CN113971463A - Heat supply pipeline risk distribution analysis method and routing inspection path planning system - Google Patents
Heat supply pipeline risk distribution analysis method and routing inspection path planning system Download PDFInfo
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Abstract
The invention belongs to the technical field of pipeline inspection, and particularly relates to a heat supply pipeline risk distribution analysis method and an inspection path planning system, wherein the included thermal pipeline risk analysis and inspection path planning method based on a digital twin model comprises the following steps: constructing a heating system digital twin model of a heating system, and identifying and correcting the heating system digital twin model; constructing a pipe section risk assessment model according to the identified and corrected digital twin model of the heat supply system; generating a threshold value of a comprehensive risk assessment index based on a basic data information base of the pipe section and the accumulated historical process data; before each inspection, calculating and acquiring a comprehensive risk assessment value index of each pipe section according to actual operation data of the system; and acquiring an optimal routing inspection path by adopting a genetic algorithm according to the comprehensive risk assessment value index of each pipe section, so that the optimal routing inspection path is generated intelligently, the workload of routing inspection equipment is greatly reduced, the cruising anxiety of the routing inspection equipment is eliminated, and the routing inspection accuracy and effectiveness are realized.
Description
Technical Field
The invention belongs to the technical field of pipeline inspection, and particularly relates to a heat supply pipeline risk distribution analysis method and an inspection path planning system.
Background
Aiming at the practical problems that the routing inspection of sections along the line is inconvenient, the routing inspection of the heat supply pipe network along the line is difficult under the severe weather environment, the potential safety hazard of old underground pipe networks is large, effective routing inspection measures are lacked and the like caused by the complex topographic environment of part of the heat supply pipe network corridor, and various routing inspection devices are gradually applied to the routing inspection of the heat supply pipe network.
However, the routing inspection device is limited by the regulations of communication, battery technology and related laws and regulations, the routing inspection distance and the working time of the routing inspection device are limited at the present stage, and therefore the routing inspection path of the thermal pipeline routing inspection device needs to be scientifically and reasonably planned urgently, the transmission bandwidth requirement of routing inspection information acquired by the routing inspection device is reduced, precious electric quantity is saved, the single routing inspection task intensity of the routing inspection device is greatly reduced, the service life of the routing inspection device is prolonged, the routing inspection accuracy and effectiveness of the routing inspection device are improved, and the safe and stable operation of a thermal pipe network is ensured.
Therefore, a new heat supply pipeline risk distribution analysis method and a routing inspection path planning system need to be designed based on the above technical problems.
Disclosure of Invention
The invention aims to provide a heat supply pipeline risk distribution analysis method and a routing inspection path planning system.
In order to solve the technical problem, the invention provides a thermal pipeline risk analysis and routing inspection path planning method based on a digital twin model, which comprises the following steps:
constructing a heating system digital twin model of a heating system, and identifying and correcting the heating system digital twin model;
constructing a pipe section risk assessment model according to the identified and corrected digital twin model of the heat supply system, acquiring a comprehensive risk assessment value index of each pipe section, and comparing the comprehensive risk assessment value index with a threshold value; and
and obtaining an optimal routing inspection path according to the comprehensive risk assessment value index of each pipe section.
Further, the method for constructing the digital twin model of the heat supply system and identifying and correcting the digital twin model of the heat supply system comprises the following steps:
constructing a physical model, a logic model, a simulation model and a data driving model of the heat supply system, and constructing a digital twin model of the heat supply system according to the physical model, the logic model, the simulation model and the data driving model;
setting remote transmission points of pressure and temperature parameters in the heat supply system, identifying and correcting the digital twin model of the heat supply system according to the preprocessed operation data, and correcting the parameters in the digital twin model of the heat supply system through reverse identification of a pipeline resistance coefficient and a heat transfer coefficient.
