CN112527928A - Pipeline protection area dividing method and device and readable storage medium - Google Patents
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Abstract
The disclosure relates to a pipeline protection area dividing method and device and a readable storage medium, and belongs to the technical field of pipeline risk control. The method comprises the following steps: acquiring Location Based Service (LBS) data corresponding to a target pipeline, wherein the LBS data is obtained by acquiring data of a terminal located in an area range corresponding to the target pipeline; inputting LBS data corresponding to the target pipeline into the algorithm model, and outputting to obtain statistical data corresponding to the target pipeline, wherein the statistical data is used for indicating data which belongs to the region range and is used for comparing with the data requirement of the high consequence area; and when the statistical data meet the data requirement of the high consequence area, marking the target pipeline as the high consequence area pipeline. According to the method, the LBS data in the area range corresponding to the target pipeline is collected, the collected LBS data is input into the algorithm model, and then the statistical data output by the algorithm model is compared with the data requirement of the high consequence area, so that the statistical efficiency and accuracy are improved.
Description
Technical Field
The present disclosure relates to the field of pipeline risk control technologies, and in particular, to a method and a system for partitioning a pipeline protection area, and a readable storage medium.
Background
With the rapid development of oil and gas pipeline arrangement, higher requirements are put forward on the division of a high-back fruit area of a pipeline, wherein the high-back-end area refers to an area where the public safety is endangered and the environment is greatly damaged when the pipeline leaks.
In the related art, when a high-consequence area of a pipeline is divided, the division is mainly completed in a manual line seeking mode. On the basis of dividing the administration range of the pipeline according to the length, a specially-assigned person is configured to carry out patrol work nearby the pipeline. The patrol staff estimates the pedestrian flow along the line, and mainly learns and reports the construction project when finding a new construction project near the pipeline.
However, the statistical method of the related data is inefficient, and the statistical result is inaccurate.
Disclosure of Invention
The embodiment of the disclosure provides a method and a device for dividing a pipeline protection area and a readable storage medium, which can solve the problems that a large amount of human resources are consumed in a statistical mode of related data, the statistical efficiency is low, and the statistical result is inaccurate easily caused. The technical scheme is as follows:
in one aspect, a method for dividing a pipeline protection area is provided, and the method includes:
acquiring Location Based Service (LBS) data corresponding to a target pipeline, wherein the LBS data is obtained by acquiring data of a terminal located in an area range corresponding to the target pipeline and is used for indicating the moving speed and the position of the terminal;
inputting the LBS data corresponding to the target pipeline into an algorithm model, and outputting to obtain statistical data corresponding to the target pipeline, wherein the statistical data is used for indicating data which belong to an area range corresponding to the target pipeline and are used for comparing with data requirements of a high consequence area, and the statistical data comprises real-time people flow data in the area range corresponding to the target pipeline and traffic flow statistical data in the area range corresponding to the target pipeline;
and when the statistical data meet the data requirement of the high consequence area, marking the target pipeline as the high consequence area pipeline.
In an optional embodiment, the LBS data corresponding to the target pipe includes LBS location data of a terminal in an area range corresponding to the target pipe, and the LBS location data includes speed information and location information of the terminal, where the speed information is used to indicate the moving speed of the terminal, and the location information is used to indicate the located location of the terminal.
In an alternative embodiment, the LBS data for a terminal connected to a mobile communication equipment operator is obtained from an LBS data server of the mobile communication equipment operator.
In an optional embodiment, the inputting the LBS data corresponding to the target pipeline into an algorithm model, and outputting to obtain statistical data corresponding to the target pipeline includes:
and inputting the LBS position data into the demographic model, and outputting to obtain the demographic data in the area range corresponding to the target pipeline.
In an optional embodiment, the inputting the LBS location data into the demographic model and outputting the demographic data within an area corresponding to the target pipeline includes:
inputting the LBS position data into the long-term demographic model, and outputting to obtain the resident population and personnel flow data;
when the statistical data meet the data requirement of the high consequence area, marking the target pipeline as the high consequence area pipeline, including:
and when the resident population and personnel flow direction data meet the data requirement of the high consequence area, marking the target pipeline as the high consequence area pipeline.
In an optional embodiment, the inputting the LBS location data into the demographic model and outputting the demographic data within an area corresponding to the target pipeline includes:
inputting the LBS position data into the real-time demographic model, and outputting to obtain the real-time people flow data corresponding to the target pipeline;
when the statistical data meet the data requirement of the high consequence area, marking the target pipeline as the high consequence area pipeline, including:
and when the real-time people flow data meet the data requirement of the high consequence area, marking the target pipeline as the high consequence area pipeline.
