Method, device and equipment for identifying electrified state of tower and medium
By constructing an energy-adaptive and state-driven closed-loop mechanism and dynamically adjusting the sampling period, the low-power problem of pole energization state identification is solved, achieving a balance between high-sensitivity detection of sudden states and low-power operation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- GUANGZHOU POWER SUPPLY BUREAU GUANGDONG POWER GRID CO LTD
- Filing Date
- 2026-02-25
- Publication Date
- 2026-05-12
AI Technical Summary
Existing technologies struggle to accurately identify the energized state of towers under low-power conditions, resulting in insufficient equipment endurance and high maintenance costs.
By constructing a closed-loop mechanism of energy adaptive initialization and state-driven feedback adjustment, the initial sampling period is determined based on the current total available energy of the tower, the rate of change of electric field and current is calculated, and fused into a comprehensive change sensitivity judgment value. Based on the identification results, the sampling period is dynamically adjusted to achieve low-power energized state identification.
While maintaining low power consumption, it significantly improves the ability to detect sudden charged states, ensuring long-term operation in scenarios without external power supply, and achieving a balance between low power consumption and high reliability identification.
Smart Images
Figure CN122017325A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of power systems, and in particular to a method, apparatus, equipment, and medium for identifying the energized state of power poles. Background Technology
[0002] In modern power systems, accurately identifying the energized state of high-voltage distribution towers is crucial for ensuring the safe operation of the power system and the personal safety of maintenance personnel. As the physical carriers supporting and transmitting electrical energy in the power grid, the energization status of towers directly determines the procedures and risk levels for on-site operations such as maintenance and construction. Misjudging the energized state can lead to serious electric shocks, equipment damage, or even grid failures. With the increasing automation and intelligence of distribution networks, advanced applications such as remote status monitoring, automatic safety interlocking, and intelligent early warning all rely on real-time perception of the energized state of towers.
[0003] Existing technologies primarily rely on electric field sensors or current transformers deployed on power poles to continuously and frequently collect ambient electric field strength or leakage current signals. The presence of an electric field is then determined by a simple comparison with a preset fixed threshold. While this method is intuitive and easy to deploy, it suffers from significant drawbacks in terms of low power consumption. Continuous data collection and operation of the relevant components on the power pole lead to high overall system energy consumption, heavily relying on external power supplies or large-capacity batteries. In remote power pole scenarios without mains power, the equipment's battery life is severely insufficient, resulting in high maintenance costs. Summary of the Invention
[0004] This invention provides a method, apparatus, device, and medium for identifying the energized state of power poles, which can solve the problem in the prior art of identifying the energized state of power poles while maintaining low power consumption.
[0005] In a first aspect, embodiments of the present invention provide a method for identifying the energized state of a power pole, comprising: The first electric field strength, first leakage current amplitude, and first line harmonic characteristics of the tower at each first sampling time point are obtained according to the initial sampling period; wherein, the initial sampling period is determined based on the current total available energy of the tower; Based on the first electric field strength and the first leakage current amplitude, the first electric field change rate and the first current change rate corresponding to each first sampling time point are calculated, and the first comprehensive mutation sensitivity judgment value corresponding to each first sampling time point is determined based on the first electric field change rate and the first current change rate. According to the time sequence, the first comprehensive mutation sensitivity judgment value corresponding to each first sampling time point is traversed sequentially to obtain the initial energized state identification result of the tower at each first sampling time point. Based on the initial energized state identification results, the initial sampling period is adjusted to obtain the target sampling period. Based on the target sampling period, the second electric field strength, the second leakage current amplitude, and the second line harmonic characteristics of the tower at each second sampling time point are further obtained to determine the target energized state identification results of the tower at each second sampling time point.
[0006] This application's embodiments effectively solve the technical problem of identifying the energized state of power poles while maintaining low power consumption by constructing a complete closed loop from energy adaptive initialization to state-driven feedback adjustment. First, the initial sampling period is determined based on the total available energy of the power pole, constraining system power consumption within the energy supply capacity from the source, ensuring long-term operation in scenarios without external power supply—a fundamental prerequisite for low power consumption. Second, by calculating the rate of change of electric field and current and fusing them into a comprehensive abrupt change sensitivity judgment value, the core of the identification mechanism shifts from relying on absolute thresholds to sensitively capturing the "slope" of state changes, significantly improving the ability to detect sudden, short-term dangerous states such as "instantaneous power-on," thus achieving a key breakthrough in identification accuracy. Next, the sampling period is dynamically adjusted based on the initial identification results, forming an intelligent feedback closed loop: automatically extending the period to save energy when the state is stable, and shortening the period to increase monitoring frequency when a sudden change or energized state is detected. This mechanism intelligently balances power consumption and performance, allowing the system to remain in a low-power "listening" state most of the time, while quickly entering a high-response "alert" state at critical moments. Ultimately, the system organically integrates energy constraints, mutation monitoring, and adaptive adjustment, transforming the identification of the energized state of towers from a simple monitoring process that consumes a lot of energy into an intelligent sensing process that "supply on demand and respond precisely," thereby achieving a unified approach of low power consumption and high reliability identification.
