Pipeline network blockage analysis method, device, computer equipment and storage medium

Abstract

The present application relates to the field of artificial intelligence technology, and discloses a method, device, computer equipment and storage medium for analyzing pipe network blockage, wherein the method comprises: obtaining basic data and real-time monitoring data of the pipe network; analyzing and processing the basic data and real-time monitoring data using a blockage analysis model to obtain the corresponding blockage height of the pipe network, wherein the blockage analysis model is obtained by combining a hydrodynamic model with a logistic regression model and training; determining the blockage level of the pipe network based on historical defects, real-time monitoring data and blockage height in the corresponding basic data of the pipe network; and providing graded alarms according to the blockage level of the pipe network. The present application also relates to blockchain technology, and the basic data of the pipe network is stored in the blockchain. The present application facilitates relevant personnel to grasp the blockage situation of the pipe network in real time and to provide graded alarms for blockage.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence, and in particular to a pipeline blockage analysis method, device, computer equipment and storage medium. Background Art

[0002] In the process of urban construction, the construction of drainage pipe networks is indispensable. Drainage pipe networks have always been the top priority of the drainage system. Due to the particularity of sewage operation in drainage pipe networks, there is a great risk of clogging. The main sources of clogging are garbage, sludge and external foreign objects in sewage. Once the pipe network is clogged, the original drainage capacity will be limited, resulting in the inability to transport sewage in the attachment normally, high water levels in inspection wells or even overflow, polluting the living environment of residents and the appearance of the city, and it may also cause damage to the pipe network results. In the existing technology, it is often based on the presentation of topological relationships based on GIS and the data monitoring display of monitoring equipment. It can only be detected when the pipe network is completely blocked. Therefore, how to analyze and warn the clogging of the pipe network in advance has become an urgent problem to be solved. Summary of the invention

[0003] The present application provides a pipeline congestion analysis method, device, computer equipment and storage medium to solve the problem in the prior art of how to analyze and warn of pipeline congestion in advance.

[0004] To solve the above problems, the present application provides a pipeline blockage analysis method, comprising:

[0005] Obtain basic data and real-time monitoring data of the pipeline network;

[0006] According to the basic data and the real-time monitoring data, a siltation analysis model is used to perform analysis and processing to obtain the siltation height corresponding to the pipe network, wherein the siltation analysis model is obtained by combining and training a hydrodynamic model with a logistic regression model;

[0007] Determining the clogging level of the pipeline network based on historical defects, real-time monitoring data and clogging height in the basic data corresponding to the pipeline network;

[0008] A graded alarm is issued according to the blockage level of the pipeline network.

[0009] Furthermore, the basic data of the pipe network is obtained including:

[0010] Send a call request to the preset knowledge base, the call request carries a signature verification token;

[0011] Receive the signature verification result returned by the knowledge base, and when the signature verification result is passed, call the basic data in the preset knowledge base, wherein the signature verification result is obtained by the knowledge base through RSA asymmetric encryption verification according to the signature verification token.

[0012] Furthermore, the blockage height corresponding to the pipe network is obtained by analyzing and processing the basic data and the real-time monitoring data using a blockage analysis model, including:

[0013] Determine a corresponding preset range according to the basic data of each pipeline under the pipeline network, and compare and judge the real-time monitoring data with the corresponding preset range;

[0014] The real-time monitoring data outside the preset range is input into the blockage analysis model for analysis and processing to obtain the blockage height corresponding to the pipeline network.

[0015] Furthermore, before determining the blockage level of the pipeline network based on the historical defects, real-time monitoring data and blockage height in the basic data corresponding to the pipeline network, the method further includes:

[0016] According to the historical blockage conditions corresponding to each defect point in the historical defects, sorting each defect point in descending order;

[0017] According to the ranking, each defect point is assigned a corresponding risk weight.

[0018] Furthermore, the determining of the blockage level of the pipeline network based on the historical defects, real-time monitoring data and blockage height in the basic data corresponding to the pipeline network includes:

[0019] According to the historical defects, real-time monitoring data and clogging height, a risk level of each pipeline under the pipeline network is determined by using a risk prediction model, wherein the risk prediction model is obtained based on back propagation neural network training;

[0020] Matching each defect point with a risk weight with the pipeline to determine the risk weight of each pipeline;

[0021] The risk level and risk weight corresponding to each pipeline are added together to obtain the blockage level of each pipeline in the pipeline network.