Further, the method for constructing the pipe section risk assessment model according to the identified and corrected digital twin model of the heat supply system and acquiring the comprehensive risk assessment value index of each pipe section comprises the following steps:
collecting the pipe section attribute factors of pipe diameter, pipe length, pipe material, pipe age, resistance coefficient and node flow to establish a pipe network basic information database;
counting pipe section environment influence factors such as geological conditions, pipeline burial depth, construction quality and construction conditions around the pipe section;
and acquiring simulation parameters of the digital twin model, namely integrating the heat source, the pipeline, the elbow, the tee joint, the heating station, the pipe network circulating pump, the valve and the meter of the digital twin model of the heating system, and acquiring simulation data of the pipe network under various working conditions by including position information of the heat source, the pipeline, the pump, the valve and each measuring point of the heating system, and the attribute of the heat source, the size of the pipeline, the opening of the valve and the lift of the pump.
Further, the step of constructing a pipe section risk assessment model according to the identified and corrected digital twin model of the heat supply system, and acquiring the comprehensive risk assessment value index of each pipe section further comprises the following steps:
build a hierarchical model, i.e.
Taking the self attribute factors of the pipe section, the environmental influence factors of the pipe section and the simulation parameters of the digital twin model as first-level indexes;
taking the pipe diameter, the pipe length, the pipe material and the pipe age as second-level indexes under the attribute factors of the pipe section;
taking soil texture, burial depth and construction as secondary indexes under the influence factors of the pipe section environment;
taking temperature, pressure and flow as secondary indexes under simulation parameters of the digital twin model;
constructing a decision matrix, i.e.
Respectively comparing the first-level index and the second-level index pairwise;
establishing a judgment matrix for each index of the first level and the second level
Wherein,
annthe importance of the comparison among the indexes; a isijThe importance of index i compared to j; a isjiThe importance of index j compared to i;
hierarchical single ordering and consistency checking, i.e.
Solving the eigenvector of each judgment matrix by adopting a geometric mean method, multiplying the elements of the judgment matrix A by rows to obtain a new vector, opening each component of the new vector by the power of n and normalizing the obtained vector to obtain the eigenvector:
the feature vector of the primary index is omegaA(ii) a The feature vector of the secondary index is
Maximum eigenvector of judgment matrix constructed for second-level index corresponding to nth first-level index
Acquiring a characteristic root of each judgment matrix:
obtaining a consistency index CI:
a CI of 0 indicates that the matrix is consistent, and a larger CI indicates that the degree of matrix inconsistency is more serious.
Obtaining the consistency ratio CR:
and the RI is an average random consistency index, when the CR is smaller than a preset value, the consistency of the judgment matrix is considered to meet the requirement, and the obtained feature vector meets the requirement.
Obtaining a composite risk assessment value index for each pipe section, i.e.
Acquiring the weight coefficient of each secondary index: alpha-omegaBωA；
Assigning scores to the secondary indexes, and acquiring the comprehensive risk assessment value index of each pipe section according to the assigning scores and the secondary index weight coefficient:
wherein alpha isiIs a second-level index weight coefficient；BiAssigning a score to the second-level index;
and generating a threshold value of a comprehensive risk assessment index of the pipe network according to the simulation calculation data of the pipe network basic information database, the historical process data accumulated by the operation of the heat supply pipe network and the digital twin model under various working conditions, and comparing the comprehensive risk assessment index with the threshold value.
Further, the method for obtaining the optimal routing inspection path according to the comprehensive risk assessment value index of each pipe section comprises the following steps:
before each routing inspection task is carried out, carrying out primary comprehensive risk evaluation on the current pipe network system based on actual data of the current system operation to generate a comprehensive risk distribution map of the heat supply pipe network overall situation;
setting the pipe sections of which the comprehensive risk assessment value indexes exceed a preset threshold value as target pipe sections to be inspected at this time based on the comprehensive risk assessment value indexes of the current pipe sections;
acquiring geographic coordinate data of target pipe sections, and numbering the target pipe sections in sequence, wherein the number is 1,2 and 3 … t; t is the number of the pipe sections screened out according to the pipe section risk index as inspection targets;
the method comprises the steps that a road network model of an inspection target pipe section area and the cruising ability of inspection equipment are used as constraint conditions, and a genetic algorithm is adopted to search an integer subset X, wherein an arrangement pi is { v } of {1,2,3, … t }1,v2,v3,…vtMake
Taking the minimum value;
wherein d (v)i,vi+1) For inspecting target pipeline viTo the target pipeline vi+1The distance of (d);
coding by adopting sequence arrangement of traversing target pipe sections;
fitness function of
Wherein, TdIs the path length; n is a radical oftThe number of the traversal targets; c is punishmentA penalty factor;
generating an initial seed cluster by adopting a random strategy, and performing selection operation by adopting a roulette selection method;
updating the path by adopting a single-point crossing operation after the selection operation;
after the cross operation, the random cross is adopted as a mutation operation to update the path;
and stopping searching after the maximum iteration sequence is reached, outputting an optimal routing inspection path, and executing the risk pipeline section routing inspection operation according to the optimal routing inspection path.
