Intelligent diagnosis method and device for water conveying pipe network

Abstract

The embodiment of the invention provides an intelligent diagnosis method and device for a water conveying pipe network. The method comprises the steps that a target water conveying pipe network data set corresponding to the water conveying pipe network is acquired; in combination with a preset rule corresponding to each piece of data in the target water conveying pipe network data set, analyzing each piece of data in the target water conveying pipe network data set to determine an abnormal data set in the target water conveying pipe network data set; comparing each abnormal data in the abnormal data set with corresponding data in the abnormal data set, and determining a target abnormal type corresponding to each abnormal data in the abnormal data set; and matching a corresponding historical fault case set from a case library based on the target exception type, performing case matching from the historical fault case set in combination with the exception data set to obtain a target historical fault case, and obtaining a fault diagnosis conclusion corresponding to the exception data set based on the target historical fault case. The system is used for carrying out real-time monitoring and fault diagnosis on the water conveying pipe network.

Description

Technical Field

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

[0002] Water pipelines play an extremely important role in modern urban infrastructure. They are not only related to the normal operation of residents' daily lives, but also involve many aspects such as industrial production and fire safety. However, with the continuous expansion of urban scale and the increasing complexity of pipeline systems, the operation and maintenance of water pipelines face many challenges.

[0003] Traditional water pipeline inspection methods usually rely on manual inspections and manual experience to maintain the water pipeline network. Therefore, when fault judgment and processing are performed based on the experience of technical personnel, human misjudgment and omissions are prone to occur, and the fault point cannot be located quickly. 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 transport network, comprising:

[0006] Obtain a target water transportation pipeline network data set corresponding to the water transportation pipeline network;

[0007] In combination with preset rules corresponding to each data in the target water transportation pipe network data set, each data in the target water transportation pipe network data set is analyzed 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 corresponding data in the abnormal data set to determine a target abnormal type corresponding to each abnormal data in the abnormal data set;

[0009] Based on the target abnormality type, a corresponding historical fault case set is matched from a 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 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, the step of obtaining a target water transportation network dataset corresponding to the water transportation network includes:

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

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

[0014] As an optional implementation of the embodiment of the present application, the preset rules corresponding to each data in the target water transportation network data set are combined to analyze each data in the target water transportation network data set to determine an abnormal data set in the target water transportation network data set, including:

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

[0016] Based on the preset rules corresponding to the various data in the target water transportation network data set, the data in the target water transportation network data set that do 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 abnormal 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 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, including:

[0020] Based on the target anomaly type, a corresponding historical fault case set is matched from a case library, and case matching is performed from the historical fault case set in combination with the anomaly data set to obtain 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] For each data in the water transportation pipe network simulation data set, the corresponding data in the target water transportation pipe network data set is used for comparison, and the target water transportation pipe network data set is verified.

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

[0026] A data acquisition unit, used to obtain a target water transport network data set corresponding to the water transport network;

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

[0028] an abnormality identification unit, used 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;

[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 a fault cause and a fault repair measure.

[0030] As an optional implementation of the embodiment of the present application, the device also 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 data set corresponding to the water delivery network; perform a preprocessing operation on the initial water delivery network data set to obtain the target water delivery network data set.

[0032] As an optional implementation manner of the 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 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.

[0033] As an optional implementation of the embodiment of the present application, the abnormality identification unit 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 the abnormality type set corresponding to the water transportation network, and the preset range of abnormal data corresponding to each abnormal type in the abnormality 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 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 abnormal 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 abnormal type based on the target historical fault case. The corresponding 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 acquiring the target water transmission pipeline network data set corresponding to the water transmission pipeline network. When the abnormal data set is acquired, the target abnormal type corresponding to the abnormal data set is first determined, and then the corresponding target historical fault case is screened out by the target abnormal type, and then the fault diagnosis conclusion corresponding to the abnormal data set is acquired based on the target historical fault case. 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 technical solutions in the embodiments of the present application or the prior art, the drawings required in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0041] Figure 1 One of the step flow charts of the intelligent diagnosis method for water transport network provided in the embodiment of the present application;

[0042] Figure 2 The second step flow chart of the intelligent diagnosis method for water transport network provided in the embodiment of the present application;

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

[0044] Figure 4 A schematic diagram of the structure of an intelligent diagnostic device for a water transport 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 in the embodiments can be combined with each other without 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 more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related 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 article, the term "comprises" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

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

[0051] S101. Obtain a target water transportation pipeline network data set corresponding to the water transportation pipeline network.