[0007] As a preferred example of the first aspect, adjusting the initial sampling period based on each of the initial charged state identification results to obtain the target sampling period includes: If the initial charged state identification result is charged after a first preset number of consecutive cycles, the initial sampling period is adjusted according to the first preset shortening coefficient to obtain the first sampling period, and the first sampling period is used as the target sampling period. If the initial charged state identification result is non-charged after a first preset number of consecutive times, the initial sampling period is adjusted according to the first preset increase factor to obtain a second sampling period, and the second sampling period is used as the target sampling period.
[0008] In this preferred example, a combination of a continuous discrimination triggering mechanism and a coefficient-based period adjustment rule is used to achieve a balance between low power consumption and high reliability. When a continuously energized state is detected, the sampling period is automatically shortened to increase the monitoring frequency and real-time response, ensuring rapid detection of hazardous conditions. When a continuously de-energized state is detected, the sampling period is automatically extended to reduce unnecessary sampling and processing energy consumption, significantly extending the device's runtime in passive environments. This adaptive mechanism enables the system to dynamically balance energy consumption and monitoring requirements based on actual operating conditions, thereby systematically reducing average power consumption while ensuring identification accuracy. As a preferred example of the first aspect, adjusting the initial sampling period according to a first preset shortening coefficient to obtain a first sampling period includes: Multiply the initial sampling period by the first preset shortening coefficient to obtain the third sampling period; If the third sampling period is less than or equal to the preset lower bound of the sampling period, then the value of the first sampling period is determined as the preset lower bound of the sampling period; otherwise, the value of the first sampling period is determined as the third sampling period.
[0009] In this preferred example, the intelligent convergence of the sampling period within a safe range is precisely achieved by combining "coefficient shortening" and "lower bound protection." First, the period is controllably compressed by multiplying by a shortening coefficient, ensuring that the system can immediately increase the sampling frequency upon identifying a live electrical risk, enhancing the real-time tracking and response to dangerous conditions. Simultaneously, a lower bound judgment and protection mechanism for the sampling period is introduced to prevent unlimited shortening of the period from causing a surge in energy consumption or equipment overload, thus maintaining a minimum energy consumption level while ensuring necessary monitoring density. These two mechanisms work together to achieve a dynamic balance between "sufficient response" and "controllable energy consumption," thereby improving the real-time performance of state identification while maintaining overall power consumption stability.
[0010] As a preferred example of the first aspect, adjusting the initial sampling period according to a first preset amplification factor to obtain a second sampling period includes: Multiply the initial sampling period by the first preset amplification factor to obtain the fourth sampling period; If the fourth sampling period is greater than or equal to the preset lower bound of the sampling period, then the value of the second sampling period is determined as the preset upper bound of the sampling period; otherwise, the value of the second sampling period is determined as the fourth sampling period.
[0011] In this preferred example, the energy-saving effect is maximized by combining "coefficient extension" and "upper bound protection" while ensuring monitoring response capability. First, the sampling period is controllably extended by multiplying by an increase coefficient, significantly reducing the sampling and processing frequency when the state is stable, thereby greatly reducing the average power consumption of the system and extending the device's battery life in passive power supply scenarios. At the same time, an upper bound judgment and protection mechanism for the sampling period is introduced to prevent excessive extension of the period from causing serious delays in response to sudden states, ensuring the system's minimum monitoring timeliness. The two work together to achieve a balance between "deep energy saving" and "basic protection," enabling the system to operate safely in the optimal low-power range under long-term stable non-powered conditions.
[0012] As a preferred example of the first aspect, the initial sampling period is determined based on the current total available energy of the tower, including: Obtain the current total available energy of the tower, the energy consumed in a single sampling, and the expected working cycle; Divide the energy consumed in a single sampling by the total available energy to obtain a first ratio, and multiply the first ratio by the expected working cycle to obtain the initial sampling cycle.
[0013] In this preferred example, by constructing an initial sampling period calculation model based on an energy budget, proactive control and optimization of system power consumption are achieved at the source. First, by acquiring the current total available energy, energy consumption per sampling, and expected operating cycle, the system can accurately assess its own energy constraints and task requirements, laying a data foundation for subsequent adaptive scheduling. Then, by using the ratio of "energy consumption / total" to reflect the sampling capacity per unit of energy, and multiplying this by the operating cycle, the total energy constraint is rationally allocated across the entire operating time axis, thereby calculating an initial sampling frequency that matches the current energy level. This mechanism ensures that the device can continuously operate within the predetermined energy budget for the entire expected cycle, preventing premature power depletion due to overly frequent sampling from the decision-making stage, and providing a fundamental guarantee for achieving long-term, stable, low-power monitoring.
[0014] As a preferred example of the first aspect, determining the first comprehensive mutation sensitivity judgment value corresponding to each of the first sampling time points based on each of the first electric field change rates and each of the first current change rates includes: The preset electric field change rate weighting factor and the first electric field change rate corresponding to each of the first sampling time points are multiplied sequentially to obtain the first multiplication value corresponding to each of the first sampling time points; The preset current change rate weighting factor and the first current change rate corresponding to each first sampling time point are multiplied sequentially to obtain the second multiplication value corresponding to each first sampling time point; The first multiplication value and the second multiplication value corresponding to each of the first sampling time points are added together to obtain the first comprehensive mutation sensitivity judgment value corresponding to each of the first sampling time points.
