An intelligent diagnosis method and device for water transportation network

Abstract

The present application provides an intelligent diagnostic method and device for a water pipeline network. The method includes: obtaining a target water pipeline network dataset corresponding to the water pipeline network; analyzing each data in the target water pipeline network dataset in accordance with preset rules corresponding to each data in the target water pipeline network dataset to determine an abnormal dataset in the target water pipeline network dataset; comparing each abnormal data in the abnormal dataset with the corresponding data in the abnormal dataset to determine a target abnormality type corresponding to each abnormal data in the abnormal dataset; matching a corresponding historical fault case set from a case library based on the target abnormality type, performing case matching from the historical fault case set in accordance with the abnormal dataset to obtain a target historical fault case, and obtaining a fault diagnosis conclusion corresponding to the abnormal dataset based on the target historical fault case. The present application is used for real-time monitoring and fault diagnosis of water pipeline networks.

Description

Technical Field

[0001] The present disclosure relates to the technical field of water transportation pipeline networks, and in particular to an intelligent diagnosis method and device for water transportation pipeline networks. Background Art

[0002] Water pipeline networks occupy a crucial position in modern urban infrastructure. They are not only crucial for the daily lives of residents but also impact numerous other aspects, such as industrial production and fire safety. However, with the continuous expansion of urban areas and the increasing complexity of pipeline systems, the operation and maintenance of water pipeline networks face numerous challenges.

[0003] Traditional water pipeline inspection methods usually rely on manual inspections and human experience to maintain water pipelines. Therefore, when fault diagnosis and treatment are based on the experience of technicians, human misjudgment and omission are prone to occur, and the fault point cannot be quickly located. At the same time, it also consumes a lot of manpower and time, resulting in a long pipeline fault handling cycle, affecting water supply safety and user experience. Summary of the Invention

[0004] In view of this, an embodiment of the present application provides an intelligent diagnosis method and device for a water transportation network, which can be used for real-time monitoring and fault diagnosis of the water transportation network.

[0005] In a first aspect, an embodiment of the present application provides an intelligent diagnosis method for a water transportation network, comprising:

[0006] Obtain a target water transportation network dataset corresponding to the water transportation network;

[0007] Analyzing each data in the target water transportation pipe network data set in combination with preset rules corresponding to each data in the target water transportation pipe network data set to determine an abnormal data set in the target water transportation pipe network data set;

[0008] Comparing each abnormal data in the abnormal data set with the corresponding data in the abnormal data set to determine the target abnormal type corresponding to each abnormal data in the abnormal data set;

[0009] Based on the target anomaly type, a corresponding historical fault case set is matched from the case library, and case matching is performed from the historical fault case set in combination with the abnormal data set to obtain a target historical fault case, and a fault diagnosis conclusion corresponding to the abnormal data set is obtained based on the target historical fault case; the fault diagnosis conclusion includes the cause of the fault and the fault repair measures.

[0010] As an optional implementation manner of an embodiment of the present application, after matching the corresponding historical fault case set from the case library based on the target abnormality type, performing case matching from the historical fault case set in combination with the abnormal data set to obtain the target historical fault case, and obtaining the fault diagnosis conclusion corresponding to the abnormal data set based on the target historical fault case, the method also includes: outputting the fault diagnosis conclusion to the mobile terminal corresponding to the water transportation pipeline network operation and maintenance personnel.

[0011] As an optional implementation of the embodiment of the present application, obtaining a target water transportation network dataset corresponding to the water transportation network includes:

[0012] Collecting an initial water transportation network data set corresponding to the water transportation network;

[0013] A preprocessing operation is performed on the initial water transportation pipe network dataset to obtain the target water transportation pipe network dataset.

[0014] As an optional implementation of the embodiment of the present application, the analyzing each data in the target water transportation network data set in combination with the preset rules corresponding to each data in the target water transportation network data set to determine the abnormal data set in the target water transportation network data set includes:

[0015] Obtaining preset rules corresponding to each data in the target water transportation network data set from a preset rule library; the preset rule library includes the preset rules corresponding to each data in the target water transportation network data set;

[0016] Based on the preset rules corresponding to the respective data in the target water transportation pipe network data set, the data in the target water transportation pipe network data set that does not conform to the preset rules are screened out to generate the abnormal data set.

[0017] As an optional implementation of the embodiment of the present application, comparing each abnormal data in the abnormal data set with corresponding data in the abnormal data set to determine the target abnormality type corresponding to each abnormal data in the abnormal data set includes:

[0018] In combination with the preset range of abnormal data corresponding to each abnormal type in the abnormal data set, the abnormal type corresponding to each data in the abnormal data set is determined according to the size of each data in the abnormal data set; the abnormal data set includes an abnormal type set corresponding to the water transportation pipeline network, and a preset range of abnormal data corresponding to each abnormal type in the abnormal type set.

