CN117371668B - Urban pipeline flow allocation optimization method based on visual view and network flow - Google Patents
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Abstract
The invention provides an urban pipeline flow allocation optimization method based on a visual view and network flow, which relates to the technical field of domestic water supply and drainage, and comprises the steps of obtaining pipeline limit value parameters according to historical pipeline network flow, carrying out monitoring early warning device mapping layout of twin pipelines in a network visual view, and synchronizing the pipeline limit value parameters to a monitoring early warning device so as to realize risk pipeline identification positioning based on real-time network flow to obtain an early warning adjustment node set; and synchronizing the early warning adjusting node set to the pipeline flow allocation module to perform flow multistage allocation fitting, and obtaining the pipeline flow allocation parameter set to perform mapping allocation of the target city pipeline. The method solves the technical problems that the flow allocation of the urban pipeline in the prior art has one-sided performance, and the flow optimization of the local pipeline interferes with the integral operation safety of the urban pipeline. The method has the technical effects of realizing global pipeline flow adjustment of the urban water supply network, avoiding local pipeline flow optimization from interfering with the integral operation of the urban pipeline, and improving the stability of the urban water supply flow.
Description
Technical Field
The invention relates to the technical field of domestic water supply and drainage, in particular to a city pipeline flow allocation optimization method based on visual and network flows.
Background
Currently, there is a limit to flow distribution of urban pipelines, and only the demands of certain specific stages or local areas are focused, but the global demands of the whole urban pipeline network are ignored.
The one-sided method ignores the overall operation safety of the urban pipeline network, does not fully consider the interconnection interoperability and close association of the whole pipeline network, and has the risk that the optimization of the flow of the water supply pipeline of one node negatively affects other associated pipelines, thereby affecting the operation stability and safety of the whole urban pipeline system.
The prior art has unilateral performance for flow allocation of urban pipelines, and has the technical problem that the optimization of local pipeline flow interferes with the integral operation safety of the urban pipelines.
Disclosure of Invention
The application provides a city pipeline flow allocation optimization method based on a visual view and network flow, which is used for solving the technical problems that the flow allocation of the city pipeline in the prior art is unilateral and the flow optimization of the local pipeline interferes with the integral operation safety of the city pipeline.
In view of the above, the present application provides a method for optimizing urban pipeline traffic allocation based on visual and network flows.
In a first aspect of the present application, there is provided a method for optimizing urban pipeline traffic allocation based on visual and network flows, the method comprising: obtaining urban pipeline design information, wherein the urban pipeline design information is obtained through interaction of target urban pipelines; constructing a pipe network visual view, wherein the pipe network visual view is generated by constructing a digital twin model based on the urban pipeline design information, the pipe network visual view comprises K twin pipelines, and each twin pipeline is marked with pipeline parameter information; the method comprises the steps of interactively obtaining a historical pipeline network flow, wherein the historical pipeline network flow comprises K historical pipeline flow sequences, and the K historical pipeline flow sequences are provided with K pipeline monitoring site identifiers; performing limit value analysis according to the historical pipeline network flow to obtain K pipeline limit value parameters of the K twin pipelines; k monitoring early warning devices are arranged in a mapping mode according to the K twin pipelines in the pipeline network visual view according to the pipeline monitoring sites, and the K pipeline limit value parameters are synchronized to the K monitoring early warning devices; interactively obtaining a real-time pipeline network flow, synchronizing the real-time pipeline network flow to the visual view of the pipe network, and carrying out risk pipeline identification positioning based on the K monitoring early-warning devices to obtain an early-warning adjusting node set; a pipeline flow allocation module is pre-constructed, the early warning adjustment node set is synchronized to the pipeline flow allocation module to carry out flow multistage allocation fitting, and a pipeline flow allocation parameter set is obtained; and carrying out mapping allocation of the target city pipeline based on the pipeline flow allocation parameter set.
In a second aspect of the present application, there is provided a city pipeline traffic allocation optimization system based on a visual view and a network flow, the system comprising: the urban pipeline design system comprises a design information obtaining unit, a design information obtaining unit and a control unit, wherein the design information obtaining unit is used for obtaining urban pipeline design information through interaction of target urban pipelines; the view construction execution unit is used for constructing a pipe network visual view, wherein the pipe network visual view is generated by constructing a digital twin model based on the urban pipeline design information, the pipe network visual view comprises K twin pipelines, and each twin pipeline is marked with pipeline parameter information; the historical data interaction unit is used for interactively obtaining historical pipeline network flows, wherein the historical pipeline network flows comprise K historical pipeline flow sequences, and the K historical pipeline flow sequences have K pipeline monitoring site identifiers; the limit value analysis execution unit is used for carrying out limit value analysis according to the historical pipeline network flow to obtain K pipeline limit value parameters of the K twin pipelines; the monitoring limit value synchronizing unit is used for mapping and arranging K monitoring early-warning devices according to the K twin pipelines in the pipeline network visual view according to the pipeline monitoring sites, and synchronizing the K pipeline limit value parameters to the K monitoring early-warning devices; the real-time data interaction unit is used for interactively obtaining a real-time pipeline network flow, synchronizing the real-time pipeline network flow to the visual view of the pipe network, and carrying out risk pipeline identification positioning based on the K monitoring early-warning devices to obtain an early-warning adjustment node set; the allocation module construction unit is used for pre-constructing a pipeline flow allocation module, synchronizing the early warning adjustment node set to the pipeline flow allocation module to perform flow multistage allocation fitting, and obtaining a pipeline flow allocation parameter set; and the mapping allocation execution unit is used for carrying out mapping allocation of the target city pipeline based on the pipeline flow allocation parameter set.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
the method provided by the embodiment of the application comprises the steps of obtaining urban pipeline design information, wherein the urban pipeline design information is obtained through interaction of target urban pipelines; constructing a pipe network visual view, wherein the pipe network visual view is generated by constructing a digital twin model based on the urban pipeline design information, the pipe network visual view comprises K twin pipelines, and each twin pipeline is marked with pipeline parameter information; the method comprises the steps of interactively obtaining a historical pipeline network flow, wherein the historical pipeline network flow comprises K historical pipeline flow sequences, and the K historical pipeline flow sequences are provided with K pipeline monitoring site identifiers; performing limit value analysis according to the historical pipeline network flow to obtain K pipeline limit value parameters of the K twin pipelines; k monitoring early warning devices are arranged in a mapping mode according to the K twin pipelines in the pipeline network visual view according to the pipeline monitoring sites, and the K pipeline limit value parameters are synchronized to the K monitoring early warning devices; interactively obtaining a real-time pipeline network flow, synchronizing the real-time pipeline network flow to the visual view of the pipe network, and carrying out risk pipeline identification positioning based on the K monitoring early-warning devices to obtain an early-warning adjusting node set; a pipeline flow allocation module is pre-constructed, the early warning adjustment node set is synchronized to the pipeline flow allocation module to carry out flow multistage allocation fitting, and a pipeline flow allocation parameter set is obtained; and carrying out mapping allocation of the target city pipeline based on the pipeline flow allocation parameter set. The method has the advantages of realizing global pipeline flow adjustment of the urban water supply network, avoiding the optimization of local pipeline flow from interfering with the integral operation of the urban pipeline, and improving the stability of the urban water supply flow.
