Cable damp detection method and device, storage medium and electronic device

Abstract

The invention relates to the field of power system safety monitoring, and discloses a cable damp detection method and device, a storage medium and an electronic device, and the method comprises the following steps: obtaining humidity related data of a cable insulation layer, the data comprising cable surface humidity, environment humidity and cable operation parameters; constructing a humidity diffusion model, and combining the humidity diffusion characteristics of the cable material and the environmental influence; inverting the internal humidity distribution of the cable based on a humidity diffusion model and the acquired data; setting a humidity judgment threshold value according to cable operation characteristics and humidity diffusion characteristics; judging the damp state of the cable according to the relation between the humidity distribution and a humidity judgment threshold value; and outputting the damp state of the cable and generating an alarm signal when the damp state exceeds a set threshold value. According to the invention, accurate detection and dynamic monitoring of moisture in the cable can be realized, the system adapts to complex environment changes, the accuracy and reliability of cable moisture detection are improved, and an effective guarantee is provided for safe operation of the cable.

Description

Technical Field

[0001] The present invention relates to the technical field of power system safety monitoring, and in particular to a cable moisture detection method, device, storage medium and electronic device. Background Art

[0002] Cables are widely used in power transmission, communications, and control systems, and their safe operation is crucial to system stability. However, moisture in cables has long been a significant factor affecting their insulation performance and service life. Moisture penetration can degrade the dielectric properties of cable insulation and even cause serious faults such as short circuits and breakdown. Therefore, effective moisture detection within cables and timely detection and warning of moisture are crucial for ensuring safe cable operation.

[0003] Most existing cable moisture detection technologies rely on regular insulation resistance testing or applying voltage to measure leakage current. However, these methods have many limitations. First, insulation resistance testing requires the cable to be powered off, which increases the complexity of operation and maintenance and the risk of power outages. In addition, these methods are mostly based on point measurements, which makes it difficult to capture the distribution of moisture in the cable insulation layer. Especially in the early stages of moisture, the changes may be very subtle, making it difficult for traditional methods to detect them in time. In addition, current methods are generally unable to achieve real-time monitoring of the cable's humidity status. Faced with dynamic changes in ambient humidity and complex operating conditions, the detection results often have large uncertainties, which can easily lead to missed or false alarms. Summary of the Invention

[0004] In response to the deficiencies of the prior art, the present invention provides a cable moisture detection method, device, storage medium and electronic device, which can realize dynamic monitoring of moisture distribution inside the cable insulation layer and accurate judgment of the moisture status under complex environmental conditions.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A first aspect of the present invention provides a method for detecting cable moisture, comprising the following steps: Obtain humidity-related data of the cable insulation layer, including cable surface humidity, ambient humidity and cable operating parameters; A cable moisture diffusion model is constructed to describe the dynamic diffusion behavior of moisture inside the insulation layer. The model combines the moisture diffusion characteristics of the cable material and the influence of the environment. Based on the humidity diffusion model and the acquired humidity-related data, the humidity distribution inside the cable is inverted; According to the cable operation characteristics and humidity diffusion characteristics, a humidity judgment threshold is set, and the threshold is dynamically adjusted to adapt to environmental changes; Determine the moisture status of the cable based on the relationship between humidity distribution and humidity judgment threshold; Outputs the cable moisture status and generates an alarm signal when the set threshold is exceeded.

[0006] Preferably, the step of constructing the cable moisture diffusion model includes: Based on the material properties of the cable insulation layer, the humidity diffusion coefficient is determined and the diffusion coefficient is set to be spatially non-uniformly distributed; The dynamic diffusion behavior of humidity in the cable insulation layer is described by a diffusion equation, which includes the diffusion effect of the humidity gradient and the input effect of ambient moisture; Moisture penetration boundary conditions on the cable surface and insulation sealing conditions at the end points are set to ensure that the model conforms to the actual moisture characteristics of the cable.

[0007] Preferably, the construction of the humidity diffusion model includes adopting a mathematical description form based on partial differential equations, wherein the partial differential equations describe the evolution process of humidity through a time term and describe the distribution characteristics of humidity in the cable insulation layer through a spatial gradient term.