[0019] As an optional implementation of the embodiment of the present application, matching a corresponding historical fault case set from a case library based on the target abnormality type, performing case matching from the historical fault case set in combination with the abnormal data set to obtain a target historical fault case, and obtaining a fault diagnosis conclusion corresponding to the abnormal data set based on the target historical fault case, includes:

[0020] Matching a corresponding historical fault case set from a case library based on the target anomaly type, performing case matching from the historical fault case set in combination with the anomaly data set, and obtaining the target historical fault case;

[0021] Obtain the fault cause and fault repair measures corresponding to the target historical fault case to generate the fault diagnosis conclusion.

[0022] As an optional implementation of the embodiment of the present application, the method further includes:

[0023] Performing real-time simulation on the water transportation network to obtain a water transportation network simulation data set;

[0024] Each data in the water transportation pipe network simulation data set is compared with corresponding data in the target water transportation pipe network data set to verify the target water transportation pipe network data set.

[0025] In a second aspect, an embodiment of the present application provides an intelligent diagnostic device for a water transportation network, comprising:

[0026] A data acquisition unit, configured to acquire a target water transportation network data set corresponding to the water transportation network;

[0027] an analysis and reasoning unit, configured to analyze each data in the target water transportation pipe network data set in combination with a preset rule corresponding to each data in the target water transportation pipe network data set, so as to determine an abnormal data set in the target water transportation pipe network data set;

[0028] an anomaly identification unit, configured to compare each abnormal data in the abnormal data set with corresponding data in the abnormal data set, and determine a target anomaly type corresponding to each abnormal data in the abnormal data set;

[0029] A fault diagnosis unit is used to match a corresponding historical fault case set from a case library based on the target abnormality type, perform case matching from the historical fault case set in combination with the abnormal data set to obtain a target historical fault case, and obtain a fault diagnosis conclusion corresponding to the abnormal data set based on the target historical fault case; the fault diagnosis conclusion includes the cause of the fault and the fault repair measures.

[0030] As an optional implementation of the embodiment of the present application, the device further includes: a fault diagnosis conclusion output unit, which is used to output the fault diagnosis conclusion to the mobile terminal corresponding to the water transportation network operation and maintenance personnel.

[0031] As an optional implementation of the embodiment of the present application, the data acquisition unit is specifically used to acquire an initial water delivery network dataset corresponding to the water delivery network; perform preprocessing operations on the initial water delivery network dataset to obtain the target water delivery network dataset.

[0032] As an optional implementation manner of an embodiment of the present application, the analysis and reasoning unit is specifically used to obtain the preset rules corresponding to each data in the target water transportation network data set from a preset rule base; the preset rule base includes the preset rules corresponding to each type of data in the target water transportation network data set; based on the preset rules corresponding to each data in the target water transportation network data set, the data in the target water transportation network data set that does not comply with the corresponding preset rules is filtered out to generate the abnormal data set.

[0033] As an optional implementation of the embodiment of the present application, the abnormality identification unit is specifically configured to determine the abnormality type corresponding to each data in the abnormal data set according to the size of each data in the abnormal data set, in combination with a preset range of abnormal data corresponding to each abnormal type in the abnormal data set; the abnormal data set includes an abnormality type set corresponding to the water transportation pipeline network, and a preset range of abnormal data corresponding to each abnormal type in the abnormal type set;

[0034] As an optional implementation of an embodiment of the present application, the fault diagnosis unit is specifically used to match the corresponding historical fault case set from the case library based on the target abnormality type, perform case matching from the historical fault case set in combination with the abnormal data set, and obtain the target historical fault case; obtain the fault cause and fault repair measures corresponding to the target historical case to generate the fault diagnosis conclusion.

[0035] As an optional implementation of the embodiment of the present application, the device also includes: a digital pipe network real-time simulation unit, which is used to perform real-time simulation of the water transmission pipe network and obtain a water transmission pipe network simulation data set; for each data in the water transmission pipe network simulation data set, the corresponding data in the target water transmission pipe network data set is used for comparison to verify the target water transmission pipe network data set.

[0036] In a third aspect, an embodiment of the present application provides an electronic device comprising: a memory and a processor, wherein the memory is used to store a computer program; and the processor is used to enable the electronic device to implement the intelligent diagnosis method for the water transportation network described in any of the above embodiments when executing the computer program.

[0037] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a computing device, the computing device implements the intelligent diagnosis method for the water transportation network described in any of the above embodiments.

