipeline from the pipeline regions in the plurality of image regions according to the feature attribute information of the region in which the plurality of feature categories are located and the region level of the pipeline region in each of the plurality of image regions, includes:

when the target pipeline is a long oil pipeline, for any image area A in the plurality of image areas, if the image area A meets a first identification condition, determining that the pipeline area in the image area A is a high back fruit area of the target pipeline, wherein the first identification condition means that the area grade of the pipeline area in the image area A is three or four, or an environment sensitive place or a road without a traffic junction exists in the image area A, or a bungalow area included in the image area A is larger than a fifth area threshold, a low-rise building area included in the image area A is larger than a sixth area threshold, and the pipeline area in the image area A belongs to a country or a town, and the environment sensitive place comprises a natural protection area and a water source;

when the target pipeline is a long gas transmission pipeline, for any image area A in the plurality of image areas, if the image area A meets a second identification condition, determining that the pipeline area in the image area A is a high back fruit area of the target pipeline, wherein the second identification condition means that the area grade of the pipeline area in the image area A is three or four, or a population concentration place or a flammable and explosive place exists in the image area A.

Optionally, if the image area a satisfies the first recognition condition, determining that the pipe area in the image area a is a high-consequence area of the target pipe includes:

if the image area A has a road without a traffic junction, determining that the pipeline area in the image area A is a first-level high back-fruit area of the target pipeline;

if the area grade of the pipeline area in the image area A is three levels, or a natural protection area exists in the image area A, or the flat-room area included in the image area A is larger than a fifth area threshold value, the low-rise building area included in the image area A is larger than a sixth area threshold value, and the pipeline area in the image area A belongs to a village or a village, determining the pipeline area in the image area A as a second-level high back fruit area of the target pipeline;

and if the area grade of the pipeline area in the image area A is four levels or a water source exists in the image area A, determining that the pipeline area in the image area A is a fruit area with the third-level height of the target pipeline.

Optionally, if the image area a satisfies the second recognition condition, determining that the pipe area in the image area a is a high-consequence area of the target pipe includes:

if a population concentration place exists in the image area A and the potential influence radius of the pipeline included in the image area A is smaller than or equal to a fourth distance threshold, determining that the pipeline area in the image area A is a first-level high posterior fruit area of the target pipeline, wherein the potential influence radius is determined according to the outer diameter of the pipeline included in the image area A and the maximum allowable operating pressure;

if a population concentration place exists in the image area A and the potential influence radius of the pipeline included in the image area A is larger than a fourth distance threshold, or the area grade of the pipeline area in the image area A is three-grade, or an inflammable and explosive place exists in the image area A, determining that the pipeline area in the image area A is a second-grade high back fruit area of the target pipeline;

Optionally, acquiring a target remote sensing image includes:

acquiring center line position information of a target pipeline and boundary position information of a pipeline area of the target pipeline, wherein the boundary position information is used for indicating the pipeline area which takes the center line of the target pipeline as a symmetry axis and takes a first distance threshold value as a symmetry radius;

acquiring a plurality of initial remote sensing images according to the central line position information, wherein each initial remote sensing image covers a partial pipeline area of a target pipeline;

splicing the multiple initial remote sensing images according to the central line position information to obtain spliced remote sensing images, wherein the spliced remote sensing images cover all pipeline areas of a target pipeline;

and cutting the spliced remote sensing image according to the boundary position information to obtain a target remote sensing image.

acquiring sample images of a plurality of ground object types;

and training a second classification recognition model according to the geometric texture features and the spectral features of the sample images of the ground feature classes to obtain a first classification recognition model, wherein the second classification recognition model is a classification recognition model to be trained and used for recognizing the ground feature classes.

All the above optional technical solutions can be combined arbitrarily to form an optional embodiment of the present invention, which is not described in detail herein.

Fig. 3 is a schematic flow chart of another method for identifying a high fruit area of a long oil and gas pipeline according to an embodiment of the present invention, where the method may be applied to a terminal, and the terminal may be a smart phone, a tablet computer, a server, or the like. Referring to fig. 3, the method comprises the steps of:

step 301: and acquiring a target remote sensing image, wherein the target remote sensing image comprises a pipeline area which takes the central line of a target pipeline as a symmetry axis and the first distance threshold value as a symmetry radius, and the target pipeline is a long oil and gas pipeline to be researched.

The remote sensing image is a film or a photo for recording electromagnetic waves of various ground objects, and is mainly divided into an aerial photo and a satellite photo. The target remote sensing image is a remote sensing image of a pipeline region containing a target pipeline.

In the embodiment of the invention, the target remote sensing image can be directly sent by other equipment, can be obtained from an image database, and can also be obtained by splicing a plurality of initial remote sensing images sent by a remote sensing satellite. For example, a target remote sensing image of a pipeline region including a target pipeline is stored in the image database, and the terminal can directly acquire the target remote sensing image from the image database.

The first distance threshold is greater than the outer diameter of the target pipe, that is, the pipe region of the target pipe includes the target pipe, and the region range is greater than the range of the target pipe. The first distance threshold is used to indicate a vertical distance between a pipeline region boundary of the target pipeline and a center line of the target pipeline, and specifically, the first distance threshold may be a vertical distance between an influence range boundary when the maximum influence range is generated on two sides of the pipeline after an accident occurs to the target pipeline and the center line of the target pipeline, and may also be a potential influence radius of the target pipeline.

In the embodiment of the present invention, the first distance threshold may be preset, specifically, may be configured by a default terminal, or may be set by a technician according to actual needs, for example, may be set by the technician according to a potential influence radius of the target pipeline. For example, when the target pipeline is a long hydrocarbon pipeline, the potential impact radius calculated from the target pipeline outer diameter and the maximum allowable operating pressure of the target pipeline may be determined as the first distance threshold. In one example, the first distance threshold may be 200 meters, that is, the pipe region of the target pipe refers to pipe regions that are 200 meters wide from both sides of the centerline of the target pipe.

In one possible embodiment, the target remote sensing image may be obtained through the following steps 3011-3014.

Step 3011:and acquiring the central line position information of the target pipeline and the boundary position information of the pipeline area of the target pipeline, wherein the boundary position information is used for indicating the pipeline area which takes the central line of the target pipeline as a symmetry axis and takes the first distance threshold value as a symmetry radius.

The center line position information is used to indicate a center line position of the target pipeline, and may be coordinate information of a center line of the target pipeline, where the coordinate information may be GPS (Global Positioning System) coordinate information, coordinate information of a beidou System, or coordinate information of a graves System. In the embodiment of the invention, the center line position information can be configured by a terminal in a default mode, can be manually input by a user according to needs, can be obtained by a positioning instrument moving in the target pipeline and sent to the terminal, and can also be obtained by a technician holding the positioning instrument moving outside the target pipeline along the center line direction of the target pipeline and sent to the terminal by the positioning instrument. For example, the technician may hold the positioning instrument to move outside the target pipeline along the direction of the target pipeline, and send the positioning result as center line position information to the terminal through the positioning instrument.