Drawings
FIG. 1 is a flow diagram of a method for optimizing urban pipeline flow allocation based on visual and network flows in one embodiment;
FIG. 2 is a flow diagram of pipeline limit parameters in urban pipeline flow fitting optimization based on visual and network flows in one embodiment;
FIG. 3 is a flow diagram of overload pipeline location replacement analysis in urban pipeline flow deployment optimization based on visual and network flows in one embodiment;
FIG. 4 is a block diagram of a city pipeline flow fitting optimization system based on visual and network flows in one embodiment.
Reference numerals illustrate: the system comprises a design information obtaining unit 1, a view construction executing unit 2, a historical data interaction unit 3, a limit value analysis executing unit 4, a monitoring limit value synchronizing unit 5, a real-time data interaction unit 6, a deployment module construction unit 7 and a mapping deployment executing unit 8.
Detailed Description
The application provides a city pipeline flow allocation optimization method based on a visual view and network flow, which is used for solving the technical problems that the flow allocation of the city pipeline in the prior art is unilateral and the flow optimization of the local pipeline interferes with the integral operation safety of the city pipeline. The method has the advantages of realizing global pipeline flow adjustment of the urban water supply network, avoiding the optimization of local pipeline flow from interfering with the integral operation of the urban pipeline, and improving the stability of the urban water supply flow.
The technical scheme of the invention accords with related regulations on data acquisition, storage, use, processing and the like.
In the following, the technical solutions of the present invention will be clearly and completely described with reference to the accompanying drawings, and it should be understood that the described embodiments are only some embodiments of the present invention, but not all embodiments of the present invention, and that the present invention is not limited by the exemplary embodiments described herein. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention. It should be further noted that, for convenience of description, only some, but not all of the drawings related to the present invention are shown.
Example 1
As shown in fig. 1, the present application provides a method for optimizing urban pipeline traffic allocation based on a visual view and a network flow, the method comprising:
a100, obtaining urban pipeline design information, wherein the urban pipeline design information is obtained through interaction of target urban pipelines;
specifically, in this embodiment, the target city is an unspecified city in which the flow integrity adjustment in the water supply pipeline of the urban water supply network is planned, and the target city pipeline is a water supply pipeline entity actually laid in the target city.
According to the urban name information of the target city, the mechanism of the target city for urban pipeline design and planning is determined, and then the mechanism is interacted to obtain urban pipeline design information in a compliance mode, wherein the urban pipeline design information comprises pipeline layout conditions of water supply pipelines in the target city, materials and size information of pipelines at different positions in the water supply pipeline layout, and sensor layout information of unspecified types and unspecified precision for monitoring pipeline flow.
The purpose of obtaining the urban pipeline design information in this embodiment is to provide reference data for the subsequent one-to-one reduction target urban pipeline twin model construction.
A200, constructing a pipe network visual view, wherein the pipe network visual view is generated by constructing a digital twin model based on the urban pipeline design information, the pipe network visual view comprises K twin pipelines, and each twin pipeline is marked with pipeline parameter information;
a300, interactively obtaining a historical pipeline network flow, wherein the historical pipeline network flow comprises K historical pipeline flow sequences, and the K historical pipeline flow sequences have K pipeline monitoring site identifiers;
specifically, due to the advancement of the existing digital twin technology-based industrial scene construction technology, in this embodiment, the existing digital twin technology is adopted to construct and generate the pipe network visual view based on the urban pipeline design information, and the material dimension parameter of any water supply pipeline in the target urban pipeline and the arrangement condition of the flow sensor on each water supply pipeline can be intuitively known based on the pipe network visual view.
Specifically, the pipe network visual view includes the K twin pipes mapped to the total K water supply pipe entities of the target city pipe network, and each twin pipe is identified with the pipe parameter information representing the size (inner diameter-outer diameter) and material condition of the corresponding water supply pipe entity.
It will be appreciated that in constructing a water supply line network, the use of a greater flow rate line is often employed to reduce the risk of overload of the water supply line, and this embodiment is not directed to the known flow limits of water supply lines of different sizes and materials, but rather is based on analysis.
Specifically, all K water supply pipeline entities of the target city pipe network are all provided with pipeline flow sensors, so in this embodiment, K historical pipeline flow sequences are obtained by interacting K pipeline flow sensors of the K water supply pipeline entities, the K historical pipeline flow sequences adopt the K pipeline monitoring site identifiers as data source identifiers, the pipeline monitoring site identifiers are pipeline arrangement site coordinates of water supply pipelines monitored by the pipeline flow sensors, and the historical pipeline flow sequences are sequences of water flow changes along with time in the historical pipelines.
A400, carrying out limit value analysis according to the historical pipeline network flow to obtain K pipeline limit value parameters of the K twin pipelines;
in one embodiment, as shown in fig. 2, performing limit analysis according to the historical pipeline network flow to obtain K pipeline limit parameters of the K twin pipelines, a method step a400 provided in the present application further includes:
a410, positioning and obtaining K pipeline parameter information in the pipe network visual view based on the K pipeline monitoring site identifiers, wherein the K pipeline parameter information is mapped in association with the K historical pipeline flow sequences;
a420, carrying out polymerization treatment on the K twin pipelines based on the K pipeline parameter information to obtain H groups of twin polymerization pipelines, wherein the pipeline parameter information of each group of twin polymerization pipelines has consistency;
a430, aggregating the K historical pipeline flow sequences according to the H-group twin aggregation pipeline mapping to obtain H-group aggregation sensing data;
a440, serializing the H groups of aggregation sensing data to obtain H aggregation pipeline limit parameters;
a450, distributing the H polymerization pipeline limit parameters to the K twin pipelines according to the H group twin polymerization pipeline to obtain the K pipeline limit parameters.