[0052] In some embodiments, the water delivery 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 environmental protection. It is one of the indispensable infrastructures in modern society and is of great significance for promoting the sustainable development of society; therefore, from the perspective of operation and maintenance, this application uses the intelligent diagnosis method for the water delivery network to perform real-time monitoring and fault diagnosis on the water delivery network.

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

[0054] S102: 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.

[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 in this application, no specific limitation is made 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 abnormal 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 the abnormal data set is obtained, 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 according to 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, the corresponding solution is called out and provided 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 according to 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 the present application can perform interference analysis through the data in the target water transportation network data set, which mainly evaluates and processes various interference situations that may occur in the water transportation network. Specifically, different equipment and components in the network, such as water pumps, valves, pipes, etc., their operating status and parameter changes may affect each other. Interference analysis aims to detect these mutual influences to determine potential fault points or weak links, and then more comprehensively evaluate the status of the water transportation network.

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

[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 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 abnormal 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 abnormal type based on the target historical fault case. The corresponding 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 acquiring the target water transmission pipeline network data set corresponding to the water transmission pipeline network. When the abnormal data set is acquired, the target abnormal type corresponding to the abnormal data set is first determined, and then the corresponding target historical fault case is screened out by the target abnormal type, and then the fault diagnosis conclusion corresponding to the abnormal data set is acquired based on the target historical fault case. 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 using 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 using a plurality of sensors may experience data fluctuations due to the external environment, the sensors themselves, and the like. Such data fluctuations are significantly different from data fluctuations caused by conventional faults. Therefore, the data generated by unconventional faults may be filtered out by a data preprocessing method, 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 operation may include data filtering, data smoothing, normalization and other operations; specifically, data filtering can remove data that does not meet specific conditions to improve the quality and availability of data. In the embodiment of the present application, the data with format errors or extremely abnormal data in the initial water transport network data set can be removed; in the sensor measurement data, due to the influence of environmental factors, some random fluctuations may occur. Through data smoothing, these fluctuations can be reduced and the quality of data can be improved; data normalization can convert data of different dimensions to facilitate subsequent analysis and comparison.

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

[0072] S203: Acquire 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 transport network data set, that is, the developer sets the rules for the 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 based on experience. Specifically, experienced water supply and drainage engineers can also make normal and reasonable judgments on the flow range of a specific type of network branch based on previous project experience, and then set the preset rule corresponding to the network branch flow according to the normal range of the network branch flow to: the network branch flow is greater than or equal to the upper limit of the normal range of the network branch flow, and is less than or equal to the lower limit of the normal range of the network branch flow; then, after obtaining the network branch flow in the target water transport network data set at the current moment, the size of the network branch flow is compared with the upper and lower limits of the normal range of the network branch flow in the corresponding preset rules, so as to determine whether the network branch flow at the current moment is normal. Once it is outside the normal range, the network branch flow at the current moment is considered abnormal.

[0075] S204: based on the preset rules corresponding to the various data in the target water transportation network data set, filter out the data in the target water transportation network data set that do not conform to the preset rules, so as to generate the abnormal data set.

[0076] Specifically, for each data in the target water transportation network data set, the corresponding preset rules are used to judge whether each data is normal and reasonable, and the 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 abnormal type set corresponding to the water transportation network and a preset range of abnormal data corresponding to each abnormal type in the abnormal 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 matter 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, according to the size of the exception data and the relationship between each exception data, the current corresponding exception type can be obtained according to the data in the exception data set; therefore, the developer needs to set a preset range of exception data corresponding to each exception type in combination with the historical exception data of the exception type. Once there is exception data 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 set includes all historical fault cases and is saved in the case library. The case library also saves case information corresponding to each historical fault case. Specifically, the case information includes the time, fault cause, fault repair measures, and information about the person responsible for operation and maintenance, etc. After the target abnormal historical fault case is locked, the case information corresponding to the case is immediately retrieved from the case library to obtain the fault cause and fault 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 corresponding to the target historical fault case and the fault repair measures 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 the 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 performing real-time simulation of the water transportation network, it can be implemented based on digital twin technology. Digital twins usually model and simulate physical entities in the real world through sensors, data analysis and simulation technologies, so as to achieve real-time monitoring, prediction and optimization of the real world.