In an optional embodiment, the inputting the LBS data corresponding to the target pipeline into an algorithm model, and outputting to obtain statistical data corresponding to the target pipeline includes:
and inputting the LBS data into the traffic flow statistical model, and outputting to obtain traffic flow statistical data corresponding to the target pipeline.
In an optional embodiment, when the real-time people flow data and the traffic flow statistical data meet the data requirement of the high consequence area, the target pipeline is marked as the high consequence area pipeline.
In another aspect, there is provided a pipe protection area dividing apparatus, the apparatus including:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring Location Based Service (LBS) data corresponding to a target pipeline, the LBS data is obtained by acquiring data of a terminal located in an area range corresponding to the target pipeline, and the LBS data is used for indicating the moving speed and the position of the terminal;
the processing module is used for inputting the LBS data corresponding to the target pipeline into an algorithm model and outputting statistical data corresponding to the target pipeline, wherein the statistical data are used for indicating data which belong to the area range corresponding to the target pipeline and are used for comparing with the data requirement of the high consequence area;
and the marking module is used for marking the target pipeline as the high consequence area pipeline when the statistical data meet the data requirement of the high consequence area.
In another aspect, a computer readable storage medium is provided, in which at least one instruction, at least one program, a set of codes, or a set of instructions is stored, which is loaded and executed by a processor to implement the pipe protection region partitioning method as described in any one of the above.
The beneficial effects brought by the technical scheme provided by the embodiment of the disclosure at least comprise:
the LBS data in the area range corresponding to the target pipeline is collected, the collected LBS data is input into the algorithm model, and then statistical data output by the algorithm model is compared with the data requirement of the high consequence area, so that whether the target pipeline belongs to the high consequence area or not is judged, the problem that a large amount of manpower and time resources are consumed due to the adoption of modes such as manual line hunting is solved, and the efficiency and accuracy of statistics are improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
Fig. 1 is a flowchart of a method for dividing a protection area of a pipeline provided by an embodiment of the present disclosure;
fig. 2 is a flowchart of a method for dividing a protection area of a pipeline according to an embodiment of the present disclosure;
fig. 3 is a flowchart of a method for dividing a protection area of a pipeline according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of a device for dividing a pipeline protection area according to an embodiment of the present disclosure.
Detailed Description
To make the objects, technical solutions and advantages of the present disclosure more apparent, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings.
Fig. 1 is a flowchart of a method for dividing a protection area of a pipeline according to an embodiment of the present disclosure. Taking the application of the method to the terminal as an example for explanation, the method comprises the following steps:
and 101, acquiring LBS data corresponding to the target pipeline, wherein the LBS data is obtained by acquiring data of the terminal located in the area range corresponding to the target pipeline and is used for indicating the moving speed and the position of the terminal.
Location Based Service (LBS) refers to a value-added Service that obtains the Location information of a mobile terminal user through a radio communication network of an operator or an external Location mode, and provides corresponding services for the user under the support of a geographic information system platform. LBS generally performs both location and service functions between fixed or mobile users via the internet or wireless networks.
Optionally, performing LBS data acquisition on the terminal located in the area range corresponding to the target pipeline is completed on the basis that the terminal is connected to the internet. Illustratively, the user connects to the internet and transmits data through the user terminal that can be connected to the internet within the area corresponding to the target pipe, and the operator can obtain the LBS data of the terminal. Illustratively, the extent of the target pipe is related to the pipe outside diameter of the target pipe. Please refer to the following formula 1.
Equation 1:
in equation 1, r is the area range of the target pipe. Furthermore, abstracting the target pipeline as the central line of the original target pipeline, wherein the area range of the target pipeline refers to a circle with any point on the line as the center of the circle, r as the radius, and the unit of r is meter; p is the maximum allowable operating pressure of the target pipeline in megapascals. d is the outside diameter of the pipe in millimeters.
The LBS data is obtained based on the location of the terminal used by the user and the user request, and therefore, the LBS data includes the location data of the user and the spatial data of the user. The user position data mainly refers to the position and the moving speed of the user terminal, and the user space data mainly refers to information transmitted between the user terminal and the internet, such as a data request of the user terminal. In order to protect the privacy of the user, in this embodiment, only LBS location data of the user is called and collected.