[0008] Preferably, the step of inverting the humidity distribution inside the cable based on the humidity diffusion model and the acquired humidity-related data includes: The inverse problem of humidity distribution is transformed into an optimization problem by constructing a target functional, wherein the target functional includes a first constraint term for constraining the smoothness of the humidity distribution, a second constraint term for ensuring the consistency of the humidity distribution with the observed data, and a regularization term for improving the calculation stability; A numerical optimization method based on the calculus of variations is used to solve the minimum value of the target functional; The finite element discretization technology is used to discretize the continuous solution problem of humidity distribution into a linear equation system, and the iterative algorithm is used to solve the linear equation system to obtain the humidity distribution inside the cable.

[0009] Preferably, the numerical optimization method based on the calculus of variations includes: The target functional of humidity field distribution is transformed into variational form; Construct a discretized system of linear equations based on the variational form; The linear equations are solved using an iterative algorithm to obtain the humidity field distribution inside the cable insulation layer.

[0010] Preferably, the humidity judgment threshold is determined according to the following method: Calibrate the critical humidity value of cable insulation layer through experiments; Dynamically adjust the humidity threshold according to the time correction factor of the ambient humidity to adapt to the moisture accumulation effect; The moisture diffusion rate is determined based on the rate of change of humidity distribution, and the threshold is dynamically updated.

[0011] Preferably, the determining the moisture state of the cable includes: When the humidity distribution value is less than 70% of the threshold, it is determined to be in normal state; When the humidity distribution value is greater than 70% of the threshold and less than the threshold, it is determined to be in an alert state; When the humidity distribution value exceeds the threshold, it is determined to be a dangerous state and an alarm signal is issued.

[0012] A second aspect of the present invention provides a cable moisture detection device, comprising: Data acquisition module, used to obtain cable insulation layer surface humidity data and ambient humidity data; Data processing module, used to build a dynamic model of humidity diffusion and achieve optimized inversion of humidity field based on target functional; A threshold setting module is used to set the dynamic humidity threshold in real time according to the cable material properties and environmental characteristics; The state judgment module is used to judge the moisture state of the cable according to the relationship between the humidity distribution and the dynamic humidity threshold, and output a graded alarm signal.

[0013] A third aspect of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described above is implemented.

[0014] A fourth aspect of the present invention provides a storage medium having a computer program stored thereon, which implements the above method when executed by a processor.

[0015] The present invention provides a cable moisture detection method, device, storage medium, and electronic device. It has the following beneficial effects: 1. This invention constructs a humidity diffusion model that accurately describes the dynamic diffusion behavior of moisture within the cable insulation layer. Combined with multidimensional humidity data collected by sensors, it achieves a detailed inversion of humidity distribution. By utilizing dynamic threshold setting and a classification judgment mechanism, it effectively avoids misjudgments caused by changes in ambient humidity or differences in cable material properties, significantly improving the accuracy of moisture detection.

[0016] 2. This invention takes into account ambient humidity, operating temperature, and moisture accumulation effects when setting the humidity threshold. It dynamically adjusts the threshold using time and temperature correction factors. This dynamic adjustment capability allows the detection method to adapt to moisture diffusion characteristics under varying climate conditions and operating environments, improving its adaptability to complex environments.

[0017] 3. This invention continuously monitors moisture levels within the insulation layer during cable operation through real-time inversion of moisture distribution and state classification. When moisture exceeds a safe threshold, the system promptly generates an alarm signal and supports on-site and remote alarm functions, ensuring early warning before a fault occurs and reducing the risk of unexpected accidents.

[0018] 4. This invention effectively reduces the probability of false alarms caused by short-term data fluctuations or environmental changes through multiple sampling verification and alarm confirmation mechanisms. Furthermore, the humidity inversion process is constrained by the regularization term of the target functional, enhancing the numerical stability of the system and making the detection results more reliable.

[0019] 5. The humidity diffusion model and dynamic adjustment mechanism of this invention can flexibly adapt to cables with different insulation materials, specifications, and operating environments. Whether in humid areas with drastic humidity fluctuations or operating environments with large temperature differences, this method can provide stable moisture detection performance and a wide range of applications, providing a universal solution for cable safety management. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 Schematic diagram of the method flow of the present invention; Figure 2 Schematic diagram of the device structure of the present invention; Figure 3 Schematic diagram of the electronic device structure of the present invention.

