Anti-corrosion control method for transmission tower

Abstract

The invention provides a transmission tower anti-corrosion control method which comprises the following steps: acquiring a contact resistance value of a bolt joint, current environment relative humidity and galvanic couple current density in real time to obtain an initial monitoring data set; according to the contact resistance value and the galvanic couple current density in the initial monitoring data set, evaluating the development degree of a corrosion product layer, and determining that the thickness grade of the corrosion layer is a thin layer, a middle layer or a thick layer; for the high-humidity activation level and the corrosion layer thickness level, performing classified learning on safety and failure states in a historical corrosion case library by adopting a decision tree algorithm, and determining a corrosion risk current density threshold value adapted to a current node state; and according to the spatial distribution density of the nodes in the high-priority risk node cluster and the high-humidity activation level, matching a corresponding galvanic couple current suppression strategy, and generating a transmission tower anti-corrosion control instruction.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to a method for corrosion control of power transmission towers. Background Technology

[0002] As a crucial supporting structure for power systems, the corrosion control of transmission towers directly impacts the safe, stable operation and long service life of the power grid. During service, the bolted connections between the main material and the grounding down conductor at the tower legs are the most susceptible to galvanic corrosion. Severe corrosion can lead to connection failure, decreased grounding performance, and even major safety accidents such as tower collapse. Most existing corrosion prevention methods rely on coating the bolt surface with anti-corrosion paint or periodically replacing rusted bolts. While these methods do offer some protection in the early stages of bolt corrosion, a rust product layer inevitably forms on the bolt surface over time. The presence of this rust product layer significantly increases the contact resistance between the bolt and the main material. According to conventional electrochemical understanding, increased contact resistance should reduce the galvanic current density flowing through the connection, seemingly inhibiting the corrosion rate on the main material side. However, this assumption overlooks another characteristic of the rust product layer in the actual environment: its strong hygroscopicity. In high-humidity conditions, such as in coastal areas or during the rainy season, the rust product layer absorbs a large amount of moisture from the air, creating a locally humid environment rich in electrolytes at the contact surface between the bolt and the main material. This humid area not only provides ample electrolyte channels for galvanic corrosion but also causes a sharp increase in the corrosion current density in localized areas of the contact surface, leading to severe localized pitting corrosion in the main material. In other words, while the rust product layer increases resistance and suppresses galvanic current in a dry state, it forms an electrolyte-rich area due to moisture absorption in a wet state, thus exacerbating localized corrosion. For example, in dry inland areas, the rust product layer may primarily increase resistance, temporarily slowing the corrosion rate, but once it encounters sudden high-humidity weather, such as fog or heavy rain, its moisture absorption behavior quickly transforms into a corrosion accelerator. This environmentally dependent dynamic characteristic increases the complexity of corrosion prediction because conventional monitoring often struggles to capture instantaneous changes caused by humidity fluctuations. This contradictory phenomenon of dry-state inhibition and wet-state aggravation makes the influence of bolt rust on galvanic corrosion highly dynamic and nonlinear, which is the fundamental reason why traditional anti-corrosion methods are difficult to accurately judge and effectively control. How to accurately identify the real impact of bolt rust on galvanic corrosion under different environmental humidity conditions, especially the risk of local electrolyte enrichment due to rust absorbing moisture in high humidity environments, which aggravates the corrosion of the main material, has become a key problem that urgently needs to be solved in the anti-corrosion control of transmission tower connection nodes. Summary of the Invention

[0003] This invention provides a method for corrosion control of power transmission towers, mainly including:

[0004] Real-time acquisition of contact resistance at bolted connections, current relative humidity, and thermocouple current density yields an initial monitoring dataset. Based on the contact resistance value and galvanic current density in the initial monitoring dataset, the degree of development of the rust product layer is assessed, and the thickness level of the rust layer is determined to be thin, medium, or thick. The current development stage is extracted from the corrosion layer thickness level. Combined with the relative humidity of the environment in the initial monitoring dataset, the moisture absorption characteristics corresponding to the pore structure of the corrosion layer are identified. The water adsorption capacity of the corrosion layer in a high humidity environment is evaluated to obtain the high humidity activation level. For high humidity activation level and rust layer thickness level, a decision tree algorithm is used to classify and learn the safety and failure states in the historical corrosion case library to determine the corrosion risk current density threshold that is suitable for the current node state. The corrosion aggravation level is divided according to the deviation between the galvanic current density and the corrosion risk current density threshold in the initial monitoring dataset. Local corrosion aggravation risk nodes and their corresponding corrosion aggravation levels are identified. Combined with the rust layer thickness level, risk nodes are spatially grouped according to the adjacent relationship of tower number and the distance of tower leg position to determine high-priority risk node clusters. Based on the spatial distribution density of nodes within the high-priority risk node cluster and the corresponding thermocouple current suppression strategy matching the high humidity activation level, anti-corrosion control instructions for transmission towers are generated.

[0005] Furthermore, the contact resistance value at the bolt connection, the current relative humidity, and the thermocouple current density are acquired in real time to obtain the initial monitoring dataset, including: By installing contact resistance detection units at the bolted connection points of the tower leg connection nodes, a constant micro-current is applied to the contact interface between the bolt and the main material using a four-wire measurement method. The voltage drop value at both ends of the contact interface is read, and the contact resistance value at the bolted connection is obtained by dividing the voltage drop value by the constant micro-current value. A galvanic current density detection unit is installed at the bolted connection to collect the galvanic current signal generated by the potential difference between the bolt and the main material, and to calculate the galvanic current density value. An ambient humidity acquisition unit is installed in the vicinity of the bolt connection. The water vapor content in the air is periodically sampled according to a preset acquisition cycle. The water vapor content is converted into capacitance change through a humidity-sensitive capacitor element. The current ambient relative humidity is obtained based on the correspondence between the capacitance change and humidity. The contact resistance value, the thermocouple current density value, and the current relative humidity are aligned according to timestamps, and the tower number and tower leg position are used as index fields to form an initial monitoring dataset. Synchronous and continuous monitoring of key corrosion indicators at bolted connections also ensures the spatiotemporal alignment of the data.