In a second aspect, the present invention further provides a thermal pipeline risk analysis and routing inspection path planning system based on a digital twin model, including:
the model building module is used for building a heat supply system digital twin model of the heat supply system and identifying and correcting the heat supply system digital twin model;
the risk evaluation module is used for constructing a pipe section risk evaluation model according to the identified and corrected digital twin model of the heat supply system, acquiring a comprehensive risk evaluation value index of each pipe section, and comparing the comprehensive risk evaluation value index with a threshold value; and
and the path acquisition module is used for acquiring an optimal routing inspection path according to the comprehensive risk assessment value index of each pipe section.
In a third aspect, the present invention further provides an inspection system, including:
a server adapted to obtain an optimal routing inspection path;
and the inspection equipment is suitable for receiving the optimal inspection path sent by the server, and is suitable for executing the inspection operation of the risk pipe section according to the optimal inspection path.
Further, the server is suitable for obtaining the optimal routing inspection path by adopting the thermal pipeline risk analysis and routing inspection path planning method based on the digital twin model.
The method has the advantages that the digital twin model of the heat supply system is constructed, and the digital twin model of the heat supply system is identified and corrected; constructing a pipe section risk assessment model according to the identified and corrected digital twin model of the heat supply system, acquiring a comprehensive risk assessment value index of each pipe section, and comparing the comprehensive risk assessment value index with a threshold value; and acquiring an optimal routing inspection path according to the comprehensive risk assessment value index of each pipe section, so that the optimal routing inspection path is generated intelligently, the workload of routing inspection equipment is greatly reduced, the cruising anxiety of the routing inspection equipment is eliminated, and the routing inspection accuracy and effectiveness are realized.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of a thermal pipeline risk analysis and routing inspection path planning method based on a digital twin model according to the present invention;
FIG. 2 is a schematic diagram of a hierarchy of metrics in accordance with the present invention;
FIG. 3 is a schematic block diagram of a thermal pipeline risk analysis and routing inspection path planning system based on a digital twin model according to the present invention;
fig. 4 is a functional block diagram of an inspection system in accordance with the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
As shown in fig. 1 and fig. 2, the present embodiment 1 provides a thermal pipeline risk analysis and routing inspection path planning method based on a digital twin model, including: constructing a heating system digital twin model of a heating system, and identifying and correcting the heating system digital twin model; constructing a pipe section risk assessment model according to the identified and corrected digital twin model of the heat supply system, generating a threshold value of a comprehensive risk assessment index based on a basic data information base of the pipe section and accumulated historical process data, calculating and acquiring the comprehensive risk assessment value index of each pipe section according to actual operation data of the system before each inspection, and comparing the comprehensive risk assessment value index with the threshold value; and acquiring an optimal routing inspection path by adopting a genetic algorithm according to the comprehensive risk assessment value index of each pipe section, so that the optimal routing inspection path is generated intelligently, the workload of routing inspection equipment is greatly reduced, the cruising anxiety of the routing inspection equipment is eliminated, and the routing inspection accuracy and effectiveness are realized.