[0021] Among them, 100, data acquisition module; 200, data processing module; 300, threshold setting module; 400, state judgment module; 40, electronic device; 41, processor; 42, memory; 43, storage medium. DETAILED DESCRIPTION

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the present specification. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0023] Please see the attached Figure 1 The present invention provides a cable moisture detection method, which aims to achieve real-time monitoring and early warning of cable moisture conditions by acquiring humidity-related data, establishing a humidity diffusion model, inverting humidity distribution, and dynamically judging the cable moisture status.

[0024] like Figure 1 As shown, the cable moisture detection method may include the following steps: S1. Obtaining humidity-related data of the cable insulation layer; S2, construct cable moisture diffusion model; S3. Based on the humidity diffusion model and the acquired humidity-related data, invert the humidity distribution inside the cable; S4. Set the humidity judgment threshold according to the cable operation characteristics and humidity diffusion characteristics; S5. judging the moisture state of the cable based on the relationship between the humidity distribution and the humidity judgment threshold; S6: Output the cable moisture status and generate an alarm signal when it exceeds the set threshold.

[0025] The technical solution of the present invention is described in detail below in conjunction with the implementation methods of each step.

[0026] In step S1, in this embodiment, to fully implement cable moisture detection, it is first necessary to obtain humidity data on the cable insulation layer. This step is the foundation of the entire detection method. By collecting data closely related to the cable's operating status and environmental conditions, it provides input information for subsequent model construction and moisture distribution inversion.

[0027] To achieve data acquisition, this embodiment uses a combination of distributed sensors and environmental monitoring equipment, specifically including: Cable surface humidity is a direct indicator of cable moisture. In this embodiment, an exemplary humidity sensor is placed on the cable insulation surface. This sensor can capture real-time humidity changes on the cable surface, and its output signal is digitally transmitted to a data acquisition unit. For example, the humidity sensor collects data from 0% to 100% relative humidity, with a sampling frequency of 10 times per second to ensure dynamic response to humidity changes.

[0028] During data transmission, to ensure signal accuracy, this embodiment filters the humidity sensor's output signal. For example, a low-pass filter is used to suppress high-frequency noise in the signal, while a median filter is used to eliminate occasional sudden changes in values, ensuring that the humidity data truly reflects the cable surface humidity.

[0029] Changes in ambient humidity directly affect the cable's moisture content. In this embodiment, humidity monitoring equipment installed near the cable's operating environment collects ambient humidity data. This data, serving as an external input parameter for the humidity diffusion model, reflects the diffusion characteristics of moisture under different environmental conditions.

[0030] To improve the adaptability of environmental data, this embodiment further incorporates cable operating temperature monitoring. Operating temperature significantly affects the moisture diffusion coefficient. For example, the diffusion rate of moisture increases significantly at higher temperatures. Therefore, it is necessary to combine temperature and humidity data as constraints on moisture diffusion behavior. For example, the cable surface temperature is collected within a range of -40°C to 100°C, with a measurement accuracy of ±0.1°C.

[0031] In this embodiment, the acquisition unit is also connected to a cable operation monitoring system to obtain auxiliary parameters of cable operation, such as cable current load and cable operating status. These parameters, combined with humidity-related data, reflect the dynamic characteristics of moisture diffusion within the cable insulation layer. For example, under high-load operation, the cable surface temperature rises, which can exacerbate moisture penetration into the insulation layer. Therefore, this data serves as a key reference in subsequent modeling.

[0032] After data collection is complete, the data processing unit performs normalization on the acquired data. This process involves normalizing the data, converting humidity and temperature values to unitized representations. For example, humidity data is mapped to a numerical range of 0 to 1. The purpose of normalization is to ensure consistent dimensioning across data sources, facilitating subsequent input calculations for the humidity diffusion model.

[0033] In addition, this embodiment also uses missing value filling technology to handle occasional data loss during the data collection process. For example, when humidity data for a certain time period is missing, linear interpolation or nearest neighbor interpolation can be used to fill in the missing data to ensure data integrity.

[0034] It should be noted that the acquisition process of this embodiment depends on the accuracy and acquisition frequency of the sensor. Users can adjust the sensor layout density and sampling frequency according to actual needs to adapt to the operating environment of different types of cables.