[0006] Furthermore, based on the contact resistance values ​​and galvanic current densities in the initial monitoring dataset, the development degree of the corrosion product layer is assessed, and the corrosion layer thickness is determined to be thin, medium, or thick, including: The difference in contact resistance between adjacent acquisition times is calculated and divided by the time interval to obtain the contact resistance change rate. The growth trend of the rust product layer at the contact interface between the bolt and the main material is determined based on the positive and negative direction and magnitude of the contact resistance change rate. The earliest timestamp of the galvanic current density value is read as the reference galvanic current density value. The current galvanic current density value is divided by the reference galvanic current density value to obtain the current attenuation ratio. The current attenuation ratio is combined with the contact resistance change rate to form the rust layer feature vector. The contact resistance change rate and current attenuation ratio in the rust layer feature vector are compared using a preset thickness level classification threshold to determine whether the rust layer thickness level is thin, medium, or thick. The contact resistance change rate and galvanic current attenuation ratio are combined into a quantified feature vector, and combined with a clear threshold standard, transforming the previously experience-based rust state judgment into an objective and repeatable level determination.

[0007] Furthermore, the rust layer thickness is classified as thin, medium, or thick, including: if the contact resistance change rate is less than the first threshold and the current attenuation ratio is greater than the fourth threshold, it is determined to be a thin layer; If the first threshold ≤ contact resistance change rate ≤ the second threshold and the third threshold ≤ current attenuation ratio ≤ the fourth threshold, it is determined to be a middle layer; If the contact resistance change rate is greater than the second threshold and the current attenuation ratio is less than the third threshold, it is classified as a thick layer. Clarifying the criteria for grade determination improves the consistency of the assessment.

[0008] Furthermore, the current development stage is extracted from the corrosion layer thickness level. Combined with the ambient relative humidity in the initial monitoring dataset, the hygroscopic characteristics corresponding to the pore structure of the corrosion layer are identified. The water adsorption capacity of the corrosion layer in a high-humidity environment is evaluated, and the high-humidity activation level is obtained, including: The current development stage is extracted based on the rust layer thickness level. When the rust layer thickness level is thin, the current development stage is determined to be the initial stage. When the rust layer thickness level is medium, the current development stage is determined to be the intermediate stage. When the rust layer thickness level is thick, the current development stage is determined to be the late stage. The moisture absorption characteristic type is identified based on the pore structure state corresponding to the current development stage. The initial stage corresponds to strong moisture absorption characteristics, the middle stage corresponds to medium moisture absorption characteristics, and the later stage corresponds to weak moisture absorption characteristics. The current relative humidity value is obtained, and the current relative humidity value is compared with a preset high humidity threshold to determine whether the current environment is in a high humidity state. By associating the moisture absorption characteristics with the high humidity environment, the high humidity activation level is determined. The differences in moisture adsorption capacity under different corrosion states are dynamically assessed, thereby more accurately identifying the "activation" risk of high humidity environments for specific corrosion stages (such as initial strong moisture absorption), achieving early and targeted warnings for hazardous environmental windows.

[0009] Furthermore, for high humidity activation level and rust layer thickness level, a decision tree algorithm is used to classify and learn the safety and failure states in the historical corrosion case library to determine the corrosion risk current density threshold suitable for the current node state, including: Extract the high humidity activation level, rust layer thickness level, galvanic current density value and corresponding final status label for each case record from the historical corrosion case database. The final status label is divided into two categories: safe status and failure status. The extracted case records are organized into a training sample set. The training sample set is used as input, and a decision tree algorithm is used for classification learning. The decision tree algorithm uses the high humidity activation level and the corrosion layer thickness level as splitting attributes. The training sample set is recursively divided to construct a classification decision tree. The leaf nodes of the classification decision tree correspond to the category labels of safe state or failure state. For each leaf node in the classification decision tree that points to the failure state, the galvanic current density value corresponding to the historical cases falling into the leaf node is counted, and the average value of the galvanic current density value is taken as the corrosion risk current density threshold corresponding to the leaf node. Based on the current node's high humidity activation level and corrosion layer thickness level, the classification decision tree is traversed downwards from the root node to the corresponding leaf node. The corrosion risk current density threshold corresponding to that leaf node is read, resulting in a corrosion risk current density threshold adapted to the current node's state. By utilizing the decision tree algorithm to mine historical cases and learn the safety boundaries under different combinations of "high humidity activation level" and "corrosion layer thickness level," a dynamic risk current density threshold is customized for each monitoring node. This overcomes the limitations of fixed thresholds, making risk assessment more personalized and dynamically adaptable.

[0010] Furthermore, the decision tree algorithm is used to classify and learn the safety and failure states in the historical corrosion case library. This involves: at the root node, the decision tree algorithm selects one of two attributes—high humidity activation level and rust layer thickness level—as the splitting attribute, dividing the training sample set into several subsets; each subset recursively performs the splitting process until all samples in the subset belong to the same category or further splitting is impossible. At this point, leaf nodes are formed and labeled with their corresponding category tags. By describing the core recursive splitting process of the decision tree algorithm, this paper clarifies how to automatically learn and construct a clear and interpretable corrosion risk classification model based on historical data.

[0011] Furthermore, based on the deviation between the galvanic current density and the corrosion risk current density threshold in the initial monitoring dataset, corrosion aggravation levels are classified, identifying localized corrosion aggravation risk nodes and their corresponding corrosion aggravation levels. Combining the rust layer thickness level, risk nodes are spatially grouped based on the adjacency relationship of tower numbers and the distance to tower leg locations, determining high-priority risk node clusters, including: The galvanic current density of each bolted connection node is obtained. The difference between the galvanic current density and the corresponding corrosion risk current density threshold is calculated to obtain the deviation value. The deviation value is compared with the preset deviation range to classify the corrosion aggravation level as highly aggravated, moderately aggravated, or slightly aggravated. Nodes with a corrosion aggravation level of highly aggravated or moderately aggravated are marked as nodes with local corrosion aggravation risk. For the risk nodes of localized corrosion aggravation, the tower number and tower leg position identifier corresponding to each risk node are extracted. Based on the adjacency relationship of the tower number, it is determined whether different risk nodes are located on adjacent towers. Based on the tower leg position identifier, it is determined whether the risk nodes on the same tower or adjacent towers meet the preset proximity distance condition. Risk nodes that meet the proximity distance condition are assigned to the same proximity group to obtain several risk node proximity groups. For each risk node's adjacent group, the number of risk nodes within the group, the corrosion aggravation level of each risk node, and the rust layer thickness level are statistically analyzed. If the number of risk nodes in a group exceeds a preset threshold and at least one risk node has a thick rust layer, then that group is identified as a high-priority risk node cluster. Identifying spatially clustered node clusters with high risk levels helps maintenance personnel prioritize the allocation of limited maintenance resources to areas with the most concentrated risks and the highest likelihood of cascading failures.