[0038] The intelligent diagnosis method for a water transportation network provided in an embodiment of the present application specifically includes: obtaining a target water transportation network data set corresponding to the water transportation network; analyzing each data in the target water transportation network data set in combination with a preset rule corresponding to each data in the target water transportation network data set to determine an abnormal data set in the target water transportation network data set; comparing each abnormal data in the abnormal data set with the corresponding data in the abnormal data set to determine a target abnormality type corresponding to each abnormal data in the abnormal data set; matching a corresponding historical fault case set from a case library based on the target abnormality type, performing case matching from the historical fault case set in combination with the abnormal data set to obtain a target historical fault case, and obtaining a target abnormality type for the abnormal data set based on the target historical fault case. The corresponding fault diagnosis conclusion; the fault diagnosis conclusion includes the cause of the fault and the fault repair measures; the present application can intelligently monitor the abnormal data set of the water transmission pipeline network in real time by obtaining the target water transmission pipeline network data set corresponding to the water transmission pipeline network. When the abnormal data set is obtained, the target abnormal type corresponding to the abnormal data set is first determined, and then the corresponding target historical fault cases are screened out according to the target abnormal type, and then the fault diagnosis conclusion corresponding to the abnormal data set is obtained based on the target historical fault cases. Compared with the traditional method of completely manually maintaining the water transmission pipeline network, the present application can quickly obtain the abnormal data set and the fault diagnosis conclusion corresponding to the abnormal data set by monitoring the target water transmission pipeline network data set, thereby saving manpower and time and improving the operation and maintenance efficiency of the water transmission pipeline network. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0041] Figure 1 This is a flowchart of one of the steps of the intelligent diagnosis method for water transportation network provided in an embodiment of the present application;

[0042] Figure 2 This is a second flowchart of the steps of the intelligent diagnosis method for water transportation pipe networks provided in an embodiment of the present application;

[0043] Figure 3 A system architecture diagram corresponding to the intelligent diagnosis method for water transportation networks provided in an embodiment of the present application;

[0044] Figure 4 A schematic diagram of the structure of an intelligent diagnostic device for a water transportation network provided in an embodiment of the present application;

[0045] Figure 5 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0046] In order to more clearly understand the above-mentioned objectives, features and advantages of the present disclosure, the scheme of the present disclosure will be further described below. It should be noted that the embodiments of the present disclosure and the features therein can be combined with each other in the absence of conflict.

[0047] In the following description, many specific details are set forth to facilitate a full understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all of the embodiments.

[0048] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete way. In addition, in the description of the embodiments of the present application, unless otherwise specified, the meaning of "multiple" refers to two or more.

[0049] It should be noted that, in this document, the term "comprises" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article, or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0050] The present application embodiment provides an intelligent diagnosis method for a water transportation network, referring to Figure 1 As shown, the intelligent diagnosis method for the water transportation network includes the following steps S101 to S104:

[0051] S101. Obtain a target water transportation network dataset corresponding to the water transportation network.

[0052] In some embodiments, the water pipeline network is often used in scenarios such as residential water supply, commercial and industrial water supply, and therefore plays an irreplaceable and important role in ensuring residents' lives, supporting economic development, ensuring public safety, and protecting the environment. It is an indispensable piece of infrastructure in modern society and is of great significance for promoting sustainable social development. Therefore, from an operation and maintenance perspective, this application uses the intelligent diagnostic method for the water pipeline network to perform real-time monitoring and fault diagnosis of the water pipeline network.

[0053] Specifically, the target water transportation network data set is real-time data of the water transportation network. The target water transportation network data set can be collected at a certain moment using sensors and Internet of Things technology: various data of the water transportation network, specifically including network branch flow, network branch flow direction, network branch pressure, pump speed, pressure, pipe branch conductivity, sample spectrum in the pipe, pipe branch sound wave and other data.

[0054] S102: Analyze each data in the target water transportation network data set in combination with preset rules corresponding to each data in the target water transportation network data set to determine an abnormal data set in the target water transportation network data set.

[0055] In some embodiments, the preset rules corresponding to the various data may be to set a corresponding preset range for each of the various data. For example, when the branch flow of the network in the target water transportation network data set exceeds the corresponding preset range, it means that the current water transportation network has a fault, resulting in abnormal branch flow of the network, that is, the branch flow of the network exceeds the corresponding preset range; it should be noted that this application does not make specific limitations on the preset rules corresponding to the various data in the target water transportation network data set.

[0056] S103: Compare each abnormal data in the abnormal data set with corresponding data in the abnormal data set to determine a target abnormality type corresponding to each abnormal data in the abnormal data set.

[0057] In some embodiments, abnormal data ranges corresponding to different abnormal types are collected in advance. After obtaining the abnormal data set, the abnormal data ranges corresponding to each abnormal data in the abnormal data set are classified to generate the abnormal type to which each abnormal data in the abnormal data set belongs, that is, to obtain the target abnormal type.

[0058] S104. Match a corresponding historical fault case set from a case library based on the target abnormality type, perform case matching from the historical fault case set in combination with the abnormal data set to obtain a target historical fault case, and obtain a fault diagnosis conclusion corresponding to the abnormal data set based on the target historical fault case.

[0059] The fault diagnosis conclusion includes the fault cause and the fault repair measures.