[0035] In step S2, in this embodiment, a cable humidity diffusion model was constructed to describe the dynamic diffusion behavior of humidity within the cable insulation layer, combining the humidity diffusion characteristics of the cable material and the influence of the operating environment. This model aims to accurately depict the distribution and evolution of moisture in the insulation layer through physical laws and mathematical descriptions, providing a foundation for subsequent humidity distribution inversion.

[0036] To describe the basic behavior of moisture diffusion, this embodiment uses a mathematical description based on partial differential equations (PDEs). The following diffusion equation is satisfied: in: Represents the humidity field distribution, which is any position inside the insulation layer and time Humidity value below; is the moisture diffusion coefficient, which is used to describe the diffusion rate of moisture in the insulation material; is the moisture source term, which represents the input intensity of ambient moisture; It is the spatial gradient term of moisture diffusion, reflecting the conduction behavior of moisture inside the insulation layer.

[0037] In this embodiment, the moisture diffusion coefficient The setting is combined with the microscopic characteristics of the cable insulation material. For example, for the commonly used polyethylene insulation material, its diffusion coefficient is usually In order to reflect the local inhomogeneity of the material structure, this embodiment sets the diffusion coefficient to be spatially non-uniformly distributed: in, is the base diffusion coefficient of the material, is a correction factor that describes local variability. Through experimental calibration, the manufacturing process and physical defects of the insulation layer can be effectively reflected.

[0038] To ensure that the model conforms to the actual cable moisture process, this embodiment defines boundary conditions and initial conditions based on the moisture diffusion equation.

[0039] Among the boundary conditions, the penetration rate of moisture on the surface of the cable insulation layer is a key factor. This embodiment uses the following surface flux condition description: in: is the gradient of the humidity field in the surface normal direction; It is the flux of external moisture into the insulation layer, usually determined by the difference between the surface humidity and the ambient humidity.

[0040] At the cable end, due to the effect of electrical insulation, the diffusion of moisture is limited. In this embodiment, the insulation closed boundary condition is set as follows: That is, moisture cannot penetrate the endpoint.

[0041] The initial conditions describe the moisture distribution in In this embodiment, it is assumed that the initial humidity field is uniformly distributed, which can be specifically expressed as: in, It can be obtained through experiments or environmental simulations, reflecting the humidity distribution of the cable at the initial stage of moisture exposure.

[0042] This embodiment also focuses on the moisture source The moisture source term is used to describe the input intensity of external moisture to the insulation layer over time and space. For example, if the intensity of ambient moisture fluctuates over time, then It can be expressed as: in: is the moisture vapor permeability coefficient; is the ambient humidity; is the cable surface humidity.

[0043] Through the above definition, the moisture diffusion model constructed in this embodiment can not only reflect the conduction behavior of moisture inside the insulation layer, but also combine the dynamic changes of the actual operating environment to ensure the applicability and accuracy of the model.

[0044] In order to improve the numerical calculation efficiency of the model, this embodiment discretizes the continuous form of the partial differential equation and transforms it into a finite element calculation problem. Expressed as a basis function expansion in a finite-dimensional function space: in: is the basis function used to describe the moisture distribution in space; are the corresponding basis function coefficients, representing the time evolution of the humidity field.

[0045] After discretization, the moisture diffusion model is expressed as a system of linear equations in the form: in, is the stiffness matrix, is the humidity field coefficient vector, is the load vector. By numerically solving this linear equation system, the spatiotemporal distribution of the humidity field can be obtained.

[0046] Through the above steps, this embodiment constructs a cable moisture diffusion model. This model, based on the physical laws of moisture diffusion behavior and incorporating cable material properties and environmental influences, provides theoretical support and a computational framework for subsequent moisture distribution inversion and threshold determination.

[0047] For step S3, in this embodiment, in order to achieve accurate inversion of the humidity distribution inside the cable insulation layer, the humidity diffusion model and the humidity-related data obtained in step S1 are combined, and an optimization problem is constructed to establish a target functional, and a numerical solution method is used to complete the dynamic inversion of the humidity field.

[0048] First, in this embodiment, the humidity diffusion model is used to transform the humidity distribution problem into an optimization problem. The humidity diffusion model is a partial differential equation that describes the dynamic evolution of humidity, where the humidity field The distribution of and ambient moisture sources The impact of Closely related.

