Operation safety assessment device, method and equipment for corrosion-resistant cable

Abstract

The invention discloses an operation safety evaluation device, method and equipment for a corrosion-resistant cable, and belongs to the technical field of cable monitoring. The device comprises a static data determination module used for acquiring basic setting parameters of the corrosion-resistant cable as static data; the dynamic data determination module is used for acquiring environmental parameters of corrosion-resistant cable installation, including the type and the concentration of a corrosive medium, as dynamic data; the data input module is used for inputting the static data and the dynamic data into a pre-constructed corrosion analysis model to obtain an output result of the corrosion analysis model; and the operation safety evaluation module is used for evaluating the operation safety state of the corrosion-resistant cable according to the output result to obtain an occurrence time estimation result of the fault of the corrosion-resistant cable. According to the technical scheme, real-time quantitative evaluation and fault active early warning of the corrosion state of the cable can be achieved, the continuity of a power utilization system is remarkably improved, and the method is particularly suitable for closed complex environments such as submarines and ships.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of cable monitoring, and particularly relates to a running safety evaluation device, method and equipment for a corrosion-resistant cable. BACKGROUND

[0002] At present, with the rapid development of science and technology, the demand for equipment power in various industries is becoming more and more extensive, and the demand for corrosion-resistant cables is also increasing. The corrosion-resistant cable can have good corrosion resistance in an environment with corrosive gas or corrosive liquid, so as to ensure the service life and normal operation of the cable. However, with the production and use of corrosion-resistant cables, it is found that there is no good running safety evaluation mechanism at present, and because the working environment of the corrosion-resistant cable is often a closed environment, it cannot be maintained on time, and only when the corrosion-resistant cable has a problem, can it be replaced and repaired. This causes the stagnation of the power system, affecting production and life. Therefore, how to evaluate the running safety of the corrosion-resistant cable is a technical problem for those skilled in the art. SUMMARY

[0003] The embodiment of the application provides a running safety evaluation device, method and equipment for a corrosion-resistant cable, which aims to realize real-time quantitative evaluation and active fault early warning of the corrosion state of the cable by using multi-dimensional data fusion and model prediction capability, and significantly improve the continuity of the power system, and is particularly suitable for submarines, ships and other closed and complex environments.

[0004] In a first aspect, the embodiment of the application provides a running safety evaluation device for a corrosion-resistant cable, and the device comprises: a static data determination module configured to obtain basic setting parameters of the corrosion-resistant cable as static data; a dynamic data determination module configured to obtain environmental parameters of the corrosion-resistant cable installation, including the type of corrosive medium and the concentration of corrosive medium, as dynamic data; a data input module configured to input the static data and the dynamic data into a pre-constructed corrosion analysis model to obtain an output result of the corrosion analysis model; a running safety evaluation module configured to evaluate the running safety state of the corrosion-resistant cable according to the output result to obtain a failure occurrence time estimation result of the corrosion-resistant cable.

[0005] Further, the device further comprises: a running data acquisition module configured to acquire electrical data of the corrosion-resistant cable in the running process; Correspondingly, the data input module is specifically configured to: inputting the static data, the dynamic data and the electrical data into a pre-constructed corrosion analysis model to obtain an output result of the corrosion analysis model.

[0006] Further, the device further comprises: an image data acquisition module, configured to acquire image data of the insulation layer of the corrosion-resistant cable; Correspondingly, the data input module is specifically configured to: input the static data, the dynamic data and the image data into a pre-constructed corrosion analysis model to obtain an output result of the corrosion analysis model.

[0007] Further, the device further comprises: a feature recognition module, configured to recognize whether the image data has a preset image feature; The data input module is further specifically configured to: in a case where the image data has the preset image feature, input the static data, the dynamic data and the image data into a pre-constructed first corrosion analysis model to obtain an output result of the first corrosion analysis model; in a case where the image data does not have the preset image feature, input the static data, the dynamic data and the image data into a pre-constructed second corrosion analysis model to obtain an output result of the second corrosion analysis model.

[0008] Further, the preset image feature comprises: a corrosive crack appearing at the insulation layer of the corrosion-resistant cable.

[0009] Further, the dynamic data determination module is further configured to: in a case where the type of the corrosion medium or the concentration of the corrosion medium changes, generate alarm dynamic data change information; an alarm module, configured to generate alarm information in a case where the dynamic data change information satisfies a set condition.

[0010] Further, the output result of the corrosion analysis model comprises a current health score of the corrosion-resistant cable. Correspondingly, the running safety evaluation module is further configured to: compare the current health score with a pre-constructed corrosion score curve to determine a current corrosion degree of the corrosion-resistant cable.