Step 102, inputting LBS data corresponding to the target pipeline into the algorithm model, and outputting statistical data corresponding to the target pipeline, wherein the statistical data is used for indicating data which belong to a region range corresponding to the target pipeline and are used for comparing with data requirements of a high consequence area, and the statistical data comprises real-time people flow data in the region range corresponding to the target pipeline and traffic flow statistical data in the region range corresponding to the target pipeline.
The people flow data and the traffic flow data are the data which can most intuitively embody the data correlation with the requirement comparison of the high consequence area.
Optionally, the algorithm model is established by taking data information contained in the LBS data as a parameter and taking statistical data as a calculation result. After the corresponding parameters are input, the algorithm model can sort and analyze the data information contained in the LBS data, and finally output to obtain statistical data. The statistical data is data for comparison with high outcome region data requirements.
Alternatively, a high-consequence area is an area where public safety is compromised if a leak occurs in the pipe at or near the location, causing significant damage to property and the environment. In the judgment process of the high-posterior fruit zone, the probability of accidents does not need to be considered, and only what the worst case of the accidents occurs needs to be considered, so that the data used for comparing with the data requirement of the high-posterior fruit zone only needs to be collected from LBS data.
Optionally, the data for comparison with the high outcome area data requirements includes resident population and people flow direction data, real-time people flow data, and traffic flow statistics data.
The high-back fruit area has more types of judgment modes, and because the environment conditions in the area ranges corresponding to different target pipelines are different, various data need to be collected to perform data requirement comparison of the high-back fruit area meeting the environment in the area ranges corresponding to the target pipelines.
Optionally, the resident population and people flow data is used to represent the number of resident populations and the number and flow time of floating populations in the area corresponding to the target pipeline. Exemplarily, when the location data representing a terminal in the LBS data in the area range corresponding to the target pipeline is located in the area range corresponding to the target pipeline in more than 200 days of the year, the terminal can be used as resident population information, and a plurality of resident population information are combined to generate resident population data; when the LBS data representing a terminal in the area range corresponding to the target pipeline leaves the area range corresponding to the target pipeline at a certain day in the year and does not appear in the rest statistical time, the terminal can be used as the people flow information in the negative direction. And combining the plurality of personnel flow information to generate personnel flow data.
Optionally, the larger number of resident population in the area range corresponding to the target pipeline indicates that the consequence is serious when an accident occurs, and the positive number of flowing population in the target range indicates that the number of resident population in the area range corresponding to the target pipeline is increasing. Therefore, resident population and personnel flow direction data can be used as a judgment standard for judging whether a target pipeline is in a high-consequence area or not, namely the resident population and personnel flow direction data can be used for comparing with the data requirement of the high-consequence area.
The real-time people flow data can represent the real-time number of people in the area range corresponding to the target pipeline. For example, in a period of time, the number of terminals whose LBS location data in the area range corresponding to the target pipe is represented in the area range corresponding to the target pipe is 200, and each terminal may represent one user, and then the number of flows in the people flow data is 200. Further, information such as the direction of flow of the person may also be obtained and summarized.
Optionally, the number of real-time people in the area range corresponding to the target pipeline is large, which indicates that the consequence is serious when an accident occurs. Therefore, the real-time human flow data can be used as a judgment standard for judging whether the target pipeline is in the high-consequence area or not, namely the human flow data can be used for comparing with the data requirement of the high-consequence area. When the similar data and the data requirement of the high consequence area need to be compared, the real-time people stream data can be selected.
The traffic flow statistics may represent the number of vehicles, speed, and direction of travel within the area corresponding to the target duct. For example, all terminals with a speed of more than 30km/h in the LBS location data within the area corresponding to the target pipeline may be regarded as vehicle-mounted terminals, and the terminals may be regarded as one traffic information, and several pieces of traffic information may constitute traffic statistics data. The number of vehicles in the area range corresponding to the target pipeline is large, the speed is high, the traveling direction is stable, and the situation that traffic infrastructure such as roads exists in the area range corresponding to the target pipeline or a defined route exists in the area range corresponding to the target pipeline indicates that an accident is serious. Therefore, the traffic flow statistical data can be used as a judgment standard for judging whether the target pipeline is in the high-consequence area or not, namely the traffic flow statistical data can be used for comparing with the data requirement of the high-consequence area. When the similar data and the data requirement of the high consequence area need to be compared, the traffic flow statistical data can be selected.
In one example, LBS data corresponding to the target pipeline is input into an algorithm model, and the algorithm model extracts the LBS data meeting the conditions and calculates to obtain real-time people flow data. In this example, the LBS data extracted by the algorithm model includes the total number of all terminals in the area range corresponding to the target pipe within a certain period of time and the moving speed of each terminal, and the algorithm model is used for matching the LBS data with different matching conditions, so as to obtain statistical data corresponding to the different matching conditions.