[0049] In order to meet the physical characteristics and data constraints of humidity distribution, the following target functional is constructed in this embodiment: : in: The first one is the smoothness constraint of the humidity field, which ensures that the spatial variation of humidity distribution is continuous and reasonable; The second item is the consistency constraint between the humidity field and the observation data, which is determined by the weight coefficient. Adjust the influence of observation data on inversion results; The third term is the regularization term, which is calculated by the weight coefficient Improve the numerical stability of inverse problems and avoid overfitting or ill-posed problems.

[0050] In the optimization process, this embodiment uses the variational method to solve the target functional. The goal is to find the humidity field The optimal solution of the objective functional Reach a minimum value.

[0051] The solution of the variational method is based on the condition that the first-order derivative of the functional is zero. In this embodiment, the first-order variation of the target functional is first performed to obtain its corresponding weak form expression: in, is the test function, which represents a small perturbation. The above expression describes the relationship between the humidity distribution and the observed data.

[0052] Through the derivation of the weak form, the corresponding Euler-Lagrange equation is further obtained: Combined with the boundary conditions and initial conditions of the humidity diffusion model, the mathematical modeling of the humidity field inversion problem is completed.

[0053] To achieve numerical solution to the above optimization problem, this embodiment uses the finite element method to discretize the continuous problem of humidity distribution. For details, see the description of step S2.

[0054] The humidity field inversion process of this embodiment also includes data correction and update. In each iterative calculation, the new solution is compared with the current humidity distribution, and the residual is calculated to determine whether the inversion has reached the convergence condition. When the residual is lower than the set threshold (for example, 10 −6 ), the iteration stops and the final humidity distribution result is output.

[0055] In order to improve the robustness of humidity distribution inversion, this embodiment also introduces a dynamic adjustment mechanism for boundary conditions. For example, when the ambient humidity fluctuates violently, the boundary flux is adjusted according to the real-time observation data. Updates are made to enhance the model's adaptability to complex environments.

[0056] Through the above steps, this embodiment realizes the dynamic inversion of humidity field based on humidity diffusion model and observation data. The inversion result not only reflects the humidity distribution inside the insulation layer, but also provides the necessary input information for subsequent moisture state judgment and alarm.

[0057] In step S4, in this embodiment, to accurately determine the cable's moisture status, a humidity threshold is set based on the cable's operating characteristics and humidity diffusion properties. This threshold is not only based on experimentally calibrated material critical values, but also incorporates dynamic conditions such as ambient humidity and operating temperature for real-time adjustment, ensuring that the determination is more consistent with actual usage scenarios.

[0058] The basic setting of humidity judgment threshold is based on the humidity critical value of the insulation material. Indicates the maximum humidity level that the insulation layer can withstand. When the humidity exceeds this value, the insulation performance may drop rapidly. For example, for commonly used polyethylene insulation layers, the experimentally calibrated humidity critical value range is usually , the specific value varies according to the type of material and manufacturing process.

[0059] In order to adapt to the dynamic changes of the operating environment, this embodiment introduces a time correction factor based on the humidity threshold. The time correction factor is used to describe the influence of ambient humidity and operating conditions on the moisture accumulation effect, and its form is: in: is the moisture accumulation rate, which is related to the permeability characteristics of the cable surface material and the ambient humidity; It is the dynamic value of ambient humidity, and its unit is relative humidity (%).

[0060] After combining the time correction factor, the humidity judgment threshold set in this embodiment is Expressed as: This formula shows that the higher the ambient humidity and operating time, the greater the amount of moisture accumulated in the insulation layer, causing the humidity threshold to dynamically increase, thereby avoiding false alarms caused by short-term humidity fluctuations.

[0061] In order to further improve the adaptability of the humidity judgment threshold, this embodiment also combines the temperature characteristics of the cable operation to make corrections. Temperature not only affects the diffusion rate of moisture, but may also change the material properties of the insulation layer. Through experiments, the temperature correction factor of the humidity judgment threshold in this embodiment is Defined as: in: is the current operating temperature of the cable; is the reference temperature, usually set at 25°C; is the temperature correction coefficient, which reflects the sensitivity of temperature to humidity diffusion.