[0015] In this preferred example, a dual-signal fusion mechanism with configurable weights effectively improves the accuracy and reliability of abrupt change state identification. First, weighting factors are preset for both the electric field and current change rates, reflecting the different importance of various electrical characteristics in abrupt change detection and giving the fusion process a clear physical guidance. Second, multiplying each change rate by its corresponding weight quantifies the contribution of each signal, enhancing the model's adaptability to different line characteristics and environmental conditions. Finally, the weighted signal values are summed to form a unified comprehensive abrupt change sensitivity judgment value. This value combines the rapid response characteristics of electric field changes with the stable indication function of current changes, thus significantly improving the sensitivity and anti-interference capability for capturing sudden energized states such as "instantaneous power-on," providing a precise basis for subsequent "on-demand response" intelligent energy-saving scheduling.
[0016] As a preferred example of the first aspect, the step of sequentially iterating through the first comprehensive mutation sensitivity judgment values corresponding to each of the first sampling time points according to time order to obtain the initial energized state identification result of the tower at each of the first sampling time points includes: In chronological order, the first comprehensive mutation sensitivity judgment value corresponding to each of the first sampling time points is traversed sequentially. If the first comprehensive mutation sensitivity judgment value corresponding to the first sampling time point of the current traversal is greater than or equal to the preset threshold, then the first electric field strength, the first leakage current amplitude, the first line harmonic characteristics and the first comprehensive mutation sensitivity judgment value corresponding to the first sampling time point of the current traversal are used as independent variables and input into the first preset linear discriminant function to obtain the initial energized state identification result corresponding to the first sampling time point of the current traversal.
[0017] In this preferred example, a two-level discrimination mechanism based on "threshold triggering and multi-dimensional criteria" is constructed to achieve high-precision state identification while ensuring low power consumption. First, the comprehensive mutation sensitivity judgment values are traversed sequentially over time to ensure the continuity of state monitoring and the integrity of the temporal logic. Then, a preliminary screening is performed using a preset threshold; subsequent complex discrimination is only initiated when the judgment value exceeds the threshold, avoiding continuous operation of the high-energy-consuming discrimination model and achieving on-demand allocation and energy saving of computing resources. Finally, multi-dimensional electrical characteristics and mutation judgment values are input together into a preset discrimination function for comprehensive analysis, fully utilizing the complementary information between features. This significantly improves the accuracy and reliability of identifying the energized state under complex interference environments, thus achieving a balance between low power consumption and high precision at the system level.
[0018] In a second aspect, the present invention provides a power status identification device for a pole, comprising: a first identification module, a second identification module, a third identification module, and a fourth identification module; The first identification module is used to obtain the first electric field strength, the first leakage current amplitude, and the first line harmonic characteristics of the tower at each first sampling time point according to the initial sampling period; wherein, the initial sampling period is determined according to the current total available energy of the tower; The second identification module is used to calculate the first electric field change rate and the first current change rate corresponding to each of the first sampling time points based on the first electric field strength and the first leakage current amplitude, and to determine the first comprehensive mutation sensitivity judgment value corresponding to each of the first sampling time points based on the first electric field change rate and the first current change rate. The third identification module is used to sequentially traverse the first comprehensive mutation sensitivity judgment value corresponding to each of the first sampling time points in chronological order to obtain the initial energized state identification result of the tower at each of the first sampling time points. The fourth identification module is used to adjust the initial sampling period based on the initial energized state identification results to obtain a target sampling period, so as to continue to acquire the second electric field strength, second leakage current amplitude and second line harmonic characteristics of the tower at each second sampling time point based on the target sampling period, so as to determine the target energized state identification results of the tower at each second sampling time point.
[0019] As a preferred example of the second aspect, adjusting the initial sampling period based on each of the initial charged state identification results to obtain the target sampling period includes: If the initial charged state identification result is charged after a first preset number of consecutive cycles, the initial sampling period is adjusted according to the first preset shortening coefficient to obtain the first sampling period, and the first sampling period is used as the target sampling period. If the initial charged state identification result is non-charged after a first preset number of consecutive times, the initial sampling period is adjusted according to the first preset increase factor to obtain a second sampling period, and the second sampling period is used as the target sampling period.
[0020] As a preferred example of the second aspect, adjusting the initial sampling period according to a first preset shortening coefficient to obtain a first sampling period includes: Multiply the initial sampling period by the first preset shortening coefficient to obtain the third sampling period; If the third sampling period is less than or equal to the preset lower bound of the sampling period, then the value of the first sampling period is determined as the preset lower bound of the sampling period; otherwise, the value of the first sampling period is determined as the third sampling period.
[0021] As a preferred example of the second aspect, adjusting the initial sampling period according to a first preset amplification factor to obtain a second sampling period includes: Multiply the initial sampling period by the first preset amplification factor to obtain the fourth sampling period; If the fourth sampling period is greater than or equal to the preset lower bound of the sampling period, then the value of the second sampling period is determined as the preset upper bound of the sampling period; otherwise, the value of the second sampling period is determined as the fourth sampling period.