Specifically, in this embodiment, K pieces of pipeline parameter information are obtained by positioning in the pipe network visual view based on the K pipeline monitoring site identifiers, and the K pieces of pipeline parameter information and the K historical pipeline flow sequences are mapped based on K twin pipelines (or K water supply pipeline entities) in association.
And merging and grouping the twin pipelines with the same pipeline parameters (materials and sizes) based on the K pipeline parameter information to finish the polymerization treatment of the K twin pipelines, so as to obtain H groups of twin polymerization pipelines, wherein the pipeline parameter information of each group of twin polymerization pipelines has consistency.
And aggregating the K historical pipeline flow sequences according to the H-group twin aggregation pipeline mapping, and removing the aggregated data in groups to obtain H-group aggregation sensing data. And sequencing the H groups of aggregation sensing data from small to large in a group and extracting the maximum flow monitoring data according to the historical flow monitoring data to obtain H aggregation pipeline limit value parameters.
And taking the H polymerization pipeline limit value parameters as the flow limit of the water supply pipeline with the consistent H pipeline parameter information in the water supply network of the target city pipeline. And distributing the H polymerization pipeline limit parameters to the K twin pipelines according to the twin pipelines included in the H group twin polymerization pipeline to obtain the K pipeline limit parameters.
According to the method, the device and the system, the flow limit value analysis of the water supply pipelines corresponding to the same pipeline parameters is carried out according to the water supply flow data of the historical flowing pipelines, so that the technical effect of obtaining the flow limit of each water supply pipeline in the current target city pipeline is achieved, and the reference technical effect is provided for the follow-up flow regulation with stronger pertinence based on the target city pipeline.
A500, according to the pipeline monitoring sites, marking the K twin pipelines in the pipeline network visual view, mapping and arranging K monitoring early-warning devices, and synchronizing the K pipeline limit value parameters to the K monitoring early-warning devices;
a600, interactively obtaining a real-time pipeline network flow, synchronizing the real-time pipeline network flow to the visual view of the pipe network, and carrying out risk pipeline identification positioning based on the K monitoring early-warning devices to obtain an early-warning adjustment node set;
specifically, in this embodiment, K monitoring early-warning devices are mapped and arranged on the K twin pipelines in the visual view of the pipeline according to the pipeline monitoring site identifier, and the K pipeline limit value parameters are synchronized to the K monitoring early-warning devices, where the monitoring early-warning devices can compare the pipeline limit value parameters according to real-time pipeline flow data of the twin pipelines, so as to determine whether the water supply pipeline entity corresponding to the twin pipelines needs to perform flow allocation processing.
And interactively obtaining a real-time pipeline network flow, wherein the real-time pipeline network flow is the flow state of a water supply network of a target city pipeline, which is formed by K real-time pipeline flow data with timeliness and based on the specific synchronicity obtained by monitoring K pipeline flow sensors of K water supply pipeline entities.
And synchronizing the K real-time pipeline flow data of the real-time pipeline network flow to K twin pipelines in the visual pipeline network, and carrying out numerical comparison on the K real-time pipeline flow data and K pipeline limit value parameters based on the K monitoring early-warning devices so as to finish risk pipeline identification and positioning, thereby obtaining an early-warning adjusting node set, wherein the early-warning adjusting node set comprises M to-be-adjusted risk pipelines, and the to-be-adjusted risk pipelines are twin pipelines (water supply pipeline entities) of which the real-time pipeline flow data do not meet the corresponding pipeline limit value parameters.
A700, a pipeline flow allocation module is pre-constructed, the early warning adjustment node set is synchronized to the pipeline flow allocation module to perform flow multistage allocation fitting, and a pipeline flow allocation parameter set is obtained;
in one embodiment, the method step a700 provided herein further includes:
A711, pre-constructing a pipeline flow steady-state function, wherein the pipeline flow steady-state function is as follows:
wherein,for the steady-state adjustment value of the flow,is pipeline flow data of multiple time sequence nodes,the weight parameters are the weight parameters of the multi-time sequence nodes;
and A712, synchronizing the pipeline flow steady-state function to the pipeline flow allocation module.
In one embodiment, the early warning adjustment node set is synchronized to the pipeline flow allocation module to perform flow multistage allocation fitting, so as to obtain a pipeline flow allocation parameter set, and the method step a700 provided in the present application further includes:
a721, the early warning adjusting node set comprises M risk pipelines to be adjusted, wherein the M risk pipelines to be adjusted have M pipeline limit value deviation coefficients, and M is a positive integer smaller than K;
a722, synchronizing the M pipeline limit deviation coefficient mappings to the pipeline flow allocation module to obtain M risk pipeline adjustment parameters, and adding the M risk pipeline adjustment parameters to the pipeline flow allocation parameter set;
a723, synchronizing the M risk pipeline adjustment parameters to the pipe network visual view to perform flow allocation fitting to obtain a first allocation visual view, wherein the K twin pipelines in the first allocation visual view have K adjustment pipeline flow parameters;
724, identifying and positioning the risk pipelines of the first allocation visual view based on the K monitoring early-warning devices to obtain a second early-warning adjusting node set, wherein the second early-warning adjusting node set comprises W second risk pipelines to be adjusted;
a725, adopting the pipeline flow allocation module to perform adjustment analysis on the W second risk pipelines to be adjusted to obtain W second risk pipeline adjustment parameters, and adding the W second risk pipeline adjustment parameters to the pipeline flow allocation parameter set;
a726, synchronizing the M risk pipeline adjusting parameters and the W second risk pipeline adjusting parameters to the pipe network visual view to perform flow allocation fitting to obtain a second allocation visual view;
and A727, by analogy, carrying out flow multistage allocation fitting, and carrying out multi-round updating on the pipeline flow allocation parameter set until the risk pipeline identification positioning result based on the K monitoring early-warning devices is 0.
Specifically, in this embodiment, the pipe flow steady-state function for calculating the water flow parameter in the pipe when the single water supply pipe is stably supplied in the target city pipe is pre-constructed, and the pipe flow steady-state function is as follows:
Wherein,for the steady-state adjustment value of the flow,is pipeline flow data of multiple time sequence nodes,the weight parameters are the weight parameters of the multi-time sequence nodes;
and synchronizing the pipeline flow steady-state function to the pipeline flow allocation module so as to calculate or calculate the pipeline water flow parameters when different pipelines perform stable water supply based on the pipeline flow allocation module.