[0062] Taking into account the time correction factor and the temperature correction factor, the final humidity judgment threshold value is expressed as follows: Through the above formula, the humidity judgment threshold of this embodiment can adapt to the complex environmental conditions of cable operation and achieve dynamic adjustment.

[0063] This embodiment also implements real-time calculation of the humidity threshold. This real-time threshold calculation relies on data input from the ambient humidity sensor and the operating temperature monitoring module, and is performed online by the data processing unit. For example, the threshold update frequency can be set to 1 minute or less to meet actual monitoring needs.

[0064] In order to improve the stability of the humidity judgment threshold, this embodiment also introduces a threshold smoothing mechanism. Specifically, the threshold change in a short period of time is smoothed by a weighted average method, and the expression is: in: is the time step of the smoothing window; is a weighting coefficient, which is usually assigned in descending order according to the time step.

[0065] Through the above steps, the humidity judgment threshold set in this embodiment can not only reflect the basic humidity tolerance of the cable material, but also combine the dynamic influence of ambient humidity, operating temperature and moisture accumulation effect, thereby improving the accuracy and reliability of moisture state judgment.

[0066] In step S5, this embodiment establishes a moisture determination mechanism based on the relationship between the humidity distribution and the dynamic humidity threshold set in step S4 to determine the cable's moisture status. This mechanism analyzes and classifies the humidity distribution to provide a graded assessment of the cable's moisture status, providing a basis for subsequent alarms and maintenance.

[0067] First, in this embodiment, the peak value of the humidity field distribution is As the core judgment parameter, Indicates the maximum point of humidity value in humidity distribution, and its physical meaning is the area with the most serious moisture inside the insulation layer. , using the inverted humidity field The discrete data can be extracted by the following expression: in, is the geometric area of the cable insulation layer.

[0068] Next, this embodiment uses the peak value of the humidity field Dynamic humidity judgment threshold Compare them and determine the moisture condition of the cable based on the relationship between the two.

[0069] When humidity peaks When the humidity level is lower than 70% of the dynamic humidity judgment threshold, it is considered that the humidity level is within the safe range. At this time, the cable is in a normal state and no intervention measures are required. The mathematical description of this state is: When humidity peaks When the humidity level is between 70% and 100% of the dynamic humidity judgment threshold, it indicates that the moisture level is close to the dangerous threshold. At this time, the cable is in an alert state and requires enhanced monitoring and subsequent inspections. The mathematical description of this state is: When humidity peaks When the dynamic humidity judgment threshold is exceeded, it means that the moisture level has exceeded the safe tolerance range of the cable material, the cable is in a dangerous state, and immediate repair measures are required. The mathematical description of this state is: In this embodiment, in order to improve the reliability of the judgment, the humidity change rate is also combined Humidity change rate Indicates the rate of moisture diffusion, and the calculation formula is: By analyzing the humidity change rate, the trend of moisture diffusion can be further predicted. When the humidity is too high, it is considered that the diffusion speed is too fast and it may cause failure in a short time. At this time, the warning should be triggered in advance.

[0070] In order to realize the automation of status judgment, this embodiment designs a logical judgment process: 1. First, extract the peak value in the humidity distribution ; 2. Dynamic humidity judgment threshold Perform comparisons to determine the current status of the cable; 3. If it is judged to be a warning or dangerous state, further analyze the humidity change rate If the humidity diffuses quickly, the alarm level will be increased.

[0071] The above logic is implemented through the programming of the state judgment module, and all calculations are automatically completed by the control system. For example, the state judgment module can dynamically adjust the judgment conditions by real-time monitoring of humidity distribution and environmental parameters to ensure the applicability of the judgment.

[0072] In step S6, this embodiment implements a mechanism to effectively monitor and provide early warning of cable moisture conditions, based on the cable humidity determined in step S5. This mechanism includes a status output module and an alarm generation module, capable of recording cable status in real time, indicating the moisture level, and triggering an alarm when the set threshold is exceeded.

[0073] First, in this embodiment, the cable moisture status is output by classification. The classification is based on the humidity peak value. Dynamic humidity judgment threshold The status output module generates a status report based on the classification results and outputs it to the monitoring interface, which specifically includes the following: When the cable is in normal condition, the condition is met The status output module records the humidity distribution and generates a regular status report. The report includes parameters such as peak humidity value, ambient humidity and operating temperature, and also marks that there are no abnormalities in the current operation.

