Pipeline corrosion monitoring method, device and equipment fused with end cloud collaboration and storage medium

Abstract

The invention discloses a pipeline corrosion monitoring method, device and equipment fusing end-cloud collaboration and a storage medium, and the method comprises the steps: obtaining potential monitoring data of potential monitoring equipment, and determining whether the potential monitoring data meets a preset triggering feature condition or not; if the potential monitoring data meets the preset triggering characteristic condition, acquiring target environment sound data corresponding to the potential monitoring data based on sound monitoring equipment corresponding to the potential monitoring equipment; inputting the target environment sound data into a target reasoning neural network for reasoning prediction to obtain interference source information corresponding to the environment sound data; and sending the interference source information, the potential monitoring data and the target environment sound data to a cloud server. The influence of the interference source is compatible when the pipeline is evaluated, the comprehensive corrosion and local corrosion conditions of the pipeline can be estimated more accurately, pipeline corrosion leakage accidents are avoided, and the monitoring accuracy of comprehensive corrosion and local corrosion of the pipeline is improved.

Description

Technical Field

[0001] The present application relates to the field of pipeline monitoring technology, and in particular to a pipeline corrosion monitoring method, device, equipment and storage medium integrating terminal and cloud collaboration. Background Art

[0002] At present, pipeline corrosion is one of the main causes of oil and gas pipeline accidents, and the anti-corrosion layer combined with cathodic protection measures is the main technical means to prevent oil and gas pipeline corrosion perforation or failure. In the process of manufacturing, transportation, construction and transportation, it is impossible to avoid damage or aging of the anti-corrosion layer. The cathodic protection system is an important "line of defense" for pipeline protection against local failure of the anti-corrosion layer.

[0003] Each pipeline company still manually collects the pipeline cathodic protection power-on potential and uses it to evaluate the effectiveness of pipeline cathodic protection. There are problems such as difficulty in measuring the power-off potential (eliminating the influence of IR drop), difficulty in ensuring the timeliness of manual measurement and data quality, inaccurate evaluation of the power-on potential, and increased workload of on-site personnel due to data collection. Currently, some intelligent cathodic protection equipment that automatically collects cathode potential data has emerged. It can collect and report potential data at a set time, or automatically trigger data collection and reporting functions when the potential offset exceeds the set threshold. It only solves the difficulty of manual measurement and cannot evaluate the impact of interference sources.

[0004] In densely populated urban environments, underground pipelines are complex and the environment along the pipelines varies. Among them, urban subways, light rails, trams, high-speed railways, high-voltage lines, etc. will bring serious stray current interference sources, which will interfere with the verification of pipeline corrosion assessment and even lead to erroneous assessments.

[0005] Therefore, how to be compatible with the influence of interference sources when evaluating pipelines and improve the monitoring accuracy of overall corrosion and local corrosion of pipelines is a problem that needs to be solved urgently. Summary of the invention

[0006] The main purpose of this application is to provide a pipeline corrosion monitoring method, device, equipment and storage medium that integrates end-cloud collaboration, aiming to solve the technical problem of how to be compatible with the influence of interference sources when evaluating pipelines and improve the monitoring accuracy of overall corrosion and local corrosion of pipelines.

[0007] To achieve the above objectives, the present application provides a pipeline corrosion monitoring method integrating terminal and cloud collaboration, and the pipeline corrosion monitoring method integrating terminal and cloud collaboration comprises the following steps:

[0008] Acquire potential monitoring data of a potential monitoring device, and determine whether the potential monitoring data meets a preset trigger characteristic condition, wherein each pipeline monitoring point corresponding to the pipeline to be monitored is provided with a potential monitoring device and a sound monitoring device;

[0009] If the potential monitoring data meets the preset trigger characteristic condition, obtaining the target environment sound data corresponding to the potential monitoring data based on the sound monitoring device corresponding to the potential monitoring device;

[0010] Inputting the target environmental sound data into the target inference neural network for inference prediction to obtain interference source information corresponding to the environmental sound data;

[0011] The interference source information, the potential monitoring data and the target environmental sound data are sent to a cloud server, wherein the cloud server predicts the corrosion degree of the pipeline based on the interference source information, the potential monitoring data and the target environmental sound data.

[0012] Furthermore, the step of determining whether the potential monitoring data meets a preset trigger characteristic condition includes:

[0013] Based on the potential monitoring data, determining the potential change slope, jump amplitude and frequency;

[0014] Whether the potential monitoring data meets a preset trigger characteristic condition is determined based on the potential change slope, jump amplitude and frequency.

[0015] Furthermore, the step of determining whether the potential monitoring data meets a preset trigger characteristic condition based on the potential change slope, jump amplitude and frequency includes:

[0016] Determine whether there is a target potential slope greater than the preset slope among the various potential change slopes and whether the jump amplitude and frequency meet the preset requirements. If there is a target potential slope greater than the preset slope and the jump amplitude and frequency meet the preset requirements, it is determined that the potential monitoring data meets the preset trigger characteristic conditions.