The early warning adjusting node set comprises M risk pipelines to be adjusted, and M historical pipeline flow sequences are obtained through mapping and calling of the K historical pipeline flow sequences based on the M risk pipelines to be adjusted.
And carrying out interval call on the M historical pipeline flow sequences at preset data acquisition intervals to obtain M groups of interval flow sequences, wherein interval flow data in each group of interval flow sequences have acquisition time identifiers, and M groups of interval flow sequences are used as M risk pipelines to be regulated and have M pipeline limit value deviation coefficients.
A first pipeline limit deviation coefficient is randomly called based on the M pipeline limit deviation coefficients, the first pipeline limit deviation coefficient is mapped and synchronized into a pipeline flow steady-state function of the pipeline flow allocation module, in the pipeline flow steady-state function, And configuring weights according to the time sequence data and the distance between the current time by the weight parameters of the multi-time sequence nodes, wherein the lower the weight assignment of the time sequence nodes which are far away from the current time is.
Based on the followingAnd calculating the weight parameters of the multiple time sequence nodes to obtain first risk pipeline adjusting parameters of a first pipeline limit value deviation coefficient, wherein the first risk pipeline adjusting parameters are the optimal flow values of the first risk pipeline to be adjusted in a stable state.
And obtaining M risk pipeline adjusting parameters by adopting the same method, and adding the M risk pipeline adjusting parameters to the pipeline flow allocation parameter set to serve as adjusting parameters for adjusting pipeline flow of the M risk pipelines to be adjusted.
It will be appreciated that when the flow rate of a single conduit changes, the conduit connected or indirectly connected to that conduit may also experience a fluctuation in flow rate that may cause the flow rate in the conduit connected or indirectly connected to that conduit to exceed its conduit limit parameter.
Based on this, the embodiment synchronizes the M risk pipeline adjustment parameters to the pipe network visual view, and in the pipe network visual view, performs flow allocation fitting, where the flow allocation fitting is to replace M real-time pipeline flow data of M risk pipelines to be adjusted by using the M risk pipeline adjustment parameters in the pipe network visual view, so as to obtain a linkage reaction caused by flow changes of the M risk pipelines to flow of pipelines connected or indirectly connected to the M risk pipelines to be adjusted, thereby obtaining the first allocation visual view of K adjustment pipeline flow parameters characterizing the K twin pipelines under the linkage reaction.
It should be understood that the chain reaction herein is that the flow rate change of the upstream pipeline to be regulated affects the flow rate change of the downstream pipeline, for example, the flow rate of the upstream pipeline is changed from 3m to 2 m/s, the flow rate distribution of the upstream pipeline to two downstream pipelines connected with the upstream pipeline is 2:1, and then the flow rates of the two downstream pipelines are changed into (2/3) m/s and (1/3) m/s. And by analogy, combining the K real-time pipeline flow parameters of the K twin pipelines, and obtaining the K regulating pipeline flow parameters of the K twin pipelines under the condition that the M risk pipelines to be regulated are subjected to flow allocation based on the M real-time pipeline flow data in the pipe network visual view.
Further, the first allocation visual risk pipeline identification positioning is performed based on the K monitoring early warning devices, and a second early warning adjusting node set is obtained, wherein the second early warning adjusting node set comprises W second risk pipelines to be adjusted.
Based on the same processing method, adopting the pipeline flow allocation module to perform adjustment analysis of the W second risk pipelines to be adjusted to obtain W second risk pipeline adjustment parameters, and adding the W second risk pipeline adjustment parameters to the pipeline flow allocation parameter set;
Synchronizing the M risk pipeline adjusting parameters and the W second risk pipeline adjusting parameters to the pipe network visual view to perform flow allocation fitting to obtain a second allocation visual view; and by analogy, carrying out flow multistage allocation fitting, and carrying out multi-round updating on the pipeline flow allocation parameter set until the risk pipeline identification positioning result based on the K monitoring early-warning devices is 0.
According to the embodiment, through carrying out the associated pipeline flow change analysis based on the single pipeline flow adjustment, the overall pipeline flow adjustment of the urban water supply network is realized, the problem that the local pipeline flow optimization interferes with the integral operation of the urban pipeline is avoided, and the technical effect of improving the urban water supply flow stability is achieved.
A800, mapping and allocating the target city pipeline based on the pipeline flow allocation parameter set.
In one embodiment, as shown in fig. 3, mapping deployment of the target city pipeline is performed based on the pipeline flow deployment parameter set, and then, a method step a800 provided in the present application further includes:
a810, pre-constructing a pipeline adjustment detection window and a monitoring early warning trigger limit value;
a820, interacting the K monitoring early-warning devices based on the pipeline adjusting detection window to obtain K groups of early-warning trigger records, wherein each group of early-warning trigger records comprises a historical deviation coefficient set and an early-warning trigger time set;
A830, extracting K early warning trigger frequencies based on the K groups of early warning trigger records, traversing the K early warning trigger frequencies by adopting the monitoring early warning trigger limit, and screening to obtain a pipeline to be replaced;
a840, obtaining a deviation coefficient set to be replaced and a trigger time set to be replaced based on the pipeline to be replaced in the K groups of early warning trigger record mapping calls;
a850, synchronizing the deviation coefficient set to be replaced to a pipeline model analysis module to obtain model parameters to be replaced;
a860, performing interval analysis based on the trigger time set to be replaced to obtain an early warning trigger interval extremum;
and A870, carrying out positioning replacement on the pipeline to be replaced according to the early warning trigger interval extremum and the parameter of the model to be replaced.
Specifically, in this embodiment, mapping allocation of the target city pipeline is performed based on the pipeline flow allocation parameter set, so as to implement that any water supply pipeline in the target city pipeline is within the corresponding pipeline limit value parameter.
Furthermore, in this embodiment, the operation and maintenance processing of the entity water supply pipelines is performed according to the situation that the flow rate of each entity water supply pipeline exceeds the pipeline limit value parameter in a certain period of time.
Specifically, the embodiment pre-constructs the pipeline adjustment detection window for analyzing whether the data time span of the physical water supply pipeline for size specification improvement is performed, and pre-constructs the monitoring early warning trigger limit value for judging the number of times that the water supply pipeline exceeds the corresponding pipeline limit value parameter in a period of time representing whether the water supply pipeline size specification replacement is performed.