[0011] In a second aspect, an embodiment of the present application provides a running safety evaluation method of a corrosion-resistant cable, and the method comprises: acquiring basic setting parameters of the corrosion-resistant cable as static data; acquire environmental parameters of the corrosion-resistant cable installation, including a type of corrosion medium and a concentration of the corrosion medium, as dynamic data; input the static data and the dynamic data into a pre-constructed corrosion analysis model to obtain an output result of the corrosion analysis model; evaluate a running safety state of the corrosion-resistant cable according to the output result to obtain a failure occurrence time estimation result of the corrosion-resistant cable.

[0012] Further, the method further includes: acquire electrical data of the corrosion-resistant cable during running; Correspondingly, input the static data and the dynamic data into a pre-constructed corrosion analysis model to obtain an output result of the corrosion analysis model, including: input the static data, the dynamic data and the electrical data into a pre-constructed corrosion analysis model to obtain an output result of the corrosion analysis model.

[0013] In a third aspect, an electronic device is provided, which includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, and the program or instructions, when executed by the processor, implement the steps of the method described above.

[0014] In a fourth aspect, a readable storage medium is provided, which stores a program or instructions, and the program or instructions, when executed by a processor, implement the steps of the method described above.

[0015] In the embodiments of the present application, the static data determination module is configured to acquire basic setting parameters of the corrosion-resistant cable as static data; the dynamic data determination module is configured to acquire environmental parameters of the corrosion-resistant cable installation, including a type of corrosion medium and a concentration of the corrosion medium, as dynamic data; the data input module is configured to input the static data and the dynamic data into a pre-constructed corrosion analysis model to obtain an output result of the corrosion analysis model; and the running safety evaluation module is configured to evaluate a running safety state of the corrosion-resistant cable according to the output result to obtain a failure occurrence time estimation result of the corrosion-resistant cable. In this technical solution, the static data determination module acquires basic setting parameters of the corrosion-resistant cable, the dynamic data determination module captures environmental parameters, and after inputting the two types of data into the corrosion analysis model, the running safety evaluation module outputs the failure occurrence time estimation result. This solution realizes real-time running safety evaluation of the corrosion-resistant cable, can quantitatively evaluate the corrosion state of the cable and actively warn of failures, improves the continuity and safety of the power system, and is especially suitable for submarines, ships and other closed and complex environments. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 is a structural schematic diagram of a running safety evaluation device of a corrosion-resistant cable provided by an embodiment of the present application; Figure 2 is a structural schematic diagram of a running safety evaluation device of a corrosion-resistant cable provided by an embodiment of the present application; Figure 3 is a flow schematic diagram of a running safety evaluation method of a corrosion-resistant cable provided by an embodiment of the present application; Figure 4 is a structural schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to make the objects, technical solutions and advantages of the present application clearer, the specific embodiments of the present application are further described in detail below with reference to the drawings. It can be understood that the specific embodiments described herein are only used to explain the present application, but not to limit the present application. In addition, it should be noted that, for the convenience of description, only parts related to the present application are shown in the drawings, but not all contents. Before discussing the example embodiments in more detail, it should be mentioned that some example embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the operations (or steps) as sequential processes, many of the operations can be implemented in parallel, concurrently or simultaneously. In addition, the order of the operations can be rearranged. The processes can be terminated when the operations are completed, but can also have additional steps not included in the drawings. The processes can correspond to methods, functions, procedures, subroutines, subprograms, etc.

[0018] The technical solutions in the embodiments of the present application will be described clearly below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art belong to the scope of protection of the present application.

[0019] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are generally of a kind, and do not limit the number of objects, for example, the first object can be one or more. In addition, "and / or" in the specification and claims means at least one of the connected objects, and the character " / ", generally represents that the objects before and after are in an "or" relationship.

[0020] The running safety evaluation device, method and equipment of the corrosion-resistant cable provided by the embodiments of the present application will be described in detail below with reference to the specific embodiments and application scenarios thereof in combination with the accompanying drawings.

[0021] Embodiment one Figure 1 is a structural schematic diagram of the running safety evaluation device of the corrosion-resistant cable provided by the first embodiment of the present application. As shown in Figure 1 , specifically comprises: The static data determination module 110 is configured to obtain the basic setting parameters of the corrosion-resistant cable as static data. Firstly, the present application is applicable to the running safety monitoring and evaluation scene of the corrosion-resistant cable. Based on the above use scenario, it can be understood that the execution subject of the present application can be a management system or a management platform of the corrosion-resistant cable, which can run on a terminal device or a server.

[0022] The corrosion-resistant cable can be a cable prepared by using a nano composite coating technology, for example, a coating containing graphene-titanium dioxide composite nanoparticles can be coated on the outer layer to make it have corrosion resistance. The internal conductor is made of a new alloy material, such as copper-nickel-molybdenum alloy, which has high conductivity and corrosion resistance.