The boundary position information is used to indicate a boundary position of the pipe region, and may be coordinate information of the boundary of the pipe region. Specifically, the boundary position information may be determined according to a center line of the target pipeline and the first distance threshold, or may be determined according to a plurality of key point position information of the pipeline region of the target pipeline, for example, the plurality of key point position information may be position information of a corner of the target pipeline. In the embodiment of the present invention, the boundary position information may be configured by default by the terminal, may be manually input by the user, or may be sent to the terminal by other devices. For example, after the positioning instrument acquires the centerline position information of the target pipeline, the boundary position information is determined according to the centerline position information and the first distance threshold, and then the boundary position information is sent to the terminal.

In one possible embodiment, in determining the boundary region of the target pipe, the GPS coordinates of each point on the centerline of the target pipe may be acquired, and then the GPS coordinates of the boundary location of the pipe region of the target pipe may be determined based on the GPS coordinates of each point and the first distance threshold. For example, assuming that the first distance threshold is 200 meters, the center line of the target pipeline is located by using a handheld GPS locator, and the GPS coordinate of a certain point at the center position of the straight line segment of the target pipeline is (109.88836, 35.23849), two points on both sides of the target pipeline, which are perpendicular to the direction of the pipe wall and are 200 meters away from the GPS coordinate, may be determined as two boundary points of the pipeline region of the target pipeline.

It should be noted that the above numerical values are only exemplary numerical values given in the embodiment of the present invention, and in practical applications, the above numerical values may also be other values, which are not specifically limited in the embodiment of the present invention.

Step 3012:and acquiring a plurality of initial remote sensing images according to the central line position information, wherein each initial remote sensing image covers part of the pipeline area of the target pipeline.

Because the distance of the target pipeline is long, the remote sensing image acquired with high resolution cannot contain the complete target pipeline, and if one remote sensing image can contain the complete target pipeline, the resolution of the remote sensing image is low, and the high consequence area of the target pipeline cannot be accurately identified, so that a plurality of initial remote sensing images with high resolution need to be acquired.

Specifically, the terminal may send the centerline position information to a multi-view remote sensing satellite, and the multi-view remote sensing satellite respectively takes a plurality of initial remote sensing images covering a partial pipeline region of the target pipeline according to the centerline position information and sends the plurality of initial remote sensing images to the terminal. Specifically, when the multi-view remote sensing satellite shoots the initial remote sensing image, the central line position of the target pipeline can be taken as the middle position of each initial remote sensing image to be shot, so that each initial remote sensing image not only can comprise the segmented pipeline of the target pipeline, but also can cover the pipeline area of the segmented pipeline, namely the partial pipeline area of the target pipeline. Moreover, each initial remote sensing image can also be a high spatial resolution remote sensing image.

Further, the terminal can also send the centerline position information and the first distance threshold to a multi-view remote sensing satellite, the multi-view remote sensing satellite respectively shoots a plurality of initial remote sensing images according to the centerline position information and the first distance threshold, and the shot plurality of initial remote sensing images are sent to the terminal. In addition, the multiple initial remote sensing images may also be sent by other devices or obtained from an image database, which is not limited in the embodiment of the present invention.

Step 3013:and splicing the multiple initial remote sensing images according to the central line position information to obtain a spliced remote sensing image, wherein the spliced remote sensing image covers all the pipeline area of the target pipeline.

Specifically, when the multiple initial remote sensing images are spliced, the multiple initial remote sensing images need to be sequenced according to positions of part of target pipelines included in each initial remote sensing image in all target pipelines, all target pipelines obtained after splicing are guaranteed to be actual positions of the target pipelines, then the sequenced multiple initial remote sensing images are spliced by taking a center line of the target pipeline as a connection point, and the spliced remote sensing image is obtained.

Furthermore, before the splicing processing is carried out on the multiple initial remote sensing images, the multiple initial remote sensing images can be preprocessed through geometric correction, image registration, image cloud removal or shadow removal and the like, so that the geometric size or the image display degree of each initial remote sensing image is completely consistent, and after the preprocessing is completed, the preprocessed multiple initial remote sensing images are spliced according to the central line position information to obtain the spliced remote sensing image.

Step 3014:and cutting the spliced remote sensing image according to the boundary position information to obtain a target remote sensing image.

Because the spliced remote sensing image obtained by splicing according to the central line position information of the target pipeline may have the phenomenon of inconsistent image range or the phenomenon of overlarge range of the included pipeline area, and the identification efficiency of the high back fruit area is reduced due to the inconsistent image range or the overlarge area range, after the spliced remote sensing image is obtained, the spliced remote sensing image can be cut according to the boundary position information to obtain the target remote sensing image of the pipeline area only containing the boundary position information indication.

Step 302: and identifying a plurality of ground feature classes in the target remote sensing image through the first classification identification model so as to determine the areas where the ground feature classes are located.

The first classification recognition model is a model capable of recognizing and classifying a plurality of surface feature types in the remote sensing image and can be obtained by training according to sample images of the plurality of surface feature types. That is, the first classification recognition model can automatically recognize and classify a plurality of ground object classes in the target remote sensing image. In a specific embodiment, the first classification and identification model may be a CNN model or an RNN model, or may be a model using other algorithms, which is not limited in the embodiment of the present invention.

The surface feature generally refers to a relatively fixed object on the surface of the earth, and the surface feature is classified to obtain the surface feature. For example, the plurality of feature categories may include buildings, water systems, vegetation, land, roads, and the like, and the first classification recognition model may be a classification recognition model that can recognize buildings, water systems, vegetation, land, roads, and the like in the remote sensing image.

In one possible embodiment, the first classification can be obtained by training 3021-3023 as follows according to the sample images of the plurality of ground feature classes.

Step 3021:sample images of a plurality of surface feature classes are acquired.

The sample images of the multiple surface feature types refer to images presented by the selected samples of the multiple surface feature types in the target remote sensing image. In the embodiment of the present invention, the sample images of the multiple surface feature types may be sent by other devices, may be obtained from an image database, or may be extracted from a target remote sensing image.

For example, in one target remote sensing image, images each including buildings, water systems, vegetation, land, roads, and the like are extracted from one image area of the target remote sensing image, and the extracted images may be used as sample images of the plurality of feature types.

Step 3022:and performing feature extraction on the sample images of the plurality of ground feature types to obtain the geometric texture features and the spectral features of the sample images of the plurality of ground feature types.

The geometric texture features are rugged grooved features with spatial scales presented by sample images of ground object classes in the remote sensing images. The spectral features are electromagnetic radiation laws presented by sample images of ground object classes in the remote sensing images.

Step 3023:and training the second classification recognition model according to the geometric texture features and the spectral features of the sample images of the plurality of ground feature types to obtain the first classification recognition model.

The second classification recognition model is a classification recognition model to be trained and used for recognizing the ground feature types, and the first classification recognition model can be obtained by training the second classification recognition model by using the sample images of the ground feature types and the geometric texture features and the spectral features of the sample images of the ground feature types. In a specific embodiment, the second classification recognition model may be a CNN model or an RNN model, or may be a model using another algorithm, which is not limited in the embodiment of the present invention.