In this embodiment, the method for constructing a digital twin model of a heating system and identifying and correcting the digital twin model of the heating system includes: a heat supply system digital twin model based on a GIS geographic information system is constructed by adopting a thermal hydraulic process modeling simulation and an industrial big data method, and the established heat supply system digital twin model is identified and corrected according to the actual measurement operation data of the heat supply network; constructing a physical model, a logic model, a simulation model and a data driving model of the heat supply system, and constructing a digital twin model of the heat supply system according to the physical model, the logic model, the simulation model and the data driving model; setting remote transmission points of pressure and temperature parameters in the heat supply system, identifying and correcting a digital twin model of the heat supply system according to the preprocessed operation data, and correcting the parameters in the digital twin model of the heat supply system through reverse identification of a pipeline resistance coefficient and a heat transfer coefficient; physical model: establishing physical models of a heat source, a primary pipe network, a heat exchange station, a secondary pipe network and a heat user entity, and defining the geometric attributes and the functional attributes of the physical models according to the geometric shapes and the mechanical mechanisms of the physical entities; logic model: establishing a controllable closed-loop logic model according to the supply and demand relationship, distribution and transportation among all physical entities of the heating system, and mapping the physical model to the logic model; a simulation model: building a heat supply system simulation model based on the collected operation data, state data and physical attribute data of the heat supply system; data-driven model: based on the collected normal operation data of the heat supply system, a data fusion and deep learning algorithm is adopted to build a heat supply system data driving model, a heat source, a primary pipe network, a heat exchange station, a secondary pipe network and a heat user perform feature extraction on each input data in normal operation according to the working principle to be used as the input of the data driving model, the model outputs a corresponding output predicted value, and the data driving model is subjected to parameter optimization adjustment according to the output predicted value and the error magnitude of an actual value; digital twin model generation: carrying out virtual-real fusion on the logic model, the simulation model and the data driving model, and establishing a digital twin model of a physical entity of the heat supply system in a virtual space; identification and correction: remote transmission points of pressure and temperature parameters are additionally arranged at key positions, identification and correction are carried out on the mechanism simulation model by combining the preprocessed operation data, relevant parameters in the simulation model are corrected by reversely identifying parameters such as pipeline resistance coefficients and heat transfer coefficients, and the consistency of simulation results and physical pipeline network measurement data is promoted; constructing a digital twin model of a heat supply system pipe network, wherein a pipe network extension diagram in the model can embody the positions and elevations of structures such as a heat source, a heat exchange station, a valve well and the like, can accurately identify the specification, the length and the trend of the pipe network, and lays a foundation for the accuracy of hydraulic calculation of the heat supply network; the digital twin model of the heat supply system can simulate various data such as pressure, flow, temperature, pressure drop, specific friction resistance, heat load and the like of a heat supply network, diagnose and analyze the operation conditions of the heat supply network and heat and water power, realize the deduction and prediction of the operation of the heat supply system, intelligently alarm when the operation parameters are abnormal, establish an emergency plan library for common accidents such as pipe explosion and the like, and quickly generate an emergency operation scheme under the accident condition.
In this embodiment, the method for constructing a pipe section risk assessment model according to the identified and corrected digital twin model of the heat supply system and obtaining a comprehensive risk assessment value index of each pipe section includes: based on heat supply network simulation data obtained by the corrected heat supply system digital twin model, combining internal physical attributes such as the material and the pipe diameter of the pipe section and external influence factors such as the environmental soil property, the buried depth and the construction of the pipe section, constructing a hierarchical structure model by adopting an AHP (advanced high-performance packet) analytic hierarchy process, constructing a judgment matrix for first-level and second-level indexes, performing single-level sequencing and consistency inspection, calculating weight coefficients of all the indexes, and combining the heat supply system digital twin model and an expert diagnosis system assignment, calculating a comprehensive risk assessment index of each pipe section; collecting the self-attribute information in the pipe network including the pipe section self-attribute factors such as pipe diameter, pipe length, pipe material, pipe age, resistance coefficient, node flow and the like to establish a pipe network basic information database; counting external factors influencing the healthy and safe operation of the pipe section, such as geological conditions, buried depth of the pipe, construction quality, construction conditions around the pipe section and the like which are factors influencing the environment of the pipe section; the method comprises the steps of obtaining simulation parameters of a digital twin model, namely integrating a heat source (a boiler, a heat pump and the like), a pipeline, an elbow, a tee joint, a heating station, a pipe network circulating pump, a valve and a meter with the digital twin model of the heating system, and obtaining simulation data of the pipe network under various working conditions by including position information of the heat source (the boiler, the heat pump and the like), the pipeline, the pump, the valve and each measuring point of the heating system, and attribute parameter information of each heat network key part such as heat source attribute, pipeline size, valve opening degree and pump lift of the pump.