[0023] The basic setting parameters can be the material composition of the cable, including the above alloy ratio, the nano coating formula, the structure parameters, such as the use of aerogel-polytetrafluoroethylene composite insulation material for the insulation layer, and the thickness and other parameters thereof; the use of a multi-layer metal mesh and conductive polymer composite structure for the shielding layer, and the number of layers and the material thereof, the rated electrical parameters, such as the rated voltage, the rated current and the rated power, etc.

[0024] The static data, i.e. the basic setting parameters obtained from the corrosion-resistant cable, are data that have been determined in the cable production and manufacturing stage and basically remain unchanged subsequently.

[0025] In this scheme, the basic setting parameters can be written into the chip by implanting a micro storage chip in the cable during the cable production process. When the static data is needed, the data in the chip is read in a non-contact manner by using a dedicated RFID reading device close to the cable by using the RFID technology, so as to determine the basic setting parameters of the corrosion-resistant cable as the static data. The parameter information of the cable at the current monitoring position can also be read from the pre-constructed cable laying information database.

[0026] The dynamic data determination module 120 is configured to obtain the environmental parameters of the corrosion-resistant cable installation, including the type of corrosion medium and the concentration of corrosion medium, as dynamic data. Among them, environmental parameters refer to various parameters of the environment in which the corrosion-resistant cable is installed, including but not limited to temperature, humidity, pressure and light intensity, as well as the type and concentration of the core corrosive medium.

[0027] The type of corrosive medium can be seawater (rich in chloride ions, making it corrosive), industrial waste gases (such as sulfur dioxide and hydrogen sulfide, which are acidic gases), or chemical waste liquids (such as strong acid and strong alkali solutions, which are corrosive substances). The concentration of the corrosive medium is the content of the corrosive medium per unit volume or unit mass of the environment.

[0028] Dynamic data, in contrast to static data, refers to environmental parameter data that changes in real time with changes in time and environment.

[0029] This technical solution determines the medium type and concentration by deploying a sensor array on and around the cable surface. Simultaneously, other environmental sensors, such as temperature and humidity sensors and pressure sensors, can be used to acquire parameters such as temperature, humidity, and pressure, which are then integrated as dynamic data.

[0030] The data input module 130 is used to input the static data and the dynamic data into a pre-built corrosion analysis model to obtain the output results of the corrosion analysis model; The data input module is a software or hardware component that transmits static and dynamic data to the corrosion analysis model. The corrosion analysis model can be a comprehensive analysis model trained using a combination of Long Short-Term Memory (LSTM) and Graph Neural Network (GNN) deep learning datasets. LSTM is used to process time-series data and capture the time dependence of the corrosion process; GNN is used to analyze the spatial structural relationships of different parts of the cable, considering the corrosion differences in different locations. After training, the model can be further optimized through extensive experimental and real-world data testing.

[0031] The output results are cable corrosion-related data calculated by the model based on the input data, such as corrosion rate, predicted corrosion depth, and remaining service life of each part.

[0032] In this technical solution, the data input module uses a standardized data interface to convert and encode static and dynamic data. Then, according to the model's preset data input rules, the processed data is transmitted in an orderly manner to the input layer of the corrosion analysis model, triggering the model's calculation process and thus obtaining the model's output results.

[0033] The operational safety assessment module 140 is used to assess the operational safety status of the corrosion-resistant cable based on the output results, and obtain the estimated time of failure of the corrosion-resistant cable.

[0034] The running safety evaluation module is a software module for analyzing and judging the output results of the corrosion analysis model to evaluate the running safety state of the cable. The running safety state can be divided into different levels such as normal, early warning, and failure.

[0035] The failure occurrence time estimation result is the time point or time range at which the cable is expected to fail, which is obtained through evaluation.

[0036] In the technical solution, the output results of the corrosion analysis model, such as corrosion depth and corrosion rate, are compared with the preset safety threshold by using the safety evaluation rules and algorithms built in the running safety evaluation module. When the corrosion degree is lower than the threshold, it is determined as normal state; when it approaches the threshold, it is determined as early warning state; and when it exceeds the threshold, it is determined as failure state. Meanwhile, by combining the change trend of the corrosion rate and the remaining life prediction, and using the reinforcement learning algorithm, the uncertainty of environmental changes is comprehensively considered to estimate the occurrence time of failure, so as to obtain the failure occurrence time estimation result.

[0037] The technical solution provided in the embodiment realizes comprehensive and accurate evaluation of the running safety of the corrosion-resistant cable. In the data acquisition layer, static and dynamic data are acquired to ensure the accuracy and real-time performance of the data; in the data analysis layer, the model is constructed and evaluated by using deep learning and reinforcement learning algorithms, which can deeply analyze the corrosion condition of the cable and realize full-process coverage from current state evaluation to future failure prediction. The technical solution improves the stability of the corrosion-resistant cable running, reduces equipment downtime and safety accidents caused by cable failure, and is especially suitable for special environments such as submarines and ships that have very high requirements for cable stability.