[0017] Furthermore, if the potential monitoring data meets a preset trigger characteristic condition, the step of obtaining the target environment sound data corresponding to the potential monitoring data based on the sound monitoring device corresponding to the potential monitoring device includes:

[0018] If the potential monitoring data meets the preset trigger characteristic condition, then obtaining the environmental sound data monitored by the sound monitoring device corresponding to the potential monitoring device;

[0019] Based on the target potential slope, jump amplitude and frequency, the target environmental sound data is obtained from the environmental sound data.

[0020] Furthermore, the step of acquiring the target environmental sound data from the environmental sound data based on the target potential slope, jump amplitude and frequency includes:

[0021] Acquire a first potential slope and a second potential slope in the target potential slope;

[0022] Obtaining a first moment corresponding to the first potential slope and a second moment corresponding to the second potential slope;

[0023] Based on the first moment and the second moment, the target environmental sound data is obtained from the environmental sound data.

[0024] Furthermore, before the step of obtaining the potential monitoring data of the potential monitoring device and determining whether the potential monitoring data meets the preset trigger characteristic condition, the pipeline corrosion monitoring method integrating terminal-cloud collaboration also includes:

[0025] Receiving a first configuration parameter sent by a cloud server, wherein the cloud server trains a preset neural network model based on training data to obtain a parameter error corresponding to the configuration parameter of the preset neural network model, reversely calculating the configuration parameter of the preset neural network model based on the parameter error to obtain a second configuration parameter, and adjusting the second configuration parameter based on a distance relationship between the potential monitoring device and the interference source to obtain the first configuration parameter;

[0026] The inference neural network is updated based on the first configuration parameters to obtain a target inference neural network.

[0027] Furthermore, the interference source information includes interference source type, interference intensity, interference frequency and interference duration.

[0028] In addition, to achieve the above-mentioned purpose, the present application also provides a pipeline corrosion monitoring device integrating terminal and cloud collaboration, and the pipeline corrosion monitoring device integrating terminal and cloud collaboration comprises:

[0029] A determination module, used to obtain potential monitoring data of a potential monitoring device and determine whether the potential monitoring data meets a preset trigger characteristic condition, wherein each pipeline monitoring point corresponding to the pipeline to be monitored is provided with a potential monitoring device and a sound monitoring device;

[0030] an acquisition module, configured to acquire, if the potential monitoring data meets a preset trigger characteristic condition, target environment sound data corresponding to the potential monitoring data based on the sound monitoring device corresponding to the potential monitoring device;

[0031] A prediction module, used to input the target environmental sound data into a target inference neural network for inference prediction, and obtain interference source information corresponding to the environmental sound data;

[0032] A sending module is used to send the interference source information, the potential monitoring data and the target environmental sound data to a cloud server, wherein the cloud server predicts the corrosion degree of the pipeline based on the interference source information, the potential monitoring data and the target environmental sound data.

[0033] In addition, to achieve the above-mentioned purpose, the present application also provides a fused end-cloud collaborative pipeline corrosion monitoring device, the fused end-cloud collaborative pipeline corrosion monitoring device includes: a memory, a processor, and a fused end-cloud collaborative pipeline corrosion monitoring program stored on the memory and executable on the processor, the fused end-cloud collaborative pipeline corrosion monitoring program, when executed by the processor, implements any one or part of the steps of the aforementioned fused end-cloud collaborative pipeline corrosion monitoring method.

[0034] In addition, to achieve the above-mentioned purpose, the present application also provides a storage medium, which refers to a computer-readable storage medium, on which a pipeline corrosion monitoring program for fusion end-cloud collaboration is stored. When the pipeline corrosion monitoring program for fusion end-cloud collaboration is executed by a processor, it implements any one or part of the steps of the aforementioned pipeline corrosion monitoring method for fusion end-cloud collaboration.

[0035] The present application obtains potential monitoring data of a potential monitoring device and determines whether the potential monitoring data meets a preset trigger characteristic condition; then if the potential monitoring data meets the preset trigger characteristic condition, based on the sound monitoring device corresponding to the potential monitoring data, obtains the target environment sound data corresponding to the potential monitoring data; then the target environment sound data is input into a target inference neural network for inference prediction to obtain interference source information corresponding to the environment sound data; then the interference source information, the potential monitoring data and the target environment sound data are sent to a cloud server, wherein the cloud server predicts the corrosion degree of the pipeline based on the interference source information, the potential monitoring data and the target environment sound data, and when it is determined by the potential monitoring data that the preset trigger characteristic condition is met, the interference source information is determined according to the target environment sound data, and the interference source information is sent to the cloud server, so that the cloud server can predict pipeline corrosion based on the interference source information, thereby achieving compatibility with the influence of interference sources when evaluating pipelines, and can more accurately estimate the overall corrosion and local corrosion of pipelines, avoid the occurrence of pipeline corrosion leakage accidents, and improve the monitoring accuracy of overall corrosion and local corrosion of pipelines. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] 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.