And 103, marking the target pipeline as the pipeline of the high consequence area when the statistical data meet the data requirement of the high consequence area.
Optionally, the data obtained through the algorithm model is compared with the data requirement of the high consequence region, and when the statistical data meet the data requirement of the high consequence region, the target pipeline is marked as the pipeline of the high consequence region.
In one example, if there is a residential village around a certain section of pipeline, then the data of the applicable residential population and people flow direction is compared with the data requirement of the high-consequence area. If the data of the high consequence area is required to be that when the number of resident people is larger than m and the flow direction of people is to enable the number of people living in the village to be increased, the area range corresponding to the target pipeline is judged to be the high consequence area, the area corresponding to a certain section of pipeline is divided into n with the number of resident people larger than m, and the number of people living in the village is increased when the flow direction data of people is, the area range corresponding to the section of pipeline can be judged to be the high back consequence area, furthermore, the section of pipeline is marked as the pipeline of the high consequence area, m is larger than 0 and smaller than n, and m and n are both positive integers.
In one example, a tourist site is located around a certain section of pipeline, and real-time people flow data is compared with data requirements of a high-consequence area. If the data requirement of the high consequence area is that when the peak value gathering number of people of the target pipeline exceeds 1000 people, the area range corresponding to the target pipeline is judged to be the high consequence area, and the real-time people flow data of the area range corresponding to a certain section of pipeline shows that the peak value gathering number of people is more than 1000 people, the area range corresponding to the section of pipeline can be judged to be the high consequence area, and further the section of pipeline is marked to be the high consequence area pipeline.
In one example, if only roads are located around a certain section of pipeline, the applicable traffic flow statistical data is compared with the data requirement of the high consequence area. If the data requirement of the high consequence area is that when the number of the peripheral roads is more than three, the area range corresponding to the target pipeline is judged to be the high consequence area, and the traffic data in the area range corresponding to a certain section of pipeline is shown to be more than three roads in the area range corresponding to the pipeline, the area range corresponding to the section of pipeline can be judged to be the high consequence area, and further, the section of pipeline is marked as the high consequence area pipeline.
In summary, according to the method provided by this embodiment, the LBS data in the area range corresponding to the target pipeline is collected, the collected LBS data is input into the algorithm model, and then the statistical data output by the algorithm model is compared with the data requirement of the high consequence area, so that the statistical efficiency and accuracy are improved.
Fig. 2 is a flowchart of a method for dividing a protection area of a pipeline according to an embodiment of the present disclosure. Taking the application of the method to the terminal as an example for explanation, the method comprises the following steps:
LBS data of a terminal connected to a mobile communication equipment operator is acquired from an LBS data server of the mobile communication equipment operator, step 201.
Alternatively, the mobile communication device operator includes a communication operator providing LBS service and an internet company providing products with LBS service.
Optionally, a data request is sent to the LBS data server, where the data request includes an area range corresponding to the target pipe, and the LBS data server obtains the location information and the speed information of the terminal in the area range according to the data request, and then sends the location information and the speed information as LBS data of the terminal to the terminal for data analysis and statistics.
LBS location data is entered into a long-term demographic model, step 202.
The algorithm model is established by taking data information contained in the LBS data as a parameter and taking statistical data as a calculation result. After the corresponding parameters are input, the algorithm model can output and obtain statistical data according to the corresponding algorithm rules. The statistical data is data for comparison with high outcome region data requirements. The algorithmic model includes a long-term demographic model.
Alternatively, different algorithmic models may process the data information differently. Illustratively, the long-term demographic model processes data such as terminal speed, terminal usage time, terminal location, etc. Further, the long-term statistical model judges whether the terminal belongs to a handheld type or a vehicle-mounted type according to the terminal speed, and further judges whether the terminal can directly represent a user; after the terminal can represent a user, the long-term statistical model processes the service time and the position of the terminal to determine whether the user belongs to resident population in a target range or determines the personnel flow direction of the user, and finally, after all the terminals are judged as above, the data are summarized and output.
And step 203, outputting and obtaining resident population and personnel flow direction data of the target pipeline.
In one example, collected LBS data of the terminal is input into a long-term demographic model, and the model can obtain resident population and person flow data of the target pipeline through statistics of terminal speed, terminal use time, terminal position and the like.
LBS location data is entered into the real-time demographic model, step 204.