In the process of training the second classification recognition model, the second classification recognition model can continuously learn the geometric texture characteristics and the spectral characteristics of the sample images of the multiple ground feature classes, and can adjust the model parameters of the second classification recognition model, so that the second classification recognition model can be converted into the first classification recognition model capable of recognizing the multiple ground feature classes through training of enough sample images.

After the first classification recognition model is obtained through training, the target remote sensing image can be used as the input of the first classification recognition model, a plurality of ground feature categories in the target remote sensing image are recognized through the first classification recognition model, the range of each ground feature category in the plurality of ground feature categories is identified, and then the area where the plurality of ground feature categories are located is determined.

For example, according to the first classification recognition model, all buildings, water systems, vegetation, land, and roads in the target remote sensing image can be recognized, the ranges of the buildings, the water systems, the vegetation, the land, and the roads are respectively identified in the target remote sensing image, and the areas where the buildings, the water systems, the vegetation, the land, and the roads are respectively located can be determined.

Step 303: and determining the feature attribute information of the region where each feature type in the plurality of feature types is located according to the geographic information of the pipeline region in the target remote sensing image.

The geographic information is used for indicating the position information of the surface feature in the pipeline area, and may specifically include auxiliary information such as a GIS, an electronic map, a thematic map, administrative planning data or environmental ecological data.

Further, the feature attribute information of the area where each of the plurality of feature types is located can be determined according to the geographic information and the population information of the pipeline area in the target remote sensing image. The population information may indicate the population distribution in the pipeline area, and specifically may be the demographic data of each area counted in advance.

The feature attribute information is used to indicate specific attributes of the features, and may be information indicating functions and purposes thereof, for example, and may be used to classify each feature in detail. For example, the attribute information of the building may include residential areas (including one-storey houses, low-rise buildings and high-rise buildings), hospitals, schools, business areas, industrial areas, kindergartens, nursing homes or shopping malls, the attribute information of the water system may include rivers, lakes, reservoirs or the like, the attribute information of the vegetation may include grasslands, woodlands or the like, the attribute information of the land may include cultivated lands, wetlands, wastelands or the like, and the attribute information of the road may include highways, national roads, provincial roads, railways, rural roads or the like.

Specifically, the attribute of each of the multiple surface feature types may be assigned according to the geographic information of the pipeline area, or the geographic information and the population information of the pipeline area, so as to obtain attribute information that can identify each of the multiple surface feature types. In practical application, the feature attribute information may be manually matched and assigned to each of the plurality of feature categories by a user according to geographic information, or geographic information and population information, or may be automatically matched and assigned to each of the plurality of feature categories by a terminal directly reading various geographic information, or geographic information and population information stored in a database.

In one possible embodiment, when determining the feature attribute information of the area in which each of the plurality of feature types is located based on the geographical information of the pipe area in the target remote sensing image, the buildings in the target remote sensing image may be identified as residential areas, hospitals, schools, commercial areas, industrial areas, kindergartens, old homes, shopping malls, trade markets, temples, stadiums, squares, leisure areas, theaters, or camping grounds, the water systems may be identified as rivers, lakes, reservoirs, or the like, the roads may be identified as highways, national roads, provinces, railways, or village roads, or the like, the lands may be identified as cultivated lands, wetlands, wastelands, or the like, and the vegetation may be identified as grasslands, forest lands, or the like.

Further, after the terminal determines the feature attribute information of the region where each of the plurality of feature categories is located according to the geographic information of the pipeline region in the target remote sensing image, the feature attribute information of the region where each of the plurality of feature categories is located determined by the terminal can be checked and corrected through a manual visual method, so that the accuracy of the feature attribute information identification of the region where each of the plurality of feature categories is located is improved.

After determining the feature attribute information of the region in which each of the plurality of feature types is located according to the geographic information of the pipeline region in the target remote sensing image, the high back fruit region of the target pipeline can be identified from the pipeline region in the plurality of image regions according to the feature attribute information of the region in which each of the plurality of feature types is located.

The plurality of image areas are obtained by dividing the target remote sensing image along the pipeline direction of the target pipeline by taking the second distance threshold as an interval, that is, the length of the pipeline included in each image area is the second distance threshold. The second distance threshold may be preset, specifically configured by a default of the terminal, or set by a technician as required, for example, the second preset distance may be 2000 meters.

Specifically, according to the feature attribute information of the region where each of the plurality of feature types is located, identifying the high-consequence region of the target pipeline from the pipeline regions in the plurality of image regions may be achieved through the following steps 304-305.

Step 304: and determining the region grade of the pipeline region in each image region in the plurality of image regions according to the feature attribute information of the region in which the plurality of feature types are located.

Wherein, the region grade is used for indicating the population concentration degree of the pipeline region in each image region, and the higher the region grade, the more concentrated the population is.

Specifically, the region level of the pipe region in each of the plurality of image regions may be determined through the following steps 3041 and 3044 according to the feature attribute information of the region where the plurality of feature types are located.

Step 3041:and determining the ith image area included by the target remote sensing image according to the starting position of the pipeline included by the (i-1) th image area included by the target remote sensing image, the second distance threshold and the third distance threshold.

Wherein the second distance threshold is a length of a pipe included in the ith image. The third distance threshold is a distance between a pipe start position included in the ith image and a pipe start position included in the (i-1) th image, and the third distance threshold is smaller than the second distance threshold. In a specific embodiment, the third distance threshold may be set by a default of the terminal, or may be set by a technician as needed, for example, the technician may directly input the third distance threshold on the terminal.

Wherein i is an integer greater than or equal to 1. When i is 1, the starting position of the pipeline included in the i-1 th image area coincides with the starting position of the pipeline included in the i-th image area, and the starting positions are the starting positions of the target pipeline in the target remote sensing image. That is, the initial position of the pipeline included in the 1 st image region is the initial position of the target pipeline in the target remote sensing image.

Specifically, when i is equal to 1, the 1 st image area may be determined according to the second preset distance, and the starting position of the pipeline included in the 1 st image area is the starting position of the target pipeline in the target remote sensing image, and the length of the pipeline included in the 1 st image area is the second distance threshold. After the 1 st image area is determined, determining a pipeline position which is away from the starting position of the pipeline included in the 1 st image area by a third distance threshold value as the starting position of the pipeline included in the 2 nd image area, determining the 2 nd image area according to the second distance threshold value, enabling the length of the pipeline included in the 2 nd image area to be a second preset distance, and repeating the steps until the last image area included in the target remote sensing image is determined.

For example, assuming that the second distance threshold is 2000 meters and the third distance threshold is 200 meters, the 1 st image region includes a starting position of the pipeline as a starting position of the target pipeline in the target remote sensing image, and includes a pipeline having a length of 2000 meters. After the 1 st image area is determined, the position of the pipeline, which is 200 meters away from the starting position of the pipeline included in the 1 st image area, may be determined as the starting position of the pipeline included in the 2 nd image area, and the 2 nd image area is determined according to 2000 meters, so that the length of the pipeline included in the 2 nd image area is 2000 meters, and the above steps are repeated until the last image area included in the target remote sensing image is determined.