[0037] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use 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.

[0038] Figure 1 A flow chart of the first embodiment of the pipeline corrosion monitoring method integrating terminal-cloud collaboration provided in this application;

[0039] Figure 2 This is a schematic diagram of the module structure of a pipeline corrosion monitoring device integrating end-cloud collaboration according to an embodiment of the present application;

[0040] Figure 3 This is a schematic diagram of the module structure of the pipeline corrosion monitoring equipment that integrates end-cloud collaboration in an embodiment of the present application.

[0041] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0042] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0043] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0044] The main solution of the present application is: to obtain the potential monitoring data of the potential monitoring device, and to determine whether the potential monitoring data meets the preset trigger characteristic conditions, wherein each pipeline monitoring point corresponding to the pipeline to be monitored is provided with a potential monitoring device and a sound monitoring device; if the potential monitoring data meets the preset trigger characteristic conditions, then based on the sound monitoring device corresponding to the potential monitoring device, the target environmental sound data corresponding to the potential monitoring data is obtained; the target environmental sound data is input into the target inference neural network for inference and prediction, and the interference source information corresponding to the environmental sound data is obtained; the interference source information, the potential monitoring data and the target environmental sound data are sent to the cloud server, wherein the cloud server predicts the degree of corrosion of the pipeline based on the interference source information, the potential monitoring data and the target environmental sound data.

[0045] At present, pipeline corrosion is one of the main causes of oil and gas pipeline accidents, and the anti-corrosion layer combined with cathodic protection measures is the main technical means to prevent oil and gas pipeline corrosion perforation or failure. In the process of manufacturing, transportation, construction and transportation, it is impossible to avoid damage or aging of the anti-corrosion layer. The cathodic protection system is an important "line of defense" for pipeline protection against local failure of the anti-corrosion layer.

[0046] Each pipeline company still manually collects the pipeline cathodic protection power-on potential and uses it to evaluate the effectiveness of pipeline cathodic protection. There are problems such as difficulty in measuring the power-off potential (eliminating the influence of IR drop), difficulty in ensuring the timeliness of manual measurement and data quality, inaccurate evaluation of the power-on potential, and increased workload of on-site personnel due to data collection. Currently, some intelligent cathodic protection equipment that automatically collects cathode potential data has emerged. It can collect and report potential data at a set time, or automatically trigger data collection and reporting functions when the potential offset exceeds the set threshold. It only solves the difficulty of manual measurement and cannot evaluate the impact of interference sources.

[0047] In densely populated urban environments, underground pipelines are complex and the environment along the pipelines varies. Among them, urban subways, light rails, trams, high-speed railways, high-voltage lines, etc. will bring serious stray current interference sources, which will interfere with the verification of pipeline corrosion assessment and even lead to erroneous assessments.

[0048] Therefore, how to be compatible with the influence of interference sources when evaluating pipelines and improve the monitoring accuracy of overall corrosion and local corrosion of pipelines is a problem that needs to be solved urgently.

[0049] When the preset trigger characteristic conditions are determined by the potential monitoring data, the interference source information is determined according to the target environment sound data, and the interference source information is sent to the cloud server, so that the cloud server can predict the pipeline corrosion based on the interference source information, thereby achieving compatibility with the influence of the interference source when evaluating the pipeline, and more accurately predicting the overall corrosion and local corrosion of the pipeline, avoiding the occurrence of pipeline corrosion leakage accidents, and improving the monitoring accuracy of the overall corrosion and local corrosion of the pipeline.

[0050] It should be noted that the execution subject of this embodiment can be a pipeline corrosion monitoring device that integrates end-cloud collaboration, or a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or a pipeline corrosion monitoring device that integrates end-cloud collaboration and can achieve the above functions, etc. This embodiment does not specifically limit this. The following takes the pipeline corrosion monitoring device that integrates end-cloud collaboration as an example of the execution subject to explain this embodiment and the following embodiments.

[0051] Based on this, this application proposes a pipeline corrosion monitoring method of the first embodiment of the fusion end-cloud collaboration, please refer to Figure 1 The pipeline corrosion monitoring method integrating terminal and cloud collaboration includes steps S101 to S104:

[0052] Step S101, obtaining potential monitoring data of a potential monitoring device, and determining whether the potential monitoring data meets a preset trigger characteristic condition, wherein each pipeline monitoring point corresponding to the pipeline to be monitored is provided with a potential monitoring device and a sound monitoring device;