The algorithm model is established by taking data information contained in the LBS data as a parameter and taking statistical data as a calculation result. After the corresponding parameters are input, the algorithm model can output and obtain statistical data according to the corresponding algorithm rules. The statistical data is data for comparison with high outcome region data requirements. The algorithmic model includes a real-time demographic model.
Alternatively, different algorithmic models may process the data information differently. Illustratively, the real-time demographic model processes data such as terminal velocity, terminal location, etc. Further, the real-time demographic model judges whether the terminal belongs to a handheld type or a vehicle-mounted type according to the terminal speed, and further judges whether the terminal can directly represent a user; after the terminal is judged to represent a user, the real-time demographic model processes the position of the terminal, determines whether the terminal is in a target range, counts the number of the terminals in the target range at the same time, performs weighting processing on the number of the terminals of the handheld type and the vehicle-mounted type, and finally summarizes and outputs the data.
And step 205, outputting the obtained real-time people stream data of the target pipeline.
In one example, collected LBS data of the terminal is input into a real-time demographic model, and the model can obtain real-time people flow data of the target pipeline through data statistics such as terminal speed, terminal position and the like.
Step 206, inputting the LBS position data into the traffic flow statistical model.
The algorithm model is established by taking data information contained in the LBS data as a parameter and taking statistical data as a calculation result. After inputting the corresponding parameters, the algorithm model may output statistical data. The statistical data is data for comparison with high outcome region data requirements. The traffic flow statistical model is one of the algorithm models. The traffic flow statistical model is a complement to the real-time demographic model.
Alternatively, different algorithmic models may process the data information differently. Illustratively, the traffic flow statistical model processes data such as terminal speed, terminal position, etc. Further, the traffic flow statistical model judges whether the terminal belongs to a handheld type or a vehicle-mounted type according to the terminal speed, and further judges whether the terminal can represent a vehicle; after the terminal is judged to represent a vehicle, the traffic flow statistical data can process the position of the terminal, determine whether the terminal travels along a certain route, count the number of the terminals in a target range at the same time, and finally summarize and output the data.
And step 207, outputting to obtain the target pipeline traffic flow statistical data.
In one example, collected LBS data of the terminal is input into a real-time demographic model, and the model can calculate target pipeline traffic data according to the data of the terminal speed, the terminal position and the like. Through the traffic flow data of the target pipeline, the traffic flow direction in the target range can be obtained, some data screened out by the real-time traffic flow data of the target pipeline due to speed can be supplemented, and statistical data are more accurate.
And step 208, marking the target pipeline as the pipeline of the high consequence area when the statistical data meet the data requirement of the high consequence area.
And comparing the data obtained by the algorithm model with the data requirement of the high consequence area, and marking the target pipeline as the pipeline of the high consequence area when the statistical data meets the data requirement of the high consequence area.
The high-back fruit area has more types of judgment modes, and because the environment conditions in the area ranges corresponding to different target pipelines are different, various data need to be collected to perform data requirement comparison of the high-back fruit area meeting the environment in the area ranges corresponding to the target pipelines.
The resident population and people flow data may represent the number of resident populations and the number and flow time of floating populations in the area corresponding to the target pipeline. Resident population and people flow data can be selected when similar data needs to be compared with data requirements of high consequence areas. Optionally, when the resident population and people flow direction data meets the high consequence area data requirement, the target pipeline is marked as a high consequence area pipeline.
In one example, if there is a residential village around a certain section of pipeline, then the data of the applicable residential population and people flow direction is compared with the data requirement of the high-consequence area. If the data of the high consequence area is required to be that when the number of resident people is larger than m and the flow direction of people is to enable the number of people living in the village to be increased, the area range corresponding to the target pipeline is judged to be the high consequence area, the area corresponding to a certain section of pipeline is divided into n with the number of resident people larger than m, and the number of people living in the village is increased when the flow direction data of people is, the area range corresponding to the section of pipeline can be judged to be the high consequence area, and further the section of pipeline is marked to be the high consequence area pipeline. m and n are positive integers.
The real-time people flow data can represent the real-time number of people in the area range corresponding to the target pipeline, and when the similar data and the data requirement of the high consequence area need to be compared, the real-time people flow data can be selected. Optionally, when the real-time people flow data meets the data requirement of the high consequence area, the target pipeline is marked as the high consequence area pipeline.