It should be noted that the above numerical values are only exemplary numerical values given in the embodiment of the present invention, and in practical applications, the above numerical values may also be other numerical values, which is not specifically limited in the embodiment of the present invention.

Step 3042:and determining the feature attribute information of the plurality of feature types in the ith image area from the feature attribute information of the areas where the plurality of feature types are located.

Specifically, after the ith image area is determined, the feature attribute information of the plurality of feature types in the ith image area may be determined according to the feature attribute information of the area where the plurality of feature types in the determined target remote sensing image are located.

Step 3043:and determining the initial region grade of the pipeline region in the ith image region according to the feature attribute information of the plurality of feature types in the ith image region.

Wherein the initial regional rating is a regional rating divided according to a degree of population concentration. Specifically, the initial region level of the pipe region within the ith image region may be determined according to the feature attribute information and the region level division rule of the plurality of feature classes located in the ith image region.

The regional classification rule can be preset, can be configured by default by a terminal, and can also be set by technical personnel according to actual needs. In the following, only an exemplary region-level division rule provided by the embodiment of the present invention is taken as an example for description, and in practical applications, the region-level division rule may be other rules, which is not limited in the embodiment of the present invention.

Specifically, the ground feature attribute information of a plurality of ground feature types located in the ith image area may be used to determine the single-story area and the low-rise building area included in the ith image area, and then the initial region level of the pipeline area in the ith image area may be determined according to the ground feature attribute information of a plurality of ground feature types located in the ith image area, the single-story area and the low-rise building area included in the ith image area.

The flat house refers to a building with floors of 1 floor, and the low-floor building refers to a building with floors larger than 1 and smaller than or equal to a floor threshold value, wherein the floor threshold value is a positive integer larger than 1. Wherein, the floor threshold value is the highest floor value of the low-rise building. In practical applications, the floor threshold may be configured by default by the terminal, or may be set by a technician as needed. For example, the floor threshold may be 6, and the low-rise building refers to a building with a floor greater than 1 and less than or equal to 6.

Specifically, determining the initial region level of the pipe region in the ith image region according to the feature attribute information of the plurality of feature categories located in the ith image region, and the single-storey and low-storey building areas included in the ith image region may include the following cases:

1) and when the first image area comprises a single-storey house area smaller than a first area threshold value, the low-storey house area smaller than a second area threshold value and the ith image area does not comprise suburban population concentration places, urban areas and transportation hubs according to the feature attribute information of a plurality of feature types in the ith image area, determining that the initial region grade of the ith image area is one level.

The first area threshold and the second area threshold may be preset, specifically configured by a default of the terminal, or set by a technician as needed. For example, the first area threshold may be 1500 square meters and the second area threshold may be 450 square meters. The suburban population centralized location may include a suburban hospital, a suburban school, a suburban commercial area, a suburban industrial area, and the like.

For example, assuming that the first area threshold is 1500 square meters and the second area threshold is 450 square meters, when the ith image area includes a flat-room area smaller than 1500 square meters, includes a low-rise floor area smaller than 450 square meters, and does not include a suburban population concentration site, a downtown area, and a transportation hub, the initial area level of the ith image area may be determined to be one level.

Further, when the ith image area is determined to include a few people or no people such as wasteland, cultivated land, wetland, grassland and woodland according to the feature attribute information of the plurality of feature types in the ith image area, the initial region level of the ith image area is determined to be one level.

2) And when the first image area comprises a single-storey house area which is larger than or equal to a first area threshold value and smaller than a third area threshold value, the included low-storey house area is larger than or equal to a second area threshold value and smaller than a fourth area threshold value, and the ith image area does not comprise suburban population concentration places, urban areas and traffic junctions according to the feature attribute information of a plurality of feature types in the ith image area, determining the initial region level of the ith image area as two levels.

Wherein the third area threshold is greater than the first area threshold, and the fourth area threshold is greater than the second area threshold. Moreover, the third area threshold and the fourth area threshold may be preset, specifically configured by default of the terminal, or set by a technician as needed. For example, the third area threshold may be 1000 square meters and the fourth area threshold may be 3000 square meters.

For example, assuming that the first area threshold is 1500 square meters, the second area threshold is 450 square meters, the third area threshold is 10000 square meters, and the fourth area threshold is 3000, when the ith image area includes a single-story floor area greater than or equal to 1500 and less than 10000, includes a low-rise floor area greater than or equal to 450 and less than 3000, and does not include a suburban population concentration site, a downtown, and a transportation hub, the initial region level of the ith image area may be determined as two-level.

3) And when the first image area comprises a single-storey house area which is larger than or equal to a third area threshold value, or comprises a low-storey house area which is larger than or equal to a fourth area threshold value, or the first image area comprises a suburban area centralized place according to the feature attribute information of a plurality of feature categories positioned in the first image area, determining that the initial area grade of the first image area is three levels.

For example, assuming that the third area threshold is 10000 square meters, the fourth area threshold is 3000, the suburban population concentration location is a suburban hospital, suburban school, suburban business area or suburban industrial area, etc., when the ith image area includes a single-story floor area greater than or equal to 10000, or includes a lower-story floor area greater than or equal to 3000, or includes a suburban hospital, suburban school, suburban business area, suburban industrial area, etc., the initial area rank of the ith image area may be determined to be three levels.

4) When the ith image area is determined to comprise a downtown area or a traffic junction according to the feature attribute information of a plurality of feature types in the ith image area, determining that the initial region level of the ith image area is four levels.

The urban area may further include a high-rise building, that is, when it is determined that the ith image area includes the urban area, the high-rise building or the transportation junction according to the feature attribute information of the plurality of feature types located in the ith image area, the initial region level of the ith image area is determined to be four levels.

It should be noted that the above values and locations are only exemplary values and locations given in the embodiment of the present invention, and in practical applications, the above values and locations may also be other values and locations, and the embodiment of the present invention is not limited to this specifically.

Step 3044:and determining at least one image area which is overlapped with the ith image area from a plurality of image areas included in the target remote sensing image, and determining the maximum initial area grade in the initial area grades of the at least one image area as the area grade of the ith image area.

Specifically, since the third distance threshold is smaller than the second distance threshold, there may be at least one image region that coincides with another image region in the ith image region, and for the at least one image region that coincides with the ith image region, in the embodiment of the present invention, the largest initial region level in the initial region levels of the at least one image region may be determined as the region level of the ith image region, so that the accuracy of determining the region level may be improved.

For example, assuming that the second distance threshold is 2000 meters and the third distance threshold is 200 meters, when the 2 nd image region is determined by the initial region level, 10 image regions overlapping the 2 nd image region exist in a plurality of image regions included in the target remote sensing image, and the initial region levels of the 10 image regions are respectively one level, two levels, one level, three levels, one level, two levels, one level and two levels, that is, the maximum initial region level of the 10 image regions is three levels, so that the region level of the 2 nd image region can be determined to be three levels, that is, the pipeline region in the 2 nd image region belongs to a three-level region.

Further, after the terminal determines the area grade of the ith image area, the area grade of the ith image area can be manually checked and corrected by a manual visual method, so that the accuracy of the determination result of the area grade of the ith image area is further improved.