[0022] Furthermore, the risk level of each pipeline in the pipeline network is determined by using a risk prediction model based on the historical defects, real-time monitoring data and clogging height, including:

[0023] Determining a first liquid level of the pipe network according to the historical defects and the blockage height;

[0024] According to the first liquid level and the real-time monitoring data, using a risk prediction model to predict the change trend of the second liquid level and flow rate of each of the pipelines within a preset time in the future;

[0025] Based on the changing trends of the second liquid level and flow rate, the risk level of each of the pipelines is determined.

[0026] Furthermore, after the graded alarm is issued according to the blockage level of the pipe network, the method further includes:

[0027] receiving feedback of a real blockage depth corresponding to the blockage height;

[0028] The blockage analysis model is retrained according to the actual blockage depth.

[0029] In order to solve the above problems, the present application also provides a pipe network blockage analysis device, the device comprising:

[0030] Acquisition module, used to obtain basic data of the pipeline network and real-time monitoring data;

[0031] A height calculation module, used to analyze and process the basic data and the real-time monitoring data using a siltation analysis model to obtain the siltation height corresponding to the pipe network, wherein the siltation analysis model is obtained by combining and training a hydrodynamic model with a logistic regression model;

[0032] A level determination module, for determining the blockage level of the pipeline network based on historical defects, real-time monitoring data and blockage height in basic data corresponding to the pipeline network;

[0033] The alarm module is used to issue graded alarms according to the blockage level of the pipeline network.

[0034] In order to solve the above problem, the present application also provides a computer device, including:

[0035] at least one processor; and,

[0036] a memory communicatively connected to the at least one processor; wherein,

[0037] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the pipeline blockage analysis method as described above.

[0038] In order to solve the above problems, the present application also provides a non-volatile computer-readable storage medium, on which computer-readable instructions are stored. When the computer-readable instructions are executed by a processor, the pipe network blockage analysis method as described above is implemented.

[0039] A pipe network blockage analysis method, device, computer equipment, and storage medium provided in the embodiments of the present application have at least the following beneficial effects compared with the prior art:

[0040] By acquiring basic data of the pipeline network and real-time detection, the basic data and real-time detection data are analyzed and processed using a blockage analysis model to obtain the blockage height corresponding to the pipeline network. The blockage height in the pipeline network is judged by using the model, thereby improving accuracy and processing efficiency. The blockage analysis model is obtained by combining and training a hydrodynamic model with a logistic regression model; based on historical defects, real-time monitoring data and blockage height in the basic data corresponding to the pipeline network, the blockage level of the pipeline network is determined, and graded alarms are issued according to the blockage level of the pipeline network, so that personnel can grasp the blockage situation of the pipeline network in real time and issue graded alarms, which facilitates subsequent processing personnel to determine the priority of processing each blockage point. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the scheme in the present application, a brief introduction will be given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1 A schematic diagram of a flow chart of a pipeline network blockage analysis method provided in an embodiment of the present application;

[0043] Figure 2 A schematic diagram of a module of a pipe network blockage analysis device provided in one embodiment of the present application;

[0044] Figure 3 A schematic diagram of the structure of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0045] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0046] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly or implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0047] This application provides a pipeline network blockage analysis method. Figure 1 As shown, Figure 1 A schematic flow chart of a pipeline blockage analysis method provided in one embodiment of the present application.

[0048] In this embodiment, the pipeline network blockage analysis method includes:

[0049] S1. Obtain basic data of the pipe network and real-time monitoring data;

[0050] Specifically, in the present application, the pipe network is a drainage pipe network, which is used to transport sewage. Its basic data includes the entire layout of the entire drainage pipe network, the parameters of each drainage pipe under the drainage pipe network, the parameters of the drainage pipes include pipe length, pipe diameter, etc., and historical defects corresponding to each drainage pipe. The historical defects may be historical blockages, which may have been processed or not; the real-time monitoring data includes real-time data such as flow rate, flow velocity and liquid level of each drainage pipe under the drainage pipe network.

[0051] The basic data of the above-mentioned pipe network can be obtained from the database or input manually; the real-time monitoring data is collected and uploaded by sensors installed in each drainage pipe under the drainage pipe network.

[0052] Furthermore, the obtaining of data of each enterprise includes:

[0053] Send a call request to the preset knowledge base, the call request carries a signature verification token;

[0054] Receive the signature verification result returned by the knowledge base, and when the signature verification result is passed, call the basic data in the preset knowledge base, wherein the signature verification result is obtained by the knowledge base through RSA asymmetric encryption verification according to the signature verification token.