[0060] In some embodiments, after determining the abnormal type corresponding to each data in the abnormal data set, the corresponding historical case is determined based on the abnormal type corresponding to the abnormal data, so as to obtain the cause of the abnormal data through the historical case, that is, to obtain the fault diagnosis conclusion, and then through the historical case, call out the corresponding solution and provide it to the current operation and maintenance personnel for reference, so as to improve the operation and maintenance efficiency of the operation and maintenance personnel.

[0061] In some embodiments, after obtaining the fault diagnosis conclusion based on the abnormal data set, the fault diagnosis conclusion can be transmitted to the mobile terminal of the corresponding operation and maintenance personnel so that the operation and maintenance personnel can obtain the fault diagnosis conclusion in time and perform maintenance more clearly.

[0062] It should be noted that this application uses the data in the target water pipeline network dataset to perform interference analysis, primarily to evaluate and address various interference situations that may occur in the water pipeline network. Specifically, the operating status and parameter changes of different devices and components in the pipeline network, such as pumps, valves, and pipes, may affect each other. Interference analysis aims to detect these interactions to identify potential fault points or weak links, thereby providing a more comprehensive assessment of the status of the water pipeline network.

[0063] Therefore, the intelligent diagnosis method for the water transportation network provided in the present application will output the fault diagnosis conclusion corresponding to the abnormal data set to the mobile terminal corresponding to the water transportation network operation and maintenance personnel after obtaining the fault diagnosis conclusion.

[0064] It should be noted that the operation and maintenance personnel can log in through the login interface of the intelligent diagnosis system of the water transportation network on the mobile terminal using the corresponding permissions and account of the operation and maintenance personnel, so that after the intelligent diagnosis system for the water transportation network obtains the fault diagnosis conclusion, the fault diagnosis conclusion can be sent to the mobile terminal of the operation and maintenance personnel in a timely manner.

[0065] The intelligent diagnosis method for a water transportation network provided in an embodiment of the present application specifically includes: obtaining a target water transportation network data set corresponding to the water transportation network; analyzing each data in the target water transportation network data set in combination with a preset rule corresponding to each data in the target water transportation network data set to determine an abnormal data set in the target water transportation network data set; comparing each abnormal data in the abnormal data set with the corresponding data in the abnormal data set to determine a target abnormality type corresponding to each abnormal data in the abnormal data set; matching a corresponding historical fault case set from a case library based on the target abnormality type, performing case matching from the historical fault case set in combination with the abnormal data set to obtain a target historical fault case, and obtaining a target abnormality type for the abnormal data set based on the target historical fault case. The corresponding fault diagnosis conclusion; the fault diagnosis conclusion includes the cause of the fault and the fault repair measures; the present application can intelligently monitor the abnormal data set of the water transmission pipeline network in real time by obtaining the target water transmission pipeline network data set corresponding to the water transmission pipeline network. When the abnormal data set is obtained, the target abnormal type corresponding to the abnormal data set is first determined, and then the corresponding target historical fault cases are screened out according to the target abnormal type, and then the fault diagnosis conclusion corresponding to the abnormal data set is obtained based on the target historical fault cases. Compared with the traditional method of completely manually maintaining the water transmission pipeline network, the present application can quickly obtain the abnormal data set and the fault diagnosis conclusion corresponding to the abnormal data set by monitoring the target water transmission pipeline network data set, thereby saving manpower and time and improving the operation and maintenance efficiency of the water transmission pipeline network.

[0066] As an extension and refinement of the above embodiment, refer to Figure 2 As shown, the embodiment of the present application further provides an intelligent diagnosis method for a water transportation network, and the intelligent diagnosis method for a water transportation network includes the following S201 to S208:

[0067] S201: Collect an initial water transportation network data set corresponding to the water transportation network.

[0068] In some embodiments, the initial water delivery network data set corresponding to the water delivery network may be acquired by collecting data from a plurality of sensors provided in the water delivery network. It should be noted that the data in the initial water delivery network data set acquired by the plurality of sensors may fluctuate due to the external environment, the sensors themselves, and other reasons. Such data fluctuations are significantly different from the data fluctuations caused by conventional faults. Therefore, the data generated by unconventional faults may be filtered out first through data preprocessing methods, and smoothing and normalization may be performed to facilitate subsequent reasoning analysis and improve the accuracy of the analysis.

[0069] S202: Preprocess the initial water transportation network dataset to obtain the target water transportation network dataset.

[0070] In some embodiments, the preprocessing operations may include data filtering, data smoothing, and normalization. Specifically, data filtering can remove data that does not meet specific conditions to improve data quality and usability. In this embodiment, data with incorrect format or extremely abnormal data can be removed from the initial water pipeline network data set. Sensor measurement data may experience random fluctuations due to environmental factors. Data smoothing can reduce these fluctuations and improve data quality. Data normalization can convert data of different dimensions to facilitate subsequent analysis and comparison.