[0053] In this embodiment, the pipeline to be monitored can be an oil and gas pipeline, or other pipelines with conductive properties. Each pipeline monitoring point corresponding to the pipeline to be monitored is provided with a potential monitoring device and a sound monitoring device. For example, the potential monitoring device can be set in the underground area corresponding to the pipeline monitoring point to monitor the potential data of the anti-corrosion layer of the pipeline to be monitored. The sound monitoring device can be set in the underground area corresponding to the pipeline monitoring point, or set on the ground or above-ground area corresponding to the pipeline monitoring point to improve the accuracy of sound monitoring. The potential monitoring device and the sound monitoring device at each pipeline monitoring point can monitor data in real time or perform data monitoring. The pipeline corrosion monitoring device with integrated end-cloud collaboration can be a processor or controller set at each pipeline monitoring point. The pipeline corrosion monitoring device with integrated end-cloud collaboration is respectively connected to the potential monitoring device and the sound monitoring device to obtain the monitoring data of the potential monitoring device and the sound monitoring device, and the acquired data can be uploaded to the cloud server. Among them, the potential monitoring data can be voltage monitoring data and / or current monitoring data.

[0054] In this embodiment, the pipeline corrosion monitoring device that integrates end-cloud collaboration can obtain the potential monitoring data of the potential monitoring equipment in real time or at a fixed time, and determine whether the potential monitoring data meets the preset trigger characteristic conditions. Specifically, the potential change slope, jump amplitude and frequency can be determined based on the potential monitoring data, and whether the preset trigger characteristic conditions are met according to the changes in the potential change slope, jump amplitude and frequency. For example, when there is a target potential slope in the potential change slope that is greater than the preset slope of the preset trigger characteristic condition and the jump amplitude and frequency meet the preset requirements, it is determined that the potential monitoring data meets the preset trigger characteristic conditions.

[0055] Further, in a feasible implementation manner, step S101 may include steps S1011 to S1012:

[0056] Step S1011, determining the potential change slope, jump amplitude and frequency based on the potential monitoring data;

[0057] Step S1012, determining whether the potential monitoring data meets a preset trigger characteristic condition based on the potential change slope, jump amplitude and frequency.

[0058] Further, in a feasible implementation manner, step S1012 may include step a:

[0059] Step a, determining whether there is a target potential slope greater than a preset slope among the potential change slopes and whether the jump amplitude and frequency meet the preset requirements, wherein, if there is a target potential slope greater than the preset slope and the jump amplitude and frequency meet the preset requirements, then determining that the potential monitoring data meets the preset trigger characteristic conditions.

[0060] In this embodiment, after the potential monitoring data is obtained, the potential change slope is determined based on the potential monitoring data. Specifically, the slope calculation algorithm in the relevant technology can be used to perform slope calculation based on two data at adjacent moments in the potential monitoring data to obtain the potential change slope corresponding to the two data at each adjacent moment in the potential monitoring data, and the jump amplitude and frequency are determined based on the potential monitoring data.

[0061] In this embodiment, after obtaining the potential change slope, it is determined whether the potential monitoring device meets the preset trigger characteristic condition based on the potential change slope, the jump amplitude and the frequency. For example, when there is a target potential slope in the potential change slope that is greater than the preset slope of the preset trigger characteristic condition and the jump amplitude is greater than the threshold M, and the change frequency is less than the threshold N, it is determined that the potential monitoring data meets the preset trigger characteristic condition, that is, it is determined whether there is a target potential slope greater than the preset slope in each potential change slope and the jump amplitude and frequency meet the preset requirements. If there is a target potential slope greater than the preset slope in each potential change slope and the jump amplitude and frequency meet the preset requirements, it is determined that the potential monitoring data meets the preset trigger characteristic condition, wherein the potential change slope can be the absolute value of the corresponding slope, and the target potential slope may include a first potential slope and a second potential slope.

[0062] It should be noted that the pipeline corrosion monitoring device with integrated end-cloud collaboration is equipped with a pre-trained target inference neural network. When the potential monitoring data is obtained, the pipeline corrosion monitoring device with integrated end-cloud collaboration can input the potential monitoring data into the target inference neural network for inference and prediction, and determine whether the potential monitoring data meets the preset trigger characteristic conditions based on the output of the target inference neural network.

[0063] Step S102, if the potential monitoring data meets the preset trigger characteristic condition, then based on the potential monitoring device corresponding to the sound monitoring device, obtaining the target environment sound data corresponding to the potential monitoring data;

[0064] In this embodiment, when it is determined that the potential monitoring data meets the preset trigger characteristic condition, the target environmental sound data corresponding to the potential monitoring data is obtained based on the environmental sound data of the sound monitoring device corresponding to the potential monitoring device. Specifically, the target environmental sound data can be obtained from the environmental sound data according to the target potential slope, jump amplitude and frequency. Further, in a feasible implementation, step S102 may include steps S1021 to S1022:

[0065] Step S1021, if the potential monitoring data meets the preset trigger characteristic condition, then obtaining the environmental sound data monitored by the sound monitoring device corresponding to the potential monitoring device;

[0066] Step S1022: acquiring the target environmental sound data from the environmental sound data based on the target potential slope, jump amplitude and frequency.