[0055] Specifically, since the basic data of the pipeline network may involve urban safety issues, all abnormal data will be saved in the preset database. Therefore, when obtaining the basic data, the database will perform a signature verification step to ensure the security of the data and avoid data leakage.

[0056] By verifying the signature, we can ensure data security and avoid data leakage.

[0057] S2. Analyze and process the basic data and the real-time monitoring data using a siltation analysis model to obtain the siltation height corresponding to the pipe network, wherein the siltation analysis model is obtained by combining and training a hydrodynamic model with a logistic regression model;

[0058] Specifically, the hydrodynamic model is a mathematical model that describes the relationship between the force and movement of water flow. Based on the basic equations of fluid mechanics, a mathematical model is established to perform numerical simulation on the dynamic process of flowing water. The logistic regression model is a generalized linear regression analysis model. By combining the logistic regression model with the hydrodynamic model and training the combined model, a clogging analysis model is obtained, and then the basic data and real-time monitoring data are used for analysis and processing to obtain the clogging height of each drainage pipe under the drainage network, and the clogging height is specifically the height of possible clogging obstacles in each drainage pipe.

[0059] In the present application, the hydrodynamic model is specifically a one-dimensional hydrodynamic model, and the logistic regression model is used to adjust the parameters of the one-dimensional hydrodynamic model in real time; specifically, since the input parameters of the hydrodynamic model are specific values ​​when calculating, that is, only a fixed siltation height can be calculated, but in reality, due to the flushing of water in the pipe network, the siltation point may move, so the calculated siltation height is inaccurate at this time; therefore, after combining the hydrodynamic model with the logistic regression model, the siltation height can be calculated in real time according to the direction of water flow in the pipe network, and the real-time siltation height can be calculated by adjusting the parameters of the hydrodynamic model in real time through the logistic regression model. According to the logistic regression model, the hydrodynamic model is adjusted in real time according to the basic data and real-time monitoring data.

[0060] Since the hydrodynamic model is a mathematical model, only the logistic regression model needs to be trained here. Specifically, the logistic regression model is trained according to the historical basic data and the historical detection data to obtain the corresponding parameters. The hydrodynamic model uses the parameters to calculate to obtain the corresponding blockage height, and the blockage height is compared with the corresponding actual blockage height. The comparison result is fed back to the logistic regression model to adjust the parameters in the logistic regression model until the difference between the blockage height obtained by the logistic regression model and the hydrodynamic model using the historical basic data and the historical detection data and the corresponding actual blockage height is less than the preset value.

[0061] Furthermore, the blockage height corresponding to the pipe network is obtained by analyzing and processing the basic data and the real-time monitoring data using a blockage analysis model, including:

[0062] Determine a corresponding preset range according to the basic data of each pipeline under the pipeline network, and compare and judge the real-time monitoring data with the corresponding preset range;

[0063] The real-time monitoring data outside the preset range is input into the blockage analysis model for analysis and processing to obtain the blockage height corresponding to the pipeline network.

[0064] Specifically, under different pipe diameter sizes, there is a parameter measurement relationship between a certain liquid level height, flow rate and flow velocity. Under the pipe diameter in the basic data, when any of the detection factors actually collected has a parameter deviation, the attention level can be raised to suspect blockage. Subsequently, for the suspected blockage points, the possible blockage obstacle height, i.e., the blockage height, is calculated based on the collected detection data.

[0065] The preset range is obtained by statistically analyzing the historical detection data of the pipe network, and there are different preset ranges for pipe diameters under different basic data.

[0066] By calculating the clogging height at places in the pipeline network where clogging may occur, it is convenient to determine the clogging level at that place later.

[0067] S3, determining the clogging level of the pipeline network based on historical defects, real-time monitoring data and clogging height in the basic data corresponding to the pipeline network;

[0068] Specifically, the historical defects are divided into structural defects and functional defects according to the national standard for pipeline defects, and are divided into severity levels 1-4. The blockage level of the pipeline network is determined by real-time monitoring data and the blockage height obtained above.

[0069] Furthermore, before determining the blockage level of the pipeline network based on the historical defects, real-time monitoring data and blockage height in the basic data corresponding to the pipeline network, the method further includes:

[0070] According to the historical blockage conditions corresponding to each defect point in the historical defects, sorting each defect point in descending order;

[0071] According to the ranking, each defect point is assigned a corresponding risk weight.