And after mapping and allocation of the target city pipeline are performed based on the pipeline flow allocation parameter set, the K entity water supply pipelines recorded by the K monitoring and early warning devices trigger the K groups of early warning trigger records of the times and time of global water supply flow adjustment by the monitoring and early warning devices in a period of time, wherein each group of early warning trigger records comprises a historical deviation coefficient set and an early warning trigger time set, and it is understood that the historical deviation coefficient set is a plurality of pipeline flow data ordered based on the time sequence of the early warning trigger time set.
And extracting and obtaining the pipeline flow data quantity of K historical deviation coefficient sets in the K groups of early warning trigger records based on the K groups of early warning trigger records, wherein the pipeline flow data quantity is used as the K early warning trigger frequencies, traversing the K early warning trigger frequencies by adopting the monitoring early warning trigger limit, and screening to obtain pipelines to be replaced, wherein the pipeline quantity to be replaced is not specific, and the embodiment carries out the detailed explanation of the technical scheme by taking the pipeline quantity to be replaced as 1.
And obtaining the deviation coefficient set to be replaced and the trigger time set to be replaced, which are consistent with the historic deviation coefficient set and the early warning trigger time set in meaning included in each group of early warning trigger records, based on the mapping call of the pipeline to be replaced on the K groups of early warning trigger records.
The method for constructing the pipeline model analysis module comprises the steps of pre-constructing the pipeline model analysis module for analyzing the pipeline flow according to a deviation coefficient set to be replaced, and determining the pipeline model parameters of the pipeline specification capable of bearing the pipeline flow condition of the deviation coefficient set to be replaced. And synchronizing the deviation coefficient set to be replaced to a pipeline model analysis module to obtain the model parameters to be replaced.
And calculating the interval duration of the adjacent trigger time based on the trigger time set to be replaced, obtaining a plurality of interval durations, and further serializing the obtained plurality of interval durations to obtain the shortest interval duration as an early warning trigger interval extremum, wherein the early warning trigger interval extremum is the available time for the operation and maintenance personnel to detach and replace the pipeline, and the replacement of the pipeline to be replaced is completed in the early warning trigger interval extremum, so that the influence of pipeline replacement on pipeline network water supply of a target city pipeline can be reduced to the greatest extent.
And selecting and calling the pipeline according to the parameters of the model to be replaced, and positioning and replacing the pipeline to be replaced by selecting and calling the pipeline according to the extreme value of the early warning triggering interval.
The embodiment achieves the technical effects of adjusting the pipeline specification and selecting the pipeline replacement time according to the pipeline use condition, reducing the influence of pipeline replacement on the operation of the target city pipeline and improving the suitability of the pipeline in the target city pipeline and the flow condition.
In one embodiment, the to-be-replaced deviation coefficient set is synchronized to a pipeline model analysis module to obtain to-be-replaced model parameters, and the method step a850 provided in the present application further includes:
a851, the H-group twin polymerization pipeline is provided with H pipeline parameter identifiers;
a852, presetting a data feature extraction rule, and carrying out feature extraction on the H groups of aggregated sensing data by adopting the data feature extraction rule to obtain H groups of sample pipeline feature sets;
a853, constructing a pipeline model analysis module based on a knowledge graph, and filling data of the pipeline model analysis module by adopting the H pipeline parameter identifiers and the H groups of sample pipeline feature sets;
a854, carrying out feature extraction on the deviation coefficient set to be replaced by adopting the data feature extraction rule to obtain a feature set to be replaced;
a855, traversing the pipeline model analysis module based on the feature set to be replaced, and generating H pipeline replacement similarity indexes;
And A856, serializing the H pipeline replacement similarity indexes to obtain the parameters of the model to be replaced.
In one embodiment, the method step a855 provided herein further includes, based on traversing the pipeline model analysis module from the feature set to be replaced, generating H pipeline replacement similarity indexes:
a8551, the data feature extraction rule comprises i feature extraction indexes;
and A8552, pre-constructing a pipeline similarity index calculation formula, wherein the pipeline similarity index calculation formula is as follows:
wherein,replacing similarity index for pipeline, < >>For sample pipe characteristics, +.>For the feature to be replaced->Weight assignment for feature extraction index, +.>Cosine adjusting parameters of the features to be replaced;
a8553, obtaining a first sample pipeline feature set based on the H groups of sample pipeline feature set calls;
a8554, synchronizing the feature set to be replaced and the first sample pipeline feature set to the pipeline similarity index calculation formula to obtain a first pipeline replacement similarity index;
a8555 and so on, obtaining the H pipeline replacement similarity indexes.
Specifically, this embodiment is a refinement of step a850, and is also the best embodiment for constructing a pipeline model analysis module.
Specifically, in this embodiment, a data feature extraction rule is preset, where the data feature extraction rule includes i feature extraction indexes, and the feature extraction indexes may be median, mode, variance, and standard deviation.
And carrying out feature extraction on the H groups of aggregated sensing data by adopting the data feature extraction rule to obtain H groups of sample pipeline feature sets. And obtaining the H pipeline parameter identifiers of the H-group twin aggregation pipeline, and carrying out the identification processing of the H-group sample pipeline characteristic set.
And constructing a pipeline model analysis module based on the knowledge graph, wherein the first attribute of the pipeline model analysis module is a pipeline parameter, and the second attribute is a pipeline feature set. And adopting the H pipeline parameter identifiers as a first attribute value, and adopting the H group sample pipeline feature sets as a second attribute value to carry out data filling of the pipeline model analysis module.
And carrying out feature extraction on the deviation coefficient set to be replaced by adopting the data feature extraction rule to obtain a feature set to be replaced, traversing the pipeline model analysis module based on the feature set to be replaced, and generating H groups of feature set to be replaced-sample pipeline feature sets.
A pipeline similarity index calculation formula is pre-constructed, wherein the pipeline similarity index calculation formula is as follows:
wherein,replacing similarity index for pipeline, < >>For sample pipe characteristics, +.>For the feature to be replaced->Weight assignment for feature extraction index, +.>For cosine adjustment parameters of the feature to be replaced, the embodiment does not limit the weight assignment of the feature extraction index, and can set the numerical value according to the actual requirement.
And obtaining a first sample pipeline feature set based on the H groups of feature sets to be replaced-sample pipeline feature sets, grouping the feature data obtained based on the i feature extraction indexes and synchronizing the feature data to the pipeline similarity index calculation formula to obtain a first pipeline replacement similarity index, and the like to obtain the H pipeline replacement similarity indexes.