[0071] In an embodiment of the present application, when performing a preprocessing operation on the initial water transmission network dataset, a mathematical model and a simulation model related to the operation of the water transmission network can be used to perform a preprocessing operation on the collected initial water transmission network dataset. Then, after the initial water transmission network dataset is obtained through multiple sensors, the initial water transmission network dataset is transmitted to the mathematical model and the simulation model used for the preprocessing operation based on the Internet of Things technology.

[0072] S203: Obtaining preset rules corresponding to each data in the target water transportation network data set from a preset rule library.

[0073] The preset rule library includes the preset rules corresponding to each type of data in the target water transportation network data set.

[0074] In some embodiments, the preset rule database includes preset rules set for each data in the water transportation network dataset, that is, rules set by developers based on experience for data such as pipe network branch flow, pipe network branch flow direction, pipe network branch pressure, pump speed, pressure, pipe branch conductivity, pipe sample spectrum, pipe branch sound wave, etc. Specifically, experienced water supply and drainage engineers can also make normal and reasonable judgments on the flow range of a specific type of pipe network branch based on previous project experience, and then set the preset rule corresponding to the pipe network branch flow based on the normal range of the pipe network branch flow to: the pipe network branch flow is greater than or equal to the upper limit of the normal range of the pipe network branch flow, and is less than or equal to the lower limit of the normal range of the pipe network branch flow. Then, after obtaining the pipe network branch flow in the target water transportation network dataset at the current moment, the size of the pipe network branch flow is compared with the upper and lower limits of the normal range of the pipe network branch flow in the corresponding preset rule to determine whether the pipe network branch flow at the current moment is normal. Once it is outside the normal range, the pipe network branch flow at the current moment is determined to be abnormal.

[0075] S204 : Based on the preset rules corresponding to the respective data in the target water transportation network data set, filter out the data in the target water transportation network data set that do not comply with the preset rules to generate the abnormal data set.

[0076] Specifically, for each data in the target water transportation network data set, corresponding preset rules are used to determine whether each data is normal and reasonable, and abnormal and unreasonable data are screened out to generate the abnormal data set.

[0077] S205 : Determine the abnormal type corresponding to each data in the abnormal data set according to the preset range of abnormal data corresponding to each abnormal type in the abnormal data set and the size of each data in the abnormal data set.

[0078] The abnormal data set includes an abnormality type set corresponding to the water transportation network and a preset range of abnormal data corresponding to each abnormality type in the abnormality type set.

[0079] Specifically, common types of abnormalities may include: pipeline leakage, pipeline blockage, pipeline corrosion, abnormal pressure, etc.; among them, pipeline leakage is the most common type of failure, which may be caused by pipeline aging, loose connections or external force damage; pipeline blockage is often caused by scaling on the inner wall of the pipeline, entry of foreign objects or accumulation of sediment in the pipeline, resulting in poor water flow; pipeline corrosion is usually caused by water quality problems, unsuitable pipeline materials or long-term use, and may cause pipeline rupture in severe cases; abnormal pressure in the pipeline network may be caused by water pump failure, pipeline damage or unreasonable pipeline layout, affecting the stability of water supply.

[0080] In some embodiments, based on the size of the exception data and the relationship between each exception data, the current corresponding exception type can be obtained based on the data in the exception data set; therefore, the developer needs to combine the historical exception data of the exception type to set a preset range of exception data corresponding to each exception type. Once exception data appears within the range, the exception type of the current exception data can be clearly obtained.

[0081] S206 : Match a corresponding historical fault case set from a case library based on the target abnormality type, perform case matching from the historical fault case set in combination with the abnormality data set, and obtain the target historical fault case.

[0082] In some embodiments, the historical fault case collection includes all historical fault cases and is stored in the case library. The case library also stores case information corresponding to each historical fault case. Specifically, the case information includes the time of occurrence, fault cause, fault repair measures, and information about the person responsible for operation and maintenance. After the target abnormal historical fault case is identified, the case information corresponding to the case is immediately retrieved from the case library to obtain the fault cause and repair measures corresponding to the target historical fault case.

[0083] S207: Obtain the fault cause and fault repair measures corresponding to the target historical fault case to generate the fault diagnosis conclusion.

[0084] In the embodiment of the present application, the fault cause and the fault repair measures corresponding to the target historical fault case are used to generate the fault diagnosis conclusion corresponding to the current abnormal data set, so as to provide it to the corresponding operation and maintenance personnel.

[0085] S208: Output the fault diagnosis conclusion to the mobile terminal corresponding to the water transportation network operation and maintenance personnel.

[0086] Specifically, the mobile terminal corresponding to the operation and maintenance personnel can be a smart phone, on which a specific application (Application, APP) is installed, which is used to carry the system corresponding to the intelligent diagnosis method for the water transportation network provided in this application, so that after obtaining the fault diagnosis conclusion corresponding to the abnormal data set, the fault diagnosis conclusion is output to the smart phone corresponding to the water transportation network operation and maintenance personnel.