[0072] Specifically, before determining the blockage level of each pipeline in the real-time pipeline network based on real-time monitoring data, the historical blockage of each defect point in the historical defects in the basic data, that is, the historical blockage height of each pipeline, is used to sort the defect points in descending order, and the corresponding risk weight is assigned to each defect point according to the sorting. The size of the risk weight assigned is used to indicate the height of the historical blockage of each pipeline. For defect points with high risk weights, great attention should be paid. The historical defects are divided into the historical blockage corresponding to each defect point and the pipeline network defect level recognized according to the national standard.

[0073] Each pipeline is weighted according to its historical blockage situation, so that when the pipeline is subsequently processed, pipelines with high risk weights are given more attention, and the blockage level is subsequently determined based on the risk weights.

[0074] Furthermore, the determining of the blockage level of the pipeline network based on the historical defects, real-time monitoring data and blockage height in the basic data corresponding to the pipeline network includes:

[0075] According to the historical defects, real-time monitoring data and clogging height, a risk level of each pipeline under the pipeline network is determined by using a risk prediction model, wherein the risk prediction model is obtained based on back propagation neural network training;

[0076] Matching each defect point with a risk weight with the pipeline to determine the risk weight of each pipeline;

[0077] The risk level and risk weight corresponding to each pipeline are added together to obtain the blockage level of each pipeline in the pipeline network.

[0078] Specifically, the risk level of each pipeline in the pipeline network is determined by using the risk prediction model using the historical defects, real-time monitoring data and siltation height. For example, the historical defect is that a level 1 siltation defect already exists. The pipeline flow rate in the real-time monitoring data is a certain percentage slower than the flow rate of the defect-free pipeline, and the flow rate in the real-time monitoring data still has a downward trend over a period of time. In addition, due to the siltation height, the liquid level in the real-time monitoring data is also rising. According to the historical defects, siltation degree and the changing trend of the real-time monitoring data, the risk prediction model calculates the timeliness of the specific changing trend, and combines the speed of the timeliness to obtain the corresponding risk level of the pipeline;

[0079] By matching each defect point with a risk weight with the pipeline, so that each pipeline has its corresponding risk weight, and by combining the risk level and risk weight corresponding to each pipeline, the blockage level of each pipeline in the pipeline network is determined.

[0080] For example, there are existing pipelines 1, 2, and 3, and the pipelines 1, 2, and 3 are all connected to the same node to form a simple pipe network. Pipelines 1, 2, and 3 have historical blockages of 0.5m, 0.6m, and 0.7m in the past, respectively. By sorting and weighting pipelines 1, 2, and 3, risk weights of 0.8, 1, and 1.2 corresponding to pipelines 1, 2, and 3 are obtained, respectively; and subsequently, according to the historical defects, real-time monitoring data, and blockage heights corresponding to pipelines 1, 2, and 3, the corresponding risk levels are determined using a risk prediction model, and the risk levels of pipelines 1, 2, and 3 are obtained to be 3, 1, and 2. In this embodiment of the present application, the risk weights and risk levels corresponding to pipelines 1, 2, and 3 can be added to obtain blockage levels of 3.8, 2, and 3.2.

[0081] Back propagation neural network is referred to as BP (Back Propagation) network. It is a multi-layer neural network with three or more layers, and each layer is composed of several neurons.

[0082] By using the risk prediction model to determine the risk level of each pipeline, and combining the risk level and risk weight, the corresponding blockage level is obtained, thereby achieving accurate prediction of the blockage level.

[0083] Furthermore, the risk level of each pipeline in the pipeline network is determined by using a risk prediction model based on the historical defects, real-time monitoring data and clogging height, including:

[0084] Determining a first liquid level of the pipe network according to the historical defects and the blockage height;

[0085] According to the first liquid level and the real-time monitoring data, using a risk prediction model to predict the change trend of the second liquid level and flow rate of each of the pipelines within a preset time in the future;

[0086] Based on the changing trends of the second liquid level and flow rate, the risk level of each of the pipelines is determined.

[0087] Specifically, the historical defects are divided into structural defects and functional defects according to the national standard for pipe network defects, and are divided into severity levels of 1-4, and are divided according to national standards. For example, when a certain tree branch penetrates the pipe network, it is difficult for sensors and other detection devices to identify it, and a single tree branch stuck in the pipe network has little effect on the flow rate and liquid level of the sewage in the pipe network; but when there is sludge or garbage blocking the tree branch, combined with the influence of the tree branch, the blockage here is likely to increase rapidly.