[0022] As a preferred example of the second aspect, the initial sampling period is determined based on the current total available energy of the tower, including: Obtain the current total available energy of the tower, the energy consumed in a single sampling, and the expected working cycle; Divide the energy consumed in a single sampling by the total available energy to obtain a first ratio, and multiply the first ratio by the expected working cycle to obtain the initial sampling cycle.
[0023] As a preferred example of the second aspect, determining the first comprehensive mutation sensitivity judgment value corresponding to each of the first sampling time points based on each of the first electric field change rates and each of the first current change rates includes: The preset electric field change rate weighting factor and the first electric field change rate corresponding to each of the first sampling time points are multiplied sequentially to obtain the first multiplication value corresponding to each of the first sampling time points; The preset current change rate weighting factor and the first current change rate corresponding to each first sampling time point are multiplied sequentially to obtain the second multiplication value corresponding to each first sampling time point; The first multiplication value and the second multiplication value corresponding to each of the first sampling time points are added together to obtain the first comprehensive mutation sensitivity judgment value corresponding to each of the first sampling time points.
[0024] As a preferred example of the second aspect, the step of sequentially iterating through the first comprehensive mutation sensitivity judgment values corresponding to each of the first sampling time points according to time order to obtain the initial energized state identification result of the tower at each of the first sampling time points includes: In chronological order, the first comprehensive mutation sensitivity judgment value corresponding to each of the first sampling time points is traversed sequentially. If the first comprehensive mutation sensitivity judgment value corresponding to the first sampling time point of the current traversal is greater than or equal to the preset threshold, then the first electric field strength, the first leakage current amplitude, the first line harmonic characteristics and the first comprehensive mutation sensitivity judgment value corresponding to the first sampling time point of the current traversal are used as independent variables and input into the first preset linear discriminant function to obtain the initial energized state identification result corresponding to the first sampling time point of the current traversal.
[0025] In summary, this application's embodiments effectively solve the technical problem of identifying the energized state of towers while maintaining low power consumption by constructing a complete closed loop from energy adaptive initialization to state-driven feedback adjustment. First, the initial sampling period is determined based on the tower's current available energy, constraining system power consumption within the energy supply capacity from the source, ensuring long-term operation in scenarios without external power supply—a fundamental prerequisite for low power consumption. Second, by calculating the rate of change of electric field and current and fusing them into a comprehensive abrupt change sensitivity judgment value, the core of the identification mechanism shifts from relying on absolute thresholds to sensitively capturing the "slope" of state changes, significantly improving the ability to detect sudden, short-term dangerous states such as "instantaneous power-on," thus achieving a key breakthrough in identification accuracy. Next, the sampling period is dynamically adjusted based on the initial identification results, forming an intelligent feedback closed loop: automatically extending the period to save energy when the state is stable, and shortening the period to increase monitoring frequency when a sudden change or energized state is detected. This mechanism intelligently balances power consumption and performance, allowing the system to remain in a low-power "listening" state most of the time, while quickly entering a high-response "alert" state at critical moments. Ultimately, the system organically integrates energy constraints, mutation monitoring, and adaptive adjustment, transforming the identification of the energized state of towers from a simple monitoring process that consumes a lot of energy into an intelligent sensing process that "supply on demand and respond precisely," thereby achieving a unified approach of low power consumption and high reliability identification.
[0026] Another embodiment of the present invention provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the steps of the method for identifying the energized state of a pole as described in the present invention.
[0027] Another embodiment of the present invention also provides a computer-readable storage medium item, including: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to perform steps such as the energized state identification method for poles of the present invention. Attached Figure Description
[0028] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.
[0029] Figure 1 A flowchart illustrating an embodiment of the method for identifying the energized state of a power pole provided by the present invention; Figure 2 This is a module structure diagram of one embodiment of a power pole status identification device provided by the present invention. Detailed Implementation
[0030] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0031] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.
[0032] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.
[0033] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.
[0034] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.
[0035] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).
[0036] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.
[0037] It should be noted that the identification of the energized status of power poles is a key safety technology in the operation and maintenance of power systems. It is mainly used to determine whether transmission or distribution poles are carrying high voltage, so as to ensure the safety of operators, prevent accidents caused by misoperation, and provide basic data for equipment status monitoring, intelligent inspection and fault early warning.
[0038] Example 1 See Figure 1 To address the problem in existing technologies of difficulty in identifying the energized state of power poles while maintaining low power consumption, an embodiment of the present invention provides a method for identifying the energized state of power poles, comprising: S1. Obtain the first electric field strength, first leakage current amplitude, and first line harmonic characteristics of the tower at each first sampling time point according to the initial sampling period; wherein, the initial sampling period is determined based on the current total available energy of the tower; Specifically, the step of obtaining the first electric field strength, first leakage current amplitude, and first line harmonic characteristics of the tower at each first sampling time point according to the initial sampling period can be implemented through the following preferred scheme: Once the sampling period begins, a high-sensitivity sensor (electric field sensor, current transformer, etc.) is activated to complete an electrical feature acquisition in a very short time, reducing the overall energy consumption of the system.
[0039] Each sampling needs to record the collection timestamp. And the following perceived variables: electric field strength Leakage inductance current amplitude Line harmonic characteristics Among them, the harmonic characteristics of the line can be used to extract higher-order harmonic energy through methods such as Fourier transform.