[0038] In the embodiment, optionally, the device further comprises: The running data acquisition module is configured to acquire electrical data of the corrosion-resistant cable during running. Correspondingly, the data input module is specifically configured to: input the static data, the dynamic data, and the electrical data into a pre-constructed corrosion analysis model to obtain output results of the corrosion analysis model.

[0039] The running data acquisition module acquires and preliminarily processes the cable running data by using an integrated intelligent sensor.

[0040] The electrical data can be real-time resistance during cable operation, resistance value caused by cross-sectional area change and resistivity change of conductor material due to corrosion, current distribution detected by a Hall effect sensor array, voltage drop for monitoring voltage difference between two ends of the cable to determine the conductivity performance, partial discharge signal captured by a high-frequency pulse current sensor for weak discharge signal generated by damaged insulation layer, and the like. These data can directly reflect the corrosion damage inside the cable.

[0041] In the scheme, the sensor converts the collected analog signal into a digital signal, and transmits the digital signal to a data processing center through a low-power Bluetooth or ZigBee wireless communication protocol, so as to complete acquisition of electrical data during operation of the corrosion-resistant cable.

[0042] In the scheme, the corrosion analysis model can introduce a self-attention mechanism (Self-Attention) on the basis of the original LSTM combined with the GNN, enhance the extraction capability of the associated features among the multi-source data, and enable the model to more accurately analyze the complex relationship among the data. In addition to the original corrosion rate, depth prediction and other data, the output result can further include prediction information such as electrical performance degradation trend and potential fault type, such as insulation layer breakdown.

[0043] In the scheme, the data input module first normalizes the acquired static data, dynamic data and electrical data. Then, the data feature fusion algorithm is used to integrate the three types of data into a multi-dimensional feature vector. The feature vector is sequentially transmitted to the input layer of the corrosion analysis model through a model-specific data interface. After the model receives the data, the self-attention mechanism is used to mine the hidden association among the data, and the LSTM and GNN are combined for time series and spatial analysis, so as to finally output a more comprehensive and accurate cable corrosion state analysis result.

[0044] The technical scheme realizes multi-dimensional and full-state monitoring of the corrosion-resistant cable. By acquiring the electrical data in real time, the change of the conductivity performance and the insulation performance inside the cable caused by corrosion can be directly reflected, which makes up for the limitation of relying only on environmental parameters and basic parameters for evaluation. Meanwhile, the corrosion analysis model can deeply mine the complex relationship among the multi-source data by means of the self-attention mechanism, which significantly improves the prediction accuracy and fault diagnosis capability of the corrosion process of the cable. This technical innovation enables the system to not only predict the external corrosion condition of the cable, but also identify the internal potential fault risk in advance, further strengthens the timeliness and accuracy of the safety evaluation of the corrosion-resistant cable in operation, and provides more reliable protection for stable operation of the submarine, ship and other equipment, and reduces the probability of major accidents caused by cable failure.

[0045] In the embodiment, optionally, the dynamic data determination module is further configured to: In the case of a change in the type of the corrosive medium or the concentration of the corrosive medium, alarm dynamic data change information is generated; An alarm module is configured to generate alarm information in the case that the dynamic data change information meets a set condition.

[0046] The dynamic data determination module integrates edge computing functions on the basis of the original quantum dot sensor array and multi-source data fusion, and has real-time data processing and abnormality judgment capabilities. The alarm dynamic data change information is a structured data containing data change types, medium type changes or concentration mutations, and key information such as change amplitude and change timestamp, and is used to identify environmental parameter abnormality.

[0047] In the present scheme, the dynamic data determination module performs real-time analysis on the data and concentration data collected by the sensor. Through a preset threshold comparison algorithm, when a change in the type of the corrosive medium is detected, such as a mismatch between the spectral characteristics of the medium and the original record, or a concentration change exceeding a set threshold, such as a 20% increase in concentration within a short period of time, the module automatically extracts the data difference before and after the change, the change time, and other information, encapsulates them according to a specific data format, and generates alarm dynamic data change information.

[0048] The alarm module generates alarm information in the case that the dynamic data change information meets a set condition.

[0049] The set condition can be a simple threshold rule, such as a concentration exceeding a dangerous value, or a complex combination rule, such as a continuous increase in concentration accompanied by an abnormal increase in temperature under a specific medium type, and the rule can also be automatically optimized according to historical data by a machine learning algorithm.

[0050] The alarm information includes alarm levels such as emergency, severe, and general, alarm types such as medium change and concentration exceeding the standard, impact ranges such as specific cable areas, and suggested measures such as adjusting the ventilation system and strengthening monitoring, and is presented in various forms such as visualization and voice broadcast.