[0074] When the cable is in alert state, the condition is met The status output module generates a warning status report. In addition to recording the above parameters, the report also includes additional moisture diffusion trend information. The analysis results are used to assist operation and maintenance personnel in subsequent inspections.

[0075] When the cable is in a dangerous state, the condition is met , the status output module immediately generates a moisture alarm signal and outputs it to the monitoring interface, and at the same time starts the alarm generation module.

[0076] The alarm generation module is the core unit for responding to dangerous conditions in this embodiment. Its design goal is to achieve real-time response when thresholds are exceeded and trigger corresponding alarm measures based on different moisture levels. The specific functions of the alarm generation module are as follows: Upon receiving a dangerous condition signal, the alarm generation module immediately triggers an audible or visual signal, which is used to alert the user on-site. For example, the intensity of the alarm signal can be dynamically adjusted based on the magnitude of the humidity exceeding the threshold. For example, if the humidity peak exceeds the threshold by 10%, the alarm signal is intermittent; if the humidity peak exceeds the threshold by 20%, the alarm signal switches to continuous mode.

[0077] In addition to on-site alarms, this embodiment also supports remote alarm functions. The alarm generation module sends alarm information to the remote monitoring system through the communication module. The information content includes the humidity peak value. , ambient humidity , temperature value and humidity diffusion trends The remote monitoring system can quickly dispatch maintenance tasks based on the alarm information received.

[0078] In order to improve the reliability of the alarm, this embodiment also designs an alarm confirmation mechanism. Specifically, when a dangerous state occurs, the alarm generation module will first verify whether the humidity peak value continues to exceed the dynamic humidity judgment threshold through multiple sampling. For example, if the condition is met for 5 consecutive samplings, This mechanism can effectively avoid false alarms caused by short-term data fluctuations.

[0079] In addition, this embodiment supports the hierarchical setting of alarm signals. For example, when the humidity peaks When the humidity is only slightly higher than the threshold (such as exceeding 5%), a level 1 alarm signal is generated, which only prompts the operation and maintenance personnel to pay attention; when the humidity peak is much higher than the threshold (such as exceeding 20%), a level 2 alarm signal is generated, prompting that emergency measures need to be taken immediately.

[0080] In this embodiment, the status output module and alarm generation module operate automatically through the control system, eliminating the need for human intervention. The real-time nature of the status output and alarm signals is ensured by the module's internal fast data processing algorithm, typically completing the output and alarm within one second of a humidity status change.

[0081] In summary, this method acquires data related to the humidity of the cable insulation layer, constructs a humidity diffusion model, inverts the humidity distribution within the cable based on this model and data, dynamically sets a humidity threshold, and determines the cable's moisture status based on the relationship between the humidity distribution and the threshold, outputting the status and generating an alarm signal. By combining the physical properties of humidity diffusion, real-time dynamic data, and a numerical optimization algorithm, this method achieves precise detection and dynamic monitoring of moisture within the cable, providing reliable assurance for the safe operation of the cable. It is particularly adaptable and practical in complex environments with dynamically changing humidity.

[0082] The cable moisture detection device described below and the cable moisture detection method described above can refer to each other.

[0083] Please see the attached Figure 2 The present invention also provides a cable moisture detection device, comprising: The data acquisition module 100 is used to obtain the surface humidity data of the cable insulation layer and the ambient humidity data; The data processing module 200 is used to construct a dynamic model of humidity diffusion and realize the optimized inversion of the humidity field based on the target functional; A threshold setting module 300 is used to set a dynamic humidity threshold in real time according to cable material properties and environmental characteristics; The state judgment module 400 is used to judge the moisture state of the cable according to the relationship between the humidity distribution and the dynamic humidity threshold, and output a graded alarm signal.

[0084] The device of this embodiment can be used to execute the above method embodiment, and its principles and technical effects are similar, so they will not be repeated here.

[0085] Please see the attached Figure 3 The present invention further provides an electronic device 40, comprising: a processor 41 and a memory 42, wherein the memory 42 stores a computer program executable by the processor, and when the computer program is executed by the processor, the above method is performed.

[0086] The present invention further provides a storage medium 43 on which a computer program is stored. When the computer program is run by the processor 41 , the above method is executed.