[0012] Furthermore, based on the spatial distribution density of nodes within the high-priority risk node cluster and the corresponding thermocouple current suppression strategy matching the high humidity activation level, anti-corrosion control instructions for transmission towers are generated, including: Based on the tower number and tower leg position of each node in the high-priority risk node cluster, the spatial distribution density value is obtained by counting the number of risk nodes within a unit line length. The spatial distribution density value is combined with the high humidity activation level corresponding to each node in the cluster, and the corresponding galvanic current suppression method is queried from the pre-established suppression method matching table. Based on the obtained thermocouple current suppression method and the tower number and leg position identifier of each node in the high-priority risk node cluster, anti-corrosion control instructions for transmission towers are encapsulated according to a preset instruction format. Based on the spatial density and high-humidity activation level of the identified high-priority risk node cluster, control instructions containing specific target locations and suppression strategies are automatically matched and generated, which improves the speed of anti-corrosion response.

[0013] Furthermore, the corrosion control command includes the target tower number, the target tower leg location, and the corresponding galvanic current suppression method. This clarifies the content of the command and provides data support for maintenance personnel.

[0014] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: This invention discloses a method for corrosion control of transmission towers. By deploying contact resistance detection units and environmental humidity acquisition units at the connection nodes of the tower legs, the method acquires real-time contact resistance values ​​at bolt connections, relative humidity, and galvanic current density to form an initial monitoring dataset. Based on this dataset, the development level of the corrosion product layer is assessed, the corrosion layer thickness level is determined, and the moisture absorption characteristics of the corrosion layer's pore structure are identified by combining the relative humidity, resulting in a high-humidity activation level. Then, for the corrosion layer thickness level and the high-humidity activation level, a decision tree algorithm is used to classify and learn from a historical corrosion case library, dynamically determining the corrosion risk current density threshold applicable to the current node state. The corrosion aggravation level is classified according to the deviation between the galvanic current density and the risk threshold, identifying nodes with localized corrosion aggravation risks, and forming high-priority risk node clusters through spatial proximity grouping. Finally, based on the spatial distribution density of nodes within the cluster and the high-humidity activation level, a corresponding galvanic current suppression strategy is matched to generate precise corrosion control instructions for the transmission towers. This invention enables real-time perception and quantitative assessment of the evolution of the microstructure of the rust layer and the risk of accelerated corrosion under high humidity conditions. By integrating multi-source monitoring data and intelligent learning algorithms, it dynamically generates targeted anti-corrosion control strategies, effectively improving the early warning and proactive prevention and control capabilities of corrosion risks at bolted connection nodes of transmission towers, and significantly reducing the probability of tower failure caused by localized corrosion intensification. Attached Figure Description

[0015] Figure 1 This is a flowchart of a corrosion control method for power transmission towers according to the present invention.

[0016] Figure 2 This is a schematic diagram of a corrosion control method for power transmission towers according to the present invention.

[0017] Figure 3 This is another schematic diagram of a method for corrosion control of power transmission towers according to the present invention. Detailed Implementation

[0018] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] like Figure 1 As shown in the figure, this embodiment of a method for corrosion control of transmission towers may specifically include: S101. Real-time acquisition of contact resistance value at bolt connection, current relative humidity, and thermocouple current density to obtain initial monitoring dataset.

[0020] Contact resistance detection units are installed at the bolted connections of the tower leg joints. A constant micro-current is applied to the contact interface between the bolt and the main material using a four-wire measurement method. The voltage drop across the contact interface is read, and the contact resistance value at the bolted connection is obtained by dividing the voltage drop value by the constant micro-current value. Additionally, a galvanic current density detection unit is installed at the bolted connection to collect the galvanic current signal generated by the potential difference between the bolt and the main material. The galvanic current density value is obtained by dividing the galvanic current signal by the bolt contact area. An ambient humidity acquisition unit is installed in the vicinity of the bolted connection to periodically sample the water vapor content in the air according to a preset acquisition cycle. The water vapor content is converted into capacitance change using a humidity-sensitive capacitor element, and the current relative humidity is obtained based on the correlation between capacitance change and humidity. The contact resistance value, galvanic current density value, and current relative humidity are aligned according to timestamps, and the tower number and tower leg position are used as index fields to form an initial monitoring dataset.

[0021] In one embodiment, the contact resistance detection unit is deployed using a four-wire measurement method. Two wires are used to apply a constant micro-current to the contact interface between the bolt and the main material, while the other two wires are used to collect the voltage drop across the contact interface. The advantage of the four-wire measurement method is that the current application circuit and the voltage measurement circuit are independent of each other, and the resistance of the measuring wires themselves is not included in the measurement result, thereby improving the accuracy of the contact resistance measurement.

[0022] Specifically, the value of the constant microcurrent is usually set in the microampere range to avoid additional electrochemical effects on the bolted connection. After applying the constant microcurrent, the contact resistance detection unit reads the voltage drop across the contact interface through a high-impedance voltage acquisition circuit. Dividing the voltage drop by the constant microcurrent value yields the contact resistance value of the bolted connection.

[0023] In one possible implementation, the galvanic current density detection unit is positioned near the contact interface between the bolt and the main material. A zero-resistance galvanometer collects the galvanic current signal generated by the potential difference between the bolt and the main material. The zero-resistance galvanometer can measure the current flow between the two electrodes without introducing an additional voltage drop. After the galvanic current signal is amplified by a signal conditioning circuit, it is divided by the actual contact area between the bolt and the main material to obtain the galvanic current density value. The method for obtaining the contact area can be pre-calibrated according to the bolt specifications and washer dimensions.

[0024] For example, the ambient humidity acquisition unit uses a humidity-sensitive capacitor as the core humidity sensing device. The dielectric layer of the humidity-sensitive capacitor is composed of a polymer film. When the water vapor content in the air changes, the film adsorbs or releases water molecules, causing a corresponding change in the dielectric constant, which in turn causes a change in the capacitance value. The signal processing circuit built into the ambient humidity acquisition unit converts the capacitance change into a percentage value of the current ambient relative humidity based on a pre-calibrated relationship between the capacitance change and humidity.