[0088] Therefore, firstly, the first liquid level of the pipeline network is determined based on the historical defects and the aforementioned blockage height, which can also be indirectly equal to the minimum liquid level under the influence of the historical defects and the blockage height at the same time and under the flow of water in the pipeline network; according to the first liquid level and real-time monitoring data, the risk prediction model is used to predict the change trend of the second liquid level and flow rate of each pipeline within a preset time in the future; the second liquid level is the future predicted liquid level in the pipeline.

[0089] Based on the changing trends of the second liquid level and flow rate, the risk level of each of the pipelines is determined.

[0090] For example, when the second liquid level shows a downward trend and the flow rate shows an upward trend, the risk level is 0;

[0091] When the second liquid level shows a downward trend and the flow rate also shows a downward trend, the risk is 0;

[0092] When the second liquid level shows a slow upward trend and the flow rate also shows a downward trend, the risk level is level 2, and the slow upward trend means that within the preset time, it is predicted that the second liquid level will only increase by a height of less than 0.2 m;

[0093] When the second liquid level shows a slowly rising trend and the flow rate also shows an increasing trend, the risk level is level 1;

[0094] When the second liquid level shows a rapid upward trend and the flow rate also shows an upward trend, the risk level is level 3; the rapid increase means that within a preset time, it is predicted that the second liquid level will increase by 0.2 m or more;

[0095] When the second liquid level shows a rapid upward trend and the flow rate shows a downward trend, the risk level is level 4.

[0096] The specific value can be set freely.

[0097] By using the risk prediction model to predict the second liquid level and flow rate change trend of the pipeline within a preset time in the future, and determining the pipeline risk level based on the second liquid level and flow rate change trend, the processing efficiency and accuracy are improved.

[0098] S4. Producing graded alarms according to the blockage levels of the pipe network.

[0099] Specifically, graded alarms are issued according to the blockage levels of the pipeline network to give different levels of attention to relevant personnel.

[0100] For example, when the clogging level of the pipeline network is 0-1, the corresponding alarm level is level 1, and the corresponding pipeline at the front end is displayed in green, and the corresponding conclusion is that there is a risk but it does not affect the operation of the pipeline network;

[0101] When the clogging level of the pipe network is 1-2, the corresponding alarm level is level 2, and the corresponding pipe at the front end is displayed in blue. The corresponding conclusion is that the low liquid level affects the operation of the pipe network and needs to be cleaned up as appropriate;

[0102] The details will not be explained in detail later, and can be set according to actual conditions.

[0103] According to the alarm display and conclusion, relevant personnel can arrange relevant staff to go to the corresponding pipeline for dredging.

[0104] Furthermore, after the graded alarm is issued according to the blockage level of the pipe network, the method further includes:

[0105] receiving feedback of a real blockage depth corresponding to the blockage height;

[0106] The blockage analysis model is retrained according to the actual blockage depth.

[0107] Specifically, since relevant staff will be arranged to go to the corresponding pipeline for dredging according to the alarm, before dredging, the relevant staff will also measure the actual depth of blockage and feedback it online. According to the feedback of the actual blockage depth, the blockage analysis model will be retrained to optimize the blockage analysis model so that the results obtained in the subsequent analysis can be closer to the actual value.

[0108] By using feedback data to retrain the congestion analysis model, the congestion analysis model can be tuned and the processing effect of the model can be improved.

[0109] It should be emphasized that in order to further ensure the privacy and security of the data, all the data of the basic data can also be stored in a node of a blockchain.

[0110] The blockchain referred to in this application is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, platform product service layer, and application service layer.

[0111] By acquiring basic data of the pipeline network and real-time detection, the basic data and real-time detection data are analyzed and processed using a blockage analysis model to obtain the blockage height corresponding to the pipeline network. The blockage height in the pipeline network is judged by using the model, thereby improving accuracy and processing efficiency. The blockage analysis model is obtained by combining and training a hydrodynamic model with a logistic regression model; based on historical defects, real-time monitoring data and blockage height in the basic data corresponding to the pipeline network, the blockage level of the pipeline network is determined, and graded alarms are issued according to the blockage level of the pipeline network, so that personnel can grasp the blockage situation of the pipeline network in real time and issue graded alarms, which facilitates subsequent processing personnel to determine the priority of processing each blockage point.