[0051] In the present scheme, after receiving the dynamic data change information, the alarm module first analyzes and matches the data through a rule engine. If the preset simple threshold rule is met, the corresponding level of alarm is triggered directly. For complex combination rules, a machine learning algorithm can be used to analyze the correlation between data and determine whether a potential risk is formed. When it is confirmed that the dynamic data change information meets the set condition, the alarm module automatically assigns an alarm level according to the risk level, generates detailed alarm information based on a preset template, and pushes it to relevant personnel through multiple channels, such as a sound and light alarm system, a mobile terminal APP, and an operation and maintenance management platform. The technical solution can improve the real-time early warning capability of the corrosion-resistant cable operation safety evaluation system through the cooperative work of the dynamic data determination module and the alarm module. On the one hand, the dynamic data determination module can quickly capture the subtle changes of the corrosion medium type and concentration, avoiding the early warning lag caused by data transmission delay; on the other hand, the alarm module uses a flexible rule engine and intelligent algorithm to customize the alarm strategy for different application scenarios, which can not only handle simple threshold exceeding situations, but also identify potential risks caused by complex environmental factor combinations. This mechanism enables the system to issue an alarm in a timely manner in the early stage of corrosion risk, giving maintenance personnel more time to respond, effectively reducing the probability of cable failure caused by environmental deterioration, and improving the operation stability of electrical equipment.

[0052] In the embodiment, optionally, the output result of the corrosion analysis model includes a current health score of the corrosion-resistant cable. Correspondingly, the operation safety evaluation module is further configured to: compare the current health score with a pre-constructed corrosion score curve to determine a current corrosion degree of the corrosion-resistant cable.

[0053] The corrosion analysis model can simultaneously capture the time evolution characteristics of cable corrosion, such as corrosion rate change trend, and spatial distribution characteristics, such as corrosion degree difference of different parts.

[0054] The current health score is a quantitative index of 0-100, which is calculated by multi-dimensional feature fusion. Specifically, when calculating, the model will consider factors such as cable material residual strength, insulation performance degradation, and conductive capacity decline, and generate a health score using a weighted summation formula. The health score calculation adopts a dynamic weight mechanism, which automatically adjusts the weight of each factor according to environmental parameters. For example, in a high humidity environment, the weight of insulation performance degradation will significantly increase, making the health score more sensitive to reflect the actual state of the cable.

[0055] Correspondingly, the operation safety evaluation module can compare the current health score with a pre-constructed corrosion score curve to determine a current corrosion degree of the corrosion-resistant cable.

[0056] The corrosion score curve can be constructed by fusing accelerated life test and actual operation data. Specifically, the same specification cable samples can be subjected to gradient increasing corrosion stress, and the degradation trajectory of the health score over time is recorded to obtain a theoretical corrosion curve. Then, the theoretical curve is corrected in real time according to the actual operation data to form the corrosion score curve. For example, the current corrosion degree can be divided into five levels, level I (health score ≥ 90, slight corrosion), level II (80 ≤ health score < 90, mild corrosion), level III (60 ≤ health score < 80, moderate corrosion), level IV (40 ≤ health score < 60, severe corrosion), and level V (health score < 40, serious corrosion).

[0057] The technical solution outputs the current health score of the corrosion-resistant cable through the innovative corrosion analysis model, and compares it with the dynamically updated corrosion score curve by using the operation safety evaluation module, and comprehensively considers the multi-dimensional performance indicators such as material, insulation and conduction, to realize the precise quantitative evaluation and probabilistic judgment of the corrosion degree of the cable. Not only does it overcome the one-sidedness of traditional single parameter evaluation, but it also dynamically adapts to complex and variable environments, and can early warn potential risks when the health score shows a trend of change, effectively reducing unnecessary maintenance, improving fault prediction accuracy, and providing reliable protection for the operation safety of cables of submarines, ships and other equipment.

[0058] Embodiment Two Figure 2 is a structural schematic diagram of the operation safety evaluation device of the corrosion-resistant cable provided in Embodiment Two of the present application. The present solution makes a more optimal improvement on the above-mentioned embodiment, and the specific improvement is that the device further comprises an image data acquisition module for acquiring image data of the insulation layer of the corrosion-resistant cable; correspondingly, the data input module is specifically configured to input the static data, the dynamic data and the image data into a pre-constructed corrosion analysis model to obtain an output result of the corrosion analysis model.

[0059] As shown in Figure 2 , specifically comprising: A static data determination module 210 is configured to acquire the basic setting parameters of the corrosion-resistant cable as static data. A dynamic data determination module 220 is configured to acquire the environmental parameters of the corrosion-resistant cable installation, including the type of corrosion medium and the concentration of corrosion medium, as dynamic data. An image data acquisition module 230 is configured to acquire image data of the insulation layer of the corrosion-resistant cable. When the insulation layer of the corrosion-resistant cable is imaged, the subtle changes in its internal structure, such as the difference in material density and the generation of bubbles caused by corrosion, will be reflected in the gray scale, texture and other features of the image.