[0025] It should be noted that the contact resistance value, thermocouple current density value, and current relative humidity were all time-stamped during the data collection process. During data integration, the timestamps were used as the alignment benchmark to associate the three types of data collected at the same or similar times. At the same time, the tower number and tower leg position were used as index fields to form a structured initial monitoring dataset.

[0026] S102. Based on the contact resistance value and galvanic current density in the initial monitoring data set, assess the degree of development of the rust product layer and determine the rust layer thickness level as thin, medium or thick.

[0027] The contact resistance value sequence at the bolt connection is extracted from the initial monitoring dataset. The difference in contact resistance value between adjacent acquisition times is calculated and divided by the time interval to obtain the contact resistance change rate. Based on the positive or negative direction and magnitude of the contact resistance change rate, the growth trend of the rust product layer at the bolt-to-material interface is determined. The galvanic current density sequence is extracted from the initial monitoring dataset. The earliest timestamped galvanic current density value in this sequence is used as the reference galvanic current density value. The current galvanic current density value is divided by the reference galvanic current density value to obtain the current attenuation ratio. The current attenuation ratio and the contact resistance change rate are combined to form the rust layer feature vector. For the contact resistance change rate and current attenuation ratio in the corrosion layer feature vector, a preset thickness level classification threshold is used for comparison. If the contact resistance change rate is lower than the preset resistance change threshold and the current attenuation ratio is higher than the preset current attenuation threshold, the corrosion layer thickness level is determined to be thin. If both the contact resistance change rate and the current attenuation ratio are in the middle range of their respective thresholds, the corrosion layer thickness level is determined to be medium. If the contact resistance change rate is higher than the preset resistance change threshold and the current attenuation ratio is lower than the preset current attenuation threshold, the corrosion layer thickness level is determined to be thick. Optionally, a thermocouple current density sequence is extracted from the initial monitoring dataset, and the thermocouple current density value with the earliest timestamp is read as the reference value. The current thermocouple current density value is divided by the reference value to obtain the current attenuation ratio d. The contact resistance change rate r and the current attenuation ratio d are combined to form the corrosion layer feature vector (r, d). Based on the characteristic vector of the corrosion layer, the following thickness classification rules are used for comparison: if r < 0.08 and d > 0.92, it is determined to be a thin layer; if 0.08 ≤ r ≤ 0.45 and 0.55 ≤ d ≤ 0.92, it is determined to be a medium layer; if r > 0.45 and d < 0.55, it is determined to be a thick layer. Here, r represents the contact resistance change rate, and d represents the current attenuation ratio.

[0028] In one embodiment, when extracting the contact resistance value sequence from the initial monitoring dataset, the contact resistance values ​​are arranged in chronological order according to their timestamps to form a time-series continuous data sequence. The rate of change of contact resistance is calculated by dividing the difference between the contact resistance values ​​at two adjacent acquisition times by the corresponding time interval. A positive value of this rate of change indicates an increasing trend in contact resistance, while a negative value indicates a decreasing trend.

[0029] Specifically, during the growth of the rust product layer at the contact interface between the bolt and the main material, as the rust products gradually accumulate, the conductive channels at the contact interface are blocked by the rust layer, leading to a continuous increase in the contact resistance value. The magnitude of the change rate of contact resistance reflects the growth rate of the rust layer; a larger magnitude indicates a more significant increase in the thickness of the rust layer per unit time, while a smaller magnitude indicates that the rust layer is in a relatively stable and slow growth stage.

[0030] It should be noted that the reference galvanic current density value is selected based on the earliest timestamp value in the galvanic current density sequence. This value corresponds to the galvanic current density state at the bolted connection during the early stages of service before a significant corrosion layer has formed. The current attenuation ratio is obtained by dividing the current galvanic current density value by the reference galvanic current density value. Its value is typically between zero and one; the closer the value is to one, the smaller the galvanic current attenuation, and the closer the value is to zero, the greater the galvanic current attenuation.

[0031] In one possible implementation, the rust layer feature vector consists of two components: the contact resistance change rate and the current decay ratio. This feature vector comprehensively characterizes the development state of the rust product layer from both the resistance change dimension and the current decay dimension. When the contact resistance change rate is low and the current decay ratio is high, it indicates that the rust layer is thin and has limited blocking effect on galvanic current; when the contact resistance change rate is high and the current decay ratio is low, it indicates that the rust layer has accumulated to a considerable thickness and significantly blocks the flow of galvanic current.

[0032] For example, the threshold for classifying the thickness level is set based on the statistical characteristics of historical monitoring data of the bolted connections of transmission towers. The resistance change threshold and the current attenuation threshold correspond to the critical dividing points of the three levels: thin, medium, and thick, respectively. By comparing the two components in the rust layer feature vector with the corresponding thresholds, the category to which the rust layer thickness level of the current bolted connection belongs can be determined.

[0033] S103. Extract the current development stage from the corrosion layer thickness level, combine it with the ambient relative humidity in the initial monitoring dataset, identify the moisture absorption characteristics corresponding to the pore structure of the corrosion layer, evaluate the moisture adsorption capacity of the corrosion layer in a high humidity environment, and obtain the high humidity activation level.

[0034] For details, see Figure 3As shown, the current development stage is extracted based on the thickness level of the rust layer. If the rust layer thickness level is thin, the current development stage is determined to be the initial stage, in which the rust layer pore structure is open and loose. If the rust layer thickness level is medium, the current development stage is determined to be the intermediate stage, in which the rust layer pore structure is semi-closed. If the rust layer thickness level is thick, the current development stage is determined to be the late stage, in which the rust layer pore structure is tightly sealed. The moisture absorption characteristic type is identified based on the pore structure state corresponding to the current development stage: open and loose state corresponds to strong moisture absorption, semi-closed state corresponds to medium moisture absorption, and tightly sealed state corresponds to weak moisture absorption. The current relative humidity value is obtained from the initial monitoring dataset and compared with a preset high humidity threshold. If the current relative humidity value exceeds the high humidity threshold, the current environment is determined to be high humidity. The moisture absorption characteristic type is associated with the high humidity environment state. If the current environment state is high humidity and the moisture absorption characteristic type is strong moisture absorption, the high humidity activation level is determined to be high; if the current environment state is high humidity and the moisture absorption characteristic type is medium moisture absorption, the high humidity activation level is determined to be medium; if the current environment state is high humidity and the moisture absorption characteristic type is weak moisture absorption, the high humidity activation level is determined to be low.