[0112] This embodiment also provides a pipe network blockage analysis device, such as Figure 2 The figure shows a functional module diagram of the pipeline blockage analysis device of the present application.

[0113] The pipe network congestion analysis device 100 described in the present application can be installed in an electronic device. According to the functions to be implemented, the pipe network congestion analysis device 100 may include an acquisition module 101, a height calculation module 102, a level determination module 103, and an alarm module 104. The module described in the present application may also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0114] In this embodiment, the functions of each module / unit are as follows:

[0115] The acquisition module 101 is used to acquire basic data and real-time monitoring data of the pipe network;

[0116] Further, the acquisition module includes a request sending submodule and a data calling submodule;

[0117] A request sending submodule is used to send a call request to a preset knowledge base, wherein the call request carries a signature verification token;

[0118] The data calling submodule is used to receive the verification result returned by the knowledge base, and when the verification result is passed, call the basic data in the preset knowledge base, wherein the verification result is obtained by the knowledge base through RSA asymmetric encryption verification according to the verification token.

[0119] Through the cooperation of the request sending submodule and the data calling submodule, the security of data is guaranteed and leakage is avoided through the signature verification call method.

[0120] A height calculation module 102 is used to analyze and process the basic data and the real-time monitoring data using a siltation analysis model to obtain a siltation height corresponding to the pipe network, wherein the siltation analysis model is obtained by combining and training a hydrodynamic model with a logistic regression model;

[0121] Further, the height calculation module 102 includes a comparison submodule and a calculation submodule;

[0122] The comparison submodule is used to determine the corresponding preset range according to the basic data of each pipeline under the pipeline network, and compare and judge the real-time monitoring data with the corresponding preset range;

[0123] The calculation submodule is used to input the real-time monitoring data outside the preset range into the blockage analysis model for analysis and processing to obtain the blockage height corresponding to the pipeline network.

[0124] By cooperating with the comparison submodule and the calculation submodule, the blockage height is calculated for the places in the pipe network where blockage may occur, so as to facilitate the subsequent determination of the blockage level at that place.

[0125] A level determination module 103, for determining the blockage level of the pipeline network based on historical defects, real-time monitoring data and blockage height in the basic data corresponding to the pipeline network;

[0126] Furthermore, the pipe network blockage analysis device 100 includes a sorting module and a value assignment module;

[0127] The sorting module is used to sort each of the defect points in the historical defects in descending order according to the historical blockage conditions corresponding to each defect point;

[0128] The assignment module is used to assign a corresponding risk weight to each defect point according to the ranking.

[0129] Through the cooperation of the sorting module and the assignment module, each pipeline is weighted according to the historical blockage situation, so that when the pipeline is subsequently processed, the pipeline with a high risk weight is taken seriously, and the blockage level is judged in combination with the risk weight.

[0130] Furthermore, the level determination module 103 includes a risk level determination submodule, a matching submodule and a congestion level determination submodule;

[0131] The risk level determination submodule is used to determine the risk level of each pipeline under the pipeline network according to the historical defects, real-time monitoring data and clogging height using a risk prediction model, wherein the risk prediction model is obtained based on back propagation neural network training;

[0132] The matching submodule is used to match each defect point with a risk weight with the pipeline to determine the risk weight of each pipeline;

[0133] The blockage level determination submodule is used to add the risk level and risk weight corresponding to each pipeline to obtain the blockage level of each pipeline in the pipeline network.

[0134] Through the cooperation of the risk level determination submodule, the matching submodule and the blockage level determination submodule, the risk level of each pipeline is determined using a risk prediction model, and the corresponding blockage level is obtained according to the risk level and risk weight, thereby achieving accurate prediction of the blockage level.

[0135] Still further, the risk level determination submodule further includes a first liquid level determination unit, a prediction unit, and a risk determination unit;

[0136] The first liquid level determination unit is used to determine a first liquid level of the pipe network according to the historical defects and the blockage height;

[0137] The prediction unit is used to predict the change trend of the second liquid level and flow rate of each of the pipelines within a preset time in the future by using a risk prediction model according to the first liquid level and real-time monitoring data;

[0138] The risk determination unit is used to determine the risk level of each of the pipelines based on the change trends of the second liquid level and flow rate.

[0139] Through the cooperation of the first liquid level determination unit, the prediction unit and the risk determination unit, the risk prediction model is used to predict the second liquid level and flow rate change trend of the pipeline within a preset time in the future, and the pipeline risk level is determined according to the second liquid level and flow rate change trend, thereby improving the processing efficiency and accuracy.