[0040] Then, construct the time-series sampling dataset D, as shown in the following expression: in, This represents the electric field strength value measured during the i-th sampling. This represents the leakage inductance current value measured during the i-th sampling. This represents the harmonic component value extracted during the i-th sampling.
[0041] In a preferred embodiment, the initial sampling period is determined based on the current total available energy of the tower, including: Obtain the current total available energy of the tower, the energy consumed in a single sampling, and the expected working cycle; Divide the energy consumed in a single sampling by the total available energy to obtain a first ratio, and multiply the first ratio by the expected working cycle to obtain the initial sampling cycle.
[0042] Specifically, the formula for calculating the total amount of currently available energy is as follows: in, This represents the total amount of energy currently available. For the energy stored in the current battery, Indicates from the initial time The energy integral collected by the current transformer up to the current time t. This represents the current stored energy of solar energy.
[0043] S2. Based on the first electric field intensity and the first leakage current amplitude, calculate the first electric field change rate and the first current change rate corresponding to each first sampling time point, and determine the first comprehensive mutation sensitivity judgment value corresponding to each first sampling time point based on the first electric field change rate and the first current change rate. Specifically, the step of calculating the first electric field change rate and the first current change rate corresponding to each first sampling time point based on each first electric field strength and each first leakage inductance current amplitude can be implemented in the following preferred manner: The sliding window step size k (e.g., the first 5 samples) is used to ensure a sensitive response to short-term mutations. This represents the rate of change of the electric field within the i-th sampling window. This represents the rate of change of leakage inductance current within the i-th sampling window.
[0044] As a preferred embodiment, determining the first comprehensive mutation sensitivity judgment value corresponding to each of the first sampling time points based on each of the first electric field change rates and each of the first current change rates includes: The preset electric field change rate weighting factor and the first electric field change rate corresponding to each of the first sampling time points are multiplied sequentially to obtain the first multiplication value corresponding to each of the first sampling time points; The preset current change rate weighting factor and the first current change rate corresponding to each first sampling time point are multiplied sequentially to obtain the second multiplication value corresponding to each first sampling time point; The first multiplication value and the second multiplication value corresponding to each of the first sampling time points are added together to obtain the first comprehensive mutation sensitivity judgment value corresponding to each of the first sampling time points.
[0045] Specifically, the preset current change rate weighting factor and the preset electric field change rate weighting factor can be obtained through offline calibration and data-driven regression, as shown in the following steps: ① Calibration Data Acquisition: Under the target tower or equivalent test environment, intermittent sampling sequences are collected for stable uncharged, stable charged, and known state switching (abrupt) processes, respectively, and the corresponding data are calculated according to the formula in this step. and ; ② Rate of Change Statistics and Normalization: Take the typical amplitude of the rate of change in the calibration data to obtain the electric field rate of change scale. Scale of the rate of change of current The specific formula is shown below: ; in, The number of sampling windows participating in the statistics; A typical amplitude scale representing the rate of change of the electric field in the calibration data; This represents a typical amplitude scale for the rate of change of leakage inductance current in the calibration data.
[0046] ③ Determining and solidifying weighting ratios: To eliminate the impact of differences in dimensions and amplitudes... The impact is determined by the "equal-scale contribution" principle, and can be further normalized to... + =1: in, , As a weighting factor; and This is the scale quantity obtained in step ②. The above method can be used to... · and · It demonstrates comparable numerical contributions under typical operating conditions, thereby improving the discriminative stability of the state change rate function. Ultimately, it will... , Write the weights into the device parameters or the background model library as default weights, and allow recalibration updates under different line / tower types.
[0047] S3. According to the time sequence, the first comprehensive mutation sensitivity judgment value corresponding to each first sampling time point is traversed in turn to obtain the initial energized state identification result of the tower at each first sampling time point. As a preferred embodiment, the step of sequentially iterating through the first comprehensive mutation sensitivity judgment value corresponding to each of the first sampling time points according to time order to obtain the initial energized state identification result of the tower at each of the first sampling time points includes: In chronological order, the first comprehensive mutation sensitivity judgment value corresponding to each of the first sampling time points is traversed sequentially. If the first comprehensive mutation sensitivity judgment value corresponding to the first sampling time point of the current traversal is greater than or equal to the preset threshold, then the first electric field strength, the first leakage current amplitude, the first line harmonic characteristics and the first comprehensive mutation sensitivity judgment value corresponding to the first sampling time point of the current traversal are used as independent variables and input into the first preset linear discriminant function to obtain the initial energized state identification result corresponding to the first sampling time point of the current traversal.
[0048] Specifically, the expression for the first preset linear discriminant function can be as follows: in, For the feature weight vector, As bias terms, both can be obtained through on-site sample training to adapt to different environments and interferences; This is the result of linear discrimination.
[0049] S4. Adjust the initial sampling period based on the initial energized state identification results to obtain the target sampling period, and continue to acquire the second electric field strength, second leakage current amplitude and second line harmonic characteristics of the tower at each second sampling time point based on the target sampling period, so as to determine the target energized state identification results of the tower at each second sampling time point.