[0035] In one embodiment, there is a correspondence between the thickness level of the rust layer and the current development stage: a thin layer corresponds to the initial stage, a medium layer to the intermediate stage, and a thick layer to the late stage. In the initial stage, the rust layer is mainly composed of ferric hydroxide and ferric hydroxide; the crystal structure is not yet fully developed, and the pores are interconnected, forming an open and loose state. In the intermediate stage, the rust layer gradually transforms into a mixture of magnetite and ferric oxide; some pores are filled by newly generated rust products, forming a semi-closed state. In the late stage, the rust layer is mainly composed of dense ferric oxide, and the pores are completely sealed, forming a tightly sealed state.

[0036] Specifically, the correlation between pore structure and hygroscopic characteristics stems from the differences in the microscopic morphology of corrosion products. Open, porous corrosion layers possess a large specific surface area and a connected pore network. When ambient humidity increases, water vapor molecules can penetrate deep into the corrosion layer along the pore channels and be adsorbed, exhibiting strong hygroscopic characteristics. Semi-closed corrosion layers have reduced pore connectivity, allowing water vapor molecules to only penetrate to the surface area, exhibiting moderate hygroscopic characteristics. Completely sealed corrosion layers have blocked pores, making it difficult for water vapor molecules to enter the interior; only trace amounts are adsorbed on the surface, exhibiting weak hygroscopic characteristics.

[0037] It should be noted that the identification of moisture absorption characteristics is based on a direct mapping of the pore structure state, without the need for additional measuring equipment. This mapping relationship is established on the physical laws between the microstructure of the rust product layer and its macroscopic moisture absorption behavior, enabling the inference of the moisture absorption capacity of the rust layer without damaging the bolted connection structure.

[0038] In one possible implementation, the high humidity threshold is set with reference to meteorological statistics of the area where the transmission tower is located. In coastal areas, where the air moisture content is high, the high humidity threshold is typically set at 75% relative humidity; in inland areas, where the air is relatively dry, the high humidity threshold is typically set at 80% relative humidity. When the current relative humidity value obtained from the initial monitoring dataset exceeds the set high humidity threshold, the current environment is determined to be in a high humidity state.

[0039] For example, the determination of a high-humidity environment and its correlation with the type of hygroscopic characteristics constitute the basis for determining the high-humidity activation level. In a high-humidity environment, a rust layer with strong hygroscopic characteristics will absorb a large amount of moisture from the air, forming an electrolyte-rich wet film at the interface between the bolt and the main material. This wet film provides ion migration channels for galvanic corrosion, significantly increasing the risk of localized corrosion aggravation, corresponding to a high activation level. A rust layer with moderate hygroscopic characteristics has a limited ability to absorb moisture, and the wet film formation range and thickness are relatively small, resulting in a moderate risk of localized corrosion aggravation, corresponding to a medium activation level. A rust layer with weak hygroscopic characteristics absorbs almost no moisture, keeping the contact interface relatively dry, resulting in a low risk of localized corrosion aggravation, corresponding to a low activation level.

[0040] Understandably, the classification of high-humidity activation levels reflects the tendency of the corrosion layer to transition from a dry to a wet state under high-humidity conditions. A high activation level means that the corrosion layer is highly susceptible to absorbing moisture and forming an electrolyte-rich zone under high-humidity conditions. This characteristic corresponds to the contradictory phenomenon of dry-state inhibition and wet-state aggravation described in the background art. Furthermore, if the current relative humidity value does not exceed the high-humidity threshold, it is determined that the current environment is not high-humidity. In this case, the moisture absorption behavior of the corrosion layer is inhibited, and the high-humidity activation level is uniformly marked as inactive. This inactive state indicates that under the current environmental conditions, the corrosion layer will not exacerbate galvanic corrosion due to moisture absorption, and the corrosion risk at the bolt connection is mainly determined by the corrosion layer thickness level itself.

[0041] Preferably, the process of determining the high humidity activation level is repeated in each acquisition cycle to track the dynamic impact of ambient humidity fluctuations on the moisture absorption state of the rust layer. When the ambient humidity suddenly rises from a low humidity state to a high humidity state, the high humidity activation level can promptly switch from an inactive state to the corresponding activation level, capturing the instantaneous changes caused by humidity fluctuations.

[0042] S104. For high humidity activation level and rust layer thickness level, a decision tree algorithm is used to classify and learn the safety and failure states in the historical corrosion case library to determine the corrosion risk current density threshold that is suitable for the current node state.

[0043] Specifically, such as Figure 2 As shown, the high humidity activation level, rust layer thickness level, galvanic current density value, and corresponding final state label are extracted from the historical corrosion case database for each case record. The final state label is divided into two categories: safe state and failure state. A safe state indicates that no significant corrosion damage has occurred at the bolted connection during its service life, while a failure state indicates that the connection performance has deteriorated due to galvanic corrosion. The extracted case records are organized into a training sample set. Using the training sample set as input, a decision tree algorithm is used for classification learning. The decision tree algorithm uses the high humidity activation level and rust layer thickness level as splitting attributes, and constructs a classification decision tree by recursively partitioning the training sample set. The leaf nodes of the classification decision tree correspond to the category label of safe state or failure state. For each leaf node in the classification decision tree pointing to the failure state, the galvanic current density value corresponding to the historical cases falling into that leaf node is counted, and the average value of the galvanic current density value is taken as the corrosion risk current density threshold corresponding to that leaf node. Based on the high humidity activation level and corrosion layer thickness level of the current node, the classification decision tree is traversed from the root node down to the corresponding leaf node. The corrosion risk current density threshold corresponding to the leaf node is read to obtain the corrosion risk current density threshold adapted to the current node state.

[0044] In one embodiment, the historical corrosion case library is constructed based on corrosion records of bolted connections accumulated during the long-term operation and maintenance of transmission towers. Each case record includes the high humidity activation level, corrosion layer thickness level, galvanic current density value, and final status label for that node during a specific period. The final status label is marked by maintenance personnel during regular inspections or bolt replacements based on the actual degree of corrosion damage. A safe state corresponds to a situation where the surface corrosion at the bolted connection is slight and does not affect the connection performance, while a failure state corresponds to a situation where there is significant pitting corrosion or an abnormally high grounding resistance at the bolted connection.

[0045] Specifically, the training sample set preparation process converts each case record into a combination of feature vectors and labels. The feature vectors consist of two discrete attributes: high humidity activation level and corrosion layer thickness level. The high humidity activation level includes three categories: high, medium, and low; the corrosion layer thickness level includes three categories: thin, medium, and thick. The labels are binary categories, representing either a safe state or a failed state. The number of samples in the training sample set should cover all feature combinations to ensure sufficient classification learning.