It should be noted that the division of the numerical values and the regional grades is only an exemplary division of the numerical values and the regional grades provided in the embodiment of the present invention, and in practical applications, the division of the numerical values and the regional grades may also be a division of other numerical values and regional grades, which is not specifically limited in the embodiment of the present invention.

Step 305: and identifying a high back fruit area of the target pipeline from the pipeline areas in the plurality of image areas according to the feature attribute information of the areas where the plurality of feature types are located and the area grade of the pipeline area in each image area in the plurality of image areas.

The target pipeline can be divided into a long oil transmission pipeline and a long gas transmission pipeline according to different transmission media of the target pipeline, and identification modes for identifying the high back fruit areas in the pipeline areas in the multiple image areas are correspondingly different according to different transmission media of the target pipeline, and specifically the identification modes can include the following two identification modes.

The first recognition mode is as follows:when the target pipeline is a long oil conveying pipeline, for any image area A in the multiple image areas, if the image area A meets the first identification condition, determining that the pipeline area in the image area A is a high posterior fruit area of the target pipeline.

The first identification condition may be configured by default by the terminal, or may be set by a technician according to actual needs. In this embodiment of the present invention, the first identification condition means that the regional level of the pipeline region in the image region a is three or four, or there is a road in an environment-sensitive place or a non-transportation junction in the image region a, or the image region a includes a flat-room area larger than a fifth area threshold, includes a low-rise building area larger than a sixth area threshold, and belongs to a country or a town.

The environment-sensitive places comprise natural protection areas and water sources, the natural protection areas comprise estuaries, forests or wetlands and the like, and the water sources comprise rivers, lakes, reservoirs and the like. Roads of non-transportation junctions include highways, national roads, provincial roads, railways and the like. A fifth area threshold and a sixth area threshold may be preset, and in an embodiment of the present invention, the fifth area threshold is greater than the first area threshold and smaller than the third area threshold, and the sixth area threshold is greater than the second area threshold and smaller than the fourth area threshold. For example, the fifth area threshold may be 5000 square meters and the sixth area threshold may be 1500 square meters.

Further, after the pipeline area in the image area A is determined to be the high back fruit area of the target long oil pipeline, the high back fruit area of the image area A can be classified. Specifically, the manner of classifying the high back fruit region of the image area a may include the following:

1) and if the road without the transportation junction exists in the image area A, determining that the pipeline area in the image area A is a first-level high back-fruit area of the target pipeline.

For example, when there are roads other than transportation junctions, such as highways, national roads, provincial roads, rural roads, and railways, in the image area a, the pipeline area in the image area a may be determined as the first-level high back-fruit area of the target pipeline.

Further, when a road with a non-traffic junction exists in an area range, in which the vertical distance from the center line of the target pipeline is less than or equal to a fifth distance threshold value, in the image area a, the pipeline area in the image area a is determined to be a first-level high back effect area of the target pipeline.

Wherein the fifth distance threshold is greater than the outer diameter of the target pipe and less than the first distance threshold, for example, the fifth distance threshold may be 50 meters. When the fifth distance threshold is 50 meters, which indicates that a road with non-transportation junctions can exist within a range of 50 meters from both sides of the center line of the target pipeline in the image area a, the pipeline area in the image area a is determined as a first-level high back-fruit area of the target pipeline.

2) If the area grade of the pipeline area in the image area A is three levels, or a natural protection area exists in the image area A, or the flat-room area included in the image area A is larger than a fifth area threshold value, the low-rise building area included in the image area A is larger than a sixth area threshold value, and the pipeline area in the image area A belongs to a village or a village, determining that the pipeline area in the image area A is a second-level high back fruit area of the target pipeline.

For example, when a region with a region level of three levels exists in the image region a, or when a single-story building area in the image region a is larger than a fifth area threshold, a low-rise building area is larger than a sixth area threshold, and the pipeline region belongs to a village or a town, or when a natural protection region such as a estuary, a forest or a wetland exists in the image region a, the pipeline region in the image region a is determined to be a second-level high fruit region of the target pipeline.

3) And if the area grade of the pipeline area in the image area A is four levels or a water source exists in the image area A, determining that the pipeline area in the image area A is a fruit area with the third-level height of the target pipeline.

For example, when a region with a region level of four exists in the image area a, or a water source such as a river, a lake, or a reservoir exists, the pipe area in the image area a may be determined as a third-level high back fruit area of the target pipe.

The second recognition mode is as follows:when the target pipeline is a long gas transmission pipeline, for any image area A in the plurality of image areas, if the image area A meets the second identification condition, determining that the pipeline area in the image area A is a high posterior fruit area of the target pipeline.

The second identification condition may be configured by default by the terminal, or may be set by a technician according to actual needs. In the embodiment of the present invention, the second identification condition means that the area level of the pipeline area in the image area a is three or four, or a place with concentrated population or a flammable and explosive place exists in the image area a.

Wherein the population concentration locations include a first concentration location and a second concentration location. The first centralized place refers to a building area such as a hospital, a college, a nursery, an old fashioned house, a prison or a mall where people are difficult to evacuate. The second centralized location refers to an open air area where people are frequently gathered, for example, an open air area where 30 or more people are gathered for at least 50 days in a year, and may be, for example, a trade market, a temple or a stadium, an amusement and leisure place, a theater or an open camp, and the like. The flammable and explosive places refer to places where combustion or explosion easily occurs, such as gas stations, oil depots and the like.

In a specific embodiment, the population centralization place and the flammable and explosive place may be identified by the first classification identification model, or may be identified by the user population and manually configured, which is not limited in the embodiment of the present invention.

Further, after the pipeline area in the image area a is determined to be the high back fruit area of the target long gas transmission pipeline, the high back fruit area of the image area a can be classified. Specifically, the manner of classifying the high back fruit region of the image area a may include the following:

1) and if the image area A exists in the place with concentrated population and the potential influence radius of the long gas transmission pipeline included in the image area A is smaller than or equal to the fourth distance threshold, determining that the pipeline area in the image area A is a first-level high back fruit area of the target pipeline.

The potential influence radius refers to the radius of an area where peripheral public safety and property are likely to be obviously influenced when the gas transmission pipeline fails, and is specifically determined by the outer diameter of the long gas transmission pipeline and the maximum allowable operation pressure of a pipe section corresponding to the outer diameter of the long gas transmission pipeline. For example, the potentially affecting radius of the long gas transmission conduit comprised by image area a may be determined by the following equation (1):

where r is the potentially affecting radius, d is the outer diameter of the pipe, and p is the maximum allowable operating pressure for the corresponding pipe segment.

And the fourth distance threshold value is the vertical distance from the boundary of the influence range to the position of the center line of the long oil and gas pipeline when the maximum influence range is generated on the two sides of the pipeline after the long oil and gas pipeline has an accident. In a specific implementation process, the fourth distance threshold may be configured by a default of the terminal, or may be set by a technician according to actual needs. For example, the fourth distance threshold may be 200 meters.