In one example, a tourist site is located around a certain section of pipeline, and real-time people flow data is compared with data requirements of a high-consequence area. If the data requirement of the high consequence area is that when the peak value gathering number of people of the target pipeline exceeds 1000 people, the area range corresponding to the target pipeline is judged to be the high consequence area, and the real-time people flow data of the area range corresponding to a certain section of pipeline shows that the peak value gathering number of people is more than 1000 people, the area range corresponding to the section of pipeline can be judged to be the high consequence area, and further the section of pipeline is marked to be the high consequence area pipeline.
The traffic flow statistics may represent the number of vehicles, speed, and direction of travel within the area corresponding to the target duct. When the similar data and the data requirement of the high consequence area need to be compared, the traffic flow statistical data can be selected.
Optionally, when both the real-time people flow data and the traffic flow statistical data meet the data requirement of the high consequence area, marking the target pipeline as the high consequence area pipeline.
In one example, if only roads are located around a certain section of pipeline, the applicable traffic flow statistical data is compared with the data requirement of the high consequence area. If the data requirement of the high consequence area is that when the number of the peripheral roads is more than three, the area range corresponding to the target pipeline is judged to be the high consequence area, and the traffic data in the area range corresponding to a certain section of pipeline is shown to be more than three roads in the area range corresponding to the pipeline, the area range corresponding to the section of pipeline can be judged to be the high consequence area, and further, the section of pipeline is marked as the high consequence area pipeline.
In summary, according to the method provided by this embodiment, the LBS data in the area range corresponding to the target pipeline is collected, the collected LBS data is input into the algorithm model, and then the statistical data output by the algorithm model is compared with the data requirement of the high consequence area, so that the statistical efficiency and accuracy are improved. Meanwhile, the algorithm model is subdivided into a plurality of methods, so that the function of the algorithm model is refined, and the statistical efficiency and accuracy are further improved.
Fig. 3 is a flowchart of a method for dividing a protection area of a pipeline according to an embodiment of the present disclosure. Taking the application of the method to the terminal as an example for explanation, the method comprises the following steps:
Alternatively, the mobile communication device operator includes a communication operator providing LBS service and an internet company providing products with LBS service. After the LBS data is called and collected for the mobile communication equipment operator, only the LBS position data corresponding to the target pipeline is collected for protecting the privacy of the user.
Optionally, the collected information is stored, and the data can be called at the required time according to the requirement of the user.
Optionally, the LBS data along the pipeline includes LBS data within a corresponding range of the target pipeline.
Step 302, performing data analysis on the collected LBS data, wherein the algorithm model comprises: and (4) carrying out statistics and analysis on resident population, personnel flow direction analysis, traffic condition statistics and real-time people flow statistics to obtain the statistics data of people flow, traffic flow and resident population along the pipeline.
The high back fruit area refers to an area which can endanger the public safety if a pipeline at or near the position leaks, and cause great damage to property and environment. In the judgment process of the high-posterior fruit zone, the probability of accidents does not need to be considered, and only what the worst case of the accidents occurs needs to be considered, so that the data used for comparing with the data requirement of the high-posterior fruit zone only needs to be collected from LBS data.
Optionally, the data for comparison with the high outcome area data requirements includes resident population and people flow direction data, real-time people flow data, and traffic flow statistics data. In one example, LBS data corresponding to the target pipeline is input into an algorithm model, and the algorithm model extracts the LBS data meeting the conditions and calculates to obtain real-time people flow data. In this example, the LBS data extracted by the algorithm model includes the total number of all terminals within the area range corresponding to the target pipe within a certain period of time.
In this embodiment, the statistics of people flow, traffic flow, and resident population along the pipeline are calculated by a plurality of models. Optionally, the data are respectively recorded and then compared in the next step, or the data enter other algorithm models to continue calculation, and other data are obtained and compared in the next step.
And 303, analyzing whether the standard domestic standard GB32167-2015 can be called a high-posterior fruit region condition or not according to the statistical data, and classifying the high-consequence region grade.
The domestic standard GB32167-2015 is an oil and gas transmission pipeline integrity management specification, wherein a division mode and a grading mode of a high-grade fruit region are specified definitely.
In one example, the resident population and person flow direction data obtained through the algorithm model are compared with 'villages, towns and the like with the number of residences more than 50 doors in 50m at two sides of the pipeline' in the standard, and when the resident population and person flow direction data show that the number of the residences is more than 50 doors at present, the pipeline is classified into a second-level high fruit area according to the standard.
Alternatively, other provisions in the domestic standard GB32167-2015 may also assist in the division of the high fruit area, such as "treat as a high consequence segment when segments identifying high fruit areas overlap or are not more than 50m apart from each other".
And step 304, displaying the identified high fruit area and high result area grades.