In this embodiment, the constructing a pipe segment risk assessment model according to the identified and corrected digital twin model of the heat supply system, and obtaining the comprehensive risk assessment value index of each pipe segment further includes: calculating the risk assessment index of each pipe section by adopting an AHP analytic hierarchy process: establishing a hierarchical structure model, namely taking the self attribute factors of the pipe section, the environmental influence factors of the pipe section and the simulation parameters of the digital twin model as 3 primary indexes, selecting secondary indexes with different numbers for each primary index according to the typicality and the representativeness principle of the index selection, and taking the pipe diameter, the pipe length, the pipe material and the pipe age as 4 secondary indexes under the self attribute factors of the pipe section; taking soil texture, burial depth and construction as 3 secondary indexes under the influence factors of the pipe section environment; taking temperature, pressure and flow as 3 secondary indexes under the simulation parameters of the digital twin model; constructing a judgment matrix, namely comparing the first-level index and the second-level index pairwise according to different importance of different indexes, and adopting a nine-level division system as a quantitative judgment basis for importance comparison among the indexes as shown in table 1;
table 1: relative importance of each index
Respectively comparing the first-level index and the second-level index pairwise;
establishing a judgment matrix for each index of the first level and the second level
Wherein,
annthe importance of the comparison among the indexes; a isijThe importance of index i compared to j; a isjiThe importance of index j compared to i;
hierarchical single ordering and consistency checking, i.e.
Solving the eigenvector of each judgment matrix by adopting a geometric mean method, multiplying the elements of the judgment matrix A by rows to obtain a new vector, opening each component of the new vector by the power of n and normalizing the obtained vector to obtain the eigenvector:
the feature vector of the primary index is omegaA(ii) a The feature vector of the secondary index is
Constructing a maximum eigenvector of a judgment matrix for the second-level index corresponding to the nth first-level index;
acquiring a characteristic root of each judgment matrix:
obtaining a consistency index CI:
a CI of 0 indicates that the matrix is consistent, and a larger CI indicates that the degree of matrix inconsistency is more serious.
Obtaining the consistency ratio CR:
wherein, RI is an average random consistency index, and when CR is smaller than a preset value (e.g., 0.1), it is considered that the consistency of the judgment matrix meets the requirement, and the obtained feature vector meets the requirement.
Acquiring the comprehensive risk assessment value index of each pipe section, namely acquiring the weight coefficient of each secondary index: alpha-omegaBωA(ii) a Assigning scores to the secondary indexes, and acquiring the comprehensive risk assessment value index of each pipe section according to the assigning scores and the secondary index weight coefficient:
wherein alpha isiThe second-level index weight coefficient; b isiAssigning a score to the second-level index; according to the pipe network basic information database, historical process data accumulated by the operation of the heat supply pipe network and the digital twin modelSimulating and calculating data under the type working condition to generate a threshold value of a comprehensive risk evaluation index of the pipe network; before each inspection, calculating and acquiring a comprehensive risk assessment value index of each pipe section according to actual operation data of the system, comparing the comprehensive risk assessment value index with a threshold value, and when the comprehensive risk assessment value index exceeds the threshold value, indicating that abnormal fluctuation of the current operation parameters deviates from a normal value or the geological and construction conditions of the area where the current operation parameters are located have large influence on the safe operation of the pipe network, and the method needs to pay attention; and establishing a pipe section risk evaluation model, comprehensively considering various factors influencing the safe operation of the pipe section, evaluating the risk index of each pipe section, and providing a scientific basis for the routing inspection task planning of routing inspection equipment.