[0050] In a preferred embodiment, adjusting the initial sampling period based on each of the initial charged state identification results to obtain the target sampling period includes: If the initial charged state identification result is charged after a first preset number of consecutive cycles, the initial sampling period is adjusted according to the first preset shortening coefficient to obtain the first sampling period, and the first sampling period is used as the target sampling period. If the initial charged state identification result is non-charged after a first preset number of consecutive times, the initial sampling period is adjusted according to the first preset increase factor to obtain a second sampling period, and the second sampling period is used as the target sampling period.
[0051] In a preferred embodiment, adjusting the initial sampling period according to a first preset shortening coefficient to obtain a first sampling period includes: Multiply the initial sampling period by the first preset shortening coefficient to obtain the third sampling period; If the third sampling period is less than or equal to the preset lower bound of the sampling period, then the value of the first sampling period is determined as the preset lower bound of the sampling period; otherwise, the value of the first sampling period is determined as the third sampling period.
[0052] In a preferred embodiment, adjusting the initial sampling period according to a first preset amplification factor to obtain a second sampling period includes: Multiply the initial sampling period by the first preset amplification factor to obtain the fourth sampling period; If the fourth sampling period is greater than or equal to the preset lower bound of the sampling period, then the value of the second sampling period is determined as the preset upper bound of the sampling period; otherwise, the value of the second sampling period is determined as the fourth sampling period.
[0053] Furthermore, when the energy budget is insufficient to support the current sampling strategy, the system enters a sleep mode for protection or waits for energy compensation before resuming sampling. Subsequently, the monitoring frequency will be further increased to improve the response capability to emergencies; the upper and lower limits of the sampling period are dynamically constrained by the energy budget, and exceeding these limits triggers energy compensation or entry into a sleep mode for protection. The final status is uploaded via the wireless module.
[0054] In summary, this application's embodiments effectively solve the technical problem of identifying the energized state of towers while maintaining low power consumption by constructing a complete closed loop from energy adaptive initialization to state-driven feedback adjustment. First, the initial sampling period is determined based on the tower's current available energy, constraining system power consumption within the energy supply capacity from the source, ensuring long-term operation in scenarios without external power supply—a fundamental prerequisite for low power consumption. Second, by calculating the rate of change of electric field and current and fusing them into a comprehensive abrupt change sensitivity judgment value, the core of the identification mechanism shifts from relying on absolute thresholds to sensitively capturing the "slope" of state changes, significantly improving the ability to detect sudden, short-term dangerous states such as "instantaneous power-on," thus achieving a key breakthrough in identification accuracy. Next, the sampling period is dynamically adjusted based on the initial identification results, forming an intelligent feedback closed loop: automatically extending the period to save energy when the state is stable, and shortening the period to increase monitoring frequency when a sudden change or energized state is detected. This mechanism intelligently balances power consumption and performance, allowing the system to remain in a low-power "listening" state most of the time, while quickly entering a high-response "alert" state at critical moments. Ultimately, the system organically integrates energy constraints, mutation monitoring, and adaptive adjustment, transforming the identification of the energized state of towers from a simple monitoring process that consumes a lot of energy into an intelligent sensing process that "supply on demand and respond precisely," thereby achieving a unified approach of low power consumption and high reliability identification.
[0055] Example 2 like Figure 2 As shown, based on the above method embodiments, corresponding device embodiments are provided; An embodiment of the present invention provides a power status identification device for a power pole, comprising: a first identification module 21, a second identification module 22, a third identification module 23, and a fourth identification module 24; The first identification module 21 is used to obtain the first electric field intensity, the first leakage current amplitude, and the first line harmonic characteristics of the tower at each first sampling time point according to the initial sampling period; wherein, the initial sampling period is determined according to the current available total energy of the tower; The second identification module 22 is used to calculate the first electric field change rate and the first current change rate corresponding to each first sampling time point based on each first electric field strength and each first leakage current amplitude, and to determine the first comprehensive mutation sensitivity judgment value corresponding to each first sampling time point based on each first electric field change rate and each first current change rate. The third identification module 23 is used to sequentially traverse the first comprehensive mutation sensitivity judgment value corresponding to each of the first sampling time points in chronological order to obtain the initial energized state identification result of the tower at each of the first sampling time points. The fourth identification module 24 is used to adjust the initial sampling period based on the initial energized state identification results to obtain a target sampling period, so as to continue to acquire the second electric field strength, second leakage current amplitude and second line harmonic characteristics of the tower at each second sampling time point based on the target sampling period, so as to determine the target energized state identification results of the tower at each second sampling time point.
[0056] In a preferred embodiment, adjusting the initial sampling period based on each of the initial charged state identification results to obtain the target sampling period includes: If the initial charged state identification result is charged after a first preset number of consecutive cycles, the initial sampling period is adjusted according to the first preset shortening coefficient to obtain the first sampling period, and the first sampling period is used as the target sampling period. If the initial charged state identification result is non-charged after a first preset number of consecutive times, the initial sampling period is adjusted according to the first preset increase factor to obtain a second sampling period, and the second sampling period is used as the target sampling period.
[0057] In a preferred embodiment, adjusting the initial sampling period according to a first preset shortening coefficient to obtain a first sampling period includes: Multiply the initial sampling period by the first preset shortening coefficient to obtain the third sampling period; If the third sampling period is less than or equal to the preset lower bound of the sampling period, then the value of the first sampling period is determined as the preset lower bound of the sampling period; otherwise, the value of the first sampling period is determined as the third sampling period.