For example, assuming that the fourth distance threshold is 200 meters, it may be determined that the pipe area within the image area a is a first-level high posterior fruit region of the target pipe when there is a population concentration site such as a hospital, a college, a nursery, an aged care, a prison, a mall, a trade market, a temple, or a stadium in the image area a and the potential radius of influence of the pipe is less than or equal to 200 meters.

2) And if the image area A exists in a place with concentrated population and the potential influence radius of the pipeline included in the image area A is larger than a fourth distance threshold, or the area grade of the pipeline area in the image area A is three-grade, or the image area A exists in a flammable and combustible place, determining that the pipeline area in the image area A is a second-grade high back fruit area of the target pipeline.

For example, assuming that the fourth distance threshold is 200 meters, a conduit area within the image area a may be determined to be a second highest posterior fruit zone of the target conduit when there is a population-centered site such as a hospital, college, nursery, nursing home, prison, mall, trade market, temple, or stadium in the image area a and the potential radius of influence of the conduit is greater than 200 meters.

3) And if the area grade of the pipeline area in the image area A is four, determining that the pipeline area in the image area A is a three-level high back fruit area of the target pipeline.

Further, after the terminal determines the area grade of the pipeline area in the image area a, the area grade of the pipeline area in the image area a determined by the terminal can be manually checked and corrected by a manual visual method, so that the accuracy of the determination result of the area grade of the pipeline area in the image area a is further improved.

Fig. 4 is a schematic structural diagram of an identification device for a high fruit zone of a long oil and gas pipeline provided by an embodiment of the invention. Referring to fig. 4, the apparatus may include:

the first obtaining module 401 is configured to obtain a target remote sensing image, where the target remote sensing image includes a pipeline region that uses a center line of a target pipeline as a symmetry axis and a first distance threshold as a symmetry radius, the first distance threshold is greater than an outer diameter of the target pipeline, and the target pipeline is a long oil and gas pipeline to be researched.

The first determining module 402 is configured to identify, through a first classification identification model, a plurality of surface feature classes in the target remote sensing image to determine a region where the plurality of surface feature classes are located, where the first classification identification model is obtained by training according to sample images of the plurality of surface feature classes.

The second determining module 403 is configured to determine, according to the geographic information of the pipeline region in the target remote sensing image, surface feature attribute information of a region where each of the plurality of surface feature types is located.

The identifying module 404 is configured to identify a high posterior fruit region of the target pipeline from pipeline regions in multiple image regions according to the feature attribute information of the regions where the multiple feature types are located, where the multiple image regions are obtained by dividing the target remote sensing image along the pipeline direction of the target pipeline with the second distance threshold as an interval.

Optionally, the identification module comprises:

the first determining unit is used for determining the region grade of the pipeline region in each image region in the plurality of image regions according to the feature attribute information of the region where the plurality of feature types are located, and the region grade is used for indicating the population concentration degree of the pipeline region in each image region;

the first identification unit is used for identifying a high-back fruit area of the target pipeline from the pipeline areas in the plurality of image areas according to the feature attribute information of the areas where the plurality of feature types are located and the area grade of the pipeline area in each image area in the plurality of image areas.

Optionally, the first determination unit includes:

the first determining subunit is used for determining the ith image area included by the target remote sensing image according to the initial position of the pipeline included by the (i-1) th image area included by the target remote sensing image, the second distance threshold and the third distance threshold;

the second determining subunit is used for determining the feature attribute information of the plurality of feature types in the ith image area from the feature attribute information of the area where the plurality of feature types are located;

the third determining subunit is used for determining the initial region level of the pipeline region in the ith image region according to the feature attribute information of a plurality of feature types in the ith image region;

and the fourth determining subunit is used for determining at least one image area which is overlapped with the ith image area from a plurality of image areas included in the target remote sensing image, and determining the maximum initial area grade in the initial area grades of the at least one image area as the area grade of the ith image area.

Optionally, the third determining subunit is specifically configured to:

Optionally, the first identification unit comprises:

a first identifying subunit, configured to, when the target pipeline is a long oil pipeline, determine, for any image area a in the plurality of image areas, if the image area a satisfies a first identifying condition, that the pipeline area in the image area a is a high posterior fruit area of the target pipeline, where the first identifying condition is that the area grade of the pipeline area in the image area a is three or four, or that a road in an environment-sensitive place or a non-traffic junction exists in the image area a, or that a bungalow area included in the image area a is greater than a fifth area threshold, a low-rise building area included in the image area a is greater than a sixth area threshold, and the pipeline area in the image area a belongs to a country or a town, and the environment-sensitive place includes a natural protection area and a water source;

and the second identification subunit is used for determining that the pipeline area in the image area A is a high posterior fruit area of the target pipeline if the image area A meets a second identification condition, wherein the second identification condition is that the area grade of the pipeline area in the image area A is three or four, or a centralized population place or a flammable and explosive place exists in the image area A, for any image area A in the plurality of image areas when the target pipeline is a long gas transmission pipeline.

Optionally, the first identifier unit is specifically configured to:

Optionally, the second identifier unit is specifically configured to:

Optionally, the first obtaining module includes:

the device comprises a first acquisition unit, a second acquisition unit and a control unit, wherein the first acquisition unit is used for acquiring the central line position information of a target pipeline and the boundary position information of a pipeline area of the target pipeline, and the boundary position information is used for indicating the pipeline area which takes the central line of the target pipeline as a symmetry axis and takes a first distance threshold value as a symmetry radius;

the first processing unit is used for splicing a plurality of initial remote sensing images according to the central line position information to obtain spliced remote sensing images, and the spliced remote sensing images cover all pipeline areas of the target pipeline;

and the second processing unit is used for cutting the spliced remote sensing image according to the boundary position information to obtain a target remote sensing image.

Optionally, the apparatus further comprises:

the second acquisition module is used for acquiring sample images of a plurality of ground object types;

and the training module is used for training a second classification recognition model according to the geometric texture characteristics and the spectral characteristics of the sample images of the plurality of ground feature types to obtain a first classification recognition model, wherein the second classification recognition model is a classification recognition model to be trained and used for recognizing the ground feature types.

It should be noted that: the identification device for the high fruit zone of the long oil and gas pipeline provided by the embodiment is only exemplified by the division of the functional modules when identifying the high result zone of the long oil and gas pipeline, and in practical application, the function distribution can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules so as to complete all or part of the functions described above. In addition, the device for identifying the high back fruit zone of the long oil and gas pipeline provided by the embodiment and the method embodiment for identifying the high back fruit zone of the long oil and gas pipeline belong to the same concept, and the specific implementation process is described in the method embodiment and is not described herein again.

Fig. 5 is a schematic structural diagram of a terminal 500 according to an embodiment of the present invention. The terminal 500 may be: a smart phone, a tablet computer, an MP3 player (Moving Picture Experts Group Audio Layer III, motion video Experts compression standard Audio Layer 3), an MP4 player (Moving Picture Experts Group Audio Layer IV, motion video Experts compression standard Audio Layer 4), a notebook computer, or a desktop computer. Terminal 500 may also be referred to by other names such as user equipment, portable terminal, laptop terminal, desktop terminal, and the like.