And serializing the H pipeline replacement similarity indexes to obtain pipeline parameter identifiers of first attribute values corresponding to minimum values of the pipeline replacement similarity indexes, and taking the pipeline parameter identifiers as parameters of the model to be replaced.
The embodiment achieves the technical effects of performing the specification analysis of the adaptive pipeline according to the pipeline flow condition, thereby effectively adjusting the pipeline with the flow adaptation defect and improving the water supply operation stability of the target city pipeline.
Example two
As shown in fig. 4, there is provided a city pipeline flow fitting optimization system based on visual and network flows, comprising: the system comprises a design information obtaining unit 1, a view construction executing unit 2, a historical data interaction unit 3, a limit value analysis executing unit 4, a monitoring limit value synchronizing unit 5, a real-time data interaction unit 6, a deployment module construction unit 7 and a mapping deployment executing unit 8, wherein:
A design information obtaining unit 1, configured to obtain urban pipeline design information, where the urban pipeline design information is obtained through interaction with a target urban pipeline;
the view construction execution unit 2 is used for constructing a pipe network visual view, wherein the pipe network visual view is generated by constructing a digital twin model based on the urban pipeline design information, the pipe network visual view comprises K twin pipelines, and each twin pipeline is marked with pipeline parameter information;
a historical data interaction unit 3, configured to obtain a historical pipeline network flow in an interaction manner, where the historical pipeline network flow includes K historical pipeline flow sequences, and the K historical pipeline flow sequences have K pipeline monitoring site identifiers;
the limit value analysis execution unit 4 is used for carrying out limit value analysis according to the historical pipeline network flow to obtain K pipeline limit value parameters of the K twin pipelines;
the monitoring limit value synchronizing unit 5 is used for mapping and arranging K monitoring early-warning devices according to the K twin pipelines in the pipeline network visual view according to the pipeline monitoring site identification, and synchronizing the K pipeline limit value parameters to the K monitoring early-warning devices;
the real-time data interaction unit 6 is used for interactively obtaining a real-time pipeline network flow, synchronizing the real-time pipeline network flow to the visual view of the pipe network, and carrying out risk pipeline identification positioning based on the K monitoring early-warning devices to obtain an early-warning adjustment node set;
The allocation module construction unit 7 is used for pre-constructing a pipeline flow allocation module, synchronizing the early warning adjustment node set to the pipeline flow allocation module to perform flow multistage allocation fitting, and obtaining a pipeline flow allocation parameter set;
and the mapping allocation executing unit 8 is used for carrying out mapping allocation of the target city pipeline based on the pipeline flow allocation parameter set.
In one embodiment, the limit value analysis execution unit 4 further includes:
positioning and obtaining K pipeline parameter information in the pipe network visual view based on the K pipeline monitoring site identifiers, wherein the K pipeline parameter information is mapped in association with the K historical pipeline flow sequences;
carrying out polymerization treatment on the K twin pipelines based on the K pipeline parameter information to obtain H groups of twin polymerization pipelines, wherein the pipeline parameter information of each group of twin polymerization pipelines has consistency;
the K historical pipeline flow sequences are mapped and aggregated according to the H-group twin aggregation pipeline to obtain H-group aggregation sensing data;
serializing the H groups of aggregation sensing data to obtain H aggregation pipeline limit value parameters;
and distributing the H polymerization pipeline limit parameters to the K twin pipelines according to the H group twin polymerization pipelines to obtain the K pipeline limit parameters.
In one embodiment, the deployment module construction unit 7 further comprises:
pre-constructing a pipeline flow steady-state function, wherein the pipeline flow steady-state function is as follows:
wherein,for the steady-state adjustment value of the flow,is pipeline flow data of multiple time sequence nodes,the weight parameters are the weight parameters of the multi-time sequence nodes;
and synchronizing the pipeline flow steady-state function to the pipeline flow allocation module.
In one embodiment, the deployment module construction unit 7 further comprises:
the early warning adjusting node set comprises M risk pipelines to be adjusted, wherein the M risk pipelines to be adjusted have M pipeline limit deviation coefficients, and M is a positive integer smaller than K;
synchronizing the M pipeline limit deviation coefficient mappings to the pipeline flow allocation module to obtain M risk pipeline adjustment parameters, and adding the M risk pipeline adjustment parameters to the pipeline flow allocation parameter set;
synchronizing the M risk pipeline adjustment parameters to the pipe network visual view to perform flow allocation fitting to obtain a first allocation visual view, wherein the K twin pipelines in the first allocation visual view have K adjustment pipeline flow parameters;
performing risk pipeline identification positioning of the first allocation visual view based on the K monitoring early-warning devices to obtain a second early-warning adjusting node set, wherein the second early-warning adjusting node set comprises W second risk pipelines to be adjusted;
Adopting the pipeline flow allocation module to perform adjustment analysis on the W second risk pipelines to be adjusted to obtain W second risk pipeline adjustment parameters, and adding the W second risk pipeline adjustment parameters to the pipeline flow allocation parameter set;
synchronizing the M risk pipeline adjusting parameters and the W second risk pipeline adjusting parameters to the pipe network visual view to perform flow allocation fitting to obtain a second allocation visual view;
and by analogy, carrying out flow multistage allocation fitting, and carrying out multi-round updating on the pipeline flow allocation parameter set until the risk pipeline identification positioning result based on the K monitoring early-warning devices is 0.
In one embodiment, the mapping deployment execution unit 8 further comprises:
pre-constructing a pipeline adjustment detection window and a monitoring early warning trigger limit value;
the K monitoring early-warning devices are interacted based on the pipeline adjusting detection window to obtain K groups of early-warning trigger records, wherein each group of early-warning trigger records comprises a historical deviation coefficient set and an early-warning trigger time set;
obtaining K early warning trigger frequencies based on the K groups of early warning trigger records, traversing the K early warning trigger frequencies by adopting the monitoring early warning trigger limit, and screening to obtain a pipeline to be replaced;
Obtaining a deviation coefficient set to be replaced and a trigger time set to be replaced based on the pipeline to be replaced in the K groups of early warning trigger record mapping calls;
synchronizing the deviation coefficient set to be replaced to a pipeline model analysis module to obtain model parameters to be replaced;
performing interval analysis based on the to-be-replaced trigger time set to obtain an early warning trigger interval extremum;
and carrying out positioning replacement on the pipeline to be replaced according to the early warning trigger interval extremum and the parameter of the model to be replaced.