[0058] In a preferred embodiment, adjusting the initial sampling period according to a first preset amplification factor to obtain a second sampling period includes: Multiply the initial sampling period by the first preset amplification factor to obtain the fourth sampling period; If the fourth sampling period is greater than or equal to the preset lower bound of the sampling period, then the value of the second sampling period is determined as the preset upper bound of the sampling period; otherwise, the value of the second sampling period is determined as the fourth sampling period.
[0059] In a preferred embodiment, the initial sampling period is determined based on the current total available energy of the tower, including: Obtain the current total available energy of the tower, the energy consumed in a single sampling, and the expected working cycle; Divide the energy consumed in a single sampling by the total available energy to obtain a first ratio, and multiply the first ratio by the expected working cycle to obtain the initial sampling cycle.
[0060] As a preferred embodiment, determining the first comprehensive mutation sensitivity judgment value corresponding to each of the first sampling time points based on each of the first electric field change rates and each of the first current change rates includes: The preset electric field change rate weighting factor and the first electric field change rate corresponding to each of the first sampling time points are multiplied sequentially to obtain the first multiplication value corresponding to each of the first sampling time points; The preset current change rate weighting factor and the first current change rate corresponding to each first sampling time point are multiplied sequentially to obtain the second multiplication value corresponding to each first sampling time point; The first multiplication value and the second multiplication value corresponding to each of the first sampling time points are added together to obtain the first comprehensive mutation sensitivity judgment value corresponding to each of the first sampling time points.
[0061] As a preferred embodiment, the step of sequentially iterating through the first comprehensive mutation sensitivity judgment value corresponding to each of the first sampling time points according to time order to obtain the initial energized state identification result of the tower at each of the first sampling time points includes: In chronological order, the first comprehensive mutation sensitivity judgment value corresponding to each of the first sampling time points is traversed sequentially. If the first comprehensive mutation sensitivity judgment value corresponding to the first sampling time point of the current traversal is greater than or equal to the preset threshold, then the first electric field strength, the first leakage current amplitude, the first line harmonic characteristics and the first comprehensive mutation sensitivity judgment value corresponding to the first sampling time point of the current traversal are used as independent variables and input into the first preset linear discriminant function to obtain the initial energized state identification result corresponding to the first sampling time point of the current traversal.
[0062] For more detailed steps and working principles of this embodiment, please refer to the relevant description in Embodiment 1, but not limited to these descriptions.
[0063] In summary, this application's embodiments effectively solve the technical problem of identifying the energized state of towers while maintaining low power consumption by constructing a complete closed loop from energy adaptive initialization to state-driven feedback adjustment. First, the initial sampling period is determined based on the tower's current available energy, constraining system power consumption within the energy supply capacity from the source, ensuring long-term operation in scenarios without external power supply—a fundamental prerequisite for low power consumption. Second, by calculating the rate of change of electric field and current and fusing them into a comprehensive abrupt change sensitivity judgment value, the core of the identification mechanism shifts from relying on absolute thresholds to sensitively capturing the "slope" of state changes, significantly improving the ability to detect sudden, short-term dangerous states such as "instantaneous power-on," thus achieving a key breakthrough in identification accuracy. Next, the sampling period is dynamically adjusted based on the initial identification results, forming an intelligent feedback closed loop: automatically extending the period to save energy when the state is stable, and shortening the period to increase monitoring frequency when a sudden change or energized state is detected. This mechanism intelligently balances power consumption and performance, allowing the system to remain in a low-power "listening" state most of the time, while quickly entering a high-response "alert" state at critical moments. Ultimately, the system organically integrates energy constraints, mutation monitoring, and adaptive adjustment, transforming the identification of the energized state of towers from a simple monitoring process that consumes a lot of energy into an intelligent sensing process that "supply on demand and respond precisely," thereby achieving a unified approach of low power consumption and high reliability identification.
[0064] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can implement the method for identifying the energized state of a tower provided by any of the above-described method embodiments of the present invention.
[0065] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.
[0066] Example 3 Based on the above embodiments of the method for identifying the energized state of poles, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the method for identifying the energized state of poles according to any embodiment of the present invention.
[0067] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.
[0068] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.
[0069] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.
[0070] Example 4 Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the pole energization status identification method described in any of the above-described method embodiments of the present invention.
[0071] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.
[0072] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.
Claims
1. A method for identifying the energized state of a power pole, characterized in that, include: The first electric field strength, first leakage current amplitude, and first line harmonic characteristics of the tower at each first sampling time point are obtained according to the initial sampling period; wherein, the initial sampling period is determined based on the current total available energy of the tower; Based on the first electric field strength and the first leakage current amplitude, the first electric field change rate and the first current change rate corresponding to each first sampling time point are calculated, and the first comprehensive mutation sensitivity judgment value corresponding to each first sampling time point is determined based on the first electric field change rate and the first current change rate. According to the time sequence, the first comprehensive mutation sensitivity judgment value corresponding to each first sampling time point is traversed sequentially to obtain the initial energized state identification result of the tower at each first sampling time point. Based on the initial energized state identification results, the initial sampling period is adjusted to obtain the target sampling period. Based on the target sampling period, the second electric field strength, the second leakage current amplitude, and the second line harmonic characteristics of the tower at each second sampling time point are further obtained to determine the target energized state identification results of the tower at each second sampling time point.