[0087] Among them, the storage medium 43 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0088] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for detecting cable moisture, characterized in that: The following steps are involved: Obtain humidity-related data of the cable insulation layer, including cable surface humidity, ambient humidity and cable operating parameters; A cable moisture diffusion model is constructed to describe the dynamic diffusion behavior of moisture inside the insulation layer. The model combines the moisture diffusion characteristics of the cable material and the influence of the environment. Based on the humidity diffusion model and the acquired humidity-related data, the humidity distribution inside the cable is inverted; According to the cable operation characteristics and humidity diffusion characteristics, a humidity judgment threshold is set, and the threshold is dynamically adjusted to adapt to environmental changes; Determine the moisture status of the cable based on the relationship between humidity distribution and humidity judgment threshold; Outputs the cable moisture status and generates an alarm signal when the set threshold is exceeded.

2. The cable moisture detection method according to claim 1, characterized in that: The steps of constructing the cable moisture diffusion model include: Based on the material properties of the cable insulation layer, the humidity diffusion coefficient is determined and the diffusion coefficient is set to be spatially non-uniformly distributed; The dynamic diffusion behavior of humidity in the cable insulation layer is described by a diffusion equation, which includes the diffusion effect of the humidity gradient and the input effect of ambient moisture; Moisture penetration boundary conditions on the cable surface and insulation sealing conditions at the end points are set to ensure that the model conforms to the actual moisture characteristics of the cable.

3. The cable moisture detection method according to claim 2, characterized in that: The construction of the humidity diffusion model includes adopting a mathematical description form based on partial differential equations, wherein the partial differential equations describe the evolution process of humidity through a time term and describe the distribution characteristics of humidity in the cable insulation layer through a spatial gradient term.

4. The cable moisture detection method according to claim 1, characterized in that: The step of inverting the humidity distribution inside the cable based on the humidity diffusion model and the acquired humidity-related data includes: The inverse problem of humidity distribution is transformed into an optimization problem by constructing a target functional, wherein the target functional includes a first constraint term for constraining the smoothness of the humidity distribution, a second constraint term for ensuring the consistency of the humidity distribution with the observed data, and a regularization term for improving the calculation stability; A numerical optimization method based on the calculus of variations is used to solve the minimum value of the target functional; The finite element discretization technology is used to discretize the continuous solution problem of humidity distribution into a linear equation system, and the iterative algorithm is used to solve the linear equation system to obtain the humidity distribution inside the cable.

5. The cable moisture detection method according to claim 4, characterized in that: The numerical optimization method based on the calculus of variations includes: The target functional of humidity field distribution is transformed into variational form; Construct a discretized system of linear equations based on the variational form; The linear equations are solved using an iterative algorithm to obtain the humidity field distribution inside the cable insulation layer.

6. The cable moisture detection method according to claim 1, characterized in that: The humidity judgment threshold is determined according to the following method: Calibrate the critical humidity value of the cable insulation layer through experiments; Dynamically adjust the humidity threshold according to the time correction factor of the ambient humidity to adapt to the moisture accumulation effect; The moisture diffusion rate is determined based on the rate of change of humidity distribution, and the threshold is dynamically updated.

7. The cable moisture detection method according to claim 1, characterized in that: Determining the moisture state of the cable includes: When the humidity distribution value is less than 70% of the threshold, it is determined to be in normal state; When the humidity distribution value is greater than 70% of the threshold and less than the threshold, it is determined to be in an alert state; When the humidity distribution value exceeds the threshold, it is determined to be a dangerous state and an alarm signal is issued.

8. A cable moisture detection device, used to implement the cable moisture detection method according to any one of claims 1 to 7, characterized in that: include: Data acquisition module, used to obtain cable insulation layer surface humidity data and ambient humidity data; Data processing module, used to build a dynamic model of humidity diffusion and achieve optimized inversion of humidity field based on target functional; A threshold setting module is used to set the dynamic humidity threshold in real time according to the cable material properties and environmental characteristics; The state judgment module is used to judge the moisture state of the cable according to the relationship between the humidity distribution and the dynamic humidity threshold, and output a graded alarm signal.

9. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the cable moisture detection method according to any one of claims 1 to 7 is implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

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