[0067] Further, in a feasible implementation manner, step S1022 may include steps b1 to b3:

[0068] Step b1, obtaining a first potential slope and a second potential slope in the target potential slope;

[0069] Step b2, obtaining a first moment corresponding to the first potential slope and a second moment corresponding to the second potential slope;

[0070] Step b3: acquiring the target ambient sound data from the ambient sound data based on the first moment and the second moment.

[0071] In this embodiment, if the potential monitoring data meets the preset trigger characteristic conditions, the environmental sound data monitored by the sound monitoring device corresponding to the potential monitoring device is obtained. Specifically, the environmental sound data within the time monitoring interval corresponding to the potential monitoring data can be obtained from all the sound data monitored by the sound monitoring device, that is, the time monitoring interval of the environmental sound data is the same as that of the potential monitoring data.

[0072] After obtaining the ambient sound data, the target ambient sound data is obtained from the ambient sound data based on the target potential slope, jump amplitude and frequency. Specifically, the target time monitoring interval corresponding to the target potential slope is determined, and the sound data within the target time monitoring interval is obtained from the ambient sound data as the target ambient sound data, so as to accurately obtain the target ambient sound data.

[0073] Specifically, when there is an interference source, the slope corresponding to the potential monitoring data enters a rapid change stage and then a stable stage. Then, when the interference source leaves, the slope corresponding to the potential monitoring data enters a rapid change stage again. Thus, the target potential slope has a first potential slope and a second potential slope. After obtaining the potential monitoring data, the first potential slope and the second potential slope in the target potential slope are obtained.

[0074] A first potential slope and a second potential slope are obtained, and a first moment corresponding to the first potential slope and a second moment corresponding to the second potential slope are obtained, wherein the time interval between the first moment and the second moment is greater than a preset time interval. For example, the time interval between the first moment and the second moment may be the time taken for a high-speed railway, a subway, or a tram to pass through, or the time interval between the first moment and the second moment may also be the continuous time interval of high-voltage line strong power transmission.

[0075] In this embodiment, after obtaining the first moment and the second moment, target ambient sound data is obtained in the ambient sound data based on the first moment and the second moment, that is, the data between the first moment and the second moment in the ambient sound data is used as the target ambient sound data.

[0076] Step S103, inputting the target environmental sound data into the target inference neural network for inference prediction to obtain interference source information corresponding to the environmental sound data;

[0077] In this embodiment, after the target environmental sound data is obtained, the target environmental sound data is input into the target inference neural network for inference prediction to obtain the interference source information corresponding to the environmental sound data. The interference sources include high-speed rail, subway, tram, or high-voltage line transmission, etc. The interference source information includes the type of interference source, interference intensity, interference frequency and interference duration.

[0078] Step S104, sending the interference source information, the potential monitoring data and the target environment sound data to a cloud server, wherein the cloud server predicts the corrosion degree of the pipeline based on the interference source information, the potential monitoring data and the target environment sound data.

[0079] In this embodiment, after the target environmental sound data is obtained, the interference source information, the potential monitoring data and the target environmental sound data are sent to the cloud server. The cloud server can predict the degree of corrosion of the pipeline based on the interference source information, the potential monitoring data and the target environmental sound data. Specifically, the target potential monitoring data can be obtained from the potential monitoring data according to the first moment and the second moment. The target potential monitoring data is the data between the first moment and the second moment in the potential monitoring data.

[0080] The pipeline corrosion monitoring method integrating terminal and cloud collaboration proposed in this embodiment obtains the potential monitoring data of the potential monitoring device and determines whether the potential monitoring data meets the preset trigger characteristic condition; then if the potential monitoring data meets the preset trigger characteristic condition, based on the sound monitoring device corresponding to the potential monitoring device, obtains the target environment sound data corresponding to the potential monitoring data; then inputs the target environment sound data into the target inference neural network for inference prediction to obtain the interference source information corresponding to the environment sound data; then sends the interference source information, the potential monitoring data and the target environment sound data to the cloud server, wherein the cloud server predicts the corrosion degree of the pipeline based on the interference source information, the potential monitoring data and the target environment sound data, and when it is determined by the potential monitoring data that the preset trigger characteristic condition is met, determines the interference source information according to the target environment sound data, and sends the interference source information to the cloud server, so that the cloud server can predict the pipeline corrosion based on the interference source information, thereby realizing the compatibility of the influence of the interference source when evaluating the pipeline, and can more accurately estimate the overall corrosion and local corrosion of the pipeline, avoid the occurrence of pipeline corrosion leakage accidents, and improve the monitoring accuracy of overall corrosion and local corrosion of the pipeline.