[0087] As an extension and refinement of the above embodiment, an intelligent diagnosis method for a water transportation network provided in an embodiment of the present application further includes the following steps 1 to 3:

[0088] Step 1: Perform real-time simulation on the water transportation network to obtain a water transportation network simulation data set.

[0089] In an embodiment of the present application, by performing real-time simulation on the water transportation network, on the one hand, the abnormal data set can be inferred by the analysis of the above steps S201 to S204, and on the other hand, the abnormal data set can be obtained by comparing the water transportation network simulation data set with the current real-time water transportation network data; thereby verifying whether the data in the abnormal data set inferred by the analysis of steps S201 to S204 is accurate, and then real-time monitoring of the water transportation network is performed.

[0090] Specifically, when conducting real-time simulation of the water transportation network, it can be achieved based on digital twin technology. Digital twins usually model and simulate physical entities in the real world through sensors, data analysis and simulation technologies to achieve real-time monitoring, prediction and optimization of the real world.

[0091] Step 2: For each data in the water transportation pipe network simulation data set, compare it with the corresponding data in the target water transportation pipe network data set to verify the target water transportation pipe network data set.

[0092] It should be noted that the present application can also perform predictive simulation on the water transportation network, obtain a water transportation network simulation data set, perform fault prediction based on the water transportation network prediction data set, and then provide feedback to the operation and maintenance personnel to deploy corresponding fault prevention measures in advance to reduce the probability of fault occurrence. At the same time, the corresponding historical fault cases are obtained based on the fault prediction results, and maintenance suggestions are provided to the operation and maintenance personnel based on the historical fault cases, helping the network management personnel to perform preventive maintenance in advance and extend the service life of the network equipment.

[0093] As an extension and refinement of the above embodiment, the embodiment of the present application provides an intelligent diagnosis method for a water transportation network corresponding to an intelligent diagnosis system 300 for a water transportation network, referring to Figure 3 As shown in the figure, the system architecture includes: a data acquisition module 31, an analysis and reasoning module 32, an anomaly identification module 33 and a fault diagnosis module 34, wherein:

[0094] The data acquisition module 31 is used to obtain a target water transportation network data set corresponding to the water transportation network.

[0095] The analysis and reasoning module 32 is used to analyze the various data in the target water transportation network data set in combination with the preset rules corresponding to the various data in the target water transportation network data set to determine the abnormal data set in the target water transportation network data set; specifically, the analysis and reasoning module 32 also includes a rule base 321 for storing the preset rules.

[0096] The anomaly identification module 33 is configured to compare each abnormal data in the abnormal data set with corresponding data in the abnormal data set to determine a target anomaly type corresponding to each abnormal data in the abnormal data set.

[0097] The fault diagnosis module 34 is used to match the corresponding historical fault case set from the case library based on the target abnormality type, perform case matching from the historical fault case set in combination with the abnormal data set to obtain the target historical fault case, and obtain the fault diagnosis conclusion corresponding to the abnormal data set based on the target historical fault case; the fault diagnosis conclusion includes the cause of the fault and the fault repair measures; specifically, the fault diagnosis module 34 also includes a case library 341 for storing the historical fault case set.

[0098] It should be noted that the intelligent diagnosis system 300 for the water transportation network also includes: a water transportation network simulation module, which is used to perform real-time simulation of the water transportation network and obtain a water transportation network simulation data set; for each data in the water transportation network simulation data set, the corresponding data in the target water transportation network data set is used for comparison to verify the target water transportation network data set.

[0099] In combination with the above embodiments, for example, the application examples of the intelligent diagnosis method for water transportation pipe networks provided by this application in real life can be specifically:

[0100] A system corresponding to the intelligent diagnosis method for a water transportation network provided by an embodiment of the present application is deployed in a certain city's water transportation network. By installing flow sensors, pressure sensors, and conductivity sensors at key nodes of the pipeline network, the above sensors are used to collect a real-time initial water transportation network data set in the pipeline network, and the initial water transportation network data set is preprocessed. The preset rules corresponding to the above data are obtained from the rule library, and a preliminary judgment is made to obtain an abnormal data set. At this time, it is found that the abnormal data set includes flow abnormality data. The corresponding target abnormality type is further determined based on the flow abnormality data, that is, pipeline leakage. Then, combined with the historical pipeline leakage case set in the case library, the corresponding target historical fault case is confirmed, and the fault cause and fault repair measures are obtained from the information corresponding to the target historical fault case, that is, there is a leakage risk in the pipeline corresponding to the flow sensor, and a detailed diagnostic report is generated for reference by operation and maintenance personnel.