In one embodiment, the mapping deployment execution unit 8 further comprises:
the H-group twin polymerization pipeline is provided with H pipeline parameter identifiers;
presetting a data feature extraction rule, and carrying out feature extraction on the H groups of aggregated sensing data by adopting the data feature extraction rule to obtain H groups of sample pipeline feature sets;
constructing a pipeline model analysis module based on a knowledge graph, and filling data of the pipeline model analysis module by adopting the H pipeline parameter identifiers and the H groups of sample pipeline feature sets;
extracting features of the deviation coefficient set to be replaced by adopting the data feature extraction rule to obtain a feature set to be replaced;
traversing the pipeline model analysis module based on the feature set to be replaced to generate H pipeline replacement similarity indexes;
And serializing the H pipeline replacement similarity indexes to obtain the parameters of the model to be replaced.
In one embodiment, the mapping deployment execution unit 8 further comprises:
the data feature extraction rule comprises i feature extraction indexes;
a pipeline similarity index calculation formula is pre-constructed, wherein the pipeline similarity index calculation formula is as follows:
wherein,replacing similarity index for pipeline, < >>For sample pipe characteristics, +.>For the feature to be replaced->Weight assignment for feature extraction index, +.>Cosine adjusting parameters of the features to be replaced;
acquiring a first sample pipeline feature set based on the H groups of sample pipeline feature set calls;
synchronizing the feature set to be replaced and the first sample pipeline feature set to the pipeline similarity index calculation formula to obtain a first pipeline replacement similarity index;
and so on, obtaining the H pipeline replacement similarity indexes.
Any of the methods or steps described above may be stored as computer instructions or programs in various non-limiting types of computer memories, and identified by various non-limiting types of computer processors, thereby implementing any of the methods or steps described above.
Based on the above-mentioned embodiments of the present invention, any improvements and modifications to the present invention without departing from the principles of the present invention should fall within the scope of the present invention.
Claims (6)
1. The urban pipeline flow allocation optimization method based on the visual view and the network flow is characterized by comprising the following steps of:
obtaining urban pipeline design information, wherein the urban pipeline design information is obtained through interaction of target urban pipelines;
constructing a pipe network visual view, wherein the pipe network visual view is generated by constructing a digital twin model based on the urban pipeline design information, the pipe network visual view comprises K twin pipelines, and each twin pipeline is marked with pipeline parameter information;
the method comprises the steps of interactively obtaining a historical pipeline network flow, wherein the historical pipeline network flow comprises K historical pipeline flow sequences, and the K historical pipeline flow sequences are provided with K pipeline monitoring site identifiers;
performing limit value analysis according to the historical pipeline network flow to obtain K pipeline limit value parameters of the K twin pipelines;
k monitoring early warning devices are arranged in a mapping mode according to the K twin pipelines in the pipeline network visual view according to the pipeline monitoring sites, and the K pipeline limit value parameters are synchronized to the K monitoring early warning devices;
Interactively obtaining a real-time pipeline network flow, synchronizing the real-time pipeline network flow to the visual view of the pipe network, and carrying out risk pipeline identification positioning based on the K monitoring early-warning devices to obtain an early-warning adjusting node set;
a pipeline flow allocation module is pre-constructed, the early warning adjustment node set is synchronized to the pipeline flow allocation module to carry out flow multistage allocation fitting, and a pipeline flow allocation parameter set is obtained;
mapping and allocating the target city pipeline based on the pipeline flow allocation parameter set;
wherein, the pre-constructed pipeline flow allocation module includes:
pre-constructing a pipeline flow steady-state function, wherein the pipeline flow steady-state function is as follows:
wherein,for the steady state regulation of the flow, +.>Pipe traffic data for multiple time-series nodes, < >>The weight parameters are the weight parameters of the multi-time sequence nodes;
synchronizing the pipeline flow steady-state function to the pipeline flow allocation module;
synchronizing the early warning adjustment node set to the pipeline flow allocation module for flow multistage allocation fitting to obtain a pipeline flow allocation parameter set, comprising:
the early warning adjusting node set comprises M risk pipelines to be adjusted, wherein the M risk pipelines to be adjusted have M pipeline limit deviation coefficients, and M is a positive integer smaller than K;
Synchronizing the M pipeline limit deviation coefficient mappings to the pipeline flow allocation module to obtain M risk pipeline adjustment parameters, and adding the M risk pipeline adjustment parameters to the pipeline flow allocation parameter set;
synchronizing the M risk pipeline adjustment parameters to the pipe network visual view to perform flow allocation fitting to obtain a first allocation visual view, wherein the K twin pipelines in the first allocation visual view have K adjustment pipeline flow parameters;
performing risk pipeline identification positioning of the first allocation visual view based on the K monitoring early-warning devices to obtain a second early-warning adjusting node set, wherein the second early-warning adjusting node set comprises W second risk pipelines to be adjusted;
adopting the pipeline flow allocation module to perform adjustment analysis on the W second risk pipelines to be adjusted to obtain W second risk pipeline adjustment parameters, and adding the W second risk pipeline adjustment parameters to the pipeline flow allocation parameter set;
synchronizing the M risk pipeline adjusting parameters and the W second risk pipeline adjusting parameters to the pipe network visual view to perform flow allocation fitting to obtain a second allocation visual view;
And by analogy, carrying out flow multistage allocation fitting, and carrying out multi-round updating on the pipeline flow allocation parameter set until the risk pipeline identification positioning result based on the K monitoring early-warning devices is 0.
2. The method of claim 1, wherein a limit analysis is performed from the historical pipeline network flow to obtain K pipeline limit parameters for the K twin pipelines, the method further comprising:
positioning and obtaining K pipeline parameter information in the pipe network visual view based on the K pipeline monitoring site identifiers, wherein the K pipeline parameter information is mapped in association with the K historical pipeline flow sequences;
carrying out polymerization treatment on the K twin pipelines based on the K pipeline parameter information to obtain H groups of twin polymerization pipelines, wherein the pipeline parameter information of each group of twin polymerization pipelines has consistency;
the K historical pipeline flow sequences are mapped and aggregated according to the H-group twin aggregation pipeline to obtain H-group aggregation sensing data;
serializing the H groups of aggregation sensing data to obtain H aggregation pipeline limit value parameters;
and distributing the H polymerization pipeline limit parameters to the K twin pipelines according to the H group twin polymerization pipelines to obtain the K pipeline limit parameters.