[0140] The alarm module 104 is used to issue graded alarms according to the blockage level of the pipeline network.

[0141] Further, the alarm module 104 includes a feedback receiving module and a retraining module;

[0142] The feedback receiving module is used to receive feedback on the real blockage depth corresponding to the blockage height;

[0143] The retraining module is used to retrain the blockage analysis model according to the actual blockage depth.

[0144] Through the cooperation of the feedback receiving module and the retraining module, the feedback data is used to retrain the congestion analysis model, so as to optimize the congestion analysis model and improve the processing effect of the model.

[0145] By adopting the above-mentioned device, the pipeline congestion analysis device 100 uses the acquisition module 101, the height calculation module 102, the level determination module 103 and the alarm module 104 in coordination, obtains the basic data of the pipeline network and real-time detection, and uses the congestion analysis model to analyze and process the basic data and real-time detection data to obtain the congestion height corresponding to the pipeline network. By using the model to make a judgment on the congestion height in the pipeline network, the accuracy and processing efficiency are improved. The congestion analysis model is obtained by combining and training a hydrodynamic model with a logistic regression model; then, based on the historical defects, real-time monitoring data and congestion height in the corresponding basic data of the pipeline network, the congestion level of the pipeline network is determined, and graded alarms are issued according to the congestion level of the pipeline network, so that personnel can grasp the congestion situation of the pipeline network in real time and issue graded alarms, which is convenient for subsequent processing personnel to determine the priority of processing each congestion point.

[0146] The present application also provides a computer device. Figure 3 , Figure 3 This is a basic structural block diagram of the computer device in this embodiment.

[0147] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected through a system bus. It should be noted that the figure only shows a computer device 4 with components 41-43, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0148] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0149] The memory 41 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 41 can be an internal storage unit of the computer device 4, such as a hard disk or memory of the computer device 4. In other embodiments, the memory 41 can also be an external storage device of the computer device 4, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. equipped on the computer device 4. Of course, the memory 41 can also include both the internal storage unit of the computer device 4 and its external storage device. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions of the pipe network blockage analysis method, etc. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or are to be output.

[0150] The processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to run computer-readable instructions stored in the memory 41 or process data, such as computer-readable instructions for running the pipe network blockage analysis method.

[0151] The network interface 43 may include a wireless network interface or a wired network interface. The network interface 43 is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0152] This embodiment implements the steps of the pipeline blockage analysis method as described in the above embodiment when the processor executes computer-readable instructions stored in the memory. By acquiring basic data and real-time detection of the pipeline network, the basic data and real-time detection data are analyzed and processed using a blockage analysis model to obtain the blockage height corresponding to the pipeline network. The blockage height in the pipeline network is judged by using the model, thereby improving accuracy and processing efficiency. The blockage analysis model is obtained by combining and training a hydrodynamic model with a logistic regression model. Then, based on historical defects, real-time monitoring data and blockage height in the basic data corresponding to the pipeline network, the blockage level of the pipeline network is determined, and graded alarms are issued according to the blockage level of the pipeline network, thereby facilitating personnel to grasp the blockage situation of the pipeline network in real time and issue graded alarms, thereby facilitating subsequent processing personnel to determine the priority of processing each blockage point.

[0153] The embodiment of the present application also provides a computer-readable storage medium, which stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to execute the steps of the pipeline blockage analysis method as described above, by acquiring basic data and real-time detection of the pipeline network, using a blockage analysis model to analyze and process the basic data and real-time detection data, and obtain the blockage height corresponding to the pipeline network, and by using the model to make a judgment on the blockage height in the pipeline network, the accuracy and processing efficiency are improved. The blockage analysis model is obtained by combining and training a hydrodynamic model with a logistic regression model; then, based on historical defects, real-time monitoring data and blockage height in the basic data corresponding to the pipeline network, the blockage level of the pipeline network is determined, and graded alarms are issued according to the blockage level of the pipeline network, so that personnel can grasp the blockage situation of the pipeline network in real time, and graded alarms are issued, so that subsequent processing personnel can determine the priority of processing each blockage point.

[0154] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0155] The pipe network blockage analysis device, computer equipment, and computer-readable storage medium of the above-mentioned embodiments of the present application have the same technical effects as the pipe network blockage analysis method of the above-mentioned embodiments, and will not be expanded here.