[0060] The image data includes an insulating layer surface topography image, and in some scenarios facilitating operation, can also include internal structure tomography images and the like information, and can be stored in the form of three-dimensional point cloud data combined with two-dimensional texture images.

[0061] The data input module 240 is specifically configured to: input the static data, the dynamic data and the image data into a pre-constructed corrosion analysis model to obtain an output result of the corrosion analysis model; The corrosion analysis model can realize deep feature extraction and fusion analysis of multi-modal data.

[0062] The output result can include comprehensive information such as cable corrosion rate, corrosion position prediction, insulating layer damage degree evaluation, and remaining service life prediction.

[0063] In the scheme, the data input module first performs standardization processing on the static data and the dynamic data, and converts them into a unified data format. For the image data, a self-supervised learning algorithm is used for feature extraction, and three-dimensional point cloud or two-dimensional image is converted into a feature vector. Then, through a multi-modal data fusion algorithm, the feature vectors of the three types of data are spliced and weighted fused to generate a composite feature matrix containing full-dimensional information of the cable. Finally, the matrix is input into the embedding layer of the corrosion analysis model to output the analysis result of the corrosion state of the cable.

[0064] The running safety evaluation module 250 is configured to evaluate the running safety state of the corrosion-resistant cable according to the output result to obtain an occurrence time estimation result of a fault of the corrosion-resistant cable.

[0065] The technical scheme provided by the embodiment realizes all-around and visualized evaluation of the running safety of the corrosion-resistant cable by introducing an image data acquisition module and a multi-modal data processing mechanism. The image data acquisition module can directly capture the microscopic damage of the insulating layer, making up for the deficiency of the traditional parameter monitoring that cannot directly observe the internal defects of the material. The data input module can deeply mine the potential correlation among the static, dynamic and image data through multi-modal data fusion and deep learning algorithms, significantly improving the accuracy of corrosion state evaluation and the timeliness of fault prediction. This way not only can timely discover early damage of the cable insulating layer, but also can assist operation and maintenance personnel to quickly locate the problem area through visual images, formulate accurate maintenance strategies, and is especially suitable for submarines, ships and other complex environments with extremely high requirements for cable reliability, effectively reducing the risk of cable failure.

[0066] On the basis of the above embodiments, optionally, the device further comprises: The feature recognition module is configured to recognize whether the image data has a preset image feature or not; The data input module is also specifically used for: In the case that the image data has the preset image feature, inputting the static data, the dynamic data and the image data into a first corrosion analysis model constructed in advance to obtain an output result of the first corrosion analysis model; In the case that the image data does not have the preset image feature, inputting the static data, the dynamic data and the image data into a second corrosion analysis model constructed in advance to obtain an output result of the second corrosion analysis model.

[0067] The preset image feature can include a crack with a surface width of an insulation layer greater than 0.1 millimeter, a hole with a diameter exceeding 0.1 millimeter, irregular bubbles or a drumming phenomenon, etc. These features are key basis for judging the corrosion degree and type of the cable.

[0068] In the scheme, after receiving the image data, the feature recognition module first pre-processes the image, including noise reduction, normalization and contrast enhancement operations, to improve the image quality. Then, the image is feature-extracted, and the extracted features are similarity-calculated with a preset image feature template. If there is a region in the image with a similarity to the preset feature template exceeding a set threshold, such as 85%, it is determined that the image data has the preset image feature; otherwise, it is determined that the image data does not have the preset image feature. In the case that the image data has the preset image feature, inputting the static data, the dynamic data and the image data into a first corrosion analysis model constructed in advance to obtain an output result of the first corrosion analysis model; in the case that the image data does not have the preset image feature, inputting the static data, the dynamic data and the image data into a second corrosion analysis model constructed in advance to obtain an output result of the second corrosion analysis model. The first corrosion analysis model can be used for image data with obvious corrosion features, focusing on analyzing the rapid development trend of cable corrosion and the influence of local severe corrosion area, and can more accurately predict the short-term failure risk. The second corrosion analysis model focuses more on analyzing potential weak corrosion signs in the image data and the influence of environmental parameters and basic parameters on long-term corrosion of the cable, and is used for evaluating the long-term health status and slow development of the corrosion process of the cable.

[0069] The output result, according to the difference of the input model, the first corrosion analysis model outputs the results of short-term cable corrosion rate acceleration prediction, severe corrosion area expansion trend, etc.; the second corrosion analysis model outputs the results of cable corrosion trend in the future months to years, overall health status evolution, etc.