[0081] In a feasible implementation, before step S101, the pipeline corrosion monitoring method integrating terminal and cloud collaboration may further include steps S201 to S202:

[0082] Step S201, receiving a first configuration parameter sent by a cloud server, wherein the cloud server trains a preset neural network model based on training data to obtain a parameter error corresponding to the configuration parameter of the preset neural network model, reversely calculates the configuration parameter of the preset neural network model based on the parameter error to obtain a second configuration parameter, and adjusts the second configuration parameter based on a distance relationship between the potential monitoring device and the interference source to obtain the first configuration parameter;

[0083] Step S202: updating the inference neural network based on the first configuration parameters to obtain a target inference neural network.

[0084] In this embodiment, the server can receive training data at regular intervals, and the cloud server trains the preset neural network model based on the training data to obtain parameter errors corresponding to the configuration parameters of the preset neural network model. That is, the cloud server inputs the training data into the preset neural network model for training to perform error calculations on the configuration parameters of the preset neural network to obtain parameter errors.

[0085] After obtaining the parameter error, the cloud server obtains the configuration parameters of the preset neural network model, and reversely calculates the configuration parameters of the preset neural network model based on the parameter error to obtain the second configuration parameters.

[0086] Afterwards, the cloud server adjusts the second configuration parameter based on the distance relationship between the potential monitoring device and the interference source, obtains the first configuration parameter, and sends the first configuration parameter to the potential monitoring device.

[0087] After receiving the first configuration parameter, the potential monitoring device updates the inference neural network based on the first configuration parameter to obtain the target inference neural network to achieve timed update of the target inference neural network.

[0088] The pipeline corrosion monitoring method of fusion end-cloud collaboration proposed in this embodiment receives a first configuration parameter sent by a cloud server, wherein the cloud server trains a preset neural network model based on training data to obtain a parameter error corresponding to the configuration parameter of the preset neural network model, reversely calculates the configuration parameter of the preset neural network model based on the parameter error to obtain a second configuration parameter, and adjusts the second configuration parameter based on the distance relationship between the potential monitoring device and the interference source to obtain the first configuration parameter; then updates the inference neural network based on the first configuration parameter to obtain a target inference neural network, and performs model training on the cloud server and regularly pushes the trained first configuration parameter to the potential monitoring device on the edge side, and the potential monitoring device updates the inference neural network based on the first configuration parameter to reduce the cost and energy consumption of the pipeline corrosion monitoring device of fusion end-cloud collaboration on the edge side, reduces the cost of data bandwidth, and also reduces the energy consumption of the pipeline corrosion monitoring device of fusion end-cloud collaboration, while reducing the cost of the pipeline corrosion monitoring device of fusion end-cloud collaboration on the edge side and the long-term use cost of the system.

[0089] The present application also provides a pipeline corrosion monitoring device integrating terminal and cloud collaboration. Figure 2 The pipeline corrosion monitoring device integrating terminal and cloud collaboration includes:

[0090] The determination module 10 is used to obtain the potential monitoring data of the potential monitoring device and determine whether the potential monitoring data meets the preset trigger characteristic condition, wherein each pipeline monitoring point corresponding to the pipeline to be monitored is provided with a potential monitoring device and a sound monitoring device;

[0091] An acquisition module 20, configured to acquire target environment sound data corresponding to the potential monitoring data based on the sound monitoring device corresponding to the potential monitoring device if the potential monitoring data meets a preset trigger characteristic condition;

[0092] A prediction module 30 is used to input the target environmental sound data into a target inference neural network for inference prediction to obtain interference source information corresponding to the environmental sound data;

[0093] The sending module 40 is used to send the interference source information, the potential monitoring data and the target environmental sound data to the cloud server, wherein the cloud server predicts the corrosion degree of the pipeline based on the interference source information, the potential monitoring data and the target environmental sound data.

[0094] The fused end-cloud collaborative pipeline corrosion monitoring device provided in the embodiment of the present application adopts the fused end-cloud collaborative pipeline corrosion monitoring method in the above embodiment, which can solve the technical problem of how to be compatible with the influence of interference sources when evaluating pipelines and improve the monitoring accuracy of overall corrosion and local corrosion of pipelines. Compared with the prior art, the beneficial effects of the fused end-cloud collaborative pipeline corrosion monitoring device provided in the embodiment of the present application are the same as the beneficial effects of the fused end-cloud collaborative pipeline corrosion monitoring method provided in the above embodiment, and the other technical features of the fused end-cloud collaborative pipeline corrosion monitoring device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0095] It should also be noted that the prediction module 30 in the pipeline corrosion monitoring device with integrated end-cloud collaboration can be only on the edge side or only on the cloud server side. Or it can be on both the end-cloud side or deployed on both the end-cloud side at the same time. The sending module 40 can send all the data or selectively send only part of the data.

[0096] The present application provides a pipeline corrosion monitoring device with integrated end-cloud collaboration, and the pipeline corrosion monitoring device with integrated end-cloud collaboration includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the pipeline corrosion monitoring method with integrated end-cloud collaboration of any one or part of the items in the above-mentioned embodiment one.