[0091] Step 2: for each data in the water transportation network simulation data set, compare it with the corresponding data in the target water transportation network data set to verify the target water transportation 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, deploy corresponding fault prevention measures in advance, and reduce the probability of failure. At the same time, according to the fault prediction results, the corresponding historical fault cases are obtained, and then 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-mentioned embodiment, the intelligent diagnosis method for a water transportation network provided in the embodiment of the present application corresponds to an intelligent diagnosis system 300 for a water transportation network, referring to Figure 3 As shown, the system architecture diagram includes: a data acquisition module 31, an analysis and reasoning module 32, an abnormality 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 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; 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 used to compare each abnormal data in the abnormal data set with the corresponding data in the abnormal data set to determine the 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 network provided by the present 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 water transportation network in a certain city. Flow sensors, pressure sensors, and conductivity sensors are installed at key nodes of the network, and real-time initial water transportation network data sets in the network are collected through the above sensors. The initial water transportation network data sets are preprocessed, and preset rules corresponding to the above data are obtained from a rule library. A preliminary judgment is made to obtain an abnormal data set. At this time, it is found that the abnormal data set includes abnormal flow data. The corresponding target abnormal type is further determined based on the abnormal flow data, that is, pipeline leakage. Then, the corresponding target historical fault case is confirmed in combination with the historical pipeline leakage case set in the case library, 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 at the position corresponding to the flow sensor, and a detailed diagnosis report is generated for reference by operation and maintenance personnel.

[0101] For another example, a system corresponding to the intelligent diagnosis method for water transportation pipelines provided by the embodiment of the present application is deployed in a large industrial park to collect the speed and pressure data of 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, the pressure anomaly of a certain pump station is identified. After comparison with the defect feature library, it is determined to be an internal fault of the pump station. Through case library matching, it is confirmed that the performance degradation is caused by the aging of a component of the pump station. The system generates maintenance suggestions 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, the embodiment of the present application also provides an intelligent diagnostic device for water transportation pipelines. This embodiment corresponds to the aforementioned system embodiment. For the sake of 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 water transportation pipelines in this embodiment can correspond to all the contents of the aforementioned method embodiment.

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

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

[0105] An analysis and reasoning unit 402 is used 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] An anomaly identification unit 403 is used to compare each abnormal data in the abnormal data set with the corresponding data in the abnormal data set to determine the target anomaly 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 also 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 data set corresponding to the water delivery network; perform a preprocessing operation on the initial water delivery network data set to obtain the target water delivery network data set.

[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 the abnormality type set corresponding to the water transportation network, and the preset range of abnormal data corresponding to each abnormal type in the abnormal type set;

[0112] As an optional implementation of the 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 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 predict faults based on the water transmission pipeline network prediction data set.

[0114] Based on the same inventive concept, an embodiment of the present disclosure also 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 a computer program; the processor 502 is used to execute the intelligent diagnosis system for the water transportation network 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, on which a computer program is stored. 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 adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media that include 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) or 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 include permanent and non-permanent, removable and non-removable storage media. Storage media can be implemented by any method or technology to store information, and the information 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 technology, compact disk read-only memory (CD-ROM), digital versatile disk (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 temporary computer readable media (transitory 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 it. 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 replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. An intelligent diagnosis method for a water transport network, characterized in that: include: Obtain a target water transportation pipeline network data set corresponding to the water transportation pipeline network; In combination with preset rules corresponding to each data in the target water transportation pipe network data set, each data in the target water transportation pipe network data set is analyzed 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 corresponding data in the abnormal data set to determine 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 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; 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 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 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 data set corresponding to the water transportation network includes: Collecting an initial water transportation pipeline network data set corresponding to the water transportation pipeline network; A preprocessing operation is performed on the initial water transportation pipe network data set to obtain the target water transportation pipe network data set.

4. The method according to claim 1, characterized in that: The combining of preset rules corresponding to each data in the target water transportation pipe network data set and analyzing 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: Acquire preset rules corresponding to each data in the target water transportation pipe network data set from a preset rule base; the preset rule base includes the preset rules corresponding to each data in the target water transportation pipe network data set; Based on the preset rules corresponding to the various data in the target water transportation network data set, the data in the target water transportation network data set that do 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 abnormal 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 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.

6. The method according to claim 1, characterized in that The matching of 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, includes: Based on the target anomaly type, a corresponding historical fault case set is matched from a case library, and case matching is performed from the historical fault case set in combination with the anomaly data set to obtain 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; For each data in the water transportation pipe network simulation data set, the corresponding data in the target water transportation pipe network data set is used for comparison, and the target water transportation pipe network data set is verified.

8. An intelligent diagnosis method for a water transport network, characterized in that: include: A data acquisition unit, used to obtain a target water transport network data set corresponding to the water transport network; An analysis and reasoning unit, configured to analyze each data in the target water transportation pipeline network data set in combination with a preset rule corresponding to each data in the target water transportation pipeline network data set, so as to determine an abnormal data set in the target water transportation pipeline network data set; an abnormality identification unit, used 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; A fault diagnosis unit, configured 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 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 transport network as described in claim 8 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 as described in claim 8.

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