[0070] The technical solution can finely process the operation safety evaluation of the corrosion-resistant cable. The data input module dynamically selects different corrosion analysis models according to the image feature recognition result, and performs special analysis on the corrosion conditions of the cable at different stages. In this way, the adaptability of the evaluation system to complex corrosion scenes can be improved. For the cable with obvious corrosion features, the first corrosion analysis model can realize accurate early warning of short-term failure, helping the operation and maintenance personnel to take emergency maintenance measures in time. For the cable with weak corrosion signs, the second corrosion analysis model can effectively predict the long-term corrosion trend, facilitating the development of a scientific preventive maintenance plan.

[0071] On the basis of the above-mentioned embodiments, optionally, the preset image features include corrosion cracks appearing at the insulation layer of the corrosion-resistant cable.

[0072] The corrosion cracks are linear or network cracks formed on the surface or inside the cable insulation layer and caused by chemical corrosion. The crack width is usually between 5-500 microns, the depth can penetrate 10%-80% of the insulation layer, and the edge presents irregular jagged shape.

[0073] The technical solution accurately identifies the corrosion cracks of the cable insulation layer, and adopts a double-model switching mechanism to consider short-term failure warning and long-term aging evaluation, realizes intelligent management of the whole life cycle of the cable, and can improve the evaluation accuracy of the operation safety of the corrosion-resistant cable.

[0074] The operation safety evaluation device of the corrosion-resistant cable in the embodiments of the present application can be a device, or a component, an integrated circuit or a chip in a terminal. The device can be a mobile electronic device or a non-mobile electronic device. Illustratively, the mobile electronic device can be a mobile phone, a tablet computer, a notebook computer, a palm computer, a vehicle-mounted electronic device, a wearable device, an ultra-mobile personal computer (UMPC), a netbook or a personal digital assistant (PDA), etc., and the non-mobile electronic device can be a server, a network attached storage (NAS), a personal computer (PC), a television (TV), a teller machine or a self-service machine, etc., and the embodiments of the present application are not limited in this regard.

[0075] The operation safety evaluation device of the corrosion-resistant cable in the embodiments of the present application can be a device with an operating system. The operating system can be an Android operating system, an IOS operating system, or other possible operating systems, and the embodiments of the present application are not limited in this regard.

[0076] Example 3 Figure 3 This is a flowchart illustrating the operational safety assessment method for corrosion-resistant cables provided in Embodiment 3 of this application. Figure 3 As shown, the specific steps include the following: S301, Obtain the basic setup parameters of the corrosion-resistant cable as static data; S302, Obtain environmental parameters for the installation of corrosion-resistant cables, including the type and concentration of the corrosive medium, as dynamic data; S303, Input the static data and the dynamic data into the pre-constructed corrosion analysis model to obtain the output results of the corrosion analysis model; S304. Based on the output results, the operational safety status of the corrosion-resistant cable is evaluated to obtain the estimated time of failure of the corrosion-resistant cable.

[0077] Furthermore, the method also includes: Obtain the electrical data of the corrosion-resistant cable during operation; Accordingly, the static data and the dynamic data are input into a pre-constructed corrosion analysis model to obtain the output results of the corrosion analysis model, including: The static data, the dynamic data, and the electrical data are input into a pre-constructed corrosion analysis model to obtain the output results of the corrosion analysis model.

[0078] The method for assessing the operational safety of corrosion-resistant cables provided in this application has the same functions and execution process as the apparatus in the above embodiments, and achieves the corresponding beneficial effects. To avoid repetition, it will not be described again here.

[0079] Example 4 like Figure 4 As shown, this application embodiment also provides an electronic device 400, including a processor 401, a memory 402, and a program or instructions stored in the memory 402 and executable on the processor 401. When the program or instructions are executed by the processor 401, they implement the various processes of the above-described embodiment of the corrosion-resistant cable operation safety assessment device and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0080] It should be noted that the electronic devices in the embodiments of this application include the mobile electronic devices and non-mobile electronic devices described above.

[0081] Example 5 The embodiment of the application further provides a readable storage medium, which stores a program or instructions, and the program or instructions are executed by a processor to realize each process of the operation safety evaluation device of the corrosion-resistant cable and achieve the same technical effects. To avoid repetition, details are not described herein.

[0082] The processor is the processor in the electronic device in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0083] It should be noted that in this document, the terms "comprising", "including", or any other variant thereof are intended to cover non-exclusive inclusions, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such a process, method, article or device. Without more limitations, the element defined by the statement "including a" does not exclude the presence of another identical element in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the method and device in the embodiments of the application is not limited to the order of performing the functions shown or discussed, and can also include performing the functions in a substantially simultaneous manner or in reverse order, for example, the described method can be performed in an order different from that described, and various steps can also be added, omitted or combined. In addition, the features described with reference to certain examples can be combined in other examples.