[0097] Reference below Figure 3 , which shows a schematic diagram of the structure of a pipeline corrosion monitoring device with integrated terminal-cloud collaboration suitable for implementing the embodiment of the present application. The pipeline corrosion monitoring device with integrated terminal-cloud collaboration in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3The shown integrated end-cloud collaborative pipeline corrosion monitoring device is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0098] like Figure 3 As shown, the pipeline corrosion monitoring device for fusion end-cloud collaboration may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM: Read Only Memory) 1002 or the program loaded from the storage device 1003 to the random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the pipeline corrosion monitoring device for fusion end-cloud collaboration are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other via a bus 1005. The input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the fused end-cloud collaborative pipeline corrosion monitoring device to communicate wirelessly or wired with other devices to exchange data. Although the figure shows a fused end-cloud collaborative pipeline corrosion monitoring device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have instead.

[0099] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0100] The fused end-cloud collaborative pipeline corrosion monitoring device provided in the present application adopts the fused end-cloud collaborative pipeline corrosion monitoring method in the above-mentioned embodiment, which can solve the technical problem of how to be compatible with the influence of interference sources when evaluating pipelines and improve the monitoring accuracy of overall corrosion and local corrosion of pipelines. Compared with the prior art, the beneficial effects of the fused end-cloud collaborative pipeline corrosion monitoring device provided in the present application are the same as the beneficial effects of the fused end-cloud collaborative pipeline corrosion monitoring method provided in the above-mentioned embodiment, and the other technical features in the fused end-cloud collaborative pipeline corrosion monitoring device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0101] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0102] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0103] The present application provides a storage medium, which refers to a computer-readable storage medium having computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the fused end-cloud collaborative pipeline corrosion monitoring method of any one or part of the above-mentioned embodiments.

[0104] The storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0105] The above-mentioned computer-readable storage medium may be included in the pipeline corrosion monitoring device for fusion end-cloud collaboration; or it may exist independently without being assembled into the pipeline corrosion monitoring device for fusion end-cloud collaboration.

[0106] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the fused end-cloud collaborative pipeline corrosion monitoring device, the fused end-cloud collaborative pipeline corrosion monitoring device: obtains the potential monitoring data of the potential monitoring device, and determines whether the potential monitoring data meets the preset trigger characteristic conditions, wherein each pipeline monitoring point corresponding to the pipeline to be monitored is provided with a potential monitoring device and a sound monitoring device; if the potential monitoring data meets the preset trigger characteristic conditions, then based on the sound monitoring device corresponding to the potential monitoring device, obtains the target environmental sound data corresponding to the potential monitoring data; inputs the target environmental sound data into the target inference neural network for inference prediction, and obtains the interference source information corresponding to the environmental sound data; sends the interference source information, the potential monitoring data and the target environmental sound data to the cloud server, wherein the cloud server predicts the corrosion degree of the pipeline based on the interference source information, the potential monitoring data and the target environmental sound data.

[0107] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0108] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0109] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0110] The storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned fusion end-cloud collaborative pipeline corrosion monitoring method, which can solve the technical problem of how to be compatible with the influence of interference sources when evaluating pipelines and improve the monitoring accuracy of overall corrosion and local corrosion of pipelines. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the fusion end-cloud collaborative pipeline corrosion monitoring method provided in the above-mentioned embodiment, and will not be repeated here.

[0111] An embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned pipeline corrosion monitoring method with integrated end-cloud collaboration.

[0112] The computer program product provided by the present application can solve the technical problem of how to be compatible with the influence of interference sources when evaluating pipelines and improve the monitoring accuracy of overall corrosion and local corrosion of pipelines. Compared with the prior art, the beneficial effects of the computer program product provided by the embodiment of the present application are the same as the beneficial effects of the pipeline corrosion monitoring method with integrated terminal-cloud collaboration provided by the above embodiment, which will not be repeated here.

[0113] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A pipeline corrosion monitoring method integrating terminal and cloud collaboration, characterized in that: The pipeline corrosion monitoring method integrating terminal and cloud collaboration includes the following steps: Acquire potential monitoring data of a potential monitoring device, and determine whether the potential monitoring data meets a preset trigger characteristic condition, wherein each pipeline monitoring point corresponding to the pipeline to be monitored is provided with a potential monitoring device and a sound monitoring device; If the potential monitoring data meets the preset trigger characteristic condition, obtaining the target environment sound data corresponding to the potential monitoring data based on the sound monitoring device corresponding to the potential monitoring device; Inputting the target environmental sound data into the target inference neural network for inference prediction to obtain interference source information corresponding to the environmental sound data; The interference source information, the potential monitoring data and the target environmental sound data are sent to a cloud server, wherein the cloud server predicts the corrosion degree of the pipeline based on the interference source information, the potential monitoring data and the target environmental sound data.