[0101] For another example, a system corresponding to the intelligent diagnostic method for water supply networks provided in an embodiment of the present application is deployed in a large industrial park to collect speed and pressure data from each pump station in the park's water supply system. Through simulation analysis of the model library and combined with the anomaly detection rules in the rule library, a pressure anomaly at a certain pump station is identified. After comparison with the defect feature library, it is determined to be an internal fault in the pump station. Through case library matching, it is confirmed that the performance degradation is caused by the aging of a component in the pump station. The system generates maintenance recommendations and notifies the operation and maintenance personnel to perform maintenance.

[0102] Based on the same inventive concept, as an implementation of the above-mentioned system, an embodiment of the present application further provides an intelligent diagnostic device for a water transportation network. This embodiment corresponds to the aforementioned system embodiment. For ease of reading, this embodiment will no longer repeat the details of the aforementioned method embodiment one by one, but it should be clear that the intelligent diagnostic device for a water transportation network in this embodiment can correspond to and implement all the contents of the aforementioned method embodiment.

[0103] The present invention provides an intelligent diagnostic device for water pipe networks. Figure 4 This is a schematic diagram of the structure of the intelligent diagnostic device for water transportation network, as shown in Figure 4 As shown, the intelligent diagnostic device for the water transportation network includes the following:

[0104] The data acquisition unit 401 is used to obtain a target water transportation network data set corresponding to the water transportation network;

[0105] An analysis and reasoning unit 402 is configured to analyze each data in the target water transportation network data set in combination with a preset rule corresponding to each data in the target water transportation network data set to determine an abnormal data set in the target water transportation network data set;

[0106] Anomaly identification unit 403, configured to compare each abnormal data in the abnormal data set with corresponding data in the abnormal data set, and determine a target abnormality type corresponding to each abnormal data in the abnormal data set;

[0107] The fault diagnosis unit 404 is used to match the corresponding historical fault case set from the case library based on the target abnormality type, perform case matching from the historical fault case set in combination with the abnormal data set to obtain the target historical fault case, and obtain the fault diagnosis conclusion corresponding to the abnormal data set based on the target historical fault case; the fault diagnosis conclusion includes the cause of the fault and the fault repair measures.

[0108] As an optional implementation of the embodiment of the present application, the device further includes: a fault diagnosis conclusion output unit, which is used to output the fault diagnosis conclusion to the mobile terminal corresponding to the water transportation network operation and maintenance personnel.

[0109] As an optional implementation of the embodiment of the present application, the data acquisition unit 401 is specifically used to acquire an initial water delivery network dataset corresponding to the water delivery network; perform preprocessing operations on the initial water delivery network dataset to obtain the target water delivery network dataset.

[0110] As an optional implementation of the embodiment of the present application, the analysis and reasoning unit 402 is specifically used to obtain the preset rules corresponding to each data in the target water transportation network data set from the preset rule base; the preset rule base includes the preset rules corresponding to each type of data in the target water transportation network data set; based on the preset rules corresponding to each data in the target water transportation network data set, the data in the target water transportation network data set that does not comply with the corresponding preset rules is filtered out to generate the abnormal data set.

[0111] As an optional implementation of the embodiment of the present application, the abnormality identification module 403 is specifically used to determine the abnormality type corresponding to each data in the abnormal data set according to the size of each data in the abnormal data set, in combination with the preset range of abnormal data corresponding to each abnormal type in the abnormal data set; the abnormal data set includes an abnormality type set corresponding to the water transportation pipeline network, and a preset range of abnormal data corresponding to each abnormal type in the abnormal type set;

[0112] As an optional implementation of an embodiment of the present application, the fault diagnosis unit 404 is specifically used to match the corresponding historical fault case set from the case library based on the target abnormality type, perform case matching from the historical fault case set in combination with the abnormal data set, and obtain the target historical fault case; obtain the fault cause and fault repair measures corresponding to the target historical case to generate the fault diagnosis conclusion.

[0113] As an optional implementation of the embodiment of the present application, the device also includes: a digital pipeline network real-time simulation unit, which is used to perform real-time simulation of the water transmission pipeline network to obtain a water transmission pipeline network simulation data set; for each data in the water transmission pipeline network simulation data set, compare it with the corresponding data in the target water transmission pipeline network data set to verify the target water transmission pipeline network data set; simulate a water transmission pipeline network prediction data set of the water transmission pipeline network for a preset time in the future to perform fault prediction based on the water transmission pipeline network prediction data set.

[0114] Based on the same inventive concept, an embodiment of the present disclosure further provides an electronic device. Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure, such as Figure 5As shown, the electronic device provided in this embodiment includes: a memory 501 and a processor 502, wherein the memory 501 is used to store computer programs; the processor 502 is used to execute the intelligent diagnosis system for water transportation pipelines provided in the above embodiment when executing the computer program.

[0115] Based on the same inventive concept, an embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the computing device implements the intelligent diagnostic system for the water transportation network provided in the above embodiment.

[0116] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code.