[0084] From the above description of the embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment method can be realized by means of software and necessary general hardware platform, of course, it can also be realized by hardware, but in many cases, the former is a better embodiment. Based on such understanding, the technical solutions of the application can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a plurality of instructions for making a terminal (which can be a mobile phone, computer, server, or network equipment, etc.) execute the method described in each embodiment of the application.

[0085] The embodiments of the present application are described above with reference to the accompanying drawings, but the present application is not limited to the specific embodiments described above, and the specific embodiments described above are merely illustrative, but not restrictive, and those of ordinary skill in the art can make many forms without departing from the purpose of the present application and the scope protected by the claims under the inspiration of the present application, which all belong to the protection of the present application.

[0086] The above are only the preferred embodiments of the present application and the technical principles used. The present application is not limited to the specific embodiments described herein, and various obvious changes, readjustments and substitutions made by those skilled in the art without departing from the scope of the present application do not fall within the scope of the present application. Therefore, although the present application is described in more detail through the above embodiments, the present application is not limited to the above embodiments, and more other equivalent embodiments can be included without departing from the concept of the present application, and the scope of the present application is determined by the scope of the claims.

Claims

1. A device for assessing the operational safety of corrosion-resistant cables, characterized in that, The device includes: The static data determination module is used to obtain the basic setup parameters of the corrosion-resistant cable as static data. The dynamic data determination module is used to acquire environmental parameters for the installation of corrosion-resistant cables, including the type and concentration of the corrosive medium, as dynamic data. The data input module is used to input the static data and the dynamic data into the pre-built corrosion analysis model to obtain the output results of the corrosion analysis model; The operation safety assessment module is used to assess the operational safety status of the corrosion-resistant cable based on the output results, and obtain the estimated time of failure of the corrosion-resistant cable.

2. The operational safety assessment device for corrosion-resistant cables according to claim 1, characterized in that, The device further includes: The operation data acquisition module is used to acquire the electrical data of the corrosion-resistant cable during operation; Accordingly, the data input module is specifically used for: The static data, the dynamic data, and the electrical data are input into a pre-constructed corrosion analysis model to obtain the output results of the corrosion analysis model.

3. The operational safety assessment device for corrosion-resistant cables according to claim 1, characterized in that, The device further includes: An image data acquisition module is used to acquire image data of the insulation layer of the corrosion-resistant cable; Accordingly, the data input module is specifically used for: The static data, the dynamic data, and the image data are input into a pre-constructed corrosion analysis model to obtain the output results of the corrosion analysis model.

4. The operational safety assessment device for corrosion-resistant cables according to claim 3, characterized in that, The device further includes: The feature recognition module is used to identify whether the image data contains preset image features; The data input module is also specifically used for: When the image data has preset image features, the static data, the dynamic data, and the image data are input into a pre-constructed first corrosion analysis model to obtain the output result of the first corrosion analysis model; If the image data does not contain preset image features, the static data, the dynamic data, and the image data are input into a pre-constructed second corrosion analysis model to obtain the output results of the second corrosion analysis model.

5. The operational safety assessment device for corrosion-resistant cables according to claim 4, characterized in that, The preset image feature includes: a corrosive crack appearing in the insulation layer of the corrosion-resistant cable.

6. The operational safety assessment device for corrosion-resistant cables according to claim 1, characterized in that, The dynamic data determination module is further used for: When the type or concentration of the corrosive medium changes, alarm dynamic data change information is generated; The alarm module is used to generate alarm information when the dynamic data change information meets or exceeds the set conditions.

7. The operational safety assessment device for corrosion-resistant cables according to claim 1, characterized in that, The output of the corrosion analysis model includes the current health score of the corrosion-resistant cable; Accordingly, the operational security assessment module is also used for: The current health score is compared with a pre-constructed corrosion score curve to determine the current degree of corrosion of the corrosion-resistant cable.

8. A method for assessing the operational safety of corrosion-resistant cables, characterized in that, The method includes: Obtain the basic setup parameters of the corrosion-resistant cable as static data; Obtain environmental parameters for the installation of corrosion-resistant cables, including the type and concentration of the corrosive medium, as dynamic data; The static data and the dynamic data are input into a pre-built corrosion analysis model to obtain the output results of the corrosion analysis model; The operational safety status of the corrosion-resistant cable is evaluated based on the output results, and the predicted time of failure of the corrosion-resistant cable is obtained.

9. The method for assessing the operational safety of corrosion-resistant cables according to claim 8, characterized in that, The method further includes: Obtain the electrical data of the corrosion-resistant cable during operation; Accordingly, the static data and the dynamic data are input into a pre-constructed corrosion analysis model to obtain the output results of the corrosion analysis model, including: The static data, the dynamic data, and the electrical data are input into a pre-constructed corrosion analysis model to obtain the output results of the corrosion analysis model.

10. An electronic device, characterized in that, It includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method for assessing the operational safety of a corrosion-resistant cable as described in any one of claims 8-9.

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