2. The pipeline corrosion monitoring method integrating terminal and cloud collaboration as claimed in claim 1 is characterized in that: The step of determining whether the potential monitoring data meets a preset trigger characteristic condition comprises: Based on the potential monitoring data, determining the potential change slope, jump amplitude and frequency; Whether the potential monitoring data meets a preset trigger characteristic condition is determined based on the potential change slope, jump amplitude and frequency.

3. The pipeline corrosion monitoring method integrating terminal and cloud collaboration as claimed in claim 2 is characterized in that: The step of determining whether the potential monitoring data meets the preset trigger characteristic condition based on the potential change slope, jump amplitude and frequency includes: Determine whether there is a target potential slope greater than the preset slope among the various potential change slopes and whether the jump amplitude and frequency meet the preset requirements. If there is a target potential slope greater than the preset slope and the jump amplitude and frequency meet the preset requirements, it is determined that the potential monitoring data meets the preset trigger characteristic conditions.

4. The pipeline corrosion monitoring method integrating terminal and cloud collaboration as claimed in claim 3 is characterized in that: If the potential monitoring data meets the preset trigger characteristic condition, the step of obtaining the target environment sound data corresponding to the potential monitoring data based on the potential monitoring device corresponding to the sound monitoring device includes: If the potential monitoring data meets the preset trigger characteristic condition, then obtaining the environmental sound data monitored by the sound monitoring device corresponding to the potential monitoring device; Based on the target potential slope, jump amplitude and frequency, the target environmental sound data is obtained from the environmental sound data.

5. The pipeline corrosion monitoring method of integrated terminal-cloud collaboration as claimed in claim 4 is characterized in that: The step of acquiring the target environmental sound data from the environmental sound data based on the target potential slope, jump amplitude and frequency includes: Acquire a first potential slope and a second potential slope in the target potential slope; Obtaining a first moment corresponding to the first potential slope and a second moment corresponding to the second potential slope; Based on the first moment and the second moment, the target environmental sound data is obtained from the environmental sound data.

6. The pipeline corrosion monitoring method integrating terminal and cloud collaboration as claimed in claim 1 is characterized in that: Before the step of acquiring the potential monitoring data of the potential monitoring device and determining whether the potential monitoring data meets the preset trigger characteristic condition, the pipeline corrosion monitoring method integrating terminal-cloud collaboration also includes: Receiving a first configuration parameter sent by a cloud server, wherein the cloud server trains a preset neural network model based on training data to obtain a parameter error corresponding to the configuration parameter of the preset neural network model, reversely calculating the configuration parameter of the preset neural network model based on the parameter error to obtain a second configuration parameter, and adjusting the second configuration parameter based on a distance relationship between the potential monitoring device and the interference source to obtain the first configuration parameter; The inference neural network is updated based on the first configuration parameters to obtain a target inference neural network.

7. The pipeline corrosion monitoring method of fusion terminal-cloud collaboration as described in any one of claims 1 to 6 is characterized in that: The interference source information includes interference source type, interference intensity, interference frequency and interference duration.

8. A pipeline corrosion monitoring device integrating terminal and cloud collaboration, characterized in that: The pipeline corrosion monitoring device integrating terminal and cloud collaboration includes: A determination module, used to obtain potential monitoring data of a potential monitoring device and determine whether the potential monitoring data meets a preset trigger characteristic condition, wherein each pipeline monitoring point corresponding to the pipeline to be monitored is provided with a potential monitoring device and a sound monitoring device; an acquisition module, configured to acquire, if the potential monitoring data meets a preset trigger characteristic condition, target environment sound data corresponding to the potential monitoring data based on the sound monitoring device corresponding to the potential monitoring device; A prediction module, used to input the target environmental sound data into a target inference neural network for inference prediction, and obtain interference source information corresponding to the environmental sound data; A sending module is used to send the interference source information, the potential monitoring data and the target environmental sound data to a cloud server, wherein the cloud server predicts the corrosion degree of the pipeline based on the interference source information, the potential monitoring data and the target environmental sound data.

9. A pipeline corrosion monitoring device integrating terminal and cloud collaboration, characterized in that: The fused end-cloud collaborative pipeline corrosion monitoring device includes: a memory, a processor, and a fused end-cloud collaborative pipeline corrosion monitoring program stored in the memory and executable on the processor. When the fused end-cloud collaborative pipeline corrosion monitoring program is executed by the processor, the steps of the fused end-cloud collaborative pipeline corrosion monitoring method as described in any one or part of claims 1 to 7 are implemented.

10. A storage medium, characterized in that: The storage medium refers to a computer-readable storage medium, on which a fused end-cloud collaborative pipeline corrosion monitoring program is stored. When the fused end-cloud collaborative pipeline corrosion monitoring program is executed by a processor, the steps of the fused end-cloud collaborative pipeline corrosion monitoring method as described in any one or part of claims 1 to 7 are implemented.

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