[0046] It should be noted that the decision tree algorithm constructs itself using a top-down recursive partitioning method. At the root node, the algorithm selects one of two attributes—high humidity activation level or corrosion layer thickness level—as the splitting attribute, dividing the training sample set into several subsets. The splitting attribute is chosen based on its ability to distinguish between safe and failed states in the training sample set; attributes with stronger distinguishing abilities are prioritized. Each subset continues the above splitting process recursively until all samples in the subset belong to the same category or further splitting is impossible. At this point, leaf nodes are formed and labeled with their corresponding category tags.

[0047] In one possible implementation, after the classification decision tree is constructed, threshold statistics are performed on each leaf node pointing to the failure state. Each leaf node corresponds to a specific combination of high humidity activation level and corrosion layer thickness level. Historical cases falling into this leaf node all satisfy this combination of conditions and the final state is failure. The galvanic current density values ​​of these historical cases are extracted, and the minimum value is taken as the corrosion risk current density threshold for this leaf node. The reason for selecting the minimum value is that the minimum value represents the lower limit of the galvanic current density that leads to the failure state under this combination of features. Once the galvanic current density of the current node reaches or exceeds this lower limit, there is a high risk of corrosion failure.

[0048] For example, suppose the combined conditions for a certain leaf node are high humidity activation level and medium corrosion layer thickness. There are several historical cases falling into this leaf node, and the galvanic current density values ​​of each case are distributed within a certain range. The minimum value among them is taken as the corrosion risk current density threshold for this leaf node. This threshold characterizes the critical current density value for corrosion failure at the bolted connection under the conditions of high activation level and medium corrosion layer thickness.

[0049] Understandably, the process of determining the corrosion risk current density threshold for the current node is achieved by traversing the classification decision tree. Based on the current node's high humidity activation level and corrosion layer thickness level, starting from the root node of the classification decision tree, it sequentially determines which branch condition each attribute value of the current node satisfies, traversing downwards along the branches that satisfy the conditions until a leaf node is reached. If the leaf node corresponds to a failure state, the pre-calculated corrosion risk current density threshold is read; if the leaf node corresponds to a safe state, it indicates that no failure has occurred in historical cases under the current feature combination conditions, and the corrosion risk current density threshold is set to the preset upper limit. Furthermore, the dynamic determination of the corrosion risk current density threshold allows different nodes to have differentiated risk assessment standards under different state conditions. Nodes with higher high humidity activation levels and thicker corrosion layers typically have lower corrosion risk current density thresholds, indicating that these nodes are more sensitive to galvanic current density; nodes with lower high humidity activation levels and thinner corrosion layers typically have higher corrosion risk current density thresholds, indicating that these nodes have a larger safety margin.

[0050] S105. Based on the deviation between the galvanic current density and the risk threshold in the initial monitoring dataset, the corrosion aggravation level is classified, the risk nodes of local corrosion aggravation and their corresponding corrosion aggravation levels are identified, and the risk nodes are spatially grouped according to the adjacent relationship of the tower number and the distance of the tower leg position, in combination with the rust layer thickness level, to determine the high-priority risk node cluster.

[0051] The galvanic current density values ​​of each bolted connection node are obtained from the initial monitoring dataset. The difference between these values ​​and the corresponding corrosion risk current density thresholds is calculated to obtain the deviation value. This deviation value is compared with a preset deviation range. If the deviation value exceeds the high deviation threshold, the corrosion aggravation level is determined to be highly aggravated; if the deviation value is between the high and low deviation thresholds, the corrosion aggravation level is determined to be moderately aggravated; and if the deviation value is below the low deviation threshold, the corrosion aggravation level is determined to be slightly aggravated. Nodes with highly or moderately aggravated corrosion are marked as nodes at risk of localized corrosion aggravation. For these nodes, the tower number and tower leg location identifier are extracted. The adjacency relationship of the tower numbers determines whether different risk nodes are located on adjacent towers. The tower leg location identifier determines whether risk nodes on the same or adjacent towers meet a preset proximity condition. Risk nodes meeting the proximity condition are grouped into the same proximity group, resulting in several risk node proximity groups. For each risk node's neighboring group, the number of risk nodes in the group, the corrosion aggravation level of each risk node, and the rust layer thickness level are counted. If the number of risk nodes in the group exceeds the preset threshold and at least one risk node has a thick rust layer, then the group is identified as a high-priority risk node cluster.

[0052] In one embodiment, the deviation value is calculated by subtracting the corresponding corrosion risk current density threshold from the galvanic current density value. A positive deviation value indicates that the current galvanic current density has exceeded the risk threshold, and the larger the deviation value, the more severe the exceedance; a negative or zero deviation value indicates that the current galvanic current density has not yet reached the risk threshold and is within a relatively safe range.

[0053] Specifically, the classification of corrosion severity levels is based on the comparison between the deviation value and a preset deviation threshold. A high deviation threshold corresponds to the critical point where the galvanic current density significantly exceeds the risk threshold, while a low deviation threshold corresponds to the critical point where the galvanic current density slightly exceeds the risk threshold. When the deviation value exceeds the high deviation threshold, it indicates that the galvanic corrosion at the bolted connection has entered a rapid development stage, and is identified as highly aggravated; when the deviation value is between the two thresholds, it indicates that the galvanic corrosion is developing at a moderate rate, and is identified as moderately aggravated; when the deviation value is below the low deviation threshold, it indicates that the galvanic corrosion is still within a controllable range, and is identified as slightly aggravated.

[0054] It should be noted that the adjacency of tower numbers reflects the physical arrangement of towers along the transmission line. Towers along the transmission line are numbered sequentially according to the line's direction, and towers with consecutive numbers are usually geographically close to each other. Leg position markers are used to distinguish different legs on the same tower; common four-corner towers have four legs, located at the four cardinal directions of the tower base.

[0055] In one possible implementation, spatial proximity determination involves two levels: proximity determination at the tower level and proximity determination at the leg level. If two risk nodes are located on adjacent towers or on the same tower, they are considered to meet the tower proximity condition. Given that the tower proximity condition is met, the leg position identifiers are further used to determine whether the two risk nodes meet the proximity distance condition. Legs in adjacent positions on the same tower or corresponding positions on adjacent towers typically meet the proximity distance condition.