In this embodiment, the method for obtaining the optimal routing inspection path according to the comprehensive risk assessment value index of each pipe section includes: before each routing inspection task is carried out, carrying out primary comprehensive risk evaluation on the current pipe network system based on actual data of the current system operation to generate a comprehensive risk distribution map of the heat supply pipe network overall situation; before each inspection, calculating a comprehensive risk assessment value index of each pipe section in the current time period based on a digital twin model of a heat supply system, and selecting the pipe section of which the comprehensive risk assessment value index exceeds a set threshold value as a target pipe section of the inspection; acquiring geographic coordinate data of a target pipe section according to a digital twin model of a heating system, numbering the target pipe section, and converting routing inspection path planning into a typical traveling salesman problem; coding is carried out according to the sequence arrangement of traversing target pipe sections, iteration and updating are carried out on the path through selection, crossing and mutation operations, when the maximum iteration times are reached, the optimal routing inspection path is output, and routing inspection equipment receives the routing inspection path and carries out routing inspection operation; setting the pipe sections of which the comprehensive risk assessment value indexes exceed a preset threshold value as target pipe sections to be inspected at this time based on the comprehensive risk assessment value indexes of the current pipe sections; acquiring geographic coordinate data of target pipe sections, and numbering the target pipe sections in sequence, wherein the number is 1,2 and 3 … t; t is the number of the pipe sections screened out according to the pipe section risk index as inspection targets;
at the moment, the routing inspection path generation is converted into a typical solution for the problem of the traveling salesman, and the road network model of the target pipe section area and the continuation of the routing inspection equipment are inspectedUsing the genetic algorithm to search for an arrangement pi ═ v { (v) of an integer subset X ═ {1,2,3, … t } as a constraint condition of navigation ability1,v2,v3,…vtMake
Taking the minimum value;
wherein d (v)i,vi+1) For inspecting target pipeline viTo the target pipeline vi+1The distance of (d); coding by adopting sequence arrangement of traversing target pipe sections; fitness function of
Wherein, TdIs the path length; n is a radical oftThe number of the traversal targets; c is a penalty coefficient; generating an initial seed cluster by adopting a random strategy, and performing selection operation by adopting a roulette selection method; updating the path by adopting a single-point crossing operation after the selection operation; after the cross operation, the random cross is adopted as a mutation operation to update the path; stopping searching after the maximum iteration sequence is reached, outputting an optimal routing inspection path, and executing the risk pipeline section routing inspection operation according to the optimal routing inspection path; through the screened high-risk target pipe sections, an optimal routing inspection path is intelligently generated by adopting a genetic algorithm, the workload of routing inspection equipment is greatly reduced, the cruising anxiety of the routing inspection equipment is eliminated, and the accuracy and the effectiveness of routing inspection are realized.
Example 2
As shown in fig. 3, on the basis of embodiment 1, embodiment 2 further provides a thermal pipeline risk analysis and routing inspection path planning system based on a digital twin model, which includes: the model building module is used for building a heat supply system digital twin model of the heat supply system and identifying and correcting the heat supply system digital twin model; the risk evaluation module is used for constructing a pipe section risk evaluation model according to the identified and corrected digital twin model of the heat supply system and acquiring a comprehensive risk evaluation value index of each pipe section; and the path acquisition module is used for acquiring the optimal routing inspection path according to the comprehensive risk assessment value index of each pipe section.
In this embodiment, specific functions of each module have been described in detail in embodiment 1, and are not described in detail in this embodiment.
In this embodiment, the modules may be integrated into one processor module, or the modules may exist separately and installed separately in the device.
Example 3
As shown in fig. 4, on the basis of embodiment 1, embodiment 3 further provides an inspection system, including: a server adapted to obtain an optimal routing inspection path; and the inspection equipment is suitable for receiving the optimal inspection path sent by the server, and is suitable for executing the inspection operation of the risk pipe section according to the optimal inspection path.
In this embodiment, the server is adapted to obtain the optimal inspection path by using the thermal pipeline risk analysis and inspection path planning method based on the digital twin model in embodiment 1.