In general, the terminal 500 includes: a processor 501 and a memory 502.

The processor 501 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and so on. The processor 501 may be implemented in at least one hardware form of a DSP (Digital Signal Processing), an FPGA (Field-Programmable Gate Array), and a PLA (Programmable Logic Array). The processor 501 may also include a main processor and a coprocessor, where the main processor is a processor for Processing data in an awake state, and is also called a Central Processing Unit (CPU); a coprocessor is a low power processor for processing data in a standby state. In some embodiments, the processor 501 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content required to be displayed on the display screen. In some embodiments, processor 501 may also include an AI (Artificial Intelligence) processor for processing computational operations related to machine learning.

Memory 502 may include one or more computer-readable storage media, which may be non-transitory. Memory 502 may also include high-speed random access memory, as well as non-volatile memory, such as one or more magnetic disk storage devices, flash memory storage devices. In some embodiments, a non-transitory computer readable storage medium in memory 502 is used to store at least one instruction for execution by processor 501 to implement the method of identifying a high fruit zone of a long hydrocarbon pipeline provided by the method embodiments herein.

In some embodiments, the terminal 500 may further optionally include: a peripheral interface 503 and at least one peripheral. The processor 501, memory 502 and peripheral interface 503 may be connected by a bus or signal lines. Each peripheral may be connected to the peripheral interface 503 by a bus, signal line, or circuit board. Specifically, the peripheral device includes: at least one of radio frequency circuitry 504, touch screen display 505, camera 506, audio circuitry 507, positioning components 508, and power supply 509.

The peripheral interface 503 may be used to connect at least one peripheral related to I/O (Input/Output) to the processor 501 and the memory 502. In some embodiments, the processor 501, memory 502, and peripheral interface 503 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 501, the memory 502, and the peripheral interface 503 may be implemented on a separate chip or circuit board, which is not limited in this embodiment.

The Radio Frequency circuit 504 is used for receiving and transmitting RF (Radio Frequency) signals, also called electromagnetic signals. The radio frequency circuitry 504 communicates with communication networks and other communication devices via electromagnetic signals. The rf circuit 504 converts an electrical signal into an electromagnetic signal to transmit, or converts a received electromagnetic signal into an electrical signal. Optionally, the radio frequency circuit 504 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a subscriber identity module card, and so forth. The radio frequency circuitry 504 may communicate with other terminals via at least one wireless communication protocol. The wireless communication protocols include, but are not limited to: metropolitan area networks, various generation mobile communication networks (2G, 3G, 4G, and 5G), Wireless local area networks, and/or WiFi (Wireless Fidelity) networks. In some embodiments, the rf circuit 504 may further include NFC (Near Field Communication) related circuits, which are not limited in this application.

The display screen 505 is used to display a UI (User Interface). The UI may include graphics, text, icons, video, and any combination thereof. When the display screen 505 is a touch display screen, the display screen 505 also has the ability to capture touch signals on or over the surface of the display screen 505. The touch signal may be input to the processor 501 as a control signal for processing. At this point, the display screen 505 may also be used to provide virtual buttons and/or a virtual keyboard, also referred to as soft buttons and/or a soft keyboard. In some embodiments, the display screen 505 may be one, providing the front panel of the terminal 500; in other embodiments, the display screens 505 may be at least two, respectively disposed on different surfaces of the terminal 500 or in a folded design; in still other embodiments, the display 505 may be a flexible display disposed on a curved surface or on a folded surface of the terminal 500. Even more, the display screen 505 can be arranged in a non-rectangular irregular figure, i.e. a shaped screen. The Display screen 505 may be made of LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode), and other materials.

The camera assembly 506 is used to capture images or video. Optionally, camera assembly 506 includes a front camera and a rear camera. Generally, a front camera is disposed at a front panel of the terminal, and a rear camera is disposed at a rear surface of the terminal. In some embodiments, the number of the rear cameras is at least two, and each rear camera is any one of a main camera, a depth-of-field camera, a wide-angle camera and a telephoto camera, so that the main camera and the depth-of-field camera are fused to realize a background blurring function, and the main camera and the wide-angle camera are fused to realize panoramic shooting and VR (Virtual Reality) shooting functions or other fusion shooting functions. In some embodiments, camera assembly 506 may also include a flash. The flash lamp can be a monochrome temperature flash lamp or a bicolor temperature flash lamp. The double-color-temperature flash lamp is a combination of a warm-light flash lamp and a cold-light flash lamp, and can be used for light compensation at different color temperatures.

Audio circuitry 507 may include a microphone and a speaker. The microphone is used for collecting sound waves of a user and the environment, converting the sound waves into electric signals, and inputting the electric signals to the processor 501 for processing, or inputting the electric signals to the radio frequency circuit 504 to realize voice communication. For the purpose of stereo sound collection or noise reduction, a plurality of microphones may be provided at different portions of the terminal 500. The microphone may also be an array microphone or an omni-directional pick-up microphone. The speaker is used to convert electrical signals from the processor 501 or the radio frequency circuit 504 into sound waves. The loudspeaker can be a traditional film loudspeaker or a piezoelectric ceramic loudspeaker. When the speaker is a piezoelectric ceramic speaker, the speaker can be used for purposes such as converting an electric signal into a sound wave audible to a human being, or converting an electric signal into a sound wave inaudible to a human being to measure a distance. In some embodiments, audio circuitry 507 may also include a headphone jack.

The positioning component 508 is used for positioning the current geographic Location of the terminal 500 for navigation or LBS (Location Based Service). The Positioning component 508 may be a Positioning component based on the united states GPS (Global Positioning System), the chinese beidou System, the russian graves System, or the european union's galileo System.

Power supply 509 is used to power the various components in terminal 500. The power source 509 may be alternating current, direct current, disposable or rechargeable. When power supply 509 includes a rechargeable battery, the rechargeable battery may support wired or wireless charging. The rechargeable battery may also be used to support fast charge technology.

In some embodiments, terminal 500 also includes one or more sensors 510. The one or more sensors 510 include, but are not limited to: acceleration sensor 511, gyro sensor 512, pressure sensor 513, fingerprint sensor 514, optical sensor 515, and proximity sensor 516.

The acceleration sensor 511 may detect the magnitude of acceleration on three coordinate axes of the coordinate system established with the terminal 500. For example, the acceleration sensor 511 may be used to detect components of the gravitational acceleration in three coordinate axes. The processor 501 may control the touch screen 505 to display the user interface in a landscape view or a portrait view according to the gravitational acceleration signal collected by the acceleration sensor 511. The acceleration sensor 511 may also be used for acquisition of motion data of a game or a user.

The gyro sensor 512 may detect a body direction and a rotation angle of the terminal 500, and the gyro sensor 512 may cooperate with the acceleration sensor 511 to acquire a 3D motion of the user on the terminal 500. The processor 501 may implement the following functions according to the data collected by the gyro sensor 512: motion sensing (such as changing the UI according to a user's tilting operation), image stabilization at the time of photographing, game control, and inertial navigation.