[0056] For example, the determination of high-priority risk node clusters comprehensively considers three factors: the number of risk nodes within the group, the degree of corrosion aggravation, and the thickness of the rust layer. A number of risk nodes exceeding a preset threshold indicates a concentrated distribution of multiple corrosion risk points in the area; the presence of risk nodes with a thick rust layer indicates that the rust product layer in the area has developed to a high degree, exhibiting strong moisture absorption capacity and corrosion aggravation potential in high-humidity environments. Adjacent groups that simultaneously meet both of these conditions are identified as high-priority risk node clusters, and the nodes within these clusters should receive priority in subsequent corrosion prevention and control.

[0057] S106. Generate anti-corrosion control instructions for transmission towers based on the spatial distribution density of nodes in the high-priority risk node cluster and the corresponding thermocouple current suppression strategy that matches the high humidity activation level.

[0058] Based on the tower numbers and leg positions of each node within the high-priority risk node cluster, the spatial distribution density value is obtained by counting the number of risk nodes within a unit line length. This spatial distribution density value is then combined with the high-humidity activation level corresponding to each node within the cluster, and the corresponding thermocouple current suppression method is retrieved from a pre-established suppression method matching table. Based on the retrieved thermocouple current suppression method and the tower numbers and leg position identifiers of each node within the high-priority risk node cluster, a transmission tower corrosion prevention control instruction is encapsulated according to a preset instruction format. This instruction includes the target tower number, target leg position, and the corresponding thermocouple current suppression method field.

[0059] In one embodiment, the spatial distribution density value is calculated based on the length of the line segment covered by the high-priority risk node cluster. The number of risk nodes in the cluster is divided by the segment length to obtain the risk node distribution density per unit length. The higher the spatial distribution density value, the more concentrated the risk nodes are in that segment, and the more obvious the regional characteristics of the corrosion risk.

[0060] Specifically, the suppression mode matching table uses the combination of spatial distribution density value range and high humidity activation level as index conditions, and pre-stores the galvanic current suppression modes corresponding to different condition combinations. When the spatial distribution density value is high and the high humidity activation level is high, the matching table returns a suppression mode with stronger intensity; when the spatial distribution density value is low and the high humidity activation level is low, the matching table returns a normal suppression mode.

[0061] For example, the anti-corrosion control command for transmission towers is encapsulated in a structured data format. The target tower number field identifies the location of the tower to be addressed, the target tower leg location field locates the specific bolt connection node, and the galvanic current suppression method field indicates the type of anti-corrosion control measure to be taken. This anti-corrosion control command can be sent to on-site maintenance terminals or automated control equipment via a communication network for execution.

[0062] If the technical solution of this application involves personal information, the product using this solution has clearly informed the user of the personal information processing rules and obtained the user's voluntary consent before processing the personal information. If sensitive personal information is involved, the user's separate consent has been obtained before processing, and the "express consent" requirement is met. For example, a clear sign is placed at the collection device such as a camera to inform the user that they have entered the collection area, and the user's voluntary entry is considered as consent; or the processing device clearly indicates the processing rules and obtains authorization through pop-up windows or by asking the user to upload information themselves. The personal information processing rules include the processor, the purpose of processing, the processing method, and the types of personal information.

[0063] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. The present invention has been described in detail with reference to preferred embodiments. Those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications and substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for corrosion control of transmission towers, characterized in that, The method includes: Real-time acquisition of contact resistance at bolted connections, current relative humidity, and thermocouple current density yields an initial monitoring dataset. Based on the contact resistance value and galvanic current density in the initial monitoring dataset, the degree of development of the rust product layer is assessed, and the thickness level of the rust layer is determined to be thin, medium, or thick. The current development stage is extracted from the corrosion layer thickness level. Combined with the relative humidity of the environment in the initial monitoring dataset, the moisture absorption characteristics corresponding to the pore structure of the corrosion layer are identified. The water adsorption capacity of the corrosion layer in a high humidity environment is evaluated to obtain the high humidity activation level. For high humidity activation level and rust layer thickness level, a decision tree algorithm is used to classify and learn the safety and failure states in the historical corrosion case library to determine the corrosion risk current density threshold that is suitable for the current node state. The corrosion aggravation level is divided according to the deviation between the galvanic current density and the corrosion risk current density threshold in the initial monitoring dataset. Local corrosion aggravation risk nodes and their corresponding corrosion aggravation levels are identified. Combined with the rust layer thickness level, risk nodes are spatially grouped according to the adjacent relationship of tower number and the distance of tower leg position to determine high-priority risk node clusters. Based on the spatial distribution density of nodes within the high-priority risk node cluster and the corresponding thermocouple current suppression strategy matching the high humidity activation level, anti-corrosion control instructions for transmission towers are generated.

2. The corrosion control method for transmission towers according to claim 1, characterized in that, The initial monitoring dataset is obtained by acquiring the contact resistance value at the bolt connection, the current relative humidity, and the thermocouple current density in real time, including: By installing contact resistance detection units at the bolted connection points of the tower leg connection nodes, a constant micro-current is applied to the contact interface between the bolt and the main material using a four-wire measurement method. The voltage drop value at both ends of the contact interface is read, and the contact resistance value at the bolted connection is obtained by dividing the voltage drop value by the constant micro-current value. A galvanic current density detection unit is installed at the bolted connection to collect the galvanic current signal generated by the potential difference between the bolt and the main material, and to calculate the galvanic current density value. An ambient humidity acquisition unit is installed in the vicinity of the bolt connection. The water vapor content in the air is periodically sampled according to a preset acquisition cycle. The water vapor content is converted into capacitance change through a humidity-sensitive capacitor element. The current ambient relative humidity is obtained based on the correspondence between the capacitance change and humidity. The contact resistance value, the thermocouple current density value, and the current relative humidity are aligned according to the timestamp, and the tower number and tower leg position are used as index fields to form an initial monitoring dataset.

3. The corrosion control method for transmission towers according to claim 1, characterized in that, The process of assessing the development degree of the rust product layer based on the contact resistance value and galvanic current density in the initial monitoring data, and determining the rust layer thickness level as thin, medium, or thick, includes: The difference in contact resistance between adjacent acquisition times is calculated and divided by the time interval to obtain the contact resistance change rate. The growth trend of the rust product layer at the contact interface between the bolt and the main material is determined based on the positive and negative direction and magnitude of the contact resistance change rate. The earliest timestamp of the galvanic current density value is read as the reference galvanic current density value. The current galvanic current density value is divided by the reference galvanic current density value to obtain the current attenuation ratio. The current attenuation ratio is combined with the contact resistance change rate to form the rust layer feature vector. Based on the contact resistance change rate and current attenuation ratio in the characteristic vector of the corrosion layer, the thickness level of the corrosion layer is determined to be thin, medium, or thick.