In conclusion, the digital twin model of the heat supply system is constructed, and the digital twin model of the heat supply system is identified and corrected; constructing a pipe section risk assessment model according to the identified and corrected digital twin model of the heat supply system, and acquiring a comprehensive risk assessment value index of each pipe section; and acquiring an optimal routing inspection path according to the comprehensive risk assessment value index of each pipe section, so that the optimal routing inspection path is generated intelligently, the workload of routing inspection equipment is greatly reduced, the cruising anxiety of the routing inspection equipment is eliminated, and the routing inspection accuracy and effectiveness are realized.
In light of the foregoing description of the preferred embodiment of the present invention, many modifications and variations will be apparent to those skilled in the art without departing from the spirit and scope of the invention. The technical scope of the present invention is not limited to the content of the specification, and must be determined according to the scope of the claims.
Claims (8)
1. A thermal pipeline risk analysis and routing inspection path planning method based on a digital twin model is characterized by comprising the following steps:
constructing a heating system digital twin model of a heating system, and identifying and correcting the heating system digital twin model;
constructing a pipe section risk assessment model according to the identified and corrected digital twin model of the heat supply system, acquiring a comprehensive risk assessment value index of each pipe section, and comparing the comprehensive risk assessment value index with a threshold value; and
and obtaining an optimal routing inspection path according to the comprehensive risk assessment value index of each pipe section.
2. The thermal pipeline risk analysis and inspection path planning method based on the digital twin model as claimed in claim 1,
the method for constructing the digital twin model of the heat supply system and identifying and correcting the digital twin model of the heat supply system comprises the following steps:
constructing a physical model, a logic model, a simulation model and a data driving model of the heat supply system, and constructing a digital twin model of the heat supply system according to the physical model, the logic model, the simulation model and the data driving model;
setting remote transmission points of pressure and temperature parameters in the heat supply system, identifying and correcting the digital twin model of the heat supply system according to the preprocessed operation data, and correcting the parameters in the digital twin model of the heat supply system through reverse identification of a pipeline resistance coefficient and a heat transfer coefficient.
3. The thermal pipeline risk analysis and inspection path planning method based on the digital twin model as claimed in claim 2,
the method for constructing the pipe section risk assessment model according to the identified and corrected digital twin model of the heat supply system and acquiring the comprehensive risk assessment value index of each pipe section comprises the following steps:
collecting the pipe section attribute factors of pipe diameter, pipe length, pipe material, pipe age, resistance coefficient and node flow to establish a pipe network basic information database;
counting pipe section environment influence factors such as geological conditions, pipeline burial depth, construction quality and construction conditions around the pipe section;
and acquiring simulation parameters of the digital twin model, namely integrating the heat source, the pipeline, the elbow, the tee joint, the heating station, the pipe network circulating pump, the valve and the meter of the digital twin model of the heating system, and acquiring simulation data of the pipe network under various working conditions by including position information of the heat source, the pipeline, the pump, the valve and each measuring point of the heating system, and the attribute of the heat source, the size of the pipeline, the opening of the valve and the lift of the pump.
4. The thermal pipeline risk analysis and inspection path planning method based on the digital twin model as claimed in claim 3,
the method for constructing the pipe section risk assessment model according to the identified and corrected digital twin model of the heat supply system and acquiring the comprehensive risk assessment value index of each pipe section further comprises the following steps:
build a hierarchical model, i.e.
Taking the self attribute factors of the pipe section, the environmental influence factors of the pipe section and the simulation parameters of the digital twin model as first-level indexes;
taking the pipe diameter, the pipe length, the pipe material and the pipe age as second-level indexes under the attribute factors of the pipe section;
taking soil texture, burial depth and construction as secondary indexes under the influence factors of the pipe section environment;
taking temperature, pressure and flow as secondary indexes under simulation parameters of the digital twin model;
constructing a decision matrix, i.e.
Respectively comparing the first-level index and the second-level index pairwise;
establishing a judgment matrix for each index of the first level and the second level
Wherein,
annthe importance of the comparison among the indexes; a isijThe importance of index i compared to j; a isjiIs a fingerThe importance of mark j compared to i;
hierarchical single ordering and consistency checking, i.e.