[0117] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0118] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0119] Computer-readable media includes both permanent and non-permanent, removable and non-removable storage media. Storage media can implement any method or technology for storing information, which can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media, such as modulated data signals and carrier waves.

[0120] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.

Claims

1. An intelligent diagnostic method for a water transportation network, characterized in that: include: Obtain a target water transportation network dataset corresponding to the water transportation network; Analyzing each data in the target water transportation pipe network data set in combination with preset rules corresponding to each data in the target water transportation pipe network data set to determine an abnormal data set in the target water transportation pipe network data set; Comparing each abnormal data in the abnormal data set with data corresponding to the abnormal type, and determining a target abnormal type corresponding to each abnormal data in the abnormal data set; Matching a corresponding historical fault case set from a case library based on the target anomaly type, performing case matching from the historical fault case set in combination with the anomaly data set to obtain a target historical fault case, and obtaining a fault diagnosis conclusion corresponding to the anomaly data set based on the target historical fault case; The fault diagnosis conclusion includes the fault cause and the fault repair measures.

2. The method according to claim 1, characterized in that After matching a corresponding historical fault case set from a case library based on the target anomaly type, performing case matching from the historical fault case set in combination with the anomaly data set to obtain a target historical fault case, and obtaining a fault diagnosis conclusion corresponding to the anomaly data set based on the target historical fault case, the method further includes: The fault diagnosis conclusion is output to the mobile terminal corresponding to the water transportation network operation and maintenance personnel.

3. The method according to claim 1, characterized in that The step of obtaining a target water transportation network dataset corresponding to the water transportation network includes: Collecting an initial water transportation network data set corresponding to the water transportation network; A preprocessing operation is performed on the initial water transportation pipe network dataset to obtain the target water transportation pipe network dataset.

4. The method according to claim 1, wherein The analyzing each data in the target water transportation pipe network data set in combination with a preset rule corresponding to each data in the target water transportation pipe network data set to determine an abnormal data set in the target water transportation pipe network data set includes: Obtaining preset rules corresponding to each data in the target water transportation network data set from a preset rule library; the preset rule library includes the preset rules corresponding to each data in the target water transportation network data set; Based on the preset rules corresponding to the respective data in the target water transportation pipe network data set, the data in the target water transportation pipe network data set that does not conform to the preset rules are screened out to generate the abnormal data set.

5. The method according to claim 1, characterized in that The step of comparing each abnormal data in the abnormal data set with corresponding data in the abnormal data set to determine a target abnormality type corresponding to each abnormal data in the abnormal data set includes: In combination with the preset range of abnormal data corresponding to each abnormal type in the abnormal data set, the target abnormal type corresponding to each data in the abnormal data set is determined according to the size of each data in the abnormal data set; the abnormal data set includes an abnormal type set corresponding to the water transportation pipeline network, and the preset range of abnormal data corresponding to each abnormal type in the abnormal type set.

6. The method according to claim 1, wherein The matching of a corresponding historical fault case set from a case library based on the target abnormality type, performing case matching from the historical fault case set in combination with the abnormal data set to obtain a target historical fault case, and obtaining a fault diagnosis conclusion corresponding to the abnormal data set based on the target historical fault case, includes: Matching a corresponding historical fault case set from a case library based on the target anomaly type, performing case matching from the historical fault case set in combination with the anomaly data set, and obtaining the target historical fault case; Obtain the fault cause and fault repair measures corresponding to the target historical fault case to generate the fault diagnosis conclusion.

7. The method according to claim 1, characterized in that The method further comprises: Performing real-time simulation on the water transportation network to obtain a water transportation network simulation data set; Each data in the water transportation pipe network simulation data set is compared with corresponding data in the target water transportation pipe network data set to verify the target water transportation pipe network data set.

8. An intelligent diagnostic device for a water transport network, characterized in that: include: A data acquisition unit, configured to acquire a target water transportation network data set corresponding to the water transportation network; an analysis and reasoning unit, configured to analyze each data in the target water transportation pipe network data set in combination with a preset rule corresponding to each data in the target water transportation pipe network data set, so as to determine an abnormal data set in the target water transportation pipe network data set; an anomaly identification unit, configured to compare each abnormal data in the abnormal data set with data corresponding to the abnormal type, and determine a target abnormal type corresponding to each abnormal data in the abnormal data set; a fault diagnosis unit, configured to match a corresponding historical fault case set from a case library based on the target anomaly type, perform case matching from the historical fault case set in combination with the anomaly data set to obtain a target historical fault case, and obtain a fault diagnosis conclusion corresponding to the anomaly data set based on the target historical fault case; The fault diagnosis conclusion includes the fault cause and the fault repair measures.

9. An electronic device, characterized in that: include: A memory and a processor, wherein the memory is used to store a computer program; and the processor is used to enable the electronic device to implement the intelligent diagnosis method for a water transportation network as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a computing device, the computing device implements the intelligent diagnosis method for a water transportation network according to any one of claims 1 to 7.

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