Optionally, the identified high fruit zone and high consequence zone ratings are displayed after a relevant approval.
In summary, according to the method provided by this embodiment, the LBS data in the area range corresponding to the target pipeline is collected, the collected LBS data is input into the algorithm model, and then the statistical data output by the algorithm model is compared with the data requirement of the high consequence area, so that the statistical efficiency and accuracy are improved. Meanwhile, in the embodiment, a more standardized comparison mode is used through a method for the standard national standard, so that the accuracy is higher, and the identified high-posterior fruit zone and the high-consequence zone grade are displayed, so that the identified result is more visual.
Fig. 4 is a schematic structural diagram of a device for dividing a pipeline protection area according to an embodiment of the present disclosure. The device can be realized by software, hardware or a combination of the two to become all or part of the terminal of the pipeline deformation detection method. The device includes:
an acquiring module 401, configured to acquire Location Based Service (LBS) data corresponding to the target pipe, where the LBS data is obtained by acquiring data of a terminal located in an area range corresponding to the target pipe, and the LBS data is used to indicate a moving speed and a location of the terminal
A processing module 402, configured to input LBS data corresponding to the target pipeline into the algorithm model, and output statistical data corresponding to the target pipeline, where the statistical data is used to indicate data in an area range corresponding to the target pipeline for comparing with the data requirement of the high consequence area
And a marking module 403, configured to mark the target pipeline as a high consequence area pipeline when the statistical data meets the data requirement of the high consequence area.
In an optional embodiment, the LBS data corresponding to the target pipe includes LBS location data of a terminal in an area range corresponding to the target pipe, and the LBS location data includes speed information and location information of the terminal, where the speed information is used to indicate the moving speed of the terminal, and the location information is used to indicate the location of the terminal.
In an optional embodiment, the acquiring module 401 is further configured to acquire, from an LBS data server of a mobile communication device operator, LBS data of a terminal connected to the mobile communication device operator.
In an optional embodiment, the processing module 402 is further configured to input the LBS location data into the demographic model, and output the obtained population data in an area range corresponding to the target pipeline.
In an alternative embodiment, the demographic model includes: a long-term demographic model, the demographic data comprising resident population and people flow direction data derived from the long-term demographic model output;
the processing module 402 is further configured to input the LBS location data into the long-term demographic model, and output to obtain the resident population and person flow direction data;
and the marking module 403 is further configured to mark the target pipeline as a high consequence area pipeline when the resident population and personnel flow direction data meet the data requirement of the high consequence area.
In an optional embodiment, the processing module 402 is further configured to input the LBS location data into the real-time demographic model, and output the real-time people flow data corresponding to the target pipe;
the labeling module 403 is further configured to label the target pipeline as a high consequence area pipeline when the real-time people stream data meets the data requirement of the high consequence area.
In an optional embodiment, the algorithm model further includes a traffic statistical model, and the statistical data further includes traffic statistical data obtained by outputting the traffic statistical model;
the processing module 402 is further configured to input the LBS data into the traffic flow statistical model, and output the traffic flow statistical data corresponding to the target pipeline.
In an optional embodiment, the labeling module 403 is further configured to label the target pipeline as the high consequence area pipeline when the real-time people flow data meets the requirement of the high consequence area data.
In summary, the device provided in this embodiment improves the efficiency and accuracy of statistics by acquiring the LBS data within the area range corresponding to the target pipeline, inputting the acquired LBS data into the algorithm model, and comparing the statistical data output by the algorithm model with the data requirement of the high consequence area.
An exemplary embodiment of the present disclosure further provides a computer-readable storage medium, in which at least one instruction, at least one program, a code set, or a set of instructions is stored, and the at least one instruction, the at least one program, the code set, or the set of instructions is loaded and executed by the processor to implement the pipeline deformation degree detection method provided by the above-mentioned method embodiments. For example, the computer-readable storage medium may be a Read-Only Memory (ROM), a Random Access Memory (RAM), a Compact Disc Read-Only Memory (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, and the like.
All the above-mentioned optional technical solutions can be combined arbitrarily to form the optional embodiments of the present invention, and are not described herein again.
The above description is only exemplary of the present disclosure and is not intended to limit the present disclosure, so that any modification, equivalent replacement, or improvement made within the spirit and principle of the present disclosure should be included in the scope of the present disclosure.