Solving the eigenvector of each judgment matrix by adopting a geometric mean method, multiplying the elements of the judgment matrix A by rows to obtain a new vector, opening each component of the new vector by the power of n and normalizing the obtained vector to obtain the eigenvector:
the feature vector of the primary index is omegaA(ii) a The feature vector of the secondary index is
Maximum eigenvector of judgment matrix constructed for second-level index corresponding to nth first-level index
Acquiring a characteristic root of each judgment matrix:
obtaining a consistency index CI:
a CI of 0 indicates that the matrix is consistent, and a larger CI indicates that the degree of matrix inconsistency is more serious.
Obtaining the consistency ratio CR:
and the RI is an average random consistency index, when the CR is smaller than a preset value, the consistency of the judgment matrix is considered to meet the requirement, and the obtained feature vector meets the requirement.
Obtaining a composite risk assessment value index for each pipe section, i.e.
Acquiring the weight coefficient of each secondary index: alpha-omegaBωA；
Assigning scores to the secondary indexes, and acquiring the comprehensive risk assessment value index of each pipe section according to the assigning scores and the secondary index weight coefficient:
wherein alpha isiThe second-level index weight coefficient; b isiAssigning a score to the second-level index;
and generating a threshold value of a comprehensive risk assessment index of the pipe network according to the simulation calculation data of the pipe network basic information database, the historical process data accumulated by the operation of the heat supply pipe network and the digital twin model under various working conditions, and comparing the comprehensive risk assessment index with the threshold value.
5. The thermal pipeline risk analysis and inspection path planning method based on the digital twin model as claimed in claim 4,
the method for obtaining the optimal routing inspection path according to the comprehensive risk assessment value index of each pipe section comprises the following steps:
before each routing inspection task is carried out, carrying out primary comprehensive risk evaluation on the current pipe network system based on actual data of the current system operation to generate a comprehensive risk distribution map of the heat supply pipe network overall situation;
setting the pipe sections of which the comprehensive risk assessment value indexes exceed a preset threshold value as target pipe sections to be inspected at this time based on the comprehensive risk assessment value indexes of the current pipe sections;
acquiring geographic coordinate data of target pipe sections, and numbering the target pipe sections in sequence, wherein the number is 1,2 and 3 … t; t is the number of the pipe sections screened out according to the pipe section risk index as inspection targets;
adopt the Chinese character' YiThe algorithm searches for a permutation pi ═ v { v } of the integer subset X ═ {1,2,3, … t }1,v2,v3,…vtMake
Taking the minimum value;
wherein d (v)i,vi+1) For inspecting target pipeline viTo the target pipeline vi+1The distance of (d);
coding by adopting sequence arrangement of traversing target pipe sections;
fitness function of
Wherein, TdIs the path length; n is a radical oftThe number of the traversal targets; c is a penalty coefficient;
generating an initial seed cluster by adopting a random strategy, and performing selection operation by adopting a roulette selection method;
updating the path by adopting a single-point crossing operation after the selection operation;
after the cross operation, the random cross is adopted as a mutation operation to update the path;
and stopping searching after the maximum iteration sequence is reached, outputting an optimal routing inspection path, and executing the risk pipeline section routing inspection operation according to the optimal routing inspection path.
6. A thermal pipeline risk analysis and routing inspection path planning system based on a digital twin model is characterized by comprising:
the model building module is used for building a heat supply system digital twin model of the heat supply system and identifying and correcting the heat supply system digital twin model;
the risk evaluation module is used for constructing a pipe section risk evaluation model according to the identified and corrected digital twin model of the heat supply system, acquiring a comprehensive risk evaluation value index of each pipe section, and comparing the comprehensive risk evaluation value index with a threshold value; and
and the path acquisition module is used for acquiring an optimal routing inspection path according to the comprehensive risk assessment value index of each pipe section.
7. An inspection system, comprising:
a server adapted to obtain an optimal routing inspection path;
and the inspection equipment is suitable for receiving the optimal inspection path sent by the server, and is suitable for executing the inspection operation of the risk pipe section according to the optimal inspection path.
8. The inspection system according to claim 7,
the server is suitable for acquiring an optimal inspection path by adopting the thermal pipeline risk analysis and inspection path planning method based on the digital twin model according to any one of claims 1 to 5.
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