2. The method for identifying the energized state of a power pole as described in claim 1, characterized in that, The step of adjusting the initial sampling period based on the initial charged state identification results to obtain the target sampling period includes: If the initial charged state identification result is charged after a first preset number of consecutive cycles, the initial sampling period is adjusted according to the first preset shortening coefficient to obtain the first sampling period, and the first sampling period is used as the target sampling period. If the initial charged state identification result is non-charged after a first preset number of consecutive times, the initial sampling period is adjusted according to the first preset increase factor to obtain a second sampling period, and the second sampling period is used as the target sampling period.
3. The method for identifying the energized state of a power pole as described in claim 2, characterized in that, The step of adjusting the initial sampling period according to a first preset shortening coefficient to obtain a first sampling period includes: Multiply the initial sampling period by the first preset shortening coefficient to obtain the third sampling period; If the third sampling period is less than or equal to the preset lower bound of the sampling period, then the value of the first sampling period is determined as the preset lower bound of the sampling period; otherwise, the value of the first sampling period is determined as the third sampling period.
4. The method for identifying the energized state of a power pole as described in claim 2, characterized in that, The step of adjusting the initial sampling period according to a first preset amplification factor to obtain a second sampling period includes: Multiply the initial sampling period by the first preset amplification factor to obtain the fourth sampling period; If the fourth sampling period is greater than or equal to the preset lower bound of the sampling period, then the value of the second sampling period is determined as the preset upper bound of the sampling period; otherwise, the value of the second sampling period is determined as the fourth sampling period.
5. The method for identifying the energized state of a power pole as described in claim 1, characterized in that, The initial sampling period is determined based on the current total available energy of the tower, including: Obtain the current total available energy of the tower, the energy consumed in a single sampling, and the expected working cycle; Divide the energy consumed in a single sampling by the total available energy to obtain a first ratio, and multiply the first ratio by the expected working cycle to obtain the initial sampling cycle.
6. The method for identifying the energized state of a power pole as described in claim 1, characterized in that, The step of determining the first comprehensive mutation sensitivity judgment value corresponding to each of the first sampling time points based on each of the first electric field change rates and each of the first current change rates includes: The preset electric field change rate weighting factor and the first electric field change rate corresponding to each of the first sampling time points are multiplied sequentially to obtain the first multiplication value corresponding to each of the first sampling time points; The preset current change rate weighting factor and the first current change rate corresponding to each first sampling time point are multiplied sequentially to obtain the second multiplication value corresponding to each first sampling time point; The first multiplication value and the second multiplication value corresponding to each of the first sampling time points are added together to obtain the first comprehensive mutation sensitivity judgment value corresponding to each of the first sampling time points.
7. The method for identifying the energized state of a power pole as described in claim 1, characterized in that, The step of sequentially iterating through the first comprehensive mutation sensitivity judgment value corresponding to each of the first sampling time points according to time order to obtain the initial energized state identification result of the tower at each of the first sampling time points includes: In chronological order, the first comprehensive mutation sensitivity judgment value corresponding to each of the first sampling time points is traversed sequentially. If the first comprehensive mutation sensitivity judgment value corresponding to the first sampling time point of the current traversal is greater than or equal to the preset threshold, then the first electric field strength, the first leakage current amplitude, the first line harmonic characteristics and the first comprehensive mutation sensitivity judgment value corresponding to the first sampling time point of the current traversal are used as independent variables and input into the first preset linear discriminant function to obtain the initial energized state identification result corresponding to the first sampling time point of the current traversal.
8. A device for identifying the energized status of a power pole, characterized in that, include: The first identification module, the second identification module, the third identification module, and the fourth identification module; The first identification module is used to obtain the first electric field strength, the first leakage current amplitude, and the first line harmonic characteristics of the tower at each first sampling time point according to the initial sampling period; wherein, the initial sampling period is determined according to the current total available energy of the tower; The second identification module is used to calculate the first electric field change rate and the first current change rate corresponding to each of the first sampling time points based on the first electric field strength and the first leakage current amplitude, and to determine the first comprehensive mutation sensitivity judgment value corresponding to each of the first sampling time points based on the first electric field change rate and the first current change rate. The third identification module is used to sequentially traverse the first comprehensive mutation sensitivity judgment value corresponding to each of the first sampling time points in chronological order to obtain the initial energized state identification result of the tower at each of the first sampling time points. The fourth identification module is used to adjust the initial sampling period based on the initial energized state identification results to obtain a target sampling period, so as to continue to acquire the second electric field strength, second leakage current amplitude and second line harmonic characteristics of the tower at each second sampling time point based on the target sampling period, so as to determine the target energized state identification results of the tower at each second sampling time point.
9. A terminal device, characterized in that, The method includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the method for identifying the energized state of a tower as described in any one of claims 1-7.
10. A computer-readable storage medium, characterized in that, include: A stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the energized state identification method for a tower as described in any one of claims 1-7.