Claims (10)
1. A method for dividing a pipeline protection area is characterized by comprising the following steps:
acquiring Location Based Service (LBS) data corresponding to a target pipeline, wherein the LBS data is obtained by acquiring data of a terminal located in an area range corresponding to the target pipeline and is used for indicating the moving speed and the position of the terminal;
inputting the LBS data corresponding to the target pipeline into an algorithm model, and outputting to obtain statistical data corresponding to the target pipeline, wherein the statistical data is used for indicating data which belong to an area range corresponding to the target pipeline and are used for comparing with data requirements of a high consequence area, and the statistical data comprises real-time people flow data in the area range corresponding to the target pipeline and traffic flow statistical data in the area range corresponding to the target pipeline;
and when the statistical data meet the data requirement of the high consequence area, marking the target pipeline as the high consequence area pipeline.
2. The method of claim 1,
the LBS data corresponding to the target pipeline comprises LBS position data of a terminal in an area range corresponding to the target pipeline, and the LBS position data comprises speed information and position information of the terminal, wherein the speed information is used for indicating the moving speed of the terminal, and the position information is used for indicating the position of the terminal.
3. The method of claim 1, wherein the collecting LBS data corresponding to the target pipeline comprises:
acquiring LBS data of a terminal connected with a mobile communication equipment operator from an LBS data server of the mobile communication equipment operator.
4. The method of claim 2, wherein the algorithmic model comprises a demographic model, and the statistical data comprises demographic data derived by the demographic model output;
inputting the LBS data corresponding to the target pipeline into an algorithm model, and outputting to obtain statistical data corresponding to the target pipeline, wherein the statistical data comprises:
and inputting the LBS position data into the demographic model, and outputting to obtain the demographic data in the area range corresponding to the target pipeline.
5. The method of claim 4, wherein the demographic model comprises: a long-term demographic model, the demographic data comprising resident population and people flow direction data derived from the long-term demographic model output;
inputting the LBS position data into the demographic model, and outputting to obtain the demographic data in the area range corresponding to the target pipeline, wherein the method comprises the following steps:
inputting the LBS position data into the long-term demographic model, and outputting to obtain the resident population and personnel flow data;
when the statistical data meet the data requirement of the high consequence area, marking the target pipeline as the high consequence area pipeline, including:
and when the resident population and personnel flow direction data meet the data requirement of the high consequence area, marking the target pipeline as the high consequence area pipeline.
6. The method of claim 4, wherein the demographic model comprises: a real-time demographic model, the demographic data comprising the real-time demographic data obtained through the real-time demographic model output;
inputting the LBS position data into the demographic model, and outputting to obtain the demographic data in the area range corresponding to the target pipeline, wherein the method comprises the following steps:
inputting the LBS position data into the real-time demographic model, and outputting to obtain the real-time people flow data corresponding to the target pipeline;
when the statistical data meet the data requirement of the high consequence area, marking the target pipeline as the high consequence area pipeline, including:
and when the real-time people flow data meet the data requirement of the high consequence area, marking the target pipeline as the high consequence area pipeline.
7. The method according to claim 6, wherein the algorithm model further comprises a traffic flow statistical model, and the statistical data further comprises the traffic flow statistical data obtained through the output of the traffic flow statistical model;
inputting the LBS data corresponding to the target pipeline into an algorithm model, and outputting to obtain statistical data corresponding to the target pipeline, wherein the statistical data comprises:
and inputting the LBS data into the traffic flow statistical model, and outputting to obtain the traffic flow statistical data corresponding to the target pipeline.
8. The method of claim 7, wherein when the real-time people flow data meets the high consequence area data requirement, marking the target pipeline as a high consequence area pipeline comprises:
and when the real-time people flow data and the traffic flow statistical data meet the data requirement of the high consequence area, marking the target pipeline as the high consequence area pipeline.
9. An apparatus for pipeline protection zone partitioning, the apparatus comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring Location Based Service (LBS) data corresponding to a target pipeline, the LBS data is obtained by acquiring data of a terminal located in an area range corresponding to the target pipeline, and the LBS data is used for indicating the moving speed and the position of the terminal;
the processing module is used for inputting the LBS data corresponding to the target pipeline into an algorithm model and outputting statistical data corresponding to the target pipeline, wherein the statistical data are used for indicating data which belong to the area range corresponding to the target pipeline and are used for comparing with the data requirement of the high consequence area;
and the marking module is used for marking the target pipeline as the high consequence area pipeline when the statistical data meet the data requirement of the high consequence area.
10. A computer-readable storage medium having stored therein at least one instruction, at least one program, set of codes, or set of instructions, which is loaded and executed by a processor to implement the pipe protection region partitioning method according to any one of claims 1 to 8.
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