3. The method of claim 2, wherein mapping deployment of the target city pipeline is performed based on the pipeline flow deployment parameter set, after which the method further comprises:
pre-constructing a pipeline adjustment detection window and a monitoring early warning trigger limit value;
the K monitoring early-warning devices are interacted based on the pipeline adjusting detection window to obtain K groups of early-warning trigger records, wherein each group of early-warning trigger records comprises a historical deviation coefficient set and an early-warning trigger time set;
obtaining K early warning trigger frequencies based on the K groups of early warning trigger records, traversing the K early warning trigger frequencies by adopting the monitoring early warning trigger limit value, and screening to obtain a pipeline to be replaced;
obtaining a deviation coefficient set to be replaced and a trigger time set to be replaced based on the pipeline to be replaced in the K groups of early warning trigger record mapping calls;
synchronizing the deviation coefficient set to be replaced to a pipeline model analysis module to obtain model parameters to be replaced;
performing interval analysis based on the to-be-replaced trigger time set to obtain an early warning trigger interval extremum;
and carrying out positioning replacement on the pipeline to be replaced according to the early warning trigger interval extremum and the parameter of the model to be replaced.
4. The method of claim 3, wherein synchronizing the set of coefficient of variation to be replaced to a pipeline model analysis module obtains model parameters to be replaced, the method further comprising:
the H-group twin polymerization pipeline is provided with H pipeline parameter identifiers;
presetting a data feature extraction rule, and carrying out feature extraction on the H groups of aggregated sensing data by adopting the data feature extraction rule to obtain H groups of sample pipeline feature sets;
constructing a pipeline model analysis module based on a knowledge graph, and filling data of the pipeline model analysis module by adopting the H pipeline parameter identifiers and the H groups of sample pipeline feature sets;
extracting features of the deviation coefficient set to be replaced by adopting the data feature extraction rule to obtain a feature set to be replaced;
traversing the pipeline model analysis module based on the feature set to be replaced to generate H pipeline replacement similarity indexes;
and serializing the H pipeline replacement similarity indexes to obtain the parameters of the model to be replaced.
5. The method of claim 4, wherein H pipe replacement similarity indices are generated based on traversing the pipe model analysis module from a feature set to be replaced, the method further comprising:
The data feature extraction rule comprises i feature extraction indexes;
a pipeline similarity index calculation formula is pre-constructed, wherein the pipeline similarity index calculation formula is as follows:
wherein,replacing similarity index for pipeline, < >>For sample pipe characteristics, +.>For the feature to be replaced->Weight assignment for feature extraction index, +.>Cosine adjusting parameters of the features to be replaced;
acquiring a first sample pipeline feature set based on the H groups of sample pipeline feature set calls;
synchronizing the feature set to be replaced and the first sample pipeline feature set to the pipeline similarity index calculation formula to obtain a first pipeline replacement similarity index;
and so on, obtaining the H pipeline replacement similarity indexes.
6. Urban pipeline flow allocation optimization system based on visual view and network flow, characterized in that it comprises:
the urban pipeline design system comprises a design information obtaining unit, a design information obtaining unit and a control unit, wherein the design information obtaining unit is used for obtaining urban pipeline design information through interaction of target urban pipelines;
the view construction execution unit is used for constructing a pipe network visual view, wherein the pipe network visual view is generated by constructing a digital twin model based on the urban pipeline design information, the pipe network visual view comprises K twin pipelines, and each twin pipeline is marked with pipeline parameter information;
The historical data interaction unit is used for interactively obtaining historical pipeline network flows, wherein the historical pipeline network flows comprise K historical pipeline flow sequences, and the K historical pipeline flow sequences have K pipeline monitoring site identifiers;
the limit value analysis execution unit is used for carrying out limit value analysis according to the historical pipeline network flow to obtain K pipeline limit value parameters of the K twin pipelines;
the monitoring limit value synchronizing unit is used for mapping and arranging K monitoring early-warning devices according to the K twin pipelines in the pipeline network visual view according to the pipeline monitoring sites, and synchronizing the K pipeline limit value parameters to the K monitoring early-warning devices;
the real-time data interaction unit is used for interactively obtaining a real-time pipeline network flow, synchronizing the real-time pipeline network flow to the visual view of the pipe network, and carrying out risk pipeline identification positioning based on the K monitoring early-warning devices to obtain an early-warning adjustment node set;
the allocation module construction unit is used for pre-constructing a pipeline flow allocation module, synchronizing the early warning adjustment node set to the pipeline flow allocation module to perform flow multistage allocation fitting, and obtaining a pipeline flow allocation parameter set;
The mapping allocation execution unit is used for carrying out mapping allocation of the target city pipeline based on the pipeline flow allocation parameter set;
the deployment module construction unit is further configured to:
pre-constructing a pipeline flow steady-state function, wherein the pipeline flow steady-state function is as follows:
wherein,for the steady state regulation of the flow, +.>Pipe traffic data for multiple time-series nodes, < >>The weight parameters are the weight parameters of the multi-time sequence nodes;
synchronizing the pipeline flow steady-state function to the pipeline flow allocation module;
the early warning adjusting node set comprises M risk pipelines to be adjusted, wherein the M risk pipelines to be adjusted have M pipeline limit deviation coefficients, and M is a positive integer smaller than K;
synchronizing the M pipeline limit deviation coefficient mappings to the pipeline flow allocation module to obtain M risk pipeline adjustment parameters, and adding the M risk pipeline adjustment parameters to the pipeline flow allocation parameter set;
synchronizing the M risk pipeline adjustment parameters to the pipe network visual view to perform flow allocation fitting to obtain a first allocation visual view, wherein the K twin pipelines in the first allocation visual view have K adjustment pipeline flow parameters;
Performing risk pipeline identification positioning of the first allocation visual view based on the K monitoring early-warning devices to obtain a second early-warning adjusting node set, wherein the second early-warning adjusting node set comprises W second risk pipelines to be adjusted;
adopting the pipeline flow allocation module to perform adjustment analysis on the W second risk pipelines to be adjusted to obtain W second risk pipeline adjustment parameters, and adding the W second risk pipeline adjustment parameters to the pipeline flow allocation parameter set;
synchronizing the M risk pipeline adjusting parameters and the W second risk pipeline adjusting parameters to the pipe network visual view to perform flow allocation fitting to obtain a second allocation visual view;
and by analogy, carrying out flow multistage allocation fitting, and carrying out multi-round updating on the pipeline flow allocation parameter set until the risk pipeline identification positioning result based on the K monitoring early-warning devices is 0.
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