The pressure sensor 513 may be disposed on a side bezel of the terminal 500 and/or an underlying layer of the touch display screen 505. When the pressure sensor 513 is disposed on the side frame of the terminal 500, a user's holding signal of the terminal 500 may be detected, and the processor 501 performs left-right hand recognition or shortcut operation according to the holding signal collected by the pressure sensor 513. When the pressure sensor 513 is disposed at the lower layer of the touch display screen 505, the processor 501 controls the operability control on the UI interface according to the pressure operation of the user on the touch display screen 505. The operability control comprises at least one of a button control, a scroll bar control, an icon control and a menu control.

The fingerprint sensor 514 is used for collecting a fingerprint of the user, and the processor 501 identifies the identity of the user according to the fingerprint collected by the fingerprint sensor 514, or the fingerprint sensor 514 identifies the identity of the user according to the collected fingerprint. Upon recognizing that the user's identity is a trusted identity, the processor 501 authorizes the user to perform relevant sensitive operations including unlocking the screen, viewing encrypted information, downloading software, paying, and changing settings, etc. The fingerprint sensor 514 may be provided on the front, back, or side of the terminal 500. When a physical button or a vendor Logo is provided on the terminal 500, the fingerprint sensor 514 may be integrated with the physical button or the vendor Logo.

The optical sensor 515 is used to collect the ambient light intensity. In one embodiment, the processor 501 may control the display brightness of the touch display screen 505 based on the ambient light intensity collected by the optical sensor 515. Specifically, when the ambient light intensity is high, the display brightness of the touch display screen 505 is increased; when the ambient light intensity is low, the display brightness of the touch display screen 505 is turned down. In another embodiment, processor 501 may also dynamically adjust the shooting parameters of camera head assembly 506 based on the ambient light intensity collected by optical sensor 515.

A proximity sensor 516, also referred to as a distance sensor, is typically disposed on the front panel of the terminal 500. The proximity sensor 516 is used to collect the distance between the user and the front surface of the terminal 500. In one embodiment, when the proximity sensor 516 detects that the distance between the user and the front surface of the terminal 500 gradually decreases, the processor 501 controls the touch display screen 505 to switch from the bright screen state to the dark screen state; when the proximity sensor 516 detects that the distance between the user and the front surface of the terminal 500 becomes gradually larger, the processor 501 controls the touch display screen 505 to switch from the screen-rest state to the screen-on state.

That is, not only is an embodiment of the present invention provide a terminal including a processor and a memory for storing executable instructions of the processor, where the processor is configured to execute the method in the embodiment shown in fig. 2 or 3, but also an embodiment of the present invention provides a computer-readable storage medium, where a computer program is stored in the storage medium, and when the computer program is executed by the processor, the method for identifying a high fruit region of a long oil and gas pipeline in the embodiment shown in fig. 2 or 3 can be implemented.

Those skilled in the art will appreciate that the configuration shown in fig. 5 is not intended to be limiting of terminal 500 and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components may be used.

It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.

The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims

1. A method for identifying a high fruit zone of a long oil and gas pipeline is characterized by comprising the following steps:

acquiring central line position information of a target pipeline and boundary position information of a pipeline area of the target pipeline, wherein the boundary position information is used for indicating the pipeline area which takes the central line of the target pipeline as a symmetrical axis and takes a first distance threshold value as a symmetrical radius;

sending the central line position information and the first distance threshold value to a multi-view remote sensing satellite, and acquiring a plurality of initial remote sensing images through the multi-view remote sensing satellite, wherein each initial remote sensing image covers a part of pipeline area of the target pipeline, and the central line of the target pipeline is the middle position of the initial remote sensing image;

according to the boundary position information, cutting the spliced remote sensing image to obtain a target remote sensing image;

the target remote sensing image comprises a pipeline area which takes the central line of the target pipeline as a symmetry axis and the first distance threshold value as a symmetry radius, the first distance threshold value is larger than the outer diameter of the target pipeline, and the target pipeline is a long oil and gas pipeline to be researched;

determining the feature attribute information of the region where each feature type in the plurality of feature types is located according to the geographic information of the pipeline region in the target remote sensing image, wherein the geographic information is used for indicating the position information of the features in the pipeline region;

2. The method of claim 1, wherein the identifying the high consequence region of the target pipeline from the pipeline regions in the plurality of image regions according to the feature attribute information of the regions in which the plurality of feature types are located comprises:

3. The method of claim 2, wherein determining the region level of the pipe region in each of the plurality of image regions according to the feature attribute information of the region in which the plurality of feature types are located comprises:

4. The method of claim 3, wherein determining an initial zone rank of a pipe region within the ith image region based on the terrain attribute information for the plurality of terrain categories located in the ith image region comprises:

5. The method of claim 2, wherein the identifying the high consequence region of the target pipeline from the pipeline regions in the plurality of image regions based on the terrain attribute information of the regions in which the plurality of terrain categories are located and the regional grade of the pipeline region in each of the plurality of image regions comprises:

6. The method of claim 5, wherein determining that the pipe region within the image region A is a high consequence region of the target pipe if the image region A satisfies a first recognition condition comprises:

7. The method of claim 5, wherein determining that the pipe region within the image region A is a high consequence region of the target pipe if the image region A satisfies a second recognition condition comprises:

8. The method of any one of claims 1-7, wherein prior to identifying the plurality of surface feature classes in the target remote sensing image via the first classification identification model, further comprising:

acquiring sample images of the plurality of ground object categories;

9. An identification device for a high back fruit zone of a long oil and gas pipeline, characterized in that the device comprises:

the device comprises an acquisition module, a detection module and a control module, wherein the acquisition module is used for acquiring the central line position information of a target pipeline and the boundary position information of a pipeline area of the target pipeline, and the boundary position information is used for indicating the pipeline area which takes the central line of the target pipeline as a symmetry axis and takes a first distance threshold value as a symmetry radius; sending the central line position information and the first distance threshold value to a multi-view remote sensing satellite, and acquiring a plurality of initial remote sensing images through the multi-view remote sensing satellite, wherein each initial remote sensing image covers a part of pipeline area of the target pipeline, and the central line of the target pipeline is the middle position of the initial remote sensing image; splicing the multiple initial remote sensing images according to the central line position information to obtain spliced remote sensing images, wherein the spliced remote sensing images cover all pipeline areas of the target pipeline; cutting the spliced remote sensing image according to the boundary position information to obtain a target remote sensing image, wherein the target remote sensing image comprises a pipeline area which takes the central line of the target pipeline as a symmetrical axis and the first distance threshold value as a symmetrical radius, the first distance threshold value is larger than the outer diameter of the target pipeline, and the target pipeline is a long oil and gas pipeline to be researched;

the second determination module is used for determining the feature attribute information of the region where each feature type in the plurality of feature types is located according to the geographic information of the pipeline region in the target remote sensing image, wherein the geographic information is used for indicating the position information of the features in the pipeline region;

10. An identification device for a high back fruit zone of a long oil and gas pipeline, characterized in that the device comprises:

a processor and a memory for storing processor-executable instructions;

wherein the processor is configured to perform the method of any one of claims 1-8.

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