[0156] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A method for analyzing pipe network blockage, characterized in that: The method comprises: Obtain basic data of the pipeline network and real-time monitoring data; According to the basic data and the real-time monitoring data, a siltation analysis model is used to perform analysis and processing to obtain the siltation height corresponding to the pipe network, wherein the siltation analysis model is obtained by combining and training a hydrodynamic model with a logistic regression model; According to the historical blockage conditions corresponding to each defect point in the historical defects in the basic data corresponding to the pipeline network, sort the defect points in descending order; According to the ranking, assigning a corresponding risk weight to each defect point; Determining the clogging level of the pipeline network based on historical defects, real-time monitoring data and clogging height in the basic data corresponding to the pipeline network; Producing graded alarms according to the blockage level of the pipe network; Wherein, determining the blockage level of the pipeline network based on historical defects, real-time monitoring data and blockage height in the basic data corresponding to the pipeline network includes: According to the historical defects, real-time monitoring data and clogging height, a risk level of each pipeline under the pipeline network is determined by using a risk prediction model, wherein the risk prediction model is obtained based on back propagation neural network training; Matching each defect point with a risk weight with the pipeline to determine the risk weight of each pipeline; The risk level and risk weight corresponding to each pipeline are added together to obtain the blockage level of each pipeline in the pipeline network.

2. The pipe network blockage analysis method according to claim 1, characterized in that: The basic data of the pipeline network obtained includes: Send a call request to the preset knowledge base, the call request carries a signature verification token; Receive the signature verification result returned by the knowledge base, and when the signature verification result is passed, call the basic data in the preset knowledge base, wherein the signature verification result is obtained by the knowledge base through RSA asymmetric encryption verification according to the signature verification token.

3. The pipe network blockage analysis method according to claim 1, characterized in that: The blockage analysis model is used to analyze and process the basic data and the real-time monitoring data to obtain the blockage height corresponding to the pipe network, which includes: Determine a corresponding preset range according to the basic data of each pipeline under the pipeline network, and compare and judge the real-time monitoring data with the corresponding preset range; The real-time monitoring data outside the preset range is input into the blockage analysis model for analysis and processing to obtain the blockage height corresponding to the pipeline network.

4. The pipe network blockage analysis method according to claim 1, characterized in that: Determining the risk level of each pipeline under the pipeline network by using a risk prediction model based on the historical defects, real-time monitoring data and clogging height includes: Determining a first liquid level of the pipe network according to the historical defects and the blockage height; According to the first liquid level and the real-time monitoring data, using a risk prediction model to predict the change trend of the second liquid level and flow rate of each of the pipelines within a preset time in the future; Based on the changing trends of the second liquid level and flow rate, the risk level of each of the pipelines is determined.

5. The pipe network blockage analysis method according to claim 1, characterized in that: After the step of issuing graded alarms according to the blockage levels of the pipe network, the method further includes: receiving feedback of a real blockage depth corresponding to the blockage height; The blockage analysis model is retrained according to the actual blockage depth.

6. A pipe network blockage analysis device, characterized in that: The device comprises: Acquisition module, used to obtain basic data of the pipeline network and real-time monitoring data; A height calculation module, used to analyze and process the basic data and the real-time monitoring data using a siltation analysis model to obtain the siltation height corresponding to the pipe network, wherein the siltation analysis model is obtained by combining and training a hydrodynamic model with a logistic regression model; Wherein, the pipe network blockage analysis device includes a sorting module and an assignment module; The sorting module is used to sort each defect point in the historical defects in the basic data corresponding to the pipeline network in descending order according to the historical blockage situation corresponding to each defect point; The assignment module is used to assign a corresponding risk weight to each defect point according to the ranking; A level determination module, for determining the blockage level of the pipeline network based on historical defects, real-time monitoring data and blockage height in basic data corresponding to the pipeline network; Wherein, the level determination module includes: A risk level determination submodule, for determining the risk level of each pipeline under the pipeline network according to the historical defects, real-time monitoring data and clogging height using a risk prediction model, wherein the risk prediction model is obtained based on back propagation neural network training; A matching submodule, used for matching each defect point with a risk weight with the pipeline to determine the risk weight of each pipeline; A blockage level determination submodule, used to assign corresponding risk weights to each of the defect points according to the ranking; The alarm module is used to issue graded alarms according to the blockage level of the pipeline network.

7. A computer device, characterized in that: The computer device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the pipeline blockage analysis method according to any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the pipeline blockage analysis method according to any one of claims 1 to 5 is implemented.

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