4. The corrosion control method for transmission towers according to claim 3, characterized in that, Determining the thickness level of the rust layer as thin, medium, or thick includes: If the contact resistance change rate is less than the first threshold and the current decay ratio is greater than the fourth threshold, it is determined to be a thin layer; If the first threshold ≤ contact resistance change rate ≤ the second threshold and the third threshold ≤ current attenuation ratio ≤ the fourth threshold, it is determined to be a middle layer; If the contact resistance change rate is greater than the second threshold and the current decay ratio is less than the third threshold, it is determined to be a thick layer.

5. The corrosion control method for transmission towers according to claim 1, characterized in that, The process involves extracting the current development stage from the corrosion layer thickness level, combining it with the ambient relative humidity in the initial monitoring dataset, identifying the hygroscopic characteristics corresponding to the pore structure of the corrosion layer, evaluating the moisture adsorption capacity of the corrosion layer in a high-humidity environment, and obtaining the high-humidity activation level, including: The current development stage is extracted based on the rust layer thickness level. When the rust layer thickness level is thin, the current development stage is determined to be the initial stage. When the rust layer thickness level is medium, the current development stage is determined to be the intermediate stage. When the rust layer thickness level is thick, the current development stage is determined to be the late stage. The moisture absorption characteristic type is identified based on the pore structure state corresponding to the current development stage. The initial stage corresponds to strong moisture absorption characteristics, the middle stage corresponds to medium moisture absorption characteristics, and the later stage corresponds to weak moisture absorption characteristics. The current relative humidity value is obtained, and the current relative humidity value is compared with a preset high humidity threshold to determine whether the current environment is in a high humidity state. The moisture absorption characteristic type is associated with the high humidity environment state to determine the high humidity activation level.

6. The corrosion control method for transmission towers according to claim 1, characterized in that, For the high humidity activation level and rust layer thickness level, a decision tree algorithm is used to classify and learn the safety and failure states in the historical corrosion case library to determine the corrosion risk current density threshold suitable for the current node state, including: Extract the high humidity activation level, rust layer thickness level, galvanic current density value and corresponding final status label for each case record from the historical corrosion case database. The final status label is divided into two categories: safe status and failure status. The extracted case records are organized into a training sample set. The training sample set is used as input, and a decision tree algorithm is used for classification learning. The decision tree algorithm uses the high humidity activation level and the corrosion layer thickness level as splitting attributes. The training sample set is recursively divided to construct a classification decision tree. The leaf nodes of the classification decision tree correspond to the category labels of safe state or failure state. For each leaf node in the classification decision tree that points to the failure state, the galvanic current density value corresponding to the historical cases falling into the leaf node is counted, and the average value of the galvanic current density value is taken as the corrosion risk current density threshold corresponding to the leaf node. Based on the high humidity activation level and corrosion layer thickness level of the current node, the classification decision tree is traversed from the root node down to the corresponding leaf node. The corrosion risk current density threshold corresponding to the leaf node is read to obtain the corrosion risk current density threshold adapted to the current node state.

7. The corrosion control method for transmission towers according to claim 1, characterized in that, The process of using a decision tree algorithm to classify and learn the safety and failure states in the historical corrosion case library includes: at the root node, the decision tree algorithm selects one of two attributes, high humidity activation level and rust layer thickness level, as the splitting attribute to divide the training sample set into several subsets; each subset recursively performs the splitting process until all samples in the subset belong to the same category or cannot be split further, at which point leaf nodes are formed and the corresponding category labels are marked.

8. The corrosion control method for transmission towers according to claim 1, characterized in that, The corrosion aggravation level is classified based on the deviation between the galvanic current density and the corrosion risk current density threshold in the initial monitoring dataset. Local corrosion aggravation risk nodes and their corresponding corrosion aggravation levels are identified. Combined with the rust layer thickness level, risk nodes are spatially grouped based on the adjacency relationship of tower numbers and the distance to tower leg positions to determine high-priority risk node clusters, including: The galvanic current density of each bolted connection node is obtained. The difference between the galvanic current density and the corresponding corrosion risk current density threshold is calculated to obtain the deviation value. The deviation value is compared with the preset deviation range to classify the corrosion aggravation level as highly aggravated, moderately aggravated, or slightly aggravated. Nodes with a corrosion aggravation level of highly aggravated or moderately aggravated are marked as nodes with local corrosion aggravation risk. For the risk nodes of localized corrosion aggravation, the tower number and tower leg position identifier corresponding to each risk node are extracted. Based on the adjacency relationship of the tower number, it is determined whether different risk nodes are located on adjacent towers. Based on the tower leg position identifier, it is determined whether the risk nodes on the same tower or adjacent towers meet the preset proximity distance condition. Risk nodes that meet the proximity distance condition are assigned to the same proximity group to obtain several risk node proximity groups. For each risk node's neighboring group, the number of risk nodes in the group, the corrosion aggravation level of each risk node, and the rust layer thickness level are counted. If the number of risk nodes in the group exceeds a preset threshold and at least one risk node has a thick rust layer, then the group is identified as a high-priority risk node cluster.

9. The corrosion control method for transmission towers according to claim 1, characterized in that, The process of generating transmission tower corrosion prevention control instructions based on the matching of the spatial distribution density of nodes within the high-priority risk node cluster with the high humidity activation level and the corresponding thermocouple current suppression strategy includes: Based on the tower number and tower leg position of each node in the high-priority risk node cluster, the spatial distribution density value is obtained by counting the number of risk nodes within a unit line length. The spatial distribution density value is combined with the high humidity activation level corresponding to each node in the cluster, and the corresponding galvanic current suppression method is queried from the pre-established suppression method matching table. Based on the obtained thermocouple current suppression method and the tower number and tower leg position identifier of each node in the high-priority risk node cluster, the transmission tower corrosion prevention control instruction is encapsulated according to the preset instruction format.

10. The corrosion control method for transmission towers according to claim 9, characterized in that, The corrosion control command includes the target tower number, the target tower leg position, and the corresponding thermocouple current suppression method field.

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